diff --git a/.github/registry_schema.json b/.github/registry_schema.json index d4b6962406..c617d4f08a 100644 --- a/.github/registry_schema.json +++ b/.github/registry_schema.json @@ -31,6 +31,9 @@ "date": { "type": "string", "format": "date" + }, + "archived": { + "type": "boolean" } }, "required": ["title", "path", "tags", "authors"], diff --git a/.github/scripts/check_notebooks.py b/.github/scripts/check_notebooks.py new file mode 100644 index 0000000000..0bcf540346 --- /dev/null +++ b/.github/scripts/check_notebooks.py @@ -0,0 +1,61 @@ +import subprocess +import sys +from pathlib import Path + +import nbformat + + +def get_changed_notebooks(base_ref: str = "origin/main") -> list[Path]: + """ + Returns a list of changed notebook paths in the current git branch + compared to the specified base reference. + """ + result = subprocess.run( + ["git", "diff", "--name-only", base_ref, "--", "*.ipynb"], + capture_output=True, + text=True, + check=True, + ) + return [Path(line.strip()) for line in result.stdout.splitlines() if line.strip()] + + +def is_valid_notebook(path: Path) -> bool: + """ + Checks if the notebook at the given path is valid by attempting to read it + with nbformat. + """ + try: + with open(path, "r", encoding="utf-8") as f: + nbformat.read(f, as_version=4) + return True + except Exception as e: + print(f"{path}: INVALID - {e}") + return False + + +def main() -> None: + """ + Main function to validate the format of changed notebooks. + """ + changed_notebooks = get_changed_notebooks() + if not changed_notebooks: + print("No changed .ipynb files to validate.") + sys.exit(0) + + print(f"Validating {len(changed_notebooks)} notebook(s)...") + errors = 0 + for path in changed_notebooks: + if not path.exists(): + continue # skip deleted files + if not is_valid_notebook(path): + errors += 1 + + if errors: + print(f"{errors} invalid notebook(s) found.") + sys.exit(1) + else: + print("All changed notebooks are valid.") + + +if __name__ == "__main__": + main() diff --git a/.github/workflows/validate-notebooks.yaml b/.github/workflows/validate-notebooks.yaml new file mode 100644 index 0000000000..bf2e6326fa --- /dev/null +++ b/.github/workflows/validate-notebooks.yaml @@ -0,0 +1,25 @@ +name: Validate Changed Notebooks + +on: [pull_request] + +jobs: + validate-notebooks: + name: Validate Notebooks + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v3 + with: + fetch-depth: 0 # needed for git diff to work + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.12' + + - name: Install dependencies + run: pip install nbformat + + - name: Validate changed .ipynb files + run: python .github/scripts/check_notebooks.py diff --git a/.gitignore b/.gitignore index 62c34036f3..16d2ebb7e6 100644 --- a/.gitignore +++ b/.gitignore @@ -137,6 +137,12 @@ dmypy.json *.DS_Store tmp_* examples/fine-tuned_qa/local_cache/* +examples/multimodal/.local_cache/* # PyCharm files .idea/ +.cursorignore + +# VS Code files +.vscode/ +.cursorignore diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 266b82223e..c790e96543 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,48 +1,7 @@ -# Welcome, AI Chef +# Contributing the the cookbook -The OpenAI Cookbook is a community-driven resource aimed at sharing knowledge in a way that is accessible, engaging, and enriching for all AI builders. +The OpenAI Cookbook is a collection of useful patterns and examples of working with the OpenAI platform, provided as a community resource. -Before contributing, read through the existing issues and pull requests to see if someone else is already working on something similar. That way you can avoid duplicating efforts. +> Contributions are reviewed on a best-effort basis - we can't provide guarantees around when or if content contributions will be reviewed or merged. -## What makes a good contribution? - -Generally, we have found that the best contributions to the Cookbook are **useful**, **novel** or **creative**, or a combination of these. - -- **Useful:** Involves concepts or techniques that can be applied broadly and often, and can translate to practical use-cases and solving real-world problems. If you're doing something often, chances are others are too, and having reusable examples to reference can be very helpful. -- **Novel:** Showcases new developments or techniques. Look out for new research on how to best use LLMs, or new models and capabilities in the API. -- **Creative:** Uses LLMs in creative and innovative ways, or combines multiple APIs and tools in novel ways. - -Additionally, we strive to maintain a **neutral** tone, and aim for **high quality** writing. - -- **Neutral:** Maintains a neutral stance on tools and products. While it's natural to have preferences for particular tools, a good guide avoids over-evangelizing or marketing specific products, ensuring integrity and inclusivity. -- **High quality:** Well structured, clear and complete. Writing good content ensures others can fully benefit from it. See the rubric below for more details on how we assess the quality of submissions to the Cookbook. - -## Rubric - -To ensure the quality of submissions, we have established a rubric that assesses each contribution on various areas. The purpose of this rating system is to maintain a high standard of quality, relevance, and uniqueness. Each area is rated on a scale from 1 to 4. Contributions that score lower than a 3 in any of the areas will generally be rejected. - -We encourage contributors to familiarize themselves with this rubric before writing content. Understanding the criteria not only increases the chances of your contribution being accepted, but also helps in creating a resource that is comprehensive, clear, and beneficial for all users. - -For additional advice on writing good documentation, refer to [What Makes Documentation Good](https://cookbook.openai.com/what_makes_documentation_good). - -| Criteria | Description | Score | -| ------------ | --------------------------------------------------------------------------------------------------- | ----- | -| Relevance | Is the content related to building with OpenAI technologies? Is it useful to others? | | -| Uniqueness | Does the content offer new insights or unique information compared to existing documentation? | | -| Clarity | Is the language easy to understand? Are things well-explained? Is the title clear? | | -| Correctness | Are the facts, code snippets, and examples correct and reliable? Does everything execute correctly? | | -| Conciseness | Is the content concise? Are all details necessary? Can it be made shorter? | | -| Completeness | Is the content thorough and detailed? Are there things that weren’t explained fully? | | -| Grammar | Are there grammatical or spelling errors present? | | - -### Breakdown - -| Criteria | 4 | 3 | 2 | 1 | -| ------------ | --------------------------------------------- | ----------------------------------------- | --------------------------------------------- | ------------------------------------------ | -| Relevance | Relevant and useful. | Relevant but not very useful. | Tangentially relevant. | Not relevant. | -| Uniqueness | Completely unique with fresh insights. | Unique with minor overlaps. | Some unique aspects, but significant overlap. | Many similar guides/examples. | -| Clarity | Clear language and structure. | Clear language, unclear structure. | Some sections unclear. | Confusing and unclear. | -| Correctness | Completely error free. | Code works, minor improvements needed. | Few errors and warnings. | Many errors, code doesn't execute. | -| Conciseness | Cannot be reduced in any section, or overall. | Mostly short, but could still be reduced. | Some long sections, and/or long overall. | Very long sections and overall, redundant. | -| Completeness | Complete and detailed. | Mostly complete, minor additions needed. | Lacks some explanations. | Missing significant portions. | -| Grammar | Perfect grammar. | Correct grammar, few typos. | Some spelling/grammatical errors. | Numerous spelling/grammatical errors. | +Stay tuned to this page for further guidance on cookbook contributions as they become available 🙏 diff --git a/LICENSE b/LICENSE index b3841f631d..e5ad2c5aa3 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 OpenAI +Copyright (c) 2025 OpenAI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index 61a91c480d..b48159e102 100644 --- a/README.md +++ b/README.md @@ -9,22 +9,12 @@ > ✨ Navigate at [cookbook.openai.com](https://cookbook.openai.com) -Example code and guides for accomplishing common tasks with the [OpenAI API](https://platform.openai.com/docs/introduction). To run these examples, you'll need an OpenAI account and associated API key ([create a free account here](https://beta.openai.com/signup)). Set an environment variable called `OPENAI_API_KEY` with your API key. Alternatively, in most IDEs such as Visual Studio Code, you can create an `.env` file at the root of your repo containing `OPENAI_API_KEY=`, which will be picked up by the notebooks. +Example code and guides for accomplishing common tasks with the [OpenAI API](https://platform.openai.com/docs/introduction). To run these examples, you'll need an OpenAI account and associated API key ([create a free account here](https://platform.openai.com/signup)). Set an environment variable called `OPENAI_API_KEY` with your API key. Alternatively, in most IDEs such as Visual Studio Code, you can create an `.env` file at the root of your repo containing `OPENAI_API_KEY=`, which will be picked up by the notebooks. Most code examples are written in Python, though the concepts can be applied in any language. For other useful tools, guides and courses, check out these [related resources from around the web](https://cookbook.openai.com/related_resources). -## Contributing +## License -The OpenAI Cookbook is a community-driven resource. Whether you're submitting an idea, fixing a typo, adding a new guide, or improving an existing one, your contributions are greatly appreciated! - -Before contributing, read through the existing issues and pull requests to see if someone else is already working on something similar. That way you can avoid duplicating efforts. - -If there are examples or guides you'd like to see, feel free to suggest them on the [issues page](https://github.com/openai/openai-cookbook/issues). - -If you'd like to contribute new content, make sure to read through our [contribution guidelines](/CONTRIBUTING.md). We welcome high-quality submissions of new examples and guides, as long as they meet our criteria and fit within the scope of the cookbook. - -The contents of this repo are automatically rendered into [cookbook.openai.com](https://cookbook.openai.com) based on [registry.yaml](/registry.yaml). - -[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=468576060&machine=basicLinux32gb&location=EastUs) +MIT License diff --git a/articles/related_resources.md b/articles/related_resources.md index 0cd19a7d95..9db0539a4b 100644 --- a/articles/related_resources.md +++ b/articles/related_resources.md @@ -7,6 +7,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a - [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc. - [Baserun](https://baserun.ai/): A paid product for testing, debugging, and monitoring LLM-based apps - [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces. +- [ElatoAI](https://github.com/akdeb/ElatoAI): A platform for running OpenAI Realtime API Speech on ESP32 on Arduino using Deno Edge Runtime and Supabase. - [Embedchain](https://github.com/embedchain/embedchain): A Python library for managing and syncing unstructured data with LLMs. - [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices. - [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control. diff --git a/authors.yaml b/authors.yaml index 1657bbee4a..e3779b7812 100644 --- a/authors.yaml +++ b/authors.yaml @@ -2,6 +2,50 @@ # You can optionally customize how your information shows up cookbook.openai.com over here. # If your information is not present here, it will be pulled from your GitHub profile. +rajpathak-openai: + name: "Raj Pathak" + website: "https://www.linkedin.com/in/rajpathakopenai/" + avatar: "https://avatars.githubusercontent.com/u/208723614?s=400&u=c852eed3be082f7fbd402b5a45e9b89a0bfed1b8&v=4" + +chelseahu-openai: + name: "Chelsea Hu" + website: "https://www.linkedin.com/in/chelsea-tsaiszuhu/" + avatar: "https://avatars.githubusercontent.com/u/196863678?v=4" + +prashantmital-openai: + name: "Prashant Mital" + website: "https://www.linkedin.com/in/pmital/" + avatar: "https://avatars.githubusercontent.com/u/173949238?v=4" + +theophile-oai: + name: "Theophile Sautory" + website: "https://www.linkedin.com/in/theophilesautory" + avatar: "https://avatars.githubusercontent.com/u/206768658?v=4" + +robert-tinn: + name: "Robert Tinn" + website: "https://www.linkedin.com/in/robert-tinn/" + avatar: "https://avatars.githubusercontent.com/u/208724428?v=4" + +minh-hoque: + name: "Minhajul Hoque" + website: "https://www.linkedin.com/in/minhajul-hoque-83242b163/" + avatar: "https://avatars.githubusercontent.com/u/84698472?v=4" + +shikhar-cyber: + name: "Shikhar Kwatra" + website: "https://www.linkedin.com/in/shikharkwatra/" + avatar: "https://avatars.githubusercontent.com/u/189049238?v=4" + +danbell-openai: + name: "Dan Bell" + website: "https://www.linkedin.com/in/dan-bell-b69721b1/" + avatar: "https://avatars.githubusercontent.com/u/201846729?v=4" + +billchen-openai: + name: "Bill Chen" + website: "https://www.linkedin.com/in/billchen99/" + avatar: "https://avatars.githubusercontent.com/u/198814448?v=4" 0hq: name: "Will Depue" @@ -43,6 +87,11 @@ ibigio: website: "https://twitter.com/ilanbigio" avatar: "https://pbs.twimg.com/profile_images/1841544725654077440/DR3b8DMr_400x400.jpg" +willhath-openai: + name: "Will Hathaway" + website: "https://www.willhath.com" + avatar: "https://media.licdn.com/dms/image/v2/D4E03AQEHOtMrHtww4Q/profile-displayphoto-shrink_200_200/B4EZRR64p9HgAc-/0/1736541178829?e=2147483647&v=beta&t=w1rX0KhLZaK5qBkVLkJjmYmfNMbsV2Bcn8InFVX9lwI" + jhills20: name: "James Hills" website: "https://twitter.com/jamesmhills" @@ -111,13 +160,13 @@ aaronwilkowitz-openai: charuj: name: "Charu Jaiswal" website: "https://www.linkedin.com/in/charu-j-8a866471" - avatar: "https://avatars.githubusercontent.com/u/18404643?v=4" + avatar: "https://avatars.githubusercontent.com/u/18404643?v=4" rupert-openai: name: "Rupert Truman" website: "https://www.linkedin.com/in/rupert-truman/" avatar: "https://avatars.githubusercontent.com/u/171234447" - + keelan-openai: name: "Keelan Schule" website: "https://www.linkedin.com/in/keelanschule/" @@ -156,8 +205,8 @@ evanweiss-openai: girishd: name: "Girish Dusane" website: "https://www.linkedin.com/in/girishdusane/" - avatar: "https://avatars.githubusercontent.com/u/272708" - + avatar: "https://avatars.githubusercontent.com/u/272708" + lxing-oai: name: "Luke Xing" website: "https://www.linkedin.com/in/lukexing/" @@ -212,7 +261,7 @@ erickgort: name: "Erick Gort" website: "https://www.linkedin.com/in/erick-gort-32ab1678/" avatar: "https://avatars.githubusercontent.com/u/189261906?v=4" - + kylecote-tray: name: "Kyle Cote" website: "https://github.com/kylecote-tray" @@ -226,13 +275,18 @@ MW-OAI: dwigg-openai: name: "Danny Wigg" website: "https://www.linkedin.com/in/dannywigg/" - avatar: "https://media.licdn.com/dms/image/v2/C4D03AQEjMSl0pMR_qw/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1587647134114?e=1743033600&v=beta&t=XmULCSmk6V6YFmlyBggxj5uJeoYYuaYUKgcByKlS0K0" + avatar: "https://avatars.githubusercontent.com/u/4661060?v=4" msingh-openai: name: "Mandeep Singh" website: "https://github.com/msingh-openai" avatar: "https://avatars.githubusercontent.com/u/168678187?v=4" +akashdeepdeb: + name: "Akashdeep Deb" + website: "https://github.com/akdeb" + avatar: "https://avatars.githubusercontent.com/u/20175219" + ted-at-openai: name: "Ted Sanders" website: "https://github.com/ted-at-openai" @@ -241,4 +295,84 @@ ted-at-openai: MikeHeaton: name: "Mike Heaton" website: "https://github.com/MikeHeaton" - avatar: "https://avatars.githubusercontent.com/u/11911723?v=4" \ No newline at end of file + avatar: "https://avatars.githubusercontent.com/u/11911723?v=4" + +thli-openai: + name: "Thomas Li" + website: "https://www.linkedin.com/in/thli/" + avatar: "https://avatars.githubusercontent.com/u/189043632?v=4" + +erikakettleson-openai: + name: "Erika Kettleson" + website: "https://www.linkedin.com/in/erika-kettleson-85763196/" + avatar: "https://avatars.githubusercontent.com/u/186107044?v=4" + +jannik-maierhofer: + name: "Jannik Maierhöfer" + website: "https://www.linkedin.com/in/maierhoefer/" + avatar: "https://avatars.githubusercontent.com/u/48529566?v=4" + +josiah-openai: + name: "Josiah Grace" + website: "https://www.linkedin.com/in/josiahbgrace" + avatar: "https://avatars.githubusercontent.com/u/181146311?v=4" + +vishnu-oai: + name: "Vishnu Chopra" + website: "https://www.linkedin.com/in/vishnu-chopra/" + avatar: "https://avatars.githubusercontent.com/u/206769912?v=4" + +nm-openai: + name: "Noah MacCallum" + website: "https://x.com/noahmacca" + avatar: "https://avatars.githubusercontent.com/u/171723556" + +julian-openai: + name: "Julian Lee" + website: "https://x.com/julianl093" + avatar: "https://avatars.githubusercontent.com/u/199828632" + +rzhao-openai: + name: "Randy Zhao" + website: "https://www.linkedin.com/in/randy-zhao-27433616b" + avatar: "https://avatars.githubusercontent.com/u/208724779?v=4" + +brandonbaker-openai: + name: "Brandon Baker" + website: "https://www.linkedin.com/in/brandonbaker18" + avatar: "https://avatars.githubusercontent.com/u/208719822" + +tompakeman-oai: + name: "Tom Pakeman" + website: "https://www.linkedin.com/in/tom-pakeman/" + avatar: "https://avatars.githubusercontent.com/u/204937754" + +alistair-openai: + name: "Alistair Gillespie" + website: "https://www.linkedin.com/in/alistair-gillespie/" + avatar: "https://avatars.githubusercontent.com/u/210626148" + +phundal-openai: + name: "Patrick Hundal" + website: "https://www.linkedin.com/in/phundal/" + avatar: "https://avatars.githubusercontent.com/u/189161955" + +rkoenig-openai: + name: "Robin Koenig" + website: "https://www.linkedin.com/in/robinkoenig/" + avatar: "https://media.licdn.com/dms/image/v2/C5603AQEqUVtbts8Huw/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1558137883581?e=1753920000&v=beta&t=jcm-qNJfmgVJsS6uNHxHu5T2nQoUWkXivthzxTJMWqA" + +joshbickett: + name: "Josh Bickett" + website: "https://www.linkedin.com/in/josh-bickett-4219b166/" + avatar: "https://avatars.githubusercontent.com/u/42594239" + +lupie: + name: "Lucie Lozinski" + website: "https://twitter.com/thisloops" + avatar: "https://avatars.githubusercontent.com/u/6293148" + +alexl-oai: + name: "Alex Lowden" + website: "https://www.linkedin.com/in/alex-lowden01/" + avatar: "https://avatars.githubusercontent.com/u/215167546" diff --git a/examples/Assistants_API_overview_python.ipynb b/examples/Assistants_API_overview_python.ipynb index 272cff5a5c..dc3a74afe3 100644 --- a/examples/Assistants_API_overview_python.ipynb +++ b/examples/Assistants_API_overview_python.ipynb @@ -193,7 +193,7 @@ " 'response_format': 'auto',\n", " 'temperature': 1.0,\n", " 'tool_resources': {'code_interpreter': None, 'file_search': None},\n", - " 'top_p': 1.0}" + " 'top_p': 1.0}", " 'tools': [],\n", " 'response_format': 'auto',\n", " 'temperature': 1.0,\n", @@ -260,7 +260,7 @@ " 'created_at': 1736340398,\n", " 'metadata': {},\n", " 'object': 'thread',\n", - " 'tool_resources': {'code_interpreter': None, 'file_search': None}}" + " 'tool_resources': {'code_interpreter': None, 'file_search': None}}", " 'object': 'thread',\n", " 'tool_resources': {'code_interpreter': None, 'file_search': None}}" ] @@ -840,7 +840,7 @@ " 'response_format': 'auto',\n", " 'temperature': 1.0,\n", " 'tool_resources': {'code_interpreter': {'file_ids': []}, 'file_search': None},\n", - " 'top_p': 1.0}" + " 'top_p': 1.0}", " 'tools': [{'type': 'code_interpreter'}],\n", " 'response_format': 'auto',\n", " 'temperature': 1.0,\n", @@ -1475,7 +1475,7 @@ " 'usage': None,\n", " 'temperature': 1.0,\n", " 'top_p': 1.0,\n", - " 'tool_resources': {}}" + " 'tool_resources': {}}", " 'strict': False},\n", " 'type': 'function'}],\n", " 'truncation_strategy': {'type': 'auto', 'last_messages': None},\n", @@ -1647,7 +1647,7 @@ " 'usage': None,\n", " 'temperature': 1.0,\n", " 'top_p': 1.0,\n", - " 'tool_resources': {}}" + " 'tool_resources': {}}", " 'strict': False},\n", " 'type': 'function'}],\n", " 'truncation_strategy': {'type': 'auto', 'last_messages': None},\n", diff --git a/examples/Context_summarization_with_realtime_api.ipynb b/examples/Context_summarization_with_realtime_api.ipynb new file mode 100644 index 0000000000..fd2b344cb8 --- /dev/null +++ b/examples/Context_summarization_with_realtime_api.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Context Summarization with Realtime API\n", + "## 1. Overview\n", + "Build an end‑to‑end **voice bot** that listens to your mic, speaks back in real time and **summarises long conversations** so quality never drops.\n", + "\n", + "### What You’ll Learn\n", + "1. **Live microphone streaming** → OpenAI *Realtime* (voice‑to‑voice) endpoint.\n", + "2. **Instant transcripts & speech playback** on every turn.\n", + "3. **Conversation state container** that stores **every** user/assistant message.\n", + "4. **Automatic “context trim”** – when the token window becomes very large (configurable), older turns are compressed into a summary.\n", + "5. **Extensible design** you can adapt to support customer‑support bots, kiosks, or multilingual assistants.\n", + "\n", + "\n", + "### Prerequisites\n", + "\n", + "| Requirement | Details |\n", + "|-------------|---------|\n", + "| **Python ≥ 3.10** | Will ensure that you don't hit any issues |\n", + "| **OpenAI API key** | Set `OPENAI_API_KEY` in your shell or paste inline (*not ideal for prod*) |\n", + "| Mic + speakers | Grant OS permission if prompted |\n", + "\n", + "\n", + "**Need help setting up the key?** \n", + "> Follow the [official quick‑start guide](https://platform.openai.com/docs/quickstart#step-2-set-your-api-key).\n", + "\n", + "\n", + "*Notes:*\n", + "> 1. GPT-4o-Realtime supports a 128k token context window, though in certain use cases, you may notice performance degrade as you stuff more tokens into the context window.\n", + "> 2. Token window = all tokens (words and audio tokens) the model currently keeps in memory for the session.x\n", + "\n", + "### One‑liner install (run in a fresh cell)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run once to install or upgrade dependencies (comment out if already installed)\n", + "# !pip install --upgrade openai websockets sounddevice simpleaudio" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Standard library imports\n", + "import os\n", + "import sys\n", + "import io\n", + "import json\n", + "import base64\n", + "import pathlib\n", + "import wave\n", + "from dataclasses import dataclass, field\n", + "from typing import List, Literal\n", + "\n", + "# Third-party imports\n", + "import asyncio\n", + "import numpy as np\n", + "import sounddevice as sd # microphone capture\n", + "import simpleaudio # speaker playback\n", + "import websockets # WebSocket client\n", + "import openai # OpenAI Python SDK >= 1.14.0" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Set your API key safely\n", + "openai.api_key = os.getenv(\"OPENAI_API_KEY\", \"\")\n", + "if not openai.api_key:\n", + " raise ValueError(\"OPENAI_API_KEY not found – please set env var or edit this cell.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Token Utilisation – Text vs Voice\n", + "\n", + "Large‑token windows are precious, every extra token you use costs latency + money. \n", + "For **audio** the input token window increases much faster than for plain text because amplitude, timing, and other acoustic details must be represented.\n", + "\n", + "In practice you’ll often see **≈ 10 ×** more tokens for the *same* sentence in audio versus text.\n", + "\n", + "\n", + "* GPT-4o realtime accepts up to **128k tokens** and as the token size increases, instruction adherence can drift.\n", + "* Every user/assistant turn consumes tokens → the window **only grows**.\n", + "* **Strategy**: Summarise older turns into a single assistant message, keep the last few verbatim turns, and continue.\n", + "\n", + "\"drawing\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Helper Functions\n", + "The following helper functions will enable us to run the full script." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Conversation State\n", + "Unlike HTTP-based Chat Completions, the Realtime API maintains an open, **stateful** session with two key components:\n", + "\n", + "| Component | Purpose |\n", + "|----------------|---------|\n", + "| **Session** | Controls global settings — model, voice, modalities, VAD, etc. |\n", + "| **Conversation** | Stores turn-by-turn messages between user and assistant — both audio and text. |\n", + "\n", + "This notebook wraps these components inside a simple `ConversationState` object to keep your logic clean, track history, and manage summarization when context windows fill up." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class Turn:\n", + " \"\"\"One utterance in the dialogue (user **or** assistant).\"\"\"\n", + " role: Literal[\"user\", \"assistant\"]\n", + " item_id: str # Server‑assigned identifier\n", + " text: str | None = None # Filled once transcript is ready\n", + "\n", + "@dataclass\n", + "class ConversationState:\n", + " \"\"\"All mutable data the session needs — nothing more, nothing less.\"\"\"\n", + " history: List[Turn] = field(default_factory=list) # Ordered log\n", + " waiting: dict[str, asyncio.Future] = field(default_factory=dict) # Pending transcript fetches\n", + " summary_count: int = 0\n", + "\n", + " latest_tokens: int = 0 # Window size after last reply\n", + " summarising: bool = False # Guard so we don’t run two summaries at once" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A quick helper to peek at the transcript:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def print_history(state) -> None:\n", + " \"\"\"Pretty-print the running transcript so far.\"\"\"\n", + " print(\"—— Conversation so far ———————————————\")\n", + " for turn in state.history:\n", + " text_preview = (turn.text or \"\").strip().replace(\"\\n\", \" \")\n", + " print(f\"[{turn.role:<9}] {text_preview} ({turn.item_id})\")\n", + " print(\"——————————————————————————————————————————\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 · Streaming Audio\n", + "We’ll stream raw PCM‑16 microphone data straight into the Realtime API.\n", + "\n", + "The pipeline is: mic ─► async.Queue ─► WebSocket ─► Realtime API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.1 Capture Microphone Input\n", + "We’ll start with a coroutine that:\n", + "\n", + "* Opens the default mic at **24 kHz, mono, PCM‑16** (one of the [format](https://platform.openai.com/docs/api-reference/realtime-sessions/create#realtime-sessions-create-input_audio_format) Realtime accepts). \n", + "* Slices the stream into **≈ 40 ms** blocks. \n", + "* Dumps each block into an `asyncio.Queue` so another task (next section) can forward it to OpenAI." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "async def mic_to_queue(pcm_queue: asyncio.Queue[bytes]) -> None:\n", + " \"\"\"\n", + " Capture raw PCM‑16 microphone audio and push ~CHUNK_DURATION_MS chunks\n", + " to *pcm_queue* until the surrounding task is cancelled.\n", + "\n", + " Parameters\n", + " ----------\n", + " pcm_queue : asyncio.Queue[bytes]\n", + " Destination queue for PCM‑16 frames (little‑endian int16).\n", + " \"\"\"\n", + " blocksize = int(SAMPLE_RATE_HZ * CHUNK_DURATION_MS / 1000)\n", + "\n", + " def _callback(indata, _frames, _time, status):\n", + " if status: # XRuns, device changes, etc.\n", + " print(\"⚠️\", status, file=sys.stderr)\n", + " try:\n", + " pcm_queue.put_nowait(bytes(indata)) # 1‑shot enqueue\n", + " except asyncio.QueueFull:\n", + " # Drop frame if upstream (WebSocket) can’t keep up.\n", + " pass\n", + "\n", + " # RawInputStream is synchronous; wrap in context manager to auto‑close.\n", + " with sd.RawInputStream(\n", + " samplerate=SAMPLE_RATE_HZ,\n", + " blocksize=blocksize,\n", + " dtype=\"int16\",\n", + " channels=1,\n", + " callback=_callback,\n", + " ):\n", + " try:\n", + " # Keep coroutine alive until cancelled by caller.\n", + " await asyncio.Event().wait()\n", + " finally:\n", + " print(\"⏹️ Mic stream closed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.2 Send Audio Chunks to the API\n", + "\n", + "Our mic task is now filling an `asyncio.Queue` with raw PCM‑16 blocks. \n", + "Next step: pull chunks off that queue, **base‑64 encode** them (the protocol requires JSON‑safe text), and ship each block to the Realtime WebSocket as an `input_audio_buffer.append` event." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper function to encode audio chunks in base64\n", + "b64 = lambda blob: base64.b64encode(blob).decode()\n", + "\n", + "async def queue_to_websocket(pcm_queue: asyncio.Queue[bytes], ws):\n", + " \"\"\"Read audio chunks from queue and send as JSON events.\"\"\"\n", + " try:\n", + " while (chunk := await pcm_queue.get()) is not None:\n", + " await ws.send(json.dumps({\n", + " \"type\": \"input_audio_buffer.append\",\n", + " \"audio\": b64(chunk),\n", + " }))\n", + " except websockets.ConnectionClosed:\n", + " print(\"WebSocket closed – stopping uploader\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.3 Handle Incoming Events \n", + "Once audio reaches the server, the Realtime API pushes a stream of JSON events back over the **same** WebSocket. \n", + "Understanding these events is critical for:\n", + "\n", + "* Printing live transcripts \n", + "* Playing incremental audio back to the user \n", + "* Keeping an accurate [`Conversation State`](https://platform.openai.com/docs/api-reference/realtime-server-events/conversation/created) so context trimming works later \n", + "\n", + "\n", + "| Event type | When it arrives | Why it matters | Typical handler logic |\n", + "|------------|-----------------|---------------|-----------------------|\n", + "| **`session.created`** | Immediately after the WebSocket handshake | Confirms the session is open and provides the `session.id`. | Log the ID for traceability and verify the connection. |\n", + "| **`session.updated`** | After you send a `session.update` call | Acknowledges that the server applied new session settings. | Inspect the echoed settings and update any local cache. |\n", + "| **`conversation.item.created`** (user) | A few ms after the user stops speaking (client VAD fires) | Reserves a timeline slot; transcript may still be **`null`**. | Insert a *placeholder* user turn in `state.history` marked “pending transcript”. |\n", + "| **`conversation.item.retrieved`** | ~100 – 300 ms later, once audio transcription is complete | Supplies the final user transcript (with timing). | Replace the placeholder with the transcript and print it if desired. |\n", + "| **`response.audio.delta`** | Every 20 – 60 ms while the assistant is speaking | Streams PCM‑16 audio chunks (and optional incremental text). | Buffer each chunk and play it; optionally show partial text in the console. |\n", + "| **`response.done`** | After the assistant’s last token | Signals both audio & text are complete; includes usage stats. | Finalize the assistant turn, update `state.latest_tokens`, and log usage. |\n", + "| **`conversation.item.deleted`** | Whenever you prune with `conversation.item.delete` | Confirms a turn was removed, freeing tokens on the server. | Mirror the deletion locally so your context window matches the server’s. |\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Detect When to Summarise\n", + "The Realtime model keeps a **large 128 k‑token window**, but quality can drift long before that limit as you stuff more context into the model.\n", + "\n", + "Our goal: **auto‑summarise** once the running window nears a safe threshold (default **2 000 tokens** for the notebook), then prune the superseded turns both locally *and* server‑side.\n", + "\n", + "We monitor latest_tokens returned in `response.done`. When it exceeds SUMMARY_TRIGGER and we have more than KEEP_LAST_TURNS, we spin up a background summarisation coroutine.\n", + "\n", + "We compress everything except the last 2 turns into a single French paragraph, then:\n", + "\n", + "1. Insert that paragraph as a new assistant message at the top of the conversation.\n", + "\n", + "2. Delete the message items that was used for the summary.\n", + "\n", + "We will later ask the Voice agent what language was the summary to test if the Summary insertion into Realtime API Conversation Context was successful." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "async def run_summary_llm(text: str) -> str:\n", + " \"\"\"Call a lightweight model to summarise `text`.\"\"\"\n", + " resp = await asyncio.to_thread(lambda: openai.chat.completions.create(\n", + " model=SUMMARY_MODEL,\n", + " temperature=0,\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"Summarise in French the following conversation \"\n", + " \"in one concise paragraph so it can be used as \"\n", + " \"context for future dialogue.\"},\n", + " {\"role\": \"user\", \"content\": text},\n", + " ],\n", + " ))\n", + " return resp.choices[0].message.content.strip()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "async def summarise_and_prune(ws, state):\n", + " \"\"\"Summarise old turns, delete them server‑side, and prepend a single summary\n", + " turn locally + remotely.\"\"\"\n", + " state.summarising = True\n", + " print(\n", + " f\"⚠️ Token window ≈{state.latest_tokens} ≥ {SUMMARY_TRIGGER}. Summarising…\",\n", + " )\n", + " old_turns, recent_turns = state.history[:-KEEP_LAST_TURNS], state.history[-KEEP_LAST_TURNS:]\n", + " convo_text = \"\\n\".join(f\"{t.role}: {t.text}\" for t in old_turns if t.text)\n", + " \n", + " if not convo_text:\n", + " print(\"Nothing to summarise (transcripts still pending).\")\n", + " state.summarising = False\n", + "\n", + " summary_text = await run_summary_llm(convo_text) if convo_text else \"\"\n", + " state.summary_count += 1\n", + " summary_id = f\"sum_{state.summary_count:03d}\"\n", + " state.history[:] = [Turn(\"assistant\", summary_id, summary_text)] + recent_turns\n", + " \n", + " print_history(state) \n", + "\n", + " # Create summary on server\n", + " await ws.send(json.dumps({\n", + " \"type\": \"conversation.item.create\",\n", + " \"previous_item_id\": \"root\",\n", + " \"item\": {\n", + " \"id\": summary_id,\n", + " \"type\": \"message\",\n", + " \"role\": \"assistant\",\n", + " \"content\": [{\"type\": \"text\", \"text\": summary_text}],\n", + " },\n", + " }))\n", + "\n", + " # Delete old items\n", + " for turn in old_turns:\n", + " await ws.send(json.dumps({\n", + " \"type\": \"conversation.item.delete\",\n", + " \"item_id\": turn.item_id,\n", + " }))\n", + "\n", + " print(f\"✅ Summary inserted ({summary_id})\")\n", + " \n", + " state.summarising = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function lets us poll for transcripts over time. This is useful in cases where the user's audio hasn't been transcribed immediately, so we can retrieve the final result later." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "async def fetch_full_item(\n", + " ws, item_id: str, state: ConversationState, attempts: int = 1\n", + "):\n", + " \"\"\"\n", + " Ask the server for a full conversation item; retry up to 5× if the\n", + " transcript field is still null. Resolve the waiting future when done.\n", + " \"\"\"\n", + " # If there is already a pending fetch, just await it\n", + " if item_id in state.waiting:\n", + " return await state.waiting[item_id]\n", + "\n", + " fut = asyncio.get_running_loop().create_future()\n", + " state.waiting[item_id] = fut\n", + "\n", + " await ws.send(json.dumps({\n", + " \"type\": \"conversation.item.retrieve\",\n", + " \"item_id\": item_id,\n", + " }))\n", + " item = await fut\n", + "\n", + " # If transcript still missing retry (max 5×)\n", + " if attempts < 5 and not item.get(\"content\", [{}])[0].get(\"transcript\"):\n", + " await asyncio.sleep(0.4 * attempts)\n", + " return await fetch_full_item(ws, item_id, state, attempts + 1)\n", + "\n", + " # Done – remove the marker\n", + " state.waiting.pop(item_id, None)\n", + " return item\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. End‑to‑End Workflow Demonstration\n", + "\n", + "Run the two cells below to launch an interactive session. Interrupt the cell stop recording.\n", + "\n", + "> **Note:** \n", + "> This notebook uses `SUMMARY_TRIGGER = 2000` and `KEEP_LAST_TURNS = 2` to make summarization easier to demo quickly. \n", + "> In production, you should tune these values based on your application's needs. \n", + "> - A typical `SUMMARY_TRIGGER` falls between **20,000–32,000 tokens**, depending on how performance degrades with larger context for your use case." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Audio/config knobs\n", + "SAMPLE_RATE_HZ = 24_000 # Required by pcm16\n", + "CHUNK_DURATION_MS = 40 # chunk size for audio capture\n", + "BYTES_PER_SAMPLE = 2 # pcm16 = 2 bytes/sample\n", + "SUMMARY_TRIGGER = 2_000 # Summarise when context ≥ this\n", + "KEEP_LAST_TURNS = 2 # Keep these turns verbatim\n", + "SUMMARY_MODEL = \"gpt-4o-mini\" # Cheaper, fast summariser" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# --------------------------------------------------------------------------- #\n", + "# 🎤 Realtime session #\n", + "# --------------------------------------------------------------------------- #\n", + "async def realtime_session(model=\"gpt-4o-realtime-preview\", voice=\"shimmer\", enable_playback=True):\n", + " \"\"\"\n", + " Main coroutine: connects to the Realtime endpoint, spawns helper tasks,\n", + " and processes incoming events in a big async‑for loop.\n", + " \"\"\"\n", + " state = ConversationState() # Reset state for each run\n", + "\n", + " pcm_queue: asyncio.Queue[bytes] = asyncio.Queue()\n", + " assistant_audio: List[bytes] = []\n", + "\n", + " # ----------------------------------------------------------------------- #\n", + " # Open the WebSocket connection to the Realtime API #\n", + " # ----------------------------------------------------------------------- #\n", + " url = f\"wss://api.openai.com/v1/realtime?model={model}\"\n", + " headers = {\"Authorization\": f\"Bearer {openai.api_key}\", \"OpenAI-Beta\": \"realtime=v1\"}\n", + "\n", + " async with websockets.connect(url, extra_headers=headers, max_size=1 << 24) as ws:\n", + " # ------------------------------------------------------------------- #\n", + " # Wait until server sends session.created #\n", + " # ------------------------------------------------------------------- #\n", + " while json.loads(await ws.recv())[\"type\"] != \"session.created\":\n", + " pass\n", + " print(\"session.created ✅\")\n", + "\n", + " # ------------------------------------------------------------------- #\n", + " # Configure session: voice, modalities, audio formats, transcription #\n", + " # ------------------------------------------------------------------- #\n", + " await ws.send(json.dumps({\n", + " \"type\": \"session.update\",\n", + " \"session\": {\n", + " \"voice\": voice,\n", + " \"modalities\": [\"audio\", \"text\"],\n", + " \"input_audio_format\": \"pcm16\",\n", + " \"output_audio_format\": \"pcm16\",\n", + " \"input_audio_transcription\": {\"model\": \"gpt-4o-transcribe\"},\n", + " },\n", + " }))\n", + "\n", + " # ------------------------------------------------------------------- #\n", + " # Launch background tasks: mic capture → queue → websocket #\n", + " # ------------------------------------------------------------------- #\n", + " mic_task = asyncio.create_task(mic_to_queue(pcm_queue))\n", + " upl_task = asyncio.create_task(queue_to_websocket(pcm_queue, ws))\n", + "\n", + " print(\"🎙️ Speak now (Ctrl‑C to quit)…\")\n", + "\n", + " try:\n", + " # ------------------------------------------------------------------- #\n", + " # Main event loop: process incoming events from the websocket #\n", + " # ------------------------------------------------------------------- #\n", + " async for event_raw in ws:\n", + " event = json.loads(event_raw)\n", + " etype = event[\"type\"]\n", + "\n", + " # --------------------------------------------------------------- #\n", + " # User just spoke ⇢ conversation.item.created (role = user) #\n", + " # --------------------------------------------------------------- #\n", + " if etype == \"conversation.item.created\" and event[\"item\"][\"role\"] == \"user\":\n", + " item = event[\"item\"]\n", + " text = None\n", + " if item[\"content\"]:\n", + " text = item[\"content\"][0].get(\"transcript\")\n", + " \n", + " state.history.append(Turn(\"user\", event[\"item\"][\"id\"], text))\n", + " \n", + " # If transcript not yet available, fetch it later\n", + " if text is None:\n", + " asyncio.create_task(fetch_full_item(ws, item[\"id\"], state))\n", + "\n", + " # --------------------------------------------------------------- #\n", + " # Transcript fetched ⇢ conversation.item.retrieved #\n", + " # --------------------------------------------------------------- #\n", + " elif etype == \"conversation.item.retrieved\":\n", + " content = event[\"item\"][\"content\"][0]\n", + " # Fill missing transcript in history\n", + " for t in state.history:\n", + " if t.item_id == event[\"item\"][\"id\"]:\n", + " t.text = content.get(\"transcript\")\n", + " break\n", + "\n", + " # --------------------------------------------------------------- #\n", + " # Assistant audio arrives in deltas #\n", + " # --------------------------------------------------------------- #\n", + " elif etype == \"response.audio.delta\":\n", + " assistant_audio.append(base64.b64decode(event[\"delta\"]))\n", + "\n", + " # --------------------------------------------------------------- #\n", + " # Assistant reply finished ⇢ response.done #\n", + " # --------------------------------------------------------------- #\n", + " elif etype == \"response.done\":\n", + " for item in event[\"response\"][\"output\"]:\n", + " if item[\"role\"] == \"assistant\":\n", + " txt = item[\"content\"][0][\"transcript\"]\n", + " state.history.append(Turn(\"assistant\", item[\"id\"], txt))\n", + " # print(f\"\\n🤖 {txt}\\n\")\n", + " state.latest_tokens = event[\"response\"][\"usage\"][\"total_tokens\"]\n", + " print(f\"—— response.done (window ≈{state.latest_tokens} tokens) ——\")\n", + " print_history(state)\n", + " \n", + " # Fetch any still‑missing user transcripts\n", + " for turn in state.history:\n", + " if (turn.role == \"user\"\n", + " and turn.text is None\n", + " and turn.item_id not in state.waiting):\n", + " asyncio.create_task(\n", + " fetch_full_item(ws, turn.item_id, state)\n", + " )\n", + "\n", + " # Playback collected audio once reply completes\n", + " if enable_playback and assistant_audio:\n", + " simpleaudio.play_buffer(b\"\".join(assistant_audio), 1, BYTES_PER_SAMPLE, SAMPLE_RATE_HZ)\n", + " assistant_audio.clear()\n", + "\n", + " # Summarise if context too large – fire in background so we don't block dialogue\n", + " if state.latest_tokens >= SUMMARY_TRIGGER and len(state.history) > KEEP_LAST_TURNS and not state.summarising:\n", + " asyncio.create_task(summarise_and_prune(ws, state))\n", + "\n", + " except KeyboardInterrupt:\n", + " print(\"\\nStopping…\")\n", + " finally:\n", + " mic_task.cancel()\n", + " await pcm_queue.put(None)\n", + " await upl_task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the realtime session (this cell blocks until you stop it)\n", + "await realtime_session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```raw\n", + "session.created ✅\n", + "🎙️ Speak now (Ctrl‑C to quit)…\n", + "—— response.done (window ≈979 tokens) ——\n", + "—— Conversation so far ———————————————\n", + "[user ] Can you tell me a quick story? (item_BTuMOcpUqp8qknKhLzlkA)\n", + "[assistant] Once upon a time, in a cozy little village, there was a cat named Whiskers who was always getting into trouble. One sunny day, Whiskers found a mysterious glowing stone in the garden. Curious, he pawed at it, and poof! The stone granted him the ability to talk to birds. Whiskers and his new bird friends had grand adventures, solving mysteries and exploring the village. And from that day on, Whiskers was known as the most adventurous cat in the village. The end. (item_BTuMPRWxqpv0ph6QM46DK)\n", + "——————————————————————————————————————————\n", + "—— response.done (window ≈2755 tokens) ——\n", + "—— Conversation so far ———————————————\n", + "[user ] Can you tell me a quick story? (item_BTuMOcpUqp8qknKhLzlkA)\n", + "[assistant] Once upon a time, in a cozy little village, there was a cat named Whiskers who was always getting into trouble. One sunny day, Whiskers found a mysterious glowing stone in the garden. Curious, he pawed at it, and poof! The stone granted him the ability to talk to birds. Whiskers and his new bird friends had grand adventures, solving mysteries and exploring the village. And from that day on, Whiskers was known as the most adventurous cat in the village. The end. (item_BTuMPRWxqpv0ph6QM46DK)\n", + "[user ] Can you tell me three extremely funny stories? (item_BTuNN64LdULM21OyC4vzN)\n", + "[assistant] Sure, let's dive into some giggle-worthy tales: **Story One:** There was a forgetful baker named Benny who baked a hundred cakes for a big wedding. But on the big day, he forgot where he put them! The entire town joined in to find the missing cakes, only to discover Benny had stored them in his neighbor's garage, thinking it was his pantry. The wedding turned into a town-wide cake feast! **Story Two:** A mischievous dog named Sparky loved to play pranks. One day, he swapped his owner's phone with a squeaky toy, causing a hilarious mix-up of barks, squeaks, and confused calls. Sparky's owner ended up having a full conversation with the mailman, all in squeaks! **Story Three:** In a small town, a parrot named Polly became a local celebrity for reciting tongue twisters. One day, Polly challenged the mayor to a tongue twister duel. The mayor, tongue-tied and laughing, declared Polly the official town jester. Polly squawked with pride, and the town rang with laughter for days. (item_BTuNNpNxki5ynSQ5c3Xsa)\n", + "——————————————————————————————————————————\n", + "⚠️ Token window ≈2755 ≥ 2000. Summarising…\n", + "—— Conversation so far ———————————————\n", + "[assistant] L'utilisateur a demandé une histoire rapide, et l'assistant a raconté celle d'un chat nommé Whiskers qui, après avoir trouvé une pierre mystérieuse dans son jardin, a obtenu le pouvoir de parler aux oiseaux. Avec ses nouveaux amis oiseaux, Whiskers a vécu de grandes aventures, résolvant des mystères et explorant le village, devenant ainsi le chat le plus aventurier du village. (sum_001)\n", + "[user ] Can you tell me three extremely funny stories? (item_BTuNN64LdULM21OyC4vzN)\n", + "[assistant] Sure, let's dive into some giggle-worthy tales: **Story One:** There was a forgetful baker named Benny who baked a hundred cakes for a big wedding. But on the big day, he forgot where he put them! The entire town joined in to find the missing cakes, only to discover Benny had stored them in his neighbor's garage, thinking it was his pantry. The wedding turned into a town-wide cake feast! **Story Two:** A mischievous dog named Sparky loved to play pranks. One day, he swapped his owner's phone with a squeaky toy, causing a hilarious mix-up of barks, squeaks, and confused calls. Sparky's owner ended up having a full conversation with the mailman, all in squeaks! **Story Three:** In a small town, a parrot named Polly became a local celebrity for reciting tongue twisters. One day, Polly challenged the mayor to a tongue twister duel. The mayor, tongue-tied and laughing, declared Polly the official town jester. Polly squawked with pride, and the town rang with laughter for days. (item_BTuNNpNxki5ynSQ5c3Xsa)\n", + "——————————————————————————————————————————\n", + "✅ Summary inserted (sum_001)\n", + "—— response.done (window ≈2147 tokens) ——\n", + "—— Conversation so far ———————————————\n", + "[assistant] L'utilisateur a demandé une histoire rapide, et l'assistant a raconté celle d'un chat nommé Whiskers qui, après avoir trouvé une pierre mystérieuse dans son jardin, a obtenu le pouvoir de parler aux oiseaux. Avec ses nouveaux amis oiseaux, Whiskers a vécu de grandes aventures, résolvant des mystères et explorant le village, devenant ainsi le chat le plus aventurier du village. (sum_001)\n", + "[user ] Can you tell me three extremely funny stories? (item_BTuNN64LdULM21OyC4vzN)\n", + "[assistant] Sure, let's dive into some giggle-worthy tales: **Story One:** There was a forgetful baker named Benny who baked a hundred cakes for a big wedding. But on the big day, he forgot where he put them! The entire town joined in to find the missing cakes, only to discover Benny had stored them in his neighbor's garage, thinking it was his pantry. The wedding turned into a town-wide cake feast! **Story Two:** A mischievous dog named Sparky loved to play pranks. One day, he swapped his owner's phone with a squeaky toy, causing a hilarious mix-up of barks, squeaks, and confused calls. Sparky's owner ended up having a full conversation with the mailman, all in squeaks! **Story Three:** In a small town, a parrot named Polly became a local celebrity for reciting tongue twisters. One day, Polly challenged the mayor to a tongue twister duel. The mayor, tongue-tied and laughing, declared Polly the official town jester. Polly squawked with pride, and the town rang with laughter for days. (item_BTuNNpNxki5ynSQ5c3Xsa)\n", + "[user ] (item_BTuPLaCv8ATdIwAQ2rLgO)\n", + "[assistant] Sure! The first summary I provided between us was in French. (item_BTuPLa7BaSQToGCVOmfBK)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "We had a conversation with our Voice AI. After several turns, the total token count reached SUMMARY_MAX, which triggered the conversation summarization step. This generated a summary of the earlier messages.\n", + "\n", + "Since there were N = 4 total messages, we summarized the first N - 2 = 2 messages:\n", + "```txt\n", + "—— Conversation so far ———————————————\n", + "[user ] Can you tell me a quick story? (item_BTuMOcpUqp8qknKhLzlkA)\n", + "[assistant] Once upon a time, in a cozy little village, there was a cat named Whiskers who was always getting into trouble. One sunny day, Whiskers found a mysterious glowing stone in the garden. Curious, he pawed at it, and poof! The stone granted him the ability to talk to birds. Whiskers and his new bird friends had grand adventures, solving mysteries and exploring the village. And from that day on, Whiskers was known as the most adventurous cat in the village. The end. (item_BTuMPRWxqpv0ph6QM46DK)\n", + "```\n", + "\n", + "We then created a summary in French and inserted it into the conversation history using the root: true flag. This ensured the summary appeared as the first message in the conversation. After that, we deleted the original items, using `\"type\": \"conversation.item.delete\"`, that were summarized.\n", + "\n", + "To validate the summary insertion, we asked the Voice AI what language the summary was in. It correctly responded:\n", + "\n", + "```txt\n", + "[assistant] Sure! The first summary I provided between us was in French. (item_BTuPLa7BaSQToGCVOmfBK)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 · Real‑World Applications\n", + "\n", + "Context summarisation can be useful for **long‑running voice experiences**. \n", + "Here are a use case ideas:\n", + "\n", + "| Use‑case | Added Value | Why Useful |\n", + "|----------|-------------|------------|\n", + "| **Customer‑support voicebot** | 24/7 natural phone tree; auto‑generate ticket summaries | Summarizes long customer calls for efficient handoff and record-keeping, reducing agent workload and improving response quality. |\n", + "| **Language tutor** | Real‑time conversation practice with corrective feedback | Helps track learner progress and highlights recurring mistakes, enabling personalized feedback and more effective language acquisition. |\n", + "| **AI therapist / coach** | Safe, always‑available listener that remembers sessions | Maintains continuity across sessions by recalling key topics and emotional tone, supporting a more empathetic and effective experience. |\n", + "| **Meeting assistant** | Live transcripts + concise action‑item recap in Slack | Distills lengthy meetings into actionable summaries, saving team members time and ensuring important points are not missed. |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6 · Next Steps & Further Reading\n", + "Try out the notebook and try integrating context summary into your application.\n", + "\n", + "Few things you can try:\n", + "| Try this… | What you’ll learn |\n", + "|-----------|------------------|\n", + "| **A/B test summarisation**
Run your eval suite with summarisation *on* vs *off*. | Whether trimming actually improves quality for your domain—and how it affects latency & cost. |\n", + "| **Swap summary styles**
Change the system prompt to bullet points, JSON, English vs French, etc. | Which format the downstream assistant absorbs best; how language choice influences follow‑up answers. |\n", + "| **Vary thresholds**
Play with `SUMMARY_TRIGGER_TOKENS` (2 k → 8 k). | The sweet spot between model drift and summarisation overhead. |\n", + "| **Cost tracing**
Log `usage.total_tokens` before/after summarisation. | Concrete ROI: token savings per hour of conversation. |\n", + "\n", + "\n", + "### Resources:\n", + "- [OpenAI Realtime Guide](https://platform.openai.com/docs/guides/realtime)\n", + "- [OpenAI Realtime Conversations](https://platform.openai.com/docs/guides/realtime-conversations)\n", + "- [OpenAI Realtime API Reference](https://platform.openai.com/docs/api-reference/realtime)\n", + "- [Voice AI and Voice Agents](https://voiceaiandvoiceagents.com/)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Data-intensive-Realtime-apps.ipynb b/examples/Data-intensive-Realtime-apps.ipynb new file mode 100644 index 0000000000..a94390131e --- /dev/null +++ b/examples/Data-intensive-Realtime-apps.ipynb @@ -0,0 +1,624 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Practical guide to data-intensive apps with the Realtime API\n", + "\n", + "This cookbook serves as a practical guide to help AI Engineers maximize the effectiveness of OpenAI's Realtime API, specifically when dealing with data-intensive function calls. We'll focus on scenarios common in speech-to-speech agents, where vast amounts of data must be handled smoothly and efficiently.\n", + "\n", + "This post won't cover the basics of setting up a Realtime API solution. Instead, you'll gain clear insights and actionable strategies to enhance the performance and reliability of your real-time conversational agents. It addresses specific challenges unique to handling large amounts of data in real-time conversational contexts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is the Realtime API?\n", + "\n", + "Before we dive in, let’s quickly recap the API for those who are new. The OpenAI Realtime API is a recent offering that supports low-latency, multimodal interactions—such as speech-to-speech conversations and live transcription. Picture scenarios like real-time voice-based customer support or live movie transcriptions. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is a data-intensive function call?\n", + "\n", + "Agents need access to tools and relevant data to perform their tasks. For instance, a financial analyst agent might pull real-time market data. In many cases, services already exist in your environment that expose this information through APIs.\n", + "\n", + "Historically, APIs weren’t designed with agents in mind and often return large volumes of data, depending on the service. As engineers, we frequently wrap these APIs with function calls to accelerate agent development—which makes perfect sense. Why reinvent what already exists?\n", + "\n", + "If not carefully optimized, these data-intensive function calls can quickly overwhelm the Realtime API—leading to slow responses or even failures to process user requests." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting the stage\n", + "\n", + "Our example centers on an NBA Scouting Agent that calls multiple functions to deliver in-depth analysis of upcoming draft prospects. To demonstrate practical guidelines for Realtime API interactions, we use large, realistic payloads inspired by NBA draft prospects. Below, you’ll find a monolithic `searchDraftProspects` function defined in the Realtime session to set the stage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```json\n", + "// \"Hey, pull up point guards projected in the top 10 in the 2025 draft\"\n", + "{\n", + " \"type\": \"session.update\",\n", + " \"session\": {\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"function\",\n", + " \"name\": \"searchDraftProspects\",\n", + " \"description\": \"Search draft prospects for a given year e.g., Point Guard\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"sign\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The player position\",\n", + " \"enum\": [\n", + " \"Point Guard\",\n", + " \"Shooting Guard\",\n", + " \"Small Forward\",\n", + " \"Power Forward\",\n", + " \"Center\",\n", + " \"Any\"\n", + " ]\n", + " },\n", + " year: { type: \"number\", description: \"Draft year e.g., 2025\" },\n", + " mockDraftRanking: { type: \"number\", description: \"Predicted Draft Ranking\" },\n", + " },\n", + " \"required\": [\"position\", \"year\"]\n", + " }\n", + " }\n", + " ],\n", + " \"tool_choice\": \"auto\",\n", + " }\n", + "}\n", + "```\n", + "\n", + "The searchDraftProspects function call returns a hefty payload. The example’s structure and size are drawn from real-world scenarios we’ve encountered.\n", + "\n", + "```json\n", + "// Example Payload\n", + "{\n", + " \"status\": {\n", + " \"code\": 200,\n", + " \"message\": \"SUCCESS\"\n", + " },\n", + " \"found\": 4274,\n", + " \"offset\": 0,\n", + " \"limit\": 10,\n", + " \"data\": [\n", + " {\n", + " \"prospectId\": 10001,\n", + " \"data\": {\n", + " \"ProspectInfo\": {\n", + " \"league\": \"NCAA\",\n", + " \"collegeId\": 301,\n", + " \"isDraftEligible\": true,\n", + " \"Player\": {\n", + " \"personalDetails\": {\n", + " \"firstName\": \"Jalen\",\n", + " \"lastName\": \"Storm\",\n", + " \"dateOfBirth\": \"2003-01-15\",\n", + " \"nationality\": \"USA\"\n", + " },\n", + " \"physicalAttributes\": {\n", + " \"position\": \"PG\",\n", + " \"height\": {\n", + " \"feet\": 6,\n", + " \"inches\": 4\n", + " },\n", + " \"weightPounds\": 205\n", + " },\n", + " \"hometown\": {\n", + " \"city\": \"Springfield\",\n", + " \"state\": \"IL\"\n", + " }\n", + " },\n", + " \"TeamInfo\": {\n", + " \"collegeTeam\": \"Springfield Tigers\",\n", + " \"conference\": \"Big West\",\n", + " \"teamRanking\": 12,\n", + " \"coach\": {\n", + " \"coachId\": 987,\n", + " \"coachName\": \"Marcus Reed\",\n", + " \"experienceYears\": 10\n", + " }\n", + " }\n", + " },\n", + " \"Stats\": {\n", + " \"season\": \"2025\",\n", + " \"gamesPlayed\": 32,\n", + " \"minutesPerGame\": 34.5,\n", + " \"shooting\": {\n", + " \"FieldGoalPercentage\": 47.2,\n", + " \"ThreePointPercentage\": 39.1,\n", + " \"FreeThrowPercentage\": 85.6\n", + " },\n", + " \"averages\": {\n", + " \"points\": 21.3,\n", + " \"rebounds\": 4.1,\n", + " \"assists\": 6.8,\n", + " \"steals\": 1.7,\n", + " \"blocks\": 0.3\n", + " }\n", + " },\n", + " \"Scouting\": {\n", + " \"evaluations\": {\n", + " \"strengths\": [\"Court vision\", \"Clutch shooting\"],\n", + " \"areasForImprovement\": [\"Defensive consistency\"]\n", + " },\n", + " \"scouts\": [\n", + " {\n", + " \"scoutId\": 501,\n", + " \"name\": \"Greg Hamilton\",\n", + " \"organization\": \"National Scouting Bureau\"\n", + " }\n", + " ]\n", + " },\n", + " \"DraftProjection\": {\n", + " \"mockDraftRanking\": 5,\n", + " \"lotteryPickProbability\": 88,\n", + " \"historicalComparisons\": [\n", + " {\n", + " \"player\": \"Chris Paul\",\n", + " \"similarityPercentage\": 85\n", + " }\n", + " ]\n", + " },\n", + " \"Media\": {\n", + " \"highlightReelUrl\": \"https://example.com/highlights/jalen-storm\",\n", + " \"socialMedia\": {\n", + " \"twitter\": \"@jstorm23\",\n", + " \"instagram\": \"@jstorm23_ig\"\n", + " }\n", + " },\n", + " \"Agent\": {\n", + " \"agentName\": \"Rick Allen\",\n", + " \"agency\": \"Elite Sports Management\",\n", + " \"contact\": {\n", + " \"email\": \"rallen@elitesports.com\",\n", + " \"phone\": \"555-123-4567\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " // ... Many thousands of tokens later.\n", + " ]\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Guiding principles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Break down unwieldy functions into smaller ones with clear roles and responsibilities\n", + "\n", + "It almost goes without saying—when building function calls, your top priority is to design clear, well-defined functions. This makes it easy to trim response sizes and avoid overwhelming the model. Each function call should be straightforward to explain, sharply scoped, and return only the information needed for its purpose. Overlapping responsibilities between functions inevitably invites confusion.\n", + "\n", + "For example, we can limit the `searchDraftProspects` function call to return only general details—such as player stats—for each prospect, dramatically reducing the response size. If more information is needed, the new `getProspectDetails` function call provides expanded details. There’s no universal solution; the right approach depends on your use case and data model.\n", + "\n", + "```json\n", + "{\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"function\",\n", + " \"name\": \"searchDraftProspects\",\n", + " \"description\": \"Search NBA draft prospects by position, draft year, and projected ranking, returning only general statistics to optimize response size.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"position\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The player's basketball position.\",\n", + " \"enum\": [\n", + " \"Point Guard\",\n", + " \"Shooting Guard\",\n", + " \"Small Forward\",\n", + " \"Power Forward\",\n", + " \"Center\",\n", + " \"Any\"\n", + " ]\n", + " },\n", + " \"year\": {\n", + " \"type\": \"number\",\n", + " \"description\": \"Draft year, e.g., 2025\"\n", + " },\n", + " \"maxMockDraftRanking\": {\n", + " \"type\": \"number\",\n", + " \"description\": \"Maximum predicted draft ranking (e.g., top 10)\"\n", + " }\n", + " },\n", + " \"required\": [\"position\", \"year\"]\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"function\",\n", + " \"name\": \"getProspectDetails\",\n", + " \"description\": \"Fetch detailed information for a specific NBA prospect, including comprehensive stats, agent details, and scouting reports.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"playerName\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Full name of the prospect (e.g., Jalen Storm)\"\n", + " },\n", + " \"year\": {\n", + " \"type\": \"number\",\n", + " \"description\": \"Draft year, e.g., 2025\"\n", + " },\n", + " \"includeAgentInfo\": {\n", + " \"type\": \"boolean\",\n", + " \"description\": \"Include agent information\"\n", + " },\n", + " \"includeStats\": {\n", + " \"type\": \"boolean\",\n", + " \"description\": \"Include detailed player statistics\"\n", + " },\n", + " \"includeScoutingReport\": {\n", + " \"type\": \"boolean\",\n", + " \"description\": \"Include scouting report details\"\n", + " }\n", + " },\n", + " \"required\": [\"playerName\", \"year\"]\n", + " }\n", + " }\n", + " ],\n", + " \"tool_choice\": \"auto\"\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. As conversations unfold, optimize the context\n", + "\n", + "Realtime conversations allow for generous 30-minute sessions—but the rolling context window only supports ~16,000 tokens (depending on the model snapshot, context window limitations are improving). As a result, you may notice performance gradually decline during extended exchanges. As conversations progress and more function calls are made, the conversation state can expand quickly with both important information and unnecessary noise—so it’s important to focus on keeping the most relevant details. This approach helps maintain strong performance and reduces cost." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**i) Periodically summarize the conversation state**\n", + "\n", + "Periodically summarizing the conversation as it unfolds is an excellent way to reduce context size—cutting both cost and latency.\n", + "\n", + "See @Minhajul's' epic guide on implementing automatic summarization in Realtime conversations ([link](https://cookbook.openai.com/examples/context_summarization_with_realtime_api))." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**ii) Periodically remind the the model of its role and responsibilities**\n", + "\n", + "Data-heavy payloads can quickly fill the context window. If you notice the model losing track of instructions or available tools, periodically remind it of its system prompt and tools by calling `session.update`—this keeps it focused on its role and responsibilities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Data processing and optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**i) Use filtering in your function calls to trim data-heavy responses down to only the essential fields needed to answer the question**\n", + "\n", + "Generally, fewer tokens returned by function calls lead to better quality responses. Common pitfalls occur when function calls return excessively large payloads spanning thousands of tokens. Focus on applying filters in each function call, either at the data-level or function-level, to minimize response sizes.\n", + "\n", + "```json\n", + "// Filtered response\n", + "{\n", + " \"status\": {\n", + " \"code\": 200,\n", + " \"message\": \"SUCCESS\"\n", + " },\n", + " \"found\": 4274,\n", + " \"offset\": 0,\n", + " \"limit\": 5,\n", + " \"data\": [\n", + " {\n", + " \"zpid\": 7972122,\n", + " \"data\": {\n", + " \"PropertyInfo\": {\n", + " \"houseNumber\": \"19661\",\n", + " \"directionPrefix\": \"N \",\n", + " \"streetName\": \"Central\",\n", + " \"streetSuffix\": \"Ave\",\n", + " \"city\": \"Phoenix\",\n", + " \"state\": \"AZ\",\n", + " \"postalCode\": \"85024\",\n", + " \"zipPlusFour\": \"1641\"\n", + " \"bedroomCount\": 2,\n", + " \"bathroomCount\": 2,\n", + " \"storyCount\": 1,\n", + " \"livingAreaSize\": 1089,\n", + " \"livingAreaSizeUnits\": \"Square Feet\",\n", + " \"yearBuilt\": \"1985\"\n", + " }\n", + "\t\t }\n", + "\t\t\t}\n", + "\t\t]\n", + "\t\t// ... \n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**ii) Flatten hierarchical payloads—without losing key information**\n", + "\n", + "Hierarchical payloads from API calls can sometimes include repeated level titles—like \"ProspectInfo\" or \"Stats\"—which may add extra noise and make things harder for the model to process. As you explore ways to make your data more efficient, you might try flattening these structures by trimming away some of the unnecessary labels. This can help improve performance, but consider what information is important to keep for your particular use case.\n", + "\n", + "```json\n", + "// Flattened payload\n", + "{\n", + " \"status\": {\n", + " \"code\": 200,\n", + " \"message\": \"SUCCESS\"\n", + " },\n", + " \"found\": 4274,\n", + " \"offset\": 0,\n", + " \"limit\": 2,\n", + " \"data\": [\n", + " {\n", + " \"prospectId\": 10001,\n", + " \"league\": \"NCAA\",\n", + " \"collegeId\": 301,\n", + " \"isDraftEligible\": true,\n", + " \"firstName\": \"Jalen\",\n", + " \"lastName\": \"Storm\",\n", + " \"position\": \"PG\",\n", + " \"heightFeet\": 6,\n", + " \"heightInches\": 4,\n", + " \"weightPounds\": 205,\n", + " \"hometown\": \"Springfield\",\n", + " \"state\": \"IL\",\n", + " \"collegeTeam\": \"Springfield Tigers\",\n", + " \"conference\": \"Big West\",\n", + " \"teamRanking\": 12,\n", + " \"coachId\": 987,\n", + " \"coachName\": \"Marcus Reed\",\n", + " \"gamesPlayed\": 32,\n", + " \"minutesPerGame\": 34.5,\n", + " \"FieldGoalPercentage\": 47.2,\n", + " \"ThreePointPercentage\": 39.1,\n", + " \"FreeThrowPercentage\": 85.6,\n", + " \"averagePoints\": 21.3,\n", + " \"averageRebounds\": 4.1,\n", + " \"averageAssists\": 6.8,\n", + " \"stealsPerGame\": 1.7,\n", + " \"blocksPerGame\": 0.3,\n", + " \"strengths\": [\"Court vision\", \"Clutch shooting\"],\n", + " \"areasForImprovement\": [\"Defensive consistency\"],\n", + " \"mockDraftRanking\": 5,\n", + " \"lotteryPickProbability\": 88,\n", + " \"highlightReelUrl\": \"https://example.com/highlights/jalen-storm\",\n", + " \"agentName\": \"Rick Allen\",\n", + " \"agency\": \"Elite Sports Management\",\n", + " \"contactEmail\": \"rallen@elitesports.com\"\n", + " },\n", + "\t\t...\n", + " }\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**iii) Experiment with different data formats**\n", + "\n", + "The way you structure your data has a direct impact on how well the model processes and summarizes API responses. In our experience, clear, key-based formats like JSON or YAML help the model interpret data more accurately than tabular formats such as Markdown. Large tables, especially, tend to overwhelm the model—resulting in less fluent and less accurate outputs. Still, it’s worth experimenting with different formats to find what works best for your use case.\n", + "\n", + "```yaml\n", + "status:\n", + " code: 200\n", + " message: \"SUCCESS\"\n", + "found: 4274\n", + "offset: 0\n", + "limit: 10\n", + "data:\n", + " - prospectId: 10001\n", + " data:\n", + " ProspectInfo:\n", + " league: \"NCAA\"\n", + " collegeId: 301\n", + " isDraftEligible: true\n", + " Player:\n", + " firstName: \"Jalen\"\n", + " lastName: \"Storm\"\n", + " position: \"PG\"\n", + " heightFeet: 6\n", + " heightInches: 4\n", + " weightPounds: 205\n", + " hometown: \"Springfield\"\n", + " state: \"IL\"\n", + " TeamInfo:\n", + " collegeTeam: \"Springfield Tigers\"\n", + " conference: \"Big West\"\n", + " teamRanking: 12\n", + " coachId: 987\n", + " coachName: \"Marcus Reed\"\n", + " Stats:\n", + " gamesPlayed: 32\n", + " minutesPerGame: 34.5\n", + " FieldGoalPercentage: 47.2\n", + " ThreePointPercentage: 39.1\n", + " FreeThrowPercentage: 85.6\n", + " averagePoints: 21.3\n", + " averageRebounds: 4.1\n", + " averageAssists: 6.8\n", + " stealsPerGame: 1.7\n", + " blocksPerGame: 0.3\n", + " Scouting:\n", + " strengths:\n", + " - \"Court vision\"\n", + " - \"Clutch shooting\"\n", + " areasForImprovement:\n", + " - \"Defensive consistency\"\n", + " DraftProjection:\n", + " mockDraftRanking: 5\n", + " lotteryPickProbability: 88\n", + " Media:\n", + " highlightReelUrl: \"https://example.com/highlights/jalen-storm\"\n", + " Agent:\n", + " agentName: \"Rick Allen\"\n", + " agency: \"Elite Sports Management\"\n", + " contactEmail: \"rallen@elitesports.com\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. After data-heavy function calls, follow up with hint prompts\n", + "\n", + "Underlying models often struggle to transition smoothly from data-heavy responses to accurate answers. To improve fluency and accuracy when working with complex data, provide a function call hint immediately after the function call. These hints guide the model on the specific task—teaching it how to interpret key fields and domain-specific values.\n", + "\n", + "The following example illustrates an effective hint prompt.\n", + "\n", + "```javascript\n", + "// Function call hint\n", + "let prospectSearchPrompt = `\n", + "Parse NBA prospect data and provide a concise, engaging response.\n", + "\n", + "General Guidelines\n", + "- Act as an NBA scouting expert.\n", + "- Highlight key strengths and notable attributes.\n", + "- Use conversational language.\n", + "- Mention identical attributes once.\n", + "- Ignore IDs and URLs.\n", + "\n", + "Player Details\n", + "- State height conversationally (\"six-foot-eight\").\n", + "- Round weights to nearest 5 lbs.\n", + "\n", + "Stats & Draft Info\n", + "- Round stats to nearest whole number.\n", + "- Use general terms for draft ranking (\"top-five pick\").\n", + "Experience\n", + "- Refer to players as freshman, sophomore, etc., or mention professional experience.\n", + "- Location & TeamMention hometown city and state/country.\n", + "- Describe teams conversationally.\n", + "\n", + "Skip (unless asked explicitly)\n", + "- Exact birth dates\n", + "- IDs\n", + "- Agent/contact details\n", + "- URLs\n", + "\n", + "Examples\n", + "- \"Jalen Storm, a dynamic six-foot-four point guard from Springfield, Illinois, averages 21 points per game.\"\n", + "- \"Known for his clutch shooting, he's projected as a top-five pick.\"\n", + "\n", + "Important: Respond based strictly on provided data, without inventing details.\n", + "`;\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, we first append the function call result to the conversation. Then, we emit a response from the Realtime API with the hint prompt. Voilà—the model gracefully handles all the information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```javascript\n", + "// Add new conversation item for the model\n", + "const conversationItem = {\n", + " type: 'conversation.item.create',\n", + " previous_item_id: output.id,\n", + " item: {\n", + " call_id: output.call_id,\n", + " type: 'function_call_output',\n", + " output: `Draft Prospect Search Results: ${result}`\n", + " }\n", + "};\n", + "\n", + "dataChannel.send(JSON.stringify(conversationItem));\n", + "\n", + "// Emit a response from the model including the hint prompt\n", + "const event = {\n", + " type: 'response.create',\n", + " conversation: \"none\",\n", + " response: {\n", + " instructions: prospectSearchPrompt # function call hint\n", + " }\n", + "};\n", + "\n", + "dataChannel.send(JSON.stringify(event));\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wrapping up\n", + "\n", + "Building effective agents with the Realtime API is an ongoing process of exploration and adaptation.\n", + "\n", + "**Summary of Key Recommendations**\n", + "\n", + "- **Filter data:** Only include fields and details that are directly relevant to the user’s request or the model’s next step. Trim the rest.\n", + "- **Flatten and simplify structures:** Reduce deeply nested or redundant data. Present information in a way that’s easy for both models and humans to scan.\n", + "- **Prefer clear, structured formats:** Use JSON (or YAML) with consistent field names and minimal noise. Avoid large tables or markdown for data-heavy responses.\n", + "- **Guide the model with hint prompts:** After returning lots of data, follow up with a targeted prompt that explains exactly what the model should extract or summarize.\n", + "\n", + "Remember—experimentation is essential. Realtime models keep improving, and we’ll continue sharing tips to help you get the most out of the Realtime API." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/Embedding_Wikipedia_articles_for_search.ipynb b/examples/Embedding_Wikipedia_articles_for_search.ipynb index 54dd1e6db9..0a2b6bf333 100644 --- a/examples/Embedding_Wikipedia_articles_for_search.ipynb +++ b/examples/Embedding_Wikipedia_articles_for_search.ipynb @@ -571,7 +571,7 @@ "\n", "Now that we've split our library into shorter self-contained strings, we can compute embeddings for each.\n", "\n", - "(For large embedding jobs, use a script like [api_request_parallel_processor.py](api_request_parallel_processor.py) to parallelize requests while throttling to stay under rate limits.)" + "(For large embedding jobs, use a script like [api_request_parallel_processor.py](https://github.com/openai/openai-cookbook/blob/main/examples/api_request_parallel_processor.py) to parallelize requests while throttling to stay under rate limits.)" ] }, { diff --git a/examples/File_Search_Responses.ipynb b/examples/File_Search_Responses.ipynb new file mode 100644 index 0000000000..aaa49f4445 --- /dev/null +++ b/examples/File_Search_Responses.ipynb @@ -0,0 +1,688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2dfbaf53-32de-4b8c-bd1c-d27371a87f81", + "metadata": {}, + "source": [ + "# Using file search tool in the Responses API\n", + "\n", + "Although RAG can be overwhelming, searching amongst PDF file shouldn't be complicated. One of the most adopted options as of now is parsing your PDF, defining your chunking strategies, uploading those chunks to a storage provider, running embeddings on those chunks of texts and storing those embeddings in a vector database. And that's only the setup — retrieving content in our LLM workflow also requires multiple steps.\n", + "\n", + "This is where file search — a hosted tool you can use in the Responses API — comes in. It allows you to search your knowledge base and generate an answer based on the retrieved content. In this cookbook, we'll upload those PDFs to a vector store on OpenAI and use file search to fetch additional context from this vector store to answer the questions we generated in the first step. Then, we'll initially create a small set of questions based on PDFs extracted from OpenAI's blog ([openai.com/news](https://openai.com/news)).\n", + "\n", + "_File search was previously available on the Assistants API. It's now available on the new Responses API, an API that can be stateful or stateless, and with from new features like metadata filtering_\n", + "\n", + "### Set up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47480955-9dd4-4837-8b4c-6821bb48306b", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install PyPDF2 pandas tqdm openai -q" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f6feaf3e-a2be-4c74-bad5-0c37bbe110b5", + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "from tqdm import tqdm\n", + "import concurrent\n", + "import PyPDF2\n", + "import os\n", + "import pandas as pd\n", + "import base64\n", + "\n", + "client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))\n", + "dir_pdfs = 'openai_blog_pdfs' # have those PDFs stored locally here\n", + "pdf_files = [os.path.join(dir_pdfs, f) for f in os.listdir(dir_pdfs)]" + ] + }, + { + "cell_type": "markdown", + "id": "43e5cb9c-fc99-45e2-bd79-9c9ba5b410cc", + "metadata": {}, + "source": [ + "### Creating Vector Store with our PDFs\n", + "\n", + "We will create a Vector Store on OpenAI API and upload our PDFs to the Vector Store. OpenAI will read those PDFs, separate the content into multiple chunks of text, run embeddings on those and store those embeddings and the text in the Vector Store. It will enable us to query this Vector Store to return relevant content based on a query." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a6823030-9110-4143-ab7c-a223182eb7e0", + "metadata": {}, + "outputs": [], + "source": [ + "def upload_single_pdf(file_path: str, vector_store_id: str):\n", + " file_name = os.path.basename(file_path)\n", + " try:\n", + " file_response = client.files.create(file=open(file_path, 'rb'), purpose=\"assistants\")\n", + " attach_response = client.vector_stores.files.create(\n", + " vector_store_id=vector_store_id,\n", + " file_id=file_response.id\n", + " )\n", + " return {\"file\": file_name, \"status\": \"success\"}\n", + " except Exception as e:\n", + " print(f\"Error with {file_name}: {str(e)}\")\n", + " return {\"file\": file_name, \"status\": \"failed\", \"error\": str(e)}\n", + "\n", + "def upload_pdf_files_to_vector_store(vector_store_id: str):\n", + " pdf_files = [os.path.join(dir_pdfs, f) for f in os.listdir(dir_pdfs)]\n", + " stats = {\"total_files\": len(pdf_files), \"successful_uploads\": 0, \"failed_uploads\": 0, \"errors\": []}\n", + " \n", + " print(f\"{len(pdf_files)} PDF files to process. Uploading in parallel...\")\n", + "\n", + " with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n", + " futures = {executor.submit(upload_single_pdf, file_path, vector_store_id): file_path for file_path in pdf_files}\n", + " for future in tqdm(concurrent.futures.as_completed(futures), total=len(pdf_files)):\n", + " result = future.result()\n", + " if result[\"status\"] == \"success\":\n", + " stats[\"successful_uploads\"] += 1\n", + " else:\n", + " stats[\"failed_uploads\"] += 1\n", + " stats[\"errors\"].append(result)\n", + "\n", + " return stats\n", + "\n", + "def create_vector_store(store_name: str) -> dict:\n", + " try:\n", + " vector_store = client.vector_stores.create(name=store_name)\n", + " details = {\n", + " \"id\": vector_store.id,\n", + " \"name\": vector_store.name,\n", + " \"created_at\": vector_store.created_at,\n", + " \"file_count\": vector_store.file_counts.completed\n", + " }\n", + " print(\"Vector store created:\", details)\n", + " return details\n", + " except Exception as e:\n", + " print(f\"Error creating vector store: {e}\")\n", + " return {}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5cb6cba0-931e-426a-88aa-34a62cc7158c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vector store created: {'id': 'vs_67d06b9b9a9c8191bafd456cf2364ce3', 'name': 'openai_blog_store', 'created_at': 1741712283, 'file_count': 0}\n", + "21 PDF files to process. Uploading in parallel...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████| 21/21 [00:09<00:00, 2.32it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'total_files': 21,\n", + " 'successful_uploads': 21,\n", + " 'failed_uploads': 0,\n", + " 'errors': []}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "store_name = \"openai_blog_store\"\n", + "vector_store_details = create_vector_store(store_name)\n", + "upload_pdf_files_to_vector_store(vector_store_details[\"id\"])" + ] + }, + { + "cell_type": "markdown", + "id": "e5f4ade3-2b3e-4df6-a441-c1ee3ea73172", + "metadata": {}, + "source": [ + "### Standalone vector search\n", + "\n", + "Now that our vector store is ready, we are able to query the Vector Store directly and retrieve relevant content for a specific query. Using the new [vector search API](https://platform.openai.com/docs/api-reference/vector-stores/search), we're able to find relevant items from our knowledge base without necessarily integrating it in an LLM query." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "980323d0-0112-4c9e-9b90-67719739026f", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"What's Deep Research?\"\n", + "search_results = client.vector_stores.search(\n", + " vector_store_id=vector_store_details['id'],\n", + " query=query\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c6045a2e-a75f-48c0-89f4-841ef722d24f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3502 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.9813588865322393\n", + "3493 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.9522476825143714\n", + "3634 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.9397930296526796\n", + "2774 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.9101975747303771\n", + "3474 of character of content from Deep research System Card _ OpenAI.pdf with a relevant score of 0.9036647613464299\n", + "3123 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.887120981288272\n", + "3343 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.8448454849432881\n", + "3262 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.791345286655509\n", + "3271 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.7485530025091963\n", + "2721 of character of content from Introducing deep research _ OpenAI.pdf with a relevant score of 0.734033360849088\n" + ] + } + ], + "source": [ + "for result in search_results.data:\n", + " print(str(len(result.content[0].text)) + ' of character of content from ' + result.filename + ' with a relevant score of ' + str(result.score))" + ] + }, + { + "cell_type": "markdown", + "id": "d4b0b4ec-ea13-429a-a1b7-7bac3d2ea014", + "metadata": {}, + "source": [ + "We can see that different size (and under-the-hood different texts) have been returned from the search query. They all have different relevancy score that are calculated by our ranker which uses hybrid search.\n", + "\n", + "### Integrating search results with LLM in a single API call\n", + "\n", + "However instead of querying the vector store and then passing the data into the Responses or Chat Completion API call, an even more convenient way to use this search results in an LLM query would be to plug use file_search tool as part of OpenAI Responses API." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a153cb6e-e94b-4b55-a557-4f34fd3022bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files used: {'Introducing deep research _ OpenAI.pdf'}\n", + "Response:\n", + "Deep Research is a new capability introduced by OpenAI that allows users to conduct complex, multi-step research tasks on the internet efficiently. Key features include:\n", + "\n", + "1. **Autonomous Research**: Deep Research acts as an independent agent that synthesizes vast amounts of information across the web, enabling users to receive comprehensive reports similar to those produced by a research analyst.\n", + "\n", + "2. **Multi-Step Reasoning**: It performs deep analysis by finding, interpreting, and synthesizing data from various sources, including text, images, and PDFs.\n", + "\n", + "3. **Application Areas**: Especially useful for professionals in fields such as finance, science, policy, and engineering, as well as for consumers seeking detailed information for purchases.\n", + "\n", + "4. **Efficiency**: The output is fully documented with citations, making it easy to verify information, and it significantly speeds up research processes that would otherwise take hours for a human to complete.\n", + "\n", + "5. **Limitations**: While Deep Research enhances research capabilities, it is still subject to limitations, such as potential inaccuracies in information retrieval and challenges in distinguishing authoritative data from unreliable sources.\n", + "\n", + "Overall, Deep Research marks a significant advancement toward automated general intelligence (AGI) by improving access to thorough and precise research outputs.\n" + ] + } + ], + "source": [ + "query = \"What's Deep Research?\"\n", + "response = client.responses.create(\n", + " input= query,\n", + " model=\"gpt-4o-mini\",\n", + " tools=[{\n", + " \"type\": \"file_search\",\n", + " \"vector_store_ids\": [vector_store_details['id']],\n", + " }]\n", + ")\n", + "\n", + "# Extract annotations from the response\n", + "annotations = response.output[1].content[0].annotations\n", + " \n", + "# Get top-k retrieved filenames\n", + "retrieved_files = set([result.filename for result in annotations])\n", + "\n", + "print(f'Files used: {retrieved_files}')\n", + "print('Response:')\n", + "print(response.output[1].content[0].text) # 0 being the filesearch call" + ] + }, + { + "cell_type": "markdown", + "id": "e6c7b7b3-7d63-4630-95e7-76cf8080477e", + "metadata": {}, + "source": [ + "We can see that `gpt-4o-mini` was able to answer a query that required more recent, specialised knowledge about OpenAI's Deep Research. It used content from the file `Introducing deep research _ OpenAI.pdf` that had chunks of texts that were the most relevant. If we want to go even deeper in the analysis of chunk of text retrieved, we can also analyse the different texts that were returned by the search engine by adding `include=[\"output[*].file_search_call.search_results\"]` to our query.\n", + "\n", + "## Evaluating performance\n", + "\n", + "What is key for those information retrieval system is to also measure the relevance & quality of files retrieved for those answers. The following steps of this cookbook will consist in generating an evaluation dataset and calculating different metrics over this generated dataset. This is an imperfect approach and we'll always recommend to have a human-verified evaluation dataset for your own use-cases, but it will show you the methodology to evaluate those. It will be imperfect because some of the questions generated might be generic (e.g: What's said by the main stakeholder in this document) and our retrieval test will have a hard time to figure out which document that question was generated for." + ] + }, + { + "cell_type": "markdown", + "id": "93291578-d04a-4e71-8ecb-9f0f647e68c3", + "metadata": {}, + "source": [ + "### Generating questions\n", + "\n", + "We will create functions that will read through the PDFs we have locally and generate a question that can only be answered by this document. Therefore it'll create our evaluation dataset that we can use after." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a1274ce-a468-489a-9206-0ff6ba82e8e7", + "metadata": {}, + "outputs": [], + "source": [ + "def extract_text_from_pdf(pdf_path):\n", + " text = \"\"\n", + " try:\n", + " with open(pdf_path, \"rb\") as f:\n", + " reader = PyPDF2.PdfReader(f)\n", + " for page in reader.pages:\n", + " page_text = page.extract_text()\n", + " if page_text:\n", + " text += page_text\n", + " except Exception as e:\n", + " print(f\"Error reading {pdf_path}: {e}\")\n", + " return text\n", + "\n", + "def generate_questions(pdf_path):\n", + " text = extract_text_from_pdf(pdf_path)\n", + "\n", + " prompt = (\n", + " \"Can you generate a question that can only be answered from this document?:\\n\"\n", + " f\"{text}\\n\\n\"\n", + " )\n", + "\n", + " response = client.responses.create(\n", + " input=prompt,\n", + " model=\"gpt-4o\",\n", + " )\n", + "\n", + " question = response.output[0].content[0].text\n", + "\n", + " return question" + ] + }, + { + "cell_type": "markdown", + "id": "7850d17f-832f-4a03-8216-5200d2db6b17", + "metadata": {}, + "source": [ + "If we run the function generate_question for the first PDF file we will be able to see the kind of question it generates." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4d19e4f5-a193-4787-aad1-8547173d36f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'What new capabilities will ChatGPT have as a result of the partnership between OpenAI and Schibsted Media Group?'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "generate_questions(pdf_files[0])" + ] + }, + { + "cell_type": "markdown", + "id": "dc2e4e26-3396-4a3b-83a9-db9ae1597e41", + "metadata": {}, + "source": [ + "We can now generate all the questions for all the PDFs we've got stored locally." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0fec6e6c-13b6-4498-b49c-d20e28b39ce9", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate questions for each PDF and store in a dictionary\n", + "questions_dict = {}\n", + "for pdf_path in pdf_files:\n", + " questions = generate_questions(pdf_path)\n", + " questions_dict[os.path.basename(pdf_path)] = questions" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2e04371b-32ef-48f9-833a-84f53b7399fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'OpenAI partners with Schibsted Media Group _ OpenAI.pdf': 'What is the purpose of the partnership between Schibsted Media Group and OpenAI announced on February 10, 2025?',\n", + " 'OpenAI and the CSU system bring AI to 500,000 students & faculty _ OpenAI.pdf': 'What significant milestone did the California State University system achieve by partnering with OpenAI, making it the first of its kind in the United States?',\n", + " '1,000 Scientist AI Jam Session _ OpenAI.pdf': 'What was the specific AI model used during the \"1,000 Scientist AI Jam Session\" event across the nine national labs?',\n", + " 'Announcing The Stargate Project _ OpenAI.pdf': 'What are the initial equity funders and lead partners in The Stargate Project announced by OpenAI, and who holds the financial and operational responsibilities?',\n", + " 'Introducing Operator _ OpenAI.pdf': 'What is the name of the new model that powers the Operator agent introduced by OpenAI?',\n", + " 'Introducing NextGenAI _ OpenAI.pdf': 'What major initiative did OpenAI launch on March 4, 2025, and which research institution from Europe is involved as a founding partner?',\n", + " 'Introducing the Intelligence Age _ OpenAI.pdf': \"What is the name of the video generation tool used by OpenAI's creative team to help produce their Super Bowl ad?\",\n", + " 'Operator System Card _ OpenAI.pdf': 'What is the preparedness score for the \"Cybersecurity\" category according to the Operator System Card?',\n", + " 'Strengthening America’s AI leadership with the U.S. National Laboratories _ OpenAI.pdf': \"What is the purpose of OpenAI's agreement with the U.S. National Laboratories as described in the document?\",\n", + " 'OpenAI GPT-4.5 System Card _ OpenAI.pdf': 'What is the Preparedness Framework rating for \"Cybersecurity\" for GPT-4.5 according to the system card?',\n", + " 'Partnering with Axios expands OpenAI’s work with the news industry _ OpenAI.pdf': \"What is the goal of OpenAI's new content partnership with Axios as announced in the document?\",\n", + " 'OpenAI and Guardian Media Group launch content partnership _ OpenAI.pdf': 'What is the main purpose of the partnership between OpenAI and Guardian Media Group announced on February 14, 2025?',\n", + " 'Introducing GPT-4.5 _ OpenAI.pdf': 'What is the release date of the GPT-4.5 research preview?',\n", + " 'Introducing data residency in Europe _ OpenAI.pdf': 'What are the benefits of data residency in Europe for new ChatGPT Enterprise and Edu customers according to the document?',\n", + " 'The power of personalized AI _ OpenAI.pdf': 'What is the purpose of the \"Model Spec\" document published by OpenAI for ChatGPT?',\n", + " 'Disrupting malicious uses of AI _ OpenAI.pdf': \"What is OpenAI's mission as stated in the document?\",\n", + " 'Sharing the latest Model Spec _ OpenAI.pdf': 'What is the release date of the latest Model Spec mentioned in the document?',\n", + " 'Deep research System Card _ OpenAI.pdf': \"What specific publication date is mentioned in the Deep Research System Card for when the report on deep research's preparedness was released?\",\n", + " 'Bertelsmann powers creativity and productivity with OpenAI _ OpenAI.pdf': 'What specific AI-powered solutions is Bertelsmann planning to implement for its divisions RTL Deutschland and Penguin Random House according to the document?',\n", + " 'OpenAI’s Economic Blueprint _ OpenAI.pdf': 'What date and location is scheduled for the kickoff event of OpenAI\\'s \"Innovating for America\" initiative as mentioned in the Economic Blueprint document?',\n", + " 'Introducing deep research _ OpenAI.pdf': 'What specific model powers the \"deep research\" capability in ChatGPT that is discussed in this document, and what are its main features designed for?'}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "questions_dict" + ] + }, + { + "cell_type": "markdown", + "id": "eea9bd1b-f746-4442-9f1b-aa31b5c766c6", + "metadata": {}, + "source": [ + "We now have a dictionary of `filename:question` that we can loop through and ask gpt-4o(-mini) about without providing the document, and gpt-4o should be able to find the relevant document in the Vector Store." + ] + }, + { + "cell_type": "markdown", + "id": "dbda554b-c3d4-4b07-9028-b41670c2fa20", + "metadata": {}, + "source": [ + "We'll convert our dictionary into a dataframe and process it using gpt-4o-mini. We will look out for the expected file " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "968d54af-55c0-4b21-9ed8-c57811f9700f", + "metadata": {}, + "outputs": [], + "source": [ + "rows = []\n", + "for filename, query in questions_dict.items():\n", + " rows.append({\"query\": query, \"_id\": filename.replace(\".pdf\", \"\")})\n", + "\n", + "# Metrics evaluation parameters\n", + "k = 5\n", + "total_queries = len(rows)\n", + "correct_retrievals_at_k = 0\n", + "reciprocal_ranks = []\n", + "average_precisions = []\n", + "\n", + "def process_query(row):\n", + " query = row['query']\n", + " expected_filename = row['_id'] + '.pdf'\n", + " # Call file_search via Responses API\n", + " response = client.responses.create(\n", + " input=query,\n", + " model=\"gpt-4o-mini\",\n", + " tools=[{\n", + " \"type\": \"file_search\",\n", + " \"vector_store_ids\": [vector_store_details['id']],\n", + " \"max_num_results\": k,\n", + " }],\n", + " tool_choice=\"required\" # it will force the file_search, while not necessary, it's better to enforce it as this is what we're testing\n", + " )\n", + " # Extract annotations from the response\n", + " annotations = None\n", + " if hasattr(response.output[1], 'content') and response.output[1].content:\n", + " annotations = response.output[1].content[0].annotations\n", + " elif hasattr(response.output[1], 'annotations'):\n", + " annotations = response.output[1].annotations\n", + "\n", + " if annotations is None:\n", + " print(f\"No annotations for query: {query}\")\n", + " return False, 0, 0\n", + "\n", + " # Get top-k retrieved filenames\n", + " retrieved_files = [result.filename for result in annotations[:k]]\n", + " if expected_filename in retrieved_files:\n", + " rank = retrieved_files.index(expected_filename) + 1\n", + " rr = 1 / rank\n", + " correct = True\n", + " else:\n", + " rr = 0\n", + " correct = False\n", + "\n", + " # Calculate Average Precision\n", + " precisions = []\n", + " num_relevant = 0\n", + " for i, fname in enumerate(retrieved_files):\n", + " if fname == expected_filename:\n", + " num_relevant += 1\n", + " precisions.append(num_relevant / (i + 1))\n", + " avg_precision = sum(precisions) / len(precisions) if precisions else 0\n", + " \n", + " if expected_filename not in retrieved_files:\n", + " print(\"Expected file NOT found in the retrieved files!\")\n", + " \n", + " if retrieved_files and retrieved_files[0] != expected_filename:\n", + " print(f\"Query: {query}\")\n", + " print(f\"Expected file: {expected_filename}\")\n", + " print(f\"First retrieved file: {retrieved_files[0]}\")\n", + " print(f\"Retrieved files: {retrieved_files}\")\n", + " print(\"-\" * 50)\n", + " \n", + " \n", + " return correct, rr, avg_precision" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ee6d3084-5fae-4a26-8fd2-d269ffbc60ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, 1.0, 1.0)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "process_query(rows[0])" + ] + }, + { + "cell_type": "markdown", + "id": "ba088faf-2945-48b3-a3de-412da1ee81fc", + "metadata": {}, + "source": [ + "Recall & Precision are at 1 for this example, and our file ranked first so we're having a MRR and MAP = 1 on this example.\n", + "\n", + "We can now execute this processing on our set of questions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6f1e1cc2-0128-48cc-9e4c-5eb416c21347", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 62%|███████████████████▏ | 13/21 [00:07<00:03, 2.57it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected file NOT found in the retrieved files!\n", + "Query: What is OpenAI's mission as stated in the document?\n", + "Expected file: Disrupting malicious uses of AI _ OpenAI.pdf\n", + "First retrieved file: Introducing the Intelligence Age _ OpenAI.pdf\n", + "Retrieved files: ['Introducing the Intelligence Age _ OpenAI.pdf']\n", + "--------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 71%|██████████████████████▏ | 15/21 [00:14<00:06, 1.04s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected file NOT found in the retrieved files!\n", + "Query: What is the purpose of the \"Model Spec\" document published by OpenAI for ChatGPT?\n", + "Expected file: The power of personalized AI _ OpenAI.pdf\n", + "First retrieved file: Sharing the latest Model Spec _ OpenAI.pdf\n", + "Retrieved files: ['Sharing the latest Model Spec _ OpenAI.pdf', 'Sharing the latest Model Spec _ OpenAI.pdf', 'Sharing the latest Model Spec _ OpenAI.pdf', 'Sharing the latest Model Spec _ OpenAI.pdf', 'Sharing the latest Model Spec _ OpenAI.pdf']\n", + "--------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████| 21/21 [00:15<00:00, 1.38it/s]\n" + ] + } + ], + "source": [ + "with ThreadPoolExecutor() as executor:\n", + " results = list(tqdm(executor.map(process_query, rows), total=total_queries))\n", + "\n", + "correct_retrievals_at_k = 0\n", + "reciprocal_ranks = []\n", + "average_precisions = []\n", + "\n", + "for correct, rr, avg_precision in results:\n", + " if correct:\n", + " correct_retrievals_at_k += 1\n", + " reciprocal_ranks.append(rr)\n", + " average_precisions.append(avg_precision)\n", + "\n", + "recall_at_k = correct_retrievals_at_k / total_queries\n", + "precision_at_k = recall_at_k # In this context, same as recall\n", + "mrr = sum(reciprocal_ranks) / total_queries\n", + "map_score = sum(average_precisions) / total_queries" + ] + }, + { + "cell_type": "markdown", + "id": "6bc74d02-7ee9-4cc3-b48f-5c205c3fdfcb", + "metadata": {}, + "source": [ + "The outputs logged above would either show that a file wasn't ranked first when our evaluation dataset expected it to rank first or that it wasn't found at all. As we can see from our imperfect evaluation dataset, some questions were generic and expected another doc, which our retrieval system didn't specifically retrieved for this question." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7a32ec63-8f39-4085-b123-f2593eb702d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metrics at k=5:\n", + "Recall@5: 0.9048\n", + "Precision@5: 0.9048\n", + "Mean Reciprocal Rank (MRR): 0.9048\n", + "Mean Average Precision (MAP): 0.8954\n" + ] + } + ], + "source": [ + "# Print the metrics with k\n", + "print(f\"Metrics at k={k}:\")\n", + "print(f\"Recall@{k}: {recall_at_k:.4f}\")\n", + "print(f\"Precision@{k}: {precision_at_k:.4f}\")\n", + "print(f\"Mean Reciprocal Rank (MRR): {mrr:.4f}\")\n", + "print(f\"Mean Average Precision (MAP): {map_score:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "34d19556-8d99-4c53-8800-eec54948a674", + "metadata": {}, + "source": [ + "With this cookbook we were able to see how to:\n", + "- Generate a dataset of evaluations using PDF context-stuffing (leveraging vision modality of 4o) and traditional PDF readers\n", + "- Create a vector store and populate it with PDF\n", + "- Get an LLM answer to a query, leveraging a RAG system available out-of-the-box with `file_search` tool call in OpenAI's Response API\n", + "- Understand how chunks of texts are retrieved, ranked and used as part of the Response API\n", + "- Measure accuracy, precision, retrieval, MRR and MAP on the dataset of evaluations previously generated\n", + "\n", + "By using file search with Responses, you can simplify RAG architecture and leverage this in a single API call using the new Responses API. File storage, embeddings, retrieval all integrated in one tool!\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (myenv)", + "language": "python", + "name": "myenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Fine_tuning_direct_preference_optimization_guide.ipynb b/examples/Fine_tuning_direct_preference_optimization_guide.ipynb new file mode 100644 index 0000000000..0f3e759bcb --- /dev/null +++ b/examples/Fine_tuning_direct_preference_optimization_guide.ipynb @@ -0,0 +1,698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-Tuning Techniques: Choosing Between SFT, DPO, and RFT (Including a Guide to DPO)\n", + " \n", + "*This guide is for developers and ML practitioners who have some experience with OpenAIʼs APIs and wish to use their fine-tuned models for research or other appropriate uses. OpenAI’s services are not intended for the personalized treatment or diagnosis of any medical condition and are subject to our [applicable terms](https://openai.com/policies/).*\n", + " \n", + "This guide discusses fine-tuning methods supported by OpenAI, specifically highlighting what each method is best for and not best for, to help you identify the most suitable technique for your use case. It then provides an in-depth look at one particular method — Direct Preference Optimization (DPO) — and provides links to existing guides for the other techniques.\n", + " \n", + "**What is fine-tuning?** Fine-tuning is the process of continuing training on a smaller, domain-specific dataset to optimize a model for a specific task. There are two main reasons why we would typically fine-tune:\n", + "1. Improve model performance on a specific task \n", + "2. Improve model efficiency (reduce the number of tokens needed, distill expertise into a smaller model, etc.)\n", + " \n", + "Currently, the OpenAI platform supports four fine-tuning methods:\n", + "- **Supervised fine-tuning (SFT):** this technique employs traditional supervised learning using input-output pairs to adjust model parameters. The training process adjusts model weights to minimize the difference between predicted and target outputs across the provided examples. The model will replicate features that it finds in provided pairs. \n", + "- **Vision fine-tuning:** this technique extends supervised fine-tuning to multimodal data by processing both text and image in a unified training framework. The training process adjusts model weights to minimize errors across text-image pairs and as a result improve the model's understanding of image inputs. \n", + "- **Direct preference optimization (DPO):** this technique uses pairwise comparisons (e.g., preferred and rejected example responses) to optimize a model to favor certain outputs over others. The model learns to replicate the preference patterns found in the provided comparison data. \n", + "- **Reinforcement fine-tuning (RFT):** this technique uses reinforcement learning with a reward signal (via a grader or reward model) to fine-tune the model for complex objectives. In RFT, the model generates outputs for given prompts during training, and each output is evaluated for quality. The model's parameters are then updated to maximize the reward, reinforcing behaviors that lead to better outcomes. This iterative feedback loop encourages the model to improve reasoning or decision-making strategies. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To help you select the appropriate fine-tuning technique, the table below summarizes the scenarios each method is best suited for, as well as those for which it is not well suited:\n", + "\n", + "| **Technique** | **Good For** | **Not Good For** |\n", + "| ---------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- |\n", + "| **Supervised fine-tuning (SFT)** | Emphasizing knowledge already present in the model.
Customizing response structure or tone.
Generating content in a specific format.
Teaching complex instructions or correcting instruction-following failures.
Optimizing cost/latency (saving tokens from prompt or distilling). | Adding entirely new knowledge (consider RAG instead).
Tasks with subjective quality. |\n", + "| **Vision fine-tuning** | Specialized visual recognition tasks (e.g., image classification).
Domain-specific image understanding.
Correcting failures in instruction following for complex prompts. | Purely textual tasks.
Generalized visual tasks without specific context.
General image understanding. |\n", + "| **Direct preference optimization (DPO)** | Aligning model outputs with subjective preferences (tone, politeness).
Refining outputs via human-rated feedback.
Achieving nuanced behavioral alignment. | Learning completely new tasks.
Tasks without clear human preference signals. |\n", + "| **Reinforcement fine-tuning (RFT)** | Complex domain-specific tasks that require advanced reasoning.
Refining existing partial capabilities (fostering emergent behaviours).
Tasks with measurable feedback.
Scenarios with limited explicit labels where reward signals can be defined. | Tasks where the model has no initial skill.
Tasks without clear feedback or measurable signals. |\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Today, there are pre-existing Cookbooks for: \n", + "\n", + "- Supervised fine-tuning (SFT): (1) [How to fine-tune chat models](https://cookbook.openai.com/examples/how_to_finetune_chat_models) (2) [Leveraging model distillation to fine-tune a model](https://cookbook.openai.com/examples/leveraging_model_distillation_to_fine-tune_a_model)\n", + "- Vision fine-tuning: [Vision fine-tuning on GPT-4o for visual question answering](https://cookbook.openai.com/examples/multimodal/vision_fine_tuning_on_gpt4o_for_visual_question_answering)\n", + "- Reinforcement fine-tuning (RFT): (1) [Reinforcement fine-tuning (RFT)](https://cookbook.openai.com/examples/reinforcement_fine_tuning), (2) [Reinforcement fine-tuning for healthbench QA](https://cookbook.openai.com/examples/fine-tuned_qa/reinforcement_finetuning_healthbench)\n", + "\n", + "Direct preference optimization (DPO) will be covered in this guide." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Guide to Direct Preference Optimization**\n", + " \n", + "As mentioned above, [Direct Preference Optimization (DPO)](https://platform.openai.com/docs/guides/direct-preference-optimization) is an alignment technique for fine-tuning language models using pairwise preference data (e.g., ranked pairs of responses). DPO directly optimizes a model to favor certain outputs over others using explicit pairwise comparisons, typically from human preferences. This approach simplifies alignment and eliminates the need for a separate reward model or complex reinforcement learning procedures, making DPO a lightweight alternative to techniques such as Reinforcement Learning from Human Feedback (RLHF).\n", + " \n", + "When should you use DPO? DPO excels in scenarios when response quality is subjective, cannot be measured objectively, or when nuanced criteria such as tone, style, appropriateness, or clarity matter - typically cases where multiple valid outputs exist. Example applications where DPO is particularly effective in aligning AI responses include: \n", + "- Enhancing Conversational AI Responses\n", + "- Improving Code Generation Quality & Style\n", + "- Ensuring Compliance with Legal, Ethical & Safety Standards \n", + "- Controlling Brand Voice, Professionalism, & Tone\n", + "- Customizing Creative Outputs & User Experience\n", + "\n", + "By fine-tuning on explicit pairs of preferred vs non‑preferred completions, DPO aligns model outputs to these nuanced preferences. The below table gives examples of pairwise preference data for a fictional AI assistant that represents an organization, where preferred responses are clear, professional, and aligned with brand standards.\n", + " \n", + "| **Example Question** | **Chosen Response** | **Rejected Response** |\n", + "|------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------|\n", + "| **Q1:** *How do I review your product?* | To submit a product review, please visit your account dashboard, select the product, and click ‘Write a review.’ Share your honest experience, rate key features, and submit when ready. | Yo, just leave some quick stars or whatever, it’s chill! |\n", + "| **Q2:** *How do I review your product?* | We welcome your feedback! In the ‘Reviews’ section on the product page, click ‘Leave a Review,’ rate it, and add your comments about what you liked or areas for improvement. | Just scribble something—doesn’t matter what, honestly. |\n", + "| **Q3:** *How to troubleshoot this particular error?* | To address the error ‘X101,’ first clear your cache, then verify your internet connection. If the issue remains, follow our step-by-step guide at [Support → Troubleshooting → Error X101]. | Just reboot it, I guess. If it doesn't work, you're on your own! |\n", + " \n", + "In this guide, weʼll walk through how to apply DPO using the fine-tuning API. You will learn key steps to take in order to successfully run preference fine-tuning jobs for your use-cases.\n", + " \n", + "Here’s what we’ll cover:\n", + " \n", + "- **1. Recommended Workflow**\n", + "- **2. Demonstration Scenario**\n", + "- **3. Generating the Dataset**\n", + "- **4. Benchmarking the Base Model**\n", + "- **5. Fine-Tuning**\n", + "- **6. Using your Fine-Tuned Model**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **1. Recommended Workflow**\n", + " \n", + "OpenAI recommends the following workflow: \n", + "1. Performing Supervised Fine-Tuning (SFT) on a subset of your preferred responses. \n", + "2. Using the SFT fine-tuned model as the starting point, apply DPO using preference comparison data. \n", + " \n", + "Performing Supervised Fine-Tuning (SFT) before Direct Preference Optimization (DPO) enhances model alignment and overall performance by establishing a robust initial policy, ensuring the model already prefers correct responses. This reduces the magnitude of weight updates during DPO, stabilizing training and preventing overfitting by allowing DPO to efficiently refine subtle nuances. Consequently, the combined SFT-then-DPO workflow converges faster and yields higher-quality results.\n", + "\n", + "In this guide, we'll focus exclusively on applying Direct Preference Optimization (DPO). However, depending on your use case, you may find performance gains from first performing Supervised Fine-Tuning (SFT). If so, you can follow the SFT guide linked above, save the resulting model ID, and use that as the starting point for your DPO job." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **2. Demonstration Scenario**\n", + "\n", + "To make things concrete, let’s walk through fine-tuning a customer-facing AI assistant to follow a fictional brand’s voice and style. Imagine Good Vibes Corp, an organization that prides itself on a friendly, enthusiastic tone with a personal touch. \n", + " \n", + "They want their customer AI assistant to answer queries in a way that reflects these brand guidelines (e.g. an upbeat attitude, polite language, and a friendly sign-off), and prefer those responses over more generic or curt answers. This is a good scenario for DPO: there’s no objectively correct answer format, but there is a preferred style.\n", + " \n", + "DPO will help the model learn from comparisons which style is preferred. We'll outline the steps to: (1) generate a synthetic preference dataset of prompts with paired responses (one in the desired brand voice and one not). (2) Evaluate base model performance using the OpenAI evals API. (3) Prepare and upload the data in the required JSONL format for preference fine-tuning. (4) Fine-tune the model with DPO using the OpenAI fine-tuning API. (5) Evaluate the fine-tuned model using the OpenAI evals API to show how the brand-style preference improved.\n", + "\n", + "We are going to synthesize a dataset for this demonstration. First, let’s create a seed bank of questions to generate more variations from.\n", + "\n", + "Let’s get started!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install openai nest-asyncio --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "PROMPT_SEED_POOL = [\n", + " \"Hi, I ordered a gadget last week. When will it arrive?\",\n", + " \"Your product stopped working after two days. Can I get help?\",\n", + " \"Do you offer discounts for long-term customers?\",\n", + " \"Can I change the shipping address for my order?\",\n", + " \"What is your return policy for damaged items?\",\n", + " \"My tracking number hasn't updated in three days—can you check the status?\",\n", + " \"How long is the warranty on your products, and how do I submit a claim?\",\n", + " \"Can I add gift wrapping to my order before it ships?\",\n", + " \"Do you accept PayPal or other alternative payment methods?\",\n", + " \"Is there an option to expedite shipping if my order hasn't left the warehouse yet?\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **3. Generating the Dataset**\n", + "\n", + "Next, we’ll define functions to take each prompt from our seed bank and generate related questions. We’ll create a dataset of preference pairs by first generating these prompt variations, then producing both a preferred and a rejected response for every prompt. \n", + "\n", + "This dataset is synthetic and serves to illustrate the mechanics of Direct Preference Optimization — when developing your own application you should collect or curate a high-quality, preference dataset. Note: the volume of data required for DPO depends on the use case; generally more is better (thousands to tens of thousands), and for preference pairs the ordering logic should be consistent (e.g. if A > B and B > C, then A > C)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n", + "from openai import AsyncOpenAI\n", + "from typing import List, Dict, Any\n", + "\n", + "async_client = AsyncOpenAI()\n", + "\n", + "SYSTEM_PROMPT = \"You are a customer-support assistant.\"\n", + "\n", + "\n", + "async def _generate_related_questions_from_prompt(\n", + " prompt: str, k: int, sem: asyncio.Semaphore, *, model: str\n", + ") -> List[str]:\n", + " \"\"\"Return *k* distinct customer-service questions related to the given prompt.\"\"\"\n", + " out: List[str] = []\n", + " async with sem:\n", + " for _ in range(k):\n", + " resp = await async_client.responses.create(\n", + " model=model,\n", + " input=[\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": (\n", + " \"Return ONE distinct, realistic customer-service question \"\n", + " \"related in topic or theme to the following question, \"\n", + " \"but NOT a direct paraphrase.\"\n", + " ),\n", + " },\n", + " {\"role\": \"user\", \"content\": prompt},\n", + " ],\n", + " temperature=0.9,\n", + " max_output_tokens=60,\n", + " )\n", + " out.append(resp.output_text.strip())\n", + " return out\n", + "\n", + "\n", + "async def expand_prompt_pool(\n", + " prompts: List[str], *, k: int = 3, concurrency: int = 32, model: str\n", + ") -> List[str]:\n", + " \"\"\"Expand each prompt into *k* related questions using the given model.\"\"\"\n", + " sem = asyncio.Semaphore(concurrency)\n", + " tasks = [\n", + " _generate_related_questions_from_prompt(p, k, sem, model=model) for p in prompts\n", + " ]\n", + " results = await asyncio.gather(*tasks)\n", + " return [v for sub in results for v in sub]\n", + "\n", + "\n", + "async def _generate_preference_pair(\n", + " prompt: str, sem: asyncio.Semaphore, *, model: str\n", + ") -> Dict[str, Any]:\n", + " \"\"\"Generate a preference pair for the given prompt.\"\"\"\n", + " async with sem:\n", + " friendly_task = async_client.responses.create(\n", + " model=model,\n", + " input=[\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": (\n", + " \"You are Good Vibes Corp's exceptionally energetic, outrageously friendly and \"\n", + " \"enthusiastic support agent.\"\n", + " ),\n", + " },\n", + " {\"role\": \"user\", \"content\": prompt},\n", + " ],\n", + " temperature=0.7, # higher temperature to increase creativity & on-brand tone adherence\n", + " max_output_tokens=80,\n", + " )\n", + " blunt_task = async_client.responses.create(\n", + " model=model,\n", + " input=[\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"You are a terse, factual support agent with no empathy or politeness.\",\n", + " },\n", + " {\"role\": \"user\", \"content\": prompt},\n", + " ],\n", + " temperature=0.3, # lower temperature to limit creativity & emphasize tonal difference\n", + " max_output_tokens=80,\n", + " )\n", + " friendly, blunt = await asyncio.gather(friendly_task, blunt_task)\n", + " return {\n", + " \"input\": {\n", + " \"messages\": [\n", + " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n", + " {\"role\": \"user\", \"content\": prompt},\n", + " ]\n", + " },\n", + " \"preferred_output\": [\n", + " {\"role\": \"assistant\", \"content\": friendly.output_text}\n", + " ],\n", + " \"non_preferred_output\": [\n", + " {\"role\": \"assistant\", \"content\": blunt.output_text}\n", + " ],\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, using these defined functions we'll build our dataset by generating friendly versus blunt response pairs. The friendly responses reflect the brand's desired communication style. We'll do this asynchronously for efficiency, creating a dataset suited for Direct Preference Optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset ready with 500 pairs.\n" + ] + } + ], + "source": [ + "import math\n", + "import nest_asyncio\n", + "\n", + "\n", + "async def build_dataset(\n", + " *,\n", + " pair_count: int = 500,\n", + " concurrency: int = 8,\n", + " expand_prompt_pool_model: str,\n", + " generate_preference_pair_model: str,\n", + ") -> List[Dict[str, Any]]:\n", + " \"\"\"Return *pair_count* preference pairs (single-shot expansion).\"\"\"\n", + "\n", + " seed = PROMPT_SEED_POOL\n", + " deficit = max(0, pair_count - len(seed))\n", + " k = max(1, math.ceil(deficit / len(seed)))\n", + "\n", + " expanded = await expand_prompt_pool(\n", + " seed,\n", + " k=k,\n", + " concurrency=concurrency,\n", + " model=expand_prompt_pool_model,\n", + " )\n", + " prompt_bank = (seed + expanded)[:pair_count]\n", + "\n", + " sem = asyncio.Semaphore(concurrency)\n", + " tasks = [\n", + " _generate_preference_pair(p, sem, model=generate_preference_pair_model)\n", + " for p in prompt_bank\n", + " ]\n", + " return await asyncio.gather(*tasks)\n", + "\n", + "\n", + "nest_asyncio.apply()\n", + "pairs = await build_dataset(\n", + " pair_count=500,\n", + " concurrency=8,\n", + " expand_prompt_pool_model=\"gpt-4.1-mini-2025-04-14\",\n", + " generate_preference_pair_model=\"gpt-4.1-mini-2025-04-14\",\n", + ")\n", + "print(f\"Dataset ready with {len(pairs)} pairs.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **4. Benchmarking the Base Model**\n", + "\n", + "Below, we split our dataset into training, validation, and testing sets. We also show a sample from the training dataset, which demonstrates a clear difference between the preferred (friendly, on-brand) and non-preferred (blunt, neutral) responses for that input pair." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input': {'messages': [{'role': 'system',\n", + " 'content': 'You are a customer-support assistant.'},\n", + " {'role': 'user',\n", + " 'content': 'Hi, I ordered a gadget last week. When will it arrive?'}]},\n", + " 'preferred_output': [{'role': 'assistant',\n", + " 'content': 'Hey there, awesome friend! 🌟 Thanks a bunch for reaching out! I’d LOVE to help you track down your gadget so you can start enjoying it ASAP! 🎉 Could you please share your order number or the email you used to place the order? Let’s make this delivery magic happen! 🚀✨'}],\n", + " 'non_preferred_output': [{'role': 'assistant',\n", + " 'content': 'Provide your order number for delivery status.'}]}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# set dataset sizes\n", + "n = len(pairs)\n", + "n_train = int(0.8 * n)\n", + "n_val = int(0.1 * n)\n", + "n_test = n - n_train - n_val\n", + "\n", + "# split dataset into train, test & validation\n", + "train_pairs = pairs[:n_train]\n", + "val_pairs = pairs[n_train : n_train + n_val]\n", + "test_pairs = pairs[n_train + n_val :]\n", + "train_pairs[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To assess the model's performance prior to fine-tuning, we'll use an automated grader (LLM-as-a-Judge) to score each response for friendliness and empathy. The grader will assign a score from 0 to 4 for each answer, allowing us to compute a mean baseline score for the base model. \n", + "\n", + "To do this, we first generate responses for the base model on the test set, then use the OpenAI evals API to create and run an evaluation with an automated grader. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "async def generate_responses(\n", + " testset,\n", + " model,\n", + " temperature=0.0,\n", + " max_output_tokens=80,\n", + " concurrency=8,\n", + "):\n", + " \"\"\"\n", + " Generate responses for each prompt in the testset using the OpenAI responses API.\n", + " Returns: List of dicts: [{\"prompt\": ..., \"response\": ...}, ...]\n", + " \"\"\"\n", + " async_client = AsyncOpenAI()\n", + " sem = asyncio.Semaphore(concurrency)\n", + "\n", + " async def get_response(prompt):\n", + " async with sem:\n", + " resp = await async_client.responses.create(\n", + " model=model,\n", + " input=[\n", + " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n", + " {\"role\": \"user\", \"content\": prompt},\n", + " ],\n", + " temperature=temperature,\n", + " max_output_tokens=max_output_tokens,\n", + " )\n", + " return {\"prompt\": prompt, \"response\": resp.output_text}\n", + "\n", + " tasks = [get_response(item[\"item\"][\"input\"]) for item in testset]\n", + " results = await asyncio.gather(*tasks)\n", + " return results\n", + "\n", + "\n", + "# generate responses for the base model over the test set\n", + "base_model = \"gpt-4.1-mini-2025-04-14\"\n", + "testset = [\n", + " {\"item\": {\"input\": pair[\"input\"][\"messages\"][1][\"content\"]}} for pair in test_pairs\n", + "]\n", + "responses = await generate_responses(testset, model=base_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll use the OpenAI evals API to create & run an evaluation with an automated grader, starting by defining the rubric for the LLM-as-a-Judge. Note: we will access responses via data logging, so in order for this to work, you'll need to be in an org where data logging isn't disabled (through zdr, etc.). If you aren't sure if this is the case for you, go to https://platform.openai.com/logs?api=responses and see if you can see the responses you just generated." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "JUDGE_SYSTEM = \"\"\"\n", + "You judge whether a reply matches Good Vibes Corp's desired tone:\n", + "energetic, super-friendly, enthusiastic.\n", + "\n", + "Score 0-4 (higher = more energy):\n", + "\n", + "4 - Highly enthusiastic: multiple upbeat phrases / emojis / exclamations, clear empathy, proactive help.\n", + "3 - Energetic & friendly: visible enthusiasm cue (≥1 emoji OR exclamation OR upbeat phrase), warm second-person tone.\n", + "2 - Pleasant: polite & positive but lacks obvious enthusiasm cues.\n", + "1 - Neutral: correct, businesslike, minimal warmth.\n", + "0 - Rude, negative, or unhelpful.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "\n", + "sync_client = OpenAI()\n", + "\n", + "# set judge model\n", + "judge_model = \"gpt-4.1-2025-04-14\"\n", + "\n", + "# create the evaluation\n", + "logs_eval = sync_client.evals.create(\n", + " name=\"Good Vibes Corp Tone Eval\",\n", + " data_source_config={\n", + " \"type\": \"logs\",\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"score_model\",\n", + " \"name\": \"General Evaluator\",\n", + " \"model\": judge_model,\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": JUDGE_SYSTEM,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": (\n", + " \"**User input**\\n\"\n", + " \"{{item.input}}\\n\"\n", + " \"**Response to evaluate**\\n\"\n", + " \"{{sample.output_text}}\"\n", + " ),\n", + " },\n", + " ],\n", + " \"range\": [0, 4],\n", + " \"pass_threshold\": 2,\n", + " }\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# run the evaluation\n", + "base_run = sync_client.evals.runs.create(\n", + " name=base_model,\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\"type\": \"responses\", \"limit\": len(test_pairs)},\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average score: 2.525\n" + ] + } + ], + "source": [ + "# score base model\n", + "base_data = sync_client.evals.runs.output_items.list(\n", + " eval_id=logs_eval.id, run_id=base_run.id\n", + ").data\n", + "base_scores = [s.results[0][\"score\"] for s in base_data]\n", + "print(\"Average score:\", sum(base_scores) / len(base_scores))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **5. Fine-Tuning**\n", + "\n", + "With a baseline established, we can now fine-tune the model using the training set and DPO. This process will teach the model to prefer responses that align with our desired style, based on the preference pairs we created earlier.\n", + "\n", + "Note: **beta (β)** is a unique fine-tuning hyperparameter for Direct Preference Optimization (DPO). It’s a floating-point number ranging between 0 and 2, controlling the balance between preserving a model’s existing behavior and adapting to new, preference-aligned responses.\n", + "- High β (close to 2): makes the model more conservative, strongly favoring previous behavior. The fine-tuned model will show minimal deviations from its original style or characteristics, emphasizing consistency and avoiding abrupt changes.\n", + "- Moderate β (around 1): balances between adherence to prior behavior and adaptation to new preferences. Recommended as a sensible starting point for most practical scenarios.\n", + "- Low β (close to 0): encourages aggressive adaptation, causing the model to prioritize newly provided preferences more prominently. This might result in significant stylistic shifts and greater alignment with explicit preferences but could lead to unexpected or overly specialized outputs.\n", + "\n", + "Technically, beta scales the difference in log-probabilities in the DPO loss; a larger β causes the sigmoid-based loss function to saturate with smaller probability differences, yielding smaller weight updates (thus preserving old behavior). It is recommended to experiment systematically with the β value to achieve optimal results tailored to your specific use-case and desired trade-offs between stability and adaptation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine-tuning job created: job_id = ftjob-5QPmA36QezFRGoXjuvIAPuAQ\n" + ] + } + ], + "source": [ + "import io\n", + "import json\n", + "\n", + "# create training file\n", + "train_buf = io.BytesIO(\"\\n\".join(json.dumps(p) for p in train_pairs).encode())\n", + "train_buf.name = \"train.jsonl\"\n", + "train_file_id = sync_client.files.create(file=train_buf, purpose=\"fine-tune\").id\n", + "\n", + "# create validation file\n", + "val_buf = io.BytesIO(\"\\n\".join(json.dumps(p) for p in val_pairs).encode())\n", + "val_buf.name = \"val.jsonl\"\n", + "val_file_id = sync_client.files.create(file=val_buf, purpose=\"fine-tune\").id\n", + "\n", + "# create a fine-tuning job\n", + "ft = sync_client.fine_tuning.jobs.create(\n", + " model=base_model,\n", + " training_file=train_file_id,\n", + " validation_file=val_file_id,\n", + " method={\n", + " \"type\": \"dpo\",\n", + " \"dpo\": {\n", + " \"hyperparameters\": {\n", + " \"n_epochs\": 2,\n", + " \"beta\": 0.1,\n", + " \"batch_size\": 8,\n", + " }\n", + " },\n", + " },\n", + ")\n", + "print(f\"Fine-tuning job created: job_id = {ft.id}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **6. Using your Fine-Tuned Model**\n", + "\n", + "Once fine-tuning is complete, we'll evaluate the DPO-tuned model on the same test set. By comparing the mean scores before and after fine-tuning, as well as reviewing example outputs, we can see how the model's alignment with our preferences has improved." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# generate responses\n", + "job = sync_client.fine_tuning.jobs.retrieve(ft.id)\n", + "if job.status == \"succeeded\":\n", + " responses = await generate_responses(testset, model=job.fine_tuned_model)\n", + "\n", + " post_run = sync_client.evals.runs.create(\n", + " name=ft.id,\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\"type\": \"responses\", \"limit\": len(test_pairs)},\n", + " },\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Δ mean: 0.45\n", + "\n", + "=== SAMPLE COMPARISON ===\n", + "Prompt:\n", + " Can I upgrade to faster delivery if my package is still being processed?\n", + "\n", + "Base model reply: \n", + " Whether you can upgrade to express shipping while your order is still being processed depends on the store's policies. Generally, many stores allow shipping upgrades before the order is shipped. \n", + "\n", + "To assist you better, could you please provide your order number or the name of the store you ordered from? Alternatively, you can contact the store's customer service directly to request the upgrade. \n", + "\n", + "DPO-tuned model reply \n", + " Hi! I’d be happy to help with that. If your package hasn’t shipped yet, there’s a good chance we can upgrade your delivery speed. Could you please provide me with your order number? I’ll check the status and let you know the available options for faster delivery.\n" + ] + } + ], + "source": [ + "# get scores from the evaluation\n", + "post_data = sync_client.evals.runs.output_items.list(\n", + " eval_id=logs_eval.id, run_id=post_run.id\n", + ").data\n", + "post_scores = [s.results[0][\"score\"] for s in post_data]\n", + "\n", + "# print scores & a sample comparison from the test set for illustration\n", + "print(\n", + " \"Δ mean:\",\n", + " sum(t - b for b, t in zip(base_scores, post_scores)) / len(base_scores),\n", + ")\n", + "print(\"\\n=== SAMPLE COMPARISON ===\")\n", + "idx = 0\n", + "print(f\"Prompt:\\n {testset[idx]['item']['input']}\\n\")\n", + "print(f\"Base model reply: \\n {base_data[idx].sample.output[0].content} \\n\")\n", + "print(f\"DPO-tuned model reply \\n {post_data[idx].sample.output[0].content}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv311", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/GPT_with_vision_for_video_understanding.ipynb b/examples/GPT_with_vision_for_video_understanding.ipynb index 6f1fe384e2..e92d4a3f32 100644 --- a/examples/GPT_with_vision_for_video_understanding.ipynb +++ b/examples/GPT_with_vision_for_video_understanding.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Processing and narrating a video with GPT-4o's visual capabilities and the TTS API\n", + "# Processing and narrating a video with GPT-4.1-mini's visual capabilities and GPT-4o TTS API\n", "\n", - "This notebook demonstrates how to use GPT's visual capabilities with a video. GPT-4o doesn't take videos as input directly, but we can use vision and the 128K context window to describe the static frames of a whole video at once. We'll walk through two examples:\n", + "This notebook demonstrates how to use GPT's visual capabilities with a video. Although GPT-4.1-mini doesn't take videos as input directly, we can use vision and the 1M token context window to describe the static frames of a whole video at once. We'll walk through two examples:\n", "\n", - "1. Using GPT-4o to get a description of a video\n", - "2. Generating a voiceover for a video with GPT-o and the TTS API\n" + "1. Using GPT-4.1-mini to get a description of a video\n", + "2. Generating a voiceover for a video with GPT-4o TTS API\n" ] }, { @@ -25,7 +25,6 @@ "import time\n", "from openai import OpenAI\n", "import os\n", - "import requests\n", "\n", "client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"\"))" ] @@ -118,38 +117,43 @@ "name": "stdout", "output_type": "stream", "text": [ - "Title: \"Epic Wildlife Showdown: Wolves vs. Bison in the Snow\"\n", - "\n", - "Description: \n", - "Witness the raw power and strategy of nature in this intense and breathtaking video! A pack of wolves face off against a herd of bison in a dramatic battle for survival set against a stunning snowy backdrop. See how the wolves employ their cunning tactics while the bison demonstrate their strength and solidarity. This rare and unforgettable footage captures the essence of the wild like never before. Who will prevail in this ultimate test of endurance and skill? Watch to find out and experience the thrill of the wilderness! 🌨️🦊🐂 #Wildlife #NatureDocumentary #AnimalKingdom #SurvivalOfTheFittest #NatureLovers\n" + "Witness the raw power and strategy of nature in this intense wildlife encounter captured in stunning detail. A determined pack of wolves surrounds a lone bison on a snowy plain, showcasing the relentless dynamics of predator and prey in the wild. As the wolves close in, the bison stands its ground amidst the swirling snow, illustrating a gripping battle for survival. This rare footage offers an up-close look at the resilience and instincts that govern life in the animal kingdom, making it a must-watch for nature enthusiasts and wildlife lovers alike. Experience the drama, tension, and beauty of this extraordinary moment frozen in time.\n" ] } ], "source": [ - "PROMPT_MESSAGES = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " \"These are frames from a video that I want to upload. Generate a compelling description that I can upload along with the video.\",\n", - " *map(lambda x: {\"image\": x, \"resize\": 768}, base64Frames[0::50]),\n", - " ],\n", - " },\n", - "]\n", - "params = {\n", - " \"model\": \"gpt-4o\",\n", - " \"messages\": PROMPT_MESSAGES,\n", - " \"max_tokens\": 200,\n", - "}\n", + "response = client.responses.create(\n", + " model=\"gpt-4.1-mini\",\n", + " input=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": (\n", + " \"These are frames from a video that I want to upload. Generate a compelling description that I can upload along with the video.\"\n", + " )\n", + " },\n", + " *[\n", + " {\n", + " \"type\": \"input_image\",\n", + " \"image_url\": f\"data:image/jpeg;base64,{frame}\"\n", + " }\n", + " for frame in base64Frames[0::25]\n", + " ]\n", + " ]\n", + " }\n", + " ],\n", + ")\n", "\n", - "result = client.chat.completions.create(**params)\n", - "print(result.choices[0].message.content)" + "print(response.output_text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Generating a voiceover for a video with GPT-4 and the TTS API\n" + "## 2. Generating a voiceover for a video with GPT-4.1 and the GPT-4o TTS API\n" ] }, { @@ -168,49 +172,43 @@ "name": "stdout", "output_type": "stream", "text": [ - "In the frozen expanses of the North American wilderness, a battle unfolds—a testament to the harsh realities of survival.\n", - "\n", - "The pack of wolves, relentless and coordinated, closes in on the mighty bison. Exhausted and surrounded, the bison relies on its immense strength and bulk to fend off the predators.\n", - "\n", - "But the wolves are cunning strategists. They work together, each member playing a role in the hunt, nipping at the bison's legs, forcing it into a corner.\n", - "\n", - "The alpha female leads the charge, her pack following her cues. They encircle their prey, tightening the noose with every passing second.\n", - "\n", - "The bison makes a desperate attempt to escape, but the wolves latch onto their target, wearing it down through sheer persistence and teamwork.\n", - "\n", - "In these moments, nature's brutal elegance is laid bare—a primal dance where only the strongest and the most cunning can thrive.\n", - "\n", - "The bison, now overpowered and exhausted, faces its inevitable fate. The wolves have triumphed, securing a meal that will sustain their pack for days to come.\n", - "\n", - "And so, the cycle of life continues, as it has for millennia, in this unforgiving land where the struggle for survival is an unending battle.\n" + "In the frozen expanse of the winter landscape, a coordinated pack of wolves moves with calculated precision. Their target, a lone bison, is powerful but vulnerable when isolated. The wolves encircle their prey, their numbers overwhelming, displaying the brutal reality of survival in the wild. As the bison struggles to break free, reinforcements from the herd arrive just in time, charging into the pack. A dramatic clash unfolds, where strength meets strategy in the perpetual battle for life. Here, in the heart of nature’s harshest conditions, every moment is a testament to endurance and the delicate balance of predator and prey.\n" ] } ], "source": [ - "PROMPT_MESSAGES = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " \"These are frames of a video. Create a short voiceover script in the style of David Attenborough. Only include the narration.\",\n", - " *map(lambda x: {\"image\": x, \"resize\": 768}, base64Frames[0::60]),\n", - " ],\n", - " },\n", - "]\n", - "params = {\n", - " \"model\": \"gpt-4o\",\n", - " \"messages\": PROMPT_MESSAGES,\n", - " \"max_tokens\": 500,\n", - "}\n", + "result = client.responses.create(\n", + " model=\"gpt-4.1-mini\",\n", + " input=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": (\n", + " \"These are frames of a video. Create a short voiceover script in the style of David Attenborough. Only include the narration.\"\n", + " )\n", + " },\n", + " *[\n", + " {\n", + " \"type\": \"input_image\",\n", + " \"image_url\": f\"data:image/jpeg;base64,{frame}\"\n", + " }\n", + " for frame in base64Frames[0::25]\n", + " ]\n", + " ]\n", + " }\n", + " ]\n", + ")\n", "\n", - "result = client.chat.completions.create(**params)\n", - "print(result.choices[0].message.content)" + "print(result.output_text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can pass the script to the TTS API where it will generate an mp3 of the voiceover:\n" + "Now, we can work with the GPT-4o TTS model and provide it a set of instructions on how the voice should sound. You can play around with the voice models and instructers at [OpenAI.fm](openai.fm). We can then pass in the script we generated above with GPT-4.1-mini and generate audio of the voiceover:\n" ] }, { @@ -223,7 +221,7 @@ "text/html": [ "\n", " \n", " " @@ -238,28 +236,38 @@ } ], "source": [ - "response = requests.post(\n", - " \"https://api.openai.com/v1/audio/speech\",\n", - " headers={\n", - " \"Authorization\": f\"Bearer {os.environ['OPENAI_API_KEY']}\",\n", - " },\n", - " json={\n", - " \"model\": \"tts-1-1106\",\n", - " \"input\": result.choices[0].message.content,\n", - " \"voice\": \"onyx\",\n", - " },\n", + "instructions = \"\"\"\n", + "Voice Affect: Calm, measured, and warmly engaging; convey awe and quiet reverence for the natural world.\n", + "\n", + "Tone: Inquisitive and insightful, with a gentle sense of wonder and deep respect for the subject matter.\n", + "\n", + "Pacing: Even and steady, with slight lifts in rhythm when introducing a new species or unexpected behavior; natural pauses to allow the viewer to absorb visuals.\n", + "\n", + "Emotion: Subtly emotive—imbued with curiosity, empathy, and admiration without becoming sentimental or overly dramatic.\n", + "\n", + "Emphasis: Highlight scientific and descriptive language (“delicate wings shimmer in the sunlight,” “a symphony of unseen life,” “ancient rituals played out beneath the canopy”) to enrich imagery and understanding.\n", + "\n", + "Pronunciation: Clear and articulate, with precise enunciation and slightly rounded vowels to ensure accessibility and authority.\n", + "\n", + "Pauses: Insert thoughtful pauses before introducing key facts or transitions (“And then... with a sudden rustle...”), allowing space for anticipation and reflection.\n", + "\"\"\"\n", + "\n", + "audio_response = response = client.audio.speech.create(\n", + " model=\"gpt-4o-mini-tts\",\n", + " voice=\"echo\",\n", + " instructions=instructions,\n", + " input=result.output_text,\n", + " response_format=\"wav\"\n", ")\n", "\n", - "audio = b\"\"\n", - "for chunk in response.iter_content(chunk_size=1024 * 1024):\n", - " audio += chunk\n", - "Audio(audio)" + "audio_bytes = audio_response.content\n", + "Audio(data=audio_bytes)" ] } ], "metadata": { "kernelspec": { - "display_name": "openai", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/examples/Generate_Images_With_GPT_Image.ipynb b/examples/Generate_Images_With_GPT_Image.ipynb new file mode 100644 index 0000000000..0a22744676 --- /dev/null +++ b/examples/Generate_Images_With_GPT_Image.ipynb @@ -0,0 +1,673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "760354d7", + "metadata": {}, + "source": [ + "# Generate and edit images with GPT Image" + ] + }, + { + "cell_type": "markdown", + "id": "3e51d6ca", + "metadata": {}, + "source": [ + "In this cookbook, you'll learn how to use GPT Image, our new large language model with image generation capabilities.\n", + "\n", + "This model has world knowledge and can generate images leveraging this broad understanding of the world.\n", + "It is also much better at instruction following and producing photorealistic images compared to our previous-generation image models, DallE 2 and 3. \n", + "\n", + "To learn more about image generation, refer to our [guide](https://platform.openai.com/docs/guides/image-generation?image-generation-model=gpt-image-1)." + ] + }, + { + "cell_type": "markdown", + "id": "3b30f3ad", + "metadata": {}, + "source": [ + "## Set up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2811f97", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install pillow openai -U" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8eda6833", + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "import os\n", + "from openai import OpenAI\n", + "from PIL import Image\n", + "from io import BytesIO\n", + "from IPython.display import Image as IPImage, display" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e022f680", + "metadata": {}, + "outputs": [], + "source": [ + "client = OpenAI()\n", + "# Set your API key if not set globally\n", + "#client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1eb23b94", + "metadata": {}, + "outputs": [], + "source": [ + "# Create imgs/ folder\n", + "folder_path = \"imgs\"\n", + "os.makedirs(folder_path, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "id": "33146f60", + "metadata": {}, + "source": [ + "## Generate an image\n", + "\n", + "GPT Image 1 is great at instruction-following, meaning you can prompt the model to generate images with very detailed instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4a5607d", + "metadata": {}, + "outputs": [], + "source": [ + "prompt1 = \"\"\"\n", + "Render a realistic image of this character:\n", + "Blobby Alien Character Spec Name: Glorptak (or nickname: \"Glorp\")\n", + "Visual Appearance Body Shape: Amorphous and gelatinous. Overall silhouette resembles a teardrop or melting marshmallow, shifting slightly over time. Can squish and elongate when emotional or startled.\n", + "Material Texture: Semi-translucent, bio-luminescent goo with a jelly-like wobble. Surface occasionally ripples when communicating or moving quickly.\n", + "Color Palette:\n", + "- Base: Iridescent lavender or seafoam green\n", + "- Accents: Subsurface glowing veins of neon pink, electric blue, or golden yellow\n", + "- Mood-based color shifts (anger = dark red, joy = bright aqua, fear = pale gray)\n", + "Facial Features:\n", + "- Eyes: 3–5 asymmetrical floating orbs inside the blob that rotate or blink independently\n", + "- Mouth: Optional—appears as a rippling crescent on the surface when speaking or emoting\n", + "- No visible nose or ears; uses vibration-sensitive receptors embedded in goo\n", + "- Limbs: None by default, but can extrude pseudopods (tentacle-like limbs) when needed for interaction or locomotion. Can manifest temporary feet or hands.\n", + "Movement & Behavior Locomotion:\n", + "- Slides, bounces, and rolls.\n", + "- Can stick to walls and ceilings via suction. When scared, may flatten and ooze away quickly.\n", + "Mannerisms:\n", + "- Constant wiggling or wobbling even at rest\n", + "- Leaves harmless glowing slime trails\n", + "- Tends to absorb nearby small objects temporarily out of curiosity\n", + "\"\"\"\n", + "\n", + "img_path1 = \"imgs/glorptak.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dae9821a", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the image\n", + "result1 = client.images.generate(\n", + " model=\"gpt-image-1\",\n", + " prompt=prompt1,\n", + " size=\"1024x1024\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "15d182da", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the image to a file and resize/compress for smaller files\n", + "image_base64 = result1.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)\n", + "\n", + "# Adjust this if you want a high-quality Glorptak\n", + "image = Image.open(BytesIO(image_bytes))\n", + "image = image.resize((300, 300), Image.LANCZOS)\n", + "image.save(img_path1, format=\"JPEG\", quality=80, optimize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1084fac0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the result\n", + "display(IPImage(img_path1))" + ] + }, + { + "cell_type": "markdown", + "id": "e0101d66", + "metadata": {}, + "source": [ + "### Customize the output\n", + "\n", + "You can customize the following output properties:\n", + "- Quality can be `low`, `medium`, `high` or `auto` (default value)\n", + "- Size can be `1024x1024` (square), `1536x1024` (portrait), `1024x1536` (landscape) or `auto` (default)\n", + "- You can adjust the compression level (from 0-100%) for JPEG and WEBP formats\n", + "- You can choose to generate an image with a transparent background (only available for PNG or WEBP)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6df3dbe3", + "metadata": {}, + "outputs": [], + "source": [ + "prompt2 = \"generate a portrait, pixel-art style, of a grey tabby cat dressed as a blond woman on a dark background.\"\n", + "img_path2 = \"imgs/cat_portrait_pixel.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1bb40de7", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the image\n", + "result2 = client.images.generate(\n", + " model=\"gpt-image-1\",\n", + " prompt=prompt2,\n", + " quality=\"low\",\n", + " output_compression=50,\n", + " output_format=\"jpeg\",\n", + " size=\"1024x1536\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4de5aa8e", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the image to a file and resize/compress for smaller files\n", + "image_base64 = result2.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)\n", + "\n", + "image = Image.open(BytesIO(image_bytes))\n", + "image = image.resize((250, 375), Image.LANCZOS)\n", + "image.save(img_path2, format=\"JPEG\", quality=80, optimize=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "03290401", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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yV6k159M0FoYmWF4YgAzHkHOOxrDobhtd1aKTUpWEj/pZ2OPfHyqa17Xrrqq/kiij9C2l+Jww3c575/am2u9Ow6VYpeC4mdwwAXAwM/P716DQ6d4VeT8mZmpyKbqHSOb/AEMWcou7rZIjOFUA8e1Smh6jLb6ybaCEhRt4HbGKsWpWtvP0Cpkwzqgce+R5qsdNvuF1es6B0KZ+wwP8VbUt0XYmtr4FdWupLnqDAGCoxkd93irFrGrw6dos0sQjW5dcKCo71WdCFxqGs3l6yj0Y2wSB34FJ64x1nVrLT1KJvbBbOR5P+1G3lJ+A3cNiXRVhc3kx1TUJSsS52jtu/wDM1J63HeXsx/JzwxQKvdHGf2HNT2vyR6X06YEQMkUQAKDGPFVDoHTpL6OW/lmxCTtKY+Lj5/eubt1zCqqJ1p+k3cswa5uTIvfk4zWo9Fa0buYWdx6AkjIWMlgC2B2A/wDO1VLU4I96xQFlODnHj9qjZ5xYSJJbysl1EchgCCDSM+JaiNPsbjl7T4NwvoP5UaxsoMhORjk0i9kmIxIw3BR296y7SuuriS226nOFFvgRFVOX99x59hWkWWoxXVpbS71YzRrINpyfiHmsLNgljk1JF6EtyTTELq2/mkOrMD2yCOKhdRtII45GKqpHZcZ3VcbqKdoGEe1mUcfFxUO0EcrAXKrlu+RSKaY2MrMY13p6+jurjUlYT2zvwvJdcnsF9hTi0uo4reOMxNvHBPnmtOvLJGt5I4kO0EjHb+9Ui9t4YJzKkIdR8LjOa0MeoeWNS8CXBRdoof4lWUVzBBf2indF8Eu2Pls/1E/LGPvWeit6ltreWJwIlktZl2vGexHkGsU16FbfWr6KOP041ncIoGAF3HGPlW16dn3R9t+DP1ePa968jEf3712x4NcA4rrHPPmtIpnI5ocURHAoUHQ+aPx3NAZPege9ABZoZoYovtQB1FG80iRxI0jscBVGSfoK9VdG9IWfT+nhLOMCNm3M0hDlj8zjmsb/AAT0Nr/Wbi8IjMUCY+JckHK8jjjvXoy2jLPDbWoYxr8ZZ/OP/wC1571fO5SWFdLs0tFj2xc2Pbd4IrUkRInGMe9RTz7VkuGZEGeDI2O/jmudWuDaKRLwoB58CsW666p1S41M2thfD8gVU7BGh+LnnOM1l4ME9RPYuC3OaxR3MunVnV8FjaytC8c9wG2BUkUlTg8kZ7VlF3danrKYuI5pIg27+VG2Mf3ru2hjik/P6nLiNTuYlclvtjmpCX8QYLMBNJ06JowMbnXaD9hit3TaKOnXxVv7M/LqHk4bpE9oNiIY9rW7hFGQSpGaR63KxaZbpycyA8n6Uy0n8SGnmSG8treFG7lFJGfpmrnJotl1lpoksp1E361UOFA+xpsm4STmRjUo1E4lhM/SsiBgF9AkA+eO1Z5pvrW9tdQmKVVfOBsOD3rR9UsZNN0yC0llIyuCVPt3qv3TYDRRv8AxyfpRjlSZ2cSM6YuJUsbyKR/TLHhW88VG9OBj1rZtKhZInIJ+eD/3qXSBVbcckk880pDamCSO7jKKWbOR39qZuXP7F7XwS/WJkOj3Wd7NwFUecnxTPpZJ49EjjjR1dcAnB44FT1xp6apEJZpZI4VBEgUgcDnPNRf/ABVo2nW4toHb4B/p5z9fNJjbjSGOk7YbwXUQaVw4kbgFgeKgb5rqPfJPA+5+CWHf502vut8ylwEmUHjeT2/ekZOsbW6jxOHBA4HcU2MJLwRc4/YrHZpcwOcFQvYCnWj9R6lpjugkhMaKsa/y8kKD/wBqLTrqG/T+VtUecEU31G3X1CpUGNh+oe9RnjjlW2aCE5Q+UWbz05qVpqGk2kkF3DPK0AZwjglTj+odwfrTWVj+ZZSQSvbI7Vgeg6hqnTGp/mrGQKM/Eh5WRexB+2ee4rX+ltSm6m0j+JPAtvOJDE8aMducZyPlz5rG1mjli+UeUXdPmU3T7JLUZHs542kZ/RlBUYX/AB+9UvUC1rdSsi77RzyfOfpVj1VbwxC3aQPt+IecVCzxNcYhyRHkMzDvkeKqYXtZZmrRG2FvIyCZJMRFj8BPI5qg/ihpfpPb36h2d2MbkfpVR+n6HJrQBALe/Cq2IipIyfNRnVlp+Z0m5W6AaHYzhQTnIGQf7Vo6bL7eVS8FfLDfBxMUrpjXKHt9K7x3r0pjHHehRtjxRUHTodhR4zXFGc471wAE0RPHNHRE10DY/wABrpYbXUFMm3c/I+WFrbhcHTtOe5Q5djsAJ4H/AJivN34VarBaPcWzzbLiRtyr23jA4HueO1bXr2pR33TsS27PGJpQoP8AUO4NeX9Qg46hyZr6d3jSKzHcXGqahJqV6qLvUxqsYOCP3+tULrNYrbXCqRqkYVCNmB75rQ1g9CNIYgQi9v3qidd4N9CzqpTkD3J81LRSvMGoj/GcdK6Z/wASX8DX0hS2WQBAQCpHuRSOv6Bb6Z1Pd2EyIIWOYGVdu5ffHjsasVhAbTT4YbSMowUMGXvzz/vTnW9Hv+oNMs7qBopdasf5ZjLYM0fJ4/5viPsMCtWU3u74KDhcK8mb9Qx6XYaNDZNaONU9cyeuDhTFgADH1BpUajGJ9O/4N/N2d3FCDcEycPIMkngDjtxU11ToVzr8VrNYW7LdoNksUhAYH27/ADqx/h9o8HRWnX9/1FGu+4gMfChzH7cEcHNHvxUE339CYxd0WbV5W1Xpew1RWLEoA74IBYcN/wDIGoKysjdJvPCd84o/w/1n+KdKanYOUazs5/5PgkPuc/3p/aYS2VI2IZiQoA8Z7UqnC4ltPckyImjERZg2GB4AFISRzy2y7QTzhcU/1O3lWNmJAA4IbvmnPSMlrNqMFldqxaQ5Vs4AHP7VLdxZxrmhPr3VRofSFnpkLiTUL4h3B42g57fL4feqppWi6JYiOXrK5lgtniyrIhcl8cDgGm342alHedaAWZxFZxrCfYurNkj5cirReafb9YdDQCzRfzEUS4Ze4cLyv74rjftxi3wn2Jl85NLwY/p0Flea4lvc3LQWLylfVUZIUtxxj2qza30rZWC3r6fePdWkbD0JpFClxgZ4wPOab9LdL3A1IT6nA8NrbtuZ3GAdp8fsat1tAeseoY4LKB49CtHH5mYDjgZxnkfvT8mV7qi+F2KUb4KnF0vrunwR3JjEUcyh0xIDuHg8GlrPWJbWVrfVFkZewx/nmtX1nVbdvTihiSO3hHpxLnIIHHFUfX9Ntr6Caa5Ajb+lh3+lEcm/8kNePb+LIm5cyKxiYOn9xVi/CjUWsOo5Q25klQAR78DOe+DVGsJhbhk9TAb381J6NJINatpo3IZJFKnA4ycH/Joz4t+OUDuOe2akeguoCEiF7AAGHDA8gVXRIruzJgBhk8+fNWS4hSa19L1fgJzzVTtsh5Y352NgEe2a8uvs2V1RB6zbTszXLErHDxjPgmlr3Zc2canhAn6T5BGKLVVnjtb1XlZ1YZAznHIo4YMaFG82S5C5z4HGBVpPixfkw/WI0h1i9jiUKiTOqgDAAB8U1+dSXVMUcWv3ghcuC5Yk+GJORUb44r1ON3BP9GLNVJo5ND70DQqZEFdDkdhXNGDgUADuMeKLFDPNCgBzp121jfwXMYy0bZAxnxivQOlahaat0lpt5ayPGHlK+m4AYcsDwCfIrzp3qY6ReSPqjTDCxDNOqnHkZ7VQ12lWaO66aLGnzPG68M9AzSqtszkcAHn3rMdevFuNTtppQfTilDMpGcAn/wCq067tinqDJwcMBWe9YRQPcMrbo53RTjxgZrF0MlHIaWeNwHc99tMcqHdEeF28Uva6mBOhBZOecnOaqugWlzPZPl2MCsQBntye1SkKqCFBIxW04LozVJ9lpv4rbVbhdQsJ/wAjct/6qwgbXPuQR3+nyqL12w0S+tJV1Hqe6Mw/RBHEQv0OUP8AmutFb0pfgCHJ5DdiKmLzTbaS3kl9MerjPHYGkNKMrJqKkuiC6N/J2SXVrYxbQ2C5b9TcH+9Wi1hCxghMsuWBPiqro3NyYo9plz8ZxjFaBNFHHpew7S7Jjv713J2Sj0QM0kM43SkNt8eKrmlXJtupDMXwsaEJjuD8v71Maoht7GQwRZYDIOf8VRNL1f8AMX8jsPiXkjOPlTMcLTITlTVnaafB1P1Rc2slxDBNIxxPMpxkc+KumidDdW9N21wul6zpU1vLhmQqTj5jkYqrQaDJJqEt9t2wsxbjn+1TWnRrbytK8uV/0dqMvKpdfRCCTdtEgem7y5vtvUWpwiyVdzR2qEGQ+xzn4T581K3Nxb29kmn2kQt9OjG1Y0z8Qznk/XNQ93qSq+6PaceKg9U1WNgxuX2L7cmlxxtjLjHlDrUtUjWQKgAVOEWo67Zr8KJS20n9INNLedJ1leJgFHZiO9LWMU0szvIzEAZAqwo0K3WyC1nTxGrCKPa47baV6RSUStv91A/cVYb+3RIxNIxIxg5Gc0zsYx/EY2jAUSuoUduc11z+DOKHyVG5adC0VsEkYYQ1VZCf4hdFchC5x/vVmgDQQSJIGLnt8qrFzcx2m9pRyz4A+ea8pBs2yM1iV11K2g25RwSzCnOpNDb6ee+ETfjvkAZP+Kb2gN1cS3G3Bb4R8v8AzFL6n6cdtJ6i7wsTbsnxg5q0u0hbMF1a8S+1O5uY1KRyuWUEYOCc8/OmoFL3zRNfXDW6BITIdijsFzwBSFeriqikjElywUKFCpHAUYPFDHFDNABUDQPtQoALzV6/BjS4tT65tvWZla1Q3MZBGN6soGfccniqKK2X8BdBkN+95PDLBMW2xuyH4k4zjPfkVU12T28MmO08d2RGtXtk/wCkqjLtJzWc9X6Sfy7ziJpZo2wqr4B7/XFarfzx3GpflxLHuSPcSxCc5xjFRupafZTQOr3cXqPnIyBtNeZxtwlaNZu1TMG0jU/ylzJYylViY+ePi+Z+9TctpOqCSKEmM99oP7131v0vFLeK9le6fCMc+pMqbj71aOldFvp9FWOXUdOlZDgtFcq+4Y988GtxZk4KaM/26k4kf07pZlUSllZWXJGeVNThsZAgjtl+Nj8RfPPzqSvtEbTunDcaXAL28RRgwP6mTkZ+EZ4xmn+l2Uc+u3Nvd2t0IY0DpIEYK3Pk0iWVt2OjBJUZDfPdaZ1BK8cZZz2CKSDgfSlR1PqbAhrCUjGDhG5/tWn9d9MStZHVOnoPUuwuGRySSPkp+Q8VlEWtzfw26a5eJLuIMNvbkZ4x9qn70pJNRvwTx4MUuJTafL6IjXtb1i5H8uCWCLGCNpwfrkU96H6cNw6XEmG3ZDLjtS3T15qet3sNqbdJo5W2BtmAD8z2rWrHpaTSIIUWFDvPcNgr9cU2WeUFskqYl4ISanCTf98FVutOe2dI4ziMD9APP7UxuNNJQkmNWPktzVl1pHstWttytMshK/Cm4gAEioG0vU1DU7y3nhkCRN+oxkY78UtTbVg4q6IDUrC5W3k9BlJHYscYqq/w26eX+bKQfPJNTGo3Wo3N09rbQyrFk/EykZpOz0S5kOby5Ma57A5zVyL2rllWSTfAyuY1gRFZyqr3IPekzqcu0LYxSOF74BOf2q0DR9OkcFTLKuB/WSCae20VtYq3o28a5GO9ceVfRL22Vq1le+tDJdxlSM/CciproR7afqWJJIfW2qNvwkhD7k9h96gtQuRLqMMcDEh3CbUOd2TjFab0ZpK6VYz3dxatFcznAB4wgxwR75Bqprsix4nfkdpouc1+i2a3cpb2LyOOe3Hz7VmuqzNcyb1lCmJS2CcZ80+6q1dZnRfUZhkALyAcdzUJag3YuXGNu3jPftWPgx0tzNKTrgmtDlMkMMhGTnLYrvXofzFu0eWG9WXcvPcY/wB6R6Zbapib+gk8VI6iFKIYRwpO4MKm3U+CHfZ56voPyl7PACWETtHk9zg4pKrR+IVo6auLraohlAjXB53KOcj71Vq9Rhye5BSMbJDZJxDoUKFNIB+9A8jtQ7d6McjtQBzVt6RhsNVgXS7iKNLmRikcpHLE9snHFVI0tZ3M1ndRXFu+yaM7lbAOD9DSs2NzjSdMnjnslbNZsvwnhLbrq7iK5xgS4P8AmtIZrTRdDay02cC8QEKASWQHJzu8+K8+W3VXUFzcxxQ3PqzOw2qsCEk/IYrdumunJ7qWG9upGllJwyr+nAPjisDXLLjr3pWaWneOVuCokNKs5EgOo6hcM6uMEONx/fNcXsNtKJHhmZd5JGVJBqW6mvWhgW3BQSNxjOOKjISul6Ss9wym4dv5QIwPkRWap88FvbxbKl1r0/d3Wk+mJLcIDvDO6oQOfc/OozoS7sukCV1S7a6W4Hx20Vs0qKffeMjx7VP6lZDVWeW/VZbgjCZ8H5VAz6br+nyfm7zXJLbT0OTHAwDY9hkVqabInBwb4KeaD3bl2a1oS6Y2kym3mlhspm3qRmIqDjAAIBHarQkcpMbJf25QD9JXBb+9YJcfiVHeakYWt5Ws9x9KMqOV9yc5z96vemdRzTx2kltpNqyyOUWQschcZyfFdninHmSCMoy6ZpJAJ3bh8Q5x2rzh+J3TunwderFAyx20pG4Jz8Rx7e+TWhat+IelW8s9veSOZITjaoHxZ8LzVC1Kwver9XN9AqaZYoMJvI9Rjjg45p2n3Qe6XCF5akqXLNL6GsrXT9ESGztkRichtvLH5mrJdwyei4W4T1CMLkdjWM6Xr130xci017NwjHK3UfCqvbJ4FXhtfmnkha2a1urGVCfWSTcfoccUjJCSe5jYyXSJa4stUTSYtpsJbpYfimIG31PJA5wKYQx6rJaEMunLdMBkiPhuPpVbk1DRrFZokjdW3lSqufPHHNM9UWS7sYbfSwkrRJkB2O7jtwKkosOkWM2OuyKd9pbKBkE5U5qB1TVbnTY/SuUtI448gl4wf81m0nUmoWlzJBdzSq/KhMAbacjrLXGSNJ7lLmFOBHJEmCPbtVyOnl+qKzzxJyfqzTxILeW3hbdz60QCjnxwP96htSeF90ttfNCjZwjsXz980n/GdDmLi96YtlZiT6kcjDn9xTKHRre+vA2mXKyRlgxhPDRjPY0yMVB27X/BBycuqZNdAdMXupX1pqE8I/hkTsS/qgMHXBHHcjOK0nUr0WrGHfvIbAzmpDSrdLLp63ESsCFywBPBqCuka7uV2soeRsljXntVneoy2+l0ammxLFAYapo+7THucguSX5Tt8qh+mbY3U86OwVgPPar3fK38PMSsDke+Dn6VRWY6fdMWAJ7tgdqnhm3FxOyXkch/4Xe7P1KxxkHFTDI08Rz+k88HmoyX/wDIwZbgJ2IAzS2kyyo00E4O4oME9jipS+/JEq34gaBNf2UL2XLQsWKnA3ZwO/yrKSCMg9636b1JLogA7CACtZZ1T0jLpS3V3DIDaRkYEn6+SB4GPNavp+pSXtzf9FLV4Xe+JVB2os0dCtcoB5NDxQIo6AOT3pzZ2F1eOq20Mj5O3IU7c/XsKbVrH4bQvN0/BEpVVmkcZxn+o8n6VV1eo/x8e+h+nwrNPa2Sf4bdMQw28PrRE3RO6UD4sN7KcccYrZLWOWyttwHor4G7biktJ0WDSLOP8uWZzgks3NN9fvP5ey4GQOVGec/WvKajK8k3OT5NiEUkox6I28lgvdWtvV3GMqVOPfnzUX1Dbs+oJaxtuG7cucttFSuiQpJMXc7lHv4p1DCp1GeRchgNikc8DNV1KmOI4xmzt0ICBzzucA/t7VQ+p7QatqDCSdkO3KnkgnPtVy6id2nEYIUKO+O9Vey23OsxLLuGzPJ88HmrenySXysVOCa5M1vtMNpM24SRz+xJ/cCi/iOo2LbBdOUxweauHVUIfUklmBwibQcdxk8f3qAvbdLiz3lAuWwrH2rbw6nek5mdlwbb28GmfhXoGl6qqajORPdDBO7DDPttP0rRde0uKa1DW0cSMmP0xhSB9q8xdIdTXPS2uxTQu3ohsSohxuXz98VtkH4gadqexlvdscgAAZTw3zpeqw5FK1yjuDJFqumSh0GO5iMN/GjR7T/SGJ59yKonUnSraKxbQ7iWJF5KSEuPtmri3Ven2sTPLfo47/SqN1n1+l2rQ6WolB4LOpFQwRy7qS4J5pQq2+SI0XUbeOKf+IhjdyOR6hICjt4Pakpbi/jnEun3sTbjj4QAQPHOaqrIs7erMxMjf2p3EvpxqIePJIbzWi8KuymsrqiSi6jVpJLXWrVbqAghmjjUSKfcN3/vQt9Otb2wNzp7ORG2JI5P1KPHNRIszMTsQhvLUdvPcabI6wyEK3Eik8MPmKk4V+HZHd/5LgF2wyUXaQPnmmVu8kE2+NmQgg/CcealLizjuYGubN1CqBuQcEHyAPlUfGXwRkgGpxpqiLTTs3Pp3U21DpyGaRsStCCVxnGR70npig3e+Q9u3OKrv4b6uJ9JurGZF9WBVVHHkc4/xVo0y3aaR9qFwU5AOK8nnxe1klA3MU90FIs8WmrLasIwnnyCf3qk9SaTJFettjAXbksVHfFT+h3wsrgW049It8I47Gpe8hS7ikWb03DKVDFe1RhJrkHwzNrC4WMtC4AXPJxS9vODPnDPtJ2/IVJ6zodu1s8YiAl7qRgE/eqvYSi1ndZyUaIY+HnmrEWpq0ccaLFLsY70zGCO23OfvUH1VYtrGhXUAb03bGGK57MD/tU1p8purYFASpOdrHP3oaj6QjeBtwdlzxxjmuQk4TTXaIySlGjzljGRQ+1G36jj3NFXrjDDPNDPFA9/ahuOMeK6By5yp8cV6L6EsJZNLtb66t4bc+kqrGhzkAYz9+/3rzoex+lej+h7gp0bZQs5kmVSTjvg8/2rH9ZdYo/2XtB+bNAguWuoQWYjB5yOBVf1WRbvUYRFICCpU+3enkF00GkqQM7uScZ80j09YC7uJZiWJjGQMjP7V5tcs1VxyLIY7C24TJbuRS+k7445psbQcsoNKXenySrEI1YDfnHuK46oL2VpaJAzKWyGx3xx3ojF9sHJPhFd1+f8zeYyDKBzgU1srRVQzbFV378c070vTHu9Ra4lMhTGS/g9uPrQ1m4tdOtZmmmjTnaq7vizn96bC3wjkqXBU9StH1e5aOMECJzmojraBbHTYBtRcMcbTgdu31qxaLHcRq2oYPozN6qrnI5qi/iZeLdX4Cu2xFwUBO0HJ5x71oaVOeVQXSK+d7cbkUu0Vrq9VfdvNT9zpj2E4YPuRlGOKHTumLKVmcMF4II8+9Xi+sfzVuNsasQoA8HOK3cmVRdGTDG5K2URoXj5ljG35jvQS2LRu8aHb7e1We40e4e3Km3JkU/CxYcGl7HTkmsDHL8E7KRkDgHNc91VZJYndFMWMk5Bz/y4pRS6qRsYYOcnxVjm0n0rYAR7pOxbOKjUsWVCZG5/0gg1JZEyLg0IW04iYr6gy3nPammpn1J8jnPlfNSf5QSQgRxhZB2wKQFkw+GXh85wKE1dg0xpbjajbsgnt4pOYbckZbjxUnLav2ePCnkYpS3sldWLfCOw967uXZyhTozVjp2rokiAR3DKjE+OcD/Na/p90IJWww2P5z4rELuKFXYBWK4PHannT+vz6RG1sV3QseMufhHsPlWZrtF738sOy7pdRt/jl0aRrTzz6m8kDKdgDAE96k9C6jZR6U8BTBHDN+9VXQNRtZbdpnkT1HxgF8n596eq0aTQbuVSTcTjkgmsqWLaqaL6kmXbVCl1YTXTKq+mueDggVk8lwbi9eMJkSN3H1rSJ5VksZdgcQMmPiHFZ1AM6gPTXCocYIxXdP5s4y22itaWkSYGRwD2/tTe/KTCSSRipUYXPAxT0sJIYzwTjvmqd+IuqxWuhzWizGO5k2mMLkE/EM8jtwDUsWN5MiivJCc1CLbMfP6j9aLNHQ+5r1piBnvQ8c0D27UMcGgAvB+YxW7/AIf69YXlhaAGJLn0WhMO8FwQQN2PY4z96wp0eMgOrKSMgMMZHvTzRNSl0jVLe9g/XEwOM43D2PyqnrNMtTjryuh+nze1K/B6jEqnQ3jUlWKHOR2Oae9FsDKsnwlnQggn51Xeg9etuotD9aW3eIZ2sSBgnHyqf6dgjt70wh9oU/C2O/1/evKPHLHJxkuUbO9Sjx5LE2qxZYOhG34chRVDnuJte6iuLZmURWpI7eT7Y9sVYdQEiyu6lWweQKo0mqpp+rXVwksUc7kgp7kZ8fPNci5TtM7GKj0Xy6dNP0p1hQkomTg9zWR61Jc6zrb26qUCvvOVzxWiNdl9Odu7Om4Dxk81nMevR2cd1PNGTO8xwFAB7D+1O09ttxXJyUfDJjqLUI7LQJIrV1EoQCJDjA5HissvLe5vZtjKT5LVYPVlv1We6kDM3xBc4xx4pbp5UbVW43YUgAitXRY1hv7Ia3E/bTfkmekNBE2lF2bbHCDhR3Y8/wCKl7YQRzxpMf6gDt7gZpLSdDupc7pwsSkkICQWzzxSp0C8F4yQNuOQAwB4ps5KTdsqQhtXRLaza21jbxSxyKQ/xAE8/THeomxshJabtsisDwfFFNpd7DIFvGkEgPwh1Jz9KlLm7awKwsrbWTcQwxS+lSZOiC1WyQwIIpdz5y2D2FMYNLCDLRpnOTkZqatpVliMsce2NT3xwB8zUVql4biZnQkKuRhexHimQcnwQkkuRBYbWOViGj3HIOBmieG3Ztw7Z7lccU6stHnuow+30UPO56ew6IVVmkkDnsBzU9yXkgk34IK5hSfHwEADApkLH+Yx3lT3A8Va7i0tLdAJI2eYnwcAfaml0lqId04Xt8IzjNdWSjnstlN1O1/Uz4J8MKg5lINWi4kEuYo1cxr4/wDuoSWF3uAMbR7Edq7718Ghh0PtpSfbGUdxNAI9jD4G3LkZ5/8ABUwOrtQY4m9Fl8kRAHNMriyCkYb7d80ylgXIwR7ZzRtx5O1ZzLhcL8GjdMdYLNbNbXxUAoTnhB34pnayhrpmRgyFmIIOe9UdImCMAR96f6TqzWIMEqAqDgH2HvSJ6KrlD/YrvURi9rZptnMiIo9UcD2zWXfijcevrsO3OwQjblcH9Rq9aZexuqbTGyf6veojq3pZdamS5tJhDKqbMScq3JP270jSSjhzXPgM8Xkx1Eyuix8zU3e9M6taM4exnaNP/wBiplSPeoUjBwe4rejOMvxdmW049gom/Se/ajzzmhUzhp950Lc9QQW15Yzwqywojh2bA4GMcH3NVTT+lrmTqiLSbsrG2/DNzgj5Gn/QXWJ6fnZbpWlgcjknO3xk/IVq0PV0NzGVjgiYsNxeIDgeKxMmbU6W4Va8M0oY8Wf5Lh+SX063tNBsv4fYl3t0TGSc81Xl1W7sNWVDLmPeAcDPHtR3msMsO/lePPc1n3UWs/mbxmEQ9QD9SucCqGmwzzSe5dljLOOKKPQktubizWZZFVGAO05z3rNdctFstQu3mCESPuRiCcH50n0J1jFK0GlyRTC7mJIOMpgD3znx7Vb+obH17J3Yqx/VyOTVeWOWnybZIZCayRuLI/pjUWvtNYSr8SttBxgEVl/UJ26jcQn4X9Q7lHitH0GbbbFVOFjzkHxWbdTZHUt+mAWMuR+wqzo0lllQyrcbHdtDtt0JHw7eKluibVbm+uSxdY0JPH6u1R1uCIFBOW7fSp/otA1veW8UgVvV3E+R2/7VexviQ31KPxh/f/on9PHoapGqtN6ch2fFgYJ7EfKpqz9bS9SYM6yxyj9Kk5H79qS0dDJqBeMMzjk4AOMeaete3UmpRLcQRx+m2VLd2+VKlyzPQSGK/vFn2zO8JwOQAv8A9UtqqadsaS6jCsE4I/UefnTizElu07MsRU/FxzVZTT1uroSzH4O+0EmorlgxveBdRiSOxjNrbKf5jt3b/wA4p/HpWhWVmpl9djwCwYAE0+Gm2YA3XBVjztx4/ejuLE3FmqRSSBFbPEakn281Ny8Ijt55IW/1SwtDstraY8DDPg8e1Rn8RluWLASqngDipNtNitCRLiWYg4DpjbTSYukbSelGoX281ONeDpGPKrJIXL7+SCT2qtalqi3C+ghbAY5Pg/Sl9e1FZ3SKJhuGQ/GPtUfa2qhS325FdckkaOl011N9gjuYoQQ5KvjIx2+9Nom9a5dwSy579sUjcRNNKwBXg4zmnjSGC2VWAA/1CuVXXbLltun0hpdzvG2ByT2yK5sdPa5lO+NiDznOAD70hExur1c5wWA+1XFY5o022ifzdowcduKtwWyP7MHWZnlyON8IZDQDHEpkOFOMYPeoLU9KKSv6YcYJBzzV1lvHEKb1y4wD9agtVlyC5K7j+oDuBU4TlZTnBUQNnqc2mOkbkGIHOMZqxWOuSXB2WkfrIPifAHw/KqtqipJGRuycD4vaoW21K8sJHNpOYmYbWwAcj71zLpY5VaXIY87xun0azr2tWllpZkmkOWBCKRkscdqxRuWJ45NOLu7mu5mluZWklY5JP/btTfFN0mmWnT55ZDPmeV8dB+exox27UMnxQFWhBzjJ570qssyDCyyKD4DkZriiJz3rh0sOj6pczJ+VY5jP9RJLD+9Wy00SF4VlkTDZxnNVfpEq8ixuPgEm4nt4xWn3mh3E4E1rbsyYChUJyeKpZZxxSrqyxjjLIueaKXaae7dR2sNs+2b1Bypx2OT2+Vb1eox05YCisxTLe4Psao3RHR17FrK6pqUCxLGT6aSEhhx3x2NW/Wb+OG8d9+xVJOSf3rE9SzLJNKPNGho8binaKzbWv5BL6WUsIQchm48c/wB6y+ZmvdfvJ8g733A/9quPW/UL3cD2OltvRxiVwMgj2FUzSLaRJ2EgIz8qbpcbhGWSXbNHHBuStFkVCFXnCjvipPo0hdWniHILYyeTjimgXbzk7PcVzplwLLWRK2VVwcN86Zhdtr7HepY7xJ/TNX/hyWqidJmEpyCFWou6knGoi5mhDBVIVTkAnjn+1TOlzl4I3eQF22kZxinV1ZtqDRlpI9qD4kLEEj7VFPnkyCHivJpbZikAV8YA5HP3pDSbWRhJLcRMhHHfgfvU/wDw7ZKpt8bOzZcnFSYhihAAY7cY5Nc3LwBU4F2FyFwAcnJHP/anEU/oFiFiJIzgn/GKlbizhmgkUJy7ZyGI/wA1B3EVpCxzGAV8tIcZoTsEr6Im8xm4eYlpX/T8WNox/wDyqfqeoGM+lHtkDAhiG7c091vVBOzx2ilSeGPGPtUCIDjLKcnncD3qbml0aOm0Tb3ZER8luySbm7e5o7gsYzHEdwIGQBjFO7liAI0UNkHPPauLWHZG2CQ3n50buLZobKdIStLVY490mVYj3ppfsFilDDevyNTBXahBHBB7kGoTVsxqdpwXOBx3qWJuc6F52sWNy+jvQLMm39fGc9iTirFE0sbrtVixA5GabadKE0yCEKTIgAyBxxU7aSh05B3YA9vrV2cjy8FfJFSRN6saOCFblmx2plrttD6aLFIrNnwRn71OvcBN2QzZyPlVanb05iXBKknmiDbdnJ/RCzptUhwM9s+9VW/iEE+0eRmrdexCW4dkZthHGarGsr/OyB24OKuQZVkRx5oqFCmkQdhXS9ia55rpf80AcnFH4oUBQBa+jLYy7iXCENuwfIxWo9N9R3Gmhkliknt/9IbaVPy+VUnpG1SPTYWmQFnTIVhjz71PTyLYKgXYZCeVHes/PGOW4yVlrFcFaLBr/wCI6xRFLOynhcfCcyDOfft2qjS6lfaod17cySZAwPY01165ju77bECrZyRjuaeWKqAqkIdq9hVPJgx4V8VybHpqllblLoUjgUQEhmDAHx3oWEA9Tez8D5d6dLsBwMIPOBSRY+vkEEj+ntmkW2bdJDzMYKjcSCfNJago2B4yPh+XejWQMuSFA+fJFEZlIYK+W74HY1GNxkmdyKM4OL6ZdOlL4zWcSs7FQACCO1Xq0uYioLhu2AAvtWS9FXgS5MLyMoL9hz3JrVbSeIFdyNOOwGOAfc03MqZ5lKm4vwO5ZQjK67lbGBgYJoXF48cYZ2Ug/wCoGkLu6Z0XFuqjwN3eoDW+oobXTitwoZhyqs2WP0FKSZJRb4Q61TXAsMiukCt/qCkeefOKzXWtbmvmaJQFhUnG1j8fPekdX1WTUpSSDFE3JQNkE00TLKFCjA7fDUuEa2m0mz5S7CiVnVs9sjGaUYAKu4g/9qIjnGBikpmAyn6cdqj2y90hNIt0rHJ2gnmnSKoUrnC59u9JQEDCk4b28VzcTiLbuXOfftmuu26OKkrDvQsSN6ch3+B4xUJIDc30SnBXOee1SEz7ypdF5BIwajLQhtUzxgNnFW9LGnbMz1Wf8VLyWeO2Zhut1AGec+/mpuBgkQLRnI5ZgK4hZHtSxVUUkc++a6Yrt2Z3KRjIXipydmLFUNLqNNobeArHmoPVo1aMJExJXI81ISOLadlZePHHFNbnMjk7N5Oc4HApsVTFy5KzJKY2xktULqsokik+DBOCTirLIFilbbu7dzUTqkAktZWbIz24xVqL5KzRVqFChTyAK6XzXPmjHANABUpAgkmRT2JxSdONOf0763fyrg1xgaxpkDRabbF+XVQCPYfKi1pkAEmMScEE06T8tNErSlROFG3BPam2rIfyxI2sF5+dZ6dy5LlfGiqxSSSXryMcmpSykkLDewAz3IqMskD3DMPuKnbdgU2jGxeAPP1qvqX8je9NhWFDxiAoIGc+wBpnMrh94I9s+aXClVIRVAHkUTJu+FiuDVRcGpLkSsJSy8gDnPbNOj8TEZPPI4qKX+TOAHUfWpUqjAHIyP6hxmuzVOyON2qEbZzaalHKn6cgnArUtLu2kiWSMlVccnd2481mE4YQybPIx/8AdSvSuqtArJIwZcdm5Ap0f5IV9GPr8Sx5N66f/JfNa1P8jbM0ZO0Lwe/NZtf3U2o3BmujlsYAzxin2q6mb+QLGAIAeCcgZ98VFeltkLLz9qQ3XBd0en2Le+2chSsmey/Sl2LFfhwW/wCUc1yuWchgPuK6QhASGGPBxUWy8kI5ckEA4Hy5rhwSxPZh8s5pdk3ksrAeR865KBRkYOe2a6mFBCISIcqAP2phqMmyKOMqMAkZPNSB3JE28ZPJHFQV3O0q/GSRnyMYpmNW7F5ZbULPIiw7WYNhSBjxxUZp77r4HHckV3CyBJvhJGCOPHBpnaXHpTpgZOcYq7gjTZjeoy34zQLK5aOBVZomQfpx3qTJIjDj27d88VVlibCvE3wEZbjtmpazEjwBVdcgjGa7KKMqLYrLGJg7tHkqPJppIpS1ZkG1yMEZqXEYELY2k4ycHjNRVxbK6vLyG+R4rkWdaKpdNKJnDhd3imN5IZI2jPGfY9qkNRjYTlmAzgDio90zkH4X+dW0VZFSYYYiipS5XbcSLwSGIpOrAsFddhXI7114roHJp1pyM95CEHxBs01FSGgpv1W1UsFDOBnNcfQGiI06tGWzvGCOK51SQywF2fDKc4PencmZYoUDfEOc5phrAlhsJMgbXO3mqS5ZZfREad8E5cjgHFWC0aNgT6eG8sFPNVezmI4ZuQc4+VWqxX1Io5F2lSM+9UtUqk7PS+myUsUaF2QZBGA3vnFN5Fk9UEKMfSurltq4yoJOMYprFO5kMUjKOOKrxTL8mroVwkysGC59xgmjicoPTK8IMD502Ikjk4xg85VaWniMqYBKuecipV4IJ+RZWZiTIMDuB7U1MrW0u8MQnJY+9JJd+gjBs7gcBu+KOTbdIQXwzD6VOFwlfgTmjHNBxY4t7kPIdv6X8fOn5J/qZVHzGarNtdNbO0OQSGBB8VYoZ1mQfCCx9hUMsGnYzT5E1t8nUfDYyo8nA70ptQggNz4pvOdi7zhQPPY13kkI5bK4yCOM0osJ+BJGYSENhsdz2xSoxjIAwaLchQlOSfA7iiQbcltvbjPeuvk4gpZGWNyfY478cVAs3rE78D2J5qTuJlfeMtwD27VAtKBu+Jd5Pb2p+KJWzT6HEAEUcg75B5x8qhw4N8vkE8AVICRTG+5huwajYY8XcRIxk8fPirmFVJmVrucdIuljKTb7IwQTxUnp0m0AFhnPIAzmovQ2X0iTksoBI/05qWt1RnYjajDnJP8AtUp+TKiPXlY8rtI9gDmmFxcJADGxk5zwVzn70V1JcjcBCCh7sM1FSu8bfFg57cdqjGJ2TGF03xM+4keKYzhRHuzl85waeaiLhXXKjbjcCoHNNp13xFjntzxVldFdlNvDm6l/6jSOaWvf/dzY7bjSNWEKBR9qKjzXQCp3pZZb2NlOGXkGmlSOg2sl5qAjhC52kkseAOK4+gXLL5ZXSi2ifcwYDnnuac6rJHcaUV4Y53ZHJ81CNZSxEBMttOCQKWEsyRsPU+EjsMc1V2q7Q/c6pkLDuE/IHxVO2c72u1Wb4W52jxUHdtidRk0uqGRFBc/COPOKRqIJvk1vTMjUGl4LNG63YPA44ztpKYCLIJDvnCg1EJLOFGx3yPI4pORpZGy276kZqqsX7Nd5rXQ/kZmC72Zd3sSKVXcZCuc496jSWMe0/p7HijRA6nG8D5813aRU+SRvYNiqSD8zjinFiwYHyMjv2FNrW6/kGCRGbIK8HHFJKrJJiMlVqG1tUxm5J2hHU4mSd3iZSgHOKk9FvYntl3lRKvw4JyTTILyRySeck0xMgtboNw2fFT2747RW725710WG8lMlpggK2eAR3o7SR3i2tKo24AAA7UzkupHgDHcF7gA96Yy4Ygnj6mlLHxQ+WXm0TkyXJINuwKHvkDiom9uJlBEjYx/pPem6yvG5Azt9s1xJGueHA+gzTIQp8i55Ny4EZLt2GN335zSKIzbsEfMHvQkAXsPv710kq9iwXHuaf10VG7fyEpioIwNpprPOwkUg9jkGnUxVzuUnP70zn3ESDHamQK2dWmiz6BI80jOrhfJXPerZCytChVTycHjiq50lYRnT1unb4vTDADjHvT6WQF8AttY9hUp8syYcLke39yqgRMSrN2NM72SDcikgvx25pYvbSR7bhTleAvPH3qOvILRmxBkYOSGzzXIpHZDG83eucyMVzgL7CuZXCxlTuBoXWxZBtyeOc+9FPJCsfxDLfKnCvJR9QwL64x23mm9ONQOb+4I/1mm9WV0JBQoUK6AKn+j8LqS5C/GNuT4FQFWLpsJBMkzoWK84BxUJ9HY9l5EbLG2w5wOwpk1vFKrFgu75cUtDNJKWw/8ALx4A4+9NryYxfEy5Pb61VSZYbIjVbaNYwy53Z8mmltK3nnHipG4lZlA2EDvz5qIu8xTbsgZOQRUpR3RGaXL7eT9MlY7omLbkAKeBSouVI7/HjnNMbaYtGARyT4pV0buJCf2qm4qz0Mcjq0Ks5IG3BGOK6hkYqRtUYP3pNVKqjYyvYg0jKT6jckH965XgHJrkevDhd6OWcHnFciV2kJfOMYOKa217MDgsCDxjApb1AXABGT3ocWuwU0+UPI41LoWwQPFcX8MTR/AgUn2FM0Uq5w+SOc04kZ+A7E8YGDUaadk7UotNDWK5khJiY/DnIpyJN3fBFN7i3Mw3IMFeCecmubO5J/lyj9PAyMVNq1aExk4va3wOCwJPA48Vy5ywbBxjtS0ZQuSNxrh4CSTG23P+oZqNryNadWhCWU4XAx/g0xnkVpju8cDApzNAYwSx+I8cDvTCWP05dp44zToJFXLKQsHKoSvbvTZ5iytkDmlTxH3OaaMGxkZxU4pCMkmlRc+ml9fSAuf0LgDOO1SVtLsjf1QRjABWoTQm26egjYAkY4+dTNvCWiHjHfipSRlJ8kgYkniVzGcH370ymgQlkRSrZPJHinNsQAUbPb/eubplg2yElgFIx9qgnRIrVzC2989gxFIXETiAsgDY75p7JKs7Mse4eWyM5pN5Y4rd0fucc5p6boTwUO8ObqYnjLGk8fOlr5g95Oy9i5IpCrKEgod6Md6BHNdAKrvo1nFLYB9uHxjOKpB7VomkqsWmwsAGUgHHzxSsrpE4K2IMjabMCrZRl5x7UHuoZDmQM474PNPLyQSBg/JPy4qP/K7wWiK47HPalLnlk3a6OpLhZBnaVTHFQeouTG5+LCknNSMv8hf5pXAOCFOTUdqUsRgdVyPhOM1NEG2L6VIrxq2MZGOakmC923CoDSQDbgnHB7e4qT9IhCQST4IqpkjUmei02RyxK0Plk24FDarLgsDk0wBkT+hqWhupQAvwj2pTh9FhZF0wS2oZ/h4j8UnBKIg2V7fenxdlBdsEn+1BZAf6QPpQpfYbFdoRW4iYjJZffApwU3AjJ4PcikBZrI+9S2CPamzyyQXGGY7BxnFFJ9HN7j+SJSaTlFG4kVH3ts0jhoxz7Yo/XyQwkUmlTOzKCXXI4A4oinE7JxmqYx3HJBzke1LqGfJklb5c964uYdwMgxkd8mklBfhc5AyanwxPMXTFmJUEKc01OGlzJzjyecU9jj4I2sPGDQkhVeOxPzriaRKUXJWNpQnp5XlsVEXl0YsxBeSO+akJiV35xwCciq/ctvndh2J4qzhhb5MzW5XBUu2Wro+59KICdd8W7HIPHzq4CXEQeMkIxPPY4FUzpZ0SyYOpILHkVYrWWOOTO1/TPcDk1LIrZn43SJQsIYiysS2OM+aYSXYY7jlm5Gw9h9Kcb2ZW9IfD3waZTrDEu+RD7nHvS4pDG/oZSeoSfhA5Jxmmt9EZLZzwGA7+3NLtIS25RhT2rmeVDAdykMTinCikyf8AqNn3os/KlLoBbmUL2DEUnn5U8UFR4NChXQC881ebW9VLOGJM4AA5HyoUKVPk7F0PQHMLM4G09yKbwox3En4fahQpaGvsaTwoZOe2eag9eRYvT2E4bPfxQoUyPZCXQno55AY/DkCp+A7SwXBBPbHahQqpn/I2/T3/ABINI2JGXI45I5pKW3JI2HJ+dChSbaL8opoKMyCM7ySn1pWJxJk5xj5UKFD6sWnTSHNswkBG4hgK5mgUxlXweeCKFClt1IekpR5GRtGQZYqR2yO9d+gUQk4I8UKFT3Ni5Y1Ho7WZDhGHJ44pKeAROSrcfKhQqXToi/lFt+BSO6IJAPxfOm8s7SORgcChQqaglyLc20kNLlswtj2I5+lQDKQcE80KFW8HRja/80WjpWMSWjA+HqXnl9KQKox44oUKJfkysvxHMVzIqMNqkMPNN3lMw2NkDPihQrlJHbGpfbII3GcGiumAGPI+VChQcRUbz/3Uv/UaRxQoU8Wf/9k=", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the result\n", + "display(IPImage(img_path2))" + ] + }, + { + "cell_type": "markdown", + "id": "203ba5d4", + "metadata": {}, + "source": [ + "### Transparent background\n", + "\n", + "You can use the `background` property to request a transparent background, but if you include in your prompt that you want a transparent background, it will be set to `transparent` by default. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "51b9f60f", + "metadata": {}, + "outputs": [], + "source": [ + "prompt3 = \"generate a pixel-art style picture of a green bucket hat with a pink quill on a transparent background.\"\n", + "img_path3 = \"imgs/hat.png\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c3e8a066", + "metadata": {}, + "outputs": [], + "source": [ + "result3 = client.images.generate(\n", + " model=\"gpt-image-1\",\n", + " prompt=prompt3,\n", + " quality=\"low\",\n", + " output_format=\"png\",\n", + " size=\"1024x1024\"\n", + ")\n", + "image_base64 = result3.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b9929907", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the image to a file and resize/compress for smaller files\n", + "image_base64 = result3.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)\n", + "\n", + "image = Image.open(BytesIO(image_bytes))\n", + "image = image.resize((250, 250), Image.LANCZOS)\n", + "image.save(img_path3, format=\"PNG\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a0a0694c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 250 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Show the result\n", + "display(IPImage(img_path3))" + ] + }, + { + "cell_type": "markdown", + "id": "24aa76b7", + "metadata": {}, + "source": [ + "## Edit images\n", + "\n", + "GPT Image can also accept image inputs, and use them to create new images. You can also provide a mask if you don't want the model to change a specific part of the input image.\n", + "\n", + "You can use a maximum of 10 input images, and if you use a mask, it will be applied to the first image provided in the `image` array." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3e4ae773", + "metadata": {}, + "outputs": [], + "source": [ + "prompt_edit = \"\"\"\n", + "Combine the images of the cat and the hat to show the cat wearing the hat while being perched in a tree, still in pixel-art style.\n", + "\"\"\"\n", + "img_path_edit = \"imgs/cat_with_hat.jpg\"" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "be995b49", + "metadata": {}, + "outputs": [], + "source": [ + "img1 = open(img_path2, \"rb\")\n", + "img2 = open(img_path3, \"rb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3e46de51", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the new image\n", + "result_edit = client.images.edit(\n", + " model=\"gpt-image-1\",\n", + " image=[img1,img2], \n", + " prompt=prompt_edit,\n", + " size=\"1024x1536\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "30fad662", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the image to a file and resize/compress for smaller files\n", + "image_base64 = result_edit.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)\n", + "\n", + "image = Image.open(BytesIO(image_bytes))\n", + "image = image.resize((250, 375), Image.LANCZOS)\n", + "image.save(img_path_edit, format=\"JPEG\", quality=80, optimize=True) " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9016913d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the result\n", + "display(IPImage(img_path_edit))" + ] + }, + { + "cell_type": "markdown", + "id": "cb06c713", + "metadata": {}, + "source": [ + "## Edit an image with a mask\n", + "\n", + "You can also provide a mask along with your input images (if there are several, the mask will be applied on the first one) to edit only the part of the input image that is not covered by the mask. Please note that the model might still edit some parts of the image inside the mask, but it will avoid it. \n", + "\n", + "Important note: the mask should contain an alpha channel. If you're generating it manually, for example using an image editing software, make sure you include this alpha channel. " + ] + }, + { + "cell_type": "markdown", + "id": "e34eedd7", + "metadata": {}, + "source": [ + "#### Generating the mask\n", + "\n", + "For this example, we'll use our model to generate the mask automatically for us. The mask might not be exact, but it will be enough for our purposes. \n", + "If you need to have an exact mask, feel free to use an image segmentation model." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "fe2ef0fc", + "metadata": {}, + "outputs": [], + "source": [ + "img_path_mask = \"imgs/mask.png\"\n", + "prompt_mask = \"generate a mask delimiting the entire character in the picture, using white where the character is and black for the background. Return an image in the same size as the input image.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "70329b98", + "metadata": {}, + "outputs": [], + "source": [ + "img_input = open(img_path1, \"rb\")\n", + "\n", + "# Generate the mask\n", + "result_mask = client.images.edit(\n", + " model=\"gpt-image-1\",\n", + " image=img_input, \n", + " prompt=prompt_mask\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e3eb0193", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the image to a file and resize/compress for smaller files\n", + "image_base64 = result_mask.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)\n", + "\n", + "image = Image.open(BytesIO(image_bytes))\n", + "image = image.resize((300, 300), Image.LANCZOS)\n", + "image.save(img_path_mask, format=\"PNG\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "1660ae01", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dKgQeaVho2Phx49Ej9SIlZFlWp9NNmTLFgWCAYM14nm9qarLjw6QYA9PIhFA+xU3qldwp7xre3L17nNEYLFh4Bxowc5Zw7gCngcIiqqMhsXBpIdq01LT09HSe5+3VBBLCOb17C1Lb2YtwHEdRFNQ+04gDBgZ57NjRnZqbUWLZmDlz/hdN6wSfwq5ngd4bP2FCdNdoLA3sFcV/4TueOGki+KIOfuVjx95tR0iQJ3hjsLFnzwTtKCEtZvPkTJ1qNBptHJugIpbBYJg3bx7MtO27teCRMmxgQMCUewXfBD1SzwfECzYZOLJMBysNVVVVERFdKJFOLdYveGiBptYJpcY8//zfbNnFD6fHEQTxyiuvOP4g8PED3+OmCvucCbcCVCUmJubWrRoH9y5JorN27VrbM2Yg9kCSJOx10E49G2m3VGNjY/Zd2fBEbY4sFEVJeW2wtCD0g4ObobnmbIGUlBStzZYVxuN1sDlbZe7cuaLoOLqFF3K1zGZz//79bUm5pGlhKxBBEOvWrVPl8Oo7AsNSVVXV1Jyc1luH5cU1QkPDVq9eDfUvnFKOAJJX14ojGm7z9WRAB7a/956zShtKmw9iY7sJ1sPHp805nmAAdc1TnTVr1mjNEZUjadS2bdsGZw1uXSk4MTHx+b/9raSkBAyYs+4LPXnu3DkfH70317wgPdsYwqJCSEjIuXP5kZERPMeTlBOel2VZmqZzc3MnT55UXn5DWoCWAjYkKYRSCYLIyMhYu3ZtTk4OfITQKtByUINffvnl5MkThYWFFy5cyMzM7NEjITs7Gzb1O/0pIGg8Yfy4/V997cgSrltDeoMZnHbffU6fjIFZKygoePDBB0NDQ1vfOjo6etPGTbBxUbM20IoO2slY7KyeeMc7chz3yccfe3mM1JP1kKZplmXf3f7u/HnzYb3e6aM4QRDXrl394osvCy9fPvLTTxaG0dH05Mn3zJ37YHh4uCush2vhCTFiIiygg22HRdHffUXemSIDFri+vj4lJaWzafGIG9CcHhkUdPXaVUeqS3RAx3vqhGHeeTMoT8UiTtRXrVrlzeEZjw0Nw/g9cuTIrqaunGsShcFKgL41hw1hf4Ho18HRa06/qYdBid7EwoUL4bhybwzPePbRaDzPT7n3XsF1dGXONFR5gpg+Ja5J4HF8tkOJ8ZjExMR7773XS49P4z1UCSFVPzg4+L777oP1OrVbhNyBpUuX2rE9xQMgPXVnPawZjB8/rqvJJDo5nvmYngFN0xzH3XXXXVmDB3Mc533G0EN31kMYZv48IV1T7bYgd4YT91IsX76c8D6cGm/WDBDpjo+PP3PmjJ/BIDwkztC0DS8MlcJezdTU1EuXLnnbWgXlqXHRmTNn+vn5Md4ZcHPPIkC+vr4PLXjIC/O5PVBAYZU599ixgQMGcCxLed0cwy3hxcSA4uLilJSU+vp6jdQBUQbKI/fRJyYmpqWkCntVUQOdBGQmuDqgHRsbCycoe1V4xtOUEFi7dq3Bz9CpeYXdoy5kfhCeC+QGwQlNrn5SnueXPPoobOEnvAbS85JFhw4d8uOPh6XcTldLJ4zZUs0iTxrCIfEdsslOnz5dXV09fPhw+IOLNoRznJC7mjl48Im8PIqkWM4b91W4N6AA//nPf5yyhddGbt6srqqslH7s9CGBmt9UUVRUNHv2bJ1YSWDG9Ptra2uddcx4e6mk//jHPzxsOPMiM0gQxIjhwxWoIgFSeOXKlTlz5nSP7x4TE5OVlTVnzhzpuFx32b7UJtI40tDQ8Oqrr8J2EJoW7GF89+7l5dddp4ScSF1dXULPBDuq2rkrHqOI8IXt37dfAR0AJc/NzW3djGXLHoeD4+EEIt6tkJvxf/3rX3BcjFRYbeHChTcdO/DUFizid/fhhx96jzH0EODbGjt2rGLFlOAuX3zxxcABA6RiZDAQpKenHz58uFmk7nTIrhawOrf4888/H3XXXdCxvr4GgiD8/f3ffvtt6c0KNIbjuOHDhnuDHnrOVhv4qr766islXUEQR4ulaf369YGBgbAjDsIYer3+T0uXFhUVwjthf5PWDCOIu7y79u7dO3bsWKlLoaJcWmpq7tGjStp2RmzSkSNHICSL6RZuo4Hjxo3neftPordbXEAu8/Ly+vbtC56bNHiHhYUtW7bs+LHj8verro2ge/KyV9XV1VveeguOHwbHXirouHDhwurqameVyerseayPL3vcmzf7ug0tp8NTP/98VK2ICAhobW3tkkeXQKskkwgyPX7cuB07dtSI55MCHMeBp6qYQjYfeS/rn7q6uv379v3xj3+MiYmRdya03M/gt/WdrfBO5XuVE3MDbt261aOHULbcWyI0bgpIzLz589SNSUoTv507d0ZHC0cdwdAgH8Xj4uIeeeSR3bt3X70qVNywUkgnlvSEGCP4wFaKx/N8TU3NwYMHV65c2atXL6lt0FrJhvfv3/+YaMBVtNuM2Owvv9wH26bVEC7EBiCK7e/vX1BQYN8p6k5E0CVxcfLatatz586FFsIue8FDlQUYAgIChw4d+uKLL+bm5taI8UYrwGu1iIBySoCCwcPKaXmzBdrQmvz88++88868efPi4uKklsAwAepHiVufaZpe8dRT9Q0NyrugrYEGPPTQQ94QoXFXYIBcvny5FiQGkMzO9u3bTSaTIOjCQEHJJV7+CLGxsaNG3fW///rXTz/99Ndff4UJmIOwLFtZWZmXl7d79+7Vq1fPnTs3LS1NftoE2BapVZJ8jxs//qgYg9FIxX5W5MaNG11NJg9eNnTjuBOUbQ4KCjp37lxkZKQDx545GcF7E9PZrl69umrVqu3bt8N4AWItGXBIWZZnY+p0OqPRmJCQYDKZeib0TErqpdPrevVKNhqDAwMD9QI6mtYJhQJ4juFYc4MZtsPW3Kw5d+7czZqblwoKfjt7tqiosLi4pKqqyirV0yrDTmoDQRBDhwx5asWK6dOni2UaGYrSSkySFWtG/nvnzhkzZ+p0Oqiq7Floop/tBETqueeeUzJJzQ6TuHfvF2npadDm1pYQDBHUiergYUmS9PX1DQwKDA0NDQsLCwkNDQoKgvB9B2XF4cpwU7lSya1fv779Pv74Y2iq6i59m4CPM2PmDI90St1YBUGqYmNja27VKBljtG8RvKG+ftPGjfHx8fIoSHu2XVJLwEaLJH0ELt7m6Q5wQoZ06/T09HfffddiEVrIaTjVjhVrSZaUlISGhXXqUDrEtcCIuHnzZu3MBttDEu7KysqNGzdKVrFNw9geZId0/FmYi8ptSFZW1o4dO5qamqxaqFkY0dN5++23PdIYuiUwHCYkJNTX12vWDLaXF9bQ0PD221sHDx4sf5zWHqODyFf8JEwm06JFi7784kupx1TPHLCZ5g6EhB7UQ/WBfI63twjZjO1F5DUIrAdKP+Yey125cmViYqL80WAxww6nCz7S5twyPT19wYL5W7duvX69TLq7veqnmsay4mh78eLF4OBg5w5YSKeB0X3atPut0o7dBSlBGX6sq6vbvWvX8uXLBw8e3PrMalAt+RRRDvy1TXU1mUyTJk3asGH98ePHOVmshRHWHRn3MH6tgOnr5s2bPckYut/5hFAMr1di4uGffgoLDdXOsoQdwJ48nUyYLly4cOrUqYMHD3711Vc3b94sLCy0/WrdunWLiorKzMwcNWpUz8TEpF69QkJCpL8KMU+CoN3fgLDiikX26Ozvv/seCikQ7o87fSViNJ7yNfgeOHBg4MCBbnbqWDtYrdq1/JawMJbTp083NDRUVNwoKLhcUVFBiUsRnKBNQkUcg8F34MCBwcHBPM8bDH69U5L9DH7yK0NijYeFE1mWoygyPz9/4IAB5sZGcCjUbpT3OaIffPCB9iOi9gHZno742JC85hbBKrthxP7ZtGmTx2ywcBtLCNkSixcv3rJlCyOcxalzn7Z3Gh7+EWmzAifUTpcsp41rFR4DwzA6nW7q1Km7d+/2AKfUPb42OAawe/f4X345GRAQQBAk+GaId8KJDvz169f7DxhQVlrq7iUS3WC2QJLNSvjmm28EBQWJwRjUQK+GEuXBZDJtfXsLBOfc2gtwAyWkKMERfeyxpRMnTmQYRulgDE77NQlN0wzDTJ58z7JljzMM49bBJ9It1iSSkpLy8vJ8fX2dOeZ55plUXgQvBpbNZnPfvn0uXSpwX6dU0+MHZDNTFPXO1nf8/f3hN867utOuhKh28g9BBAQEvPba3+E8GcI90fTsClyOVatWDR8x3H5HFP1Jz0UnSsiUKVMeeOAB9103JrV+sMSQIQcPHbJey0aQFoRtzQRxrbQ0PS0NahO71/I9qVl3FPQtMDBg27vvNm9dRQ1E2oKiKJbjoqOjVz/7rJseeU9p2Qy+8H9eSE5OdvfYF6KMtDz22GNZWVkqxM89Mj4IfTpu3Lh9+/YJfaqjPadUOOIaWJajaerEiRNDhgyRNqkQbgKlzcOuQ0JCYBu1MBVEDWwTt5ExJaBpimGZfv36PffccyzLupfrpLm20jTNcdy6devi4uLcrjcVBYem26EpwYFasXJlZlaWe0VKSQ06omNGj/76m28gSVftFiHuBCvqXl5eXlZmJg/n/mreKdVWdBQcUT8/v9c3bnTyujziHdDismH//v0f+/Of3cWN4jWlhOCIPv7446mpqW4X43JjtG4q7JGiNWvWxMREw55mQvOQmsqLj4qKOnPmjNFo9PiFQY0Gpj3iWRiW1dH0e++9t2DBArfYbUhpyhdds2ZNqFg5xrM1UEODnyc+i06M0MyfP3/M6DFuEaEhteNCZGRk5OXlCW2iSFyWQBwBdO/w4cPDhg2DjTiEVtFQYIbn+bVr14JTihqIOCfxeOjQ++67T+O5bJpw5puXJcaM+frrr93CeXAznPIla0JSOgfLsTRFnz17dsCAAVLBf0KTqG8JoWvWrFmjdkM8FKcoD+mua/cpKSn33y/Uidby4E5pYTY4adKkESNGoBlEnA7P80899RSt02l5WqiyEkIg9K9//au6zUA8Eloc4gcMGDBlyhQtG0NK9T4aNXLksGHDtNxHiLvz3888A2tghAYhVVVC6JS/PPOMVEkSQVwx0GdmZU2aNEmjAz2v3owbgqIZGRnHjx+naFpYGkQQFwBJpAcOHBw9OlubFdlUs4SQEzNr1iydTqf9xCLEfaFpmuf5kSNH9OvXT5vGUJ1qayRJsizr6+s7e/ZsMZqs/koJ4sFwYib3n//8Z0KTqCP9FE3zBDFx4sRevXq5y5YTxH2hRes3NScnJCSEZVmtZSZTqgSMeI4jeP7hhx9W4+aI10GKU8Gw8PARI0eQJKk1j1QFEwQJtYmJiePHj3fro3YRN4LnBHPz4AMPajB/TR0lJAhi4cMLDQaDBn0DxCOhaEHqJk+ebIqK0prUKa2EJEkyDBMYELhg3nxRIbXlGCCeCikKXnBw8Ow5c6RZopcqYfMU+b6pMd26iSEZDQ1IiGdDUcIBMvMXLKBoSlOrYkorIbjjs2bPFl5oyzNHPByKogme75ORkZyUrKlgBKX88mBMTMzYMWOEY89ENx1BFINlWZ1ON27cOCk2oQUUbQcc7TJ69OiAgACG0dbkGPEGSFHkpubkaCpdWVElhGKs0++/X8wdR2cUURqwfkOGDo2NjdVOQURK4QXT0NDQkaNGQdKMYrdGkN9jpCzr7+8/CoTQ25QQHjgzMzM8PFw42NHpviha1g7AzpEQQ4MjRowgtAFJkJRi9UNA60aOHEnwrnHHcYLZAdg5txuDESNGaGT7Dk/wlGKDJCje8GHDBN3HkAyiEqQoe4mJidHR0RopM62QJYQJYWBAQELPnnjYC6IiJElyLGcwGPr06aOFaSEU/1XCEoLWdYmIiO4ajUqIqAvHC05ZZuYgjYiiQsMAjDeDBg7U++i1lj6LeBukKH5pqWlSCpeXHI0mPOroMWO08NiIl0OCEqanQw6N2s1RRAnFbDXOR68fKyqh6l444uWQoh8WYjSGhBhV90hhicL1txFLPnaPj+/Ro4f4IyohoiokSRC8EKGIjlFdCRVdrA8PD/fx9RWDwordE7EB75sckATJ84ROpwsICFBdCWGd0OXAQ4aHh2kqaxZpxivHRF4MTERERhAaQDklHDQoE6MyiEbgRTk0RZlUt4TCobiK3apr166K3QtBbFHC0NAQQn0UcUfBBY2N7aaBUQdBfkev9yE0gBI1XmDUiYlBJUS0Ba+NyZESxX/hUXU6netvhSCdwGAwEGoDuaMuvodo+gwGg7+/n6vvhSCdQq/XExpAocBMQGBAQEAguqOIpggKCiI0AOXqZSLQOmNQcHBwsGvvhCCdFMv+/fqpvnYtJnArMjXV+/j4+vqiJUQ0RWlZmRZkUqmtTCSl+qMiiFWw8Oeff/YiJdTpsLYaojl02hBLhZTQx0cTq6IIIkcby4RKHZfNaeRxEUQEXNDLly9rYcleoZN6GYtFkfsgSCeUsLqqmvAed1QLRQQQxEoJa27VaMISKnMbdEYRrcEwzM2b1V6RtgaIg42giXgoIaI6PC+cwlBaWlpcVOxFlrC+vr6url54hTqIqA0vCqHZbL5VW6u6EipR8hCesKam5ubNm6o/MIJIVFZVamGlXhlLeJsSIjaCY5XrgGTRkydOwsG1hMevE4Ll4ziOYXCVohOoPz57OqxWIvakEuuEUO33coEmFkYRBCgTs7dVh1SmxkyzEhYWohIiWoAU54H55/K14PYLgRnF3J7GxkalboUgHSIqYfmNcuG1BqyCQmlrBEGcO3dO/D9OdtwX9eXVcXiepynqVk3Nr7/+qvqOXuUW68EFvXTpknQWB+KeOuI5Xx7LcWazmdAASh6NRtTW1jIsQynn/3of2LWdlEZCGyihE2DxCwoKampqSJLC2AyiIhwniN+VkpK62jo4L8yL5oT19fUci6fBuC3qy6ozn6Suvl48IEx954FUrAI3SZJms/nixYu4SuGuqC+uzgRKUWtBFIU5oTKtoCiKZdniYjFpXe14FOLNUOKqdUJCD39/fy0YQ+W2MsGjtow7njWoIm4FKYpi167RiYmJ3pPAbZ2th1sKEXVhWZam6e7du5MkqYVwvUJKyPGCCxoSatTI2OOF4Mhnxdx5c8U5ocrSyJOKtICkKJ7junbt+ttvvwUHB2vBEUe8HJZjaYrOycn57LPP9HqdxcJ4uCWkKaH89tChQ4KDg1mW1a4GorHwGkiC5Djurbe2JCUnWyyMisczKZe2xvP8jOkzNRIUbhetDg52ouGe1kiM1GSK+vLLL5OTe1ssFp1OR6lhHgSHmCRIl0ZK4Gm7deuWn58Pdbi1awkRL4PjOIqiKisrH1u69KOPP5Y22nMcp6S1cO3RaCRJ0jTNcdzzzz/v6+vLcUKVKxfeD0ED2BkoiuI4LiwsbMdHH721eXNcXBwrAmELwTaKMynClZAu3dQAgwrLsk8++eSrr74KcWFC46gfLUOUBqZLYBL37Nnz6aeffvfdd1VVVdIbRLnlWdckXZIukjiwfmDQ//KXZ9ate0Gw+4LniwKOaBRWZiTKSkt/PXXq+wPf79+3P+9EXlNjE5hNkiRdUZmGdE1mjKB+d4266+m/PD1p0iTUQMQt4HkeZkwQyADO/fbbvv37//nPfx46dMhKwp0F6fT1QIIgHnzwwcWLF2dnZ1sNMAjiRtpI3F4Qcd++fa+//vqePXvg9040iaRz57gBAQGffPLJPffcIz0JaiDi1nAiNE2DDdyzZ8+SJUtKSkqcpYdOmxOSIn4Gw2d79owePdpisVAU5QXqh2EcL4IVVY6m6ZKSKzk5U/LyTjhRDx2+hAjHcV98sXfChIkWi0XF/AMBVA3EZTAWRqfXVVZVjh837tix4+AAOnZJ0gkZM6CBzz33nCY0sIOBBRfQEIfR6XUMy4SFhn344Q4/Pz/HE6Gd4I7CSBAbG3vp4iWKFmK4uByvLdAvcAEMw+h0unlz533w4QcOOqVOKG8BKpecnEzRQgUn1EDN4QVfCK94QjLP8yzLDhs+zPE0TJ50UnkLHx8f+dIKgiijBgzDqJILKVQQpukjh484ZQhwmjt65uxZP4PBaqETQRRYT7eIziGpiOPNcRzLsnq9/sSJE8OGDYMKwo7ooXOWKMAnfvrp/3rxxZekMK7jl0U0hAYmlpwIVEkjCOL69etnzpw5kXfi+PHjN29W79q9m+c40mUGwEr5Dx06+MADDxYXFzsjOuokQOuee+5ZyIVlGMFPgNcI4iCc6HbC6wZzw86dO6fdf394eLhcAg8c+F4UPGdKnXiopgj7+2VPnTq1ZMkSuKlT3GDhEs4a4GCEGDfu7h9+OCS1GHaFIIjdMC16lZ+fv3r16t69e8tFTqfT+fj4kCQ5ZvQYu+RN8C3lWERgqil/342Kio8++ignJwdW4MRFAOdYXSd7GJIXOnHixM8++wyeAvXQmdwmGB4OJxoinucvXbq0dOljAQEBkphJSWQARQv6sH//fucaw4rKykOHDr3++uvTpk0zmUyt5dxZOF8PpTWTkSNHvrFpU3pGBuxfdu6NEM+Gbcn73/LWWytWrKi5dQvKZsPMsE2pGzly5IEDB1iOo20QNlhOq6mpKSu7TlNkg7lRr9eZG8wnTp64WX3zxIkT5/LP/fbbuYqKG/K7KL/p3n5omoYJdPfu3W/erIaOc9b4hHg8rOg9lZWVLVq0CCRKCH52OAGDUf6zzz6z0RiCQBYWFoYYQ/R6fXs76KE0hJXhdT6uuzZ4z9u2beN53mKxKPL1IR6igd98801sbKy0lfaOwgZ6kpqS2tjYyLDWM7o2AV196eWXQVbhLroWXK54MlzoJYLb4O/nj8WdEBvhxGWAoqKiqVOnFhcX6/V6G90/lmUpijpz9sybb7xBU7QtpSjAiV3x1FML5s8HIwHmFAYCGAsIZXCRckASqb+f3/nz5zE8g9iIRVSGpUsfE6yTj94OkYuIiKioqBCV6M7GUJoovfHGpsSewtEUEkp4oVLLXXVd8fjFEKPxXH5+REQEGkPvWlm3C14MljSazb1TUgsLLxMEyYunJ9gOTetYllm+fPmGDRsgx9r2+5rN5iNHjhz64dC333ybm5tbU1MjXZMkCdcZRqEKvuu+Logm7dq1i+f5JpwTIneCEy2TxWJJT0+3L/8R4ig6ne6HH37s1HKF1TuLioq2b98+ceJEg8Egl2dXBPlFBXSZFkKLMzIy6sVTUTGHBrHRHX322WfFGAltt9SlpaULp9K3WnO/47KkxWKRf+TCxQvvv//+Qw891NXUFa7vmpRMV3ou0COTJ01qbGxstoe4VIG0DycoAFdZWdW1a1e7pzCgJ39YvFgQuaYmO5bHxNQZRh7IqKio+MebbyYm9nS6Hioxc4AWjx079sqVK9DLVoMNgsgBj2nZsmXSodZ2AB9cv369YF3t0kMAEtkkJ66mpmbxH/7gllsUaNGv6NrV9N7296TukB5PCGOhTiItNDU18Ty/bt06R5QQJocEQbz88sug2A7KGBgPeP3UU8vdUw9bWpyVmblhw4aSkhKrhwR33PHOQtwaRhQAhmFGjBjhoKBLevjUihXSxR1snqSKDzz4gLP00FVl8DsuykYQRFBQUHZ29oQJE8LDwpOSk3v1SgwKCpK/mWGYNj/eMknA9Q5PgxedI8gOnTV79r937qRomnOsoCCEWFmWnTlz5pYtW4xGI8MwDq7+sRxLkVRRUVF6elpdXb3jO+tFJSQVLUNGiVjpWFxc3NChQwcOHDhs2LCUlJSwsLA7XgfSxOH5Jf1E1XQ7+BYLA57n+fPnly1btnfvXp1O13ogtg+4VJ8+fbZs2ZKVlQVDPMihPQ0W/yFJMikp6fz5807Z1KuO1MIQBQv6VpWqIiMjY6JjMvpkUBTVMzHRGGwMCTH6+flFRET06JHg6ysUs4HV/9ZAOEvKNkSd1BowaErenVwTqquq/+f1/3nllVdqa2udW2ReylCjaOqJJ55YuWIl7EuCiU9nDz+Do6YtFktc9+7Xy8qco4TKGsK2gY4AZ7WDR/Lz8xO+NJpO7NkzLCwsKCg4LS01NDQsKalXSkpKfHy81TxeOmhOgVPmPCDJBRQDXtsiWDa6HnBZeHNr41NXV5d3PG/vF3s/+OCDoqIipx/zICFpS2Rk5COPPDJ37tzk5GT4kyR18ubJn04+LQQZW7FixYYNG5xlBlX/6q1p79vqOG/Ix8enR3z8kKFDhwwZkpWVlZCQEBgYKNdJ+4Y9z0ba0aPYONXU1NTQ0HDx4sXS0tLc3Nxjx3JPnvxVzFCzPlHPFQgVPls03NfXd+zYMbNmzR4/Yby0EG+F1BJ5z1RWVK5atWrzW5udVV1Gi0rYAVJfSKMU/LeV/STDw8NMJlOvXr0GDBhwV3Z2au+ULhFdbndZm8sTeJtOSopHUSRF3Rbc4zjuypUrZnNjZWXF5YKCmlu3qqqqqqurpa20NE3HxEQLuZSCV+IfFRUVFhbm7++nFwtMSIYD7CnHcY2NjRaLpaq66nz++R9//LGkpKSoqKiisrKyoqLN45w5RSomSdEa+NFoNA4ePHh0dnZsbFy//v0MYsVAgiDCwsJCQkKkTisqLDp1+tTBgwc/+OCDK1euOLe+k4eIoGQ/W08ywQMZMXLk1JycCRMmREVFyf8EXQk7aDw1uiNTvNuiEbW1t06fPnPk8OEfD/946VJBQ0NDQUGB2WzurHiJVpQgSQqifKCEcM/2PgIhSikqQyiO5G3dLi2kODYJUhQZFRUTHQ1uAlhvqG7oCofZ0wSueZori5jK/djw8PDs7OwxY8b069u3e3yPiMgIn1YnZ0hDMmgjfFXupJnNOtA8wZOmMcD18vIzp05/f/BA3rHjR4/+fPXatTavccc4vqTYtq/X/T6vEn8gtAHZfozQChjCXLGdwn1ky9nDnsHgFx8fHxkZERwUPGTokLS0tNTU1ISEhPayNOSftZotyP1kqxeui53Ig43ym7ZeQW5qavr555+/+vrrb7/+5tTpU5WVlVYaItfYzi58dfCk7lGLpf1nkbwG+ejhCrxCCVsPe22OZz4+PnFxcekZGSaTKbFnz6Tk5FCjMSk52cfXN8Ro7NSNOv7COnbA2nSJbd91xvP8zeqblVWV+fn5BQUFeXl5hw4dOnv2bGvFc5uaRR6NmwVmnIt8BtjB6khwcLBer4+P7xER0UVH6/z9/YKNxpiY6LCwMFqnM0WZunQJj4iIFHex0f4B/gRP6Gg6IjLSFW2uFIFsB57ni4uLb4o0NNSXFF/57dxvFovlWmlp0eXC2ro6s7lB/rCoeNrEq5WwNVaxmc6KrE6nCwoK5HnhRXR0NFxFsLywCtrc1YJX0zMxMTAwSNo2Li7VNhs/HU1fKy0tLCzU6/Wwz1Xc6Sq0geP4ioobN8pvkJSwWHzHhoFfesfVV0R1UAnvgGQq5S6ifBlXRdsCkxb51EU+b0SL5y6gEjqNNrW07dSkDhdCmlNM2spkkukVqpiHIPpLajcCQbwZN1r8QhDPxJW11hAEsQFUQQRBEMS7QXcUQVQGVRBBEARBEARREXRHEQRBEARBEARBEARBVAIDMwiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIIj38ftBQtJ/Wn6U/m0zt6blr2RblTJsycVp+z02flK6n/y19Hertsh+lD9sW1dtvw1k+79pqw0dfbADWnrzjp9t+Us7T9LeBaSHtLpR6ze0eYnb/9TRI9vwCO19lJDLXJv/beOp2u0KeUVm614TTlVs55HlYi1+UDyCsa0Ptyk0bXRsOx1BEsT/B9If67npuHIhAAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the mask\n", + "display(IPImage(img_path_mask))" + ] + }, + { + "cell_type": "markdown", + "id": "2d66b14d", + "metadata": {}, + "source": [ + "#### Creating an alpha channel\n", + "This step is optional, if you want to turn a black & white image into a mask with an alpha channel that can be used in the Image Edit API." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "2ea97104", + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Load your black & white mask as a grayscale image\n", + "mask = Image.open(img_path_mask).convert(\"L\")\n", + "\n", + "# 2. Convert it to RGBA so it has space for an alpha channel\n", + "mask_rgba = mask.convert(\"RGBA\")\n", + "\n", + "# 3. Then use the mask itself to fill that alpha channel\n", + "mask_rgba.putalpha(mask)\n", + "\n", + "# 4. Convert the mask into bytes\n", + "buf = BytesIO()\n", + "mask_rgba.save(buf, format=\"PNG\")\n", + "mask_bytes = buf.getvalue()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7aa9c7f7", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the resulting file\n", + "img_path_mask_alpha = \"imgs/mask_alpha.png\"\n", + "with open(img_path_mask_alpha, \"wb\") as f:\n", + " f.write(mask_bytes)" + ] + }, + { + "cell_type": "markdown", + "id": "ca8fdead", + "metadata": {}, + "source": [ + "#### Editing with the mask\n", + "When using a mask, we still need the prompt the model describing the entiring resulting image, not just the area that is masked. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "40ee1402", + "metadata": {}, + "outputs": [], + "source": [ + "prompt_mask_edit = \"A strange character on a colorful galaxy background, with lots of stars and planets.\"\n", + "mask = open(img_path_mask_alpha, \"rb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "65b5487a", + "metadata": {}, + "outputs": [], + "source": [ + "result_mask_edit = client.images.edit(\n", + " model=\"gpt-image-1\", \n", + " prompt=prompt_mask_edit,\n", + " image=img_input,\n", + " mask=mask,\n", + " size=\"1024x1024\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "06ee85a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Display result\n", + "\n", + "img_path_mask_edit = \"imgs/mask_edit.png\"\n", + "\n", + "image_base64 = result_mask_edit.data[0].b64_json\n", + "image_bytes = base64.b64decode(image_base64)\n", + "\n", + "image = Image.open(BytesIO(image_bytes))\n", + "image = image.resize((300, 300), Image.LANCZOS)\n", + "image.save(img_path_mask_edit, format=\"JPEG\", quality=80, optimize=True)\n", + " \n", + "display(IPImage(img_path_mask_edit))" + ] + }, + { + "cell_type": "markdown", + "id": "ae87ecb7", + "metadata": {}, + "source": [ + "## Wrapping up" + ] + }, + { + "cell_type": "markdown", + "id": "28f2e4b8", + "metadata": {}, + "source": [ + "In this cookbook, we've seen how to use our new image generation model, GPT Image, to either generate new images from scratch, or use reference images. We've also covered how to create a mask with an alpha channel to apply it to an input image, to guide the image edition even further. \n", + "\n", + "Feel free to use this as a starting point to explore other use cases, and if you're looking for some inspiration, check out the [image gallery](https://platform.openai.com/docs/guides/image-generation?image-generation-model=gpt-image-1&gallery=open#generate-images) in our docs. \n", + "\n", + "Happy building!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (notebooks-env)", + "language": "python", + "name": "notebooks-env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/How_to_call_functions_for_knowledge_retrieval.ipynb b/examples/How_to_call_functions_for_knowledge_retrieval.ipynb index 3b8e118753..bfb1a29d37 100644 --- a/examples/How_to_call_functions_for_knowledge_retrieval.ipynb +++ b/examples/How_to_call_functions_for_knowledge_retrieval.ipynb @@ -54,7 +54,6 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import arxiv\n", "import ast\n", "import concurrent\n", diff --git a/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb b/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb index f1c1860192..a29a52bee4 100644 --- a/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb +++ b/examples/How_to_combine_GPT4o_with_RAG_Outfit_Assistant.ipynb @@ -331,16 +331,16 @@ " \"content\": [\n", " {\n", " \"type\": \"text\",\n", - " \"text\": \"\"\"Given an image of an item of clothing, analyze the item and generate a JSON output with the following fields: \"items\", \"category\", and \"gender\". \n", + " \"text\": f\"\"\"Given an image of an item of clothing, analyze the item and generate a JSON output with the following fields: \"items\", \"category\", and \"gender\".\n", " Use your understanding of fashion trends, styles, and gender preferences to provide accurate and relevant suggestions for how to complete the outfit.\n", " The items field should be a list of items that would go well with the item in the picture. Each item should represent a title of an item of clothing that contains the style, color, and gender of the item.\n", " The category needs to be chosen between the types in this list: {subcategories}.\n", " You have to choose between the genders in this list: [Men, Women, Boys, Girls, Unisex]\n", " Do not include the description of the item in the picture. Do not include the ```json ``` tag in the output.\n", - " \n", + "\n", " Example Input: An image representing a black leather jacket.\n", "\n", - " Example Output: {\"items\": [\"Fitted White Women's T-shirt\", \"White Canvas Sneakers\", \"Women's Black Skinny Jeans\"], \"category\": \"Jackets\", \"gender\": \"Women\"}\n", + " Example Output: {{\"items\": [\"Fitted White Women's T-shirt\", \"White Canvas Sneakers\", \"Women's Black Skinny Jeans\"], \"category\": \"Jackets\", \"gender\": \"Women\"}}\n", " \"\"\",\n", " },\n", " {\n", diff --git a/examples/Multiclass_classification_for_transactions.ipynb b/examples/Multiclass_classification_for_transactions.ipynb index d9c4befb33..c5ee388f83 100644 --- a/examples/Multiclass_classification_for_transactions.ipynb +++ b/examples/Multiclass_classification_for_transactions.ipynb @@ -25,18 +25,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload\n", - "%pip install openai 'openai[datalib]' 'openai[embeddings]' transformers\n" + "%pip install openai 'openai[datalib]' 'openai[embeddings]' transformers scikit-learn matplotlib plotly pandas scipy\n" ] }, { "cell_type": "code", - "execution_count": 311, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -47,8 +47,8 @@ "import os\n", "\n", "COMPLETIONS_MODEL = \"gpt-4\"\n", - "\n", - "client = openai.OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"\"))" + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", + "client = openai.OpenAI()" ] }, { @@ -70,184 +70,34 @@ }, { "cell_type": "code", - "execution_count": 312, + "execution_count": 152, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "359" - ] - }, - "execution_count": 312, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of transactions: 359\n", + " Date Supplier Description \\\n", + "0 21/04/2016 M & J Ballantyne Ltd George IV Bridge Work \n", + "1 26/04/2016 Private Sale Literary & Archival Items \n", + "2 30/04/2016 City Of Edinburgh Council Non Domestic Rates \n", + "3 09/05/2016 Computacenter Uk Kelvin Hall \n", + "4 09/05/2016 John Graham Construction Ltd Causewayside Refurbishment \n", + "\n", + " Transaction value (£) \n", + "0 35098.0 \n", + "1 30000.0 \n", + "2 40800.0 \n", + "3 72835.0 \n", + "4 64361.0 \n" + ] } ], "source": [ "transactions = pd.read_csv('./data/25000_spend_dataset_current.csv', encoding= 'unicode_escape')\n", - "len(transactions)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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021/04/2016M & J Ballantyne LtdGeorge IV Bridge Work35098.0
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230/04/2016City Of Edinburgh CouncilNon Domestic Rates40800.0
309/05/2016Computacenter UkKelvin Hall72835.0
409/05/2016John Graham Construction LtdCausewayside Refurbishment64361.0
\n", - "
" - ], - "text/plain": [ - " Date Supplier Description \\\n", - "0 21/04/2016 M & J Ballantyne Ltd George IV Bridge Work \n", - "1 26/04/2016 Private Sale Literary & Archival Items \n", - "2 30/04/2016 City Of Edinburgh Council Non Domestic Rates \n", - "3 09/05/2016 Computacenter Uk Kelvin Hall \n", - "4 09/05/2016 John Graham Construction Ltd Causewayside Refurbishment \n", - "\n", - " Transaction value (£) \n", - "0 35098.0 \n", - "1 30000.0 \n", - "2 40800.0 \n", - "3 72835.0 \n", - "4 64361.0 " - ] - }, - "execution_count": 313, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transactions.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": {}, - "outputs": [], - "source": [ - "def request_completion(prompt):\n", - "\n", - " completion_response = openai.chat.completions.create(\n", - " prompt=prompt,\n", - " temperature=0,\n", - " max_tokens=5,\n", - " top_p=1,\n", - " frequency_penalty=0,\n", - " presence_penalty=0,\n", - " model=COMPLETIONS_MODEL)\n", - "\n", - " return completion_response\n", - "\n", - "def classify_transaction(transaction,prompt):\n", - "\n", - " prompt = prompt.replace('SUPPLIER_NAME',transaction['Supplier'])\n", - " prompt = prompt.replace('DESCRIPTION_TEXT',transaction['Description'])\n", - " prompt = prompt.replace('TRANSACTION_VALUE',str(transaction['Transaction value (£)']))\n", - "\n", - " classification = request_completion(prompt).choices[0].message.content.replace('\\n','')\n", - "\n", - " return classification\n", - "\n", - "# This function takes your training and validation outputs from the prepare_data function of the Finetuning API, and\n", - "# confirms that each have the same number of classes.\n", - "# If they do not have the same number of classes the fine-tune will fail and return an error\n", - "\n", - "def check_finetune_classes(train_file,valid_file):\n", - "\n", - " train_classes = set()\n", - " valid_classes = set()\n", - " with open(train_file, 'r') as json_file:\n", - " json_list = list(json_file)\n", - " print(len(json_list))\n", - "\n", - " for json_str in json_list:\n", - " result = json.loads(json_str)\n", - " train_classes.add(result['completion'])\n", - " #print(f\"result: {result['completion']}\")\n", - " #print(isinstance(result, dict))\n", - "\n", - " with open(valid_file, 'r') as json_file:\n", - " json_list = list(json_file)\n", - " print(len(json_list))\n", - "\n", - " for json_str in json_list:\n", - " result = json.loads(json_str)\n", - " valid_classes.add(result['completion'])\n", - " #print(f\"result: {result['completion']}\")\n", - " #print(isinstance(result, dict))\n", - "\n", - " if len(train_classes) == len(valid_classes):\n", - " print('All good')\n", - "\n", - " else:\n", - " print('Classes do not match, please prepare data again')\n" + "print(f\"Number of transactions: {len(transactions)}\")\n", + "print(transactions.head())\n" ] }, { @@ -262,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ @@ -273,38 +123,54 @@ "\n", "Transaction:\n", "\n", - "Supplier: SUPPLIER_NAME\n", - "Description: DESCRIPTION_TEXT\n", - "Value: TRANSACTION_VALUE\n", + "Supplier: {}\n", + "Description: {}\n", + "Value: {}\n", "\n", - "The classification is:'''\n" + "The classification is:'''\n", + "\n", + "def format_prompt(transaction):\n", + " return zero_shot_prompt.format(transaction['Supplier'], transaction['Description'], transaction['Transaction value (£)'])\n", + "\n", + "def classify_transaction(transaction):\n", + "\n", + " \n", + " prompt = format_prompt(transaction)\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": prompt},\n", + " ]\n", + " completion_response = openai.chat.completions.create(\n", + " messages=messages,\n", + " temperature=0,\n", + " max_tokens=5,\n", + " top_p=1,\n", + " frequency_penalty=0,\n", + " presence_penalty=0,\n", + " model=COMPLETIONS_MODEL)\n", + " label = completion_response.choices[0].message.content.replace('\\n','')\n", + " return label\n" ] }, { "cell_type": "code", - "execution_count": 315, + "execution_count": 155, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Building Improvement\n" + "Transaction: M & J Ballantyne Ltd George IV Bridge Work 35098.0\n", + "Classification: Building Improvement\n" ] } ], "source": [ "# Get a test transaction\n", "transaction = transactions.iloc[0]\n", - "\n", - "# Interpolate the values into the prompt\n", - "prompt = zero_shot_prompt.replace('SUPPLIER_NAME',transaction['Supplier'])\n", - "prompt = prompt.replace('DESCRIPTION_TEXT',transaction['Description'])\n", - "prompt = prompt.replace('TRANSACTION_VALUE',str(transaction['Transaction value (£)']))\n", - "\n", "# Use our completion function to return a prediction\n", - "completion_response = request_completion(prompt)\n", - "print(completion_response.choices[0].text)\n" + "print(f\"Transaction: {transaction['Supplier']} {transaction['Description']} {transaction['Transaction value (£)']}\")\n", + "print(f\"Classification: {classify_transaction(transaction)}\")\n" ] }, { @@ -319,44 +185,45 @@ }, { "cell_type": "code", - "execution_count": 291, + "execution_count": 156, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "/var/folders/3n/79rgh27s6l7_l91b9shw0_nr0000gp/T/ipykernel_81921/2775604370.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" + " test_transactions['Classification'] = test_transactions.apply(lambda x: classify_transaction(x),axis=1)\n" ] } ], "source": [ "test_transactions = transactions.iloc[:25]\n", - "test_transactions['Classification'] = test_transactions.apply(lambda x: classify_transaction(x,zero_shot_prompt),axis=1)\n" + "test_transactions['Classification'] = test_transactions.apply(lambda x: classify_transaction(x),axis=1)\n" ] }, { "cell_type": "code", - "execution_count": 292, + "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - " Building Improvement 14\n", - " Could not classify 5\n", - " Literature & Archive 3\n", - " Software/IT 2\n", - " Utility Bills 1\n", - "Name: Classification, dtype: int64" + "Classification\n", + "Building Improvement 17\n", + "Literature & Archive 3\n", + "Software/IT 2\n", + "Could not classify 2\n", + "Utility Bills 1\n", + "Name: count, dtype: int64" ] }, - "execution_count": 292, + "execution_count": 157, "metadata": {}, "output_type": "execute_result" } @@ -367,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 293, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -493,7 +360,7 @@ " Wavetek Ltd\n", " Kelvin Hall\n", " 87589.0\n", - " Could not classify\n", + " Building Improvement\n", " \n", " \n", " 12\n", @@ -525,7 +392,7 @@ " Wavetek Ltd\n", " Kelvin Hall\n", " 65692.0\n", - " Could not classify\n", + " Building Improvement\n", " \n", " \n", " 16\n", @@ -581,7 +448,7 @@ " Creative Video Productions Ltd\n", " Kelvin Hall\n", " 26866.0\n", - " Could not classify\n", + " Building Improvement\n", " \n", " \n", " 23\n", @@ -631,35 +498,35 @@ "23 15/08/2016 John Graham Construction Ltd Causewayside Refurbishment \n", "24 24/08/2016 ECG Facilities Service Facilities Management Charge \n", "\n", - " Transaction value (£) Classification \n", - "0 35098.0 Building Improvement \n", - "1 30000.0 Literature & Archive \n", - "2 40800.0 Utility Bills \n", - "3 72835.0 Software/IT \n", - "4 64361.0 Building Improvement \n", - "5 53690.0 Building Improvement \n", - "6 365344.0 Building Improvement \n", - "7 26506.0 Software/IT \n", - "8 32777.0 Building Improvement \n", - "9 32777.0 Building Improvement \n", - "10 32317.0 Could not classify \n", - "11 87589.0 Could not classify \n", - "12 381803.0 Building Improvement \n", - "13 32832.0 Building Improvement \n", - "14 1700000.0 Building Improvement \n", - "15 65692.0 Could not classify \n", - "16 139845.0 Building Improvement \n", - "17 28500.0 Literature & Archive \n", - "18 33800.0 Literature & Archive \n", - "19 30113.0 Building Improvement \n", - "20 32317.0 Could not classify \n", - "21 32795.0 Building Improvement \n", - "22 26866.0 Could not classify \n", - "23 196807.0 Building Improvement \n", - "24 32795.0 Building Improvement " + " Transaction value (£) Classification \n", + "0 35098.0 Building Improvement \n", + "1 30000.0 Literature & Archive \n", + "2 40800.0 Utility Bills \n", + "3 72835.0 Software/IT \n", + "4 64361.0 Building Improvement \n", + "5 53690.0 Building Improvement \n", + "6 365344.0 Building Improvement \n", + "7 26506.0 Software/IT \n", + "8 32777.0 Building Improvement \n", + "9 32777.0 Building Improvement \n", + "10 32317.0 Could not classify \n", + "11 87589.0 Building Improvement \n", + "12 381803.0 Building Improvement \n", + "13 32832.0 Building Improvement \n", + "14 1700000.0 Building Improvement \n", + "15 65692.0 Building Improvement \n", + "16 139845.0 Building Improvement \n", + "17 28500.0 Literature & Archive \n", + "18 33800.0 Literature & Archive \n", + "19 30113.0 Building Improvement \n", + "20 32317.0 Could not classify \n", + "21 32795.0 Building Improvement \n", + "22 26866.0 Building Improvement \n", + "23 196807.0 Building Improvement \n", + "24 32795.0 Building Improvement " ] }, - "execution_count": 293, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -692,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -784,19 +651,19 @@ "4 27926 Building Improvement " ] }, - "execution_count": 317, + "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('./data/labelled_transactions.csv')\n", - "df.head()\n" + "df.head()" ] }, { "cell_type": "code", - "execution_count": 318, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -865,19 +732,19 @@ "1 Supplier: John Graham Construction Ltd; Descri... " ] }, - "execution_count": 318, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['combined'] = \"Supplier: \" + df['Supplier'].str.strip() + \"; Description: \" + df['Description'].str.strip() + \"; Value: \" + str(df['Transaction value (£)']).strip()\n", - "df.head(2)\n" + "df.head(2)" ] }, { "cell_type": "code", - "execution_count": 319, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -886,7 +753,7 @@ "101" ] }, - "execution_count": 319, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } @@ -896,28 +763,27 @@ "tokenizer = GPT2TokenizerFast.from_pretrained(\"gpt2\")\n", "\n", "df['n_tokens'] = df.combined.apply(lambda x: len(tokenizer.encode(x)))\n", - "len(df)\n" + "len(df)" ] }, { "cell_type": "code", - "execution_count": 320, + "execution_count": 162, "metadata": {}, "outputs": [], "source": [ - "embedding_path = './data/transactions_with_embeddings_100.csv'\n" + "embedding_path = './data/transactions_with_embeddings_100.csv'" ] }, { "cell_type": "code", - "execution_count": 321, + "execution_count": 163, "metadata": {}, "outputs": [], "source": [ "from utils.embeddings_utils import get_embedding\n", - "\n", - "df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x, model='gpt-4'))\n", - "df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x, model='gpt-4'))\n", + "df['babbage_similarity'] = df.combined.apply(lambda x: get_embedding(x))\n", + "df['babbage_search'] = df.combined.apply(lambda x: get_embedding(x))\n", "df.to_csv(embedding_path)\n" ] }, @@ -935,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": 309, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -982,8 +848,8 @@ " Other\n", " Supplier: Creative Video Productions Ltd; Desc...\n", " 136\n", - " [-0.009802100248634815, 0.022551486268639565, ...\n", - " [-0.00232666521333158, 0.019198870286345482, 0...\n", + " [-0.02898375503718853, -0.02881557121872902, 0...\n", + " [-0.02879939414560795, -0.02867320366203785, 0...\n", " \n", " \n", " 1\n", @@ -995,8 +861,8 @@ " Building Improvement\n", " Supplier: John Graham Construction Ltd; Descri...\n", " 140\n", - " [-0.009065819904208183, 0.012094118632376194, ...\n", - " [0.005169447045773268, 0.00473341578617692, -0...\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", " \n", " \n", " 2\n", @@ -1008,8 +874,8 @@ " Building Improvement\n", " Supplier: Morris & Spottiswood Ltd; Descriptio...\n", " 141\n", - " [-0.009000026620924473, 0.02405017428100109, -...\n", - " [0.0028343256562948227, 0.021166473627090454, ...\n", + " [0.013581369072198868, -0.003978211898356676, ...\n", + " [0.013593776151537895, -0.0037341134157031775,...\n", " \n", " \n", " 3\n", @@ -1021,8 +887,8 @@ " Building Improvement\n", " Supplier: John Graham Construction Ltd; Descri...\n", " 140\n", - " [-0.009065819904208183, 0.012094118632376194, ...\n", - " [0.005169447045773268, 0.00473341578617692, -0...\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", " \n", " \n", " 4\n", @@ -1034,8 +900,8 @@ " Building Improvement\n", " Supplier: John Graham Construction Ltd; Descri...\n", " 140\n", - " [-0.009065819904208183, 0.012094118632376194, ...\n", - " [0.005169447045773268, 0.00473341578617692, -0...\n", + " [-0.02408558875322342, -0.02881370671093464, 0...\n", + " [-0.024109570309519768, -0.02880912832915783, ...\n", " \n", " \n", "\n", @@ -1064,21 +930,21 @@ "4 Supplier: John Graham Construction Ltd; Descri... 140 \n", "\n", " babbage_similarity \\\n", - "0 [-0.009802100248634815, 0.022551486268639565, ... \n", - "1 [-0.009065819904208183, 0.012094118632376194, ... \n", - "2 [-0.009000026620924473, 0.02405017428100109, -... \n", - "3 [-0.009065819904208183, 0.012094118632376194, ... \n", - "4 [-0.009065819904208183, 0.012094118632376194, ... \n", + "0 [-0.02898375503718853, -0.02881557121872902, 0... \n", + "1 [-0.024112487211823463, -0.02881261520087719, ... \n", + "2 [0.013581369072198868, -0.003978211898356676, ... \n", + "3 [-0.024112487211823463, -0.02881261520087719, ... \n", + "4 [-0.02408558875322342, -0.02881370671093464, 0... \n", "\n", " babbage_search \n", - "0 [-0.00232666521333158, 0.019198870286345482, 0... \n", - "1 [0.005169447045773268, 0.00473341578617692, -0... \n", - "2 [0.0028343256562948227, 0.021166473627090454, ... \n", - "3 [0.005169447045773268, 0.00473341578617692, -0... \n", - "4 [0.005169447045773268, 0.00473341578617692, -0... " + "0 [-0.02879939414560795, -0.02867320366203785, 0... \n", + "1 [-0.024112487211823463, -0.02881261520087719, ... \n", + "2 [0.013593776151537895, -0.0037341134157031775,... \n", + "3 [-0.024112487211823463, -0.02881261520087719, ... \n", + "4 [-0.024109570309519768, -0.02880912832915783, ... " ] }, - "execution_count": 309, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -1086,7 +952,7 @@ "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, accuracy_score\n", + "from sklearn.metrics import classification_report\n", "from ast import literal_eval\n", "\n", "fs_df = pd.read_csv(embedding_path)\n", @@ -1096,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 310, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -1121,12 +987,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n", - "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" + "/Users/vishnu/code/openai-cookbook/cookbook_env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/Users/vishnu/code/openai-cookbook/cookbook_env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", + "/Users/vishnu/code/openai-cookbook/cookbook_env/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" ] } ], @@ -1172,14 +1038,13 @@ "### Building Fine-tuned Classifier\n", "\n", "We'll need to do some data prep first to get our data ready. This will take the following steps:\n", - "- First we'll list out our classes and replace them with numeric identifiers. Making the model predict a single token rather than multiple consecutive ones like 'Building Improvement' should give us better results\n", - "- We also need to add a common prefix and suffix to each example to aid the model in making predictions - in our case our text is already started with 'Supplier' and we'll add a suffix of '\\n\\n###\\n\\n'\n", - "- Lastly we'll aid a leading whitespace onto each of our target classes for classification, again to aid the model" + "- To prepare our training and validation sets, we'll create a set of message sequences. The first message for each will be the user prompt formatted with the details of the transaction, and the final message will be the expected classification response from the model\n", + "- Our test set will contain the initial user prompt for each transaction, along with the corresponding expected class label. We will then use the fine-tuned model to generate the actual classification for each transaction." ] }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1188,7 +1053,7 @@ "101" ] }, - "execution_count": 210, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1200,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1246,9 +1111,9 @@ " 26866\n", " Other\n", " Supplier: Creative Video Productions Ltd; Desc...\n", - " 12\n", - " [-0.009630300104618073, 0.009887108579277992, ...\n", - " [-0.008217384107410908, 0.025170527398586273, ...\n", + " 136\n", + " [-0.028885245323181152, -0.028660893440246582,...\n", + " [-0.02879939414560795, -0.02867320366203785, 0...\n", " \n", " \n", " 1\n", @@ -1259,9 +1124,9 @@ " 74806\n", " Building Improvement\n", " Supplier: John Graham Construction Ltd; Descri...\n", - " 16\n", - " [-0.006144719664007425, -0.0018709596479311585...\n", - " [-0.007424891460686922, 0.008475713431835175, ...\n", + " 140\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", + " [-0.02414606139063835, -0.02883070334792137, 0...\n", " \n", " \n", " 2\n", @@ -1272,9 +1137,9 @@ " 56448\n", " Building Improvement\n", " Supplier: Morris & Spottiswood Ltd; Descriptio...\n", - " 17\n", - " [-0.005225738976150751, 0.015156379900872707, ...\n", - " [-0.007611643522977829, 0.030322374776005745, ...\n", + " 141\n", + " [0.013593776151537895, -0.0037341134157031775,...\n", + " [0.013561442494392395, -0.004199974238872528, ...\n", " \n", " \n", " 3\n", @@ -1285,9 +1150,9 @@ " 164691\n", " Building Improvement\n", " Supplier: John Graham Construction Ltd; Descri...\n", - " 16\n", - " [-0.006144719664007425, -0.0018709596479311585...\n", - " [-0.007424891460686922, 0.008475713431835175, ...\n", + " 140\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", " \n", " \n", " 4\n", @@ -1298,9 +1163,9 @@ " 27926\n", " Building Improvement\n", " Supplier: John Graham Construction Ltd; Descri...\n", - " 16\n", - " [-0.006144719664007425, -0.0018709596479311585...\n", - " [-0.007424891460686922, 0.008475713431835175, ...\n", + " 140\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", + " [-0.024112487211823463, -0.02881261520087719, ...\n", " \n", " \n", "\n", @@ -1322,28 +1187,28 @@ "4 Causewayside Refurbishment 27926 Building Improvement \n", "\n", " combined n_tokens \\\n", - "0 Supplier: Creative Video Productions Ltd; Desc... 12 \n", - "1 Supplier: John Graham Construction Ltd; Descri... 16 \n", - "2 Supplier: Morris & Spottiswood Ltd; Descriptio... 17 \n", - "3 Supplier: John Graham Construction Ltd; Descri... 16 \n", - "4 Supplier: John Graham Construction Ltd; Descri... 16 \n", + "0 Supplier: Creative Video Productions Ltd; Desc... 136 \n", + "1 Supplier: John Graham Construction Ltd; Descri... 140 \n", + "2 Supplier: Morris & Spottiswood Ltd; Descriptio... 141 \n", + "3 Supplier: John Graham Construction Ltd; Descri... 140 \n", + "4 Supplier: John Graham Construction Ltd; Descri... 140 \n", "\n", " babbage_similarity \\\n", - "0 [-0.009630300104618073, 0.009887108579277992, ... \n", - "1 [-0.006144719664007425, -0.0018709596479311585... \n", - "2 [-0.005225738976150751, 0.015156379900872707, ... \n", - "3 [-0.006144719664007425, -0.0018709596479311585... \n", - "4 [-0.006144719664007425, -0.0018709596479311585... \n", + "0 [-0.028885245323181152, -0.028660893440246582,... \n", + "1 [-0.024112487211823463, -0.02881261520087719, ... \n", + "2 [0.013593776151537895, -0.0037341134157031775,... \n", + "3 [-0.024112487211823463, -0.02881261520087719, ... \n", + "4 [-0.024112487211823463, -0.02881261520087719, ... \n", "\n", " babbage_search \n", - "0 [-0.008217384107410908, 0.025170527398586273, ... \n", - "1 [-0.007424891460686922, 0.008475713431835175, ... \n", - "2 [-0.007611643522977829, 0.030322374776005745, ... \n", - "3 [-0.007424891460686922, 0.008475713431835175, ... \n", - "4 [-0.007424891460686922, 0.008475713431835175, ... " + "0 [-0.02879939414560795, -0.02867320366203785, 0... \n", + "1 [-0.02414606139063835, -0.02883070334792137, 0... \n", + "2 [0.013561442494392395, -0.004199974238872528, ... \n", + "3 [-0.024112487211823463, -0.02881261520087719, ... \n", + "4 [-0.024112487211823463, -0.02881261520087719, ... " ] }, - "execution_count": 211, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1354,22 +1219,22 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "( class_id class\n", - " 0 0 Literature & Archive\n", - " 1 1 Utility Bills\n", - " 2 2 Building Improvement\n", - " 3 3 Software/IT\n", - " 4 4 Other,\n", + " 0 0 Other\n", + " 1 1 Literature & Archive\n", + " 2 2 Software/IT\n", + " 3 3 Utility Bills\n", + " 4 4 Building Improvement,\n", " 5)" ] }, - "execution_count": 212, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -1383,7 +1248,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 181, "metadata": {}, "outputs": [ { @@ -1407,145 +1272,50 @@ " \n", " \n", " \n", - " Unnamed: 0\n", - " Date\n", - " Supplier\n", - " Description\n", - " Transaction value (£)\n", - " Classification\n", - " combined\n", - " n_tokens\n", - " babbage_similarity\n", - " babbage_search\n", - " class_id\n", - " prompt\n", + " messages\n", + " class\n", " \n", " \n", " \n", " \n", " 0\n", - " 0\n", - " 15/08/2016\n", - " Creative Video Productions Ltd\n", - " Kelvin Hall\n", - " 26866\n", + " [{'role': 'user', 'content': 'You are a data e...\n", " Other\n", - " Supplier: Creative Video Productions Ltd; Desc...\n", - " 12\n", - " [-0.009630300104618073, 0.009887108579277992, ...\n", - " [-0.008217384107410908, 0.025170527398586273, ...\n", - " 4\n", - " Supplier: Creative Video Productions Ltd; Desc...\n", " \n", " \n", " 1\n", - " 51\n", - " 31/03/2017\n", - " NLS Foundation\n", - " Grant Payment\n", - " 177500\n", - " Other\n", - " Supplier: NLS Foundation; Description: Grant P...\n", - " 11\n", - " [-0.022305507212877274, 0.008543581701815128, ...\n", - " [-0.020519884303212166, 0.01993306167423725, -...\n", - " 4\n", - " Supplier: NLS Foundation; Description: Grant P...\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Building Improvement\n", " \n", " \n", " 2\n", - " 70\n", - " 26/06/2017\n", - " British Library\n", - " Legal Deposit Services\n", - " 50056\n", - " Other\n", - " Supplier: British Library; Description: Legal ...\n", - " 11\n", - " [-0.01019938476383686, 0.015277703292667866, -...\n", - " [-0.01843327097594738, 0.03343546763062477, -0...\n", - " 4\n", - " Supplier: British Library; Description: Legal ...\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Building Improvement\n", " \n", " \n", " 3\n", - " 71\n", - " 24/07/2017\n", - " ALDL\n", - " Legal Deposit Services\n", - " 27067\n", - " Other\n", - " Supplier: ALDL; Description: Legal Deposit Ser...\n", - " 11\n", - " [-0.008471488021314144, 0.004098685923963785, ...\n", - " [-0.012966590002179146, 0.01299362163990736, 0...\n", - " 4\n", - " Supplier: ALDL; Description: Legal Deposit Ser...\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Building Improvement\n", " \n", " \n", " 4\n", - " 100\n", - " 24/07/2017\n", - " AM Phillip\n", - " Vehicle Purchase\n", - " 26604\n", - " Other\n", - " Supplier: AM Phillip; Description: Vehicle Pur...\n", - " 10\n", - " [-0.003459023078903556, 0.004626389592885971, ...\n", - " [-0.0010945454705506563, 0.008626140654087067,...\n", - " 4\n", - " Supplier: AM Phillip; Description: Vehicle Pur...\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Building Improvement\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Unnamed: 0 Date Supplier \\\n", - "0 0 15/08/2016 Creative Video Productions Ltd \n", - "1 51 31/03/2017 NLS Foundation \n", - "2 70 26/06/2017 British Library \n", - "3 71 24/07/2017 ALDL \n", - "4 100 24/07/2017 AM Phillip \n", - "\n", - " Description Transaction value (£) Classification \\\n", - "0 Kelvin Hall 26866 Other \n", - "1 Grant Payment 177500 Other \n", - "2 Legal Deposit Services 50056 Other \n", - "3 Legal Deposit Services 27067 Other \n", - "4 Vehicle Purchase 26604 Other \n", - "\n", - " combined n_tokens \\\n", - "0 Supplier: Creative Video Productions Ltd; Desc... 12 \n", - "1 Supplier: NLS Foundation; Description: Grant P... 11 \n", - "2 Supplier: British Library; Description: Legal ... 11 \n", - "3 Supplier: ALDL; Description: Legal Deposit Ser... 11 \n", - "4 Supplier: AM Phillip; Description: Vehicle Pur... 10 \n", - "\n", - " babbage_similarity \\\n", - "0 [-0.009630300104618073, 0.009887108579277992, ... \n", - "1 [-0.022305507212877274, 0.008543581701815128, ... \n", - "2 [-0.01019938476383686, 0.015277703292667866, -... \n", - "3 [-0.008471488021314144, 0.004098685923963785, ... \n", - "4 [-0.003459023078903556, 0.004626389592885971, ... \n", - "\n", - " babbage_search class_id \\\n", - "0 [-0.008217384107410908, 0.025170527398586273, ... 4 \n", - "1 [-0.020519884303212166, 0.01993306167423725, -... 4 \n", - "2 [-0.01843327097594738, 0.03343546763062477, -0... 4 \n", - "3 [-0.012966590002179146, 0.01299362163990736, 0... 4 \n", - "4 [-0.0010945454705506563, 0.008626140654087067,... 4 \n", - "\n", - " prompt \n", - "0 Supplier: Creative Video Productions Ltd; Desc... \n", - "1 Supplier: NLS Foundation; Description: Grant P... \n", - "2 Supplier: British Library; Description: Legal ... \n", - "3 Supplier: ALDL; Description: Legal Deposit Ser... \n", - "4 Supplier: AM Phillip; Description: Vehicle Pur... " + " messages class\n", + "0 [{'role': 'user', 'content': 'You are a data e... Other\n", + "1 [{'role': 'user', 'content': 'You are a data e... Building Improvement\n", + "2 [{'role': 'user', 'content': 'You are a data e... Building Improvement\n", + "3 [{'role': 'user', 'content': 'You are a data e... Building Improvement\n", + "4 [{'role': 'user', 'content': 'You are a data e... Building Improvement" ] }, - "execution_count": 215, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -1553,145 +1323,51 @@ "source": [ "ft_df_with_class = ft_prep_df.merge(class_df,left_on='Classification',right_on='class',how='inner')\n", "\n", - "# Adding a leading whitespace onto each completion to help the model\n", - "ft_df_with_class['class_id'] = ft_df_with_class.apply(lambda x: ' ' + str(x['class_id']),axis=1)\n", - "ft_df_with_class = ft_df_with_class.drop('class', axis=1)\n", - "\n", - "# Adding a common separator onto the end of each prompt so the model knows when a prompt is terminating\n", - "ft_df_with_class['prompt'] = ft_df_with_class.apply(lambda x: x['combined'] + '\\n\\n###\\n\\n',axis=1)\n", - "ft_df_with_class.head()\n" + "# Creating a list of messages for the fine-tuning job. The user message is the prompt, and the assistant message is the response from the model\n", + "ft_df_with_class['messages'] = ft_df_with_class.apply(lambda x: [{\"role\": \"user\", \"content\": format_prompt(x)}, {\"role\": \"assistant\", \"content\": x['class']}],axis=1)\n", + "ft_df_with_class[['messages', 'class']].head()\n" ] }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 169, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " prompt completion\n", - "ordering \n", - "0 Supplier: Sothebys; Description: Literary & Ar... 0\n", - "1 Supplier: Sotheby'S; Description: Literary & A... 0\n", - "2 Supplier: City Of Edinburgh Council; Descripti... 1\n", - "2 Supplier: John Graham Construction Ltd; Descri... 2\n", - "3 Supplier: John Graham Construction Ltd; Descri... 2" - ] - }, - "execution_count": 236, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# This step is unnecessary if you have a number of observations in each class\n", - "# In our case we don't, so we shuffle the data to give us a better chance of getting equal classes in our train and validation sets\n", - "# Our fine-tuned model will error if we have less classes in the validation set, so this is a necessary step\n", + "# Create train/validation split\n", + "samples = ft_df_with_class[\"messages\"].tolist()\n", + "train_df, valid_df = train_test_split(samples, test_size=0.2, random_state=42)\n", "\n", - "import random\n", - "\n", - "labels = [x for x in ft_df_with_class['class_id']]\n", - "text = [x for x in ft_df_with_class['prompt']]\n", - "ft_df = pd.DataFrame(zip(text, labels), columns = ['prompt','class_id']) #[:300]\n", - "ft_df.columns = ['prompt','completion']\n", - "ft_df['ordering'] = ft_df.apply(lambda x: random.randint(0,len(ft_df)), axis = 1)\n", - "ft_df.set_index('ordering',inplace=True)\n", - "ft_df_sorted = ft_df.sort_index(ascending=True)\n", - "ft_df_sorted.head()\n" + "def write_to_jsonl(list_of_messages, filename):\n", + " with open(filename, \"w+\") as f:\n", + " for messages in list_of_messages:\n", + " object = { \n", + " \"messages\": messages\n", + " }\n", + " f.write(json.dumps(object) + \"\\n\")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 186, "metadata": {}, "outputs": [], "source": [ - "# This step is to remove any existing files if we've already produced training/validation sets for this classifier\n", - "#!rm transactions_grouped*\n", - "\n", - "# We output our shuffled dataframe to a .jsonl file and run the prepare_data function to get us our input files\n", - "ft_df_sorted.to_json(\"transactions_grouped.jsonl\", orient='records', lines=True)\n", - "!openai tools fine_tunes.prepare_data -f transactions_grouped.jsonl -q\n" + "# Write the train/validation split to jsonl files\n", + "train_file_name, valid_file_name = \"transactions_grouped_train.jsonl\", \"transactions_grouped_valid.jsonl\"\n", + "write_to_jsonl(train_df, train_file_name)\n", + "write_to_jsonl(valid_df, valid_file_name)\n" ] }, { "cell_type": "code", - "execution_count": 322, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "31\n", - "8\n", - "All good\n" - ] - } - ], + "outputs": [], "source": [ - "# This functions checks that your classes all appear in both prepared files\n", - "# If they don't, the fine-tuned model creation will fail\n", - "check_finetune_classes('transactions_grouped_prepared_train.jsonl','transactions_grouped_prepared_valid.jsonl')\n" + "# Upload the files to OpenAI\n", + "train_file = client.files.create(file=open(train_file_name, \"rb\"), purpose=\"fine-tune\")\n", + "valid_file = client.files.create(file=open(valid_file_name, \"rb\"), purpose=\"fine-tune\")" ] }, { @@ -1700,22 +1376,29 @@ "metadata": {}, "outputs": [], "source": [ - "# This step creates your model\n", - "!openai api fine_tunes.create -t \"transactions_grouped_prepared_train.jsonl\" -v \"transactions_grouped_prepared_valid.jsonl\" --compute_classification_metrics --classification_n_classes 5 -m curie\n", - "\n", - "# You can use following command to get fine tuning job status and model name, replace the job name with your job\n", - "#!openai api fine_tunes.get -i ft-YBIc01t4hxYBC7I5qhRF3Qdx\n" + "# Create the fine-tuning job\n", + "fine_tuning_job = client.fine_tuning.jobs.create(training_file=train_file.id, validation_file=valid_file.id, model=\"gpt-4o-2024-08-06\")\n", + "# Get the fine-tuning job status and model name\n", + "status = client.fine_tuning.jobs.retrieve(fine_tuning_job.id)" ] }, { "cell_type": "code", - "execution_count": 323, + "execution_count": 209, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine tuned model id: ft:gpt-4o-2024-08-06:openai::BKr3Xy8U\n" + ] + } + ], "source": [ - "# Congrats, you've got a fine-tuned model!\n", - "# Copy/paste the name provided into the variable below and we'll take it for a spin\n", - "fine_tuned_model = 'curie:ft-personal-2022-10-20-10-42-56'\n" + "# Once the fine-tuning job is complete, you can retrieve the model name from the job status\n", + "fine_tuned_model = client.fine_tuning.jobs.retrieve(fine_tuning_job.id).fine_tuned_model\n", + "print(f\"Fine tuned model id: {fine_tuned_model}\")" ] }, { @@ -1730,7 +1413,7 @@ }, { "cell_type": "code", - "execution_count": 324, + "execution_count": 210, "metadata": {}, "outputs": [ { @@ -1754,222 +1437,64 @@ " \n", " \n", " \n", - " prompt\n", - " completion\n", + " messages\n", + " expected_class\n", " \n", " \n", " \n", " \n", " 0\n", - " Supplier: Wavetek Ltd; Description: Kelvin Hal...\n", - " 2\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Utility Bills\n", " \n", " \n", " 1\n", - " Supplier: ECG Facilities Service; Description:...\n", - " 1\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Literature & Archive\n", " \n", " \n", " 2\n", - " Supplier: M & J Ballantyne Ltd; Description: G...\n", - " 2\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Literature & Archive\n", " \n", " \n", " 3\n", - " Supplier: Private Sale; Description: Literary ...\n", - " 0\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Literature & Archive\n", " \n", " \n", " 4\n", - " Supplier: Ex Libris; Description: IT equipment...\n", - " 3\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " prompt completion\n", - "0 Supplier: Wavetek Ltd; Description: Kelvin Hal... 2\n", - "1 Supplier: ECG Facilities Service; Description:... 1\n", - "2 Supplier: M & J Ballantyne Ltd; Description: G... 2\n", - "3 Supplier: Private Sale; Description: Literary ... 0\n", - "4 Supplier: Ex Libris; Description: IT equipment... 3" - ] - }, - "execution_count": 324, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_set = pd.read_json('transactions_grouped_prepared_valid.jsonl', lines=True)\n", - "test_set.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": {}, - "outputs": [], - "source": [ - "test_set['predicted_class'] = test_set.apply(lambda x: openai.chat.completions.create(model=fine_tuned_model, prompt=x['prompt'], max_tokens=1, temperature=0, logprobs=5),axis=1)\n", - "test_set['pred'] = test_set.apply(lambda x : x['predicted_class']['choices'][0]['text'],axis=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "metadata": {}, - "outputs": [], - "source": [ - "test_set['result'] = test_set.apply(lambda x: str(x['pred']).strip() == str(x['completion']).strip(), axis = 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True 4\n", - "False 4\n", - "Name: result, dtype: int64" - ] - }, - "execution_count": 327, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_set['result'].value_counts()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Performance is not great - unfortunately this is expected. With only a few examples of each class, the above approach with embeddings and a traditional classifier worked better.\n", - "\n", - "A fine-tuned model works best with a great number of labelled observations. If we had a few hundred or thousand we may get better results, but lets do one last test on a holdout set to confirm that it doesn't generalise well to a new set of observations" - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DateSupplierDescriptionTransaction value (£)
10123/10/2017City Building LLPCausewayside Refurbishment53147.0
10230/10/2017ECG Facilities ServiceFacilities Management Charge35758.0
10330/10/2017ECG Facilities ServiceFacilities Management Charge35758.0
10406/11/2017John Graham Construction LtdCausewayside Refurbishment134208.0
10506/11/2017ALDLLegal Deposit Services27067.0[{'role': 'user', 'content': 'You are a data e...Building Improvement
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" ], "text/plain": [ - " Date Supplier Description \\\n", - "101 23/10/2017 City Building LLP Causewayside Refurbishment \n", - "102 30/10/2017 ECG Facilities Service Facilities Management Charge \n", - "103 30/10/2017 ECG Facilities Service Facilities Management Charge \n", - "104 06/11/2017 John Graham Construction Ltd Causewayside Refurbishment \n", - "105 06/11/2017 ALDL Legal Deposit Services \n", - "\n", - " Transaction value (£) \n", - "101 53147.0 \n", - "102 35758.0 \n", - "103 35758.0 \n", - "104 134208.0 \n", - "105 27067.0 " + " messages expected_class\n", + "0 [{'role': 'user', 'content': 'You are a data e... Utility Bills\n", + "1 [{'role': 'user', 'content': 'You are a data e... Literature & Archive\n", + "2 [{'role': 'user', 'content': 'You are a data e... Literature & Archive\n", + "3 [{'role': 'user', 'content': 'You are a data e... Literature & Archive\n", + "4 [{'role': 'user', 'content': 'You are a data e... Building Improvement" ] }, - "execution_count": 330, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "holdout_df = transactions.copy().iloc[101:]\n", - "holdout_df.head()\n" + "# Create a test set with the expected class labels\n", + "test_set = pd.read_json(valid_file_name, lines=True)\n", + "test_set['expected_class'] = test_set.apply(lambda x: x['messages'][-1]['content'], axis=1)\n", + "test_set.head()" ] }, { "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [], - "source": [ - "holdout_df['combined'] = \"Supplier: \" + holdout_df['Supplier'].str.strip() + \"; Description: \" + holdout_df['Description'].str.strip() + '\\n\\n###\\n\\n' # + \"; Value: \" + str(df['Transaction value (£)']).strip()\n", - "holdout_df['prediction_result'] = holdout_df.apply(lambda x: openai.chat.completions.create(model=fine_tuned_model, prompt=x['combined'], max_tokens=1, temperature=0, logprobs=5),axis=1)\n", - "holdout_df['pred'] = holdout_df.apply(lambda x : x['prediction_result']['choices'][0]['text'],axis=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 333, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1993,203 +1518,115 @@ " \n", " \n", " \n", - " Date\n", - " Supplier\n", - " Description\n", - " Transaction value (£)\n", - " combined\n", - " prediction_result\n", - " pred\n", + " messages\n", + " expected_class\n", + " response\n", + " predicted_class\n", " \n", " \n", " \n", " \n", - " 101\n", - " 23/10/2017\n", - " City Building LLP\n", - " Causewayside Refurbishment\n", - " 53147.0\n", - " Supplier: City Building LLP; Description: Caus...\n", - " {'id': 'cmpl-63YDadbYLo8xKsGY2vReOFCMgTOvG', '...\n", - " 2\n", - " \n", - " \n", - " 102\n", - " 30/10/2017\n", - " ECG Facilities Service\n", - " Facilities Management Charge\n", - " 35758.0\n", - " Supplier: ECG Facilities Service; Description:...\n", - " {'id': 'cmpl-63YDbNK1D7UikDc3xi5ATihg5kQEt', '...\n", - " 2\n", - " \n", - " \n", - " 103\n", - " 30/10/2017\n", - " ECG Facilities Service\n", - " Facilities Management Charge\n", - " 35758.0\n", - " Supplier: ECG Facilities Service; Description:...\n", - " {'id': 'cmpl-63YDbwfiHjkjMWsfTKNt6naeqPzOe', '...\n", - " 2\n", - " \n", - " \n", - " 104\n", - " 06/11/2017\n", - " John Graham Construction Ltd\n", - " Causewayside Refurbishment\n", - " 134208.0\n", - " Supplier: John Graham Construction Ltd; Descri...\n", - " {'id': 'cmpl-63YDbWAndtsRqPTi2ZHZtPodZvOwr', '...\n", - " 2\n", - " \n", - " \n", - " 105\n", - " 06/11/2017\n", - " ALDL\n", - " Legal Deposit Services\n", - " 27067.0\n", - " Supplier: ALDL; Description: Legal Deposit Ser...\n", - " {'id': 'cmpl-63YDbDu7WM3svYWsRAMdDUKtSFDBu', '...\n", - " 2\n", - " \n", - " \n", - " 106\n", - " 27/11/2017\n", - " Maggs Bros Ltd\n", - " Literary & Archival Items\n", - " 26500.0\n", - " Supplier: Maggs Bros Ltd; Description: Literar...\n", - " {'id': 'cmpl-63YDbxNNI8ZH5CJJNxQ0IF9Zf925C', '...\n", - " 0\n", + " 0\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Utility Bills\n", + " ChatCompletion(id='chatcmpl-BKrC0S1wQSfM9ZQfcC...\n", + " Utility Bills\n", " \n", " \n", - " 107\n", - " 30/11/2017\n", - " Glasgow City Council\n", - " Kelvin Hall\n", - " 42345.0\n", - " Supplier: Glasgow City Council; Description: K...\n", - " {'id': 'cmpl-63YDb8R1FWu4bjwM2xE775rouwneV', '...\n", - " 2\n", + " 1\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Literature & Archive\n", + " ChatCompletion(id='chatcmpl-BKrC1BTr0DagbDkC2s...\n", + " Literature & Archive\n", " \n", " \n", - " 108\n", - " 11/12/2017\n", - " ECG Facilities Service\n", - " Facilities Management Charge\n", - " 35758.0\n", - " Supplier: ECG Facilities Service; Description:...\n", - " {'id': 'cmpl-63YDcAPsp37WhbPs9kwfUX0kBk7Hv', '...\n", - " 2\n", + " 2\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Literature & Archive\n", + " ChatCompletion(id='chatcmpl-BKrC1H3ZeIW5cz2Owr...\n", + " Literature & Archive\n", " \n", " \n", - " 109\n", - " 11/12/2017\n", - " John Graham Construction Ltd\n", - " Causewayside Refurbishment\n", - " 159275.0\n", - " Supplier: John Graham Construction Ltd; Descri...\n", - " {'id': 'cmpl-63YDcML2welrC3wF0nuKgcNmVu1oQ', '...\n", - " 2\n", + " 3\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Literature & Archive\n", + " ChatCompletion(id='chatcmpl-BKrC1wdhaMP0Q7YmYx...\n", + " Literature & Archive\n", " \n", " \n", - " 110\n", - " 08/01/2018\n", - " ECG Facilities Service\n", - " Facilities Management Charge\n", - " 35758.0\n", - " Supplier: ECG Facilities Service; Description:...\n", - " {'id': 'cmpl-63YDc95SSdOHnIliFB2cjMEEm7Z2u', '...\n", - " 2\n", + " 4\n", + " [{'role': 'user', 'content': 'You are a data e...\n", + " Building Improvement\n", + " ChatCompletion(id='chatcmpl-BKrC20c5pkpngy1xDu...\n", + " Building Improvement\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Date Supplier Description \\\n", - "101 23/10/2017 City Building LLP Causewayside Refurbishment \n", - "102 30/10/2017 ECG Facilities Service Facilities Management Charge \n", - "103 30/10/2017 ECG Facilities Service Facilities Management Charge \n", - "104 06/11/2017 John Graham Construction Ltd Causewayside Refurbishment \n", - "105 06/11/2017 ALDL Legal Deposit Services \n", - "106 27/11/2017 Maggs Bros Ltd Literary & Archival Items \n", - "107 30/11/2017 Glasgow City Council Kelvin Hall \n", - "108 11/12/2017 ECG Facilities Service Facilities Management Charge \n", - "109 11/12/2017 John Graham Construction Ltd Causewayside Refurbishment \n", - "110 08/01/2018 ECG Facilities Service Facilities Management Charge \n", + " messages expected_class \\\n", + "0 [{'role': 'user', 'content': 'You are a data e... Utility Bills \n", + "1 [{'role': 'user', 'content': 'You are a data e... Literature & Archive \n", + "2 [{'role': 'user', 'content': 'You are a data e... Literature & Archive \n", + "3 [{'role': 'user', 'content': 'You are a data e... Literature & Archive \n", + "4 [{'role': 'user', 'content': 'You are a data e... Building Improvement \n", "\n", - " Transaction value (£) combined \\\n", - "101 53147.0 Supplier: City Building LLP; Description: Caus... \n", - "102 35758.0 Supplier: ECG Facilities Service; Description:... \n", - "103 35758.0 Supplier: ECG Facilities Service; Description:... \n", - "104 134208.0 Supplier: John Graham Construction Ltd; Descri... \n", - "105 27067.0 Supplier: ALDL; Description: Legal Deposit Ser... \n", - "106 26500.0 Supplier: Maggs Bros Ltd; Description: Literar... \n", - "107 42345.0 Supplier: Glasgow City Council; Description: K... \n", - "108 35758.0 Supplier: ECG Facilities Service; Description:... \n", - "109 159275.0 Supplier: John Graham Construction Ltd; Descri... \n", - "110 35758.0 Supplier: ECG Facilities Service; Description:... \n", - "\n", - " prediction_result pred \n", - "101 {'id': 'cmpl-63YDadbYLo8xKsGY2vReOFCMgTOvG', '... 2 \n", - "102 {'id': 'cmpl-63YDbNK1D7UikDc3xi5ATihg5kQEt', '... 2 \n", - "103 {'id': 'cmpl-63YDbwfiHjkjMWsfTKNt6naeqPzOe', '... 2 \n", - "104 {'id': 'cmpl-63YDbWAndtsRqPTi2ZHZtPodZvOwr', '... 2 \n", - "105 {'id': 'cmpl-63YDbDu7WM3svYWsRAMdDUKtSFDBu', '... 2 \n", - "106 {'id': 'cmpl-63YDbxNNI8ZH5CJJNxQ0IF9Zf925C', '... 0 \n", - "107 {'id': 'cmpl-63YDb8R1FWu4bjwM2xE775rouwneV', '... 2 \n", - "108 {'id': 'cmpl-63YDcAPsp37WhbPs9kwfUX0kBk7Hv', '... 2 \n", - "109 {'id': 'cmpl-63YDcML2welrC3wF0nuKgcNmVu1oQ', '... 2 \n", - "110 {'id': 'cmpl-63YDc95SSdOHnIliFB2cjMEEm7Z2u', '... 2 " + " response predicted_class \n", + "0 ChatCompletion(id='chatcmpl-BKrC0S1wQSfM9ZQfcC... Utility Bills \n", + "1 ChatCompletion(id='chatcmpl-BKrC1BTr0DagbDkC2s... Literature & Archive \n", + "2 ChatCompletion(id='chatcmpl-BKrC1H3ZeIW5cz2Owr... Literature & Archive \n", + "3 ChatCompletion(id='chatcmpl-BKrC1wdhaMP0Q7YmYx... Literature & Archive \n", + "4 ChatCompletion(id='chatcmpl-BKrC20c5pkpngy1xDu... Building Improvement " ] }, - "execution_count": 333, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "holdout_df.head(10)\n" + "# Apply the fine-tuned model to the test set\n", + "test_set['response'] = test_set.apply(lambda x: openai.chat.completions.create(model=fine_tuned_model, messages=x['messages'][:-1], temperature=0),axis=1)\n", + "test_set['predicted_class'] = test_set.apply(lambda x: x['response'].choices[0].message.content, axis=1)\n", + "\n", + "test_set.head()" ] }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 212, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - " 2 231\n", - " 0 27\n", - "Name: pred, dtype: int64" - ] - }, - "execution_count": 334, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "result\n", + "True 20\n", + "False 1\n", + "Name: count, dtype: int64\n", + "F1 Score: 0.9296066252587991\n", + "Raw Accuracy: 0.9523809523809523\n" + ] } ], "source": [ - "holdout_df['pred'].value_counts()\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Well those results were similarly underwhelming - so we've learned that with a dataset with a small number of labelled observations, either zero-shot classification or traditional classification with embeddings return better results than a fine-tuned model.\n", + "# Calculate the accuracy of the predictions\n", + "from sklearn.metrics import f1_score\n", + "test_set['result'] = test_set.apply(lambda x: str(x['predicted_class']).strip() == str(x['expected_class']).strip(), axis = 1)\n", + "test_set['result'].value_counts()\n", + "\n", + "print(test_set['result'].value_counts())\n", "\n", - "A fine-tuned model is still a great tool, but is more effective when you have a larger number of labelled examples for each class that you're looking to classify" + "print(\"F1 Score: \", f1_score(test_set['expected_class'], test_set['predicted_class'], average=\"weighted\"))\n", + "print(\"Raw Accuracy: \", test_set['result'].value_counts()[True] / len(test_set))\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "cookbook_env", "language": "python", "name": "python3" }, @@ -2203,7 +1640,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/examples/Orchestrating_agents.ipynb b/examples/Orchestrating_agents.ipynb index d0f2084a29..9d28f87c0a 100644 --- a/examples/Orchestrating_agents.ipynb +++ b/examples/Orchestrating_agents.ipynb @@ -68,7 +68,7 @@ " \"1. First, ask probing questions and understand the user's problem deeper.\\n\"\n", " \" - unless the user has already provided a reason.\\n\"\n", " \"2. Propose a fix (make one up).\\n\"\n", - " \"3. ONLY if not satesfied, offer a refund.\\n\"\n", + " \"3. ONLY if not satisfied, offer a refund.\\n\"\n", " \"4. If accepted, search for the ID and then execute refund.\"\n", " \"\"\n", ")\n", @@ -575,7 +575,7 @@ "messages.extend(response)\n", "\n", "\n", - "user_query = \"Actually, I want a refund.\" # implitly refers to the last item\n", + "user_query = \"Actually, I want a refund.\" # implicitly refers to the last item\n", "print(\"User:\", user_query)\n", "messages.append({\"role\": \"user\", \"content\": user_query})\n", "response = run_full_turn(refund_agent, messages) # refund agent" @@ -596,7 +596,7 @@ "\n", "Now that agent can express the _intent_ to make a handoff, we must make it actually happen. There's many ways to do this, but there's one particularly clean way.\n", "\n", - "For the agent functions we've defined so far, like `execute_refund` or `place_order` they return a string, which will be provided to the model. What if instead, we return an `Agent` object to indate which agent we want to transfer to? Like so:" + "For the agent functions we've defined so far, like `execute_refund` or `place_order` they return a string, which will be provided to the model. What if instead, we return an `Agent` object to indicate which agent we want to transfer to? Like so:" ] }, { diff --git a/examples/RAG_with_graph_db.ipynb b/examples/RAG_with_graph_db.ipynb index 221b63072a..2ba945397a 100644 --- a/examples/RAG_with_graph_db.ipynb +++ b/examples/RAG_with_graph_db.ipynb @@ -620,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 84, "id": "83100e64", "metadata": {}, "outputs": [], @@ -629,7 +629,7 @@ "client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"\"))\n", "\n", "# Define the entities to look for\n", - "def define_query(prompt, model=\"gpt-4-1106-preview\"):\n", + "def define_query(prompt, model=\"gpt-4o\"):\n", " completion = client.chat.completions.create(\n", " model=model,\n", " temperature=0,\n", @@ -1220,7 +1220,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "14f76f9d", "metadata": {}, "outputs": [], @@ -1230,7 +1230,7 @@ "from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser\n", "\n", "\n", - "llm = ChatOpenAI(temperature=0, model=\"gpt-4\")\n", + "llm = ChatOpenAI(temperature=0, model=\"gpt-4o\")\n", "\n", "# LLM chain consisting of the LLM and a prompt\n", "llm_chain = LLMChain(llm=llm, prompt=prompt)\n", diff --git a/examples/Reinforcement_Fine_Tuning.ipynb b/examples/Reinforcement_Fine_Tuning.ipynb new file mode 100644 index 0000000000..6bd67eefd2 --- /dev/null +++ b/examples/Reinforcement_Fine_Tuning.ipynb @@ -0,0 +1,2124 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **Exploring Model Graders for Reinforcement Fine-Tuning**\n", + "\n", + "*This guide is for developers and ML practitioners who already know their way around OpenAIʼs APIs, have a basic understanding of reinforcement fine-tuning (RFT), and wish to use their fine-tuned models for research or other appropriate uses. OpenAI’s services are not intended for the personalized treatment or diagnosis of any medical condition and are subject to our [applicable terms](https://openai.com/policies/).*\n", + "\n", + "[Reinforcement fine-tuning (RFT)](https://platform.openai.com/docs/guides/reinforcement-fine-tuning) of reasoning models consists in running reinforcement learning on of top the models to improve their reasoning performance by exploring the solution space and reinforcing strategies that result in a higher reward. RFT helps the model make sharper decisions and interpret context more effectively. \n", + "\n", + "\n", + "In this guide, weʼll walk through how to apply RFT to the OpenAI `o4-mini` reasoning model, using a task from the life sciences research domain: predicting outcomes from doctor-patient transcripts and descriptions, which is a necessary assessment in many health research studies. We'll use a subset of the medical-o1-verifiable-problem [dataset](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-verifiable-problem/viewer/default/train?row=0). You will learn key steps to take in order to succesfully run RFT jobs for your use-cases.\n", + "\n", + "Here’s what we’ll cover:\n", + "\n", + "- **[1. Setup](#1-setup)**\n", + "- **[2. Gathering the dataset](#2-gathering-the-dataset)**\n", + "- **[3. Benchmarking the base model](#3-benchmarking-the-base-model)**\n", + "- **[4. Defining your grader](#4-defining-your-grader)**\n", + "- **[5. Training](#5-training)**\n", + "- **[6. Using your fine-tuned model](#6-using-your-fine-tuned-model)**\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **1. Setup**\n", + "\n", + "Even strong reasoning models can miss the mark when it comes to expert-level behavior-especially in domains like medicine, where nuance and exactness matter. Imagine a model trying to extract [ICD-10](https://www.cms.gov/medicare/coding-billing/icd-10-codes) codes from a transcript: even if it understands the gist, it may not use the precise terminology expected by medical professionals. \n", + "\n", + "Other great candidates for RFT include topics like ledger normalization or tiering fraud risk- settings in which you want precise, reliable, and repeatable reasoning. Checkout our [RFT use-cases guide](https://platform.openai.com/docs/guides/rft-use-cases) for great examples. \n", + "\n", + "In our case, weʼll focus on teaching `o4-mini` to become better at predicting the outcomes of clinical conversations and descriptions. Specifically, we want to see if RFT can boost the accuracy of the prediction. \n", + "\n", + "Along the way, weʼll talk about how to write effective graders, how they guide the modelʼs learning, and how to watch out for classic reward-hacking pitfalls. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **2. Gathering the Dataset**\n", + "\n", + "Letʼs start off by loading the dataset from Hugging Face. Weʼre interested in samples framed as a description of a patient case with an associated question, followed by the correct answer. These represent real world transcripts where a physician is summarizing a case and assigning an outcome. For any use-case, verifying the accuracy of the gold level answers is critical and requires careful consideration. Here, we will trust the dataset quality." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered samples: 9169\n" + ] + } + ], + "source": [ + "import re\n", + "from datasets import load_dataset\n", + "ds = load_dataset(\"FreedomIntelligence/medical-o1-verifiable-problem\")\n", + "\n", + "def is_age_question(sample):\n", + " question = sample.get('Open-ended Verifiable Question', '')\n", + " # Match \"A 88-year-old\", \"An 8-year-old\", \"A 23-year-old\", etc. at the start\n", + " return re.match(r\"^(A|An) \\d{1,2}-year-old\", question) is not None\n", + "\n", + "filtered_samples = [s for s in ds[\"train\"] if is_age_question(s)]\n", + "print(f\"Filtered samples: {len(filtered_samples)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the advantages of RFT is that it doesnʼt need thousands of samples to start making a difference. Thanks to trajectory sampling and the feedback loop during training, the model learns not just correct behaviors, but also patterns to avoid. This means we can see solid gains even with small datasets.\n", + "\n", + "For this run, weʼll randomly sample 100 training and 100 test examples and slightly normalize them." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training samples: 100\n", + "Number of test samples: 100\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "# Set a random seed for reproducibility\n", + "random.seed(42)\n", + "\n", + "# Randomly select 100 training samples from filtered_samples\n", + "train_samples = random.sample(filtered_samples, min(100, len(filtered_samples)))\n", + "\n", + "# Remove training samples from filtered_samples to avoid overlap\n", + "remaining_samples = [s for s in filtered_samples if s not in train_samples]\n", + "\n", + "# Randomly select 100 test samples from the remaining samples (no overlap)\n", + "test_samples = random.sample(remaining_samples, min(100, len(remaining_samples)))\n", + "\n", + "print(f\"Number of training samples: {len(train_samples)}\")\n", + "print(f\"Number of test samples: {len(test_samples)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Standardize the 'Ground-True Answer' fields to all lowercase in train and test samples\n", + "for sample in train_samples:\n", + " if 'Ground-True Answer' in sample and isinstance(sample['Ground-True Answer'], str):\n", + " sample['Ground-True Answer'] = sample['Ground-True Answer'].lower()\n", + "\n", + "for sample in test_samples:\n", + " if 'Ground-True Answer' in sample and isinstance(sample['Ground-True Answer'], str):\n", + " sample['Ground-True Answer'] = sample['Ground-True Answer'].lower()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll convert these samples to `jsonl` format, as expected by the [reinforcement finetuning API](https://platform.openai.com/docs/api-reference/fine-tuning/reinforcement-input)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "def convert_to_jsonl_format(samples, filename):\n", + " with open(filename, \"w\") as f:\n", + " for sample in samples:\n", + " user_content = sample.get(\"Open-ended Verifiable Question\", \"\")\n", + " reference_answer = sample.get(\"Ground-True Answer\", \"\")\n", + " json_obj = {\n", + " \"messages\": [\n", + " {\"role\": \"user\", \"content\": user_content}\n", + " ],\n", + " \"reference_answer\": reference_answer\n", + " }\n", + " f.write(json.dumps(json_obj) + \"\\n\")\n", + "\n", + "def load_jsonl(filename):\n", + " samples = []\n", + " with open(filename, \"r\") as f:\n", + " for line in f:\n", + " samples.append(json.loads(line))\n", + " return samples\n", + "\n", + "# Save the datasets to jsonl files\n", + "convert_to_jsonl_format(train_samples, \"data/medical_01_verifiable_problem_train.jsonl\")\n", + "convert_to_jsonl_format(test_samples, \"data/medical_01_verifiable_problem_val.jsonl\")\n", + "\n", + "# Load the datasets back from jsonl files\n", + "train_samples_loaded = load_jsonl(\"data/medical_01_verifiable_problem_train.jsonl\")\n", + "test_samples_loaded = load_jsonl(\"data/medical_01_verifiable_problem_val.jsonl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Next up: we’ll see how the base model performs out of the box-and where there’s room to grow.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **3. Benchmarking the Base Model**\n", + "\n", + "Before we fine-tune anything, we need to know where we’re starting from. Benchmarking gives us a clear picture of the model’s initial strengths and weaknesses-so we can later measure how far it’s come.\n", + "\n", + "We’ll first lean on two simple yet powerful evaluators:\n", + "\n", + "1. `clinical_phrase_binary_grader` - an exact-match checker.\n", + "2. `clinical_phrase_grader` - a softer, token-based similarity grader." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from rapidfuzz import fuzz, utils\n", + "\n", + "def clinical_phrase_grader(sample: dict, item: dict) -> float:\n", + " from rapidfuzz import fuzz, utils\n", + " score = fuzz.token_set_ratio(sample[\"output_text\"], item[\"reference_answer\"], processor=utils.default_process)\n", + " return score / 100.0\n", + "\n", + "def clinical_phrase_binary_grader(sample: dict, item: dict) -> float:\n", + " return 1.0 if sample[\"output_text\"] == item[\"reference_answer\"] else 0.0\n", + "\n", + "def combined_grader(sample: dict, item: dict, weights: list[float] = [0.85, 0.15]) -> float:\n", + " clinical_phrase_score = clinical_phrase_grader(sample, item)\n", + " binary_score = clinical_phrase_binary_grader(sample, item)\n", + " return weights[0] * clinical_phrase_score + weights[1] * binary_score" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This combination lets us track both strict correctness and partial lexical overlap. The binary grader gives a crisp 0 or 1: did the model produce an exact match? The softer one gives more nuance-how close did the output come to the gold answer? We use both because outcomes are often phrased in multiple valid ways. For instance, a model might respond with “gouty arthritis” instead of “gout.” While a human evaluator could consider this partially acceptable, a strict string match would not. Combining exact and fuzzy scoring ensures a more accurate and fair assessment of model outputs. \n", + "\n", + "We build a helper function to preprend the examples with a system prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def prepend_system_prompt_to_first_user_message(samples, system_prompt, path=None):\n", + " new_samples = []\n", + " for sample in samples:\n", + " # Deep copy to avoid mutating the original\n", + " sample_copy = json.loads(json.dumps(sample))\n", + " messages = sample_copy.get(\"messages\", [])\n", + " if messages and messages[0].get(\"role\") == \"user\" and isinstance(messages[0].get(\"content\"), str):\n", + " if not messages[0][\"content\"].startswith(system_prompt):\n", + " messages[0][\"content\"] = f\"{system_prompt}\\n\\n{messages[0]['content']}\"\n", + " new_samples.append(sample_copy)\n", + " if path is not None:\n", + " with open(path, \"w\", encoding=\"utf-8\") as f:\n", + " for item in new_samples:\n", + " f.write(json.dumps(item, ensure_ascii=False) + \"\\n\")\n", + " return new_samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "simple_prompt = \"\"\"You are an expert clinician. For each clinical vignette, respond with exactly one phrase: the single most likely outcome or phenomenon, all in lowercase. \n", + "- Do not add punctuation, articles, explanations, or commentary - output only the term itself.\n", + "- Sometimes, the expected answer can be a synonym of what you think.\n", + "- Use the standard clinical name (e.g. “thought withdrawal”, “Toxoplasma encephalitis”).\"\"\"\n", + "train_samples_loaded_simple_sys_prompt = prepend_system_prompt_to_first_user_message(\n", + " train_samples_loaded, simple_prompt, path=\"data/medical_01_verifiable_problem_train_simple_prompt.jsonl\"\n", + ")\n", + "test_samples_loaded_simple_sys_prompt = prepend_system_prompt_to_first_user_message(\n", + " test_samples_loaded, simple_prompt, path=\"data/medical_01_verifiable_problem_val_simple_prompt.jsonl\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then build a helper function to generate and store the model's predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import concurrent.futures\n", + "from tqdm import tqdm\n", + "import os\n", + "\n", + "client = OpenAI()\n", + "\n", + "def generate_model_predictions(\n", + " subset,\n", + " prompt_type,\n", + " model_name=\"o4-mini-2025-04-16\",\n", + " reasoning_effort=\"medium\",\n", + " n_runs=1,\n", + " verbose=False,\n", + "):\n", + " if isinstance(subset, str):\n", + " samples_path = f\"data/medical_01_verifiable_problem_{subset}_{prompt_type}_prompt.jsonl\"\n", + " with open(samples_path, \"r\", encoding=\"utf-8\") as f:\n", + " test_samples = [json.loads(line) for line in f if line.strip()]\n", + " else:\n", + " test_samples = [subset]\n", + "\n", + " def run_inference(item):\n", + " resp = client.responses.create(\n", + " model=model_name,\n", + " input=item[\"messages\"],\n", + " reasoning={\"effort\": reasoning_effort, \"summary\": \"detailed\"},\n", + " )\n", + " model_prediction = {'output_text': resp.output_text}\n", + " reasoning_tokens_used = resp.usage.output_tokens_details.reasoning_tokens\n", + " summaries = [seg.text for item in resp.output if item.type == \"reasoning\" for seg in item.summary]\n", + " summaries_string = \"\\n\".join(summaries)\n", + " if verbose:\n", + " print(\"Prompt: {}\".format(item[\"messages\"][0][\"content\"]))\n", + " print(f\"Model Sample: {model_prediction}\\nSolution: {item['reference_answer']}\\n\")\n", + " return {\n", + " \"model_prediction\": model_prediction[\"output_text\"],\n", + " \"input\": item,\n", + " \"reasoning_tokens_used\": reasoning_tokens_used,\n", + " \"reference_answer\": item[\"reference_answer\"],\n", + " \"summaries\": summaries_string\n", + " }\n", + "\n", + " # Ensure the predictions directory exists before any file operations\n", + " predictions_dir = os.path.join(\"data\", \"rft\", \"predictions\")\n", + " os.makedirs(predictions_dir, exist_ok=True)\n", + "\n", + " # Check if results already exist for all runs\n", + " results_per_run = []\n", + " for run_idx in range(n_runs):\n", + " run_save_path = os.path.join(\n", + " predictions_dir,\n", + " f\"{subset}_{prompt_type}_{model_name}_{reasoning_effort}_predictions_run{run_idx+1}.json\"\n", + " )\n", + " if os.path.exists(run_save_path):\n", + " print(f\"Results for run {run_idx+1} already exist at {run_save_path}. Loading results.\")\n", + " with open(run_save_path, \"r\", encoding=\"utf-8\") as f:\n", + " run_results = json.load(f)\n", + " results_per_run.append(run_results)\n", + " else:\n", + " if len(test_samples) == 1:\n", + " run_results = [run_inference(test_samples[0])]\n", + " else:\n", + " run_results = []\n", + " with concurrent.futures.ThreadPoolExecutor() as executor:\n", + " futures = [executor.submit(run_inference, item) for item in test_samples]\n", + " for future in tqdm(futures, total=len(futures), desc=f\"Generating predictions (run {run_idx+1})\"):\n", + " result = future.result()\n", + " run_results.append(result)\n", + " with open(run_save_path, \"w\", encoding=\"utf-8\") as f:\n", + " json.dump(run_results, f, ensure_ascii=False, indent=2)\n", + " results_per_run.append(run_results)\n", + "\n", + " # Return a flat list for backward compatibility\n", + " if n_runs == 1:\n", + " return results_per_run[0]\n", + " else:\n", + " return results_per_run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To generate the predictions, first make sure your API key is set:\n", + "\n", + "```bash\n", + "export OPENAI_API_KEY=...\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenAI o4-mini model\n", + "results_simple_o4mini = generate_model_predictions(\n", + " subset=\"train\",\n", + " prompt_type=\"simple\",\n", + " model_name=\"o4-mini\",\n", + " reasoning_effort=\"medium\",\n", + " n_runs=3\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# OpenAI o3 model\n", + "results_simple_o3 = generate_model_predictions(\n", + " subset=\"train\",\n", + " prompt_type=\"simple\",\n", + " model_name=\"o3\",\n", + " reasoning_effort=\"medium\",\n", + " n_runs=3\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have predictions that are ready to be evaluated.
\n", + "We'll build a helper function that allows us to easily swap in different scoring methods," + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import functools\n", + "\n", + "def evaluate_predictions_with_grader(\n", + " predictions,\n", + " grader_func=combined_grader,\n", + "):\n", + " results = []\n", + "\n", + " if isinstance(predictions, dict):\n", + " predictions = [predictions]\n", + "\n", + " def run_grading(pred):\n", + " model_prediction = {\"output_text\": pred[\"model_prediction\"]}\n", + " item = pred[\"input\"]\n", + " score = grader_func(model_prediction, item)\n", + " result = pred.copy()\n", + " result[\"score\"] = score\n", + " return result\n", + "\n", + " if len(predictions) == 1:\n", + " result = run_grading(predictions[0])\n", + " results.append(result)\n", + " else:\n", + " with concurrent.futures.ThreadPoolExecutor() as executor:\n", + " futures = [executor.submit(run_grading, pred) for pred in predictions]\n", + " for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc=\"Grading predictions\"):\n", + " results.append(future.result())\n", + "\n", + " total = len(results)\n", + " correct = sum(r[\"score\"] for r in results)\n", + " accuracy = correct / total if total else 0.0\n", + "\n", + " metrics = {\n", + " \"total_samples\": total,\n", + " \"accuracy\": accuracy,\n", + " }\n", + " print(metrics)\n", + " return metrics, results\n", + "\n", + "def run_prediction_evaluation(\n", + " model_name=\"o4-mini\",\n", + " reasoning_effort=\"medium\",\n", + " prompt_type=\"simple\",\n", + " subset=\"train\",\n", + " grader_func=combined_grader,\n", + " num_runs=3,\n", + "):\n", + " if isinstance(grader_func, functools.partial):\n", + " name = grader_func.func.__name__\n", + " mg = grader_func.keywords[\"model_grader\"]\n", + " mg_name = mg[\"name\"]\n", + " name = f\"{name}_{mg_name}\"\n", + " else:\n", + " name = getattr(grader_func, \"__name__\", getattr(grader_func, \"__class__\", type(grader_func)).__name__)\n", + " grader_func_name = name.replace(\" \", \"_\").replace(\":\", \"_\").replace(\"/\", \"_\").replace(\",\", \"_\")\n", + "\n", + " for i in range(num_runs):\n", + " preds_path = f\"data/rft/predictions/{subset}_{prompt_type}_{model_name}_{reasoning_effort}_predictions_run{i+1}.json\"\n", + " with open(preds_path, \"r\") as f:\n", + " preds = json.load(f)\n", + " metrics, results_with_scores = evaluate_predictions_with_grader(preds, grader_func=grader_func)\n", + " # Save the scored results\n", + " with open(f\"data/rft/predictions/{subset}_{prompt_type}_{model_name}_{reasoning_effort}_{grader_func_name}_predictions_run_{i+1}_scored.json\", \"w\") as f:\n", + " json.dump(results_with_scores, f, indent=2)\n", + " # Save the metrics\n", + " with open(f\"data/rft/predictions/{subset}_{prompt_type}_{model_name}_{reasoning_effort}_{grader_func_name}_predictions_run_{i+1}_metrics.json\", \"w\") as f:\n", + " json.dump(metrics, f, indent=2)\n", + " # Save the scores (if present in results_with_scores)\n", + " scores = [item.get(\"score\") for item in results_with_scores if \"score\" in item]\n", + " with open(f\"data/rft/predictions/{subset}_{prompt_type}_{model_name}_{reasoning_effort}_{grader_func_name}_predictions_run_{i+1}_scores.json\", \"w\") as f:\n", + " json.dump(scores, f, indent=2)\n", + "\n", + "def load_predictions(\n", + " model_name=\"o4-mini\",\n", + " reasoning_effort=\"medium\",\n", + " prompt_type=\"simple\",\n", + " subset=\"train\",\n", + " grader_func_name=\"clinical_phrase_grader\",\n", + " num_runs=3\n", + "):\n", + " all_predictions = []\n", + " all_metrics = []\n", + " for run in range(1, num_runs + 1):\n", + " pred_path = f\"data/rft/predictions/{subset}_{prompt_type}_{model_name}_{reasoning_effort}_{grader_func_name}_predictions_run_{run}_scored.json\"\n", + " metrics_path = f\"data/rft/predictions/{subset}_{prompt_type}_{model_name}_{reasoning_effort}_{grader_func_name}_predictions_run_{run}_metrics.json\"\n", + " try:\n", + " with open(pred_path, \"r\") as f:\n", + " predictions = json.load(f)\n", + " except FileNotFoundError:\n", + " predictions = None\n", + " try:\n", + " with open(metrics_path, \"r\") as f:\n", + " metrics = json.load(f)\n", + " except FileNotFoundError:\n", + " metrics = None\n", + " all_predictions.append(predictions)\n", + " all_metrics.append(metrics)\n", + " return all_predictions, all_metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and then run the evaluations." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 329740.88it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_samples': 100, 'accuracy': 0.5716752010712578}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 497544.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_samples': 100, 'accuracy': 0.5855097792577905}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 414456.92it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_samples': 100, 'accuracy': 0.5702082734545793}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "model_name = \"o4-mini\"\n", + "reasoning_effort = \"medium\"\n", + "prompt_type = \"simple\"\n", + "subset = \"train\"\n", + "grader_func = combined_grader\n", + "grader_func_name = \"combined_grader\"\n", + "num_runs = 3\n", + "run_prediction_evaluation(\n", + " model_name=model_name, \n", + " reasoning_effort=reasoning_effort, \n", + " prompt_type=prompt_type, \n", + " subset=subset, \n", + " grader_func=grader_func, \n", + " num_runs=num_runs\n", + ")\n", + "predictions_o4mini_medium_simple_prompt, metrics_o4mini_medium_simple_prompt = load_predictions(model_name=model_name, reasoning_effort=reasoning_effort, prompt_type=prompt_type, subset=subset, grader_func_name=grader_func_name, num_runs=num_runs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualizing the results allows us to spot trends and failure modes." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total mistakes: 84\n", + "\n", + "[Sample 16]\n", + " Model prediction: enveloped double stranded linear dna virus\n", + " Reference answer: double-stranded, enveloped dna virus\n", + " Score: 0.85\n", + "\n", + "[Sample 19]\n", + " Model prediction: gallstone ileus\n", + " Reference answer: gall stone ileus\n", + " Score: 0.8225806451612904\n", + "\n", + "[Sample 20]\n", + " Model prediction: acute rheumatic fever\n", + " Reference answer: postinfectious glomerulonephritis\n", + " Score: 0.22037037037037036\n", + "\n", + "[Sample 22]\n", + " Model prediction: amygdala\n", + " Reference answer: hippocampus\n", + " Score: 0.17894736842105263\n", + "\n", + "[Sample 23]\n", + " Model prediction: hypopituitarism\n", + " Reference answer: pituitary adenoma\n", + " Score: 0.47812499999999997\n" + ] + } + ], + "source": [ + "# Print mistakes where the model did not get the correct answer (score < 1.0)\n", + "mistakes = [\n", + " {\"index\": i, **res}\n", + " for i, res in enumerate(predictions_o4mini_medium_simple_prompt[0])\n", + " if res[\"score\"] < 1.0\n", + "]\n", + "\n", + "print(f\"\\nTotal mistakes: {len(mistakes)}\")\n", + "for m in mistakes[15:20]:\n", + " print(f\"\\n[Sample {m['index']}]\")\n", + " print(f\" Model prediction: {m['model_prediction']}\")\n", + " print(f\" Reference answer: {m['reference_answer']}\")\n", + " print(f\" Score: {m['score']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As observed above, typical failure modes fall into three categories:\n", + "1. Small differences and formatting issues, score >=0.8.\n", + "2. Partial lexical match, 0.3 < score < 0.8.\n", + "3. Lexically off-base, score < 0.3.\n", + "\n", + "We can visualize the full score distribution on the training set.\n", + "\n", + "> Note: In practice, analyzing model errors at scale often involves a mix of manual review and automated methods-like tagging failure types or clustering predictions by score and content. That workflow is beyond the scope of this guide, but it's a valuable next step once you've identified broad patterns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "scores_distribution = [m['score'] for m in predictions_o4mini_medium_simple_prompt[0]]\n", + "plt.hist(scores_distribution, alpha=0.6, label='o4-mini medium simple prompt')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare with other models and prompts, and visualize scores." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 489988.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_samples': 100, 'accuracy': 0.6150339441350683}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 507170.98it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_samples': 100, 'accuracy': 0.5901906182115139}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Grading predictions: 100%|██████████| 100/100 [00:00<00:00, 543303.63it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'total_samples': 100, 'accuracy': 0.5927679005876193}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# OpenAI o3 model\n", + "model_name = \"o3\"\n", + "reasoning_effort = \"medium\"\n", + "prompt_type = \"simple\"\n", + "subset = \"train\"\n", + "grader_func = combined_grader\n", + "grader_func_name = \"combined_grader\"\n", + "num_runs = 3\n", + "run_prediction_evaluation(model_name=model_name, reasoning_effort=reasoning_effort, prompt_type=prompt_type, subset=subset, grader_func=grader_func, num_runs=num_runs)\n", + "predictions_o3_medium_simple_prompt, metrics_o3_medium_simple_prompt = load_predictions(model_name=model_name, reasoning_effort=reasoning_effort, prompt_type=prompt_type, subset=subset, grader_func_name=grader_func_name, num_runs=num_runs)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "def average_and_std_metrics(metrics_list):\n", + " \"\"\"Returns dicts of mean and std for a list of metrics dicts.\"\"\"\n", + " if not metrics_list: return {}, {}\n", + " keys = metrics_list[0].keys()\n", + " arr = {k: np.array([m[k] for m in metrics_list]) for k in keys}\n", + " mean = {k: float(np.mean(arr[k])) for k in keys}\n", + " std = {k: float(np.std(arr[k])) for k in keys}\n", + " return mean, std\n", + "\n", + "def plot_model_accuracies(model_metrics_avg, model_metrics_std, grader_title=\"Combined Grader Accuracy\", sharey: bool = True) -> None:\n", + " \"\"\"Plots model accuracies with standard deviation error bars.\"\"\"\n", + " # Convert the nested dicts into tidy DataFrames\n", + " df_avg = pd.DataFrame(model_metrics_avg).T.reset_index().rename(columns={\"index\": \"Model\"})\n", + " df_std = pd.DataFrame(model_metrics_std).T.reset_index().rename(columns={\"index\": \"Model\"})\n", + "\n", + " # Long-form for Seaborn\n", + " long_df_avg = df_avg.melt(id_vars=\"Model\", value_vars=[\"accuracy\"], var_name=\"Metric\", value_name=\"Accuracy\")\n", + " long_df_std = df_std.melt(id_vars=\"Model\", value_vars=[\"accuracy\"], var_name=\"Metric\", value_name=\"Std\")\n", + "\n", + " # Merge avg and std for error bars\n", + " long_df = pd.merge(long_df_avg, long_df_std, on=[\"Model\", \"Metric\"])\n", + "\n", + " pretty_names = {\"accuracy\": grader_title}\n", + "\n", + " # Create a separate figure for each metric\n", + " for metric_key in [\"accuracy\"]:\n", + " metric_df = long_df[long_df[\"Metric\"] == metric_key].copy()\n", + " plt.figure(figsize=(8, 5))\n", + " # Plot bars with error bars\n", + " ax = sns.barplot(data=metric_df, x=\"Model\", y=\"Accuracy\", hue=\"Model\", palette=\"tab10\", legend=False, errorbar=None)\n", + " bars = ax.patches\n", + " # Add error bars manually\n", + " for i, row in enumerate(metric_df.itertuples()):\n", + " bar = bars[i]\n", + " x = bar.get_x() + bar.get_width() / 2\n", + " y = row.Accuracy\n", + " yerr = row.Std\n", + " ax.errorbar(x=x, y=y, yerr=yerr, fmt='none', ecolor='black', capsize=5, elinewidth=2, capthick=2, zorder=10)\n", + " plt.title(pretty_names[metric_key])\n", + " plt.ylabel(\"Accuracy\")\n", + " plt.xlabel(\"\")\n", + " if sharey: plt.ylim(0, 1)\n", + " # Annotate bars with exact values\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " ax.annotate(f\"{height:.2f}\", xy=(bar.get_x() + bar.get_width() / 2, height), xytext=(0, 6), textcoords=\"offset points\", ha='center', va='bottom', fontsize=10, fontweight='bold')\n", + " plt.xticks(rotation=15, ha=\"right\")\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "avg_metrics_o4mini_medium_simple_prompt, std_metrics_o4mini_medium_simple_prompt = average_and_std_metrics(metrics_o4mini_medium_simple_prompt)\n", + "avg_metrics_o3_medium_simple_prompt, std_metrics_o3_medium_simple_prompt = average_and_std_metrics(metrics_o3_medium_simple_prompt)\n", + "model_metrics_avg = {\n", + " \"o4-mini-medium-simple-prompt\": avg_metrics_o4mini_medium_simple_prompt,\n", + " \"o3-medium-simple-prompt\": avg_metrics_o3_medium_simple_prompt,\n", + "}\n", + "model_metrics_std = {\n", + " \"o4-mini-medium-simple-prompt\": std_metrics_o4mini_medium_simple_prompt,\n", + " \"o3-medium-simple-prompt\": std_metrics_o3_medium_simple_prompt,\n", + "}\n", + "plot_model_accuracies(model_metrics_avg, model_metrics_std, grader_title=\"Combined Grader Accuracy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the modelʼs performance has clear limits. In practice, iterating on the prompt often helps boost baseline results and get more out of the base model. However, in this case, our prompt engineering didnʼt lead to meaningful improvements-so we excluded those runs from the analysis.\n", + "\n", + "\n", + "A key requirement for RFT to work is that the base model demonstrates it can successfully complete the task for at least some examples right out of the gate. The initial accuracy of ~0.6 is a strong signal that RFT can boost performance. If the model never succeeds on your tasks, there is no training signal to hill climb on.\n", + "\n", + "\n", + "This evaluation process prepares us for the next step: guiding the model with structured, high-quality feedback from a grader.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **4. Defining Your Grader**\n", + "\n", + "The grader defines the reward function that shapes model behavior during RFT. It provides examples of desired outputs-and penalizes undesirable ones. Designing an effective grader requires both principled structure and thoughtful domain insight, and is perhaps the most important task for successful RFT. \n", + "\n", + "In this section, we will present 3 graders, show how they should be set up to fit the API, and discuss the results they yielded. We will then show how to actually launch an RFT task. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### String based grader\n", + "We began with a dual grader using our earlier evaluation functions since it provides a distribution of scores that will be aligned with the lexical proximity of the prediction to the reference answer. It provided a starting point, but the signal wasnʼt rich enough for `o4-mini` to truly learn and improve, and a first experiment showed stagnant reward during the RFT run. For the API calls, you should build the python grading function as shown below. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import inspect\n", + "\n", + "# --- Utility functions ---\n", + "def build_python_grader_payload(grader_fn) :\n", + " \"\"\"Build a payload for a python grader.\"\"\"\n", + " grader_source = inspect.getsource(grader_fn)\n", + " # Enforce function name to be `grade`\n", + " grader_source = grader_source.replace(grader_fn.__name__, \"grade\", 1)\n", + " return {\n", + " \"type\": \"python\",\n", + " \"source\": grader_source,\n", + " }\n", + "\n", + "multi_python_grader_tool_call = {\n", + " \"type\": \"multi\",\n", + " \"graders\": {\n", + " \"clinical_phrase\": {\n", + " \"name\": \"clinical_phrase_grader\",\n", + " \"image_tag\": \"2025-05-08\",\n", + " **build_python_grader_payload(clinical_phrase_grader),\n", + " },\n", + " \"clinical_phrase_binary\": {\n", + " \"name\": \"clinical_phrase_binary_grader\",\n", + " \"image_tag\": \"2025-05-08\",\n", + " **build_python_grader_payload(clinical_phrase_binary_grader),\n", + " },\n", + " },\n", + " \"calculate_output\": \"0.85 * clinical_phrase + 0.15 * clinical_phrase_binary\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a snapshot of its training curves, where the green curve is the traning set reward and the blue curve is the test set reward:\n", + "\n", + "![RFT String Grader](../images/rft_string_grader.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Grader 1\n", + "To address this limitation, we introduced a more advanced approach: the **model grader**. A model-based grader lets us embed semantic understanding and nuance into the feedback. Thatʼs especially powerful when domain-specific synonyms or fuzzy reasoning are in play. \n", + "\n", + "We used gpt-4.1 as our grader model, guided by a rubric that emphasized semantic fidelity: clinical synonymy, correct disease categorization, and conceptual alignment. Rather than focusing on superficial phrasing-e.g., \"Is this the same string?\"-the grader aimed to answer, \"Does this reflect the correct outcome or phenomenon?\" \n", + "\n", + "To ensure the grader aligned with expert expectations, we evaluated it on a subset of base model predictions. For any production use-case, domain expert reviewers should verify that model assigned scores reflect preferred answer orderings and align with domain judgment. This typically involves confirming that the model grader correctly ranks predictions according to their validity. In the scope of this cookbook, we approximated this evaluation by using OpenAI `o3` to check whether higher-quality predictions were consistently rewarded relative to their alternatives.\n", + "\n", + "From these discussions of `o3` , we iteratively update the model grader until the results are aligned. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "GRADER_PROMPT_1 = \"\"\"\n", + "System:\n", + " You are an expert medical grader. Compare the **Reference Answer** to the **Model's Answer** and produce **only** a JSON object with:\n", + " • **result**: a float between 0.0 and 1.0 \n", + " • **steps**: a list of reasoning steps (each with a `\"description\"` and a `\"conclusion\"`)\n", + "\n", + " Scoring rubric (start at 0.0, then add or subtract):\n", + " 1. Exact lexical match: **+0.15** \n", + " 2. Clinical synonym (e.g. “withdrawal of thought” ↔ “thought withdrawal”): **+0.35** \n", + " 3. Same disease family (e.g. two viral encephalitides): **+0.35** \n", + " 4. Partial term overlap (e.g. “ulcer” in both phrases): **+0.15** \n", + " 5. Completely unrelated: **-0.10**\n", + "\n", + " • If multiple criteria apply, sum their weights (max 1.0). \n", + " • Cap the final score to the [0.0, 1.0] range. \n", + " • In your **steps**, show which rule you applied and the running subtotal.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To be submitted through the API, this is how the dictionary is built." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "model_grader_1 = {\n", + " \"type\": \"score_model\",\n", + " \"name\": \"gpt41_score_model_1\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": GRADER_PROMPT_1\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Reference Answer: {{item.reference_answer}}. Model's Answer: {{sample.output_text}}\"\n", + " }\n", + " ],\n", + " \"pass_threshold\": 0.75,\n", + " \"model\": \"gpt-4.1-2025-04-14\",\n", + " \"range\": [0, 1],\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accordingly, we set up the model grader locally to check the results of the models we will fine-tune next. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from pydantic import BaseModel\n", + "from typing import List\n", + "\n", + "class GraderStep(BaseModel):\n", + " description: str\n", + " conclusion: str\n", + "\n", + "class GraderResponse(BaseModel):\n", + " result: float\n", + " steps: List[GraderStep]\n", + "\n", + "# Adapted python_model_grader to match the other graders' interface\n", + "def python_model_grader(sample, item, model_grader=model_grader_1):\n", + " \"\"\"\n", + " Calls an OpenAI model to grade the model output against the reference answer.\n", + " Expects sample to have \"output_text\", item to have \"reference_answer\".\n", + " Returns a float score (parsed from the model's JSON response).\n", + " \"\"\"\n", + " # Prepare the prompt as the grader expects\n", + " system_prompt = model_grader[\"input\"][0][\"content\"]\n", + " user_prompt = model_grader[\"input\"][1][\"content\"]\n", + " user_prompt_filled = user_prompt.replace(\"{{item.reference_answer}}\", item[\"reference_answer\"]).replace(\"{{sample.output_text}}\", sample[\"output_text\"])\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt_filled}\n", + " ]\n", + " # Call the OpenAI API with the grader's model\n", + " response = client.beta.chat.completions.parse(\n", + " model=model_grader[\"model\"],\n", + " messages=messages,\n", + " seed=model_grader.get(\"sampling_params\", {}).get(\"seed\", None),\n", + " temperature=model_grader.get(\"sampling_params\", {}).get(\"temperature\", 0),\n", + " response_format=GraderResponse,\n", + " )\n", + " # Parse the float score from the model's JSON response\n", + " parsed = response.choices[0].message.parsed\n", + " if not isinstance(parsed, GraderResponse):\n", + " raise RuntimeError(f\"Grader returned invalid structured output: {parsed!r}\")\n", + " return float(parsed.result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the rubric initially delivered sensible feedback, the model soon uncovered a loophole and began **reward-hacking**. Scores shot up-sometimes by 20-30 percentage points-not because clinical accuracy improved but because the model padded its “one phrase” answers with synonyms, doses, and full management plans. You might see `begin warfarin therapy **and** continue unfractionated heparin for ≥5 days, overlapping until the INR is in the therapeutic range (2–3)` or `chewable aspirin 325 mg stat plus nitroglycerin…` instead of the required `continue unfractionated heparin` or `aspirin` respectively. Although the system prompt is explicit-*“respond with exactly one phrase: the single most likely outcome or phenomenon”*-these verbose outputs inflate *lexical_similarity* scores without precisely adding prediction value. This experience highlights the need to continuously inspect model outputs and remain vigilant for reward-hacking behaviours that can quietly distort evaluation metrics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a snapshot of its training curves (green is training reward, blue is test reward):\n", + "\n", + "![RFT Model Hacking](../images/rft_hacking.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Grader 2\n", + "To mitigate this reward-hack, we refined the grader prompt by clarifying expectations, enforcing stricter output constraints, and supplying contrastive examples of correct versus incorrect behavior. Once again, we've iterated with `o3`, leveraging predictions from the base `o4-mini` and the previous fine-tuned model hacking examples, to design and validate our grader. Another important point of this updated grader is the reduction of the weight of the *lexical_similarity*, to ensure that *clinical_similarity* prevails." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "GRADER_PROMPT_2 = \"\"\"You are an expert medical grader.\n", + "\n", + "Compare the reference_answer (gold standard) with the model_prediction\n", + "and return **exactly** this JSON object:\n", + "\n", + "{\n", + " \"steps\": [ // each: {\"description\": \"...\", \"conclusion\": \"...\"}\n", + " …\n", + " ],\n", + " \"result\": \n", + "}\n", + "\n", + "──────────────── Input placeholders ───────────────\n", + "reference_answer:\n", + "model_prediction:\n", + "\n", + "──────────── Normalisation steps ────────────\n", + "• lowercase, strip punctuation / excess whitespace \n", + "• expand common abbreviations (e.g. cll → chronic lymphocytic leukemia) \n", + "• map both strings to ICD-10 / SNOMED concepts when possible\n", + "\n", + "──────────── Clinical layer rubric ───────────\n", + "L1 exact concept or universally accepted synonym \n", + "L2 same concept but benign modifier differs (e.g. “acute”, “left”) \n", + "L3 same disease / drug family but wrong subtype or variant \n", + "L4 same organ system but entirely different disease / intervention \n", + "L5 only partial mechanistic overlap (e.g. both vasodilators) \n", + "L6 unrelated or nonsensical\n", + "\n", + "──────────── Scoring parameters ─────────────\n", + "clinical_weight = 0.90\n", + "lexical_weight = 0.10\n", + "clinical_similarity = {1:1.00, 2:0.85, 3:0.45, 4:0.30, 5:0.10, 6:0.00}\n", + "\n", + "lexical_similarity = normalized_levenshtein(reference_answer,\n", + " model_prediction)\n", + "\n", + "# Optional penalty if a clinically critical adjective is missing\n", + "critical_modifiers = [\n", + " \"wide\", \"narrow\", \"acute\", \"chronic\", \"posteromedial\",\n", + " \"oxidized\", \"oxidised\", \"left\", \"right\"\n", + "]\n", + "modifier_pen = -0.05 if any(\n", + " w in reference_answer and w not in model_prediction\n", + " for w in critical_modifiers\n", + ") else 0.0\n", + "\n", + "# Determine layer L (1-6) per rubric above using ontology + judgment.\n", + "if L == 6:\n", + " score = 0.0\n", + "else:\n", + " score = (clinical_weight * clinical_similarity[L] +\n", + " lexical_weight * lexical_similarity) + modifier_pen\n", + "\n", + "Clamp to [0,1] and round to 3 decimals. \n", + "Output **only** the JSON.\n", + "\n", + "──────────────── Worked examples ─────────────\n", + "reference_answer: beta-thalassemia major \n", + "model_prediction: beta-thalassemia minor \n", + "reasoning: Both involve β-globin chain synthesis, but “major” causes\n", + " transfusion-dependent anemia while “minor” is largely benign;\n", + " same family, wrong subtype → **L3**. Lexical ≈ 0.83. \n", + "score = 0.90·0.45 + 0.10·0.83 = 0.488 → **0.488**\n", + "\n", + "reference_answer: ACE inhibitor \n", + "model_prediction: angiotensin-receptor blocker \n", + "reasoning: Both act on the renin–angiotensin axis yet on different\n", + " targets; only partial mechanistic overlap → **L5**.\n", + " Lexical ≈ 0.31. \n", + "score = 0.90·0.10 + 0.10·0.31 = 0.121 → **0.121**\n", + "\n", + "reference_answer: acute pancreatitis \n", + "model_prediction: pancreatitis \n", + "reasoning: Same disorder but missing timing adjective “acute”;\n", + " benign modifier difference → **L2**. Lexical ≈ 0.78. \n", + "score = 0.90·0.85 + 0.10·0.78 = 0.843 → **0.843**\n", + "\n", + "reference_answer: valproate \n", + "model_prediction: valproic acid \n", + "reasoning: Valproic acid is the active moiety of valproate; mechanisms\n", + " and indications are identical → **L1**. Lexical ≈ 0.82. \n", + "score = 0.90·1.00 + 0.10·0.82 = 0.982 → **0.982**\n", + "\n", + "reference_answer: riboflavin \n", + "model_prediction: riboflavin deficiency \n", + "reasoning: Adds “deficiency” but refers to the same vitamin (B₂);\n", + " benign modifier difference → **L2**. Lexical ≈ 0.60. \n", + "score = 0.90·0.85 + 0.10·0.60 = 0.825 → **0.825**\n", + "\n", + "reference_answer: splenectomy \n", + "model_prediction: acetaminophen overdose \n", + "reasoning: Surgical removal of the spleen has no mechanistic or anatomic\n", + " relationship to toxic drug ingestion → **L6**. \n", + "score = **0.000**\n", + "\n", + "reference_answer: ulcerative colitis \n", + "model_prediction: Crohn disease \n", + "reasoning: Both are inflammatory-bowel diseases but differ in location,\n", + " histology and management; same organ system, different disease\n", + " → **L4**. Lexical ≈ 0.38. \n", + "score = 0.90·0.30 + 0.10·0.38 = 0.308 → **0.308**\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "model_grader_2 = {\n", + " \"type\": \"score_model\",\n", + " \"name\": \"gpt41_score_model_2\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": GRADER_PROMPT_2\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Reference Answer: {{item.reference_answer}}. Model's Answer: {{sample.output_text}}\"\n", + " }\n", + " ],\n", + " \"pass_threshold\": 0.75,\n", + " \"model\": \"gpt-4.1-2025-04-14\",\n", + " \"range\": [0, 1],\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The final result was a high-signal, domain-sensitive grader that guided the model toward more appropriate and concise predictions.\n", + "\n", + "**Note on cost:** LLM graders incur token usage charges in addition to training compute. To manage costs effectively, we recommend:\n", + "1. Testing your grader locally on base model completions (and optionally synthetic ones) to ensure it aligns with your rubric or human preferences. When available, use [flex processing](https://platform.openai.com/docs/guides/flex-processing) for more efficient evaluation.\n", + "2. Starting with a small-scale RFT run to validate grader alignment and detect potential reward-hacking before scaling up.\n", + "\n", + "Let's look at how to launch the training in the next step!\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **5. Training**\n", + "\n", + "Once your prompt and grader are finalized, you can proceed to training. This section shows how to launch RFT using your final grader-but naturally, you would have already run similar commands when experimenting with earlier grader versions to evaluate their performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We make sure the grader passed API test," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "API_KEY = os.environ[\"OPENAI_API_KEY\"]\n", + "HEADERS = {\"Authorization\": f\"Bearer {API_KEY}\"}\n", + "\n", + "# Validate a grader configuration for fine-tuning\n", + "payload = {\"grader\": model_grader_2}\n", + "try:\n", + " response = requests.post(\n", + " \"https://api.openai.com/v1/fine_tuning/alpha/graders/validate\",\n", + " json=payload,\n", + " headers=HEADERS,\n", + " )\n", + " response.raise_for_status()\n", + " print(\"Grader validated\")\n", + "except requests.exceptions.RequestException as e:\n", + " print(f\"Error validating grader: {e}\")\n", + " if 'response' in locals():\n", + " print(f\"Response: {response.text}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and upload the training and test sets to the OpenAI file system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set your training and test file paths\n", + "train_file = \"data/medical_01_verifiable_problem_train_simple_prompt.jsonl\"\n", + "test_file = \"data/medical_01_verifiable_problem_val_simple_prompt.jsonl\"\n", + "\n", + "def upload_file(file_path: str) -> str:\n", + " \"\"\"Upload a file to the OpenAI platform for fine-tuning.\"\"\"\n", + " print(f\"Uploading file: {file_path}\")\n", + " with open(file_path, 'rb') as f:\n", + " response = requests.post(\n", + " \"https://api.openai.com/v1/files\",\n", + " headers=HEADERS,\n", + " files={\"file\": f},\n", + " data={\"purpose\": \"fine-tune\"}\n", + " )\n", + " response.raise_for_status()\n", + " file_id = response.json()[\"id\"]\n", + " print(f\"File uploaded successfully. File ID: {file_id}\")\n", + " return file_id\n", + "\n", + "train_file_id = train_file\n", + "if train_file.endswith(\"jsonl\"):\n", + " print(f\"Training file detected: {train_file}\")\n", + " train_file_id = upload_file(train_file)\n", + "test_file_id = test_file\n", + "if test_file and test_file.endswith(\"jsonl\"):\n", + " print(f\"test file detected: {test_file}\")\n", + " test_file_id = upload_file(test_file)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now define the hyper-parameters for our run. We will be fine-tuning `o4-mini`, with the `medium` reasoning effort. This parameter will impact the length by limiting the number of tokens the model uses to reason. We tune with a moderate compute multiplier and reasonable number of epochs, prioritizing efficiency and fast iteration. You’ll want to tailor these depending on your budget, desired generalization, and dataset difficulty." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the model and other parameters\n", + "model = \"o4-mini-2025-04-16\"\n", + "suffix = \"medical_01_verifiable_problem_gpt41_grader\"\n", + "reasoning_effort = \"medium\"\n", + "n_epochs = 5\n", + "seed = 42\n", + "grader = model_grader_2\n", + "response_format = None\n", + "compute_multiplier = 1.0\n", + "eval_samples = 1\n", + "eval_interval = 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to launch the run!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Launch the RFT job\n", + "payload = dict(\n", + " training_file=train_file_id,\n", + " validation_file=test_file_id,\n", + " model=model,\n", + " suffix=suffix,\n", + " method=dict(\n", + " type=\"reinforcement\",\n", + " reinforcement=dict(\n", + " grader=grader,\n", + " response_format=response_format,\n", + " hyperparameters=dict(\n", + " compute_multiplier=compute_multiplier,\n", + " eval_samples=eval_samples,\n", + " eval_interval=eval_interval,\n", + " n_epochs=n_epochs,\n", + " reasoning_effort=reasoning_effort,\n", + " )\n", + " )\n", + " ),\n", + " seed=seed\n", + ")\n", + "\n", + "try:\n", + " response = requests.post(\n", + " \"https://api.openai.com/v1/fine_tuning/jobs\",\n", + " json=payload,\n", + " headers=HEADERS,\n", + " )\n", + " response.raise_for_status()\n", + " job_id = response.json().get(\"id\")\n", + " if job_id:\n", + " print(\"Training job created with ID:\", job_id)\n", + " print(\n", + " f\"View the job details at: https://platform.openai.com/finetune/{job_id}\")\n", + " else:\n", + " print(\"Failed to retrieve job ID from response.\")\n", + "except requests.exceptions.RequestException as e:\n", + " print(f\"An error occurred while creating the training job: {e}\")\n", + " if 'response' in locals():\n", + " print(f\"Response: {response.text}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the [dashboard](https://platform.openai.com/finetune/) you can observe the reward plots - they let you watch overall performance improve across steps, while the per-grader charts break down specific components in the case of a *multi_grader*. Reasoning token usage trends (often decreasing as the model gets more confident) and step duration metrics give insight into efficiency. Grader latency and error count plots help ensure your grader stays performant and bug-free during the run.\n", + "\n", + "Here is a snapshot of our training curves, where the green and orange curves are for the training set, while tbe blue and red curves are for the test subset:\n", + "\n", + "![RFT Dashboard Example](../images/rft_dashboard_modelgrader2.png)\n", + "\n", + "During training, evaluation runs on the test set are logged directly to the [Evaluation API](https://platform.openai.com/evaluations?tab=runs). You can head there to track how your samples perform and get a sense of how predictions evolve over time.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **6. Using Your Fine-Tuned Model**\n", + "\n", + "When training completes, you can call your new model by its `model_id` and benchmark its improvements. Expect sharper predictions! \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# To retrieve information about a fine-tuning job (including the fine-tuned model id), use the job_id:\n", + "response = requests.get(\n", + " f\"https://api.openai.com/v1/fine_tuning/jobs/{job_id}\",\n", + " headers=HEADERS,\n", + ")\n", + "if response.ok:\n", + " data = response.json()\n", + " if data.get(\"status\") == \"succeeded\":\n", + " fine_tuned_model_id = data.get(\"fine_tuned_model\")\n", + " else:\n", + " fine_tuned_model_id = None\n", + "else:\n", + " raise Exception(f\"Request failed: {response.status_code} - {response.text}\")\n", + "print(\"Fine-tuned model id:\", fine_tuned_model_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model's prediction scores\n", + "\n", + "Let's compute the scores of our base and fine-tuned models for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating predictions (run 1): 0%| | 0/100 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "avg_metrics_o4mini_medium_simple_prompt_model_grader_2, std_metrics_o4mini_medium_simple_prompt_model_grader_2 = average_and_std_metrics(metrics_o4mini_medium_simple_prompt_model_grader_2)\n", + "avg_metrics_o3_medium_simple_prompt_model_grader_2, std_metrics_o3_medium_simple_prompt_model_grader_2 = average_and_std_metrics(metrics_o3_medium_simple_prompt_model_grader_2)\n", + "avg_metrics_ftmodel_medium_simple_prompt_model_grader_2, std_metrics_ftmodel_medium_simple_prompt_model_grader_2 = average_and_std_metrics(metrics_ftmodel_medium_simple_prompt_model_grader_2)\n", + "model_metrics_avg = {\n", + " \"o4-mini-medium-simple-prompt\": avg_metrics_o4mini_medium_simple_prompt_model_grader_2,\n", + " \"o3-medium-simple-prompt\": avg_metrics_o3_medium_simple_prompt_model_grader_2,\n", + " \"ftmodel-medium-simple-prompt\": avg_metrics_ftmodel_medium_simple_prompt_model_grader_2\n", + "}\n", + "model_metrics_std = {\n", + " \"o4-mini-medium-simple-prompt\": std_metrics_o4mini_medium_simple_prompt_model_grader_2,\n", + " \"o3-medium-simple-prompt\": std_metrics_o3_medium_simple_prompt_model_grader_2,\n", + " \"ftmodel-medium-simple-prompt\": std_metrics_ftmodel_medium_simple_prompt_model_grader_2\n", + "}\n", + "plot_model_accuracies(model_metrics_avg, model_metrics_std, grader_title=\"Model Grader 2 Accuracy\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total mistakes: 80\n", + "\n", + "[Sample 5]\n", + " Model prediction: carotid duplex ultrasound\n", + " Reference answer: carotid doppler\n", + " Score: 0.5525\n", + "\n", + "[Sample 6]\n", + " Model prediction: under fixation due to insufficient fixation time\n", + " Reference answer: incomplete fixation\n", + " Score: 0.5037037037037037\n", + "\n", + "[Sample 7]\n", + " Model prediction: acute rheumatic fever due to group a streptococcal pharyngitis mediated by type ii hypersensitivity\n", + " Reference answer: acute rheumatic fever\n", + " Score: 0.85\n", + "\n", + "[Sample 8]\n", + " Model prediction: exposure (open) method of burn treatment\n", + " Reference answer: heterograft application with sutures to secure it in place and daily washes, but no dressing\n", + " Score: 0.3031007751937985\n", + "\n", + "[Sample 9]\n", + " Model prediction: beta-lactamase production leading to enzymatic inactivation of ampicillin\n", + " Reference answer: production of beta-lactamase enzyme\n", + " Score: 0.7555555555555555\n" + ] + } + ], + "source": [ + "# Print mistakes where the model did not get the correct answer (score < 1.0)\n", + "mistakes = [\n", + " {\"index\": i, **res}\n", + " for i, res in enumerate(predictions_ftmodel_medium_simple_prompt_model_grader_2[0])\n", + " if res[\"score\"] < 1.0\n", + "]\n", + "\n", + "print(f\"\\nTotal mistakes: {len(mistakes)}\")\n", + "for m in mistakes[5:10]:\n", + " print(f\"\\n[Sample {m['index']}]\")\n", + " print(f\" Model prediction: {m['model_prediction']}\")\n", + " print(f\" Reference answer: {m['reference_answer']}\")\n", + " print(f\" Score: {m['score']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see about a 5-point boost in accuracy after fine-tuning. Looking at the first few errors, the model tends to harshly penalize answers that are close but not clinically identical-like *carotid duplex ultrasound* vs. *carotid doppler*. It also dings longer answers, even when they’re correct, like *beta-lactamase production leading to enzymatic inactivation of ampicillin*." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "o4-mini-medium-simple-prompt bin counts: [ 4. 15. 9. 7. 7. 4. 3. 5. 22. 24.]\n", + "ftmodel-medium-simple-prompt bin counts: [ 8. 15. 7. 3. 9. 7. 8. 4. 19. 20.]\n", + "Max bin count (y-axis): 24.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scores_o4 = [p['score'] for p in predictions_o4mini_medium_simple_prompt_model_grader_2[0]]\n", + "scores_ft = [p['score'] for p in predictions_ftmodel_medium_simple_prompt_model_grader_2[0]]\n", + "\n", + "# Determine common bins for both histograms\n", + "all_scores = scores_o4 + scores_ft\n", + "bins = plt.hist(all_scores, bins=10, alpha=0)[1]\n", + "\n", + "# Plot histograms and capture the counts\n", + "counts_o4, _, _ = plt.hist(\n", + " scores_o4,\n", + " bins=bins,\n", + " alpha=0.6,\n", + " label='o4-mini-medium-simple-prompt'\n", + ")\n", + "counts_ft, _, _ = plt.hist(\n", + " scores_ft,\n", + " bins=bins,\n", + " alpha=0.6,\n", + " label='ftmodel-medium-simple-prompt'\n", + ")\n", + "\n", + "plt.title(\"Model Grader 2 Score Distribution by Model\")\n", + "plt.xlabel(\"Score\")\n", + "plt.ylabel(\"Count\")\n", + "plt.ylim(top=25)\n", + "plt.legend()\n", + "\n", + "# Print the bin counts\n", + "print(\"o4-mini-medium-simple-prompt bin counts:\", counts_o4)\n", + "print(\"ftmodel-medium-simple-prompt bin counts:\", counts_ft)\n", + "print(\"Max bin count (y-axis):\", max(max(counts_o4), max(counts_ft)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the distruibution of scores, we observe that RFT helped shift the model’s predictions out of the mid-to-low score zone (0.4–0.5) and into the mid-to-high range (0.5–0.6). Since the grader emphasizes clinical similarity over lexical match, this shift reflects stronger medical reasoning-not just better phrasing-according to our *expert* grader. As observed in the 0.9-1.0 range, some verbosity crept in despite mitigations and slightly lowering scores throughout, though it often reflected more complete, semantically aligned answers. A future grader pass could better account for these cases.\n", + "\n", + "Note that, because the earlier `combined_grader` was designed to reward lexical correctness, its accuracy didnʼt improve much-which is expected. That gap reinforces why validating your model grader is critical, and why you should monitor for reward-hacking. In our case, we used `o3` to spot-check grading behavior, but domain expert review is essential. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model's reasoning\n", + "\n", + "Another important point in the analysis of the fine-tuned model are the reasoning summaries. The model may provide key information throughout these summaries, and exploring them to understand where the model fails can drive updates in the model's and the grader's system prompts. Below, we show examples of such chain of thought summaries that the model produced to show its way of answering the question:" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean reasoning_tokens_used o4-mini: 424\n", + "Mean reasoning_tokens_used o3: 353\n", + "Mean reasoning_tokens_used ftmodel: 1820\n" + ] + } + ], + "source": [ + "# Flatten the list of lists into a single list of dicts\n", + "predictions = {\n", + " \"o4-mini\": predictions_o4mini_medium_simple_prompt_model_grader_2,\n", + " \"o3\": predictions_o3_medium_simple_prompt_model_grader_2,\n", + " \"ftmodel\": predictions_ftmodel_medium_simple_prompt_model_grader_2,\n", + "}\n", + "\n", + "for model_name, predictions in predictions.items():\n", + " all_preds = [item for sublist in predictions for item in sublist]\n", + " reasoning_tokens = [p['reasoning_tokens_used'] for p in all_preds if 'reasoning_tokens_used' in p]\n", + " mean_reasoning_tokens = np.mean(reasoning_tokens)\n", + " print(f\"Mean reasoning_tokens_used {model_name}: {mean_reasoning_tokens:.0f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The fine-tuned model spends more reasoning tokens to think through the question. Let's visualize an example thanks to the reasoning summaries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classifying staging type\n", + "\n", + "The user provided a clinical scenario of a 35-year-old female with a 5 cm oral tumor and a 2 cm lymph node. They're asking how to stage it according to the TNM classification. This is a diagnosis query, so the correct answer type here is \"diagnosis.\" Considering the tumor's size, it appears to be classified as T3 since it's greater than 4 cm. Thus, I think the staging might be Stage II, but I'll confirm that.\n" + ] + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "markdown_text = results_o4mini_model_grader_2[5][\"summaries\"]\n", + "display(Markdown(markdown_text))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clarifying T staging for cancers\n", + "\n", + "I’m digging into T staging for head and neck cancers in the oral cavity. So, T1 applies to tumors 2 cm or less, T2 for those over 2 cm but not more than 4 cm, and T3 is for tumors over 4 cm. T4a indicates invasion into adjacent structures. The patient's tumor measures 5 cm, which is over 4 cm. I’m not sure if it fits T3 or T4a, since T4a involves additional invasiveness, not just size. Determining T and N staging\n", + "\n", + "I’m looking at a 5 cm tumor in the oral cavity. It seems there’s no mention of invasion into adjacent structures, so I’m categorizing it as T3 due to its size. T4a usually means invasion into structures like bone or skin. According to the TNM classification, since I see no such invasion, T classification remains T3.\n", + "\n", + "Moving on to N staging, I see there's a single lymph node of 2 cm on the same side; this fits the N1 classification for metastasis, as it’s less than 3 cm.\n" + ] + } + ], + "source": [ + "markdown_text = results_ft_model_grader_2[5][\"summaries\"]\n", + "display(Markdown(markdown_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Base `o4-mini`'s reasoning gives a quick answer but doesn’t explain how it got there. It mentions the tumor size but doesn’t walk through the actual TNM rules, and it seems unsure about the result. On the other hand, the `finetuned model` is more thoughtful - breaking down the T and N staging step by step and explaining why each part applies. The latter seems more careful, and seems to have learnt to break down the case description even more." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To push the scores further\n", + "Both the baseline `o3` and our fine-tuned `o4-mini` sometimes scored zero on the same samples-a red flag that the reference labels may be wrong. Before adding more compute, invest in data quality: have a domain expert relabel the noisy slice, analyze the model's reasoning, then tighten the grader prompt. Clean, trusted data and methodical updates almost always buys more accuracy than extra epochs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## **Conclusion**\n", + "\n", + "Weʼve looked at how to design graders that give `o4-mini` the kind of detailed feedback it needs during RFT. That signal is what helps the model actually learn and improve beyond the baseline. Model graders can be incredibly powerful for this-but only if theyʼre designed carefully. A sloppy grader or sloppy data can send the wrong signals and steer the model in the wrong direction. \n", + "\n", + "You're now ready to apply reinforcement fine-tuning on your own models using the OpenAI API. Weʼre excited to see how you push the boundaries of reasoning and tool use with custom graders and smarter model behavior!\n", + "\n", + "For troubleshooting or next steps, refer to the [OpenAI fine-tuning documentation](https://platform.openai.com/docs/guides/fine-tuning)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "jupyter-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Speech_transcription_methods.ipynb b/examples/Speech_transcription_methods.ipynb new file mode 100644 index 0000000000..5c52be698a --- /dev/null +++ b/examples/Speech_transcription_methods.ipynb @@ -0,0 +1,672 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "26a10eea", + "metadata": {}, + "source": [ + "# 🗣️ Comparing Speech-to-Text Methods with the OpenAI API\n", + "\n", + "## Overview\n", + "\n", + "This notebook provides a clear, hands-on guide for beginners to quickly get started with Speech-to-Text (STT) using the OpenAI API. You'll explore multiple practical methods, their use cases, and considerations.\n", + "\n", + "By the end you will be able to select and use the appropriate transcription method for your use use cases.\n", + "\n", + "*Note: For simplicity and ease of use, this notebook uses WAV audio files. Real-time microphone streaming (e.g., from web apps or microphones) is not utilized.*" + ] + }, + { + "cell_type": "markdown", + "id": "5120a023", + "metadata": {}, + "source": [ + "### 📊 Quick-look\n", + "| Mode | Latency to **first token** | Best for (real examples) | Advantages | Key limitations |\n", + "|--------------------------------|---------------------------|--------------------------------------------------------------|-----------------------------------------------------------|-----------------------------------------------------------|\n", + "| File upload + `stream=False` (blocking) | seconds | Voicemail, meeting recordings | Simple to set up | • No partial results, users see nothing until file finishes
• Max 25 MB per request (you must chunk long audio) |\n", + "| File upload + `stream=True` | subseconds | Voice memos in mobile apps | Simple to set up & provides a “live” feel via token streaming | • Still requires a completed file
• You implement progress bars / chunked uploads |\n", + "| Realtime WebSocket | subseconds | Live captions in webinars | True real-time; accepts a continuous audio stream | • Audio must be pcm16, g711_ulaw, or g711_alaw
• Session ≤ 30 min, reconnect & stitch
• You handle speaker-turn formatting to build the full transcript |\n", + "| Agents SDK VoicePipeline | subseconds | Internal help-desk assistant | Real-time streaming and easy to build agentic workflows | • Python-only beta
• API surface may change |" + ] + }, + { + "cell_type": "markdown", + "id": "25308313", + "metadata": {}, + "source": [ + "## Installation (one‑time)\n", + "\n", + "To set up your environment, uncomment and run the following cell in a new Python environment:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bc940358", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade -q openai openai-agents websockets sounddevice pyaudio nest_asyncio resampy httpx websocket-client" + ] + }, + { + "cell_type": "markdown", + "id": "efbbb76a", + "metadata": {}, + "source": [ + "This installs the necessary packages required to follow along with the notebook." + ] + }, + { + "cell_type": "markdown", + "id": "6d6ba036", + "metadata": {}, + "source": [ + "## Authentication\n", + "Before proceeding, ensure you have set your OpenAI API key as an environment variable named OPENAI_API_KEY. You can typically set this in your terminal or notebook environment: `export OPENAI_API_KEY=\"your-api-key-here\"`\n", + "\n", + "Verify that your API key is set correctly by running the next cell." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e4078915", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ OpenAI client ready\n" + ] + } + ], + "source": [ + "# ─── Standard Library ──────────────────────────────────────────────────────────\n", + "import asyncio\n", + "import struct\n", + "import base64 # encode raw PCM bytes → base64 before sending JSON\n", + "import json # compose/parse WebSocket messages\n", + "import os\n", + "import time\n", + "from typing import List\n", + "from pathlib import Path\n", + "\n", + "# ─── Third-Party ───────────────────────────────────────────────────────────────\n", + "import nest_asyncio\n", + "import numpy as np\n", + "from openai import OpenAI\n", + "import resampy # high-quality sample-rate conversion\n", + "import soundfile as sf # reads many audio formats into float32 arrays\n", + "import websockets # asyncio-based WebSocket client\n", + "from agents import Agent\n", + "from agents.voice import (\n", + " SingleAgentVoiceWorkflow,\n", + " StreamedAudioInput,\n", + " VoicePipeline,\n", + " VoicePipelineConfig,\n", + ")\n", + "from IPython.display import Audio, display\n", + "# ───────────────────────────────────────────────────────────────────────────────\n", + "nest_asyncio.apply()\n", + "\n", + "# ✏️ Put your key in an env-var or just replace the call below.\n", + "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n", + "\n", + "client = OpenAI(api_key=OPENAI_API_KEY)\n", + "print(\"✅ OpenAI client ready\")" + ] + }, + { + "cell_type": "markdown", + "id": "c2a95c79", + "metadata": {}, + "source": [ + "---\n", + "## 1 · Speech-to-Text with Audio File\n", + "*model = gpt-4o-transcribe*\n", + "\n", + "### When to use\n", + "* You have a completed audio file (up to 25 MB).The following input file types are supported: mp3, mp4, mpeg, mpga, m4a, wav, and webm.\n", + "* Suitable for batch processing tasks like podcasts, call-center recordings, or voice memos.\n", + "* Real-time feedback or partial results are not required." + ] + }, + { + "cell_type": "markdown", + "id": "0d2c053d", + "metadata": {}, + "source": [ + "### How it works\n", + "\n", + "\n", + "![STT Not Streaming Transcription flow](../images/speech-to-text-not-streaming.png)\n", + "\n", + "#### Benefits\n", + "\n", + "- **Ease of use:** Single HTTP request – perfect for automation or backend scripts. \n", + "- **Accuracy:** Processes the entire audio in one go, improving context and transcription quality. \n", + "- **File support:** Handles WAV, MP3, MP4, M4A, FLAC, Ogg, and more. \n", + "\n", + "#### Limitations\n", + "\n", + "- **No partial results:** You must wait until processing finishes before seeing any transcript. \n", + "- **Latency scales with duration:** Longer recordings mean longer wait times. \n", + "- **File-size cap:** Up to 25 MB (≈ 30 min at 16-kHz mono WAV). \n", + "- **Offline use only:** Not intended for real-time scenarios such as live captioning or conversational AI. " + ] + }, + { + "cell_type": "markdown", + "id": "4eeb51a7", + "metadata": {}, + "source": [ + "Let's first preview the audio file. I've downloaded the audio file from [here](https://pixabay.com/sound-effects/search/male-speech/)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ab545e4c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "AUDIO_PATH = Path('./data/sample_audio_files/lotsoftimes-78085.mp3') # change me\n", + "MODEL_NAME = \"gpt-4o-transcribe\"\n", + "\n", + "if AUDIO_PATH.exists():\n", + " display(Audio(str(AUDIO_PATH)))\n", + "else:\n", + " print('⚠️ Provide a valid audio file')" + ] + }, + { + "cell_type": "markdown", + "id": "218b7649", + "metadata": {}, + "source": [ + "Now, we can call the STT endpoint to transcribe the audio." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7ae4af8d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- TRANSCRIPT ---\n", + "\n", + "And lots of times you need to give people more than one link at a time. A band could give their fans a couple new videos from a live concert, a behind-the-scenes photo gallery, an album to purchase, like these next few links.\n", + "\n" + ] + } + ], + "source": [ + "if AUDIO_PATH.exists():\n", + " with AUDIO_PATH.open('rb') as f:\n", + " transcript = client.audio.transcriptions.create(\n", + " file=f,\n", + " model=MODEL_NAME,\n", + " response_format='text',\n", + " )\n", + " print('\\n--- TRANSCRIPT ---\\n')\n", + " print(transcript)" + ] + }, + { + "cell_type": "markdown", + "id": "765ec73a", + "metadata": {}, + "source": [ + "## 2 · Speech-to-Text with Audio File: Streaming\n", + "*model = gpt-4o-transcribe*\n", + "### When to use\n", + "- You already have a fully recorded audio file. \n", + "- You need immediate transcription results (partial or final) as they arrive. \n", + "- Scenarios where partial feedback improves UX, e.g., uploading a long voice memo.\n", + "\n", + "![STT Streaming Transcription flow](../images/speech-to-text-streaming.png)\n", + "\n", + "#### Benefits\n", + "- **Real-time feel:** Users see transcription updates almost immediately. \n", + "- **Progress visibility:** Intermediate transcripts show ongoing progress. \n", + "- **Improved UX:** Instant feedback keeps users engaged.\n", + "\n", + "#### Limitations\n", + "- **Requires full audio file upfront:** Not suitable for live audio feeds. \n", + "- **Implementation overhead:** You must handle streaming logic and progress updates yourself. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d027fdb9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "And lots of times you need to give people more than one link at a time. A band could give their fans a couple new videos from a live concert, a behind-the-scenes photo gallery, an album to purchase, like these next few links.\n", + "\n", + "And lots of times you need to give people more than one link at a time. A band could give their fans a couple new videos from a live concert, a behind-the-scenes photo gallery, an album to purchase, like these next few links.\n" + ] + } + ], + "source": [ + "if AUDIO_PATH.exists():\n", + " with AUDIO_PATH.open('rb') as f:\n", + " stream = client.audio.transcriptions.create(\n", + " file=f,\n", + " model=MODEL_NAME,\n", + " response_format='text',\n", + " stream=True\n", + ")\n", + "\n", + "for event in stream:\n", + " # If this is an incremental update, you can get the delta using `event.delta`\n", + " if getattr(event, \"delta\", None): \n", + " print(event.delta, end=\"\", flush=True)\n", + " time.sleep(0.05) # simulate real-time pacing\n", + " \n", + " # When transcription is complete, you can get the final transcript using `event.text`\n", + " elif getattr(event, \"text\", None):\n", + " print()\n", + " print(\"\\n\" + event.text)" + ] + }, + { + "cell_type": "markdown", + "id": "f42c4da4", + "metadata": {}, + "source": [ + "---\n", + "## 3 · Realtime Transcription API\n", + "*model = gpt-4o-transcribe*\n", + "### When to use\n", + "* Live captioning for real-time scenarios (e.g., meetings, demos).\n", + "* Need built-in voice-activity detection, noise suppression, or token-level log probabilities.\n", + "* Comfortable handling WebSockets and real-time event streams.\n" + ] + }, + { + "cell_type": "markdown", + "id": "88ef332f", + "metadata": {}, + "source": [ + "### How it works\n", + "\n", + "![Realtime Transcription flow](../images/realtime_api_transcription.png)\n", + "\n", + "#### Benefits\n", + "- **Ultra-low latency:** Typically 300–800 ms, enabling near-instant transcription. \n", + "- **Dynamic updates:** Supports partial and final transcripts, enhancing the user experience. \n", + "- **Advanced features:** Built-in turn detection, noise reduction, and optional detailed log-probabilities. \n", + "\n", + "#### Limitations\n", + "- **Complex integration:** Requires managing WebSockets, Base64 encoding, and robust error handling. \n", + "- **Session constraints:** Limited to 30-minute sessions. \n", + "- **Restricted formats:** Accepts only raw PCM (no MP3 or Opus); For pcm16, input audio must be 16-bit PCM at a 24kHz sample rate, single channel (mono), and little-endian byte order." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c6fa0ea1", + "metadata": {}, + "outputs": [], + "source": [ + "TARGET_SR = 24_000\n", + "PCM_SCALE = 32_767\n", + "CHUNK_SAMPLES = 3_072 # ≈128 ms at 24 kHz\n", + "RT_URL = \"wss://api.openai.com/v1/realtime?intent=transcription\"\n", + "\n", + "EV_DELTA = \"conversation.item.input_audio_transcription.delta\"\n", + "EV_DONE = \"conversation.item.input_audio_transcription.completed\"\n", + "# ── helpers ────────────────────────────────────────────────────────────────\n", + "def float_to_16bit_pcm(float32_array):\n", + " clipped = [max(-1.0, min(1.0, x)) for x in float32_array]\n", + " pcm16 = b''.join(struct.pack(' np.ndarray:\n", + " \"\"\"Return mono PCM-16 as a NumPy array.\"\"\"\n", + " data, file_sr = sf.read(path, dtype=\"float32\")\n", + " if data.ndim > 1:\n", + " data = data.mean(axis=1)\n", + " if file_sr != sr:\n", + " data = resampy.resample(data, file_sr, sr)\n", + " return data\n", + "\n", + "async def _send_audio(ws, pcm: np.ndarray, chunk: int, sr: int) -> None:\n", + " \"\"\"Producer: stream base-64 chunks at real-time pace, then signal EOF.\"\"\"\n", + " dur = 0.025 # Add pacing to ensure real-time transcription\n", + " t_next = time.monotonic()\n", + "\n", + " for i in range(0, len(pcm), chunk):\n", + " float_chunk = pcm[i:i + chunk]\n", + " payload = {\n", + " \"type\": \"input_audio_buffer.append\",\n", + " \"audio\": base64_encode_audio(float_chunk),\n", + " }\n", + " await ws.send(json.dumps(payload))\n", + " t_next += dur\n", + " await asyncio.sleep(max(0, t_next - time.monotonic()))\n", + "\n", + " await ws.send(json.dumps({\"type\": \"input_audio_buffer.end\"}))\n", + "\n", + "async def _recv_transcripts(ws, collected: List[str]) -> None:\n", + " \"\"\"\n", + " Consumer: build `current` from streaming deltas, promote it to `collected`\n", + " whenever a …completed event arrives, and flush the remainder on socket\n", + " close so no words are lost.\n", + " \"\"\"\n", + " current: List[str] = []\n", + "\n", + " try:\n", + " async for msg in ws:\n", + " ev = json.loads(msg)\n", + "\n", + " typ = ev.get(\"type\")\n", + " if typ == EV_DELTA:\n", + " delta = ev.get(\"delta\")\n", + " if delta:\n", + " current.append(delta)\n", + " print(delta, end=\"\", flush=True)\n", + " elif typ == EV_DONE:\n", + " # sentence finished → move to permanent list\n", + " collected.append(\"\".join(current))\n", + " current.clear()\n", + " except websockets.ConnectionClosedOK:\n", + " pass\n", + "\n", + " # socket closed → flush any remaining partial sentence\n", + " if current:\n", + " collected.append(\"\".join(current))\n", + "\n", + "def _session(model: str, vad: float = 0.5) -> dict:\n", + " return {\n", + " \"type\": \"transcription_session.update\",\n", + " \"session\": {\n", + " \"input_audio_format\": \"pcm16\",\n", + " \"turn_detection\": {\"type\": \"server_vad\", \"threshold\": vad},\n", + " \"input_audio_transcription\": {\"model\": model},\n", + " },\n", + " }\n", + "\n", + "async def transcribe_audio_async(\n", + " wav_path,\n", + " api_key,\n", + " *,\n", + " model: str = MODEL_NAME,\n", + " chunk: int = CHUNK_SAMPLES,\n", + ") -> str:\n", + " pcm = load_and_resample(wav_path)\n", + " headers = {\"Authorization\": f\"Bearer {api_key}\", \"OpenAI-Beta\": \"realtime=v1\"}\n", + "\n", + " async with websockets.connect(RT_URL, additional_headers=headers, max_size=None) as ws:\n", + " await ws.send(json.dumps(_session(model)))\n", + "\n", + " transcripts: List[str] = []\n", + " await asyncio.gather(\n", + " _send_audio(ws, pcm, chunk, TARGET_SR),\n", + " _recv_transcripts(ws, transcripts),\n", + " ) # returns when server closes\n", + "\n", + " return \" \".join(transcripts)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d90de5b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "And lots of times you need to give people more than one link at a time.A band could give their fans a couple new videos from a live concert, a behind-the-scenes photo galleryLike these next few linksAn album to purchase." + ] + }, + { + "data": { + "text/plain": [ + "'And lots of times you need to give people more than one link at a time. A band could give their fans a couple new videos from a live concert, a behind-the-scenes photo gallery Like these next few linksAn album to purchase. '" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transcript = await transcribe_audio_async(AUDIO_PATH, OPENAI_API_KEY)\n", + "transcript" + ] + }, + { + "cell_type": "markdown", + "id": "c20826c4", + "metadata": {}, + "source": [ + "---\n", + "## 4 · Agents SDK Realtime Transcription\n", + "*models = gpt-4o-transcribe, gpt-4o-mini*\n", + "### When to use\n", + "* Leveraging the OpenAI Agents SDK for real-time transcription and synthesis with minimal setup.\n", + "* You want to integrate transcription directly into agent-driven workflows.\n", + "* Prefer high-level management of audio input/output, WebSockets, and buffering.\n" + ] + }, + { + "cell_type": "markdown", + "id": "90bc7055", + "metadata": {}, + "source": [ + "### How it works\n", + "\n", + "![Agents Transcription flow](../images/agents_sdk_transcription.png)\n", + "\n", + "**Benefits**\n", + "\n", + "- **Minimal boilerplate:** `VoicePipeline` handles resampling, VAD, buffering, token auth, and reconnects. \n", + "- **Seamless agent integration**: Enables direct interaction with GPT agents using real-time audio transcription.\n", + "\n", + "**Limitations**\n", + "\n", + "- **Python-only beta:** not yet available in other languages; APIs may change. \n", + "- **Less control:** fine-tuning VAD thresholds or packet scheduling requires digging into SDK internals. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "754a846b", + "metadata": {}, + "outputs": [], + "source": [ + "# ── 1 · agent that replies in French ---------------------------------------\n", + "fr_agent = Agent(\n", + " name=\"Assistant-FR\",\n", + " instructions=\n", + " \"Translate the user's words into French.\",\n", + " model=\"gpt-4o-mini\",\n", + ")\n", + "\n", + "# ── 2 · workflow that PRINTS what it yields --------------------------------\n", + "class PrintingWorkflow(SingleAgentVoiceWorkflow):\n", + " \"\"\"Subclass that prints every chunk it yields (the agent's reply).\"\"\"\n", + "\n", + " async def run(self, transcription: str):\n", + " # Optionally: also print the user transcription\n", + " print()\n", + " print(\"[User]:\", transcription)\n", + " print(\"[Assistant]: \", end=\"\", flush=True)\n", + " async for chunk in super().run(transcription):\n", + " print(chunk, end=\"\", flush=True) # <-- agent (French) text\n", + " yield chunk # still forward to TTS\n", + "\n", + "\n", + "pipeline = VoicePipeline(\n", + " workflow=PrintingWorkflow(fr_agent),\n", + " stt_model=MODEL_NAME,\n", + " config=VoicePipelineConfig(tracing_disabled=True),\n", + ")\n", + "\n", + "# ── 3 · helper to stream ~40 ms chunks at 24 kHz ---------------------------\n", + "def load_and_resample(path: str, sr: int = 24_000) -> np.ndarray:\n", + " \"\"\"Return mono PCM-16 as a NumPy array.\"\"\"\n", + " data, file_sr = sf.read(path, dtype=\"float32\")\n", + " if data.ndim > 1:\n", + " data = data.mean(axis=1)\n", + " if file_sr != sr:\n", + " data = resampy.resample(data, file_sr, sr)\n", + " return data\n", + " \n", + "def audio_chunks(path: str, target_sr: int = 24_000, chunk_ms: int = 40):\n", + " # 1️⃣ reuse the helper\n", + " audio = load_and_resample(path, target_sr)\n", + "\n", + " # 2️⃣ float-32 → int16 NumPy array\n", + " pcm = (np.clip(audio, -1, 1) * 32_767).astype(np.int16)\n", + "\n", + " # 3️⃣ yield real-time sized hops\n", + " hop = int(target_sr * chunk_ms / 1_000)\n", + " for off in range(0, len(pcm), hop):\n", + " yield pcm[off : off + hop]\n", + "\n", + "# ── 4 · stream the file ----------------------------------------------------\n", + "async def stream_audio(path: str):\n", + " sai = StreamedAudioInput()\n", + " run_task = asyncio.create_task(pipeline.run(sai))\n", + "\n", + " for chunk in audio_chunks(path):\n", + " await sai.add_audio(chunk)\n", + " await asyncio.sleep(len(chunk) / 24_000) # real-time pacing\n", + "\n", + " # just stop pushing; session ends automatically\n", + " await run_task # wait for pipeline to finish" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "611c11e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[User]: And lots of times you need to give people more than one link at a time.\n", + "[Assistant]: Et souvent, vous devez donner aux gens plusieurs liens à la fois.\n", + "[User]: A band could give their fans a couple new videos from a live concert, a behind-the-scenes photo gallery.\n", + "[Assistant]: Un groupe pourrait donner à ses fans quelques nouvelles vidéos d'un concert live, ainsi qu'une galerie de photos des coulisses.\n", + "[User]: An album to purchase.\n", + "[Assistant]: " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Un album à acheter.\n", + "[User]: like these next few links.\n", + "[Assistant]: comme ces quelques liens suivants." + ] + } + ], + "source": [ + "await stream_audio(AUDIO_PATH)" + ] + }, + { + "cell_type": "markdown", + "id": "e34ebc6d", + "metadata": {}, + "source": [ + "## Conclusion \n", + "\n", + "In this notebook you explored multiple ways to convert speech to text with the OpenAI API and the Agents SDK, ranging from simple file uploads to fully-interactive, real-time streaming. Each workflow shines in a different scenario, so pick the one that best matches your product’s needs.\n", + "\n", + "### Key takeaways\n", + "- **Match the method to the use-case:** \n", + " • Offline batch jobs → file-based transcription. \n", + " • Near-real-time updates → HTTP-streaming. \n", + " • Conversational, low-latency experiences → WebSocket or Agents SDK. \n", + "- **Weigh trade-offs:** latency, implementation effort, supported formats, and session limits all differ by approach. \n", + "- **Stay current:** the models and SDK continue to improve; new features ship regularly.\n", + "\n", + "### Next steps\n", + "1. Try out the notebook!\n", + "2. Integrate your chosen workflow into your application.\n", + "3. Send us feedback! Community insights help drive the next round of model upgrades. " + ] + }, + { + "cell_type": "markdown", + "id": "e0b68b9b", + "metadata": {}, + "source": [ + "## References\n", + "* Explore the [Transcriptions API docs](https://platform.openai.com/docs/api-reference/audio).\n", + "* Read the [Realtime guide](https://platform.openai.com/docs/guides/realtime?use-case=transcription).\n", + "* Explore the [Agents SDK reference](https://openai.github.io/openai-agents-python/).\n", + "* Explore the [Agents SDK Voice Pipeline reference](https://openai.github.io/openai-agents-python/voice/)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Using_logprobs.ipynb b/examples/Using_logprobs.ipynb index ee3c336a88..e3aa394f5d 100644 --- a/examples/Using_logprobs.ipynb +++ b/examples/Using_logprobs.ipynb @@ -7,7 +7,7 @@ "# Using logprobs for classification and Q&A evaluation\n", "\n", "This notebook demonstrates the use of the `logprobs` parameter in the Chat Completions API. When `logprobs` is enabled, the API returns the log probabilities of each output token, along with a limited number of the most likely tokens at each token position and their log probabilities. The relevant request parameters are:\n", - "* `logprobs`: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message. This option is currently not available on the `gpt-4-vision-preview` model.\n", + "* `logprobs`: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.\n", "* `top_logprobs`: An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to true if this parameter is used.\n", "\n", "Log probabilities of output tokens indicate the likelihood of each token occurring in the sequence given the context. To simplify, a logprob is `log(p)`, where `p` = probability of a token occurring at a specific position based on the previous tokens in the context. Some key points about `logprobs`:\n", @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 266, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 267, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -177,7 +177,7 @@ " print(f\"\\nHeadline: {headline}\")\n", " API_RESPONSE = get_completion(\n", " [{\"role\": \"user\", \"content\": CLASSIFICATION_PROMPT.format(headline=headline)}],\n", - " model=\"gpt-4\",\n", + " model=\"gpt-4o\",\n", " )\n", " print(f\"Category: {API_RESPONSE.choices[0].message.content}\\n\")" ] @@ -191,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -205,7 +205,7 @@ { "data": { "text/html": [ - "Output token 1: Technology, logprobs: -2.4584822e-06, linear probability: 100.0%
Output token 2: Techn, logprobs: -13.781253, linear probability: 0.0%
" + "Output token 1: Technology, logprobs: 0.0, linear probability: 100.0%
Output token 2: Technology, logprobs: -18.75, linear probability: 0.0%
" ], "text/plain": [ "" @@ -227,7 +227,7 @@ { "data": { "text/html": [ - "Output token 1: Politics, logprobs: -2.4584822e-06, linear probability: 100.0%
Output token 2: Technology, logprobs: -13.937503, linear probability: 0.0%
" + "Output token 1: Politics, logprobs: -3.1281633e-07, linear probability: 100.0%
Output token 2: Polit, logprobs: -16.0, linear probability: 0.0%
" ], "text/plain": [ "" @@ -249,7 +249,7 @@ { "data": { "text/html": [ - "Output token 1: Art, logprobs: -0.009169078, linear probability: 99.09%
Output token 2: Sports, logprobs: -4.696669, linear probability: 0.91%
" + "Output token 1: Art, logprobs: -0.028133942, linear probability: 97.23%
Output token 2: Sports, logprobs: -4.278134, linear probability: 1.39%
" ], "text/plain": [ "" @@ -272,7 +272,7 @@ " print(f\"\\nHeadline: {headline}\")\n", " API_RESPONSE = get_completion(\n", " [{\"role\": \"user\", \"content\": CLASSIFICATION_PROMPT.format(headline=headline)}],\n", - " model=\"gpt-4\",\n", + " model=\"gpt-4o-mini\",\n", " logprobs=True,\n", " top_logprobs=2,\n", " )\n", @@ -292,9 +292,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As expected from the first two headlines, `gpt-4` is nearly 100% confident in its classifications, as the content is clearly technology and politics focused respectively. However, the third headline combines both sports and art-related themes, so we see the model is less confident in its selection.\n", + "As expected from the first two headlines, gpt-4o-mini is 100% confident in its classifications, as the content is clearly technology and politics focused, respectively. However, the third headline combines both sports and art-related themes, resulting in slightly lower confidence at 97%, while still demonstrating strong certainty in its classification.\n", "\n", - "This shows how important using `logprobs` can be, as if we are using LLMs for classification tasks we can set confidence theshholds, or output several potential output tokens if the log probability of the selected output is not sufficiently high. For instance, if we are creating a recommendation engine to tag articles, we can automatically classify headlines crossing a certain threshold, and send the less certain headlines for manual review." + "`logprobs` are quite useful for classification tasks. They allow us to set confidence thresholds or output multiple potential tokens if the log probability of the selected output is not sufficiently high. For instance, when creating a recommendation engine to tag articles, we can automatically classify headlines that exceed a certain threshold and send less certain ones for manual review." ] }, { @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 270, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -368,13 +368,13 @@ }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "Questions clearly answered in article

Question: What nationality was Ada Lovelace?

has_sufficient_context_for_answer: True, logprobs: -3.1281633e-07, linear probability: 100.0%

Question: What was an important finding from Lovelace's seventh note?

has_sufficient_context_for_answer: True, logprobs: -7.89631e-07, linear probability: 100.0%

Questions only partially covered in the article

Question: Did Lovelace collaborate with Charles Dickens

has_sufficient_context_for_answer: True, logprobs: -0.06993677, linear probability: 93.25%

Question: What concepts did Lovelace build with Charles Babbage

has_sufficient_context_for_answer: False, logprobs: -0.61807257, linear probability: 53.9%

" + "Questions clearly answered in article

Question: What nationality was Ada Lovelace?

has_sufficient_context_for_answer: True, logprobs: -3.1281633e-07, linear probability: 100.0%

Question: What was an important finding from Lovelace's seventh note?

has_sufficient_context_for_answer: True, logprobs: -7.89631e-07, linear probability: 100.0%

Questions only partially covered in the article

Question: Did Lovelace collaborate with Charles Dickens

has_sufficient_context_for_answer: False, logprobs: -0.008654992, linear probability: 99.14%

Question: What concepts did Lovelace build with Charles Babbage

has_sufficient_context_for_answer: True, logprobs: -0.004082317, linear probability: 99.59%

" ], "text/plain": [ "" @@ -398,7 +398,7 @@ " ),\n", " }\n", " ],\n", - " model=\"gpt-4\",\n", + " model=\"gpt-4o-mini\",\n", " logprobs=True,\n", " )\n", " html_output += f'

Question: {question}

'\n", @@ -417,7 +417,7 @@ " ),\n", " }\n", " ],\n", - " model=\"gpt-4\",\n", + " model=\"gpt-4o\",\n", " logprobs=True,\n", " top_logprobs=3,\n", " )\n", @@ -437,13 +437,6 @@ "This self-evaluation can help reduce hallucinations, as you can restrict answers or re-prompt the user when your `sufficient_context_for_answer` log probability is below a certain threshold. Methods like this have been shown to significantly reduce RAG for Q&A hallucinations and errors ([Example](https://jfan001.medium.com/how-we-cut-the-rate-of-gpt-hallucinations-from-20-to-less-than-2-f3bfcc10e4ec)) " ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -467,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -486,18 +479,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we can ask `gpt-3.5-turbo` to act as an autocomplete engine with whatever context the model is given. We can enable `logprobs` and can see how confident the model is in its prediction." + "Now, we can ask `gpt-4o-mini` to act as an autocomplete engine with whatever context the model is given. We can enable `logprobs` and can see how confident the model is in its prediction." ] }, { "cell_type": "code", - "execution_count": 274, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

Sentence: My

Predicted next token: favorite, logprobs: -0.18245785, linear probability: 83.32%

Predicted next token: dog, logprobs: -2.397172, linear probability: 9.1%

Predicted next token: ap, logprobs: -3.8732424, linear probability: 2.08%


Sentence: My least

Predicted next token: favorite, logprobs: -0.0146376295, linear probability: 98.55%

Predicted next token: My, logprobs: -4.2417912, linear probability: 1.44%

Predicted next token: favorite, logprobs: -9.748788, linear probability: 0.01%


Sentence: My least favorite

Predicted next token: food, logprobs: -0.9481721, linear probability: 38.74%

Predicted next token: My, logprobs: -1.3447137, linear probability: 26.06%

Predicted next token: color, logprobs: -1.3887696, linear probability: 24.94%


Sentence: My least favorite TV

Predicted next token: show, logprobs: -0.0007898556, linear probability: 99.92%

Predicted next token: My, logprobs: -7.711523, linear probability: 0.04%

Predicted next token: series, logprobs: -9.348547, linear probability: 0.01%


Sentence: My least favorite TV show

Predicted next token: is, logprobs: -0.2851253, linear probability: 75.19%

Predicted next token: of, logprobs: -1.55335, linear probability: 21.15%

Predicted next token: My, logprobs: -3.4928775, linear probability: 3.04%


Sentence: My least favorite TV show is

Predicted next token: \"My, logprobs: -0.69349754, linear probability: 49.98%

Predicted next token: \"The, logprobs: -1.2899293, linear probability: 27.53%

Predicted next token: My, logprobs: -2.4170141, linear probability: 8.92%


Sentence: My least favorite TV show is Breaking Bad

Predicted next token: because, logprobs: -0.17786823, linear probability: 83.71%

Predicted next token: ,, logprobs: -2.3946173, linear probability: 9.12%

Predicted next token: ., logprobs: -3.1861975, linear probability: 4.13%


" + "

Sentence: My

Predicted next token: My, logprobs: -0.08344023, linear probability: 91.99%

Predicted next token: dog, logprobs: -3.3334403, linear probability: 3.57%

Predicted next token: ap, logprobs: -3.5834403, linear probability: 2.78%


Sentence: My least

Predicted next token: My, logprobs: -0.1271426, linear probability: 88.06%

Predicted next token: favorite, logprobs: -2.1271427, linear probability: 11.92%

Predicted next token: My, logprobs: -9.127143, linear probability: 0.01%


Sentence: My least favorite

Predicted next token: My, logprobs: -0.052905332, linear probability: 94.85%

Predicted next token: food, logprobs: -4.0529056, linear probability: 1.74%

Predicted next token: color, logprobs: -5.0529056, linear probability: 0.64%


Sentence: My least favorite TV

Predicted next token: show, logprobs: -0.57662326, linear probability: 56.18%

Predicted next token: My, logprobs: -0.82662326, linear probability: 43.75%

Predicted next token: show, logprobs: -8.201623, linear probability: 0.03%


Sentence: My least favorite TV show

Predicted next token: is, logprobs: -0.70817715, linear probability: 49.25%

Predicted next token: My, logprobs: -0.70817715, linear probability: 49.25%

Predicted next token: was, logprobs: -4.833177, linear probability: 0.8%


Sentence: My least favorite TV show is

Predicted next token: My, logprobs: -0.47896808, linear probability: 61.94%

Predicted next token: one, logprobs: -1.7289681, linear probability: 17.75%

Predicted next token: the, logprobs: -2.9789681, linear probability: 5.08%


Sentence: My least favorite TV show is Breaking Bad

Predicted next token: because, logprobs: -0.034502674, linear probability: 96.61%

Predicted next token: ,, logprobs: -3.7845027, linear probability: 2.27%

Predicted next token: because, logprobs: -5.0345025, linear probability: 0.65%


" ], "text/plain": [ "" @@ -516,7 +509,7 @@ " PROMPT = \"\"\"Complete this sentence. You are acting as auto-complete. Simply complete the sentence to the best of your ability, make sure it is just ONE sentence: {sentence}\"\"\"\n", " API_RESPONSE = get_completion(\n", " [{\"role\": \"user\", \"content\": PROMPT.format(sentence=sentence)}],\n", - " model=\"gpt-3.5-turbo\",\n", + " model=\"gpt-4o-mini\",\n", " logprobs=True,\n", " top_logprobs=3,\n", " )\n", @@ -544,16 +537,16 @@ }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'My least': 'favorite', 'My least favorite TV': 'show'}" + "{'My least favorite TV show is Breaking Bad': 'because'}" ] }, - "execution_count": 275, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -571,16 +564,16 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'My least favorite': 'food', 'My least favorite TV show is': '\"My'}" + "{'My least favorite TV': 'show', 'My least favorite TV show': 'is'}" ] }, - "execution_count": 276, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -594,7 +587,7 @@ "metadata": {}, "source": [ "These are logical as well. It's pretty unclear what the user is going to say with just the prefix 'my least favorite', and it's really anyone's guess what the author's favorite TV show is.

\n", - "So, using `gpt-3.5-turbo`, we can create the root of a dynamic autocompletion engine with `logprobs`!" + "So, using `gpt-4o-mini`, we can create the root of a dynamic autocompletion engine with `logprobs`!" ] }, { @@ -613,14 +606,14 @@ }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "PROMPT = \"\"\"What's the longest word in the English language?\"\"\"\n", "\n", "API_RESPONSE = get_completion(\n", - " [{\"role\": \"user\", \"content\": PROMPT}], model=\"gpt-4\", logprobs=True, top_logprobs=5\n", + " [{\"role\": \"user\", \"content\": PROMPT}], model=\"gpt-4o\", logprobs=True, top_logprobs=5\n", ")\n", "\n", "\n", @@ -650,13 +643,13 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "The longest word in the English language, according to the Guinness World Records, is 'pneumonoultramicroscopicsilicovolcanoconiosis'. It is a type of lung disease caused by inhaling ash and sand dust." + "The longest word in the English language is often considered to be \"pneumonoultramicroscopicsilicovolcanoconiosis,\" a term referring to a type of lung disease caused by inhaling very fine silicate or quartz dust. However, it's worth noting that this word was coined more for its length than for practical use. There are also chemical names for proteins and other compounds that can be much longer, but they are typically not used in everyday language." ], "text/plain": [ "" @@ -669,7 +662,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Total number of tokens: 51\n" + "Total number of tokens: 95\n" ] } ], @@ -686,16 +679,68 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Token: Here\n", + "Log prob: -0.054242473\n", + "Linear prob: 94.72 %\n", + "Bytes: [72, 101, 114, 101] \n", + "\n", + "Token: is\n", + "Log prob: -0.0044352207\n", + "Linear prob: 99.56 %\n", + "Bytes: [32, 105, 115] \n", + "\n", + "Token: the\n", + "Log prob: -2.1008714e-06\n", + "Linear prob: 100.0 %\n", + "Bytes: [32, 116, 104, 101] \n", + "\n", + "Token: blue\n", + "Log prob: -0.0013290489\n", + "Linear prob: 99.87 %\n", + "Bytes: [32, 98, 108, 117, 101] \n", + "\n", + "Token: heart\n", + "Log prob: 0.0\n", + "Linear prob: 100.0 %\n", + "Bytes: [32, 104, 101, 97, 114, 116] \n", + "\n", + "Token: emoji\n", + "Log prob: 0.0\n", + "Linear prob: 100.0 %\n", + "Bytes: [32, 101, 109, 111, 106, 105] \n", + "\n", + "Token: and\n", + "Log prob: -0.038287632\n", + "Linear prob: 96.24 %\n", + "Bytes: [32, 97, 110, 100] \n", + "\n", + "Token: its\n", + "Log prob: 0.0\n", + "Linear prob: 100.0 %\n", + "Bytes: [32, 105, 116, 115] \n", + "\n", + "Token: name\n", + "Log prob: -1.569009e-05\n", + "Linear prob: 100.0 %\n", + "Bytes: [32, 110, 97, 109, 101] \n", + "\n", + "Token: :\n", + "\n", + "\n", + "Log prob: -0.11313002\n", + "Linear prob: 89.3 %\n", + "Bytes: [58, 10, 10] \n", + "\n", "Token: \\xf0\\x9f\\x92\n", - "Log prob: -0.0003056686\n", - "Linear prob: 99.97 %\n", + "Log prob: -0.09048584\n", + "Linear prob: 91.35 %\n", "Bytes: [240, 159, 146] \n", "\n", "Token: \\x99\n", @@ -703,31 +748,28 @@ "Linear prob: 100.0 %\n", "Bytes: [153] \n", "\n", - "Token: -\n", - "Log prob: -0.0096905725\n", - "Linear prob: 99.04 %\n", - "Bytes: [32, 45] \n", - "\n", "Token: Blue\n", - "Log prob: -0.00042042506\n", - "Linear prob: 99.96 %\n", + "Log prob: -0.023958502\n", + "Linear prob: 97.63 %\n", "Bytes: [32, 66, 108, 117, 101] \n", "\n", "Token: Heart\n", - "Log prob: -7.302705e-05\n", - "Linear prob: 99.99 %\n", + "Log prob: -6.2729996e-06\n", + "Linear prob: 100.0 %\n", "Bytes: [32, 72, 101, 97, 114, 116] \n", "\n", - "Bytes array: [240, 159, 146, 153, 32, 45, 32, 66, 108, 117, 101, 32, 72, 101, 97, 114, 116]\n", - "Decoded bytes: 💙 - Blue Heart\n", - "Joint prob: 98.96 %\n" + "Bytes array: [72, 101, 114, 101, 32, 105, 115, 32, 116, 104, 101, 32, 98, 108, 117, 101, 32, 104, 101, 97, 114, 116, 32, 101, 109, 111, 106, 105, 32, 97, 110, 100, 32, 105, 116, 115, 32, 110, 97, 109, 101, 58, 10, 10, 240, 159, 146, 153, 32, 66, 108, 117, 101, 32, 72, 101, 97, 114, 116]\n", + "Decoded bytes: Here is the blue heart emoji and its name:\n", + "\n", + "💙 Blue Heart\n", + "Joint prob: 72.19 %\n" ] } ], "source": [ "PROMPT = \"\"\"Output the blue heart emoji and its name.\"\"\"\n", "API_RESPONSE = get_completion(\n", - " [{\"role\": \"user\", \"content\": PROMPT}], model=\"gpt-4\", logprobs=True\n", + " [{\"role\": \"user\", \"content\": PROMPT}], model=\"gpt-4o\", logprobs=True\n", ")\n", "\n", "aggregated_bytes = []\n", @@ -771,71 +813,71 @@ "\n", "When looking to assess the model's confidence in a result, it can be useful to calculate perplexity, which is a measure of the uncertainty. Perplexity can be calculated by exponentiating the negative of the average of the logprobs. Generally, a higher perplexity indicates a more uncertain result, and a lower perplexity indicates a more confident result. As such, perplexity can be used to both assess the result of an individual model run and also to compare the relative confidence of results between model runs. While a high confidence doesn't guarantee result accuracy, it can be a helpful signal that can be paired with other evaluation metrics to build a better understanding of your prompt's behavior.\n", "\n", - "For example, let's say that I want to use `gpt-3.5-turbo` to learn more about artificial intelligence. I could ask a question about recent history and a question about the future:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Prompt: In a short sentence, has artifical intelligence grown in the last decade?\n", - "Response: Yes, artificial intelligence has grown significantly in the last decade. \n", - "\n", - "Tokens: Yes , artificial intelligence has grown significantly in the last decade .\n", - "Logprobs: -0.00 -0.00 -0.00 -0.00 -0.00 -0.53 -0.11 -0.00 -0.00 -0.01 -0.00 -0.00\n", - "Perplexity: 1.0564125277713383 \n", - "\n", - "Prompt: In a short sentence, what are your thoughts on the future of artificial intelligence?\n", - "Response: The future of artificial intelligence holds great potential for transforming industries and improving efficiency, but also raises ethical and societal concerns that must be carefully addressed. \n", - "\n", - "Tokens: The future of artificial intelligence holds great potential for transforming industries and improving efficiency , but also raises ethical and societal concerns that must be carefully addressed .\n", - "Logprobs: -0.19 -0.03 -0.00 -0.00 -0.00 -0.30 -0.51 -0.24 -0.03 -1.45 -0.23 -0.03 -0.22 -0.83 -0.48 -0.01 -0.38 -0.07 -0.47 -0.63 -0.18 -0.26 -0.01 -0.14 -0.00 -0.59 -0.55 -0.00\n", - "Perplexity: 1.3220795252314004 \n", - "\n" - ] - } - ], - "source": [ - "prompts = [\n", - " \"In a short sentence, has artifical intelligence grown in the last decade?\",\n", - " \"In a short sentence, what are your thoughts on the future of artificial intelligence?\",\n", - "]\n", - "\n", - "for prompt in prompts:\n", - " API_RESPONSE = get_completion(\n", - " [{\"role\": \"user\", \"content\": prompt}],\n", - " model=\"gpt-3.5-turbo\",\n", - " logprobs=True,\n", - " )\n", - "\n", - " logprobs = [token.logprob for token in API_RESPONSE.choices[0].logprobs.content]\n", - " response_text = API_RESPONSE.choices[0].message.content\n", - " response_text_tokens = [token.token for token in API_RESPONSE.choices[0].logprobs.content]\n", - " max_starter_length = max(len(s) for s in [\"Prompt:\", \"Response:\", \"Tokens:\", \"Logprobs:\", \"Perplexity:\"])\n", - " max_token_length = max(len(s) for s in response_text_tokens)\n", - " \n", - "\n", - " formatted_response_tokens = [s.rjust(max_token_length) for s in response_text_tokens]\n", - " formatted_lps = [f\"{lp:.2f}\".rjust(max_token_length) for lp in logprobs]\n", - "\n", - " perplexity_score = np.exp(-np.mean(logprobs))\n", - " print(\"Prompt:\".ljust(max_starter_length), prompt)\n", - " print(\"Response:\".ljust(max_starter_length), response_text, \"\\n\")\n", - " print(\"Tokens:\".ljust(max_starter_length), \" \".join(formatted_response_tokens))\n", - " print(\"Logprobs:\".ljust(max_starter_length), \" \".join(formatted_lps))\n", - " print(\"Perplexity:\".ljust(max_starter_length), perplexity_score, \"\\n\")" - ] - }, + "For example, let's say that I want to use `gpt-4o-mini` to learn more about artificial intelligence. I could ask a question about recent history and a question about the future:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prompt: In a short sentence, has artifical intelligence grown in the last decade?\n", + "Response: Yes, artificial intelligence has grown significantly in the last decade, advancing in capabilities and applications across various fields. \n", + "\n", + "Tokens: Yes , artificial intelligence has grown significantly in the last decade , advancing in capabilities and applications across various fields .\n", + "Logprobs: -0.00 0.00 -0.00 0.00 -0.00 -0.73 -0.00 -0.01 -0.02 -0.00 0.00 -0.02 -0.66 -0.03 -0.62 -0.47 -0.02 -0.39 -0.01 -0.20 -0.00\n", + "Perplexity: 1.1644170003987546 \n", + "\n", + "Prompt: In a short sentence, what are your thoughts on the future of artificial intelligence?\n", + "Response: The future of artificial intelligence holds immense potential for transformative advancements across various sectors, but it also requires careful consideration of ethical and societal impacts. \n", + "\n", + "Tokens: The future of artificial intelligence holds immense potential for transformative advancements across various sectors , but it also requires careful consideration of ethical and societal impacts .\n", + "Logprobs: -0.02 -0.00 0.00 -0.00 0.00 -0.05 -0.35 -0.01 -0.02 -0.64 -0.43 -0.25 -0.16 -0.51 -0.02 -0.43 -0.08 -0.07 -0.97 -0.02 -0.48 -0.00 -0.00 -0.48 -0.01 -0.58 -0.00\n", + "Perplexity: 1.2292170270768858 \n", + "\n" + ] + } + ], + "source": [ + "prompts = [\n", + " \"In a short sentence, has artifical intelligence grown in the last decade?\",\n", + " \"In a short sentence, what are your thoughts on the future of artificial intelligence?\",\n", + "]\n", + "\n", + "for prompt in prompts:\n", + " API_RESPONSE = get_completion(\n", + " [{\"role\": \"user\", \"content\": prompt}],\n", + " model=\"gpt-4o-mini\",\n", + " logprobs=True,\n", + " )\n", + "\n", + " logprobs = [token.logprob for token in API_RESPONSE.choices[0].logprobs.content]\n", + " response_text = API_RESPONSE.choices[0].message.content\n", + " response_text_tokens = [token.token for token in API_RESPONSE.choices[0].logprobs.content]\n", + " max_starter_length = max(len(s) for s in [\"Prompt:\", \"Response:\", \"Tokens:\", \"Logprobs:\", \"Perplexity:\"])\n", + " max_token_length = max(len(s) for s in response_text_tokens)\n", + " \n", + "\n", + " formatted_response_tokens = [s.rjust(max_token_length) for s in response_text_tokens]\n", + " formatted_lps = [f\"{lp:.2f}\".rjust(max_token_length) for lp in logprobs]\n", + "\n", + " perplexity_score = np.exp(-np.mean(logprobs))\n", + " print(\"Prompt:\".ljust(max_starter_length), prompt)\n", + " print(\"Response:\".ljust(max_starter_length), response_text, \"\\n\")\n", + " print(\"Tokens:\".ljust(max_starter_length), \" \".join(formatted_response_tokens))\n", + " print(\"Logprobs:\".ljust(max_starter_length), \" \".join(formatted_lps))\n", + " print(\"Perplexity:\".ljust(max_starter_length), perplexity_score, \"\\n\")" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In this example, `gpt-3.5-turbo` returned a lower perplexity score for a more deterministic question about recent history, and a higher perplexity score for a more speculative assessment about the near future. Again, while these differences don't guarantee accuracy, they help point the way for our interpretation of the model's results and our future use of them." + "In this example, `gpt-4o-mini` returned a lower perplexity score for a more deterministic question about recent history, and a higher perplexity score for a more speculative assessment about the near future. Again, while these differences don't guarantee accuracy, they help point the way for our interpretation of the model's results and our future use of them." ] }, { diff --git a/examples/Whisper_prompting_guide.ipynb b/examples/Whisper_prompting_guide.ipynb index e7cf3a532e..bebeca2495 100644 --- a/examples/Whisper_prompting_guide.ipynb +++ b/examples/Whisper_prompting_guide.ipynb @@ -413,7 +413,7 @@ ], "source": [ "# more natural, sentence-style prompt\n", - "transcribe(bbq_plans_filepath, prompt=\"\"\"\"Aimee and Shawn ate whisky, doughnuts, omelets at a BBQ.\"\"\")" + "transcribe(bbq_plans_filepath, prompt=\"\"\"Aimee and Shawn ate whisky, doughnuts, omelets at a BBQ.\"\"\")" ] }, { diff --git a/examples/agents_sdk/app_assistant_voice_agents.ipynb b/examples/agents_sdk/app_assistant_voice_agents.ipynb new file mode 100644 index 0000000000..0f9b2f649b --- /dev/null +++ b/examples/agents_sdk/app_assistant_voice_agents.ipynb @@ -0,0 +1,901 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "Let's say you're an AI lead at a consumer tech company. You have the vision of deploying a single entry point digital voice assistant with the ability to help users with any query, regardless of whether they want to take action on their account, find product information, or receive real-time guidance.\n", + "\n", + "However, turning this vision into reality can be extremely difficult - it requires building and testing the capability to handle each individual use case through text first, integrating access to the wide range of tools and systems they require, and somehow orchestrating them into a coherent experience. Then, once you’ve achieved a satisfactory level of quality (and even evaluating this can be a struggle), you face the daunting task of refactoring the entire workflow for voice interaction.\n", + "\n", + "Fortunately for you, three recent releases from OpenAI have made implementing this vision simpler than ever by providing the tools to build and orchestrate modular agentic workflows through voice with minimal configuration:\n", + "\n", + "- [**Responses API**](https://platform.openai.com/docs/api-reference/responses) - an agentic API for easy engagement with our frontier models through managed stateful conversations, tracing of responses to enable evaluation, and built-in tools for file search, web search, computer use, and more\n", + "- [**Agents SDK**](https://openai.github.io/openai-agents-python/quickstart/) - a lightweight, customizable open source framework for building and orchestrating workflows across many different agents, enabling your assistant to route inputs to the appropriate agent and to scale to support many use cases\n", + "- [**Voice agents**](https://openai.github.io/openai-agents-python/voice/quickstart/) - an extension of the Agents SDK to support the use of voice pipelines, enabling your agents to go from being text-base to being able to interpret and produce audio in just a few lines of code\n", + "\n", + "This cookbook demonstrates how to build a simple in-app voice assistant for a fictitious consumer application using the tools above. We'll create a **Triage Agent** that greets the user, determines their intent, and routes requests to one of three specialised agents:\n", + "\n", + "- **Search Agent** - performs a web search via the built-in tooling of the Responses API to provide real-time information on the user's query\n", + "- **Knowledge Agent** - utilises the file search tooling of the Responses API to retrieve information from an OpenAI managed vector database\n", + "- **Account Agent** - uses function calling to provide the ability to trigger custom actions via API\n", + "\n", + "Finally, we'll convert this workflow into a live voice assistant using the AgentsSDK's Voice funtionality, capturing microphone input, performing speech‑to‑text, routing through our agents, and responding with text‑to‑speech." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup\n", + "\n", + "To execute this cookbook, you'll need to install the following packages providing access to OpenAI's API, the Agents SDK, and libraries for audio processing. Additionally, you can set your OpenAI API key for use by the agents via the `set_default_openai_key` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install openai\n", + "%pip install openai-agents 'openai-agents[voice]'\n", + "%pip install numpy\n", + "%pip install sounddevice\n", + "%pip install os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from agents import Agent, function_tool, WebSearchTool, FileSearchTool, set_default_openai_key\n", + "from agents.extensions.handoff_prompt import prompt_with_handoff_instructions\n", + "\n", + "set_default_openai_key(\"YOUR_API_KEY\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining Agents & Tools\n", + "\n", + "Today we're going to be building an assitant for our fictitious consumer application, ACME shop, focussed on initially supporting use cases across three key use cases:\n", + "\n", + "- Answering real-time questions to inform purchasing decisions using web search\n", + "- Providing information on the available options in our product portfolio\n", + "- Providing account information to enable the user to understand their budget and spending\n", + "\n", + "To achieve this we'll be using an agentic architecture. This allows us to split the functionality for each use case into a separate agent, in turn reducing the complexity/range of tasks that a single agent could be asked to complete and increasing accuracy. Our agent architecture is relatively simple focussing on the three use cases above, but the beauty of the Agents SDK is that it is incredibly easy to extend and add aditional agents to the workflow when you want to add new functionality:\n", + "\n", + "![Agent Architecture](../../images/app_assistant_voice_agents_arch.png)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Search Agent\n", + "\n", + "Our first agent is a simple web search agent that uses the `WebSearchTool` provided by the Responses API to find real-time information on the user's query. We'll be keeping the instruction prompts simple for each of these examples, but we'll iterate later to show how to optimise the response format for your use case." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# --- Agent: Search Agent ---\n", + "search_agent = Agent(\n", + " name=\"SearchAgent\",\n", + " instructions=(\n", + " \"You immediately provide an input to the WebSearchTool to find up-to-date information on the user's query.\"\n", + " ),\n", + " tools=[WebSearchTool()],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*For more information on web search and the Responses API, be sure to check out the [Web Search and States with Responses API](https://cookbook.openai.com/examples/responses_api/responses_example) cookbook*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Knowledge Agent\n", + "\n", + "Our second agent needs to be able to answer questions on our product portfolio. To do this, we'll use the `FileSearchTool` to retrieve information from a vector store managed by OpenAI containing our company specific product information. For this, we have two options:\n", + "\n", + "1. Use the OpenAI Platform Website - go to [platform.openai.com/storage](https://platform.openai.com/storage) and create a vector store, uploading your documents of choice. Then, take the vector store ID and substitute it into the `FileSearchTool` initialisation below.\n", + "\n", + "2. Use the OpenAI API - use the `vector_stores.create` function from the OpenAI Python client to create a vector store and then the `vector_stores.files.create` function to add files to it. Once this is complete you can again use the `FileSearchTool` to search the vector store. Please see the code below for an example of how to do this, either using the example file provided or altering to your own local file path:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import os\n", + "\n", + "client = OpenAI(api_key='YOUR_API_KEY')\n", + "\n", + "def upload_file(file_path: str, vector_store_id: str):\n", + " file_name = os.path.basename(file_path)\n", + " try:\n", + " file_response = client.files.create(file=open(file_path, 'rb'), purpose=\"assistants\")\n", + " attach_response = client.vector_stores.files.create(\n", + " vector_store_id=vector_store_id,\n", + " file_id=file_response.id\n", + " )\n", + " return {\"file\": file_name, \"status\": \"success\"}\n", + " except Exception as e:\n", + " print(f\"Error with {file_name}: {str(e)}\")\n", + " return {\"file\": file_name, \"status\": \"failed\", \"error\": str(e)}\n", + "\n", + "def create_vector_store(store_name: str) -> dict:\n", + " try:\n", + " vector_store = client.vector_stores.create(name=store_name)\n", + " details = {\n", + " \"id\": vector_store.id,\n", + " \"name\": vector_store.name,\n", + " \"created_at\": vector_store.created_at,\n", + " \"file_count\": vector_store.file_counts.completed\n", + " }\n", + " print(\"Vector store created:\", details)\n", + " return details\n", + " except Exception as e:\n", + " print(f\"Error creating vector store: {e}\")\n", + " return {}\n", + " \n", + "vector_store_id = create_vector_store(\"ACME Shop Product Knowledge Base\")\n", + "upload_file(\"voice_agents_knowledge/acme_product_catalogue.pdf\", vector_store_id[\"id\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having implemented your vector store, we can now enable the knowledge agent to use the `FileSearchTool` to search the given store ID." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# --- Agent: Knowledge Agent ---\n", + "knowledge_agent = Agent(\n", + " name=\"KnowledgeAgent\",\n", + " instructions=(\n", + " \"You answer user questions on our product portfolio with concise, helpful responses using the FileSearchTool.\"\n", + " ),\n", + " tools=[FileSearchTool(\n", + " max_num_results=3,\n", + " vector_store_ids=[\"VECTOR_STORE_ID\"],\n", + " ),],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*For more information on the power of file search and the Responses API, be sure to check out the excellent cookbook on the subject where the example code above was taken from: [Doing RAG on PDFs using File Search in the Responses API](https://cookbook.openai.com/examples/file_search_responses)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Account Agent\n", + "\n", + "Whilst so far we've been using the built-in tools provided by the Agents SDK, you can define your own tools to be used by the agents to integrate with your systems with the `function_tool` decorator. Here, we'll define a simple dummy function to return account information for a given user ID for our account agent. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# --- Tool 1: Fetch account information (dummy) ---\n", + "@function_tool\n", + "def get_account_info(user_id: str) -> dict:\n", + " \"\"\"Return dummy account info for a given user.\"\"\"\n", + " return {\n", + " \"user_id\": user_id,\n", + " \"name\": \"Bugs Bunny\",\n", + " \"account_balance\": \"£72.50\",\n", + " \"membership_status\": \"Gold Executive\"\n", + " }\n", + "\n", + "# --- Agent: Account Agent ---\n", + "account_agent = Agent(\n", + " name=\"AccountAgent\",\n", + " instructions=(\n", + " \"You provide account information based on a user ID using the get_account_info tool.\"\n", + " ),\n", + " tools=[get_account_info],\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*For more information on function calling with the Agents SDK, see the [Agents SDK Documentation](https://openai.github.io/openai-agents-python/tools/#function-tools)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we'll define the triage agent that will route the user's query to the appropriate agent based on their intent. Here we're using the `prompt_with_handoff_instructions` function, which provides additional guidance on how to treat handoffs and is recommended to provide to any agent with a defined set of handoffs with a defined set of instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# --- Agent: Triage Agent ---\n", + "triage_agent = Agent(\n", + " name=\"Assistant\",\n", + " instructions=prompt_with_handoff_instructions(\"\"\"\n", + "You are the virtual assistant for Acme Shop. Welcome the user and ask how you can help.\n", + "Based on the user's intent, route to:\n", + "- AccountAgent for account-related queries\n", + "- KnowledgeAgent for product FAQs\n", + "- SearchAgent for anything requiring real-time web search\n", + "\"\"\"),\n", + " handoffs=[account_agent, knowledge_agent, search_agent],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Run the workflow\n", + "\n", + "Now that we've defined our agents, we can run the workflow on a few example queries to see how it performs." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User: What's my ACME account balance doc? My user ID is 1234567890\n", + "Your ACME account balance is £72.50. You have a Gold Executive membership.\n", + "---\n", + "User: Ooh i've got money to spend! How big is the input and how fast is the output of the dynamite dispenser?\n", + "The Automated Dynamite Dispenser can hold up to 10 sticks of dynamite and dispenses them at a speed of 1 stick every 2 seconds.\n", + "---\n", + "User: Hmmm, what about duck hunting gear - what's trending right now?\n", + "Staying updated with the latest trends in duck hunting gear can significantly enhance your hunting experience. Here are some of the top trending items for the 2025 season:\n", + "\n", + "\n", + "\n", + "**Banded Aspire Catalyst Waders** \n", + "These all-season waders feature waterproof-breathable technology, ensuring comfort in various conditions. They boast a minimal-stitch design for enhanced mobility and include PrimaLoft Aerogel insulation for thermal protection. Additional features like an over-the-boot protective pant and an integrated LED light in the chest pocket make them a standout choice. ([blog.gritroutdoors.com](https://blog.gritroutdoors.com/must-have-duck-hunting-gear-for-a-winning-season/?utm_source=openai))\n", + "\n", + "\n", + "\n", + "\n", + "**Sitka Delta Zip Waders** \n", + "Known for their durability, these waders have reinforced shins and knees with rugged foam pads, ideal for challenging terrains. Made with GORE-TEX material, they ensure dryness throughout the season. ([blog.gritroutdoors.com](https://blog.gritroutdoors.com/must-have-duck-hunting-gear-for-a-winning-season/?utm_source=openai))\n", + "\n", + "\n", + "\n", + "\n", + "**MOmarsh InvisiMan Blind** \n", + "This one-person, low-profile blind is praised for its sturdiness and ease of setup. Hunters have reported that even late-season, cautious ducks approach without hesitation, making it a valuable addition to your gear. ([bornhunting.com](https://bornhunting.com/top-duck-hunting-gear/?utm_source=openai))\n", + "\n", + "\n", + "\n", + "\n", + "**Slayer Calls Ranger Duck Call** \n", + "This double reed call produces crisp and loud sounds, effectively attracting distant ducks in harsh weather conditions. Its performance has been noted for turning the heads of ducks even at extreme distances. ([bornhunting.com](https://bornhunting.com/top-duck-hunting-gear/?utm_source=openai))\n", + "\n", + "\n", + "\n", + "\n", + "**Sitka Full Choke Pack** \n", + "A favorite among hunters, this backpack-style blind bag offers comfort and efficiency. It has proven to keep gear dry during heavy downpours and is durable enough to withstand over 60 hunts in a season. ([bornhunting.com](https://bornhunting.com/top-duck-hunting-gear/?utm_source=openai))\n", + "\n", + "\n", + "Incorporating these trending items into your gear can enhance your comfort, efficiency, and success during the hunting season. \n", + "---\n" + ] + } + ], + "source": [ + "# %%\n", + "from agents import Runner, trace\n", + "\n", + "async def test_queries():\n", + " examples = [\n", + " \"What's my ACME account balance doc? My user ID is 1234567890\", # Account Agent test\n", + " \"Ooh i've got money to spend! How big is the input and how fast is the output of the dynamite dispenser?\", # Knowledge Agent test\n", + " \"Hmmm, what about duck hunting gear - what's trending right now?\", # Search Agent test\n", + "\n", + " ]\n", + " with trace(\"ACME App Assistant\"):\n", + " for query in examples:\n", + " result = await Runner.run(triage_agent, query)\n", + " print(f\"User: {query}\")\n", + " print(result.final_output)\n", + " print(\"---\")\n", + "# Run the tests\n", + "await test_queries()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tracing\n", + "\n", + "Above we can see the outputs appear to be in line with our expectations, but one key benefit of the Agents SDK is that it includes built-in tracing which enables tracking of the flow of events during an agent run across the LLM calls, handoffs, and tools. \n", + "\n", + "Using the [Traces dashboard](https://platform.openai.com/traces), we can debug, visualize, and monitor our workflows during development and in production. As we can see below, each test query was correctly routed to the appropriate agent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Traces Dashboard](../../images/app_assistant_voice_agents.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Enabling Voice\n", + "\n", + "Having designed our workflow, here in reality we would spend time evaluating the traces and iterating on the workflow to ensure it is as effective as possible. But let's assume we're happy with the workflow, so we can now start thinking about how to convert our in-app assistant from text-based to voice-based interactions.\n", + "\n", + "To do this, we can simply leverage the classes provided by the [Agents SDK](https://openai.github.io/openai-agents-python/voice/quickstart/) to convert our text-based workflow into a a voice-based one. The `VoicePipeline` class provides an interface for transcribing audio input, executing a given agent workflow and generating a text to speech response for playback to the user, whilst the `SingleAgentVoiceWorkflow` class enables us to leverage the same agent workflow we used earlier for our text-based workflow. To provide and receive audio, we'll use the `sounddevice` library. \n", + "\n", + "End to end, the new workflow looks like this:\n", + "\n", + "![Agent Architecture 2](../../images/app_assistant_voice_agents_arch_2.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the code to enable this is as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Listening...\n", + "Assistant is responding...\n", + "---\n", + "Exiting...\n" + ] + } + ], + "source": [ + "# %%\n", + "import numpy as np\n", + "import sounddevice as sd\n", + "from agents.voice import AudioInput, SingleAgentVoiceWorkflow, VoicePipeline\n", + "\n", + "async def voice_assistant():\n", + " samplerate = sd.query_devices(kind='input')['default_samplerate']\n", + "\n", + " while True:\n", + " pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(triage_agent))\n", + "\n", + " # Check for input to either provide voice or exit\n", + " cmd = input(\"Press Enter to speak your query (or type 'esc' to exit): \")\n", + " if cmd.lower() == \"esc\":\n", + " print(\"Exiting...\")\n", + " break \n", + " print(\"Listening...\")\n", + " recorded_chunks = []\n", + "\n", + " # Start streaming from microphone until Enter is pressed\n", + " with sd.InputStream(samplerate=samplerate, channels=1, dtype='int16', callback=lambda indata, frames, time, status: recorded_chunks.append(indata.copy())):\n", + " input()\n", + "\n", + " # Concatenate chunks into single buffer\n", + " recording = np.concatenate(recorded_chunks, axis=0)\n", + "\n", + " # Input the buffer and await the result\n", + " audio_input = AudioInput(buffer=recording)\n", + "\n", + " with trace(\"ACME App Voice Assistant\"):\n", + " result = await pipeline.run(audio_input)\n", + "\n", + " # Transfer the streamed result into chunks of audio\n", + " response_chunks = []\n", + " async for event in result.stream():\n", + " if event.type == \"voice_stream_event_audio\":\n", + " response_chunks.append(event.data)\n", + "\n", + " response_audio = np.concatenate(response_chunks, axis=0)\n", + "\n", + " # Play response\n", + " print(\"Assistant is responding...\")\n", + " sd.play(response_audio, samplerate=samplerate)\n", + " sd.wait()\n", + " print(\"---\")\n", + "\n", + "# Run the voice assistant\n", + "await voice_assistant()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Executing the above code, gives us the following responses which correctly provide the same functionality as the text-based workflow." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Audio\n", + "display(Audio(\"voice_agents_audio/account_balance_response_base.mp3\"))\n", + "display(Audio(\"voice_agents_audio/product_info_response_base.mp3\"))\n", + "display(Audio(\"voice_agents_audio/trending_items_response_base.mp3\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Tip: when using tracing with voice agents, you can playback audio in the traces dashboard*\n", + "\n", + "![Audio trace](../../images/app_assistant_voice_agents_trace.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimizing Voice\n", + "\n", + "This is a great start, but we can do better. As we've simply converted our text-based agents into voice-based ones, the responses are not optimised in their output for either tone or format, meaning they feel robotic and unnatural.\n", + "\n", + "To address this, we'll need to make a few changes to our prompts.\n", + "\n", + "Firstly, we can adapt our existing agents to include a common system prompt, providing instructions on how to optimise their text response for later conversion to the voice format\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Common system prompt for voice output best practices:\n", + "voice_system_prompt = \"\"\"\n", + "[Output Structure]\n", + "Your output will be delivered in an audio voice response, please ensure that every response meets these guidelines:\n", + "1. Use a friendly, human tone that will sound natural when spoken aloud.\n", + "2. Keep responses short and segmented—ideally one to two concise sentences per step.\n", + "3. Avoid technical jargon; use plain language so that instructions are easy to understand.\n", + "4. Provide only essential details so as not to overwhelm the listener.\n", + "\"\"\"\n", + "\n", + "# --- Agent: Search Agent ---\n", + "search_voice_agent = Agent(\n", + " name=\"SearchVoiceAgent\",\n", + " instructions=voice_system_prompt + (\n", + " \"You immediately provide an input to the WebSearchTool to find up-to-date information on the user's query.\"\n", + " ),\n", + " tools=[WebSearchTool()],\n", + ")\n", + "\n", + "# --- Agent: Knowledge Agent ---\n", + "knowledge_voice_agent = Agent(\n", + " name=\"KnowledgeVoiceAgent\",\n", + " instructions=voice_system_prompt + (\n", + " \"You answer user questions on our product portfolio with concise, helpful responses using the FileSearchTool.\"\n", + " ),\n", + " tools=[FileSearchTool(\n", + " max_num_results=3,\n", + " vector_store_ids=[\"VECTOR_STORE_ID\"],\n", + " ),],\n", + ")\n", + "\n", + "# --- Agent: Account Agent ---\n", + "account_voice_agent = Agent(\n", + " name=\"AccountVoiceAgent\",\n", + " instructions=voice_system_prompt + (\n", + " \"You provide account information based on a user ID using the get_account_info tool.\"\n", + " ),\n", + " tools=[get_account_info],\n", + ")\n", + "\n", + "# --- Agent: Triage Agent ---\n", + "triage_voice_agent = Agent(\n", + " name=\"VoiceAssistant\",\n", + " instructions=prompt_with_handoff_instructions(\"\"\"\n", + "You are the virtual assistant for Acme Shop. Welcome the user and ask how you can help.\n", + "Based on the user's intent, route to:\n", + "- AccountAgent for account-related queries\n", + "- KnowledgeAgent for product FAQs\n", + "- SearchAgent for anything requiring real-time web search\n", + "\"\"\"),\n", + " handoffs=[account_voice_agent, knowledge_voice_agent, search_voice_agent],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we can instruct the default OpenAI TTS model used by the Agents SDK, `gpt-4o-mini-tts`, on how to communicate the audio output of the agent generated text with the `instructions` field. \n", + "\n", + "Here we have a huge amount of control over the output, including the ability to specify the personality, pronunciation, speed and emotion of the output. \n", + "\n", + "Below i've included a few examples on how to prompt the model for different applications." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "health_assistant= \"Voice Affect: Calm, composed, and reassuring; project quiet authority and confidence.\"\n", + "\"Tone: Sincere, empathetic, and gently authoritative—express genuine apology while conveying competence.\"\n", + "\"Pacing: Steady and moderate; unhurried enough to communicate care, yet efficient enough to demonstrate professionalism.\"\n", + "\n", + "coach_assistant=\"Voice: High-energy, upbeat, and encouraging, projecting enthusiasm and motivation.\"\n", + "\"Punctuation: Short, punchy sentences with strategic pauses to maintain excitement and clarity.\"\n", + "\"Delivery: Fast-paced and dynamic, with rising intonation to build momentum and keep engagement high.\"\n", + "\n", + "themed_character_assistant=\"Affect: Deep, commanding, and slightly dramatic, with an archaic and reverent quality that reflects the grandeur of Olde English storytelling.\"\n", + "\"Tone: Noble, heroic, and formal, capturing the essence of medieval knights and epic quests, while reflecting the antiquated charm of Olde English.\" \n", + "\"Emotion: Excitement, anticipation, and a sense of mystery, combined with the seriousness of fate and duty.\"\n", + "\"Pronunciation: Clear, deliberate, and with a slightly formal cadence.\"\n", + "\"Pause: Pauses after important Olde English phrases such as \\\"Lo!\\\" or \\\"Hark!\\\" and between clauses like \\\"Choose thy path\\\" to add weight to the decision-making process and allow the listener to reflect on the seriousness of the quest.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our configuration is going to focus on creating a friendly, warm, and supportive tone that sounds natural when spoken aloud and guides the user through the conversation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Listening...\n", + "Assistant is responding...\n", + "---\n", + "Listening...\n", + "Assistant is responding...\n", + "---\n", + "Listening...\n", + "Assistant is responding...\n", + "---\n", + "Listening...\n", + "Assistant is responding...\n" + ] + } + ], + "source": [ + "from agents.voice import TTSModelSettings, VoicePipeline, VoicePipelineConfig, SingleAgentVoiceWorkflow, AudioInput\n", + "import sounddevice as sd\n", + "import numpy as np\n", + "\n", + "# Define custom TTS model settings with the desired instructions\n", + "custom_tts_settings = TTSModelSettings(\n", + " instructions=\"Personality: upbeat, friendly, persuasive guide\"\n", + " \"Tone: Friendly, clear, and reassuring, creating a calm atmosphere and making the listener feel confident and comfortable.\"\n", + " \"Pronunciation: Clear, articulate, and steady, ensuring each instruction is easily understood while maintaining a natural, conversational flow.\"\n", + " \"Tempo: Speak relatively fast, include brief pauses and after before questions\"\n", + " \"Emotion: Warm and supportive, conveying empathy and care, ensuring the listener feels guided and safe throughout the journey.\"\n", + ")\n", + "\n", + "async def voice_assistant_optimized():\n", + " samplerate = sd.query_devices(kind='input')['default_samplerate']\n", + " voice_pipeline_config = VoicePipelineConfig(tts_settings=custom_tts_settings)\n", + "\n", + " while True:\n", + " pipeline = VoicePipeline(workflow=SingleAgentVoiceWorkflow(triage_voice_agent), config=voice_pipeline_config)\n", + "\n", + " # Check for input to either provide voice or exit\n", + " cmd = input(\"Press Enter to speak your query (or type 'esc' to exit): \")\n", + " if cmd.lower() == \"esc\":\n", + " print(\"Exiting...\")\n", + " break \n", + " print(\"Listening...\")\n", + " recorded_chunks = []\n", + "\n", + " # Start streaming from microphone until Enter is pressed\n", + " with sd.InputStream(samplerate=samplerate, channels=1, dtype='int16', callback=lambda indata, frames, time, status: recorded_chunks.append(indata.copy())):\n", + " input()\n", + "\n", + " # Concatenate chunks into single buffer\n", + " recording = np.concatenate(recorded_chunks, axis=0)\n", + "\n", + " # Input the buffer and await the result\n", + " audio_input = AudioInput(buffer=recording)\n", + "\n", + " with trace(\"ACME App Optimized Voice Assistant\"):\n", + " result = await pipeline.run(audio_input)\n", + "\n", + " # Transfer the streamed result into chunks of audio\n", + " response_chunks = []\n", + " async for event in result.stream():\n", + " if event.type == \"voice_stream_event_audio\":\n", + " response_chunks.append(event.data)\n", + " response_audio = np.concatenate(response_chunks, axis=0)\n", + "\n", + " # Play response\n", + " print(\"Assistant is responding...\")\n", + " sd.play(response_audio, samplerate=samplerate)\n", + " sd.wait()\n", + " print(\"---\")\n", + "\n", + "# Run the voice assistant\n", + "await voice_assistant_optimized()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running the above code gives us the following responses which are much more naturally worded and engaging in the delivery." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Audio(\"voice_agents_audio/account_balance_response_opti.mp3\"))\n", + "display(Audio(\"voice_agents_audio/product_info_response_opti.mp3\"))\n", + "display(Audio(\"voice_agents_audio/trending_items_response_opti.mp3\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "...And for something less subtle, we can switch to the `themed_character_assistant` instructions and receive the following responses:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Audio(\"voice_agents_audio/product_info_character.wav\"))\n", + "display(Audio(\"voice_agents_audio/product_info_character_2.wav\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion\n", + "\n", + "Voila! \n", + "\n", + "In this cookbook, we've demonstrated how to:\n", + "\n", + "- Define agents to provide specific use case functionality for our in-app voice assistant\n", + "- Leverage in-built and custom tools with the Responses API to provide agents with a range of functionality and evaluate their performance with tracing\n", + "- Orchestrate these agents using the Agents SDK\n", + "- Convert agents from text-based to voice-based interactions using the Agents SDK's Voice functionality\n", + "\n", + "The Agents SDK enables a modular approach to building your voice assistant, allowing you to work on a use case by use case basis, evaluating and iterating on each use case individually, before implementing the next and then converting the workflow from text to voice when you're ready.\n", + "\n", + "We hope this cookbook has provided you with a useful guide to help you get started with building your own in-app voice assistant!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/agents_sdk/dispute_agent.ipynb b/examples/agents_sdk/dispute_agent.ipynb new file mode 100644 index 0000000000..879117be1e --- /dev/null +++ b/examples/agents_sdk/dispute_agent.ipynb @@ -0,0 +1,492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "668c381f-e6d3-4404-bec5-81404a70bcb5", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "We recently announced our new open-source **Agents SDK**, designed to help you build agentic AI applications using a lightweight, easy-to-use package with minimal abstractions.\n", + "\n", + "This cookbook demonstrates how you can leverage the Agents SDK in combination with Stripe's API to handle dispute management, a common operational challenge many businesses face. Specifically, we focus on two real-world scenarios:\n", + "\n", + "1. **Company Mistake:** \n", + " A scenario where the company clearly made an error, such as failing to fulfill an order, where accepting the dispute the appropriate action.\n", + "\n", + "2. **Customer Dispute (Final Sale):** \n", + " A scenario where a customer knowingly disputes a transaction despite receiving the correct item and understanding that the purchase was final sale, requiring further investigation to gather supporting evidence.\n", + "\n", + "To address these scenarios, we'll introduce three distinct agents:\n", + "\n", + "- **Triage Agent:** \n", + " Determines whether to accept or escalate a dispute based on the fulfillment status of the order.\n", + "\n", + "- **Acceptance Agent:** \n", + " Handles clear-cut cases by automatically accepting disputes, providing concise reasoning.\n", + "\n", + "- **Investigator Agent:** \n", + "Performs thorough investigations into disputes by analyzing communication records and order information to collect essential evidence.\n", + "\n", + "Throughout this cookbook, we’ll guide you step-by-step, illustrating how custom agentic workflows can automate dispute management and support your business operations.\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4508e3f-520e-4294-bb73-aac2ecfcaf6b", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "Before running this cookbook, you must set up the following accounts and complete a few setup actions. These prerequisites are essential to interact with the APIs used in this project.\n", + "\n", + "#### 1. OpenAI Account\n", + "\n", + "- **Purpose:** \n", + " You need an OpenAI account to access language models and use the Agents SDK featured in this cookbook.\n", + "\n", + "- **Action:** \n", + " [Sign up for an OpenAI account](https://openai.com) if you don’t already have one. Once you have an account, create an API key by visiting the [OpenAI API Keys page](https://platform.openai.com/api-keys).\n", + "\n", + "#### 2. Stripe Account\n", + "\n", + "- **Purpose:** \n", + " A Stripe account is required to simulate payment processing, manage disputes, and interact with the Stripe API as part of our demo workflow.\n", + "\n", + "- **Action:** \n", + " Create a free Stripe account by visiting the [Stripe Signup Page](https://dashboard.stripe.com/register).\n", + "\n", + "- **Locate Your API Keys:** \n", + " Log in to your Stripe dashboard and navigate to **Developers > API keys**.\n", + "\n", + "- **Use Test Mode:** \n", + " Use your **Test Secret Key** for all development and testing.\n", + "\n", + "\n", + "#### 3. Create a .env file with your OpenAI API and Stripe API Keys\n", + "\n", + "```\n", + "OPENAI_API_KEY=\n", + "STRIPE_SECRET_KEY=\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e5daf08c-d8fb-402c-b2c7-b89996ce97f0", + "metadata": {}, + "source": [ + "### Environment Setup\n", + "First we will install the necessary dependencies, then import the libraries and write some utility functions that we will use later on." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e34c8bb-7720-432b-b0bc-d10414b6a65e", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install python-dotenv --quiet\n", + "%pip install openai-agents --quiet\n", + "%pip install stripe --quiet\n", + "%pip install typing_extensions --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "id": "8cc88805-6473-458c-b745-c9f338ce8f19", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import logging\n", + "import json\n", + "from dotenv import load_dotenv\n", + "from agents import Agent, Runner, function_tool # Only import what you need\n", + "import stripe\n", + "from typing_extensions import TypedDict, Any\n", + "# Load environment variables from .env file\n", + "load_dotenv()\n", + "\n", + "# Configure logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "# Set Stripe API key from environment variables\n", + "stripe.api_key = os.getenv(\"STRIPE_SECRET_KEY\")" + ] + }, + { + "cell_type": "markdown", + "id": "736c9314-a51d-4bc2-aa5e-a40f01bdb836", + "metadata": {}, + "source": [ + "#### Define Function Tools\n", + "This section defines several helper function tools that support the dispute processing workflow. \n", + "
\n", + " \n", + "- `get_order`, `get_phone_logs` and `get_emails` simulate external data lookups by returning order details and email/phone records based on provided identifiers.\n", + "- `retrieve_payment_intent` interacts with the Stripe API to fetch payment intent details.\n", + "- `close_dispute` automatically closes a Stripe dispute using the provided dispute ID, ensuring that disputes are properly resolved and logged.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "id": "1c04ec7b-c78a-4860-b940-75401f1f0153", + "metadata": {}, + "outputs": [], + "source": [ + "@function_tool\n", + "def get_phone_logs(phone_number: str) -> list:\n", + " \"\"\"\n", + " Return a list of phone call records for the given phone number.\n", + " Each record might include call timestamps, durations, notes, \n", + " and an associated order_id if applicable.\n", + " \"\"\"\n", + " phone_logs = [\n", + " {\n", + " \"phone_number\": \"+15551234567\",\n", + " \"timestamp\": \"2023-03-14 15:24:00\",\n", + " \"duration_minutes\": 5,\n", + " \"notes\": \"Asked about status of order #1121\",\n", + " \"order_id\": 1121\n", + " },\n", + " {\n", + " \"phone_number\": \"+15551234567\",\n", + " \"timestamp\": \"2023-02-28 10:10:00\",\n", + " \"duration_minutes\": 7,\n", + " \"notes\": \"Requested refund for order #1121, I told him we were unable to refund the order because it was final sale\",\n", + " \"order_id\": 1121\n", + " },\n", + " {\n", + " \"phone_number\": \"+15559876543\",\n", + " \"timestamp\": \"2023-01-05 09:00:00\",\n", + " \"duration_minutes\": 2,\n", + " \"notes\": \"General inquiry; no specific order mentioned\",\n", + " \"order_id\": None\n", + " },\n", + " ]\n", + " return [\n", + " log for log in phone_logs if log[\"phone_number\"] == phone_number\n", + " ]\n", + "\n", + "\n", + "@function_tool\n", + "def get_order(order_id: int) -> str:\n", + " \"\"\"\n", + " Retrieve an order by ID from a predefined list of orders.\n", + " Returns the corresponding order object or 'No order found'.\n", + " \"\"\"\n", + " orders = [\n", + " {\n", + " \"order_id\": 1234,\n", + " \"fulfillment_details\": \"not_shipped\"\n", + " },\n", + " {\n", + " \"order_id\": 9101,\n", + " \"fulfillment_details\": \"shipped\",\n", + " \"tracking_info\": {\n", + " \"carrier\": \"FedEx\",\n", + " \"tracking_number\": \"123456789012\"\n", + " },\n", + " \"delivery_status\": \"out for delivery\"\n", + " },\n", + " {\n", + " \"order_id\": 1121,\n", + " \"fulfillment_details\": \"delivered\",\n", + " \"customer_id\": \"cus_PZ1234567890\",\n", + " \"customer_phone\": \"+15551234567\",\n", + " \"order_date\": \"2023-01-01\",\n", + " \"customer_email\": \"customer1@example.com\",\n", + " \"tracking_info\": {\n", + " \"carrier\": \"UPS\",\n", + " \"tracking_number\": \"1Z999AA10123456784\",\n", + " \"delivery_status\": \"delivered\"\n", + " },\n", + " \"shipping_address\": {\n", + " \"zip\": \"10001\"\n", + " },\n", + " \"tos_acceptance\": {\n", + " \"date\": \"2023-01-01\",\n", + " \"ip\": \"192.168.1.1\"\n", + " }\n", + " }\n", + " ]\n", + " for order in orders:\n", + " if order[\"order_id\"] == order_id:\n", + " return order\n", + " return \"No order found\"\n", + "\n", + "\n", + "@function_tool\n", + "def get_emails(email: str) -> list:\n", + " \"\"\"\n", + " Return a list of email records for the given email address.\n", + " \"\"\"\n", + " emails = [\n", + " {\n", + " \"email\": \"customer1@example.com\",\n", + " \"subject\": \"Order #1121\",\n", + " \"body\": \"Hey, I know you don't accept refunds but the sneakers don't fit and I'd like a refund\"\n", + " },\n", + " {\n", + " \"email\": \"customer2@example.com\",\n", + " \"subject\": \"Inquiry about product availability\",\n", + " \"body\": \"Hello, I wanted to check if the new model of the smartphone is available in stock.\"\n", + " },\n", + " {\n", + " \"email\": \"customer3@example.com\",\n", + " \"subject\": \"Feedback on recent purchase\",\n", + " \"body\": \"Hi, I recently purchased a laptop from your store and I am very satisfied with the product. Keep up the good work!\"\n", + " }\n", + " ]\n", + " return [email_data for email_data in emails if email_data[\"email\"] == email]\n", + "\n", + "\n", + "@function_tool\n", + "async def retrieve_payment_intent(payment_intent_id: str) -> dict:\n", + " \"\"\"\n", + " Retrieve a Stripe payment intent by ID.\n", + " Returns the payment intent object on success or an empty dictionary on failure.\n", + " \"\"\"\n", + " try:\n", + " return stripe.PaymentIntent.retrieve(payment_intent_id)\n", + " except stripe.error.StripeError as e:\n", + " logger.error(f\"Stripe error occurred while retrieving payment intent: {e}\")\n", + " return {}\n", + "\n", + "@function_tool\n", + "async def close_dispute(dispute_id: str) -> dict:\n", + " \"\"\"\n", + " Close a Stripe dispute by ID. \n", + " Returns the dispute object on success or an empty dictionary on failure.\n", + " \"\"\"\n", + " try:\n", + " return stripe.Dispute.close(dispute_id)\n", + " except stripe.error.StripeError as e:\n", + " logger.error(f\"Stripe error occurred while closing dispute: {e}\")\n", + " return {}\n" + ] + }, + { + "cell_type": "markdown", + "id": "38e7a7ad-59fa-4096-8c2b-ffa3e2c295a9", + "metadata": {}, + "source": [ + "### Define the Agents\n", + "\n", + "- The **Dispute Intake Agent (investigator_agent)** is responsible for investigating disputes by gathering all relevant evidence and providing a report.\n", + "- The **Accept a Dispute Agent (accept_dispute_agent)** handles disputes that are determined to be valid by automatically closing them and providing a brief explanation for the decision.\n", + "- The **Triage Agent (triage_agent)** serves as the decision-maker by extracting the order ID from the payment intent's metadata, retrieving detailed order information, and then deciding whether to escalate the dispute to the investigator or to pass it to the accept dispute agent.\n", + "- Together, these agents form a modular workflow that automates and streamlines the dispute resolution process by delegating specific tasks to specialized agents.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "e4d20626-7ff2-4dac-9bb4-7b7af8b03add", + "metadata": {}, + "outputs": [], + "source": [ + "investigator_agent = Agent(\n", + " name=\"Dispute Intake Agent\",\n", + " instructions=(\n", + " \"As a dispute investigator, please compile the following details in your final output:\\n\\n\"\n", + " \"Dispute Details:\\n\"\n", + " \"- Dispute ID\\n\"\n", + " \"- Amount\\n\"\n", + " \"- Reason for Dispute\\n\"\n", + " \"- Card Brand\\n\\n\"\n", + " \"Payment & Order Details:\\n\"\n", + " \"- Fulfillment status of the order\\n\"\n", + " \"- Shipping carrier and tracking number\\n\"\n", + " \"- Confirmation of TOS acceptance\\n\\n\"\n", + " \"Email and Phone Records:\\n\"\n", + " \"- Any relevant email threads (include the full body text)\\n\"\n", + " \"- Any relevant phone logs\\n\"\n", + " ),\n", + " model=\"o3-mini\",\n", + " tools=[get_emails, get_phone_logs]\n", + ")\n", + "\n", + "\n", + "accept_dispute_agent = Agent(\n", + " name=\"Accept Dispute Agent\",\n", + " instructions=(\n", + " \"You are an agent responsible for accepting disputes. Please do the following:\\n\"\n", + " \"1. Use the provided dispute ID to close the dispute.\\n\"\n", + " \"2. Provide a short explanation of why the dispute is being accepted.\\n\"\n", + " \"3. Reference any relevant order details (e.g., unfulfilled order, etc.) retrieved from the database.\\n\\n\"\n", + " \"Then, produce your final output in this exact format:\\n\\n\"\n", + " \"Dispute Details:\\n\"\n", + " \"- Dispute ID\\n\"\n", + " \"- Amount\\n\"\n", + " \"- Reason for Dispute\\n\\n\"\n", + " \"Order Details:\\n\"\n", + " \"- Fulfillment status of the order\\n\\n\"\n", + " \"Reasoning for closing the dispute\\n\"\n", + " ),\n", + " model=\"gpt-4o\",\n", + " tools=[close_dispute]\n", + ")\n", + "\n", + "triage_agent = Agent(\n", + " name=\"Triage Agent\",\n", + " instructions=(\n", + " \"Please do the following:\\n\"\n", + " \"1. Find the order ID from the payment intent's metadata.\\n\"\n", + " \"2. Retrieve detailed information about the order (e.g., shipping status).\\n\"\n", + " \"3. If the order has shipped, escalate this dispute to the investigator agent.\\n\"\n", + " \"4. If the order has not shipped, accept the dispute.\\n\"\n", + " ),\n", + " model=\"gpt-4o\",\n", + " tools=[retrieve_payment_intent, get_order],\n", + " handoffs=[accept_dispute_agent, investigator_agent],\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b31c1be3-0360-42c1-93fa-d551cca9a43e", + "metadata": {}, + "source": [ + "#### Retrieve the Dispute and Initiate the Agentic Workflow\n", + "This function retrieves the dispute details from Stripe using the provided `payment_intent_id` and initiates the dispute-handling workflow by passing the retrieved dispute information to the specified `triage_agent`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "5eaaa982-80fa-4018-8b9d-e73e90f8ae0f", + "metadata": {}, + "outputs": [], + "source": [ + "async def process_dispute(payment_intent_id, triage_agent):\n", + " \"\"\"Retrieve and process dispute data for a given PaymentIntent.\"\"\"\n", + " disputes_list = stripe.Dispute.list(payment_intent=payment_intent_id)\n", + " if not disputes_list.data:\n", + " logger.warning(\"No dispute data found for PaymentIntent: %s\", payment_intent_id)\n", + " return None\n", + " \n", + " dispute_data = disputes_list.data[0]\n", + " \n", + " relevant_data = {\n", + " \"dispute_id\": dispute_data.get(\"id\"),\n", + " \"amount\": dispute_data.get(\"amount\"),\n", + " \"due_by\": dispute_data.get(\"evidence_details\", {}).get(\"due_by\"),\n", + " \"payment_intent\": dispute_data.get(\"payment_intent\"),\n", + " \"reason\": dispute_data.get(\"reason\"),\n", + " \"status\": dispute_data.get(\"status\"),\n", + " \"card_brand\": dispute_data.get(\"payment_method_details\", {}).get(\"card\", {}).get(\"brand\")\n", + " }\n", + " \n", + " event_str = json.dumps(relevant_data)\n", + " # Pass the dispute data to the triage agent\n", + " result = await Runner.run(triage_agent, input=event_str)\n", + " logger.info(\"WORKFLOW RESULT: %s\", result.final_output)\n", + " \n", + " return relevant_data, result.final_output" + ] + }, + { + "cell_type": "markdown", + "id": "83fe5866-84ec-420a-9841-49ef88f91670", + "metadata": {}, + "source": [ + "#### Scenario 1: Company Mistake (Product Not Received)\n", + "This scenario represents a situation where the company has clearly made an error—for instance, failing to fulfill or ship an order. In such cases, it may be appropriate to accept the dispute rather than contest it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57274ad2-e524-40a4-9eb8-4413bdf54c6f", + "metadata": {}, + "outputs": [], + "source": [ + "payment = stripe.PaymentIntent.create(\n", + " amount=2000,\n", + " currency=\"usd\",\n", + " payment_method = \"pm_card_createDisputeProductNotReceived\",\n", + " confirm=True,\n", + " metadata={\"order_id\": \"1234\"},\n", + " off_session=True,\n", + " automatic_payment_methods={\"enabled\": True},\n", + ")\n", + "relevant_data, triage_result = await process_dispute(payment.id, triage_agent)" + ] + }, + { + "cell_type": "markdown", + "id": "d4a48e55-2c08-4563-8a25-1d2e5d97bb33", + "metadata": {}, + "source": [ + "#### Scenario 2: Customer Dispute (Final Sale)\n", + "This scenario describes a situation where a customer intentionally disputes a transaction, despite having received the correct product and being fully aware that the purchase was clearly marked as a \"final sale\" (no refunds or returns). Such disputes typically require further investigation to collect evidence in order to effectively contest the dispute." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6aafea1-72c2-42fd-a78a-e494804b5dde", + "metadata": {}, + "outputs": [], + "source": [ + "payment = stripe.PaymentIntent.create(\n", + " amount=2000,\n", + " currency=\"usd\",\n", + " payment_method = \"pm_card_createDispute\",\n", + " confirm=True,\n", + " metadata={\"order_id\": \"1121\"},\n", + " off_session=True,\n", + " automatic_payment_methods={\"enabled\": True},\n", + ")\n", + "relevant_data, triage_result = await process_dispute(payment.id, triage_agent)" + ] + }, + { + "cell_type": "markdown", + "id": "ec050259", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this Jupyter Notebook, we explored the capabilities of the **OpenAI Agents SDK**, demonstrating how to efficiently create agent-based AI applications using a simple, Python-first approach. Specifically, we showcased the following SDK features:\n", + "\n", + "- **Agent Loop**: Manages tool calls, communicates results to the LLM, and loops until completion.\n", + "- **Handoffs**: Enables coordination and delegation tasks between multiple specialized agents.\n", + "- **Function Tools**: Converts Python functions into tools with automatic schema generation and validation.\n", + "\n", + "Additionally, the SDK offers built-in **Tracing**, accessible via the OpenAI dashboard. Tracing helps you visualize, debug, and monitor your agent workflows during both development and production phases. It also integrates smoothly with OpenAI’s evaluation, fine-tuning, and distillation tools.\n", + "\n", + "While we didn't cover it directly in this notebook, implementing **Guardrails** is strongly recommended for production applications to validate inputs and proactively detect errors.\n", + "\n", + "Overall, this notebook lays a clear foundation for further exploration, emphasizing how the OpenAI Agents SDK facilitates intuitive and effective agent-driven workflows." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/agents_sdk/evaluate_agents.ipynb b/examples/agents_sdk/evaluate_agents.ipynb new file mode 100644 index 0000000000..9f4c9861aa --- /dev/null +++ b/examples/agents_sdk/evaluate_agents.ipynb @@ -0,0 +1,2761 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "qsOnSYBqfoSL" + }, + "source": [ + "# Evaluating Agents with Langfuse\n", + "\n", + "In this cookbook, we will learn how to **monitor the internal steps (traces) of the [OpenAI agent SDK](https://github.com/openai/openai-agents-python)** and **evaluate its performance** using [Langfuse](https://langfuse.com/docs).\n", + "\n", + "This guide covers **online** and **offline evaluation** metrics used by teams to bring agents to production fast and reliably. To learn more about evaluation strategies, check out this [blog post](https://langfuse.com/blog/2025-03-04-llm-evaluation-101-best-practices-and-challenges).\n", + "\n", + "**Why AI agent Evaluation is important:**\n", + "- Debugging issues when tasks fail or produce suboptimal results\n", + "- Monitoring costs and performance in real-time\n", + "- Improving reliability and safety through continuous feedback\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "94-c-mbeVk4q" + }, + "source": [ + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZzPbsmLrfoSN" + }, + "source": [ + "## Step 0: Install the Required Libraries\n", + "\n", + "Below we install the `openai-agents` library (the OpenAI Agents SDK [link text](https://github.com/openai/openai-agents-python)), the `pydantic-ai[logfire]` OpenTelemetry instrumentation, `langfuse` and the Hugging Face `datasets` library" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "_EI_0ZfzfoSO", + "outputId": "ace75429-9836-456e-98e7-b08df97f616e" + }, + "outputs": [], + "source": [ + "%pip install openai-agents\n", + "%pip install nest_asyncio\n", + "%pip install pydantic-ai[logfire]\n", + "%pip install langfuse\n", + "%pip install datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FHRsxz1VfoSP" + }, + "source": [ + "## Step 1: Instrument Your Agent\n", + "\n", + "In this notebook, we will use [Langfuse](https://langfuse.com/) to trace, debug and evaluate our agent.\n", + "\n", + "**Note:** If you are using LlamaIndex or LangGraph, you can find documentation on instrumenting them [here](https://langfuse.com/docs/integrations/llama-index/workflows) and [here](https://langfuse.com/docs/integrations/langchain/example-python-langgraph)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "mZnxtWx9foSP" + }, + "outputs": [], + "source": [ + "import os\n", + "import base64\n", + "\n", + "# Get keys for your project from the project settings page: https://cloud.langfuse.com\n", + "os.environ[\"LANGFUSE_PUBLIC_KEY\"] = \"pk-lf-...\"\n", + "os.environ[\"LANGFUSE_SECRET_KEY\"] = \"sk-lf-...\"\n", + "os.environ[\"LANGFUSE_HOST\"] = \"https://cloud.langfuse.com\" # 🇪🇺 EU region\n", + "# os.environ[\"LANGFUSE_HOST\"] = \"https://us.cloud.langfuse.com\" # 🇺🇸 US region\n", + "\n", + "LANGFUSE_AUTH = base64.b64encode(\n", + " f\"{os.environ.get('LANGFUSE_PUBLIC_KEY')}:{os.environ.get('LANGFUSE_SECRET_KEY')}\".encode()\n", + ").decode()\n", + "\n", + "os.environ[\"OTEL_EXPORTER_OTLP_ENDPOINT\"] = os.environ.get(\"LANGFUSE_HOST\") + \"/api/public/otel\"\n", + "os.environ[\"OTEL_EXPORTER_OTLP_HEADERS\"] = f\"Authorization=Basic {LANGFUSE_AUTH}\"\n", + "\n", + "# Set your OpenAI API Key\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KQjuJNHkfoSP" + }, + "outputs": [], + "source": [ + "from opentelemetry.sdk.trace import TracerProvider\n", + "from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n", + "from opentelemetry.sdk.trace.export import SimpleSpanProcessor\n", + "\n", + "# Create a TracerProvider for OpenTelemetry\n", + "trace_provider = TracerProvider()\n", + "\n", + "# Add a SimpleSpanProcessor with the OTLPSpanExporter to send traces\n", + "trace_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))\n", + "\n", + "# Set the global default tracer provider\n", + "from opentelemetry import trace\n", + "trace.set_tracer_provider(trace_provider)\n", + "tracer = trace.get_tracer(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IWr-MQY7hKdM" + }, + "source": [ + "Pydantic Logfire offers an instrumentation for the OpenAi Agent SDK. We use this to send traces to the [Langfuse OpenTelemetry Backend](https://langfuse.com/docs/opentelemetry/get-started)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "td11AsCShBxA" + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1MQoskgIhCQi" + }, + "outputs": [], + "source": [ + "import logfire\n", + "\n", + "# Configure logfire instrumentation.\n", + "logfire.configure(\n", + " service_name='my_agent_service',\n", + "\n", + " send_to_logfire=False,\n", + ")\n", + "# This method automatically patches the OpenAI Agents SDK to send logs via OTLP to Langfuse.\n", + "logfire.instrument_openai_agents()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uulS5iGHfoSP" + }, + "source": [ + "## Step 2: Test Your Instrumentation\n", + "\n", + "Here is a simple Q&A agent. We run it to confirm that the instrumentation is working correctly. If everything is set up correctly, you will see logs/spans in your observability dashboard." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UcyynS9CfoSP", + "outputId": "5e8eb8c2-bcff-4149-ff8b-21d706c1d25b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12:01:03.401 OpenAI Agents trace: Agent workflow\n", + "12:01:03.403 Agent run: 'Assistant'\n", + "12:01:03.404 Responses API with 'gpt-4o'\n", + "Evaluating AI agents is crucial for several reasons:\n", + "\n", + "1. **Performance Verification**: Ensures that the AI performs its intended tasks accurately and efficiently, meeting the desired objectives and criteria.\n", + "\n", + "2. **Reliability and Consistency**: Assesses whether the AI provides consistent results across different scenarios and over time.\n", + "\n", + "3. **Safety and Risk Management**: Identifies potential risks or harmful behaviors that could lead to undesirable outcomes, ensuring the AI operates safely within defined limits.\n", + "\n", + "4. **Bias and Fairness**: Checks for any biases in the AI’s decision-making process to promote fairness and avoid discrimination against particular groups.\n", + "\n", + "5. **User Trust and Adoption**: Builds confidence and trust in the AI system among users and stakeholders, which is essential for widespread adoption.\n", + "\n", + "6. **Regulatory Compliance**: Ensures that the AI adheres to relevant laws, regulations, and ethical guidelines, which may vary by industry or region.\n", + "\n", + "7. **Continuous Improvement**: Provides feedback that can be used to refine and improve the AI model over time, enhancing its effectiveness and efficiency.\n", + "\n", + "8. **Integration and Compatibility**: Evaluates how well the AI integrates with existing systems and processes, ensuring compatibility and smooth operation.\n", + "\n", + "9. **Resource Optimization**: Assesses the efficiency of the AI in terms of computational resources, which can lead to cost savings and improved performance.\n", + "\n", + "Evaluating AI agents systematically and rigorously supports their development and deployment in a responsible and effective manner.\n" + ] + } + ], + "source": [ + "import asyncio\n", + "from agents import Agent, Runner\n", + "\n", + "async def main():\n", + " agent = Agent(\n", + " name=\"Assistant\",\n", + " instructions=\"You are a senior software engineer\",\n", + " )\n", + "\n", + " result = await Runner.run(agent, \"Tell me why it is important to evaluate AI agents.\")\n", + " print(result.final_output)\n", + "\n", + "loop = asyncio.get_running_loop()\n", + "await loop.create_task(main())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hPLt1hRkfoSQ" + }, + "source": [ + "Check your [Langfuse Traces Dashboard](https://cloud.langfuse.com/traces) to confirm that the spans and logs have been recorded.\n", + "\n", + "Example trace in Langfuse:\n", + "\n", + "![Example trace in Langfuse](https://langfuse.com/images/cookbook/integration_openai-agents/first-example-trace.png)\n", + "\n", + "_[Link to the trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/0195948781a9f0d78fd5e067154aa508?timestamp=2025-03-14T12%3A01%3A03.401Z&observation=64bcac3cb82d04e9)_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "onjMD-ZJfoSQ" + }, + "source": [ + "## Step 3: Observe and Evaluate a More Complex Agent\n", + "\n", + "Now that you have confirmed your instrumentation works, let's try a more complex query so we can see how advanced metrics (token usage, latency, costs, etc.) are tracked." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3qdMJh9KfoSQ", + "outputId": "04136f13-51b0-4938-afa6-ade016c01aa9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13:33:30.839 OpenAI Agents trace: Agent workflow\n", + "13:33:30.840 Agent run: 'Hello world'\n", + "13:33:30.842 Responses API with 'gpt-4o'\n", + "13:33:31.822 Function: get_weather\n", + "13:33:31.825 Responses API with 'gpt-4o'\n", + "The weather in Berlin is currently sunny.\n" + ] + } + ], + "source": [ + "import asyncio\n", + "from agents import Agent, Runner, function_tool\n", + "\n", + "# Example function tool.\n", + "@function_tool\n", + "def get_weather(city: str) -> str:\n", + " return f\"The weather in {city} is sunny.\"\n", + "\n", + "agent = Agent(\n", + " name=\"Hello world\",\n", + " instructions=\"You are a helpful agent.\",\n", + " tools=[get_weather],\n", + ")\n", + "\n", + "async def main():\n", + " result = await Runner.run(agent, input=\"What's the weather in Berlin?\")\n", + " print(result.final_output)\n", + "\n", + "loop = asyncio.get_running_loop()\n", + "await loop.create_task(main())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fjkhTgLWfoSQ" + }, + "source": [ + "### Trace Structure\n", + "\n", + "Langfuse records a **trace** that contains **spans**, which represent each step of your agent’s logic. Here, the trace contains the overall agent run and sub-spans for:\n", + "- The tool call (get_weather)\n", + "- The LLM calls (Responses API with 'gpt-4o')\n", + "\n", + "You can inspect these to see precisely where time is spent, how many tokens are used, and so on:\n", + "\n", + "![Trace tree in Langfuse](https://langfuse.com/images/cookbook/integration_openai-agents/trace-tree.png)\n", + "\n", + "_[Link to the trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/019594b5b9a27c5d497b13be71e7f255?timestamp=2025-03-14T12%3A51%3A32.386Z&display=preview&observation=6374a3c96baf831d)_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JHZAkQuefoSQ" + }, + "source": [ + "## Online Evaluation\n", + "\n", + "Online Evaluation refers to evaluating the agent in a live, real-world environment, i.e. during actual usage in production. This involves monitoring the agent’s performance on real user interactions and analyzing outcomes continuously.\n", + "\n", + "We have written down a guide on different evaluation techniques [here](https://langfuse.com/blog/2025-03-04-llm-evaluation-101-best-practices-and-challenges).\n", + "\n", + "### Common Metrics to Track in Production\n", + "\n", + "1. **Costs** — The instrumentation captures token usage, which you can transform into approximate costs by assigning a price per token.\n", + "2. **Latency** — Observe the time it takes to complete each step, or the entire run.\n", + "3. **User Feedback** — Users can provide direct feedback (thumbs up/down) to help refine or correct the agent.\n", + "4. **LLM-as-a-Judge** — Use a separate LLM to evaluate your agent’s output in near real-time (e.g., checking for toxicity or correctness).\n", + "\n", + "Below, we show examples of these metrics." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QHMvJ1QlfoSQ" + }, + "source": [ + "#### 1. Costs\n", + "\n", + "Below is a screenshot showing usage for `gpt-4o` calls. This is useful to see costly steps and optimize your agent.\n", + "\n", + "![Costs](https://langfuse.com/images/cookbook/integration_openai-agents/gpt-4o-costs.png)\n", + "\n", + "_[Link to the trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/019594b5b9a27c5d497b13be71e7f255?timestamp=2025-03-14T12%3A51%3A32.386Z&display=preview&observation=6374a3c96baf831d)_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yz0y9mn7foSQ" + }, + "source": [ + "#### 2. Latency\n", + "\n", + "We can also see how long it took to complete each step. In the example below, the entire run took 7 seconds, which you can break down by step. This helps you identify bottlenecks and optimize your agent.\n", + "\n", + "![Latency](https://langfuse.com/images/cookbook/integration_openai-agents/openai-agent-latency.png)\n", + "\n", + "_[Link to the trace](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/019594b5b9a27c5d497b13be71e7f255?timestamp=2025-03-14T12%3A51%3A32.386Z&display=timeline&observation=b12967a01b3f8bcb)_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MByq31MzfoSQ" + }, + "source": [ + "#### 3. Additional Attributes\n", + "\n", + "Opentelemetry lets you attach a set of attributes to all spans by setting [`set_attribute`](https://opentelemetry.io/docs/languages/python/instrumentation/#add-attributes-to-a-span). This allows you to set properties like a Langfuse Session ID, to group traces into Langfuse Sessions or a User ID, to assign traces to a specific user. You can find a list of all supported attributes in the [here](/docs/opentelemetry/get-started#property-mapping).\n", + "\n", + "In this example, we pass a [user_id](https://langfuse.com/docs/tracing-features/users), [session_id](https://langfuse.com/docs/tracing-features/sessions) and [trace_tags](https://langfuse.com/docs/tracing-features/tags) to Langfuse. You can also use the span attribute `input.value` and `output.value` to set the trace level input and output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CaSQNrgyfoSR" + }, + "outputs": [], + "source": [ + "from opentelemetry.sdk.trace import TracerProvider\n", + "from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n", + "from opentelemetry.sdk.trace.export import SimpleSpanProcessor\n", + "\n", + "trace_provider = TracerProvider()\n", + "trace_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))\n", + "\n", + "# Sets the global default tracer provider\n", + "from opentelemetry import trace\n", + "trace.set_tracer_provider(trace_provider)\n", + "\n", + "# Creates a tracer from the global tracer provider\n", + "tracer = trace.get_tracer(__name__)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "w5O02Ren3Kmu", + "outputId": "63a0020d-c873-46c6-d159-3a045ab681f0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13:34:49.654 OpenAI Agents trace: Agent workflow\n", + "13:34:49.655 Agent run: 'Assistant'\n", + "13:34:49.657 Responses API with 'gpt-4o'\n", + "AI agent evaluation is crucial for several reasons:\n", + "\n", + "1. **Performance Verification**: It ensures that the AI agent performs its intended tasks effectively and meets specific criteria or benchmarks.\n", + "\n", + "2. **Safety and Reliability**: Evaluation helps identify and mitigate risks, ensuring that the AI operates safely and reliably in real-world situations.\n", + "\n", + "3. **Continuous Improvement**: Analyzing performance data allows developers to refine and enhance the AI, leading to better outcomes and more efficient systems.\n", + "\n", + "4. **Transparency and Accountability**: Thorough evaluation provides transparency into how decisions are made by the AI, which is essential for accountability, especially in sensitive applications.\n", + "\n", + "5. **Bias and Fairness**: Evaluating AI systems helps detect and address potential biases, ensuring fair treatment of all users and stakeholders.\n", + "\n", + "6. **Compliance**: It ensures adherence to regulations and industry standards, which is critical for legal and ethical compliance.\n", + "\n", + "7. **User Trust**: A well-evaluated AI fosters trust among users, stakeholders, and the public, as they can be confident in its capabilities and limitations.\n", + "\n", + "8. **Resource Allocation**: Evaluation helps determine if the AI is using resources efficiently, which can be crucial for cost management and scalability.\n" + ] + } + ], + "source": [ + "input_query = \"Why is AI agent evaluation important?\"\n", + "\n", + "with tracer.start_as_current_span(\"OpenAI-Agent-Trace\") as span:\n", + " span.set_attribute(\"langfuse.user.id\", \"user-12345\")\n", + " span.set_attribute(\"langfuse.session.id\", \"my-agent-session\")\n", + " span.set_attribute(\"langfuse.tags\", [\"staging\", \"demo\", \"OpenAI Agent SDK\"])\n", + "\n", + " async def main(input_query):\n", + " agent = Agent(\n", + " name = \"Assistant\",\n", + " instructions = \"You are a helpful assistant.\",\n", + " )\n", + "\n", + " result = await Runner.run(agent, input_query)\n", + " print(result.final_output)\n", + " return result\n", + "\n", + " result = await main(input_query)\n", + "\n", + " # Add input and output values to parent trace\n", + " span.set_attribute(\"input.value\", input_query)\n", + " span.set_attribute(\"output.value\", result.final_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tEPeGdAafoSR" + }, + "source": [ + "![Example trace in Langfuse](https://langfuse.com/images/cookbook/integration_openai-agents/openai-agent-sdk-custom-attributes.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XtKiK62HfoSR" + }, + "source": [ + "#### 4. User Feedback\n", + "\n", + "If your agent is embedded into a user interface, you can record direct user feedback (like a thumbs-up/down in a chat UI). Below is an example using `IPython.display` for simple feedback mechanism.\n", + "\n", + "In the code snippet below, when a user sends a chat message, we capture the OpenTelemetry trace ID. If the user likes/dislikes the last answer, we attach a score to the trace." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 468, + "referenced_widgets": [ + "8b10018448324153af2ee1f9bd83d140", + "cebcce63ea37474ca10f1828105ca2e6", + "9153dfceabff450ead31493c3c518d4c", + "5a2b1d2255a34a7597b263755eaa14b3", + "c8b3aa3aeec046ef8acfab640c2dee17", + "ee1b1596e6ec42029fbf8b711c0fc41a", + "ecd5521cdbc34eb7a866b4b2094fd500", + "df007d6320cb4198a6dbf58485980394" + ] + }, + "id": "YI9siKKKfoSR", + "outputId": "3eb086d0-4277-4cdd-966f-10b3c12272a6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter your question: What is Langfuse?\n", + "13:54:41.574 OpenAI Agents trace: Agent workflow\n", + "13:54:41.575 Agent run: 'WebSearchAgent'\n", + "13:54:41.577 Responses API with 'gpt-4o'\n", + "Langfuse is an open-source engineering platform designed to enhance the development, monitoring, and optimization of Large Language Model (LLM) applications. It offers a suite of tools that provide observability, prompt management, evaluations, and metrics, facilitating the debugging and improvement of LLM-based solutions. ([toolkitly.com](https://www.toolkitly.com/langfuse?utm_source=openai))\n", + "\n", + "**Key Features of Langfuse:**\n", + "\n", + "- **LLM Observability:** Langfuse enables developers to monitor and analyze the performance of language models by tracking API calls, user inputs, prompts, and outputs. This observability aids in understanding model behavior and identifying areas for improvement. ([toolkitly.com](https://www.toolkitly.com/langfuse?utm_source=openai))\n", + "\n", + "- **Prompt Management:** The platform provides tools for managing, versioning, and deploying prompts directly within Langfuse. This feature allows for efficient organization and refinement of prompts to optimize model responses. ([toolkitly.com](https://www.toolkitly.com/langfuse?utm_source=openai))\n", + "\n", + "- **Evaluations and Metrics:** Langfuse offers capabilities to collect and calculate scores for LLM completions, run model-based evaluations, and gather user feedback. It also tracks key metrics such as cost, latency, and quality, providing insights through dashboards and data exports. ([toolkitly.com](https://www.toolkitly.com/langfuse?utm_source=openai))\n", + "\n", + "- **Playground Environment:** The platform includes a playground where users can interactively experiment with different models and prompts, facilitating prompt engineering and testing. ([toolkitly.com](https://www.toolkitly.com/langfuse?utm_source=openai))\n", + "\n", + "- **Integration Capabilities:** Langfuse integrates seamlessly with various tools and frameworks, including LlamaIndex, LangChain, OpenAI SDK, LiteLLM, and more, enhancing its functionality and allowing for the development of complex applications. ([toolerific.ai](https://toolerific.ai/ai-tools/opensource/langfuse-langfuse?utm_source=openai))\n", + "\n", + "- **Open Source and Self-Hosting:** Being open-source, Langfuse allows developers to customize and extend the platform according to their specific needs. It can be self-hosted, providing full control over infrastructure and data. ([vafion.com](https://www.vafion.com/blog/unlocking-power-language-models-langfuse/?utm_source=openai))\n", + "\n", + "Langfuse is particularly valuable for developers and researchers working with LLMs, offering a comprehensive set of tools to improve the performance and reliability of LLM applications. Its flexibility, integration capabilities, and open-source nature make it a robust choice for those seeking to enhance their LLM projects. \n", + "How did you like the agent response?\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b10018448324153af2ee1f9bd83d140", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(Button(description='👍', icon='thumbs-up', style=ButtonStyle()), Button(description='👎', icon='t…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Scored the trace in Langfuse\n" + ] + } + ], + "source": [ + "from agents import Agent, Runner, WebSearchTool\n", + "from opentelemetry.trace import format_trace_id\n", + "import ipywidgets as widgets\n", + "from IPython.display import display\n", + "from langfuse import Langfuse\n", + "\n", + "langfuse = Langfuse()\n", + "\n", + "# Define your agent with the web search tool\n", + "agent = Agent(\n", + " name=\"WebSearchAgent\",\n", + " instructions=\"You are an agent that can search the web.\",\n", + " tools=[WebSearchTool()]\n", + ")\n", + "\n", + "formatted_trace_id = None # We'll store the current trace_id globally for demonstration\n", + "\n", + "def on_feedback(button):\n", + " if button.icon == \"thumbs-up\":\n", + " langfuse.score(\n", + " value=1,\n", + " name=\"user-feedback\",\n", + " comment=\"The user gave this response a thumbs up\",\n", + " trace_id=formatted_trace_id\n", + " )\n", + " elif button.icon == \"thumbs-down\":\n", + " langfuse.score(\n", + " value=0,\n", + " name=\"user-feedback\",\n", + " comment=\"The user gave this response a thumbs down\",\n", + " trace_id=formatted_trace_id\n", + " )\n", + " print(\"Scored the trace in Langfuse\")\n", + "\n", + "user_input = input(\"Enter your question: \")\n", + "\n", + "# Run agent\n", + "with trace.get_tracer(__name__).start_as_current_span(\"OpenAI-Agent-Trace\") as span:\n", + "\n", + " # Run your agent with a query\n", + " result = Runner.run_sync(agent, user_input)\n", + " print(result.final_output)\n", + "\n", + " current_span = trace.get_current_span()\n", + " span_context = current_span.get_span_context()\n", + " trace_id = span_context.trace_id\n", + " formatted_trace_id = str(format_trace_id(trace_id))\n", + " langfuse.trace(id=formatted_trace_id, input=user_input, output=result.final_output)\n", + "\n", + "# Get feedback\n", + "print(\"How did you like the agent response?\")\n", + "\n", + "thumbs_up = widgets.Button(description=\"👍\", icon=\"thumbs-up\")\n", + "thumbs_down = widgets.Button(description=\"👎\", icon=\"thumbs-down\")\n", + "\n", + "thumbs_up.on_click(on_feedback)\n", + "thumbs_down.on_click(on_feedback)\n", + "\n", + "display(widgets.HBox([thumbs_up, thumbs_down]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iiemuS7YfoSR" + }, + "source": [ + "User feedback is then captured in Langfuse:\n", + "\n", + "![User feedback is being captured in Langfuse](https://langfuse.com/images/cookbook/integration_openai-agents/open-ai-agent-user-feedback.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "29KsI9xcfoSR" + }, + "source": [ + "#### 5. LLM-as-a-Judge\n", + "\n", + "LLM-as-a-Judge is another way to automatically evaluate your agent's output. You can set up a separate LLM call to gauge the output’s correctness, toxicity, style, or any other criteria you care about.\n", + "\n", + "**Workflow**:\n", + "1. You define an **Evaluation Template**, e.g., \"Check if the text is toxic.\"\n", + "2. You set a model that is used as judge-model; in this case `gpt-4o-mini`.\n", + "2. Each time your agent generates output, you pass that output to your \"judge\" LLM with the template.\n", + "3. The judge LLM responds with a rating or label that you log to your observability tool.\n", + "\n", + "Example from Langfuse:\n", + "\n", + "![LLM-as-a-Judge Evaluation Template](https://langfuse.com/images/cookbook/integration_openai-agents/evaluator-template.png)\n", + "![LLM-as-a-Judge Evaluator](https://langfuse.com/images/cookbook/integration_openai-agents/evaluator.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UGGlYrB7foSR", + "outputId": "3e2d7d06-a5be-4552-9f17-88d93dd7b600" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14:05:34.735 OpenAI Agents trace: Agent workflow\n", + "14:05:34.736 Agent run: 'WebSearchAgent'\n", + "14:05:34.738 Responses API with 'gpt-4o'\n" + ] + } + ], + "source": [ + "# Example: Checking if the agent’s output is toxic or not.\n", + "from agents import Agent, Runner, WebSearchTool\n", + "\n", + "# Define your agent with the web search tool\n", + "agent = Agent(\n", + " name=\"WebSearchAgent\",\n", + " instructions=\"You are an agent that can search the web.\",\n", + " tools=[WebSearchTool()]\n", + ")\n", + "\n", + "input_query = \"Is eating carrots good for the eyes?\"\n", + "\n", + "# Run agent\n", + "with trace.get_tracer(__name__).start_as_current_span(\"OpenAI-Agent-Trace\") as span:\n", + " # Run your agent with a query\n", + " result = Runner.run_sync(agent, input_query)\n", + "\n", + " # Add input and output values to parent trace\n", + " span.set_attribute(\"input.value\", input_query)\n", + " span.set_attribute(\"output.value\", result.final_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Izr-3LiQfoSR" + }, + "source": [ + "You can see that the answer of this example is judged as \"not toxic\".\n", + "\n", + "![LLM-as-a-Judge Evaluation Score](https://langfuse.com/images/cookbook/integration_openai-agents/llm-as-a-judge-score.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g7fN0UTkfoSR" + }, + "source": [ + "#### 6. Observability Metrics Overview\n", + "\n", + "All of these metrics can be visualized together in dashboards. This enables you to quickly see how your agent performs across many sessions and helps you to track quality metrics over time.\n", + "\n", + "![Observability metrics overview](https://langfuse.com/images/cookbook/integration_openai-agents/dashboard-dark.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zlwltgEkfoSR" + }, + "source": [ + "## Offline Evaluation\n", + "\n", + "Online evaluation is essential for live feedback, but you also need **offline evaluation**—systematic checks before or during development. This helps maintain quality and reliability before rolling changes into production." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p5R8eNQxfoSR" + }, + "source": [ + "### Dataset Evaluation\n", + "\n", + "In offline evaluation, you typically:\n", + "1. Have a benchmark dataset (with prompt and expected output pairs)\n", + "2. Run your agent on that dataset\n", + "3. Compare outputs to the expected results or use an additional scoring mechanism\n", + "\n", + "Below, we demonstrate this approach with the [search-dataset](https://huggingface.co/datasets/junzhang1207/search-dataset), which contains questions that can be answered via the web search tool and expected answers." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510, + "referenced_widgets": [ + "dbf84b0798e0432599453e370740acaa", + "f05fbf51e518417ea66a24db7b0a472e", + "27794498f97b40a6b4ef1f2e36bf317e", + "35fb6e8a89194653abcaa84f6da4f190", + "93572e5407ea443999d1da45280741f7", + "f3574545791843998157f0a3176e0ded", + "636090e9932f4ff6a76152c64c92347d", + "7928d3e09417467aaa74cb2c6cea32ca", + "1ef6f4bf46e24841916cc9c611c1498b", + "07f767145c7741bfb950cd983c757cc7", + "b620b045db0242189200a99090ea6b9f", + "33f6af1d99a9451a86e6e6690cec7e43", + "722784d0cb184f02b5250c57829cfdc4", + "e98c3c9567334b69a46ab23cd378358e", + "f0c04648902343288b4248dbf5589d7b", + "b32d7f992f064016ab548a961569b632", + "a95842a82d46492796b7d0fd45bb9795", + "9b33b4e7c6bb4a728f21258c10034066", + "6b2cbe08ebad4df8bf9e4711d78920f2", + "ed53bc55a6da404bac64993433f78ccf", + "f68c9919bd26417cb1f3950f121276e3", + "e815dd583c3243efa5d3b57672519f74", + "ea5cea15ae5741418720f77d5879ecc2", + "f8be1fdbe50649ebb614194c140e0d9a", + "bdb56cd2387e4e30a1bf82beecf481d1", + "12b08dc8912c4bfc81c81524eabc7898", + "57152a81a7a24410992ccce525f28af0", + "799212d16c814bd696d57d0cdd35d9c7", + "f6935aa898f544d2b4051ee259ec9e30", + "a049a1b305ed4660bc48f81fe1c6c0a7", + "9e40a549b3094b3892de5159dfe935ff", + "15fa9808df56469db8cc623bd127ceae", + "8948024991964982ad058f199066cc55", + "0f5356bc1d4a4e0f895fa9482bb06a7a", + "427363e7b1ce4c7387b93f2794bdedb4", + "690c3eb1f314478083b725bcb1d38a25", + "edc537f5b13348c6b831365930c0cf31", + "7f0d7cf658054c448668bebacd96a59d", + "71277158b5ec43c8aa0accb86b15952d", + "c6b284ea83ee4de0a6de5210067b4b6b", + "c248d2dba9484807bc53d23ece012644", + "2b4fb4ce8c71405d80da978589750950", + "07431a8d1a2044b6baebcef3242f41c5", + "e2941796709b4284aa3b6143b19e064e" + ] + }, + "id": "r77WUP9NfoSS", + "outputId": "7809f9f1-b496-4672-e17c-41681e66accc" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dbf84b0798e0432599453e370740acaa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "README.md: 0%| | 0.00/2.12k [00:00= 49: # For this example, we upload only the first 50 items\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QgHw2e6afoSS" + }, + "source": [ + "![Dataset items in Langfuse](https://langfuse.com/images/cookbook/integration_openai-agents/example-dataset.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xZl2W_u0foSS" + }, + "source": [ + "#### Running the Agent on the Dataset\n", + "\n", + "We define a helper function `run_openai_agent()` that:\n", + "1. Starts an OpenTelemetry span\n", + "2. Runs our agent on the prompt\n", + "3. Records the trace ID in Langfuse\n", + "\n", + "Then, we loop over each dataset item, run the agent, and link the trace to the dataset item. We can also attach a quick evaluation score if desired." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "-rYh1PBRfoSS" + }, + "outputs": [], + "source": [ + "from agents import Agent, Runner, WebSearchTool\n", + "from opentelemetry.trace import format_trace_id\n", + "\n", + "# Define your agent with the web search tool\n", + "agent = Agent(\n", + " name=\"WebSearchAgent\",\n", + " instructions=\"You are an agent that can search the web.\",\n", + " tools=[WebSearchTool(search_context_size= \"high\")]\n", + ")\n", + "\n", + "def run_openai_agent(question):\n", + " with tracer.start_as_current_span(\"OpenAI-Agent-Trace\") as span:\n", + " span.set_attribute(\"langfuse.tag\", \"dataset-run\")\n", + "\n", + " # Run your agent with a query\n", + " result = Runner.run_sync(agent, question)\n", + "\n", + " # Get the Langfuse trace_id to link the dataset run item to the agent trace\n", + " current_span = trace.get_current_span()\n", + " span_context = current_span.get_span_context()\n", + " trace_id = span_context.trace_id\n", + " formatted_trace_id = format_trace_id(trace_id)\n", + "\n", + " langfuse_trace = langfuse.trace(\n", + " id=formatted_trace_id,\n", + " input=question,\n", + " output=result.final_output\n", + " )\n", + " return langfuse_trace, result.final_output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "nF6JLCsYfoST", + "outputId": "84c3b74e-1aa3-4ef2-f285-f390095b03c0" + }, + "outputs": [], + "source": [ + "dataset = langfuse.get_dataset(langfuse_dataset_name)\n", + "\n", + "# Run our agent against each dataset item\n", + "for item in dataset.items:\n", + " langfuse_trace, output = run_openai_agent(item.input[\"text\"])\n", + "\n", + " # Link the trace to the dataset item for analysis\n", + " item.link(\n", + " langfuse_trace,\n", + " run_name=\"openai-agent-run-03\",\n", + " run_metadata={ \"search_context_size\": \"high\"}\n", + " )\n", + "\n", + " # Optionally, store a quick evaluation score for demonstration\n", + " langfuse_trace.score(\n", + " name=\"\",\n", + " value=1,\n", + " comment=\"This is a comment\"\n", + " )\n", + "\n", + "# Flush data to ensure all telemetry is sent\n", + "langfuse.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2kUYV69HfoST" + }, + "source": [ + "You can repeat this process with different:\n", + "- Search tools (e.g. different context sized for OpenAI's `WebSearchTool`)\n", + "- Models (gpt-4o-mini, o1, etc.)\n", + "- Tools (search vs. no search)\n", + "\n", + "Then compare them side-by-side in Langfuse. In this example, I did run the agent 3 times on the 50 dataset questions. For each run, I used a different setting for the context size of OpenAI's `WebSearchTool`. You can see that an increased context size also slightly increased the answer correctness from `0.89` to `0.92`. The `correct_answer` score is created by an [LLM-as-a-Judge Evaluator](https://langfuse.com/docs/scores/model-based-evals) that is set up to judge the correctness of the question based on the sample answer given in the dataset.\n", + "\n", + "![Dataset run overview](https://langfuse.com/images/cookbook/integration_openai-agents/dataset_runs.png)\n", + "![Dataset run comparison](https://langfuse.com/images/cookbook/integration_openai-agents/dataset-run-comparison.png)\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "07431a8d1a2044b6baebcef3242f41c5": { + 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+*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# Installer logs +distutils.log +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Jupyter Notebook checkpoints +.ipynb_checkpoints + +# pyenv +.python-version + +# mypy +.mypy_cache/ +.dmypy.json + +# Pyre type checker +.pyre/ + +# VS Code +.vscode/ + +# Mac OS +.DS_Store + +# Output and log directories +outputs/ +logs/ + +# Project-specific logs and outputs +*.log +*.jsonl + +# Secret keys and environment variables +.env +.env.* \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/__init__.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/__init__.py new file mode 100644 index 0000000000..8b98142f54 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/__init__.py @@ -0,0 +1 @@ +# This file marks the agents directory as a Python package. \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/config.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/config.py new file mode 100644 index 0000000000..4063d59cbc --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/config.py @@ -0,0 +1,30 @@ +from dataclasses import dataclass +from investment_agents.fundamental import build_fundamental_agent +from investment_agents.macro import build_macro_agent +from investment_agents.quant import build_quant_agent +from investment_agents.editor import build_editor_agent, build_memo_edit_tool +from investment_agents.pm import build_head_pm_agent, SpecialistRequestInput +import asyncio + +@dataclass +class InvestmentAgentsBundle: + head_pm: object + fundamental: object + macro: object + quant: object + + +def build_investment_agents() -> InvestmentAgentsBundle: + fundamental = build_fundamental_agent() + macro = build_macro_agent() + quant = build_quant_agent() + editor = build_editor_agent() + memo_edit_tool = build_memo_edit_tool(editor) + head_pm = build_head_pm_agent(fundamental, macro, quant, memo_edit_tool) + return InvestmentAgentsBundle( + head_pm=head_pm, + fundamental=fundamental, + macro=macro, + quant=quant, + ) + diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/editor.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/editor.py new file mode 100644 index 0000000000..47ec698745 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/editor.py @@ -0,0 +1,40 @@ +from agents import Agent, ModelSettings, function_tool, Runner, RunContextWrapper +from tools import write_markdown, read_file, list_output_files +from utils import load_prompt, DISCLAIMER +from pydantic import BaseModel +import json + +default_model = "gpt-4.1" + +class MemoEditorInput(BaseModel): + fundamental: str + macro: str + quant: str + pm: str + files: list[str] + +def build_editor_agent(): + tool_retry_instructions = load_prompt("tool_retry_prompt.md") + editor_prompt = load_prompt("editor_base.md") + return Agent( + name="Memo Editor Agent", + instructions=(editor_prompt + DISCLAIMER + tool_retry_instructions), + tools=[write_markdown, read_file, list_output_files], + model=default_model, + model_settings=ModelSettings(temperature=0), + ) + +def build_memo_edit_tool(editor): + @function_tool( + name_override="memo_editor", + description_override="Stitch analysis sections into a Markdown memo and save it. This is the ONLY way to generate and save the final investment report. All memos must be finalized through this tool.", + ) + async def memo_edit_tool(ctx: RunContextWrapper, input: MemoEditorInput) -> str: + result = await Runner.run( + starting_agent=editor, + input=json.dumps(input.model_dump()), + context=ctx.context, + max_turns=40, + ) + return result.final_output + return memo_edit_tool \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/fundamental.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/fundamental.py new file mode 100644 index 0000000000..b9c909d5f6 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/fundamental.py @@ -0,0 +1,28 @@ +from agents import Agent, WebSearchTool, ModelSettings +from utils import load_prompt, DISCLAIMER, repo_path +from pathlib import Path + +default_model = "gpt-4.1" +default_search_context = "medium" +RECENT_DAYS = 15 + +def build_fundamental_agent(): + tool_retry_instructions = load_prompt("tool_retry_prompt.md") + fundamental_prompt = load_prompt("fundamental_base.md", RECENT_DAYS=RECENT_DAYS) + # Set up the Yahoo Finance MCP server + from agents.mcp import MCPServerStdio + server_path = str(repo_path("mcp/yahoo_finance_server.py")) + yahoo_mcp_server = MCPServerStdio( + params={"command": "python", "args": [server_path]}, + client_session_timeout_seconds=300, + cache_tools_list=True, + ) + + return Agent( + name="Fundamental Analysis Agent", + instructions=(fundamental_prompt + DISCLAIMER + tool_retry_instructions), + mcp_servers=[yahoo_mcp_server], + tools=[WebSearchTool(search_context_size=default_search_context)], + model=default_model, + model_settings=ModelSettings(parallel_tool_calls=True, temperature=0), + ) \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/macro.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/macro.py new file mode 100644 index 0000000000..02c3354ca5 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/macro.py @@ -0,0 +1,18 @@ +from agents import Agent, WebSearchTool, ModelSettings +from tools import get_fred_series +from utils import load_prompt, DISCLAIMER + +default_model = "gpt-4.1" +default_search_context = "medium" +RECENT_DAYS = 45 + +def build_macro_agent(): + tool_retry_instructions = load_prompt("tool_retry_prompt.md") + macro_prompt = load_prompt("macro_base.md", RECENT_DAYS=RECENT_DAYS) + return Agent( + name="Macro Analysis Agent", + instructions=(macro_prompt + DISCLAIMER + tool_retry_instructions), + tools=[WebSearchTool(search_context_size=default_search_context), get_fred_series], + model=default_model, + model_settings=ModelSettings(parallel_tool_calls=True, temperature=0), + ) \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/pm.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/pm.py new file mode 100644 index 0000000000..cff4fd57c8 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/pm.py @@ -0,0 +1,69 @@ +from agents import Agent, ModelSettings, function_tool, Runner +from utils import load_prompt, DISCLAIMER +from dataclasses import dataclass +from pydantic import BaseModel +import json +import asyncio + + +class SpecialistRequestInput(BaseModel): + section: str # e.g., 'fundamental', 'macro', 'quant', or 'pm' + user_question: str + guidance: str + +# Core async functions for each specialist +async def specialist_analysis_func(agent, input: SpecialistRequestInput): + result = await Runner.run( + starting_agent=agent, + input=json.dumps(input.model_dump()), + max_turns=75, + ) + return result.final_output + +async def run_all_specialists_parallel( + fundamental, macro, quant, + fundamental_input: SpecialistRequestInput, + macro_input: SpecialistRequestInput, + quant_input: SpecialistRequestInput +): + results = await asyncio.gather( + specialist_analysis_func(fundamental, fundamental_input), + specialist_analysis_func(macro, macro_input), + specialist_analysis_func(quant, quant_input) + ) + return { + "fundamental": results[0], + "macro": results[1], + "quant": results[2] + } + +def build_head_pm_agent(fundamental, macro, quant, memo_edit_tool): + def make_agent_tool(agent, name, description): + @function_tool(name_override=name, description_override=description) + async def agent_tool(input: SpecialistRequestInput): + return await specialist_analysis_func(agent, input) + return agent_tool + fundamental_tool = make_agent_tool(fundamental, "fundamental_analysis", "Generate the Fundamental Analysis section.") + macro_tool = make_agent_tool(macro, "macro_analysis", "Generate the Macro Environment section.") + quant_tool = make_agent_tool(quant, "quantitative_analysis", "Generate the Quantitative Analysis section.") + + @function_tool(name_override="run_all_specialists_parallel", description_override="Run all three specialist analyses (fundamental, macro, quant) in parallel and return their results as a dict.") + async def run_all_specialists_tool(fundamental_input: SpecialistRequestInput, macro_input: SpecialistRequestInput, quant_input: SpecialistRequestInput): + return await run_all_specialists_parallel( + fundamental, macro, quant, + fundamental_input, macro_input, quant_input + ) + + return Agent( + name="Head Portfolio Manager Agent", + instructions=( + load_prompt("pm_base.md") + DISCLAIMER + ), + model="gpt-4.1", + #Reasoning model + #model="o4-mini", + tools=[fundamental_tool, macro_tool, quant_tool, memo_edit_tool, run_all_specialists_tool], + # Settings for a reasoning model + #model_settings=ModelSettings(parallel_tool_calls=True, reasoning={"summary": "auto", "effort": "high"}, tool_choice="auto") + model_settings=ModelSettings(parallel_tool_calls=True, tool_choice="auto", temperature=0) + ) \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/quant.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/quant.py new file mode 100644 index 0000000000..0494284f88 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/quant.py @@ -0,0 +1,27 @@ +from agents import Agent, ModelSettings +from tools import run_code_interpreter, get_fred_series, read_file, list_output_files +from utils import load_prompt, DISCLAIMER, repo_path +from pathlib import Path + +default_model = "gpt-4.1" + +def build_quant_agent(): + tool_retry_instructions = load_prompt("tool_retry_prompt.md") + quant_prompt = load_prompt("quant_base.md") + # Set up the Yahoo Finance MCP server + from agents.mcp import MCPServerStdio + server_path = str(repo_path("mcp/yahoo_finance_server.py")) + yahoo_mcp_server = MCPServerStdio( + params={"command": "python", "args": [server_path]}, + client_session_timeout_seconds=300, + cache_tools_list=True, + ) + + return Agent( + name="Quantitative Analysis Agent", + instructions=(quant_prompt + DISCLAIMER + tool_retry_instructions), + mcp_servers=[yahoo_mcp_server], + tools=[run_code_interpreter, get_fred_series, read_file, list_output_files], + model=default_model, + model_settings=ModelSettings(parallel_tool_calls=True, temperature=0), + ) \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/mcp/yahoo_finance_server.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/mcp/yahoo_finance_server.py new file mode 100644 index 0000000000..7241996cb9 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/mcp/yahoo_finance_server.py @@ -0,0 +1,486 @@ +import json +from enum import Enum +import os +import pandas as pd +import yfinance as yf +from mcp.server.fastmcp import FastMCP +from pathlib import Path +import uuid +import asyncio +import logging + +# Helper to ensure outputs dir exists and return path (repo root) +_REPO_ROOT = Path(__file__).resolve().parent.parent + +# Single shared outputs folder at the repository root +OUTPUTS_DIR = _REPO_ROOT / "outputs" + +# Ensure the directory exists +OUTPUTS_DIR.mkdir(parents=True, exist_ok=True) + +# Set up logging +LOGS_DIR = _REPO_ROOT / "logs" +LOGS_DIR.mkdir(parents=True, exist_ok=True) +LOG_FILE = LOGS_DIR / "yahoo_finance_server.log" +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s %(levelname)s %(message)s', + handlers=[logging.FileHandler(LOG_FILE), logging.StreamHandler()] +) +logger = logging.getLogger(__name__) + +# --------------------------------------------------------------------------- +# Helper: write DataFrame to /outputs and strip any timezone info +# --------------------------------------------------------------------------- + +def _strip_tz(df: pd.DataFrame) -> pd.DataFrame: + out = df.copy() + for col in out.select_dtypes(include=["datetimetz"]).columns: + out[col] = out[col].dt.tz_localize(None) + return out + +def save_df_to_csv(df, base_name): + df_clean = _strip_tz(df) + file_path = OUTPUTS_DIR / f"{base_name}.csv" + if file_path.exists(): + unique_id = uuid.uuid4().hex[:8] + file_path = OUTPUTS_DIR / f"{base_name}_{unique_id}.csv" + df_clean.to_csv(file_path, index=False) + return str(file_path), list(df_clean.columns) + +def save_json_to_file(data, base_name): + file_path = OUTPUTS_DIR / f"{base_name}.json" + if file_path.exists(): + unique_id = uuid.uuid4().hex[:8] + file_path = OUTPUTS_DIR / f"{base_name}_{unique_id}.json" + with open(file_path, "w") as f: + json.dump(data, f, indent=2) + # Schema: for dict, top-level keys; for list, type of first element or 'list'; else type + if isinstance(data, dict): + schema = list(data.keys()) + preview = {k: data[k] for k in list(data)[:PREVIEW_ROWS]} + elif isinstance(data, list): + schema = [type(data[0]).__name__] if data else ["list"] + preview = data[:PREVIEW_ROWS] + else: + schema = [type(data).__name__] + preview = data + return str(file_path), schema, preview + +class FinancialType(str, Enum): + income_stmt = "income_stmt" + quarterly_income_stmt = "quarterly_income_stmt" + balance_sheet = "balance_sheet" + quarterly_balance_sheet = "quarterly_balance_sheet" + cashflow = "cashflow" + quarterly_cashflow = "quarterly_cashflow" + +class HolderType(str, Enum): + major_holders = "major_holders" + institutional_holders = "institutional_holders" + mutualfund_holders = "mutualfund_holders" + insider_transactions = "insider_transactions" + insider_purchases = "insider_purchases" + insider_roster_holders = "insider_roster_holders" + +class RecommendationType(str, Enum): + recommendations = "recommendations" + upgrades_downgrades = "upgrades_downgrades" + +# Initialize FastMCP server +yfinance_server = FastMCP( + "yfinance", + instructions=""" +# Yahoo Finance MCP Server + +This server is used to get information about a given ticker symbol from yahoo finance. + +Available tools: +- get_historical_stock_prices: Get historical stock prices for a given ticker symbol from yahoo finance. Include the following information: Date, Open, High, Low, Close, Volume, Adj Close. +- get_stock_info: Get stock information for a given ticker symbol from yahoo finance. Include the following information: Stock Price & Trading Info, Company Information, Financial Metrics, Earnings & Revenue, Margins & Returns, Dividends, Balance Sheet, Ownership, Analyst Coverage, Risk Metrics, Other. +- get_yahoo_finance_news: Get news for a given ticker symbol from yahoo finance. +- get_stock_actions: Get stock dividends and stock splits for a given ticker symbol from yahoo finance. +- get_financial_statement: Get financial statement for a given ticker symbol from yahoo finance. You can choose from the following financial statement types: income_stmt, quarterly_income_stmt, balance_sheet, quarterly_balance_sheet, cashflow, quarterly_cashflow. +- get_holder_info: Get holder information for a given ticker symbol from yahoo finance. You can choose from the following holder types: major_holders, institutional_holders, mutualfund_holders, insider_transactions, insider_purchases, insider_roster_holders. +- get_option_expiration_dates: Fetch the available options expiration dates for a given ticker symbol. +- get_option_chain: Fetch the option chain for a given ticker symbol, expiration date, and option type. +- get_recommendations: Get recommendations or upgrades/downgrades for a given ticker symbol from yahoo finance. You can also specify the number of months back to get upgrades/downgrades for, default is 12. +""", +) + +PREVIEW_ROWS = 20 + +# --- Tool: get_historical_stock_prices --- +def get_historical_stock_prices_sync(ticker, period, interval): + logger.info(f"Called get_historical_stock_prices_sync: ticker={ticker}, period={period}, interval={interval}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + hist_data = company.history(period=period, interval=interval) + hist_data = hist_data.reset_index(names="Date") + file_base = f"{ticker}_{period}_{interval}_historical" + file_path, schema = save_df_to_csv(hist_data, file_base) + preview_json = hist_data.head(PREVIEW_ROWS).to_json(orient="records", date_format="iso") + logger.info(f"Returning historical data for {ticker}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": json.loads(preview_json) + }) + +@yfinance_server.tool( + name="get_historical_stock_prices", + description="""Get historical stock prices for a given ticker symbol from yahoo finance. Include the following information: Date, Open, High, Low, Close, Volume, Adj Close.\nArgs:\n ticker: str\n The ticker symbol of the stock to get historical prices for, e.g. \"AAPL\"\n period : str\n Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max\n Either Use period parameter or use start and end\n Default is \"1mo\"\n interval : str\n Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo\n Intraday data cannot extend last 60 days\n Default is \"1d\"\n""", +) +async def get_historical_stock_prices(ticker: str, period: str = "1mo", interval: str = "1d") -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_historical_stock_prices_sync, ticker, period, interval), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching historical stock prices"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_stock_info --- +def get_stock_info_sync(ticker): + logger.info(f"Called get_stock_info_sync: ticker={ticker}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + info = company.info + file_path, schema, preview = save_json_to_file(info, f"{ticker}_stock_info") + logger.info(f"Returning stock info for {ticker}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": preview + }) + +@yfinance_server.tool( + name="get_stock_info", + description="""Get stock information for a given ticker symbol from yahoo finance. Include the following information:\nStock Price & Trading Info, Company Information, Financial Metrics, Earnings & Revenue, Margins & Returns, Dividends, Balance Sheet, Ownership, Analyst Coverage, Risk Metrics, Other.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get information for, e.g. \"AAPL\"\n""", +) +async def get_stock_info(ticker: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_stock_info_sync, ticker), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching stock info"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_yahoo_finance_news --- +def get_yahoo_finance_news_sync(ticker): + logger.info(f"Called get_yahoo_finance_news_sync: ticker={ticker}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + try: + news = company.news + except Exception as e: + logger.error(f"Error getting news for {ticker}: {e}") + return json.dumps({"error": f"Error: getting news for {ticker}: {e}"}) + news_list = [] + for news_item in news: + if news_item.get("content", {}).get("contentType", "") == "STORY": + title = news_item.get("content", {}).get("title", "") + summary = news_item.get("content", {}).get("summary", "") + description = news_item.get("content", {}).get("description", "") + url = news_item.get("content", {}).get("canonicalUrl", {}).get("url", "") + news_list.append( + {"title": title, "summary": summary, "description": description, "url": url} + ) + if not news_list: + logger.warning(f"No news found for company with ticker {ticker}.") + return json.dumps({"error": f"No news found for company that searched with {ticker} ticker."}) + file_path, schema, preview = save_json_to_file(news_list, f"{ticker}_news") + logger.info(f"Returning news for {ticker}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": preview + }) + +@yfinance_server.tool( + name="get_yahoo_finance_news", + description="""Get news for a given ticker symbol from yahoo finance.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get news for, e.g. \"AAPL\"\n""", +) +async def get_yahoo_finance_news(ticker: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_yahoo_finance_news_sync, ticker), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching news"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_stock_actions --- +def get_stock_actions_sync(ticker): + logger.info(f"Called get_stock_actions_sync: ticker={ticker}") + try: + company = yf.Ticker(ticker) + except Exception as e: + logger.error(f"Error getting stock actions for {ticker}: {e}") + return json.dumps({"error": f"Error: getting stock actions for {ticker}: {e}"}) + actions_df = company.actions + actions_df = actions_df.reset_index(names="Date") + file_path, schema = save_df_to_csv(actions_df, f"{ticker}_actions") + preview_json = actions_df.head(PREVIEW_ROWS).to_json(orient="records", date_format="iso") + logger.info(f"Returning stock actions for {ticker}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": json.loads(preview_json) + }) + +@yfinance_server.tool( + name="get_stock_actions", + description="""Get stock dividends and stock splits for a given ticker symbol from yahoo finance.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get stock actions for, e.g. \"AAPL\"\n""", +) +async def get_stock_actions(ticker: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_stock_actions_sync, ticker), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching stock actions"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_financial_statement --- +def get_financial_statement_sync(ticker, financial_type): + logger.info(f"Called get_financial_statement_sync: ticker={ticker}, financial_type={financial_type}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + if financial_type == FinancialType.income_stmt: + financial_statement = company.income_stmt + elif financial_type == FinancialType.quarterly_income_stmt: + financial_statement = company.quarterly_income_stmt + elif financial_type == FinancialType.balance_sheet: + financial_statement = company.balance_sheet + elif financial_type == FinancialType.quarterly_balance_sheet: + financial_statement = company.quarterly_balance_sheet + elif financial_type == FinancialType.cashflow: + financial_statement = company.cashflow + elif financial_type == FinancialType.quarterly_cashflow: + financial_statement = company.quarterly_cashflow + else: + logger.error(f"Invalid financial type {financial_type} for {ticker}.") + return json.dumps({"error": f"Error: invalid financial type {financial_type}. Please use one of the following: {list(FinancialType)}."}) + df = financial_statement.transpose().reset_index(names="date") + file_path, schema = save_df_to_csv(df, f"{ticker}_{financial_type}") + preview_json = df.head(PREVIEW_ROWS).to_json(orient="records", date_format="iso") + logger.info(f"Returning financial statement for {ticker}, type={financial_type}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": json.loads(preview_json) + }) + +@yfinance_server.tool( + name="get_financial_statement", + description="""Get financial statement for a given ticker symbol from yahoo finance. You can choose from the following financial statement types: income_stmt, quarterly_income_stmt, balance_sheet, quarterly_balance_sheet, cashflow, quarterly_cashflow.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get financial statement for, e.g. \"AAPL\"\n financial_type: str\n The type of financial statement to get. You can choose from the following financial statement types: income_stmt, quarterly_income_stmt, balance_sheet, quarterly_balance_sheet, cashflow, quarterly_cashflow.\n""", +) +async def get_financial_statement(ticker: str, financial_type: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_financial_statement_sync, ticker, financial_type), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching financial statement"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_holder_info --- +def get_holder_info_sync(ticker, holder_type): + logger.info(f"Called get_holder_info_sync: ticker={ticker}, holder_type={holder_type}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + if holder_type == HolderType.major_holders: + df = company.major_holders.reset_index(names="metric") + elif holder_type == HolderType.institutional_holders: + df = company.institutional_holders + elif holder_type == HolderType.mutualfund_holders: + df = company.mutualfund_holders + elif holder_type == HolderType.insider_transactions: + df = company.insider_transactions + elif holder_type == HolderType.insider_purchases: + df = company.insider_purchases + elif holder_type == HolderType.insider_roster_holders: + df = company.insider_roster_holders + else: + logger.error(f"Invalid holder type {holder_type} for {ticker}.") + return json.dumps({"error": f"Error: invalid holder type {holder_type}. Please use one of the following: {list(HolderType)}."}) + df = df.reset_index() if df.index.name or df.index.names else df + file_path, schema = save_df_to_csv(df, f"{ticker}_{holder_type}") + preview_json = df.head(PREVIEW_ROWS).to_json(orient="records", date_format="iso") + logger.info(f"Returning holder info for {ticker}, type={holder_type}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": json.loads(preview_json) + }) + +@yfinance_server.tool( + name="get_holder_info", + description="""Get holder information for a given ticker symbol from yahoo finance. You can choose from the following holder types: major_holders, institutional_holders, mutualfund_holders, insider_transactions, insider_purchases, insider_roster_holders.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get holder information for, e.g. \"AAPL\"\n holder_type: str\n The type of holder information to get. You can choose from the following holder types: major_holders, institutional_holders, mutualfund_holders, insider_transactions, insider_purchases, insider_roster_holders.\n""", +) +async def get_holder_info(ticker: str, holder_type: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_holder_info_sync, ticker, holder_type), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching holder info"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_option_expiration_dates --- +def get_option_expiration_dates_sync(ticker): + logger.info(f"Called get_option_expiration_dates_sync: ticker={ticker}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + dates = list(company.options) + file_path, schema, preview = save_json_to_file(dates, f"{ticker}_option_expiration_dates") + logger.info(f"Returning option expiration dates for {ticker}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": preview + }) + +@yfinance_server.tool( + name="get_option_expiration_dates", + description="""Fetch the available options expiration dates for a given ticker symbol.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get option expiration dates for, e.g. \"AAPL\"\n""", +) +async def get_option_expiration_dates(ticker: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_option_expiration_dates_sync, ticker), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching option expiration dates"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_option_chain --- +def get_option_chain_sync(ticker, expiration_date, option_type): + logger.info(f"Called get_option_chain_sync: ticker={ticker}, expiration_date={expiration_date}, option_type={option_type}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + if expiration_date not in company.options: + logger.error(f"No options available for {ticker} on date {expiration_date}.") + return json.dumps({"error": f"No options available for the date {expiration_date}. You can use `get_option_expiration_dates` to get the available expiration dates."}) + if option_type not in ["calls", "puts"]: + logger.error(f"Invalid option type {option_type} for {ticker}.") + return json.dumps({"error": "Invalid option type. Please use 'calls' or 'puts'."}) + option_chain = company.option_chain(expiration_date) + df = option_chain.calls if option_type == "calls" else option_chain.puts + file_path, schema = save_df_to_csv(df, f"{ticker}_{expiration_date}_{option_type}_options") + preview_json = df.head(PREVIEW_ROWS).to_json(orient="records", date_format="iso") + logger.info(f"Returning option chain for {ticker}, date={expiration_date}, type={option_type}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": json.loads(preview_json) + }) + +@yfinance_server.tool( + name="get_option_chain", + description="""Fetch the option chain for a given ticker symbol, expiration date, and option type.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get option chain for, e.g. \"AAPL\"\n expiration_date: str\n The expiration date for the options chain (format: 'YYYY-MM-DD')\n option_type: str\n The type of option to fetch ('calls' or 'puts')\n""", +) +async def get_option_chain(ticker: str, expiration_date: str, option_type: str) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_option_chain_sync, ticker, expiration_date, option_type), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching option chain"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +# --- Tool: get_recommendations --- +def get_recommendations_sync(ticker, recommendation_type, months_back=12): + logger.info(f"Called get_recommendations_sync: ticker={ticker}, recommendation_type={recommendation_type}, months_back={months_back}") + company = yf.Ticker(ticker) + if company.isin is None: + logger.error(f"Company ticker {ticker} not found.") + return json.dumps({"error": f"Company ticker {ticker} not found."}) + try: + if recommendation_type == RecommendationType.recommendations: + df = company.recommendations + elif recommendation_type == RecommendationType.upgrades_downgrades: + upgrades_downgrades = company.upgrades_downgrades.reset_index() + cutoff_date = pd.Timestamp.now() - pd.DateOffset(months=months_back) + upgrades_downgrades = upgrades_downgrades[ + upgrades_downgrades["GradeDate"] >= cutoff_date + ] + upgrades_downgrades = upgrades_downgrades.sort_values("GradeDate", ascending=False) + latest_by_firm = upgrades_downgrades.drop_duplicates(subset=["Firm"]) + df = latest_by_firm + else: + logger.error(f"Invalid recommendation type {recommendation_type} for {ticker}.") + return json.dumps({"error": f"Invalid recommendation type {recommendation_type}."}) + df = df.reset_index() if df.index.name or df.index.names else df + file_path, schema = save_df_to_csv(df, f"{ticker}_{recommendation_type}_recommendations") + preview_json = df.head(PREVIEW_ROWS).to_json(orient="records", date_format="iso") + logger.info(f"Returning recommendations for {ticker}, type={recommendation_type}, months_back={months_back}") + return json.dumps({ + "file_path": file_path, + "schema": schema, + "preview": json.loads(preview_json) + }) + except Exception as e: + logger.error(f"Error getting recommendations for {ticker}: {e}") + return json.dumps({"error": f"Error: getting recommendations for {ticker}: {e}"}) + +@yfinance_server.tool( + name="get_recommendations", + description="""Get recommendations or upgrades/downgrades for a given ticker symbol from yahoo finance. You can also specify the number of months back to get upgrades/downgrades for, default is 12.\n\nArgs:\n ticker: str\n The ticker symbol of the stock to get recommendations for, e.g. \"AAPL\"\n recommendation_type: str\n The type of recommendation to get. You can choose from the following recommendation types: recommendations, upgrades_downgrades.\n months_back: int\n The number of months back to get upgrades/downgrades for, default is 12.\n""", +) +async def get_recommendations(ticker: str, recommendation_type: str, months_back: int = 12) -> str: + loop = asyncio.get_running_loop() + try: + return await asyncio.wait_for( + loop.run_in_executor(None, get_recommendations_sync, ticker, recommendation_type, months_back), + timeout=30 + ) + except asyncio.TimeoutError: + return json.dumps({"error": "Timeout fetching recommendations"}) + except Exception as e: + return json.dumps({"error": str(e)}) + +if __name__ == "__main__": + # Initialize and run the server + print("Starting Yahoo Finance MCP server...") + yfinance_server.run(transport="stdio") \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration.ipynb b/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration.ipynb new file mode 100644 index 0000000000..aa505230ab --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1e5b29d1", + "metadata": {}, + "source": [ + "# Multi-Agent Orchestration with OpenAI Agents SDK: Financial Portfolio Analysis Example\n", + "\n", + "## Introduction\n", + "\n", + "*This guide is for readers already familiar with OpenAI models and LLM agents, and want to see how to orchestrate a team of agents for a real-world, complex task.*\n", + "\n", + "**What You'll Learn**\n", + "\n", + "In this notebook, you'll learn how to use the OpenAI Agents SDK to design and implement a complex multi-agent collaboration system. Specifically, you'll see how to:\n", + "- Build a workflow where multiple specialist agents (Macro, Fundamental, Quantitative) collaborate under a Portfolio Manager agent to solve a challenging investment research problem.\n", + "- Use the \"agents as a tool\" approach, where a central agent orchestrates and calls other agents as tools for specific subtasks.\n", + "- Leverage all major tool types supported by the SDK (custom Python functions, managed tools like Code Interpreter and WebSearch, and external MCP servers) in a single, integrated workflow.\n", + "- Apply best practices for modularity, parallelism, and observability in agentic patterns.\n", + "\n", + "**Why this matters**\n", + "\n", + "The \"agents as a tool\" pattern is a powerful way to build transparent, auditable, and scalable multi-agent collaboration . This example demonstrates how to combine deep specialization, parallel execution, and robust orchestration using the OpenAI Agents SDK.\n", + "\n", + "By the end of this guide, you'll have a clear blueprint for building your own multi-agent workflows for research, analysis, or any complex task that benefits from expert collaboration.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ed547489", + "metadata": {}, + "source": [ + "\n", + "---\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [What is Multi-Agent Collaboration?](#what-is-multi-agent-collaboration)\n", + "2. [Collaboration Patterns: Handoff vs. Agent-as-Tool](#collaboration-patterns-handoff-vs-agent-as-tool)\n", + "3. [Architecture Overview](#architecture-overview)\n", + "4. [Supported Tool Types](#supported-tool-types)\n", + "5. [Setup](#setup)\n", + "6. [Running the Workflow](#running-the-workflow)\n", + "7. [The Head Portfolio Manager (PM) Agent](#the-head-portfolio-manager-pm-agent)\n", + "8. [Breaking Down the Head Portfolio Manager Agent](#breaking-down-the-head-portfolio-manager-agent)\n", + "9. [Example Output](#example-output)\n", + "10. [Best Practices When Building Agents](#best-practices-when-building-agents)\n", + "11. [Further Reading & Best Practices](#further-reading--best-practices)\n" + ] + }, + { + "cell_type": "markdown", + "id": "26670dad", + "metadata": {}, + "source": [ + "\n", + "---\n", + "\n", + "## What is Multi-Agent Collaboration?\n", + "\n", + "**Multi-agent collaboration** means multiple autonomous agents (LLM \"nodes\") coordinate to achieve an overarching goal that would be difficult for a single agent to handle. Instead of one monolithic prompt, each agent handles a specific subtask or expertise area, and an orchestration layer connects these agent \"nodes\" into a coherent workflow. This approach is useful for complex systems – for example, a financial analysis might be broken into macro-economic analysis, fundamental company analysis, and quantitative signal analysis, each handled by a different agent specialist. The agents share information and their results are combined to produce a final outcome.\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d5f3a58", + "metadata": {}, + "source": [ + "\n", + "### Collaboration Patterns: Handoff vs. Agent-as-Tool\n", + "\n", + "The OpenAI Agents SDK supports multiple patterns for agents to work together:\n", + "\n", + "- **Handoff Collaboration:** One agent can _handoff_ control to another agent mid-problem. In a handoff architecture, each agent knows about the others and can decide when to defer to a more appropriate agent. This is flexible for open-ended or conversational workflows, but can make it harder to maintain a global view of the task. [Read more in the SDK docs.](https://openai.github.io/openai-agents-python/handoffs/)\n", + "\n", + "- **Agent as a Tool:** In this approach, one agent (often a central planner or manager) **calls other agents as if they were tools**. Sub-agents don't take over the conversation; instead, the main agent invokes them for specific subtasks and incorporates their results. This model keeps a single thread of control (the main agent orchestrates everything) and tends to simplify coordination. **This repo uses the agent-as-tool model:** the Portfolio Manager agent remains in charge, using the other specialist agents as tools when it needs their expertise. This choice keeps the overall reasoning transparent and allows parallel execution of sub-tasks, which is ideal for complex analyses.\n", + "\n", + "For more on these collaboration patterns, see the [OpenAI Agents SDK documentation](https://openai.github.io/openai-agents-python/multi_agent/).\n", + "\n", + "---\n", + "\n", + "## Architecture Overview\n", + "\n", + "Our system follows a **hub-and-spoke design**. The **Portfolio Manager agent** is the hub (central coordinator), and the **specialist agents** are the spokes. The user's query (e.g. \"How would a planned interest rate reduction affect my GOOGL holdings?\") goes first to the Portfolio Manager. The Portfolio Manager agent is prompted to break down the problem and delegate to the appropriate specialist agents. It treats each specialist as a callable tool, invoking them for their portion of the analysis. All three report back to the Portfolio Manager, which then synthesizes a final answer for the user.\n", + "\n", + "![Multi-Agent Investment Report Workflow](../../../images/multi_agent_collab_agent_architecture.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a7a2ef1e", + "metadata": {}, + "source": [ + "\n", + "---\n", + "\n", + "## Supported Tool Types\n", + "\n", + "A key advantage of the Agents SDK is the flexibility in defining **tools** that agents can use. Tools can range from simple Python functions to external services. In this project, we use:\n", + "\n", + "- **MCP (Model Context Protocol) Server:** Used to connect agents to external tools and data sources in a standardized way. This project uses a local MCP server for Yahoo Finance data (see `mcp/yahoo_finance_server.py`). [Learn more: OpenAI MCP docs](https://openai.github.io/openai-agents-python/mcp/) | [MCP Spec](https://modelcontextprotocol.io/)\n", + "\n", + "- **OpenAI Managed Tools:** Managed tools are built-in, hosted tools provided by OpenAI that require no custom implementation. They offer powerful capabilities out of the box, such as **Code Interpreter** (for quantitative/statistical analysis) and **WebSearch** (for up-to-date news and data). These tools are easy to integrate, maintained by OpenAI, and allow agents to perform advanced actions like code execution and real-time information retrieval without additional setup.\n", + "\n", + "- **Custom Tools:** Custom tools are any Python functions you define and register as tools for your agent. The Agents SDK makes this easy: just decorate your function, and the SDK will automatically extract its name, docstring, and input schema. This is ideal for domain-specific logic, data access, or workflow extensions. \n", + " In our project, we use custom tools to access FRED economic data ([see FRED API](https://fred.stlouisfed.org/docs/api/api_key.html)) and perform file system operations.\n", + "\n", + "Custom tools give you full flexibility to extend your agent's capabilities beyond built-in or managed tools. [See the SDK docs on function tools.](https://openai.github.io/openai-agents-python/tools/#function-tools)\n", + "\n", + "> **Want to add more tools?** The SDK supports a wide range of tool types, including web search, file search, code execution, and more. [See the full list of supported tools in the SDK documentation.](https://openai.github.io/openai-agents-python/tools/)\n", + "\n", + "---\n", + "\n", + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b128b837", + "metadata": {}, + "outputs": [], + "source": [ + "# Install required dependencies\n", + "!pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "id": "21c2f377", + "metadata": {}, + "source": [ + "**Before running the workflow, set your environment variables:**\n", + "- `OPENAI_API_KEY` (for OpenAI access)\n", + "- `FRED_API_KEY` (for FRED economic data, see [FRED API key instructions](https://fred.stlouisfed.org/docs/api/api_key.html))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c70bf2c3", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "missing = []\n", + "if not os.environ.get('OPENAI_API_KEY'):\n", + " missing.append('OPENAI_API_KEY')\n", + "if not os.environ.get('FRED_API_KEY'):\n", + " missing.append('FRED_API_KEY')\n", + "\n", + "if missing:\n", + " print(f\"Missing environment variable(s): {', '.join(missing)}. Please set them before running the workflow.\")\n", + "else:\n", + " print(\"All required API keys are set.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3b2c4e5", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Running the Workflow \n", + "\n", + "Edit the question to whatever you'd like, but keep the date field to improve accuracy!\n", + "\n", + "
\n", + "Disclaimer: This example is for educational purposes only. Consult a qualified financial professional before making any investment decisions\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "04b11e29", + "metadata": {}, + "source": [ + "The workflow is kicked off by sending a user request to the Head Portfolio Manager (PM) agent. The PM agent orchestrates the entire process, delegating to specialist agents and tools as needed. You can monitor the workflow in real time using OpenAI Traces, which provide detailed visibility into every agent and tool call.\n", + "\n", + "Edit the `question` in the code below to whatever you'd like, but keep the date field to improve accuracy!\n", + "\n", + "
\n", + "Note: Depending on the complexity of the task, this request can take up to 10 minutes.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a7059b4", + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import json\n", + "import os\n", + "from pathlib import Path\n", + "from contextlib import AsyncExitStack\n", + "from agents import Runner, add_trace_processor, trace\n", + "from agents.tracing.processors import BatchTraceProcessor\n", + "from utils import FileSpanExporter, output_file\n", + "from investment_agents.config import build_investment_agents\n", + "import asyncio\n", + "\n", + "add_trace_processor(BatchTraceProcessor(FileSpanExporter()))\n", + "\n", + "async def run_workflow():\n", + " if \"OPENAI_API_KEY\" not in os.environ:\n", + " raise EnvironmentError(\"OPENAI_API_KEY not set — set it as an environment variable before running.\")\n", + "\n", + " today_str = datetime.date.today().strftime(\"%B %d, %Y\")\n", + " question = (\n", + " f\"Today is {today_str}. \"\n", + " \"How would the planned interest rate reduction effect my holdings in GOOGL if they were to happen?\"\n", + " \"Considering all the factors effecting its price right now (Macro, Technical, Fundamental, etc.), what is a realistic price target by the end of the year?\"\n", + " )\n", + " bundle = build_investment_agents()\n", + "\n", + " async with AsyncExitStack() as stack:\n", + " for agent in [getattr(bundle, \"fundamental\", None), getattr(bundle, \"quant\", None)]:\n", + " if agent is None:\n", + " continue\n", + " for server in getattr(agent, \"mcp_servers\", []):\n", + " await server.connect()\n", + " await stack.enter_async_context(server)\n", + "\n", + " print(\"Running multi-agent workflow with tracing enabled...\\n\")\n", + " with trace(\n", + " \"Investment Research Workflow\",\n", + " metadata={\"question\": question[:512]}\n", + " ) as workflow_trace:\n", + " print(\n", + " f\"\\n🔗 View the trace in the OpenAI console: \"\n", + " f\"https://platform.openai.com/traces/trace?trace_id={workflow_trace.trace_id}\\n\"\n", + " )\n", + "\n", + " response = None\n", + " try:\n", + " response = await asyncio.wait_for(\n", + " Runner.run(bundle.head_pm, question, max_turns=40),\n", + " timeout=1200\n", + " )\n", + " except asyncio.TimeoutError:\n", + " print(\"\\n❌ Workflow timed out after 20 minutes.\")\n", + "\n", + " report_path = None\n", + " try:\n", + " if hasattr(response, 'final_output'):\n", + " output = response.final_output\n", + " if isinstance(output, str):\n", + " data = json.loads(output)\n", + " if isinstance(data, dict) and 'file' in data:\n", + " report_path = output_file(data['file'])\n", + " except Exception as e:\n", + " print(f\"Could not parse investment report path: {e}\")\n", + "\n", + " print(f\"Workflow Completed Response from Agent: {response.final_output if hasattr(response, 'final_output') else response}, investment report created: {report_path if report_path else '[unknown]'}\")\n", + "\n", + "# In a Jupyter notebook cell, run:\n", + "await run_workflow()" + ] + }, + { + "cell_type": "markdown", + "id": "94273ca6", + "metadata": {}, + "source": [ + "\n", + "---\n", + "\n", + "## Breaking Down the Head Portfolio Manager Agent\n", + "\n", + "The Head Portfolio Manager (PM) agent is the orchestrator of the entire workflow. It coordinates a set of four specialist agents, each focused on a different area of expertise. This design is intentional: overloading a single agent with every possible responsibility leads to shallow, generic outputs and makes it hard to maintain or improve your system over time.\n", + "\n", + "### Why This Design?\n", + "By breaking the problem into specialized agents—each with a clear role—you get:\n", + "\n", + "- **Deeper, higher-quality research:** Each agent can focus on its domain, using the right tools and prompts for the job. The PM agent brings these perspectives together for a more nuanced, robust answer.\n", + "\n", + "- **Modularity and clarity:** You can update, test, or improve one agent without affecting the others. This makes your system easier to maintain and extend as your needs evolve.\n", + "\n", + "- **Faster results through parallelism:** Independent agents can work at the same time, dramatically reducing the time to complete complex, multi-part analyses.\n", + "\n", + "- **Consistency and auditability:** A structured, prompt-driven workflow ensures every run follows best practices, is easy to debug, and produces outputs you can trust and review.\n", + "\n", + "This approach is ideal for any application where you want depth, specialization, and reliability—whether you're building a research assistant, a decision support tool, or any system that benefits from expert collaboration and orchestration.\n", + "\n", + "**How We Implement This in Practice:**\n", + "- Each specialist agent (Fundamental, Macro, Quantitative) is wrapped as a callable tool using the SDK's `function_tool` decorator, with custom names and descriptions. This makes the PM agent's toolset explicit and LLM-friendly.\n", + "\n", + "- The Head PM agent uses the `run_all_specialists_parallel` tool to invoke all three specialists concurrently, leveraging `parallel_tool_calls=True` for maximum speed and efficiency.\n", + "\n", + "- The agent's prompt is loaded from a markdown file (`pm_base.md`), encoding not just the firm's philosophy but also detailed tool usage rules and a step-by-step workflow. This ensures every run is consistent, auditable, and aligned with best practices.\n", + "\n", + "- After gathering and reviewing the specialist outputs, the PM agent uses a dedicated memo editor tool to assemble, format, and finalize the investment report. This separation of concerns keeps the workflow modular and easy to extend.\n", + "\n", + "- The system is designed for extensibility: you can add new specialist agents, swap out tools, or update prompts without breaking the overall orchestration logic. All tool calls, agent decisions, and outputs are captured in OpenAI Traces for full transparency and debugging.\n", + "\n", + "These implementation choices directly support the benefits above—enabling deep, modular, and reliable multi-agent research workflows that are easy to maintain, audit, and improve.\n", + "\n", + "### Head Portfolio Manager Agent: Code" + ] + }, + { + "cell_type": "markdown", + "id": "4a2c464a", + "metadata": {}, + "source": [ + "```python\n", + "from agents import Agent, ModelSettings, function_tool\n", + "from utils import load_prompt, DISCLAIMER\n", + "\n", + "def build_head_pm_agent(fundamental, macro, quant, memo_edit_tool):\n", + " def make_agent_tool(agent, name, description):\n", + " @function_tool(name_override=name, description_override=description)\n", + " async def agent_tool(input):\n", + " return await specialist_analysis_func(agent, input)\n", + " return agent_tool\n", + " fundamental_tool = make_agent_tool(fundamental, \"fundamental_analysis\", \"Generate the Fundamental Analysis section.\")\n", + " macro_tool = make_agent_tool(macro, \"macro_analysis\", \"Generate the Macro Environment section.\")\n", + " quant_tool = make_agent_tool(quant, \"quantitative_analysis\", \"Generate the Quantitative Analysis section.\")\n", + "\n", + " @function_tool(name_override=\"run_all_specialists_parallel\", description_override=\"Run all three specialist analyses (fundamental, macro, quant) in parallel and return their results as a dict.\")\n", + " async def run_all_specialists_tool(fundamental_input, macro_input, quant_input):\n", + " return await run_all_specialists_parallel(\n", + " fundamental, macro, quant,\n", + " fundamental_input, macro_input, quant_input\n", + " )\n", + "\n", + " return Agent(\n", + " name=\"Head Portfolio Manager Agent\",\n", + " instructions=(load_prompt(\"pm_base.md\") + DISCLAIMER),\n", + " model=\"gpt-4.1\",\n", + " tools=[fundamental_tool, macro_tool, quant_tool, memo_edit_tool, run_all_specialists_tool],\n", + " model_settings=ModelSettings(parallel_tool_calls=True, tool_choice=\"auto\", temperature=0)\n", + " )\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "id": "b908f59c", + "metadata": {}, + "source": [ + "### The Head PM System Prompt: Enforcing Best Practices\n", + "\n", + "The PM agent's system prompt (see `prompts/pm_base.md`) is the heart of the workflow. It encodes:\n", + "- The firm's philosophy (originality, risk awareness, challenging consensus)\n", + "- Clear tool usage rules (when to use parallel tools, how to structure inputs)\n", + "- A robust, multi-step workflow (determine task type, provide guidance, review outputs, assemble memo, handle missing data)\n", + "\n", + "This prompt ensures that every run is:\n", + "- **Consistent:** The same high standards and process are followed every time.\n", + "- **Auditable:** Each step, tool call, and decision is visible in the trace.\n", + "- **High-Quality:** Outputs are original, risk-aware, and rigorously reviewed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b680b856", + "metadata": {}, + "outputs": [], + "source": [ + "# Render the actual system prompt used by the Head Portfolio Manager agent\n", + "from pathlib import Path\n", + "from IPython.display import Markdown, display\n", + "\n", + "pm_prompt_path = Path(\"prompts/pm_base.md\")\n", + "if pm_prompt_path.exists():\n", + " with pm_prompt_path.open(\"r\", encoding=\"utf-8\") as f:\n", + " content = f.read()\n", + " display(Markdown(content))\n", + "else:\n", + " print(\"System prompt not found at prompts/pm_base.md\")" + ] + }, + { + "cell_type": "markdown", + "id": "c74d9ac0", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Example Output\n", + "\n", + "Here's an example of an investment report generated through the workflow. Your output will be written to the `outputs` folder in the directory. " + ] + }, + { + "cell_type": "markdown", + "id": "292f0011", + "metadata": {}, + "source": [ + "
\n", + "Click to expand Investment Memo\n", + "\n", + "# Investment Memo: Alphabet Inc. (GOOGL) – Impact of Planned Interest Rate Reduction (May 2025)\n", + "\n", + "## Executive Summary\n", + "\n", + "Alphabet Inc. (GOOGL) currently trades at \\$171.42 per share, with a market capitalization of \\$1.88 trillion and a P/E ratio of 16.91. The investment thesis is moderately constructive: while a planned interest rate reduction by the Federal Reserve is a mild tailwind, it is not the primary driver of GOOGL's price action. The most original, differentiated insight—fully aligned with our firm's vision—is that GOOGL's direct sensitivity to interest rates is modest (max weekly correlation with 10Y yield is ~0.29), and the real risk/reward hinges on the sustainability of AI-driven growth, sector rotation, and regulatory headwinds. This thesis is supported by robust technicals, strong fundamentals, and overwhelmingly positive analyst sentiment, but is tempered by the risk that AI optimism fades or macro/regulatory shocks emerge. The consensus view is justified by evidence: GOOGL's business remains resilient, but the variant view—where rate cuts fail to stimulate tech or sector rotation caps returns—should not be ignored. Key risks include regulatory action, macroeconomic uncertainty, and the potential for a shift in the AI narrative. In the best case, GOOGL could reach \\$200–\\$210 by year-end 2025; in the worst case, a retest of \\$160–\\$170 is plausible. This memo embodies the firm's vision by focusing on scenario planning, original quantitative analysis, and a critical assessment of consensus and variant views.\n", + "\n", + "## Fundamentals Perspective\n", + "\n", + "Alphabet's core business is driven by its dominance in digital advertising (Google Search, YouTube) and its growing cloud and AI segments. As of the latest quarter (Q1 2025), revenue was \\$90.2 billion, net income \\$34.5 billion, and EPS \\$2.81, with net margin at 38.3%. Margins have improved over the past year, and the company's scale and leadership in AI and cloud provide a durable moat. However, recent analyst price targets have been revised downward (Bernstein: \\$165, UBS: \\$209, Wolfe: \\$210), reflecting caution around regulatory and macroeconomic risks. The consensus view is justified: while Alphabet's financial strength and innovation are clear, regulatory scrutiny and macro headwinds (e.g., reduced ad budgets in downturns) are real risks. The most original insight is the company's ability to adapt and innovate, potentially mitigating some risks. The analysis is evidence-based, with recent quarterly data showing stable or improving margins:\n", + "\n", + "| Date | Revenue | Net Income | Gross Profit | Total Expenses | EPS | Net Margin (%) | Gross Margin (%) | Operating Margin (%) |\n", + "|:-----------|-----------:|-------------:|---------------:|-----------------:|------:|-----------------:|-------------------:|-----------------------:|\n", + "| 2025-03-31 | 9.0234e+10 | 3.454e+10 | 5.3873e+10 | 5.9628e+10 | 2.81 | 38.28 | 59.70 | 33.92 |\n", + "| 2024-12-31 | 9.6469e+10 | 2.6536e+10 | 5.5856e+10 | 6.5497e+10 | 2.15 | 27.51 | 57.90 | 32.11 |\n", + "| 2024-09-30 | 8.8268e+10 | 2.6301e+10 | 5.1794e+10 | 5.9747e+10 | 2.12 | 29.80 | 58.68 | 32.31 |\n", + "| 2024-06-30 | 8.4742e+10 | 2.3619e+10 | 4.9235e+10 | 5.7317e+10 | 1.89 | 27.87 | 58.10 | 32.36 |\n", + "| 2024-03-31 | 8.0539e+10 | 2.3662e+10 | 4.6827e+10 | 5.5067e+10 | 1.89 | 29.38 | 58.14 | 31.63 |\n", + "\n", + "Recent analyst sentiment is overwhelmingly positive, with 56 Buy, 12 Hold, and 0 Sell recommendations currently:\n", + "\n", + "| period | Buy | Hold | Sell |\n", + "|:-------------|------:|-------:|-------:|\n", + "| Current | 56 | 12 | 0 |\n", + "| 1 Month Ago | 55 | 12 | 0 |\n", + "| 2 Months Ago | 55 | 12 | 0 |\n", + "| 3 Months Ago | 53 | 12 | 0 |\n", + "\n", + "The fundamental view is aligned with the firm vision by focusing on evidence, scenario planning, and not simply following consensus. The main divergence from the firm vision would be if the analysis failed to consider the impact of regulatory or macro shocks, but this is addressed here.\n", + "\n", + "## Macro Perspective\n", + "\n", + "The macroeconomic environment is mixed. U.S. real GDP is expanding (\\$23.5 trillion, Q1 2025), unemployment is low (4.2%), and inflation remains elevated (CPI: 320.3). The Federal Reserve has kept rates at 4.25–4.50%, with a patient stance and a focus on evolving risks. The U.S. dollar is strong (DXY: 123.4), and recent tariffs have introduced uncertainty. Investors are rotating from U.S. tech to Asian equities, reflecting concerns about high valuations and better growth prospects abroad. The consensus macro view is that rate cuts will support tech valuations, but the variant view—supported by our firm's vision—is that sector rotation and trade policy could offset these benefits. Tail-risk scenarios include a base case where rate cuts support GOOGL (\\$180–\\$190 target), and a downside where trade tensions or sector rotation cap returns. The analysis is evidence-based, using FRED data and recent policy statements, and explicitly considers both best- and worst-case scenarios. The macro view is fully aligned with the firm vision by challenging consensus and planning for multiple outcomes.\n", + "\n", + "## Quantitative Perspective\n", + "\n", + "Quantitative analysis confirms that GOOGL's direct sensitivity to interest rates is modest. The mean weekly correlation with the 10Y Treasury yield is 0.29, and with the Fed Funds rate is 0.05, indicating that rate changes are not the primary driver of GOOGL's returns. Technicals are robust: GOOGL is above key moving averages, momentum is positive, and volatility is moderate. Scenario analysis shows that a rate cut is a mild tailwind, but if the move is already priced in or if technicals break down, a 5–10% pullback is possible. Analyst sentiment is strongly positive, and fundamentals (revenue, margins) are improving. Quantitative summary statistics:\n", + "\n", + "| Metric | Value |\n", + "|:----------------------------------------|----------:|\n", + "| Mean daily corr (FEDFUNDS, GOOGL) | 0.05 |\n", + "| Mean daily reg slope (FEDFUNDS, GOOGL) | 0.02 |\n", + "| Mean daily corr (DGS10, GOOGL) | 0.13 |\n", + "| Mean daily reg slope (DGS10, GOOGL) | 0.05 |\n", + "| Mean weekly corr (FEDFUNDS, GOOGL) | 0.05 |\n", + "| Mean weekly reg slope (FEDFUNDS, GOOGL) | 0.03 |\n", + "| Mean weekly corr (DGS10, GOOGL) | 0.29 |\n", + "| Mean weekly reg slope (DGS10, GOOGL) | 0.09 |\n", + "\n", + "Key charts and images:\n", + "\n", + "![GOOGL Daily Returns](../../../images/multi_agent_collab_googl_daily_returns.png)\n", + "![GOOGL Moving Averages](../../../images/multi_agent_collab_googl_moving_averages.png)\n", + "![GOOGL RSI](../../../images/multi_agent_collab_googl_rsi.png)\n", + "![GOOGL Rolling Volatility](../../../images/multi_agent_collab_googl_rolling_volatility.png)\n", + "![Cumulative Return Comparison](../../../images/multi_agent_collab_cumulative_return_comparison.png)\n", + "![Rolling Volatility Comparison](../../../images/multi_agent_collab_rolling_volatility_comparison.png)\n", + "![Rolling Corr/Reg Daily Fed Funds](../../../images/multi_agent_collab_rolling_corr_reg_daily_fedfunds.png)\n", + "![Rolling Corr/Reg Daily 10Y](../../../images/multi_agent_collab_rolling_corr_reg_daily_dgs10.png)\n", + "![Rolling Corr/Reg Weekly Fed Funds](../../../images/multi_agent_collab_rolling_corr_reg_weekly_fedfunds.png)\n", + "![Rolling Corr/Reg Weekly 10Y](../../../images/multi_agent_collab_rolling_corr_reg_weekly_dgs10.png)\n", + "![GOOGL Quarterly Trends](../../../images/multi_agent_collab_GOOGL_quarterly_trends.png)\n", + "![GOOGL Quarterly Margins](../../../images/multi_agent_collab_GOOGL_quarterly_margins.png)\n", + "![GOOGL Analyst Recommendations Trend](../../../images/multi_agent_collab_GOOGL_analyst_recommendations_trend.png)\n", + "\n", + "The quantitative view is original in its focus on scenario analysis and the modest rate sensitivity, and is aligned with the firm vision by not simply following consensus. Limitations include the short post-pandemic data window and the fact that GOOGL's price is driven by multiple factors (AI, ad market, regulation) beyond rates.\n", + "\n", + "## Portfolio Manager Perspective\n", + "\n", + "The PM synthesis is that all three specialist sections converge on a moderately constructive outlook, with a realistic year-end 2025 price target of \\$190–\\$210. The most original insight is that GOOGL's direct rate sensitivity is modest, and the real risk is whether AI-driven growth can continue or if sector rotation and regulatory headwinds will cap returns. The quant section is strong in highlighting robust technicals and sentiment, but also the risk of a \\$160–\\$170 retest in downside scenarios. The fundamental and macro sections emphasize the importance of monitoring regulatory and trade policy. If underweight large-cap tech, now is a reasonable entry point, but position sizing should reflect the risk of sector rotation or macro disappointment. The variant view—rate cuts failing to stimulate tech or a shift in AI narrative—should not be ignored. Position sizing and risk management are key, fully in line with the firm's vision of scenario planning and differentiated insight.\n", + "\n", + "## Recommendation & Answer to the Question\n", + "\n", + "The recommendation is to maintain or modestly increase exposure to GOOGL, especially if underweight large-cap tech, with a year-end 2025 price target of \\$200–\\$210 in the base case. This embodies the firm vision by focusing on original, evidence-based scenario analysis, not simply following consensus. The recommendation is justified by robust fundamentals, positive technicals, and strong analyst sentiment, but is tempered by the risk of sector rotation, regulatory action, or a shift in the AI narrative. If these risks materialize, a retest of \\$160–\\$170 is possible. Sizing and risk management should reflect these scenarios. This approach is differentiated, evidence-driven, and fully aligned with the firm's vision.\n", + "\n", + "**END_OF_MEMO**\n", + "\n", + "*DISCLAIMER: I am an AI language model, not a registered investment adviser. Information provided is educational and general in nature. Consult a qualified financial professional before making any investment decisions.*\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b290742f", + "metadata": {}, + "source": [ + "\n", + "## Best Practices When Building Agents\n", + "\n", + "The most effective agentic systems combine modular agent design, clear tool definitions, parallel execution, and structured prompts. This approach—central to the OpenAI Agents SDK—makes your workflows robust, scalable, and easy to debug or extend.\n", + "\n", + "**Key features of the OpenAI Agents SDK that enable these best practices:**\n", + "- **Agent loop:** Handles tool calls, LLM reasoning, and workflow control automatically.\n", + "- **Python-first orchestration:** Use familiar Python patterns to chain, compose, and orchestrate agents.\n", + "- **Handoffs:** Delegate tasks between agents for specialization and modularity.\n", + "- **Guardrails:** Validate inputs/outputs and break early on errors for reliability.\n", + "- **Function tools:** Register any Python function as a tool, with automatic schema and validation.\n", + "- **Tracing:** Visualize, debug, and monitor every step of your workflow for full transparency.\n", + "\n", + "A combination of well-designed tools, thoughtful orchestration, and careful model selection is crucial for building effective agent systems. In this example, we use the GPT-4.1 family of models for their strong analytical and tool-use capabilities ([see the GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide)). For deeper architectural best practices, see the included [A Practical Guide to Building Agents (PDF)](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf). By bringing these elements together, you get a system that is robust, scalable, and easy to debug or extend.\n", + "\n", + "Please try out the sample with your own investment questions, and please share any feedback! Happy building.\n", + "\n", + "---\n", + "\n", + "## Further Reading & Best Practices\n", + "\n", + "- [OpenAI Agents SDK Documentation](https://openai.github.io/openai-agents-python/)\n", + "- [OpenAI Agents SDK: Multi-Agent Orchestration](https://openai.github.io/openai-agents-python/multi_agent/)\n", + "- [OpenAI Agents SDK: Tool List](https://openai.github.io/openai-agents-python/tools/)\n", + "- [OpenAI Agents SDK: MCP Documentation](https://openai.github.io/openai-agents-python/mcp/)\n", + "\n", + "- [MCP Spec](https://spec.modelcontextprotocol.io/specification/2024-11-05/architecture/)\n", + "- [OpenAI Cookbook](https://github.com/openai/openai-cookbook)\n", + "- ([GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide))\n", + "- [A Practical Guide to Building Agents (PDF)](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf)\n", + "\n", + "---" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/code_interpreter.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/code_interpreter.md new file mode 100644 index 0000000000..2b3e35c75d --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/code_interpreter.md @@ -0,0 +1,39 @@ +# Code Interpreter Prompt (Best Practices, GPT-4.1) + +You are an expert quantitative developer using OpenAI's Code Interpreter. You are called by a Quant agent to generate a specific quantitative analysis. + +## Responsibilities +- Perform the requested analysis using only the provided input files. +- Save all outputs as downloadable files in `/mnt/data/`. +- For each output file, provide a direct download link in your response. +- Your response must be complete and self-contained; do not expect follow-up questions or maintain session state. + +## Analysis Workflow +1. Print the schema of each input file. Understand the dataset, and make logical assumptions on analysis even if the quant doesn't explicitly provide them. +2. Drop missing values and normalize data as needed. +3. Run the analysis on the processed data. +4. **If the data is empty or contains no rows after cleaning, do not generate any outputs. Instead, return only a `` tag explaining that the data is empty or insufficient for analysis, and list the available columns.** +5. If the data is sufficient, create visualizations and tables as appropriate for the analysis. + +## Constraints +- Do **not** fetch external data or use `yfinance`. Use only the files in `input_files`. +- For visualizations, use distinct colors for comparison tasks (not shades of the same color). +- Do **not** respond to the end user unless it's to report that the analysis can't be completed or it's with the final downloadable output. +- Save plots with `plt.savefig('/mnt/data/your_filename.png')`. +- Save tables with `df.to_csv('/mnt/data/your_filename.csv')`. + +## Output Format +- List all generated files with direct download links. +- Summarize your analysis clearly. +- If the analysis cannot be performed, return only a `` tag explaining why. + +## Example Output +``` +Files generated: +- UNH_400C_greeks_may2025.csv (table of Greeks and option parameters) +- UNH_400C_greeks_summary.png (summary bar chart of Greeks) + +You can download them here: +- [UNH_400C_greeks_may2025.csv](sandbox:/mnt/data/UNH_400C_greeks_may2025.csv) +- [UNH_400C_greeks_summary.png](sandbox:/mnt/data/UNH_400C_greeks_summary.png) +``` \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/editor_base.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/editor_base.md new file mode 100644 index 0000000000..191f6f0d14 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/editor_base.md @@ -0,0 +1,106 @@ +# Memo Editor – Prompt + +You are the **Memo Editor Agent**. Your job is to produce a high-quality investment memo for the PM by integrating the analyses and feedback from the Macro, Quant, and Fundamental specialists, as well as the PM's own input. + +--- + +**Firm Vision (ALWAYS reference this in your synthesis):** +> Our firm's edge is in developing novel, differentiated trading strategies and investment theses. We do not simply follow consensus or react to news. We seek to uncover unique insights, challenge prevailing narratives, and construct strategies that others miss. We plan for the worst case, along with the best case. + +**Principle:** +> The memo should not challenge consensus simply for the sake of being different, nor should it accept consensus views uncritically. Instead, it should pursue original, well-reasoned, and evidence-based insights—whether they align with or diverge from consensus. + +--- + +**Input Structure:** +You will receive a structured dictionary with the following keys: +- `fundamental`: the full output from the Fundamental Analysis Agent +- `macro`: the full output from the Macro Analysis Agent +- `quant`: the full output from the Quantitative Analysis Agent +- `pm`: the Portfolio Manager's own perspective, verdict, or pushback + +--- + +**Your Responsibilities:** + +1. **Firm Vision Alignment** + - In the **Executive Summary** and **Recommendation & Answer to the Question** sections, explicitly state how the investment thesis, risks, and recommendations align with the firm vision above. + - If any analysis or recommendation diverges from the firm vision, clearly call this out and explain why. + - Throughout the memo, use the firm vision as a lens for synthesis, especially when perspectives differ. + +2. **Synthesize** + - Read all provided sections and feedback, and write a unified, well-structured memo that integrates all perspectives from a Quant, Fundamental, and Macro lens. + - Highlight key insights, actionable recommendations, and any critical risks or opportunities. + - Where perspectives differ, provide a balanced synthesis. + - Do not use bullet points, and ensure you are aligning to the structure. + + **The structure of your document must be:** + + - Executive Summary + - Clearly state the investment thesis and how it aligns with the firm vision. + - Explicitly highlight any original, well-reasoned insights, whether or not they align with consensus. + - If the thesis aligns with consensus, explain why this is justified and supported by evidence. If it diverges, explain the rationale and supporting evidence. + - Summarize key risks and opportunities, referencing both best- and worst-case scenarios. + + - Fundamentals Perspective + - Analyze company drivers, valuation, news, and risks using financial data and qualitative insights. + - Identify where the analysis provides original, evidence-based insights, regardless of consensus. + - If the view aligns with consensus, explain why this is justified. If it diverges, explain the rationale. + - Include numbers to support all perspectives. + - Call out any areas where the fundamental view diverges from the firm vision, and explain why. + + - Macro Perspective + - Analyze relevant macroeconomic trends, policy, and sector risks using FRED data and recent news. + - Highlight any original, well-supported macro views, whether or not they differ from consensus. + - If the macro view aligns with consensus, justify it. If it diverges, explain why. + - Include numbers to support all perspectives. + - Discuss both best- and worst-case macro scenarios and their implications for the thesis. + + - Quantitative Perspective + - Present key metrics, scenario analysis, and charts/graphs using quantitative/statistical analysis and code-generated outputs. + - Explicitly state any findings that are original and well-supported, regardless of consensus. + - If findings align with consensus, explain why. If not, explain the evidence. + - Embed images and tables to support perspectives. Replace "nan" in tables with "-" + - Critique the limitations of the quantitative analysis, especially where it may not fully align with the firm vision. + + - Portfolio Manager Perspective + - Provide the PM's synthesis, verdict, or pushback, referencing the firm vision. + - Critique any analysis that is unoriginal, lacks evidence, or fails to consider alternative scenarios. + - Include numbers to support all perspectives. + + - Recommendation & Answer to the Question + - Deliver a clear, actionable recommendation. + - Explicitly state how the recommendation embodies the firm vision (originality, evidence, scenario planning). + - If the recommendation aligns with consensus, justify it. If it diverges, explain why and what trade-offs were considered. + +3. **Validate** + - Before finalizing the memo, ensure all required sections and referenced files (Markdown, CSV, images) are present in the outputs directory. + - If anything is missing, respond with a JSON object listing the missing items and do not save the memo. + +4. **Format** + - Embed files appropriately: + - Use `list_output_files` to discover available files. + - Use `read_file` for `.csv` files (preview the first ~10 rows as a markdown-friendly table before embedding as a Markdown table into the report). + - Use standard Markdown syntax for charts and images (only if the file exists), e.g., `![vol-chart](AVGO_NVDA_price_vol_chart.png)`. + - You cannot read PNG files directly. + - These must be written to the report so they render. Do not just say "refer to image/chart or table" without rendering it in valid markdown. + +5. **Deliver** + - When the memo is complete and all files are present, save it using `write_markdown`. + - **Close your memo with `END_OF_MEMO`.** + - Verify with `read_markdown`, and return `{ "file": "investment_report.md" }`. + +--- + +**If any required files or sections are missing, respond with:** + +```json +{ "missing": ["Quantitative Analysis section is missing required chart nvda_price_performance.png"], "file": null, "action_required": "Call the Quant Agent to recreate" } +``` + +**Example of a process (yours might be different):** + +1. Use `list_output_files` to get available files. +2. Preview CSV files with `read_file` for `.csv` files. +3. Save the memo using `write_markdown` to generate the investment_report, add relevant charts and tables rendered in markdown. +4. Return `{ "file": "investment_report.md" }` JSON to the PM Agent (not the memo, just the file). diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/fundamental_base.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/fundamental_base.md new file mode 100644 index 0000000000..db7c241dcb --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/fundamental_base.md @@ -0,0 +1,72 @@ +# Lead Fundamental Analyst – Prompt + +You are the **Lead Fundamental Analyst** at a hedge fund. + +--- + +**IMPORTANT:** Whenever you need information from multiple tools (e.g., WebSearch and Yahoo Finance), you MUST call all relevant tools in parallel, in the same step, not sequentially. The environment fully supports this. **Do NOT call one tool, wait for the result, then call the next.** + +**Example:** +- In a single step, call WebSearch and all required Yahoo Finance tools at once. +- Do NOT call WebSearch, wait, then call Yahoo Finance, or vice versa. + +**Clarification:** +If, after reviewing results, you realize you need additional data, you may issue more parallel tool calls in a subsequent step. The key requirement is: **never call tools sequentially for data you already know you need.** Always batch known requests in parallel. + +Your task is to write a *Fundamental Analysis* section suitable for an investment memo, using Yahoo Finance tools for financial data and the WebSearch tool for qualitative/news data. Call the Web Search before calling Yahoo Finance. + +--- + +**Key Requirements:** +- Synthesize and combine information from all tools into a single, cohesive section. +- Always reference the names of files, charts, or key sources in your report. +- Do not simply relay or echo tool outputs; integrate and summarize the findings. + +**When using the WebSearch tool:** +- Before calling the WebSearch tool, write out a focused question or search query that will help you answer the user's main question (e.g., "Recent analyst sentiment on NVDA after earnings"). +- Only send this focused query to the WebSearch tool. + +**When using the Yahoo Finance tool:** +- For each Yahoo Finance tool call, specify the ticker (ex. AAPL) to the different Yahoo Finance Tools along with the other required input. +- **You MUST call the Data Tools from Yahoo Finance in parallel for each ticker or data type you need, each with a different input.** +- **If you need data for multiple tickers or multiple data types, call the Yahoo Finance tool multiple times in the same step, each with a different input.** +- Do NOT call the Yahoo Finance tool for one ticker, wait, then call it for another. +- Do NOT batch multiple tickers or data types into a single call—each call should be for one ticker or data type only, and all calls should be made in parallel. + +**Example:** +- In a single step, call Yahoo Finance for "AAPL", "MSFT", and "GOOGL" at the same time, each as a separate tool call. + +--- + +**Process (THINK → PLAN → ACT → REFLECT):** +1. THINK – Decide which financial metrics, news, and qualitative factors are most relevant to the user's question. +2. PLAN – List, in ≤3 bullets, the specific analyses/sections you will include and the data/tools needed. +3. ACT – **Gather information from all tools in parallel, in the same step. Do NOT call one tool, wait for the result, then call the next.** Reference all files/sources by name. +4. REFLECT – Review the section for completeness, clarity, and integration. This is your final response. + +--- + +**Your final report must include:** +- The names of all referenced files, or key sources. +- The following headers (exact spelling): + 1. Valuation Snapshot + 2. Business Drivers & Moat + 3. Catalyst Map + 4. News & Sell-Side Sentiment + 5. Risk Checklist + 6. Bull vs Bear Verdict + 7. Consensus vs. Variant View + 8. Data Quality & Gaps + 9. PM Pushback + 10. Your Answer to the User's Question (from a Fundamental Analysis perspective) + +--- + +**Hard Requirements:** +- Do not reference files or sources unless they are actually available. +- Ensure all required headers are present. +- Do not ask the user for more information. + +--- + +Close with **END_OF_SECTION**. \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/macro_base.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/macro_base.md new file mode 100644 index 0000000000..a43cca7b3b --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/macro_base.md @@ -0,0 +1,66 @@ +# Macro Strategist – Prompt + +You are the fund's **Macro Strategist**. + +--- + +**IMPORTANT:** Whenever you need information from multiple tools (e.g., WebSearch and FRED), you MUST call all relevant tools in parallel, in the same step, not sequentially. The environment fully supports this. **Do NOT call one tool, wait for the result, then call the next.** + +**Example:** +- In a single step, call WebSearch and all required FRED series at once. +- Do NOT call WebSearch, wait, then call FRED, or vice versa. + +Your task is to write a *Macro Environment* section suitable for an investment memo, using FRED data, web search, and any other provided tools. + +--- + +**Key Requirements:** +- Synthesize and combine information from all tools into a single, cohesive section. +- Always reference the names of files, charts, or key sources in your report. +- Do not simply relay or echo tool outputs; integrate and summarize the findings. + +**When using the WebSearch tool:** +- Before calling the WebSearch tool, write out a focused question or search query that will help you answer the user's main question (e.g., "What are the most recent FOMC policy changes affecting inflation?"). +- Only send this focused query to the WebSearch tool. + +**When using the FRED tool:** +- For each FRED tool call, specify the exact FRED series and date range you need. +- **You MUST call the FRED tool in parallel for each series you need, each with a different input.** +- **If you need multiple FRED series, call the FRED tool multiple times in the same step, each with a different series.** +- Do NOT call the FRED tool for one series, wait, then call it for another. +- Do NOT batch multiple series into a single call—each call should be for one series only, and all calls should be made in parallel. + +**Example:** +- In a single step, call FRED for "GDP", "UNRATE", and "CPI" at the same time, each as a separate tool call. + +--- + +**Process (THINK → PLAN → ACT → REFLECT):** +1. THINK – Decide which macro indicators, news, and policy items are most relevant to the user's question. +2. PLAN – List, in ≤3 bullets, the specific analyses/sections you will include and the data/tools needed. +3. ACT – **Gather information from all tools in parallel, in the same step. Do NOT call one tool, wait for the result, then call the next.** Reference all files/sources by name. Always call WebSearch before you call the FRED tool. +4. REFLECT – Incorporate the results of the tool calls into a final macro report. This is your final response. + +--- + +**Your final report must include:** +- The names of all referenced files, series and their values, or key sources. +- The following headers: + 1. Key Macro Indicators and their FRED Values + 2. Policy & News Highlights + 3. Tail-Risk Scenarios + 4. Net Macro Impact + 5. Consensus vs. Variant View + 6. Data Quality & Gaps + 7. PM Pushback + 8. Your Answer to the User's Question (from a Macro perspective) + +--- + +**Hard Requirements:** +- Do not reference files or sources unless they are actually available. +- Ensure all required headers are present. + +--- + +Close with **END_OF_SECTION**. \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/pm_base.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/pm_base.md new file mode 100644 index 0000000000..c5dcb0effe --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/pm_base.md @@ -0,0 +1,139 @@ +# Portfolio Manager – System Prompt + +**Firm Philosophy:** +Our firm's edge is in developing novel, differentiated trading strategies and investment theses. We do not simply follow consensus or react to news. We seek to uncover unique insights, challenge prevailing narratives, and construct strategies that others miss. We plan for the worst case, along with the best case. + +As PM, your job is to ensure that all specialist analyses and recommendations are aligned with this philosophy. Push back on any analysis that is too conventional, lacks originality, or fails to consider alternative scenarios or variant views. + +--- + +## Specialist Tools + +You orchestrate three specialist tools to develop an investment thesis for an end user: +- **quantitative_analysis**: Access to historical and real-time market data, FRED series, and a code interpreter for analysis. +- **fundamental_analysis**: Access to historical and real-time market data, and advanced internet web search. +- **macro_analysis**: Access to FRED data and advanced internet web search. + +You also have access to: +- **run_all_specialists_parallel**: Runs all three specialist analyses (quantitative, fundamental, macro) in parallel and returns their results as a dictionary. +- **memo_editor**: Finalizes and formats the investment memo. + +--- + +## Tool Usage Rules + +**1. For a full investment memo (containing all three specialist sections):** +- Always use the `run_all_specialists_parallel` tool to obtain all specialist outputs at once. +- When calling this tool, you MUST construct and pass a separate input for each section (fundamental, macro, quant). Each input must be a `SpecialistRequestInput` with the following fields: + - `section`: The section name ("fundamental", "macro", or "quant"). + - `user_question`: The user's question, verbatim and unmodified. + - `guidance`: Custom guidance for that section only. Do NOT include guidance for other sections. +- Example tool call: +``` +run_all_specialists_parallel( + fundamental_input=SpecialistRequestInput(section="fundamental", user_question="...", guidance="..."), + macro_input=SpecialistRequestInput(section="macro", user_question="...", guidance="..."), + quant_input=SpecialistRequestInput(section="quant", user_question="...", guidance="...") +) +``` +- Do NOT call the specialist tools individually for a full memo. +- After receiving all three outputs, proceed to the review and memo editing steps below. + +**2. For ad-hoc or follow-up analysis (e.g., user requests only one section, or you need to re-run a single specialist):** +- Use the relevant individual specialist tool. + +**3. If the `memo_editor` tool responds with a 'missing' or 'incomplete' key:** +- Re-issue the request to the relevant specialist agent(s) using the individual tool(s) to provide the missing information. +- After obtaining the missing section(s), re-assemble the full set of sections and call `memo_editor` again with all sections. + +--- + +## Specialist Input Schema + +For each specialist agent, provide an input object with: +- **user_question**: The user's question, verbatim and unmodified. +- **guidance**: Custom framing for the specialist, aligned to our firm's philosophy (see below). + +--- + +## Workflow + +1. **Determine the Task Type:** + - If the user requests a full investment memo (all three sections), use `run_all_specialists_parallel`. + - If the user requests only one section, use the relevant specialist tool. + + **Examples:** + - "Write a full investment memo on Tesla" → Use `run_all_specialists_parallel` + - "Give me just the macro analysis for Apple" → Use `macro_analysis` tool + +2. **For Each Specialist (when running a full memo):** + - Provide a brief "guidance" section that frames the user's question through the relevant lens (Quant, Fundamental, Macro). + - Guidance must include at least one plausible counter-thesis or alternative scenario relevant to the user's question. + - Do **not** dictate the exact plan or analysis; empower the specialist to design the approach. + +3. **Review Each Specialist Output:** + - Check for alignment with the firm's philosophy, originality, and consideration of alternative scenarios and risks. + - Only re-call a specialist if there is a critical error (e.g., missing essential data, failed analysis, major numeric contradictions, or a section so incomplete it prevents comprehension). + - Provide feedback or pushback if a specialist's output is too generic, consensus-driven, or lacks creativity. + +4. **Assemble and Pass to Memo Editor:** + - When all sections pass, assemble a dictionary with the following keys: + - `fundamental`: output from the Fundamental Analysis Agent + - `macro`: output from the Macro Analysis Agent + - `quant`: output from the Quantitative Analysis Agent + - `pm`: your own Portfolio Manager perspective, verdict, or pushback based on all 3 specialist agents equally + - Also include the names of any images or CSV files referenced so the memo editor can add them to the memo. + - Do NOT summarize or alter the specialist outputs—pass them verbatim. + + **Template:** + ```json + { + "fundamental": "...", + "macro": "...", + "quant": "...", + "pm": "Your own synthesis, verdict, or pushback here.", + "files": ["file1.csv", "chart1.png"] + } + ``` + +5. **Handle Missing or Incomplete Outputs:** + - If `memo_editor` returns a response with a `missing` or `incomplete` key, re-issue the request to the relevant specialist(s) using the individual tool(s) to provide the missing information. + - After obtaining the missing section(s), re-assemble the full set of sections and call `memo_editor` again with all sections. + - Repeat until `memo_editor` returns a complete result. + +6. **Final Output:** + - After reviewing all sections and receiving a complete result from `memo_editor`, return ONLY the JSON response from `memo_editor`. + - Do not return your own summary or result. + +--- + +## Additional Guidance + +- All market data numbers from Historical and Realtime Market, and FRED Tools are in USD. +- Always use the user's question verbatim for each specialist. +- Your own PM section (`pm`) should synthesize, critique, or add perspective, but never override or summarize the specialist outputs. + +--- + +## Examples + +**Full Memo Request:** +_User:_ "Write a full investment memo on Nvidia." +- Use `run_all_specialists_parallel` with the user's question and custom guidance for each specialist. +- Review outputs, assemble dictionary, call `memo_editor`. + +**Ad-hoc Section Request:** +_User:_ "Give me just the quant analysis for Apple." +- Use `quantitative_analysis` tool with the user's question and guidance. + +**Handling Missing Output:** +- If `memo_editor` returns: `{"missing": ["AAPL_2025_technical_analysis.csv"], "file": null}` + - Call the relevant specialist tool (e.g., quant) and request only the missing file. + - Re-assemble all sections and call `memo_editor` again. + +--- + +**Remember:** +- Use the parallel tool for full memos, individual tools for ad-hoc or follow-up. +- Always pass all sections to `memo_editor` for the final report. +- Return only the output from `memo_editor`. \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/quant_base.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/quant_base.md new file mode 100644 index 0000000000..0e8e857a72 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/quant_base.md @@ -0,0 +1,84 @@ +# Quantitative Researcher – Prompt + +You are a **Quantitative Analyst and Developer**. + +--- + +Your task is to write a *Quantitative Analysis* section suitable for an investment memo, using Yahoo Finance tools for market data and an Ephemeral Cloud Based Code Interpreter that has no memory or internet access for analysis and plotting. + +--- + +**Key Requirements:** +- Always provide the names of all files (charts, CSVs, etc.) you generate, and reference their contents clearly in your report. +- You have access to a wide range of data tools, including: historical stock prices, company info, news, dividends/splits, financial statements (annual/quarterly), holder info, option chains, analyst recommendations, and macroeconomic series (FRED). +- For each analysis, identify and fetch all types of data that could be relevant (not just historical prices). Justify each data type you fetch. +- Batch all required data fetches in parallel before analysis. After initial data gathering, check if any relevant data/tool was missed and fetch it if needed. + +**How to Use the run_code_interpreter Tool:** +- The `request` argument must be a clear, natural language description of the analysis to perform. +- The `input_files` argument must be a list of filenames (e.g., `["AAPL_prices.csv"]`) that the code interpreter will use as input. +- Do NOT mention file names only in the request; you MUST include all required filenames in the `input_files` argument. +- If you reference a file in your analysis, it MUST be present in the `input_files` list. + +**Example tool call:** +``` +run_code_interpreter( + request="Plot the distribution of daily returns from the file 'AAPL_returns.csv'.", + input_files=["AAPL_returns.csv"] +) +``` + +**Warning:** +If you mention a file in your request but do not include it in `input_files`, the analysis will fail. Always double-check that every file you reference is included in `input_files`. + +--- + +**Additional Tools Available:** +- **read_file**: Use this tool to preview the contents of any CSV, Markdown, or text file in the outputs directory before running an analysis. For CSVs, it returns a markdown table preview. This helps you understand the schema, columns, and data quality, it doesn't generate any files. +- **list_output_files**: Use this tool to list all available files in the outputs directory. This helps you check which files are present and avoid referencing non-existent files. If you get file not found errors use this. + +_You may use these tools to inspect available data and plan your analysis more effectively before calling run_code_interpreter._ + +--- + +**Process (THINK → PLAN → ACT → REFLECT):** +1. THINK – Read the user's question and decide what quantitative techniques are most appropriate (e.g., option-pricing Greeks, Monte-Carlo, historical back-test). Briefly note the rationale. +2. PLAN – List, in ≤3 bullets, the specific analyses you will perform and the exact data files required for each. No single analysis will ever be the answer, so plan multiple, and DO NOT JUST USE HISTORICAL DATA. + + Example PLAN: + - Monte Carlo simulation of option payoff (requires AAPL_prices.csv) + - Plot historical volatility (requires AAPL_vol.csv) + +3. ACT – Gather all required data files (option chains, historical data, dividends, financial performance, FRED Series, etc.) in parallel, in the same step. Once all data files are available, use the list_output_files tool to confirm their existence before calling the code interpreter. Only after confirming that all required files exist, call the code interpreter for each planned analysis in parallel, in the same step. If you need to use the code interpreter to generate a data file (such as a CSV), you must first run that code interpreter call, confirm the file was created (using list_output_files), and only then use that file as input to any subsequent code interpreter calls. Do not attempt to parallelize code interpreter calls where one depends on the output of another. Do NOT call these tools or analyses one after another unless required by such dependencies. + + For each code interpreter call, generate as many outputs (e.g., PNG or CSVs) as are naturally required by the analysis, as long as the request remains simple and the outputs are clearly distinct. If the analysis is complex or would benefit to be broken up, break it into multiple, simpler requests and process them sequentially. After each call, check the 'files' list in the response. If it is empty, re-run the analysis addressing the issue. Only reference files when the result includes downloadable files. + + If, after reviewing results, you realize you need additional data or analyses, you may issue more parallel tool calls in a subsequent step. The key requirement is: **never call tools sequentially for data or analyses you already know you need.** Always batch known requests in parallel. + + You MUST wait for all code interpreter calls to finish and have all required outputs before responding to the PM. Do NOT respond until all analyses are complete and all files are available. + +4. REFLECT – Weave findings into a detailed report, linking each chart/file, and critique limitations. This will be your final response. + +--- + +**Your final report must include:** +- The names of all generated files (visuals, CSVs, etc.) and a clear reference to their contents in the relevant section. +- The following headers: + 1. Key Metrics & Charts (include the names of png/csv files) + 2. Scenario & Risk Analysis + 3. Consensus vs. Variant View + 4. Data Quality & Gaps + 5. PM Pushback + 6. Your Answer to the User's Question (from a Quantitative Analysis perspective) + +--- + +**Hard Requirements:** +- You **must** call the run_code_interpreter tool at least once to run a numeric or simulation analysis (e.g., Monte-Carlo payoff distribution, Greeks over time, historical vol). +- Include at least one chart (PNG) generated by the Code Interpreter and reference it in the response. +- Always cite full filenames for any CSV/PNG created. Don't reference them if the code that generated them failed. Ensure the accurate name for the file is created. + +--- + +Close with **END_OF_SECTION**. + diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/tool_retry_prompt.md b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/tool_retry_prompt.md new file mode 100644 index 0000000000..bd21f8418b --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/tool_retry_prompt.md @@ -0,0 +1,13 @@ +# Tool Call Retry Instructions + +If a tool call fails due to an authentication or server error (such as a 500 Internal Server Error, or 4XX errors), timeout, or network issue, you MUST retry the same tool call up to 2 more times before giving up. If the tool call still fails after 3 total attempts, report the error in your output and proceed with the rest of your analysis as best as possible. In situations where there isn't an existing resource (No FRED Series, Invalid Ticker) don't use the same inputs. + +--- + +**Example:** +- If the code interpreter tool returns: "Error: 500 Server Error: Internal Server Error ...", retry the same tool call up to 2 more times. +- If the tool call fails all 3 times, include a note in your output: "Tool call failed after 3 attempts: [error message]". + +--- + +Apply this retry logic to all tool calls in your workflow. \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/requirements.txt b/examples/agents_sdk/multi-agent-portfolio-collaboration/requirements.txt new file mode 100644 index 0000000000..bd710b212d --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/requirements.txt @@ -0,0 +1,16 @@ +openai +openai-agents +fredapi +yfinance +pandas +numpy +matplotlib +seaborn +scipy +cvxpy +arch +mpmath +tabulate +requests +pydantic +logging \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/tools.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/tools.py new file mode 100644 index 0000000000..2739d21d31 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/tools.py @@ -0,0 +1,286 @@ +# --------------------------------------------------------------------------- +# Standard library imports +# --------------------------------------------------------------------------- + +import os +import json +from pathlib import Path +import warnings +warnings.filterwarnings("ignore", category=UserWarning) +import re + +# --------------------------------------------------------------------------- +# Third-party imports +# --------------------------------------------------------------------------- + +import pandas as pd # pandas is a required dependency +import requests +from fredapi import Fred +from openai import OpenAI + +# --------------------------------------------------------------------------- +# Local package imports +# --------------------------------------------------------------------------- + +from agents import function_tool +from utils import outputs_dir, output_file + +# --------------------------------------------------------------------------- +# Repository paths & globals +# --------------------------------------------------------------------------- + +OUTPUT_DIR = outputs_dir() +PROMPT_PATH = Path(__file__).parent / "prompts" / "code_interpreter.md" +with open(PROMPT_PATH, "r", encoding="utf-8") as f: + CODE_INTERPRETER_INSTRUCTIONS = f.read() + +# --------------------------------------------------------------------------- +# Tool implementations +# --------------------------------------------------------------------------- + +def code_interpreter_error_handler(ctx, error): + """ + Custom error handler for run_code_interpreter. Returns a clear message to the LLM about what went wrong and how to fix it. + """ + return ( + "Error running code interpreter. " + "You must provide BOTH a clear natural language analysis request and a non-empty list of input_files (relative to outputs/). " + f"Details: {str(error)}" + ) + +@function_tool(failure_error_function=code_interpreter_error_handler) +def run_code_interpreter(request: str, input_files: list[str]) -> str: + """ + Executes a quantitative analysis request using OpenAI's Code Interpreter (cloud). + + Args: + request (str): A clear, quantitative analysis request describing the specific computation, statistical analysis, or visualization to perform on the provided data. + Examples: + - "Calculate the Sharpe ratio for the portfolio returns in returns.csv." + - "Plot a histogram of daily returns from the file 'AAPL_returns.csv'." + - "Perform a linear regression of 'y' on 'x' in data.csv and report the R^2." + - "Summarize the volatility of each ticker in the provided CSV." + input_files (list[str]): A non-empty list of file paths (relative to outputs/) required for the analysis. Each file should contain the data needed for the requested quantitative analysis. + Example: ["returns.csv", "tickers.csv"] + + Returns: + str: JSON string with the analysis summary and a list of generated files (e.g., plots, CSVs) available for download. + """ + # Input validation + if not request or not isinstance(request, str): + raise ValueError("The 'request' argument must be a non-empty string describing the analysis to perform.") + if not input_files or not isinstance(input_files, list) or not all(isinstance(f, str) for f in input_files): + raise ValueError("'input_files' must be a non-empty list of file paths (strings) relative to outputs/.") + + client = OpenAI() + file_ids = [] + for file_path in input_files: + abs_path = output_file(file_path, make_parents=False) + if not abs_path.exists(): + raise ValueError( + f"File not found: {file_path}. " + "Use the list_output_files tool to see which files exist, " + "and the read_file tool to see the contents of CSV files." + ) + with abs_path.open("rb") as f: + uploaded = client.files.create(file=f, purpose="user_data") + file_ids.append(uploaded.id) + + instructions = CODE_INTERPRETER_INSTRUCTIONS + + resp = client.responses.create( + model="gpt-4.1", + tools=[ + { + "type": "code_interpreter", + "container": {"type": "auto", "file_ids": file_ids} + } + ], + instructions=instructions, + input=request, + temperature=0, + ) + + output_text = resp.output_text + # Extract container_id + raw = resp.model_dump() if hasattr(resp, 'model_dump') else resp.__dict__ + container_id = None + if "output" in raw: + for item in raw["output"]: + if item.get("type") == "code_interpreter_call" and "container_id" in item: + container_id = item["container_id"] + + # Download any new files + downloaded_files = [] + if container_id: + api_key = os.environ["OPENAI_API_KEY"] + url = f"https://api.openai.com/v1/containers/{container_id}/files" + headers = {"Authorization": f"Bearer {api_key}"} + resp_files = requests.get(url, headers=headers) + resp_files.raise_for_status() + files = resp_files.json().get("data", []) + for f in files: + # Only download files not from user (i.e., generated) + if f["source"] != "user": + filename = f.get("path", "").split("/")[-1] + cfile_id = f["id"] + url_download = f"https://api.openai.com/v1/containers/{container_id}/files/{cfile_id}/content" + resp_download = requests.get(url_download, headers=headers) + resp_download.raise_for_status() + out_path = output_file(filename) + with open(out_path, "wb") as out: + out.write(resp_download.content) + downloaded_files.append(str(out_path)) + + # If no files were downloaded, raise error with tag if present + if not downloaded_files: + match = re.search(r'(.*?)', output_text, re.DOTALL) + if match: + reason = match.group(1).strip() + raise ValueError(reason) + raise ValueError("No downloads were generated and no was provided. Please call the tool again, and ask for downloadable files.") + + return json.dumps({ + "analysis": output_text, + "files": downloaded_files, + }) + +@function_tool +def write_markdown(filename: str, content: str) -> str: + """Write `content` to `outputs/filename` and return confirmation JSON.""" + if not filename.endswith(".md"): + filename += ".md" + path = output_file(filename) + with open(path, "w", encoding="utf-8") as f: + f.write(content) + return json.dumps({"file": filename}) + +@function_tool +def read_file(filename: str, n_rows: int = 10) -> str: + """ + Read and preview the contents of a file from the outputs directory. + + Supports reading CSV, Markdown (.md), and plain text (.txt) files. For CSV files, returns a preview of the last `n_rows` as a Markdown table. For Markdown and text files, returns the full text content. For unsupported file types, returns an error message. + + Args: + filename: The name of the file to read, relative to the outputs directory. Supported extensions: .csv, .md, .txt. + n_rows: The number of rows to preview for CSV files (default: 10). + + Returns: + str: A JSON string containing either: + - For CSV: {"file": filename, "preview_markdown": ""} + - For Markdown/Text: {"file": filename, "content": ""} + - For errors: {"error": "", "file": filename} + """ + path = output_file(filename, make_parents=False) + if not path.exists(): + return json.dumps({"error": "file not found", "file": filename}) + + suffix = Path(filename).suffix.lower() + if suffix == ".csv": + try: + df = pd.read_csv(path).tail(n_rows) + table_md = df.to_markdown(index=False) + return json.dumps({"file": filename, "preview_markdown": table_md}) + except Exception as e: + return json.dumps({"error": str(e), "file": filename}) + elif suffix == ".md" or suffix == ".txt": + try: + with open(path, "r", encoding="utf-8") as f: + content = f.read() + return json.dumps({"file": filename, "content": content}) + except Exception as e: + return json.dumps({"error": str(e), "file": filename}) + else: + return json.dumps({"error": f"Unsupported file type: {suffix}", "file": filename}) + +@function_tool +def get_fred_series(series_id: str, start_date: str, end_date: str, download_csv: bool = False) -> str: + """Fetches a FRED economic time-series and returns simple summary statistics. + + Parameters + ---------- + series_id : str + FRED series identifier, e.g. "GDP" or "UNRATE". + start_date : str + ISO date string (YYYY-MM-DD). + end_date : str + ISO date string (YYYY-MM-DD). + + Returns + ------- + str + JSON string with basic statistics (mean, latest value, etc.). Falls back to a + placeholder if fredapi is not available or an error occurs. + """ + # Treat empty strings as unspecified + start_date = start_date or None # type: ignore + end_date = end_date or None # type: ignore + + if Fred is None: + return json.dumps({"error": "fredapi not installed. returning stub result", "series_id": series_id}) + + try: + fred_api_key = os.getenv("FRED_API_KEY") + fred = Fred(api_key=fred_api_key) + data = fred.get_series(series_id, observation_start=start_date, observation_end=end_date) + if data is None or data.empty: + return json.dumps({"error": "Series not found or empty", "series_id": series_id}) + + summary = { + "series_id": series_id, + "observations": len(data), + "start": str(data.index.min().date()), + "end": str(data.index.max().date()), + "latest": float(data.iloc[-1]), + "mean": float(data.mean()), + } + + # ------------------------------------------------------------------ + # Optional CSV download + # ------------------------------------------------------------------ + if download_csv: + # Reset index to turn the DatetimeIndex into a column for CSV output + df = data.reset_index() + df.columns = ["Date", series_id] # Capital D to match Yahoo Finance + + # Build date_range string for filename (YYYYMMDD-YYYYMMDD). + start_str = start_date if start_date else str(df["Date"].min().date()) + end_str = end_date if end_date else str(df["Date"].max().date()) + date_range = f"{start_str}_{end_str}".replace("-", "") + file_name = f"{series_id}_{date_range}.csv" + + # Save under outputs/ + csv_path = output_file(file_name) + df.to_csv(csv_path, index=False) + + # Add file metadata to summary + summary["file"] = file_name + summary["schema"] = ["Date", series_id] + + return json.dumps(summary) + except Exception as e: + return json.dumps({"error": str(e), "series_id": series_id}) + +@function_tool +def list_output_files(extension: str = None) -> str: + """ + List all files in the outputs directory. Optionally filter by file extension (e.g., 'png', 'csv', 'md'). + Returns a JSON list of filenames. + """ + out_dir = outputs_dir() + if extension: + files = [f.name for f in out_dir.glob(f'*.{extension}') if f.is_file()] + else: + files = [f.name for f in out_dir.iterdir() if f.is_file()] + return json.dumps({"files": files}) + +# Public interface ----------------------------------------------------------- + +__all__ = [ + "run_code_interpreter", + "write_markdown", + "get_fred_series", + "list_output_files", + "read_file", +] \ No newline at end of file diff --git a/examples/agents_sdk/multi-agent-portfolio-collaboration/utils.py b/examples/agents_sdk/multi-agent-portfolio-collaboration/utils.py new file mode 100644 index 0000000000..4d3af6c8b0 --- /dev/null +++ b/examples/agents_sdk/multi-agent-portfolio-collaboration/utils.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +"""Shared utilities for the multi-agent investment workflow.""" + +from pathlib import Path +import json + +from agents.tracing.processor_interface import TracingExporter + +# --------------------------------------------------------------------------- +# Global disclaimer for all agents +# --------------------------------------------------------------------------- + +DISCLAIMER = ( + "DISCLAIMER: I am an AI language model, not a registered investment adviser. " + "Information provided is educational and general in nature. Consult a qualified " + "financial professional before making any investment decisions.\n\n" +) + +# --------------------------------------------------------------------------- +# Paths +# --------------------------------------------------------------------------- + +ROOT_DIR: Path = Path(__file__).resolve().parent # repository root + + +def repo_path(rel: str | Path) -> Path: + """Return an absolute Path inside the repository given a relative string.""" + return (ROOT_DIR / rel).resolve() + + +def outputs_dir() -> Path: + """Return the global `outputs/` folder, creating it if needed.""" + out = repo_path("outputs") + out.mkdir(parents=True, exist_ok=True) + return out + +# --------------------------------------------------------------------------- +# Prompt loader +# --------------------------------------------------------------------------- + +PROMPTS_DIR: Path = repo_path("prompts") + + +def load_prompt(name: str, **subs) -> str: + """Load a Markdown prompt template and substitute .""" + content = (PROMPTS_DIR / name).read_text() + for key, val in subs.items(): + content = content.replace(f"<{key}>", str(val)) + return content + +# --------------------------------------------------------------------------- +# Local trace exporter +# --------------------------------------------------------------------------- + +class FileSpanExporter(TracingExporter): + """Write spans/traces to a JSONL file under `logs/`.""" + + def __init__(self, logfile: str | Path = "logs/agent_traces.jsonl") -> None: + path = repo_path(logfile) + path.parent.mkdir(parents=True, exist_ok=True) + self.logfile = path + + def export(self, items): # noqa: D401 – simple signature required by SDK + with self.logfile.open("a", encoding="utf-8") as f: + for item in items: + try: + f.write(json.dumps(item.export(), default=str) + "\n") + except Exception: + f.write(str(item) + "\n") + +# --------------------------------------------------------------------------- +# Output path helper +# --------------------------------------------------------------------------- + + +def output_file(name: str | Path, *, make_parents: bool = True) -> Path: + """Return an absolute Path under the shared outputs/ directory. + + If *name* already starts with the string "outputs/", that prefix is removed + to avoid accidentally nesting a second outputs folder (e.g. + `outputs/outputs/foo.png`). Absolute paths are returned unchanged. + """ + + path = Path(name) + + if path.is_absolute(): + return path + + # Strip leading "outputs/" if present + if path.parts and path.parts[0] == "outputs": + path = Path(*path.parts[1:]) + + final = outputs_dir() / path + + if make_parents: + final.parent.mkdir(parents=True, exist_ok=True) + + return final + +__all__ = [ + "ROOT_DIR", + "repo_path", + "outputs_dir", + "load_prompt", + "FileSpanExporter", + "output_file", +] \ No newline at end of file diff --git a/examples/agents_sdk/parallel_agents.ipynb b/examples/agents_sdk/parallel_agents.ipynb new file mode 100644 index 0000000000..a6f4e0ce48 --- /dev/null +++ b/examples/agents_sdk/parallel_agents.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running Specialized Agents in Parallel with the OpenAI Agents SDK\n", + "\n", + "Why would you want to do this?\n", + "In many production workflows you must answer several independent questions about the same piece of content.\n", + "Doing those analyses one-by-one increases latency and can increase total cost if any step fails and forces a retry.\n", + "By \"fanning out\" multiple specialized agents at the same time and then \"fanning in\" their outputs to a final “meta” agent, you're able to reduce this latency.\n", + "\n", + "This notebook present a toy example that you likely wouldn't parallelize in the real world, but that shows:\n", + "1. How to define several focused agents with the OpenAI Agents SDK.\n", + "2. How to execute them concurrently using either Python [asyncio](https://docs.python.org/3/library/asyncio.html) for lower latency, lightweight parallelization or directly through the [Agents SDK](https://openai.github.io/openai-agents-python/tools/#agents-as-tools) for ease of management and dynamic tool call planning.\n", + "3. How to gather their individual outputs and feed them into a downstream meta-agent that produces the final, user-ready answer.\n", + "4. A simple timeline visualization so you can see the latency benefit of parallelization.\n", + "\n", + "This same pattern can be adapted to real world scenarios such as customer-support triage, content moderation, or other scenarios where you might want to run multiple independent analyses on an input and merge them into a single outcome." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Install dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install openai-agents asyncio matplotlib nest_asyncio\n", + "\n", + "import time\n", + "\n", + "import asyncio\n", + "import matplotlib.pyplot as plt\n", + "import nest_asyncio\n", + "\n", + "from agents import Agent, Runner\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Define your Agents" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Agent focusing on product features\n", + "features_agent = Agent(\n", + " name=\"FeaturesAgent\",\n", + " instructions=\"Extract the key product features from the review.\"\n", + ")\n", + "\n", + "# Agent focusing on pros & cons\n", + "pros_cons_agent = Agent(\n", + " name=\"ProsConsAgent\",\n", + " instructions=\"List the pros and cons mentioned in the review.\"\n", + ")\n", + "\n", + "# Agent focusing on sentiment analysis\n", + "sentiment_agent = Agent(\n", + " name=\"SentimentAgent\",\n", + " instructions=\"Summarize the overall user sentiment from the review.\"\n", + ")\n", + "\n", + "# Agent focusing on recommendation summary\n", + "recommend_agent = Agent(\n", + " name=\"RecommendAgent\",\n", + " instructions=\"State whether you would recommend this product and why.\"\n", + ")\n", + "\n", + "parallel_agents = [\n", + " features_agent,\n", + " pros_cons_agent,\n", + " sentiment_agent,\n", + " recommend_agent\n", + "]\n", + "\n", + "# Meta-agent to combine outputs\n", + "meta_agent = Agent(\n", + " name=\"MetaAgent\",\n", + " instructions=\"You are given multiple summaries labeled with Features, ProsCons, Sentiment, and a Recommendation.\"\n", + " \" Combine them into a concise executive summary of the product review with a 1-5 star rating for each summary area.\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "starts, ends = [], []\n", + "async def run_agent(agent, review_text: str):\n", + " agent_name = agent.name\n", + "\n", + " start = time.time()\n", + " starts.append((agent_name, start))\n", + "\n", + " result = await Runner.run(agent, review_text)\n", + "\n", + " end = time.time()\n", + " ends.append((agent_name, end))\n", + "\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Create function for parallel execution" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "async def run_agents(review_text: str):\n", + " responses = await asyncio.gather(\n", + " *(run_agent(agent, review_text) for agent in parallel_agents)\n", + " )\n", + "\n", + " labeled_summaries = [\n", + " f\"### {resp.last_agent.name}\\n{resp.final_output}\"\n", + " for resp in responses\n", + " ]\n", + "\n", + " collected_summaries = \"\\n\".join(labeled_summaries)\n", + " final_summary = await run_agent(meta_agent, collected_summaries)\n", + "\n", + "\n", + " print('Final summary:', final_summary.final_output)\n", + "\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final summary: ### Executive Summary\n", + "\n", + "The AuroraSound X2 wireless noise-cancelling headphones offer a blend of premium design and advanced features. The headphones boast a matte-finish with comfortable, memory-foam padding, making them ideal for extended use. With Bluetooth 5.2, they provide seamless connectivity and stable communication. The noise-cancelling capabilities effectively reduce ambient noise and feature a well-tuned Transparency mode for essential sound transmission.\n", + "\n", + "**Audio Quality** is a highlight, delivering rich, balanced sound with customizable EQ presets including “Podcast,” “Bass Boost,” and “Concert Hall.” Intuitive touch controls allow for easy navigation, though some users report occasional misfires. The extended battery life offers over 30 hours with ANC on, with a quick-charge option for convenience.\n", + "\n", + "**Minor Limitations** include a bulky carrying case, occasional touch control issues, and limited color choices (black or white). Despite these, the overall sentiment is highly positive, with users particularly appreciating the headphones' design, connectivity, and performance. The product is recommended for those seeking high-quality audio experiences with effective noise-cancelling features.\n", + "\n", + "### Star Ratings\n", + "\n", + "- **Features**: ★★★★☆\n", + "- **Pros & Cons**: ★★★★☆\n", + "- **Sentiment**: ★★★★★\n", + "- **Recommendation**: ★★★★★\n", + "\n", + "Overall, the AuroraSound X2 headphones are a compelling choice, offering excellent value despite minor drawbacks.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "review_text = \"\"\"\n", + "I recently upgraded to the AuroraSound X2 wireless noise-cancelling headphones, and after two weeks of daily use I have quite a bit to share. First off, the design feels premium without being flashy: the matte‐finish ear cups are softly padded and rotate smoothly for storage, while the headband’s memory‐foam cushion barely presses on my temples even after marathon work calls. Connectivity is seamless—pairing with my laptop and phone took under five seconds each time, and the Bluetooth 5.2 link held rock-solid through walls and down the hallway.\n", + "\n", + "The noise-cancelling performance is genuinely impressive. In a busy café with music and chatter swirling around, flipping on ANC immediately quiets low-level ambient hums, and it even attenuates sudden noises—like the barista’s milk frother—without sounding distorted. The “Transparency” mode is equally well‐tuned: voices come through clearly, but the world outside isn’t overwhelmingly loud. Audio quality in standard mode is rich and balanced, with tight bass, clear mids, and a hint of sparkle in the highs. There’s also a dedicated EQ app, where you can toggle between “Podcast,” “Bass Boost,” and “Concert Hall” presets or craft your own curve.\n", + "\n", + "On the control front, intuitive touch panels let you play/pause, skip tracks, and adjust volume with a simple swipe or tap. One neat trick: holding down on the right ear cup invokes your phone’s voice assistant. Battery life lives up to the hype, too—over 30 hours with ANC on, and the quick‐charge feature delivers 2 hours of playtime from just a 10-minute top-up.\n", + "\n", + "That said, it isn’t perfect. For one, the carrying case is a bit bulky, so it doesn’t slip easily into a slim bag. And while the touch interface is mostly reliable, I occasionally trigger a pause when trying to adjust the cup position. The headphones also come in only two colorways—black or white—which feels limiting given the premium price point.\n", + "\"\"\"\n", + "\n", + "asyncio.get_event_loop().run_until_complete(run_agents(review_text))\n", + "\n", + "def plot_timeline(starts, ends):\n", + "\n", + " # Plot the timeline of the agents\n", + " # normalize times to zero\n", + " base = min(t for _, t in starts)\n", + " labels = [n for n, _ in starts]\n", + " start_offsets = [t - base for _, t in starts]\n", + " lengths = [ends[i][1] - starts[i][1] for i in range(len(starts))]\n", + "\n", + " plt.figure(figsize=(8, 3))\n", + " plt.barh(labels, lengths, left=start_offsets)\n", + " plt.xlabel(\"Seconds since kickoff\")\n", + " plt.title(\"Agent Execution Timeline\")\n", + " plt.show()\n", + "\n", + "plot_timeline(starts, ends)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The agents can also be parallelized directly through the SDK via the \"agent as tool\" route, adding convenience and the assistance of the planner dynamically deciding which tools to call at the expense of higher latency. This latency comes both from the additional planning API call up front, along with the higher overhead and context from the tool call objects." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final summary: **Executive Summary: AuroraSound X2 Wireless Noise-Cancelling Headphones**\n", + "\n", + "**Features (⭐️⭐️⭐️⭐️⭐️ 5/5):** The headphones boast a premium, matte-finish design with comfortable memory-foam cushioning. They offer seamless Bluetooth 5.2 connectivity, impressive noise-cancelling capabilities, and a well-tuned \"Transparency\" mode. The audio quality is rich and balanced, with customizable sound options via a dedicated EQ app. Additional features include intuitive touch controls and excellent battery life paired with a quick-charge option.\n", + "\n", + "**Pros and Cons (⭐️⭐️⭐️⭐️ 4/5):** \n", + "- **Pros:** Premium design, comfortable fit, seamless connectivity, effective noise-cancelling, clear voice input in \"Transparency\" mode, customizable audio, intuitive controls, long battery life.\n", + "- **Cons:** Bulky carrying case, occasional touch control sensitivity issues, limited color options.\n", + "\n", + "**Sentiment (⭐️⭐️⭐️⭐️ 4/5):** The overall sentiment is highly positive, with appreciation for the design, comfort, connectivity, noise-cancelling effectiveness, and audio quality. Minor drawbacks are noted but do not outweigh the benefits.\n", + "\n", + "**Recommendation (⭐️⭐️⭐️⭐️ 4/5):** Highly recommended for those seeking premium noise-cancelling headphones with versatile features and excellent audio performance. The minor drawbacks are outweighed by the comprehensive suite of high-quality features.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from agents import ModelSettings\n", + "\n", + "\n", + "meta_agent_parallel_tools = Agent(\n", + " name=\"MetaAgent\",\n", + " instructions=\"You are given multiple summaries labeled with Features, ProsCons, Sentiment, and a Recommendation.\"\n", + " \" Combine them into a concise executive summary of the product review with a 1-5 star rating for each summary area.\",\n", + " model_settings=ModelSettings(\n", + " parallel_tool_calls=True\n", + " ),\n", + " tools=[\n", + " features_agent.as_tool(\n", + " tool_name=\"features\",\n", + " tool_description=\"Extract the key product features from the review.\",\n", + " ),\n", + " pros_cons_agent.as_tool(\n", + " tool_name=\"pros_cons\",\n", + " tool_description=\"List the pros and cons mentioned in the review.\",\n", + " ),\n", + " sentiment_agent.as_tool(\n", + " tool_name=\"sentiment\",\n", + " tool_description=\"Summarize the overall user sentiment from the review.\",\n", + " ),\n", + " recommend_agent.as_tool(\n", + " tool_name=\"recommend\",\n", + " tool_description=\"State whether you would recommend this product and why.\",\n", + " ),\n", + " ],\n", + ")\n", + "\n", + "starts, ends = [], []\n", + "result = await run_agent(meta_agent_parallel_tools, review_text)\n", + "\n", + "print('Final summary:', result.final_output)\n", + "\n", + "plot_timeline(starts, ends)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "From the above, we can see two different patterns for parallelizing agents. Ultimately, the approach you use will depend on the balance you want between:\n", + "\n", + "1. Convenience vs. customization\n", + " * If you prefer convenience, the agent as tool route is the way to go. If you want to customize how agents fan in and out across multiple layers, building a graph with `asyncio.gather` might make more sense\n", + "1. Planning vs. determinism\n", + " * If you want your planner (in this case the meta agent) to dynamically decide which tools to call and the order, you should use agents as tools whereas `asyncio.gather` makes more sense if you want a deterministic order.\n", + "1. Latency sensitivity\n", + " * If you're highly sensitive to latency, you may want to use `asyncio` to avoid the additional upfront cost of planning the parallel tools and the overhead of tool outputs and longer context windows." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/agents_sdk/voice_agents_audio/account_balance_response_base.mp3 b/examples/agents_sdk/voice_agents_audio/account_balance_response_base.mp3 new file mode 100644 index 0000000000..a7beac6828 Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/account_balance_response_base.mp3 differ diff --git a/examples/agents_sdk/voice_agents_audio/account_balance_response_opti.mp3 b/examples/agents_sdk/voice_agents_audio/account_balance_response_opti.mp3 new file mode 100644 index 0000000000..0800ee3e66 Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/account_balance_response_opti.mp3 differ diff --git a/examples/agents_sdk/voice_agents_audio/product_info_character.wav b/examples/agents_sdk/voice_agents_audio/product_info_character.wav new file mode 100644 index 0000000000..4f259589b2 Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/product_info_character.wav differ diff --git a/examples/agents_sdk/voice_agents_audio/product_info_character_2.wav b/examples/agents_sdk/voice_agents_audio/product_info_character_2.wav new file mode 100644 index 0000000000..853808c2fa Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/product_info_character_2.wav differ diff --git a/examples/agents_sdk/voice_agents_audio/product_info_response_base.mp3 b/examples/agents_sdk/voice_agents_audio/product_info_response_base.mp3 new file mode 100644 index 0000000000..811cfaa7e3 Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/product_info_response_base.mp3 differ diff --git a/examples/agents_sdk/voice_agents_audio/product_info_response_opti.mp3 b/examples/agents_sdk/voice_agents_audio/product_info_response_opti.mp3 new file mode 100644 index 0000000000..2ac6d791db Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/product_info_response_opti.mp3 differ diff --git a/examples/agents_sdk/voice_agents_audio/trending_items_response_base.mp3 b/examples/agents_sdk/voice_agents_audio/trending_items_response_base.mp3 new file mode 100644 index 0000000000..dfb5a17067 Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/trending_items_response_base.mp3 differ diff --git a/examples/agents_sdk/voice_agents_audio/trending_items_response_opti.mp3 b/examples/agents_sdk/voice_agents_audio/trending_items_response_opti.mp3 new file mode 100644 index 0000000000..f75f5afb2e Binary files /dev/null and b/examples/agents_sdk/voice_agents_audio/trending_items_response_opti.mp3 differ diff --git a/examples/agents_sdk/voice_agents_knowledge/acme_product_catalogue.pdf b/examples/agents_sdk/voice_agents_knowledge/acme_product_catalogue.pdf new file mode 100644 index 0000000000..f9508e05f1 Binary files /dev/null and b/examples/agents_sdk/voice_agents_knowledge/acme_product_catalogue.pdf differ diff --git a/examples/book_translation/translate_latex_book.ipynb b/examples/book_translation/translate_latex_book.ipynb index 2ab0eefbfd..a6e10896ab 100644 --- a/examples/book_translation/translate_latex_book.ipynb +++ b/examples/book_translation/translate_latex_book.ipynb @@ -20,30 +20,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1485565" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from openai import OpenAI\n", - "import os\n", - "from transformers import GPT2Tokenizer\n", + "import tiktoken\n", + "client = OpenAI()\n", "\n", - "client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"\"))\n", - "\n", - "# OpenAI GPT-2 tokenizer is the same as GPT-3 tokenizer\n", + "# OpenAI tiktoken tokenizer: https://github.com/openai/tiktoken\n", "# we use it to count the number of tokens in the text\n", - "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n", + "tokenizer = tiktoken.get_encoding(\"o200k_base\")\n", "\n", "with open(\"data/geometry_slovenian.tex\", \"r\") as f:\n", " text = f.read()" @@ -58,25 +45,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Token indices sequence length is longer than the specified maximum sequence length for this model (1327 > 1024). Running this sequence through the model will result in indexing errors\n" + "Size of the largest chunk: 1211\n", + "Number of chunks: 5877\n" ] - }, - { - "data": { - "text/plain": [ - "1473" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -84,36 +62,37 @@ "ntokens = []\n", "for chunk in chunks:\n", " ntokens.append(len(tokenizer.encode(chunk)))\n", - "max(ntokens)" + "print(\"Size of the largest chunk: \", max(ntokens))\n", + "print(\"Number of chunks: \", len(chunks))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "It turns out that a double newline is a good separator in this case, in order not to break the flow of the text. Also no individual chunk is larger than 1500 tokens. The model we will use is text-davinci-002, which has a limit of 4096 tokens, so we don't need to worry about breaking the chunks down further.\n", + "It turns out that a double newline is a good separator in this case, in order not to break the flow of the text. Also no individual chunk is larger than 1211 tokens. The model we will use is gpt-4o, which has a limit of 16,384 tokens, so we don't need to worry about breaking the chunks down further.\n", "\n", - "We will group the shorter chunks into chunks of around 1000 tokens, to increase the coherence of the text, and decrease the frequency of breaks within the text." + "We will group the shorter chunks into chunks of around 15000 tokens, to increase the coherence of the text, and decrease the frequency of breaks within the text." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "869" + "39" ] }, - "execution_count": 21, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def group_chunks(chunks, ntokens, max_len=1000, hard_max_len=3000):\n", + "def group_chunks(chunks, ntokens, max_len=15000, hard_max_len=16000):\n", " \"\"\"\n", " Group very short chunks, to form approximately page long chunks.\n", " \"\"\"\n", @@ -165,60 +144,503 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, + "execution_count": 5, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Let $\\mathcal{I}=\\mathcal{S}_{AB} \\circ\\mathcal{S}_{CA}\n", - " \\circ\\mathcal{S}_{BC}$. By \\ref{izoZrcdrsprq} is\n", - " $\\mathcal{I}$ a mirror reflection. Let $A_1$, $B_1$ and $C_1$ be in order the center points of the lines $BC$, $AC$ and $AB$ of the triangle $ABC$.\n", - " Because it is a right triangle is $\\mathcal{I}(A_1C_1)=A_1C_1$, which\n", - " means that the line $A_1C_1$ is of this mirror reflection. It is not\n", - " difficult to prove that for the point $A'_1=\\mathcal{I}(A_1)$ (both\n", - " lie on the axis $A_1C_1$) is\n", - " $\\overrightarrow{A_1A'_1}=3\\overrightarrow{A_1C_1}$, so\n", - " $\\mathcal{I}=\\mathcal{G}_{3\\overrightarrow{A_1C_1}}$.\n", - "\n", - "\\item \\res{Given are the points $A$ and $B$ on the same side of the line\n", - "$p$.\n", - "Draw the line $XY$, which lies on the line $p$ and is consistent\n", - "with the given line $l$, so that the sum\n", - "$|AX|+|XY|+|YB|$ is minimal.}\n", - "\n", - "Let $A'=\\mathcal{G}_{\\overrightarrow{MN}}(A)$ (where $M,N\\in\n", - "p$ and $MN\\cong l$). The point $Y$ is obtained as the intersection of the lines $p$\n", - "and $X'Y$ (see also example \\ref{HeronProbl}).\n", - "\n", - "\\item \\res{Let $ABC$ be an isosceles right triangle with a right angle at the vertex $A$. What does the composite\n", - "$\\mathcal{G}_{\\overrightarrow{AB}}\\circ \\mathcal{G}_{\\overrightarrow{CA}}$ represent?}\n", - "\n", - "Let $p$ and $q$ be the simetrali of the sides $CA$ and $AB$ of the triangle\n", - "$ABC$. By \\ref{izoZrcDrsKompSrOsn} is:\n", - " $$\\mathcal{G}_{\\overrightarrow{AB}}\\circ\n", - " \\mathcal{G}_{\\overrightarrow{CA}}=\n", - " \\mathcal{S}_q\\circ\\mathcal{S}_A\\circ\\mathcal{S}_A\\circ\\mathcal{S}_p=\n", - " \\mathcal{S}_q\\circ\\mathcal{S}_p.$$ Because $ABC$ is an isosceles\n", - " right triangle with a right angle at the vertex $A$, the lines $p$ and $q$ are perpendicular and intersect at the center $S$\n", - " of the hypotenuse $BC$. Therefore\n", - " $\\mathcal{G}_{\\overrightarrow{AB}}\\circ\n", - " \\mathcal{G}_{\\overrightarrow{CA}}=\\mathcal{S}_q\n", - " \\circ\\mathcal{S}_p=\\mathcal{S}_S$.\n", - "\n", - "\\item \\res{In the same plane are given the lines\n", - "$a$, $b$ and $c$.\n", - "Draw the points $A\\in a$ and $B\\in b$\n", - "so that $\\mathcal{S}_c(A)=B$.}\n" + "Certainly! Here's the translation of the text from the LaTeX document into English, with all LaTeX commands unchanged:\n", + "\n", + "---\n", + "\n", + "\\chapter{The basics of Geometry} \\label{osn9Geom}\n", + "Let us mention that the group structure also requires the property of associativity, i.e., $\\mathcal{I}_1\\circ (\\mathcal{I}_2\\circ \\mathcal{I}_3)= (\\mathcal{I}_1\\circ \\mathcal{I}_2)\\circ \\mathcal{I}_3$ (for arbitrary isometries $\\mathcal{I}_1$, $\\mathcal{I}_2$, and $\\mathcal{I}_3$), which is automatically fulfilled in the operation of function composition. Let us also mention that the \\concept{identity} \\index{identity} $\\mathcal{E}$ from the previous axiom is a mapping for which $\\mathcal{E}(A)=A$ for every point of the plane. The mapping $\\mathcal{I}^{-1}$ is the \\concept{inverse mapping} for the isometry $\\mathcal{I}$ if $\\mathcal{I}^{-1}\\circ \\mathcal{I} =\\mathcal{I}\\circ\\mathcal{I}^{-1}=\\mathcal{E}$. According to the previous axiom, the identity and inverse mapping of each isometry are also isometries.\n", + "\n", + "Let us prove the first consequences of the axioms of congruence. First, we will consider the following properties of isometries.\n", + "\n", + "\\begin{theorem} \\label{izrekIzoB} Isometry maps a line to a line, a line segment to a line segment, a ray to a ray, a half-plane to a half-plane, an angle to an angle, and an $n$-gon to an $n$-gon.\n", + "\\end{theorem}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "According to axiom \\ref{aksIII1}, isometries preserve the relation $\\mathcal{B}$. Therefore, all points of the line segment $AB$ under the isometry $I$ are mapped to points lying on the line segment $A'B'$, where $A'=\\mathcal{I}(A)$ and $B'=\\mathcal{I}(B)$. Since the inverse mapping $\\mathcal{I}^{-1}$ is also an isometry (axiom \\ref{aksIII4}), every point of the line segment $A'B'$ is the image of some point lying on the line segment $AB$. Thus, the line segment $AB$ is mapped to the line segment $A'B'$ by the isometry $\\mathcal{I}$.\n", + "\n", + "The remaining figures from the theorem are also defined using the relation $\\mathcal{B}$, so the proof is similar to that for the line segment.\n", + "\\qed\n", + "\n", + "From the proof of the previous theorem, it follows that the endpoints of the line segment $AB$ are mapped to the endpoints of the image $A'B'$ by the isometry. Similarly, the origin of a ray is mapped to the origin of the ray, the edge of a half-plane to the edge of a half-plane, the vertex of an angle to the vertex of an angle, and the vertex of a polygon to the vertex of a polygon.\n", + "\n", + "Isometries are defined as bijective mappings that preserve the congruence of pairs of points. Is it also true that for congruent pairs of points, there exists an isometry that maps the first pair to the second? Let us provide the answer with the following theorem.\n", + "\n", + "\\begin{theorem} \\label{izrekAB} If $(A,B)\\cong (A',B')$, then there is an isometry $\\mathcal{I}$, which maps the points $A$ and $B$ to the points $A'$ and $B'$, i.e.:\n", + "$$\\mathcal{I}: A, B\\mapsto A',B'.$$\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.7.pic}\n", + "\\caption{} \\label{sl.aks.2.3.7.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "Let $C$ be a point that does not lie on the line $AB$, and $C'$ a point that does not lie on the line $A'B'$ (Figure \\ref{sl.aks.2.3.7.pic}). According to axiom \\ref{aksIII2}, there exists a unique isometry $\\mathcal{I}$ that maps the point $A$ to the point $A'$, the ray $AB$ to the ray $A'B'$, and the half-plane $ABC$ to the half-plane $A'B'C'$. Since by assumption $(A,B)\\cong (A',B')$ from the same axiom \\ref{aksIII2}, it follows that $\\mathcal{I}(B)=B'$.\n", + "\\qed\n", + "\n", + "The proof of the following theorem is similar, which will later be presented in a different form as the first theorem on the congruence of triangles.\n", + "\n", + "\\begin{theorem} \\label{IizrekABC} Let $(A,B,C)$ and $(A',B',C')$ be triplets of non-collinear points such that $$(A,B,C)\\cong (A',B',C'),$$ then there is a single isometry $\\mathcal{I}$, that maps the points $A$, $B$, and $C$ into the points $A'$, $B'$, and $C'$, i.e.:\n", + "$$\\mathcal{I}: A, B,C\\mapsto A',B',C'.$$\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.5.pic}\n", + "\\caption{} \\label{sl.aks.2.3.5.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "According to axiom \\ref{aksIII2}, there exists a unique isometry $\\mathcal{I}$ that maps the point $A$ to the point $A'$, the ray $AB$ to the ray $A'B'$, and the half-plane $ABC$ to the half-plane $A'B'C'$ (Figure \\ref{sl.aks.2.3.5.pic}). Since by assumption $(A,B,C)\\cong (A',B',C')$ from the same axiom \\ref{aksIII2}, it follows that $\\mathcal{I}(B)=B'$ and $\\mathcal{I}(C)=C'$.\n", + "\n", + "It is necessary to prove that $\\mathcal{I}$ is the only such isometry. Suppose there exists such an isometry $\\mathcal{\\widehat{I}}$ that satisfies $\\mathcal{\\widehat{I}}: A, B,C\\mapsto A',B',C'$. According to theorem \\ref{izrekIzoB}, the isometry $\\mathcal{\\widehat{I}}$ also maps the ray $AB$ to the ray $A'B'$ and the half-plane $ABC$ to the half-plane $A'B'C'$. From axiom \\ref{aksIII2}, it follows that $\\mathcal{\\widehat{I}}=\\mathcal{I}$.\n", + "\\qed\n", + "\n", + "A direct consequence is the following theorem.\n", + "\n", + "\\begin{theorem} \\label{IizrekABCident} Let $A$, $B$, and $C$ be three non-collinear points, then the identity map $\\mathcal{E}$ is the only isometry that maps points $A$, $B$, and $C$ to the same points $A$, $B$, and $C$.\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.5a.pic}\n", + "\\caption{} \\label{sl.aks.2.3.5a.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} (Figure \\ref{sl.aks.2.3.5a.pic})\n", + "\n", + "First, the identical mapping $\\mathcal{E}$, which maps the points $A$, $B$, and $C$ to the points $A$, $B$, and $C$, is an isometry according to axiom \\ref{aksIII4}. From the previous theorem \\ref{IizrekABC}, it follows that there is only one such isometry.\n", + "\\qed\n", + "\n", + "For the point $A$, we say that it is a \\index{point!fixed} \\concept{fixed point} (or \\index{point!immovable} \\concept{immovable point}) of the isometry $\\mathcal{I}$ if $\\mathcal{I}(A)=A$. The previous theorem tells us that the only isometries that have three non-collinear fixed points are identities.\n", + "\n", + "We will discuss isometries in more detail in chapter \\ref{pogIZO}, but for now, we will use them primarily to help introduce the congruence of figures. Two figures $\\Phi$ and $\\Phi'$ are \\index{figures!congruent}\\concept{congruent} (denoted $\\Phi\\cong \\Phi'$) if there exists an isometry $I$ that maps the figure $\\Phi$ to the figure $\\Phi'$.\n", + "\n", + "A direct consequence of axiom \\ref{aksIII4} is the following theorem.\n", + "\n", + "\\begin{theorem}\n", + "Congruence of figures is an equivalence relation. \\label{sklRelEkv}\n", + "\\end{theorem}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "\n", + "\\textit{Reflexivity.} For every figure $\\Phi$, it holds that $\\Phi \\cong \\Phi$, because the identical mapping $\\mathcal{E}$ is an isometry (axiom \\ref{aksIII4}) and $\\mathcal{E}:\\Phi\\rightarrow\\Phi$.\n", + "\n", + "\\textit{Symmetry.} From $\\Phi \\cong \\Phi_1$, it follows that there exists an isometry $\\mathcal{I}$ that maps the figure $\\Phi$ to the figure $\\Phi_1$. The inverse mapping $\\mathcal{I}^{-1}$, which is an isometry according to axiom \\ref{aksIII4}, maps the figure $\\Phi_1$ to the figure $\\Phi$, so $\\Phi_1 \\cong \\Phi$.\n", + "\n", + "\\textit{Transitivity.} From $\\Phi \\cong \\Phi_1$ and $\\Phi_1 \\cong \\Phi_2$, it follows that there exist such isometries $\\mathcal{I}$ and $\\mathcal{I}'$ that satisfy $\\mathcal{I}:\\Phi\\rightarrow\\Phi_1$ and $\\mathcal{I}':\\Phi_1\\rightarrow\\Phi_2$. Then the composition $\\mathcal{I}'\\circ\\mathcal{I}$, which is an isometry according to axiom \\ref{aksIII4}, maps the figure $\\Phi$ to the figure $\\Phi_2$, so $\\Phi \\cong \\Phi_2$.\n", + "\\qed\n", + "\n", + "The concept of congruence of figures also applies to line segments. Intuitively, we have associated the congruence of line segments with the congruence of pairs of points. Now we will prove the equivalence of both relations.\n", + "\n", + "\\begin{theorem} \\label{izrek(A,B)} $AB \\cong A'B' \\Leftrightarrow (A,B)\\cong (A',B')$\n", + "\\end{theorem}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "\n", + "($\\Rightarrow$) If $(A,B)\\cong (A',B')$, according to theorem \\ref{izrekAB}, there exists an isometry $\\mathcal{I}$ that maps the points $A$ and $B$ to the points $A'$ and $B'$. From theorem \\ref{izrekIzoB}, it follows that the isometry $\\mathcal{I}$ maps the line segment $AB$ to the line segment $A'B'$, i.e., $AB \\cong A'B'$.\n", + "\n", + "($\\Leftarrow$) If $AB \\cong A'B'$, there exists an isometry $\\mathcal{I}$ that maps the line segment $AB$ to the line segment $A'B'$. According to the consequence of theorem \\ref{izrekIzoB}, the endpoint of the line segment is mapped to the endpoint of the line segment. This means that either $\\mathcal{I}:A,B\\mapsto A',B'$ or $\\mathcal{I}:A,B\\mapsto B',A'$. From the first relation, it follows that $(A,B)\\cong (A',B')$, and from the second, $(A,B)\\cong (B',A')$. However, even from the second case, we get $(A,B)\\cong (A',B')$, which is a consequence of axioms \\ref{aksIII3} and \\ref{aksIII4}.\n", + "\\qed\n", + "\n", + "Due to the previous theorem, we will always write $AB\\cong A'B'$ instead of the relation $(A,B)\\cong (A',B')$ in the continuation.\n", + "\n", + "\\begin{theorem} \\label{ABnaPoltrakCX}\n", + "For each line segment $AB$ and each ray $CX$, there is exactly one point $D$ on the ray $CX$ that $AB\\cong CD$ holds.\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.5b.pic}\n", + "\\caption{} \\label{sl.aks.2.3.5b.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} Let $P$ be a point that does not lie on the line $AB$, and $Q$ a point that does not lie on the line $CX$ (Figure \\ref{sl.aks.2.3.5b.pic}). According to axiom \\ref{aksIII2}, there exists a unique isometry $\\mathcal{I}$ that maps the point $A$ to the point $C$, the ray $AB$ to the ray $CX$, and the half-plane $ABP$ to the half-plane $CXQ$. Let $D=\\mathcal{I}(C)$, then $AB \\cong CD$.\n", + "\n", + "Assume that there is another point $\\widehat{D}$ on the ray $CX$ for which $AB \\cong C\\widehat{D}$. Since the rays $CX$ and $CD$ coincide, and the isometry $\\mathcal{I}$ maps the point $A$ to the point $C$, the ray $AB$ to the ray $CD$, and the half-plane $ABP$ to the half-plane $CDQ$, it follows from axiom \\ref{aksIII2} that $\\mathcal{I}(C)=\\widehat{D}$, i.e., $\\widehat{D}=D$.\n", + "\\qed\n", + "\n", + "\\begin{theorem} \\label{izomEnaC'} Let $A$, $B$, $C$ be three non-collinear points and $A'$, $B'$ points of the edge of a half-plane $\\pi$ such that $AB \\cong A'B'$. Then there is exactly one point $C'$ in the half-plane $\\pi$ such that $AC \\cong A'C'$ and $BC \\cong B'C'$.\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.11a.pic}\n", + "\\caption{} \\label{sl.aks.2.3.11a.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} (Figure \\ref{sl.aks.2.3.11a.pic})\n", + "\n", + "According to axiom \\ref{aksIII2}, there exists a unique isometry $\\mathcal{I}$ that maps the point $A$ to the point $A'$, the ray $AB$ to the ray $A'B'$, and the half-plane $ABC$ to the half-plane $\\pi$, and it holds that $\\mathcal{I}(B)=B'$. Let $C'=\\mathcal{I}(C)$, then $AC \\cong A'C'$ and $BC \\cong B'C'$. Assume that there is such a point $\\widehat{C}'$ that lies in the half-plane $\\pi$ and satisfies $AC \\cong A'\\widehat{C}'$ and $BC \\cong B'\\widehat{C}'$. Since $AB \\cong A'B'$, according to theorem \\ref{IizrekABC}, there exists a unique isometry $\\mathcal{\\widehat{I}}$ that maps the points $A$, $B$, and $C$ to the points $A'$, $B'$, and $\\widehat{C}'$. However, it also maps the ray $AB$ to the ray $A'B'$ and the half-plane $ABC$ to the half-plane $A'B'\\widehat{C}'=\\pi$. According to axiom \\ref{aksIII2}, $\\mathcal{\\widehat{I}}=\\mathcal{I}$, and therefore $\\widehat{C}'=\\mathcal{\\widehat{I}}(C)=\\mathcal{I}(C)=C'$.\n", + "\\qed\n", + "\n", + "\\begin{theorem} \\label{izoABAB} If $\\mathcal{I}$ is an isometry that maps a points $A$ and $B$ into the same points $A$ and $B$ (i.e. $\\mathcal{I}(A)=A$ and $\\mathcal{I}(B)=B$), then it also holds for each point $X$ on the line $AB$ (i.e. $\\mathcal{I}(X)=X$).\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.8.pic}\n", + "\\caption{} \\label{sl.aks.2.3.8.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} Let $X$ be an arbitrary point on the line $AB$. Without loss of generality, assume that the point $X$ lies on the ray $AB$ (Figure \\ref{sl.aks.2.3.8.pic}). Let us prove that $\\mathcal{I}(X)=X$.\n", + "\n", + "Let $P$ be a point that does not lie on the line $AB$ and $P'=\\mathcal{I}(P)$. The isometry $\\mathcal{I}$ maps the point $A$ to the point $A$, the ray $AB$ to the ray $AB$ (or the ray $AX$ to the ray $AX$), and the half-plane $ABP$ to the half-plane $ABP'$ (or the half-plane $AXP$ to the half-plane $AXP'$). According to axiom \\ref{aksIII2}, from $AX\\cong AX$, it follows that $\\mathcal{I}(X)=X$.\n", + "\\qed\n", + "\n", + "Let us introduce new concepts related to line segments.\n", + "\n", + "We say that the line segment $EF$ is the \\index{sum!of segments}\\concept{sum of segments} $AB$ and $CD$, denoted $EF=AB+CD$, if there exists such a point $P$ on the line segment $EF$ that $AB \\cong EP$ and $CD \\cong PF$ (Figure \\ref{sl.aks.2.3.9.pic}).\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.9.pic}\n", + "\\caption{} \\label{sl.aks.2.3.9.pic}\n", + "\\end{figure}\n", + "\n", + "The line segment $EF$ is the \\index{difference!of segments}\\concept{difference of segments} $AB$ and $CD$, denoted $EF=AB-CD$, if $AB=EF+CD$ (Figure \\ref{sl.aks.2.3.9.pic}).\n", + "\n", + "In a similar way, we can also define the multiplication of a line segment by a natural and a positive rational number. For line segments $AB$ and $CD$, $AB=n\\cdot CD$ ($n\\in \\mathbb{N}$) if there exist such points $X_1$, $X_2$,..., $X_{n-1}$ that $\\mathcal{B}(X_1,X_2,\\ldots,X_{n-1})$ and $AX_1 \\cong X_1X_2 \\cong X_{n-1}B \\cong CD$ (Figure \\ref{sl.aks.2.3.10.pic}). In this case, $CD=\\frac{1}{n}\\cdot AB$.\n", + "\n", + "At this point, we will not formally prove the fact that for every line segment $PQ$ and every natural number $n$, there exists a line segment $AB$ for which $AB=n\\cdot PQ$, and a line segment $CD$ for which $CD=\\frac{1}{n}\\cdot PQ$.\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.10.pic}\n", + "\\caption{} \\label{sl.aks.2.3.10.pic}\n", + "\\end{figure}\n", + "\n", + "The multiplication of a line segment by a positive rational number is introduced in the following way. For $q=\\frac{n}{m} \\in \\mathbb{Q^+}$, it is:\n", + "$$q\\cdot AB=\\frac{n}{m}\\cdot AB = n\\cdot\\left(\\frac{1}{m}\\cdot AB\\right)$$\n", + "\n", + "If for a point $P$ of the line segment $AB$, it holds that $AP=\\frac{n}{m}\\cdot PB$, we say that the point $P$ divides the line segment $AB$ in the \\index{ratio} \\concept{ratio} $n:m$, which we write as $AP:PB=n:m$.\n", + "\n", + "The line segment $AB$ is \\index{relation!of order of segments}\\concept{longer} than the line segment $CD$, denoted $AB>CD$, if there exists such a point $P\\neq B$ on the line segment $AB$ that $CD \\cong AP$ (Figure \\ref{sl.aks.2.3.11.pic}). In this case, we also say that the line segment $CD$ is \\concept{shorter} than the line segment $AB$ (denoted $CDCD$, $AB \\angle cd$) if there exists a ray $s=SX$ in the angle $ab$ such that $\\angle as \\cong \\angle cd$ holds (Figure \\ref{sl.aks.2.3.17.pic}). In this case, the angle $cd$ is also \\concept{smaller} than the angle $ab$ ($\\angle cd< \\angle ab$). It is not difficult to prove that for two angles $ab$ and $cd$, exactly one of the relations: $\\angle ab > \\angle cd$, $\\angle ab < \\angle cd$, or $\\angle ab \\cong \\angle cd$ holds.\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.17.pic}\n", + "\\caption{} \\label{sl.aks.2.3.17.pic}\n", + "\\end{figure}\n", + "\n", + "Angles are \\index{angles!supplementary}\\concept{supplementary} if their sum is equal to a straight angle (Figure \\ref{sl.aks.2.3.18.pic}).\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.18.pic}\n", + "\\caption{} \\label{sl.aks.2.3.18.pic}\n", + "\\end{figure}\n", + "\n", + "The ray $s=SX$ is the \\index{bisector of an angle}\\concept{bisector of the angle} $\\angle pSq=\\alpha$ (Figure \\ref{sl.aks.2.3.19.pic}) if it lies in this angle and $\\angle ps \\cong \\angle sq$ holds. The carrier of this bisector is the \\index{symmetry line!of an angle}\\concept{symmetry line of the angle} $pSq$ (line $s_{\\alpha}$).\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.19.pic}\n", + "\\caption{} \\label{sl.aks.2.3.19.pic}\n", + "\\end{figure}\n", + "\n", + "Similar to the midpoint of a line segment, the following theorem holds for the bisector of an angle.\n", + "\n", + "\\begin{theorem} \\label{izrekSimetralaKota}\n", + "An angle has exactly one bisector.\n", + "\\end{theorem}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "Let $\\alpha=pSq$ be an arbitrary angle, $P$ an arbitrary point lying on the arm $Sp$ ($P\\neq S$), and $Q$ a point lying on the arm $Sq$ such that $SP\\cong SQ$.\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.20.pic}\n", + "\\caption{} \\label{sl.aks.2.3.20.pic}\n", + "\\end{figure}\n", + "\n", + "Assume that the angle $\\alpha$ is a straight angle (Figure \\ref{sl.aks.2.3.20.pic}), which determines the half-plane $\\pi$. Let $A$ be its arbitrary point. According to theorem \\ref{izomEnaC'}, there exists a unique point $B$ in the half-plane $\\pi$ such that $(P,Q,A)\\cong (Q,P,B)$. From theorem \\ref{IizrekABC}, it follows that there exists a unique isometry $\\mathcal{I}$ that maps the points $P$, $Q$, and $A$ to the points $Q$, $P$, and $B$. Let $\\mathcal{I}(B)=\\widehat{A}$. Since $(Q,P,B)\\cong(P,Q,\\widehat{A})$, according to theorem \\ref{izomEnaC'}, $\\widehat{A}=A$. Therefore:\n", + "$$\\mathcal{I}:P,Q,A,B\\mapsto Q,P,B,A.$$\n", + "Therefore, the midpoints $S$ and $L$ of the line segments $PQ$ and $AB$ are mapped onto themselves (axiom \\ref{aksIII4}), which then holds for the ray $s=SL$ and every point on it (theorem \\ref{izoABAB}). Therefore, the isometry $\\mathcal{I}$ maps the angle $pSs$ to the angle $sSq$, so $pSs\\cong sSq$, i.e., the ray $s$ is the bisector of the angle $pSq$.\n", + "\n", + "Let us prove that $s$ is the only bisector of the angle $\\alpha$. Let $\\widehat{s}=S\\widehat{L}$ be a ray that lies in the angle $\\alpha$ and $pS\\widehat{s}\\cong \\widehat{s}Sq$ holds. Then there exists an isometry $\\mathcal{\\widehat{I}}$ that maps the angle $pS\\widehat{s}$ to the angle $\\widehat{s}Sq$. This isometry maps the point $S$ to the point $S$, the ray $p$ to the ray $q$, and the half-plane $\\pi$ to the half-plane $\\pi$, so according to axiom \\ref{aksIII2}, $\\mathcal{\\widehat{I}}=\\mathcal{I}$. Therefore, $\\mathcal{I}(\\widehat{s})= \\mathcal{\\widehat{I}}(\\widehat{s})=\\widehat{s}$. If $\\widehat{L} \\notin s$, the isometry $\\mathcal{I}$ maps three non-collinear points $S$, $L$, and $\\widehat{L}$ onto themselves, and $\\mathcal{I}$ is the identity mapping (theorem \\ref{IizrekABCident}), which is not possible. Therefore, $\\widehat{L} \\in s$, i.e., $\\widehat{s}=s$.\n", + "\n", + "If $\\alpha$ is a non-convex angle, the bisector is obtained as the complementary (supplementary) ray of the ray $s$.\n", + "\\qed\n", + "\n", + "Let us prove two theorems related to adjacent and vertical angles.\\index{angles!adjacent} \\index{angles!vertical}\n", + "\n", + "\\begin{theorem}\n", + "The adjacent supplementary angles of two congruent angles are also congruent. \\label{sokota}\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.20a.pic}\n", + "\\caption{} \\label{sl.aks.2.3.20a.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} Let $\\alpha'=\\angle P'OQ$ and $\\alpha_1'=\\angle P_1'O_1Q_1$ be the adjacent angles of two congruent angles $\\alpha=\\angle POQ$ and $\\alpha_1=\\angle P_1O_1Q_1$ (Figure \\ref{sl.aks.2.3.20a.pic}). According to axiom \\ref{aksIII2}, there exists a unique isometry $\\mathcal{I}$ that maps the point $O$ to the point $O_1$, the ray $OP$ to the ray $O_1P_1$, and the half-plane $POQ$ to the half-plane $P_1O_1Q_1$. Let $Q_2=\\mathcal{I}(Q)$. Then $\\angle P_1O_1Q_2\\cong \\angle POQ$. The isometry $\\mathcal{I}$ maps the half-plane $POQ$ to the half-plane $P_1O_1Q_1$, so the point $Q_2$ (and also the ray $O_1Q_2$) lies in the half-plane $P_1O_1Q_1$. Since by assumption $\\angle POQ\\cong\\angle P_1O_1Q_1$, according to theorem \\ref{KotNaPoltrak}, $OQ_1$ and $OQ_2$ represent the same ray. Therefore, the point $Q_2$ lies on the ray $O_1Q_1$. Let $P_2'=\\mathcal{I}(P')$. Since isometries map rays to rays (theorem \\ref{izrekIzoB}), the point $P_2'$ lies on the ray $O_1P_1'$. From $\\mathcal{I}:P',O,Q\\mapsto P_2',O_1,Q_2$, it follows that the isometry $\\mathcal{I}$ maps the angle $P'OQ$ to the angle $P_2'O_1Q_2$ (theorem \\ref{izrekIzoB}), so $\\angle P'OQ\\cong \\angle P_2'O_1Q_2=\\angle P_1'O_1Q_1$.\n", + "\\qed\n", + "\n", + "\\begin{theorem} \\label{sovrsnaSkladna}\n", + "Vertical angles are congruent.\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.20b.pic}\n", + "\\caption{} \\label{sl.aks.2.3.20b.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} Let $\\alpha=\\angle POQ$ and $\\alpha'=\\angle P'OQ'$ be vertical angles, where the points $P$, $O$, $P'$ (or $Q$, $O$, $Q'$) are collinear (Figure \\ref{sl.aks.2.3.20b.pic}). The angle $\\beta=\\angle QOP'$ is the adjacent angle for both angles $\\alpha$ and $\\alpha'$. Since $\\beta\\cong\\beta$, according to the previous theorem \\ref{sokota}, $\\alpha\\cong\\alpha'$.\n", + "\\qed\n", + "\n", + "\\begin{theorem} \\label{sredZrcObstoj}\n", + "For each point $S$, there exists an isometry $\\mathcal{I}$ such that $\\mathcal{I}(S)=S$. In addition, for each point $X\\neq S$, the following holds: if $\\mathcal{I}(X)=X'$, then $S$ is the midpoint of the line segment $XX'$.\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.20c.pic}\n", + "\\caption{} \\label{sl.aks.2.3.20c.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} Let $P$ be an arbitrary point different from $S$ (Figure \\ref{sl.aks.2.3.20c.pic}). According to axiom \\ref{AksII3}, there exists such a point $Q$ on the line $SP$ that $\\mathcal{B}(P,S,Q)$ holds. Denote the half-planes determined by the edge $SP$ as $\\alpha$ and $\\alpha'$. According to axiom \\ref{aksIII2}, there exists (a unique) isometry $\\mathcal{I}$ that maps the point $S$ to the point $S$, the ray $SP$ to the ray $SQ$, and the half-plane $\\alpha$ to the half-plane $\\alpha'$.\n", + "\n", + "Denote the line $SP$ as $p$. The point $P'=\\mathcal{I}(P)$ lies on the ray $SQ$, i.e., the line $p$. Since $\\mathcal{I}:S,P \\mapsto S,P'$, according to axiom \\ref{aksIII1}, the line $SP$ is mapped to the line $SP'$, i.e., $\\mathcal{I}:p\\rightarrow p$. The image of the half-plane $\\alpha'$ with the edge $p$ is therefore a half-plane with the same edge (theorem \\ref{izrekIzoB}). This half-plane cannot be $\\alpha'$, as the isometry $\\mathcal{I}$ is a bijective mapping and by assumption maps the half-plane $\\alpha$ to the half-plane $\\alpha'$. Therefore, $\\mathcal{I}:\\alpha'\\rightarrow \\alpha$.\n", + "\n", + "Now it is clear that without loss of generality, it is sufficient to derive the proof only for points lying in the half-plane $\\alpha$ (without the edge or only the ray $SP$).\n", + "\n", + "Let $X\\in \\alpha\\setminus p$ and $X'=\\mathcal{I}(X)$. It is immediately clear that $X'\\in \\alpha'\\setminus p$. According to axiom \\ref{AksII3}, there exists such a point $X_1$ on the line $SX$ that $\\mathcal{B}(X,S,X_1)$ holds. Since $\\angle PSX$ and $\\angle P'SX_1$ are vertical angles, they are congruent according to theorem \\ref{sovrsnaSkladna}. However, from $\\mathcal{I}:S,P,X \\mapsto S,P',X'$, it follows that $\\angle PSX \\cong \\angle P'SX'$. Therefore, $\\angle P'SX_1\\cong \\angle P'SX'$ (theorem \\ref{sklRelEkv}), so according to theorem \\ref{KotNaPoltrak}, the rays $SX_1$ and $SX'$ are identical. This means that the point $X'$ lies on the ray $SX_1$, i.e., $\\mathcal{B}(X,S,X')$. Since due to $\\mathcal{I}:S,X \\mapsto S,X'$, it is also $SX\\cong SX'$, according to the definition, the point $S$ is the midpoint of the line segment $XX'$.\n", + "\n", + "Finally, let $Y$ be an arbitrary point of the ray $SP$ different from the point $S$, and $Y'=\\mathcal{I}(Y)$. The point $Y'$ lies on the ray $SQ$, so $\\mathcal{B}(Y,S,Y')$. Since due to $\\mathcal{I}:S,Y \\mapsto S,Y'$, it is also $SY\\cong SY'$, according to the definition, the point $S$ is the midpoint of the line segment $YY'$.\n", + "\\qed\n", + "\n", + "In section \\ref{odd6SredZrc}, we will specifically discuss the isometry mentioned in the previous theorem \\ref{sredZrcObstoj}.\n", + "\n", + "Let us define new types of angles. A convex angle is an \\index{angle!acute}\\concept{acute angle}, \\index{angle!right} \\concept{right angle}, or \\index{angle!obtuse}\\concept{obtuse angle} if it is smaller, equal, or greater than its adjacent angle (Figure \\ref{sl.aks.2.3.21.pic}).\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.21.pic}\n", + "\\caption{} \\label{sl.aks.2.3.21.pic}\n", + "\\end{figure}\n", + "\n", + "From the definition, it follows that acute (or obtuse) angles are those convex angles that are smaller (or greater) than a right angle.\n", + "\n", + "From theorem \\ref{izrekSimetralaKota}, it follows that a right angle exists, as the bisector divides a straight angle into two congruent adjacent angles.\n", + "\n", + "It is not difficult to prove that any two right angles are congruent and that an angle congruent to a right angle is also a right angle.\n", + "\n", + "If the sum of two angles is a right angle, we say that the angles are \\index{angles!complementary}\\concept{complementary} (Figure \\ref{sl.aks.2.3.22.pic}).\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.22.pic}\n", + "\\caption{} \\label{sl.aks.2.3.22.pic}\n", + "\\end{figure}\n", + "\n", + "Now we will introduce an extremely important relation between lines. If the lines $p$ and $q$ contain the arms of a right angle, we say that $p$ and $q$ are \\concept{perpendicular}, denoted $p \\perp q$ (Figure \\ref{sl.aks.2.3.23.pic}).\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.23.pic}\n", + "\\caption{} \\label{sl.aks.2.3.23.pic}\n", + "\\end{figure}\n", + "\n", + "From the definition itself, it is clear that perpendicularity is a symmetric relation, i.e., from $p \\perp q$, it follows that $q \\perp p$. If $p \\perp q$ and $p \\cap q=S$, we say that the line $p$ is \\index{perpendicular!lines}\\concept{perpendicular} to the line $q$ at the point $S$, or that $p$ is a \\index{perpendicular line}\\concept{perpendicular line} of the line $q$ at this point.\n", + "\n", + "The following theorem is the most important theorem characterizing the relation of perpendicularity.\n", + "\n", + "\\begin{theorem} \\label{enaSamaPravokotnica}\n", + "For each point $A$ and each line $p$, there is a unique line $n$ going through the point $A$, which is perpendicular on the line $p$.\n", + "\\end{theorem}\n", + "\n", + "\\textbf{\\textit{Proof.}}\n", + "Assume that the point $A$ does not lie on the line $p$. Let $B$ and $C$ be arbitrary points lying on the line $p$ (Figure \\ref{sl.aks.2.3.24.pic}). Denote the half-plane $BCA$ as $\\pi$, and the complementary half-plane as $\\pi_1$. According to theorem \\ref{izomEnaC'}, there exists a unique point $A_1\\in \\pi_1$ for which $(A,B,C) \\cong (A_1,B,C)$. From theorem \\ref{IizrekABC}, it follows that there exists a unique isometry $\\mathcal{I}$ that maps the points $A$, $B$, and $C$ to the points $A_1$, $B$, and $C$. Denote the line $AA_1$ as $n$. Since $A$ and $A_1$ are on different sides of the line $p$, the line $n$ intersects the line $p$ at some point $S$. From $\\mathcal{I}:B,C \\mapsto B,C$, it follows that $\\mathcal{I}(S)=S$ (theorem \\ref{izoABAB}). Therefore, the isometry $\\mathcal{I}$ maps the angle $ASB$ to the angle $A_1SB$. It follows that the angles $\\angle ASB$ and $\\angle A_1SB$ are congruent adjacent angles, so they are also right angles. Therefore, $n \\perp p$.\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.24.pic}\n", + "\\caption{} \\label{sl.aks.2.3.24.pic}\n", + "\\end{figure}\n", + "\n", + "Let us prove that $n$ is the only perpendicular line to the line $p$ through the point $A$. Let $\\widehat{n}$ be a line for which $A\\in \\widehat{n}$ and $\\widehat{n} \\perp p$. Let $\\widehat{S}$ be the intersection of the lines $\\widehat{n}$ and $p$. By assumption, $\\angle A\\widehat{S}B$ is a right angle and is congruent to its adjacent angle $\\angle B\\widehat{S}A_2$ ($A_2$ is such a point that $\\mathcal{B}(A,\\widehat{S},A_2)$ holds), which is also a right angle.\n", + "\n", + "From $\\mathcal{I}:B,C \\mapsto B,C$, it follows that $\\mathcal{I}(\\widehat{S})=\\widehat{S}$ (theorem \\ref{izoABAB}). Therefore, the isometry $\\mathcal{I}$ maps the angle $A\\widehat{S}B$ to the angle $A_1\\widehat{S}B$. It follows that the angles $\\angle A\\widehat{S}B$ and $\\angle A_1\\widehat{S}B$ are congruent, so the angle $\\angle A_1\\widehat{S}B$ is also a right angle. Therefore, the angles $A_1\\widehat{S}B$ and $A_2\\widehat{S}B$ are right angles and are therefore congruent. From this, it follows that the rays $\\widehat{S}A_1$ and $\\widehat{S}A_2$ are the same, so $A_1 \\in \\widehat{S}A_2=\\widehat{n}$, i.e., $\\widehat{n}=AA_1=n$.\n", + "\n", + "In the case when the point $A$ lies on the line $p$, the perpendicular line $n$ is the bisector of the corresponding straight angle (theorem \\ref{izrekSimetralaKota}).\n", + "\\qed\n", + "\n", + "The previous theorem has the consequence of a very important fact - the existence of pairs of disjoint lines in the plane - i.e., those that do not have common points. This is the content of the following two theorems.\n", + "\n", + "\\begin{theorem} \\label{absolGeom1}\n", + "Let $p$ and $q$ be lines perpendicular on a line $PQ$ in the points $P$ and $Q$. Then the lines $p$ and $q$ do not have a common points i.e. $p\\cap q=\\emptyset$.\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.25b.pic}\n", + "\\caption{} \\label{sl.aks.2.3.25b.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} The theorem is a direct consequence of the previous theorem \\ref{enaSamaPravokotnica}. If the lines $p$ and $q$ were to intersect at some point $S$, there would be two perpendicular lines from the point $S$ to the line $PQ$ (Figure \\ref{sl.aks.2.3.25b.pic}), which contradicts the mentioned theorem.\n", + "\\qed\n", + "\n", + "\\begin{theorem} \\label{absolGeom}\n", + "If $A$ is a point that does not lie on a line $p$, then there exists at least one line (in the same plane) passing through the point $A$ and not intersecting the line $p$ (Figure \\ref{sl.aks.2.3.25a.pic}).\n", + "\\end{theorem}\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.25a.pic}\n", + "\\caption{} \\label{sl.aks.2.3.25a.pic}\n", + "\\end{figure}\n", + "\n", + "\\textbf{\\textit{Proof.}} According to theorem \\ref{enaSamaPravokotnica}, there exists (exactly one) perpendicular line $n$ to the line $p$ that passes through the point $A$. Denote the intersection of the lines $p$ and $n$ as $A'$. From the same theorem, it follows that there exists another perpendicular line $q$ to the line $n$ at the point $A$. According to the previous theorem \\ref{absolGeom1}, $q$ is a line that passes through the point $A$ and does not have common points with the line $p$.\n", + "\\qed\n", + "\n", + "The point $A'$ is the \\index{foot}\\concept{foot} or \\index{orthogonal projection}\\concept{orthogonal projection} of the point $A$ on the line $p$ if the perpendicular line to the line $p$ through the point $A$ intersects this line at the point $A'$. We will denote it as $A'=pr_{\\perp p}(A)$ (Figure \\ref{sl.aks.2.3.25.pic}). From the previous theorem, it follows that for every point and line, there exists a unique orthogonal projection.\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.25.pic}\n", + "\\caption{} \\label{sl.aks.2.3.25.pic}\n", + "\\end{figure}\n", + "\n", + "The line that passes through the midpoint $S$ of the line segment $AB$ and is perpendicular to the line $AB$ is called the \\index{symmetry line!of a segment}\\concept{symmetry line of the segment} $AB$ and is denoted by $s_{AB}$ (Figure \\ref{sl.aks.2.3.26.pic}). The properties of the symmetry line of a segment will be discussed in the next chapter.\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.26.pic}\n", + "\\caption{} \\label{sl.aks.2.3.26.pic}\n", + "\\end{figure}\n", + "\n", + "We say that the point $A$ is \\index{symmetry!with respect to a line}\\concept{symmetric} to the point $B$ with respect to the line $s$ if $s$ is the symmetry line of the line segment $AB$. Symmetry with respect to a line (as a mapping - so-called axial reflection) will be discussed in more detail in section \\ref{odd6OsnZrc}.\n", + "\n", + "Let $S$ be a point and $AB$ a line segment. The set of all points $X$ for which $SX \\cong AB$ holds is called a \\index{circle}\\concept{circle} with \\index{center!of a circle}\\concept{center} $S$ and \\index{radius of a circle}\\concept{radius} $AB$; we denote it by $k(S,AB)$ (Figure \\ref{sl.aks.2.3.27.pic}), i.e.:\n", + "$$k(S,AB)=\\{X;\\hspace*{1mm}SX \\cong AB\\}.$$\n", + "\n", + "\\begin{figure}[!htb]\n", + "\\centering\n", + "\\input{sl.aks.2.3.27.pic}\n", + "\\caption{} \\label{sl.aks.2.3.27.pic}\n", + "\\end{figure}\n", + "\n", + "Of course, the circle is a set of points in the plane, as in this book, we only deal with plane geometry (all points and all figures belong to the same plane).\n", + "\n", + "From the definition, it is clear that for the radius, we can choose any line segment that is congruent to the line segment $AB$, i.e., any line segment $SP$, where $P$ is an arbitrary point on the circle. Since the radius is not tied to a specific line segment, we usually denote it with a lowercase letter $r$. Therefore, we can also write the circle as follows:\n", + "$$k(S,r)=\\{X;\\hspace*{1mm}SX \\cong r\\}.$$\n", + "The set\n", + "\n", + "$$\\{X;\\hspace*{1mm}SX \\leq r\\}$$\n", + "is called a \\index{disk}\\concept{disk} with center $S$ and radius $r$ (Figure \\ref{sl.aks.2.3.28.pic}) and is denoted by $\\mathcal{K}(S,r)$.\n", + "The set\n", + "$$\\{X;\\hspace*{1mm}SX < r\\}$$\n", + "is the \\index{interior!of a disk} \\concept{interior of the disk} $\\mathcal{K}(S, r)$, and its points are \\concept{interior points of the disk}.\n", + "This means that the disk is actually the union of its interior and the corresponding circle.\n", + "\n", + "The set\n", + "$$\\{X;\\hspace*{1mm}SX > r\\}$$\n", + "is called the \\index{exterior!of a disk}\\concept{exterior of the disk} $\\mathcal{K}(S, r)$ and its points are \\concept{exterior points of the disk}.\n" ] } ], "source": [ - "def translate_chunk(chunk, model='gpt-3.5-turbo',\n", + "def translate_chunk(chunk, model='gpt-4o',\n", " dest_language='English',\n", - " sample_translation=(\"\\poglavje{Osnove Geometrije} \\label{osn9Geom}\", \"\\poglavje{The basics of Geometry} \\label{osn9Geom}\")\n", - " ):\n", + " sample_translation=(\n", + " r\"\\poglavje{Osnove Geometrije} \\label{osn9Geom}\",\n", + " r\"\\chapter{The basics of Geometry} \\label{osn9Geom}\")):\n", " prompt = f'''Translate only the text from the following LaTeX document into {dest_language}. Leave all LaTeX commands unchanged\n", " \n", "\"\"\"\n", @@ -232,12 +654,12 @@ " model=model,\n", " temperature=0,\n", " top_p=1,\n", - " max_tokens=1500,\n", + " max_tokens=15000,\n", " )\n", " result = response.choices[0].message.content.strip()\n", " result = result.replace('\"\"\"', '') # remove the double quotes, as we used them to surround the text\n", " return result\n", - "print(translate_chunk(chunks[800], model='gpt-3.5-turbo', dest_language='English'))" + "print(translate_chunk(chunks[2], model='gpt-4o', dest_language='English'))" ] }, { @@ -251,909 +673,90 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dest_language = \"English\"\n", + "\n", + "translated_chunks = []\n", + "for i, chunk in enumerate(chunks):\n", + " print(str(i+1) + \" / \" + str(len(chunks)))\n", + " # translate each chunk\n", + " translated_chunks.append(translate_chunk(chunk, model='gpt-4o', dest_language=dest_language))\n", + "\n", + "# join the chunks together\n", + "result = '\\n\\n'.join(translated_chunks)\n", + "\n", + "# save the final result\n", + "with open(f\"data/geometry_{dest_language}.tex\", \"w\") as f:\n", + " f.write(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - 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"for i, chunk in enumerate(chunks):\n", - " print(str(i+1) + \" / \" + str(len(chunks)))\n", - " # translate each chunk\n", - " translated_chunks.append(translate_chunk(chunk, model='gpt-3.5-turbo', dest_language=dest_language))\n", "\n", - "# join the chunks together\n", + "# Use ThreadPoolExecutor to parallelize the translation\n", + "with ThreadPoolExecutor(max_workers=5) as executor:\n", + " # Submit all translation tasks\n", + " futures = {executor.submit(translate_chunk_wrapper, chunk, 'gpt-4o', dest_language): i for i, chunk in enumerate(chunks)}\n", + " \n", + " # Process completed tasks as they finish\n", + " for future in as_completed(futures):\n", + " i = futures[future]\n", + " try:\n", + " translated_chunk = future.result()\n", + " translated_chunks.append(translated_chunk)\n", + " print(f\"Chunk {i+1} / {len(chunks)} translated.\")\n", + " except Exception as e:\n", + " print(f\"Chunk {i+1} failed with exception: {e}\")\n", + "\n", + "# Join the translated chunks together\n", "result = '\\n\\n'.join(translated_chunks)\n", "\n", - "# save the final result\n", + "# Save the final result\n", "with open(f\"data/geometry_{dest_language}.tex\", \"w\") as f:\n", " f.write(result)" ] } ], "metadata": { - "interpreter": { - "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" - }, "kernelspec": { - "display_name": "Python 3.9.10 64-bit", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1167,10 +770,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" - }, - "orig_nbformat": 4 + "version": "3.13.2" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/chatgpt/gpt_actions_library/gpt_action_google_drive.ipynb b/examples/chatgpt/gpt_actions_library/gpt_action_google_drive.ipynb index 64d419c460..a70f062d01 100644 --- a/examples/chatgpt/gpt_actions_library/gpt_action_google_drive.ipynb +++ b/examples/chatgpt/gpt_actions_library/gpt_action_google_drive.ipynb @@ -120,7 +120,9 @@ "source": [ "### **Example OpenAPI Schema**\n", "\n", - "Once you've created a Custom GPT, copy the text below in the Actions panel. This offers an example of what you could include within functions, but . Have questions? Check out [Getting Started Example](https://platform.openai.com/docs/getting-started) to see how this step works in more detail. As well, try [ActionsGPT](https://chatgpt.com/g/g-TYEliDU6A-actionsgpt), a CustomGPT OpenAI created to help with Actions. The three examples are:\n", + "Once you've created a Custom GPT, copy the text below in the Actions panel. This offers an example of what you could include as functions of your GPT.\n", + "\n", + "Have questions? Check out [Getting Started Example](https://platform.openai.com/docs/getting-started) to see how this step works in more detail. As well, try [ActionsGPT](https://chatgpt.com/g/g-TYEliDU6A-actionsgpt), a CustomGPT OpenAI created to help with Actions. The three examples are:\n", "\n", "\n", "* **List Files**: this is the core action that lists the files in your drive. Within this are a few parameters, such as `q`, `includeItemsFromAllDrives,supportsAllDrives`\n", diff --git a/examples/chatgpt/gpt_actions_library/gpt_action_salesforce_gong.md b/examples/chatgpt/gpt_actions_library/gpt_action_salesforce_gong.md new file mode 100644 index 0000000000..faf3827d25 --- /dev/null +++ b/examples/chatgpt/gpt_actions_library/gpt_action_salesforce_gong.md @@ -0,0 +1,446 @@ +# GPT Action Library: Salesforce + Gong + +## Introduction + +This page provides an instruction & guide for developers building middleware to connect a GPT Action to a specific application. Before you proceed, make sure to first familiarize yourself with the following information: + +- [Introduction to GPT Actions](https://platform.openai.com/docs/actions) +- [Introduction to GPT Actions Library](https://platform.openai.com/docs/actions/actions-library) +- [Example of Building a GPT Action from Scratch](https://platform.openai.com/docs/actions/getting-started) + +This particular GPT Action provides an overview of how to build a GPT that retrieves information from Salesforce and Gong. This will include creating multiple custom actions which are documented in existing cookbooks. We will highlight these cookbooks in the next section. + +### Value + Example Business Use Cases + +**Value**: Users can now leverage ChatGPT's capabilities to: + +- Connect to Salesforce +- Search for customer accounts +- Retrieve Gong transcripts from previous calls + +**Example Use Cases**: + +A sales rep is preparing for an upcoming customer meeting. Using this integration, they can quickly retrieve relevant account details from Salesforce, access recent Gong call transcripts, and receive AI-generated summaries and insights structured around proven sales methodologies like MEDPICC or SPICED. This empowers the rep with a clear, actionable understanding of the customer's current state and next steps — all in minutes + +## Application Information +In this example, we are connecting to Salesforce and Gong (via a middleware). We are going to refer to existing cookbooks for basic setup and authentication instructions for Salesforce and creating a middleware. + +### Salesforce GPT Action + +Refer to our cookbook on setting up a GPT Action for Salesforce. The two settings to pay attention to in that cookbook are: + +- [Application Information](https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_action_salesforce#application-information) - this covers the necessary concepts to be familiar with in Salesforce +- [Authentication Instructions](https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_action_salesforce#authentication-instructions) - this covers creating a Connected App in Salesforce and configuring OAuth (on both Salesforce and ChatGPT) + +### Middleware GPT Action +Refer to any one of our cookbooks on creating a middleware: + +- [GPT Actions library (Middleware) - AWS](https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_middleware_aws_function) +- [GPT Actions library (Middleware) - Azure Functions](https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_middleware_azure_function) +- [GPT Actions library (Middleware) - Google Cloud Function](https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_middleware_google_cloud_function) + +### Application Prerequisites + +In addition to the prerequisites in the cookbooks above, please ensure that you have access to a Gong API key + +## Application Setup + +### Deploying a serverless function + +This serverless function will accept an array of `callIds`, fetch the transcripts from Gong and clean up the response that it sends to ChatGPT. Here is an example of what it looks like on Azure Functions (Javascript) + +```javascript +const { app } = require('@azure/functions'); +const axios = require('axios'); + +// Replace with your Gong API token +const GONG_API_BASE_URL = "https://api.gong.io/v2"; +const GONG_API_KEY = process.env.GONG_API_KEY; + +app.http('callTranscripts', { + methods: ['POST'], + authLevel: 'function', + handler: async (request, context) => { + try { + const body = await request.json(); + const callIds = body.callIds; + + if (!Array.isArray(callIds) || callIds.length === 0) { + return { + status: 400, + body: "Please provide call IDs in the 'callIds' array." + }; + } + + // Fetch call transcripts + const transcriptPayload = { filter: { callIds } }; + const transcriptResponse = await axios.post(`${GONG_API_BASE_URL}/calls/transcript`, transcriptPayload, { + headers: { + 'Authorization': `Basic ${GONG_API_KEY}`, + 'Content-Type': 'application/json' + } + }); + + const transcriptData = transcriptResponse.data; + + // Fetch extensive call details + const extensivePayload = { + filter: { callIds }, + contentSelector: { + exposedFields: { parties: true } + } + }; + + const extensiveResponse = await axios.post(`${GONG_API_BASE_URL}/calls/extensive`, extensivePayload, { + headers: { + 'Authorization': `Basic ${GONG_API_KEY}`, + 'Content-Type': 'application/json' + } + }); + + const extensiveData = extensiveResponse.data; + + // Create a map of call IDs to metadata and speaker details + const callMetaMap = {}; + extensiveData.calls.forEach(call => { + callMetaMap[call.metaData.id] = { + title: call.metaData.title, + started: call.metaData.started, + duration: call.metaData.duration, + url: call.metaData.url, + speakers: {} + }; + + call.parties.forEach(party => { + callMetaMap[call.metaData.id].speakers[party.speakerId] = party.name; + }); + }); + + // Transform transcript data into content and include metadata + transcriptData.callTranscripts.forEach(call => { + const meta = callMetaMap[call.callId]; + if (!meta) { + throw new Error(`Metadata for callId ${call.callId} not found.`); + } + + let content = ''; + call.transcript.forEach(segment => { + const speakerName = meta.speakers[segment.speakerId] || "Unknown Speaker"; + + // Combine all sentences for the speaker into a paragraph + const sentences = segment.sentences.map(sentence => sentence.text).join(' '); + content += `${speakerName}: ${sentences}\n\n`; // Add a newline between speaker turns + }); + + // Add metadata and content to the call object + call.title = meta.title; + call.started = meta.started; + call.duration = meta.duration; + call.url = meta.url; + call.content = content; + + delete call.transcript; + }); + + // Return the modified transcript data + return { + status: 200, + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(transcriptData) + }; + } catch (error) { + context.log('[ERROR]', "Error processing request:", error); + + return { + status: error.response?.status || 500, + body: { + message: "An error occurred while fetching or processing call data.", + details: error.response?.data || error.message + } + }; + } + } +}); + +``` + +Here are the dependencies that you would include in your `package.json` file + +```javascript +"dependencies": { + "@azure/functions": "^4.0.0", + "axios": "^1.7.7" + } +``` + +## ChatGPT Steps + +### Custom GPT Instructions + +Once you've created a Custom GPT, copy the text below in the Instructions panel. Have questions? Check out [Getting Started Example](https://platform.openai.com/docs/actions/getting-started) to see how this step works in more detail. + +``` +# Trigger +User enters the name of an account that they want to prepare for + +# Steps +1. Retrieve Account Names - Make a call to the `executeSOSLSearch` custom action searching for a Salesforce Account with that name (SOSL). Retrieve up to 5 accounts. This is what the query should look like - `FIND {Acme} IN ALL FIELDS RETURNING Account(Id, Name) LIMIT 5` + +2. Show the accounts in this format - `Account Name - salesforceID`. Ask the user to confirm which account they are interested in. + +3. Get Gong Call IDs for the account - For the confirmed account, make a call to `executeSOQLQuery` to get all the Gong Call IDs. It should look like this - `SELECT XXX, YYY, ZZZ +FROM Gong__Gong_Call__c +WHERE Gong__Primary_Account__c = '' +ORDER BY Gong__Call_Start__c DESC +LIMIT 2 +` + +4. Pass in the callIds to `getTranscriptsByCallIds ` + +# Trigger +User says "Summarize call" + +# Steps + +Use both the transcripts and provide the following output + +## Account Name +Print out the account name + +## Details of calls +>Please list the calls for which you retrieved the transcripts along with their dates and attendees in this table format: +>>Headers: , <Date>, <Attendees>, <Gong URL> + +## Recommended Meeting Focus Areas: +>Analyze the transcripts to identify themes, challenges, and opportunities. Based on this, generate a list of recommended focus areas for the next meeting. These should be actionable and specific to the client’s needs. Explain **why** each item should be a meeting focus. + +For each of the following insights, specify **which call and date** you sourced the insight from: + +### Metrics +Quantifiable outcomes the customer is trying to achieve. These could be cost reduction, increased revenue, user growth, efficiency gains, etc. Look for KPIs or success measures mentioned. + +### Economic Buyer +Identify if the true economic decision-maker was mentioned or involved. This includes titles, names, or hints at budget ownership or final authority. + +### Decision Criteria +What are the key factors the customer will use to evaluate solutions? These could include price, performance, support, integrations, ease of use, etc. + +### Decision Process +Describe how the customer plans to make the buying decision: stages, stakeholders involved, approval processes, timelines. + +### Paper Process +Any mention of legal, procurement, compliance, or contract-related steps and timelines should be captured here. + +### Identify Pain +Highlight the core business challenges the customer is facing, ideally in their own words. Understand what’s driving urgency. + +### Champion +Is there someone internally who is championing our solution? Mention names, roles, or behaviors that indicate advocacy (e.g., “I’m pushing this internally”). + +### (Optional) Competition +Mention any competing vendors, internal builds, or alternative solutions discussed. +``` +In the above example, replace the query in (3) to retrieves the Gong Call IDs from your custom Salesforce object. + +You will now create 2 separate actions - one to connect to Salesforce and the other to connect to the middleware that calls the Gong APIs + +### OpenAPI Schema for Salesforce custom action + +Once you've created a Custom GPT, copy the text below in the Actions panel. Have questions? Check out [Getting Started Example](https://platform.openai.com/docs/actions/getting-started) to see how this step works in more detail. + +Below is an example of what connecting to Salesforce might look like. You'll need to insert your URL in this section. + +```javascript +openapi: 3.1.0 +info: + title: Salesforce API + version: 1.0.0 + description: API for accessing Salesforce sObjects and executing queries. +servers: + - url: https://<subdomain>.my.salesforce.com/services/data/v59.0 + description: Salesforce API server +paths: + /query: + get: + summary: Execute a SOQL Query + description: Executes a given SOQL query and returns the results. + operationId: executeSOQLQuery + parameters: + - name: q + in: query + description: The SOQL query string to be executed. + required: true + schema: + type: string + responses: + '200': + description: Query executed successfully. + content: + application/json: + schema: + $ref: '#/components/schemas/QueryResult' + + /search: + get: + summary: Execute a SOSL Search + description: Executes a SOSL search based on the given query and returns matching records. + operationId: executeSOSLSearch + parameters: + - name: q + in: query + description: The SOSL search string (e.g., 'FIND {Acme}'). + required: true + schema: + type: string + responses: + '200': + description: Search executed successfully. + content: + application/json: + schema: + $ref: '#/components/schemas/SearchResult' + +components: + schemas: + QueryResult: + type: object + description: Result of a SOQL query. + properties: + totalSize: + type: integer + description: The total number of records matching the query. + done: + type: boolean + description: Indicates if the query result includes all records. + records: + type: array + description: The list of records returned by the query. + items: + $ref: '#/components/schemas/SObject' + + SearchResult: + type: object + description: Result of a SOSL search. + properties: + searchRecords: + type: array + description: The list of records matching the search query. + items: + $ref: '#/components/schemas/SObject' + + SObject: + type: object + description: A Salesforce sObject, which represents a database table record. + properties: + attributes: + type: object + description: Metadata about the sObject, such as type and URL. + properties: + type: + type: string + description: The sObject type. + url: + type: string + description: The URL of the record. + Id: + type: string + description: The unique identifier for the sObject. + additionalProperties: true +``` + +### Authentication instructions for Salesforce custom actions +Please follow the steps shown in [GPT Actions library - Salesforce](https://cookbook.openai.com/examples/chatgpt/gpt_actions_library/gpt_action_salesforce#in-chatgpt) + +### OpenAPI Schema for the middleware that connects to Gong +In this example, we are setting this up for an Azure Function that calls the Gong APIs. Replace `url` with your own Middleware URL + +``` +openapi: 3.1.0 +info: + title: Call Transcripts API + description: API to retrieve call transcripts and associated metadata by specific call IDs. + version: 1.0.1 +servers: + - url: https://<subdomain>.azurewebsites.net/api + description: Production server +paths: + /callTranscripts: + post: + operationId: getTranscriptsByCallIds + x-openai-isConsequential: false + summary: Retrieve call transcripts by call IDs + description: Fetches specific call transcripts based on the provided call IDs in the request body. + requestBody: + required: true + content: + application/json: + schema: + type: object + properties: + callIds: + type: array + description: List of call IDs for which transcripts need to be fetched. + items: + type: string + required: + - callIds + responses: + '200': + description: A successful response containing the requested call transcripts and metadata. + content: + application/json: + schema: + type: object + properties: + requestId: + type: string + description: Unique request identifier. + records: + type: object + description: Metadata about the pagination. + properties: + totalRecords: + type: integer + description: Total number of records available. + currentPageSize: + type: integer + description: Number of records in the current page. + currentPageNumber: + type: integer + description: The current page number. + callTranscripts: + type: array + description: List of call transcripts. + items: + type: object + properties: + callId: + type: string + description: Unique identifier for the call. + title: + type: string + description: Title of the call or meeting. + started: + type: string + format: date-time + description: Timestamp when the call started. + duration: + type: integer + description: Duration of the call in seconds. + url: + type: string + format: uri + description: URL to access the call recording or details. + content: + type: string + description: Transcript content of the call. + '400': + description: Invalid request. Possibly due to missing or invalid `callIds` parameter. + '401': + description: Unauthorized access due to invalid or missing API key. + '500': + description: Internal server error. +``` + + +*Are there integrations that you’d like us to prioritize? Are there errors in our integrations? File a PR or issue in our github, and we’ll take a look.* diff --git a/examples/chatgpt/gpt_actions_library/gpt_action_snowflake_direct.ipynb b/examples/chatgpt/gpt_actions_library/gpt_action_snowflake_direct.ipynb index 491aac91f8..d2494a6037 100644 --- a/examples/chatgpt/gpt_actions_library/gpt_action_snowflake_direct.ipynb +++ b/examples/chatgpt/gpt_actions_library/gpt_action_snowflake_direct.ipynb @@ -27,7 +27,7 @@ "source": [ "This particular GPT Action provides an overview of how to connect to a Snowflake Data Warehouse. This Action takes a user’s question, scans the relevant tables to gather the data schema, then writes a SQL query to answer the user’s question.\n", "\n", - "Note: This cookbook returns back a [ResultSet SQL statement](https://docs.snowflake.com/en/developer-guide/sql-api/handling-responses#getting-the-data-from-the-results), rather than the full result that is not limited by GPT Actions application/json payload limit. For production and advanced use-case, a middleware is required to return back a CSV file. You can follow instructions in the [GPT Actions - Snowflake Middleware cookbook](../gpt_action_snowflake_middleware) to implement this flow instead." + "Note: This cookbook returns back a [ResultSet SQL statement](https://docs.snowflake.com/en/developer-guide/sql-api/handling-responses#getting-the-data-from-the-results), rather than the full result that is not limited by GPT Actions application/json payload limit. For production and advanced use-case, a middleware is required to return back a CSV file. You can follow instructions in the [GPT Actions - Snowflake Middleware cookbook](../gpt_actions_library/gpt_action_snowflake_middleware) to implement this flow instead." ] }, { @@ -456,6 +456,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "* This guide is intended to illustrate general concepts and is provided for reference purposes only. We are unable to provide full support for the third party API integration. \n", "* The callback url can change if you update the YAML, double check it is correct when making changes.\n", "* _Callback URL Error:_ If you get a callback URL error in ChatGPT, pay close attention to the Post-Action Steps above. You need to add the callback URL directly into your Security Integration for the action to authenticate correctly\n", "* _Schema calls the wrong warehouse or database:_ If ChatGPT calls the wrong warehouse or database, consider updating your instructions to make it more explicit either (a) which warehouse / database should be called or (b) to require the user provide those exact details before it runs the query\n" diff --git a/examples/completions_usage_api.ipynb b/examples/completions_usage_api.ipynb index 93db9ac962..c7ce198a89 100644 --- a/examples/completions_usage_api.ipynb +++ b/examples/completions_usage_api.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ "import json\n", "\n", "# For inline plotting in Jupyter\n", - "%matplotlib inline\n" + "%matplotlib inline" ] }, { @@ -48,7 +48,54 @@ "\n", "Set up an Admin Key - https://platform.openai.com/settings/organization/admin-keys\n", "\n", - "Replace `'PLACEHOLDER'` with your actual ADMIN API key. It's best practice to load the key from an environment variable for security.\n" + "Replace `'PLACEHOLDER'` with your actual ADMIN API key. It's best practice to load the key from an environment variable for security." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Reusable function for retrieving paginated data from the API\n", + "def get_data(url, params):\n", + " # Set up the API key and headers\n", + " OPENAI_ADMIN_KEY = 'PLACEHOLDER'\n", + "\n", + " headers = {\n", + " \"Authorization\": f\"Bearer {OPENAI_ADMIN_KEY}\",\n", + " \"Content-Type\": \"application/json\",\n", + " }\n", + "\n", + " # Initialize an empty list to store all data\n", + " all_data = []\n", + "\n", + " # Initialize pagination cursor\n", + " page_cursor = None\n", + "\n", + " # Loop to handle pagination\n", + " while True:\n", + " if page_cursor:\n", + " params[\"page\"] = page_cursor\n", + "\n", + " response = requests.get(url, headers=headers, params=params)\n", + "\n", + " if response.status_code == 200:\n", + " data_json = response.json()\n", + " all_data.extend(data_json.get(\"data\", []))\n", + "\n", + " page_cursor = data_json.get(\"next_page\")\n", + " if not page_cursor:\n", + " break\n", + " else:\n", + " print(f\"Error: {response.status_code}\")\n", + " break\n", + "\n", + " if all_data:\n", + " print(\"Data retrieved successfully!\")\n", + " else:\n", + " print(\"Issue: No data available to retrieve.\")\n", + " return all_data" ] }, { @@ -65,14 +112,6 @@ } ], "source": [ - "# Set up the API key and headers\n", - "OPENAI_ADMIN_KEY = '<PLACEHOLDER>' \n", - "\n", - "headers = {\n", - " \"Authorization\": f\"Bearer {OPENAI_ADMIN_KEY}\",\n", - " \"Content-Type\": \"application/json\"\n", - "}\n", - "\n", "# Define the API endpoint\n", "url = \"https://api.openai.com/v1/organization/usage/completions\"\n", "\n", @@ -84,45 +123,18 @@ "params = {\n", " \"start_time\": start_time, # Required: Start time (Unix seconds)\n", " # \"end_time\": end_time, # Optional: End time (Unix seconds)\n", - " \"bucket_width\": \"1d\", # Optional: '1m', '1h', or '1d' (default '1d')\n", + " \"bucket_width\": \"1d\", # Optional: '1m', '1h', or '1d' (default '1d')\n", " # \"project_ids\": [\"proj_example\"], # Optional: List of project IDs\n", " # \"user_ids\": [\"user_example\"], # Optional: List of user IDs\n", " # \"api_key_ids\": [\"key_example\"], # Optional: List of API key IDs\n", - " # \"models\": [\"gpt-4o-mini-2024-07-18\"], # Optional: List of models\n", + " # \"models\": [\"o1-2024-12-17\", \"gpt-4o-2024-08-06\", \"gpt-4o-mini-2024-07-18\"], # Optional: List of models\n", " # \"batch\": False, # Optional: True for batch jobs, False for non-batch\n", " # \"group_by\": [\"model\"], # Optional: Fields to group by\n", - " \"limit\": 7, # Optional: Number of buckets to return, this will chunk the data into 7 buckets\n", + " \"limit\": 7, # Optional: Number of buckets to return, this will chunk the data into 7 buckets\n", " # \"page\": \"cursor_string\" # Optional: Cursor for pagination\n", "}\n", "\n", - "# Initialize an empty list to store all data\n", - "all_data = []\n", - "\n", - "# Initialize pagination cursor\n", - "page_cursor = None\n", - "\n", - "# Loop to handle pagination\n", - "while True:\n", - " if page_cursor:\n", - " params[\"page\"] = page_cursor\n", - "\n", - " response = requests.get(url, headers=headers, params=params)\n", - "\n", - " if response.status_code == 200:\n", - " data_json = response.json()\n", - " all_data.extend(data_json.get(\"data\", [])) \n", - "\n", - " page_cursor = data_json.get(\"next_page\")\n", - " if not page_cursor:\n", - " break \n", - " else:\n", - " print(f\"Error: {response.status_code}\")\n", - " break \n", - "\n", - "if all_data:\n", - " print(\"Data retrieved successfully!\")\n", - "else:\n", - " print(\"Issue: No data available to retrieve.\")" + "usage_data = get_data(url, params)" ] }, { @@ -146,21 +158,20 @@ "[\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734345977,\n", - " \"end_time\": 1734393600,\n", + " \"start_time\": 1736616660,\n", + " \"end_time\": 1736640000,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 300245,\n", - " \"output_tokens\": 534874,\n", - " \"num_model_requests\": 298,\n", + " \"input_tokens\": 141201,\n", + " \"output_tokens\": 9756,\n", + " \"num_model_requests\": 470,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 53120,\n", + " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", " }\n", @@ -168,20 +179,19 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734393600,\n", - " \"end_time\": 1734480000,\n", + " \"start_time\": 1736640000,\n", + " \"end_time\": 1736726400,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 8,\n", - " \"output_tokens\": 9,\n", - " \"num_model_requests\": 1,\n", + " \"input_tokens\": 45949,\n", + " \"output_tokens\": 282,\n", + " \"num_model_requests\": 150,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -190,99 +200,83 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734480000,\n", - " \"end_time\": 1734566400,\n", + " \"start_time\": 1736726400,\n", + " \"end_time\": 1736812800,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 19287,\n", - " \"output_tokens\": 1770,\n", - " \"num_model_requests\": 24,\n", + " \"input_tokens\": 3718360,\n", + " \"output_tokens\": 97756,\n", + " \"num_model_requests\": 3053,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 15104,\n", - " \"input_audio_tokens\": 47248,\n", - " \"output_audio_tokens\": 6403\n", + " \"input_cached_tokens\": 76544,\n", + " \"input_audio_tokens\": 5776,\n", + " \"output_audio_tokens\": 3166\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734566400,\n", - " \"end_time\": 1734652800,\n", + " \"start_time\": 1736812800,\n", + " \"end_time\": 1736899200,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 19162,\n", - " \"output_tokens\": 5115,\n", - " \"num_model_requests\": 38,\n", + " \"input_tokens\": 52786,\n", + " \"output_tokens\": 38204,\n", + " \"num_model_requests\": 157,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 3584,\n", - " \"input_audio_tokens\": 21218,\n", - " \"output_audio_tokens\": 12483\n", + " \"input_cached_tokens\": 5440,\n", + " \"input_audio_tokens\": 4066,\n", + " \"output_audio_tokens\": 1097\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734652800,\n", - " \"end_time\": 1734739200,\n", + " \"start_time\": 1736899200,\n", + " \"end_time\": 1736985600,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 50882,\n", - " \"output_tokens\": 24867,\n", - " \"num_model_requests\": 28,\n", + " \"input_tokens\": 35664,\n", + " \"output_tokens\": 1835,\n", + " \"num_model_requests\": 55,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 0,\n", - " \"input_audio_tokens\": 0,\n", - " \"output_audio_tokens\": 0\n", + " \"input_cached_tokens\": 192,\n", + " \"input_audio_tokens\": 2520,\n", + " \"output_audio_tokens\": 1549\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734739200,\n", - " \"end_time\": 1734825600,\n", - " \"results\": []\n", - " },\n", - " {\n", - " \"object\": \"bucket\",\n", - " \"start_time\": 1734825600,\n", - " \"end_time\": 1734912000,\n", - " \"results\": []\n", - " },\n", - " {\n", - " \"object\": \"bucket\",\n", - " \"start_time\": 1734912000,\n", - " \"end_time\": 1734998400,\n", + " \"start_time\": 1736985600,\n", + " \"end_time\": 1737072000,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 14642697,\n", - " \"output_tokens\": 164700,\n", - " \"num_model_requests\": 11300,\n", + " \"input_tokens\": 5464,\n", + " \"output_tokens\": 2667,\n", + " \"num_model_requests\": 8,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 151296,\n", + " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", " }\n", @@ -290,50 +284,61 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1734998400,\n", - " \"end_time\": 1735084800,\n", - " \"results\": []\n", - " },\n", - " {\n", - " \"object\": \"bucket\",\n", - " \"start_time\": 1735084800,\n", - " \"end_time\": 1735171200,\n", - " \"results\": []\n", - " },\n", - " {\n", - " \"object\": \"bucket\",\n", - " \"start_time\": 1735171200,\n", - " \"end_time\": 1735257600,\n", - " \"results\": []\n", - " },\n", - " {\n", - " \"object\": \"bucket\",\n", - " \"start_time\": 1735257600,\n", - " \"end_time\": 1735344000,\n", - " \"results\": []\n", + " \"start_time\": 1737072000,\n", + " \"end_time\": 1737158400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.usage.completions.result\",\n", + " \"input_tokens\": 3390547,\n", + " \"output_tokens\": 38604,\n", + " \"num_model_requests\": 2687,\n", + " \"project_id\": null,\n", + " \"user_id\": null,\n", + " \"api_key_id\": null,\n", + " \"model\": null,\n", + " \"batch\": null,\n", + " \"input_cached_tokens\": 25344,\n", + " \"input_audio_tokens\": 0,\n", + " \"output_audio_tokens\": 0\n", + " }\n", + " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735344000,\n", - " \"end_time\": 1735430400,\n", - " \"results\": []\n", + " \"start_time\": 1737158400,\n", + " \"end_time\": 1737244800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.usage.completions.result\",\n", + " \"input_tokens\": 8117824,\n", + " \"output_tokens\": 105662,\n", + " \"num_model_requests\": 6335,\n", + " \"project_id\": null,\n", + " \"user_id\": null,\n", + " \"api_key_id\": null,\n", + " \"model\": null,\n", + " \"batch\": null,\n", + " \"input_cached_tokens\": 46464,\n", + " \"input_audio_tokens\": 0,\n", + " \"output_audio_tokens\": 0\n", + " }\n", + " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735430400,\n", - " \"end_time\": 1735516800,\n", + " \"start_time\": 1737244800,\n", + " \"end_time\": 1737331200,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 84150,\n", - " \"output_tokens\": 502,\n", - " \"num_model_requests\": 232,\n", + " \"input_tokens\": 13542,\n", + " \"output_tokens\": 85,\n", + " \"num_model_requests\": 46,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -342,20 +347,25 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735516800,\n", - " \"end_time\": 1735603200,\n", + " \"start_time\": 1737331200,\n", + " \"end_time\": 1737417600,\n", + " \"results\": []\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737417600,\n", + " \"end_time\": 1737504000,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 118904,\n", - " \"output_tokens\": 57094,\n", - " \"num_model_requests\": 331,\n", + " \"input_tokens\": 29806,\n", + " \"output_tokens\": 57604,\n", + " \"num_model_requests\": 98,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -364,20 +374,19 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735603200,\n", - " \"end_time\": 1735689600,\n", + " \"start_time\": 1737504000,\n", + " \"end_time\": 1737590400,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 175320,\n", - " \"output_tokens\": 67638,\n", - " \"num_model_requests\": 515,\n", + " \"input_tokens\": 1823,\n", + " \"output_tokens\": 1467,\n", + " \"num_model_requests\": 7,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -386,20 +395,19 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735689600,\n", - " \"end_time\": 1735776000,\n", + " \"start_time\": 1737590400,\n", + " \"end_time\": 1737676800,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 1515,\n", - " \"output_tokens\": 11,\n", - " \"num_model_requests\": 5,\n", + " \"input_tokens\": 7126,\n", + " \"output_tokens\": 1896,\n", + " \"num_model_requests\": 19,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -408,64 +416,73 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735776000,\n", - " \"end_time\": 1735862400,\n", + " \"start_time\": 1737676800,\n", + " \"end_time\": 1737763200,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 527323,\n", - " \"output_tokens\": 40396,\n", - " \"num_model_requests\": 283,\n", + " \"input_tokens\": 22187,\n", + " \"output_tokens\": 822,\n", + " \"num_model_requests\": 75,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 1152,\n", - " \"input_audio_tokens\": 0,\n", - " \"output_audio_tokens\": 0\n", + " \"input_cached_tokens\": 640,\n", + " \"input_audio_tokens\": 2557,\n", + " \"output_audio_tokens\": 3103\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735862400,\n", - " \"end_time\": 1735948800,\n", + " \"start_time\": 1737763200,\n", + " \"end_time\": 1737849600,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 31027,\n", - " \"output_tokens\": 242,\n", - " \"num_model_requests\": 106,\n", + " \"input_tokens\": 30204,\n", + " \"output_tokens\": 65673,\n", + " \"num_model_requests\": 99,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", - " \"input_audio_tokens\": 12,\n", - " \"output_audio_tokens\": 29\n", + " \"input_audio_tokens\": 0,\n", + " \"output_audio_tokens\": 0\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1735948800,\n", - " \"end_time\": 1736035200,\n", + " \"start_time\": 1737849600,\n", + " \"end_time\": 1737936000,\n", + " \"results\": []\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737936000,\n", + " \"end_time\": 1738022400,\n", + " \"results\": []\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738022400,\n", + " \"end_time\": 1738108800,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 77053,\n", - " \"output_tokens\": 51801,\n", - " \"num_model_requests\": 255,\n", + " \"input_tokens\": 2541,\n", + " \"output_tokens\": 1604,\n", + " \"num_model_requests\": 14,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -474,70 +491,88 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736035200,\n", - " \"end_time\": 1736121600,\n", - " \"results\": []\n", + " \"start_time\": 1738108800,\n", + " \"end_time\": 1738195200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.usage.completions.result\",\n", + " \"input_tokens\": 68339,\n", + " \"output_tokens\": 49525,\n", + " \"num_model_requests\": 217,\n", + " \"project_id\": null,\n", + " \"user_id\": null,\n", + " \"api_key_id\": null,\n", + " \"model\": null,\n", + " \"batch\": null,\n", + " \"input_cached_tokens\": 7296,\n", + " \"input_audio_tokens\": 20033,\n", + " \"output_audio_tokens\": 3168\n", + " }\n", + " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736121600,\n", - " \"end_time\": 1736208000,\n", + " \"start_time\": 1738195200,\n", + " \"end_time\": 1738281600,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 598758,\n", - " \"output_tokens\": 575020,\n", - " \"num_model_requests\": 1941,\n", + " \"input_tokens\": 18481,\n", + " \"output_tokens\": 17500,\n", + " \"num_model_requests\": 84,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 12480,\n", - " \"input_audio_tokens\": 23843,\n", - " \"output_audio_tokens\": 2441\n", + " \"input_cached_tokens\": 2944,\n", + " \"input_audio_tokens\": 10076,\n", + " \"output_audio_tokens\": 4966\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736208000,\n", - " \"end_time\": 1736294400,\n", + " \"start_time\": 1738281600,\n", + " \"end_time\": 1738368000,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 81383,\n", - " \"output_tokens\": 38568,\n", - " \"num_model_requests\": 284,\n", + " \"input_tokens\": 1187894,\n", + " \"output_tokens\": 139134,\n", + " \"num_model_requests\": 5528,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 0,\n", - " \"input_audio_tokens\": 0,\n", - " \"output_audio_tokens\": 0\n", + " \"input_cached_tokens\": 2112,\n", + " \"input_audio_tokens\": 4935,\n", + " \"output_audio_tokens\": 993\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736294400,\n", - " \"end_time\": 1736380800,\n", + " \"start_time\": 1738368000,\n", + " \"end_time\": 1738454400,\n", + " \"results\": []\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738454400,\n", + " \"end_time\": 1738540800,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 81272,\n", - " \"output_tokens\": 208562,\n", - " \"num_model_requests\": 265,\n", + " \"input_tokens\": 7268,\n", + " \"output_tokens\": 30563,\n", + " \"num_model_requests\": 24,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -546,21 +581,20 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736380800,\n", - " \"end_time\": 1736467200,\n", + " \"start_time\": 1738540800,\n", + " \"end_time\": 1738627200,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 873554,\n", - " \"output_tokens\": 31703,\n", - " \"num_model_requests\": 413,\n", + " \"input_tokens\": 15121,\n", + " \"output_tokens\": 22866,\n", + " \"num_model_requests\": 48,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 691584,\n", + " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", " }\n", @@ -568,21 +602,20 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736467200,\n", - " \"end_time\": 1736553600,\n", + " \"start_time\": 1738627200,\n", + " \"end_time\": 1738713600,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 129753,\n", - " \"output_tokens\": 65335,\n", - " \"num_model_requests\": 184,\n", + " \"input_tokens\": 16735,\n", + " \"output_tokens\": 16177,\n", + " \"num_model_requests\": 50,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 0,\n", + " \"input_cached_tokens\": 1152,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", " }\n", @@ -590,20 +623,19 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736553600,\n", - " \"end_time\": 1736640000,\n", + " \"start_time\": 1738713600,\n", + " \"end_time\": 1738800000,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 141874,\n", - " \"output_tokens\": 9831,\n", - " \"num_model_requests\": 473,\n", + " \"input_tokens\": 6573,\n", + " \"output_tokens\": 4238,\n", + " \"num_model_requests\": 43,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -612,20 +644,19 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736640000,\n", - " \"end_time\": 1736726400,\n", + " \"start_time\": 1738800000,\n", + " \"end_time\": 1738886400,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 45949,\n", - " \"output_tokens\": 282,\n", - " \"num_model_requests\": 150,\n", + " \"input_tokens\": 1402,\n", + " \"output_tokens\": 2042,\n", + " \"num_model_requests\": 18,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", " \"input_cached_tokens\": 0,\n", " \"input_audio_tokens\": 0,\n", " \"output_audio_tokens\": 0\n", @@ -634,67 +665,70 @@ " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736726400,\n", - " \"end_time\": 1736812800,\n", + " \"start_time\": 1738886400,\n", + " \"end_time\": 1738972800,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 3718360,\n", - " \"output_tokens\": 97756,\n", - " \"num_model_requests\": 3053,\n", + " \"input_tokens\": 11847,\n", + " \"output_tokens\": 21938,\n", + " \"num_model_requests\": 47,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 76544,\n", - " \"input_audio_tokens\": 5776,\n", - " \"output_audio_tokens\": 3166\n", + " \"input_cached_tokens\": 0,\n", + " \"input_audio_tokens\": 0,\n", + " \"output_audio_tokens\": 0\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736812800,\n", - " \"end_time\": 1736899200,\n", + " \"start_time\": 1738972800,\n", + " \"end_time\": 1739059200,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 52786,\n", - " \"output_tokens\": 38204,\n", - " \"num_model_requests\": 157,\n", + " \"input_tokens\": 1993,\n", + " \"output_tokens\": 12,\n", + " \"num_model_requests\": 7,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 5440,\n", - " \"input_audio_tokens\": 4066,\n", - " \"output_audio_tokens\": 1097\n", + " \"input_cached_tokens\": 0,\n", + " \"input_audio_tokens\": 0,\n", + " \"output_audio_tokens\": 0\n", " }\n", " ]\n", " },\n", " {\n", " \"object\": \"bucket\",\n", - " \"start_time\": 1736899200,\n", - " \"end_time\": 1736937980,\n", + " \"start_time\": 1739059200,\n", + " \"end_time\": 1739145600,\n", + " \"results\": []\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1739145600,\n", + " \"end_time\": 1739208663,\n", " \"results\": [\n", " {\n", " \"object\": \"organization.usage.completions.result\",\n", - " \"input_tokens\": 9588,\n", - " \"output_tokens\": 224,\n", - " \"num_model_requests\": 34,\n", + " \"input_tokens\": 332,\n", + " \"output_tokens\": 1509,\n", + " \"num_model_requests\": 8,\n", " \"project_id\": null,\n", " \"user_id\": null,\n", " \"api_key_id\": null,\n", " \"model\": null,\n", " \"batch\": null,\n", - " \"service_tier\": null,\n", - " \"input_cached_tokens\": 192,\n", - " \"input_audio_tokens\": 349,\n", - " \"output_audio_tokens\": 499\n", + " \"input_cached_tokens\": 0,\n", + " \"input_audio_tokens\": 0,\n", + " \"output_audio_tokens\": 0\n", " }\n", " ]\n", " }\n", @@ -703,7 +737,7 @@ } ], "source": [ - "print(json.dumps(all_data, indent=2))" + "print(json.dumps(usage_data, indent=2))" ] }, { @@ -756,23 +790,21 @@ " <th>api_key_id</th>\n", " <th>model</th>\n", " <th>batch</th>\n", - " <th>service_tier</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", - " <td>2024-12-16 10:46:17</td>\n", - " <td>2024-12-17</td>\n", - " <td>1734345977</td>\n", - " <td>1734393600</td>\n", - " <td>300245</td>\n", - " <td>534874</td>\n", - " <td>53120</td>\n", + " <td>2025-01-11 17:31:00</td>\n", + " <td>2025-01-12</td>\n", + " <td>1736616660</td>\n", + " <td>1736640000</td>\n", + " <td>141201</td>\n", + " <td>9756</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>298</td>\n", - " <td>None</td>\n", + " <td>0</td>\n", + " <td>470</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", @@ -781,17 +813,16 @@ " </tr>\n", " <tr>\n", " <th>1</th>\n", - " <td>2024-12-17 00:00:00</td>\n", - " <td>2024-12-18</td>\n", - " <td>1734393600</td>\n", - " <td>1734480000</td>\n", - " <td>8</td>\n", - " <td>9</td>\n", + " <td>2025-01-12 00:00:00</td>\n", + " <td>2025-01-13</td>\n", + " <td>1736640000</td>\n", + " <td>1736726400</td>\n", + " <td>45949</td>\n", + " <td>282</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>1</td>\n", - " <td>None</td>\n", + " <td>150</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", @@ -800,17 +831,16 @@ " </tr>\n", " <tr>\n", " <th>2</th>\n", - " <td>2024-12-18 00:00:00</td>\n", - " <td>2024-12-19</td>\n", - " <td>1734480000</td>\n", - " <td>1734566400</td>\n", - " <td>19287</td>\n", - " <td>1770</td>\n", - " <td>15104</td>\n", - " <td>47248</td>\n", - " <td>6403</td>\n", - " <td>24</td>\n", - " <td>None</td>\n", + " <td>2025-01-13 00:00:00</td>\n", + " <td>2025-01-14</td>\n", + " <td>1736726400</td>\n", + " <td>1736812800</td>\n", + " <td>3718360</td>\n", + " <td>97756</td>\n", + " <td>76544</td>\n", + " <td>5776</td>\n", + " <td>3166</td>\n", + " <td>3053</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", @@ -819,17 +849,16 @@ " </tr>\n", " <tr>\n", " <th>3</th>\n", - " <td>2024-12-19 00:00:00</td>\n", - " <td>2024-12-20</td>\n", - " <td>1734566400</td>\n", - " <td>1734652800</td>\n", - " <td>19162</td>\n", - " <td>5115</td>\n", - " <td>3584</td>\n", - " <td>21218</td>\n", - " <td>12483</td>\n", - " <td>38</td>\n", - " <td>None</td>\n", + " <td>2025-01-14 00:00:00</td>\n", + " <td>2025-01-15</td>\n", + " <td>1736812800</td>\n", + " <td>1736899200</td>\n", + " <td>52786</td>\n", + " <td>38204</td>\n", + " <td>5440</td>\n", + " <td>4066</td>\n", + " <td>1097</td>\n", + " <td>157</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", @@ -838,17 +867,16 @@ " </tr>\n", " <tr>\n", " <th>4</th>\n", - " <td>2024-12-20 00:00:00</td>\n", - " <td>2024-12-21</td>\n", - " <td>1734652800</td>\n", - " <td>1734739200</td>\n", - " <td>50882</td>\n", - " <td>24867</td>\n", - " <td>0</td>\n", - " <td>0</td>\n", - " <td>0</td>\n", - " <td>28</td>\n", - " <td>None</td>\n", + " <td>2025-01-15 00:00:00</td>\n", + " <td>2025-01-16</td>\n", + " <td>1736899200</td>\n", + " <td>1736985600</td>\n", + " <td>35664</td>\n", + " <td>1835</td>\n", + " <td>192</td>\n", + " <td>2520</td>\n", + " <td>1549</td>\n", + " <td>55</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", @@ -861,32 +889,32 @@ ], "text/plain": [ " start_datetime end_datetime start_time end_time input_tokens \\\n", - "0 2024-12-16 10:46:17 2024-12-17 1734345977 1734393600 300245 \n", - "1 2024-12-17 00:00:00 2024-12-18 1734393600 1734480000 8 \n", - "2 2024-12-18 00:00:00 2024-12-19 1734480000 1734566400 19287 \n", - "3 2024-12-19 00:00:00 2024-12-20 1734566400 1734652800 19162 \n", - "4 2024-12-20 00:00:00 2024-12-21 1734652800 1734739200 50882 \n", + "0 2025-01-11 17:31:00 2025-01-12 1736616660 1736640000 141201 \n", + "1 2025-01-12 00:00:00 2025-01-13 1736640000 1736726400 45949 \n", + "2 2025-01-13 00:00:00 2025-01-14 1736726400 1736812800 3718360 \n", + "3 2025-01-14 00:00:00 2025-01-15 1736812800 1736899200 52786 \n", + "4 2025-01-15 00:00:00 2025-01-16 1736899200 1736985600 35664 \n", "\n", " output_tokens input_cached_tokens input_audio_tokens \\\n", - "0 534874 53120 0 \n", - "1 9 0 0 \n", - "2 1770 15104 47248 \n", - "3 5115 3584 21218 \n", - "4 24867 0 0 \n", + "0 9756 0 0 \n", + "1 282 0 0 \n", + "2 97756 76544 5776 \n", + "3 38204 5440 4066 \n", + "4 1835 192 2520 \n", "\n", " output_audio_tokens num_model_requests project_id user_id api_key_id \\\n", - "0 0 298 None None None \n", - "1 0 1 None None None \n", - "2 6403 24 None None None \n", - "3 12483 38 None None None \n", - "4 0 28 None None None \n", + "0 0 470 None None None \n", + "1 0 150 None None None \n", + "2 3166 3053 None None None \n", + "3 1097 157 None None None \n", + "4 1549 55 None None None \n", "\n", - " model batch service_tier \n", - "0 None None None \n", - "1 None None None \n", - "2 None None None \n", - "3 None None None \n", - "4 None None None " + " model batch \n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None " ] }, "execution_count": 5, @@ -899,41 +927,53 @@ "records = []\n", "\n", "# Iterate through the data to extract bucketed data\n", - "for bucket in all_data: \n", + "for bucket in usage_data:\n", " start_time = bucket.get(\"start_time\")\n", " end_time = bucket.get(\"end_time\")\n", " for result in bucket.get(\"results\", []):\n", - " records.append({\n", - " \"start_time\": start_time,\n", - " \"end_time\": end_time,\n", - " \"input_tokens\": result.get(\"input_tokens\", 0),\n", - " \"output_tokens\": result.get(\"output_tokens\", 0),\n", - " \"input_cached_tokens\": result.get(\"input_cached_tokens\", 0),\n", - " \"input_audio_tokens\": result.get(\"input_audio_tokens\", 0),\n", - " \"output_audio_tokens\": result.get(\"output_audio_tokens\", 0),\n", - " \"num_model_requests\": result.get(\"num_model_requests\", 0),\n", - " \"project_id\": result.get(\"project_id\"),\n", - " \"user_id\": result.get(\"user_id\"),\n", - " \"api_key_id\": result.get(\"api_key_id\"),\n", - " \"model\": result.get(\"model\"),\n", - " \"batch\": result.get(\"batch\"),\n", - " \"service_tier\": result.get(\"service_tier\")\n", - " })\n", + " records.append(\n", + " {\n", + " \"start_time\": start_time,\n", + " \"end_time\": end_time,\n", + " \"input_tokens\": result.get(\"input_tokens\", 0),\n", + " \"output_tokens\": result.get(\"output_tokens\", 0),\n", + " \"input_cached_tokens\": result.get(\"input_cached_tokens\", 0),\n", + " \"input_audio_tokens\": result.get(\"input_audio_tokens\", 0),\n", + " \"output_audio_tokens\": result.get(\"output_audio_tokens\", 0),\n", + " \"num_model_requests\": result.get(\"num_model_requests\", 0),\n", + " \"project_id\": result.get(\"project_id\"),\n", + " \"user_id\": result.get(\"user_id\"),\n", + " \"api_key_id\": result.get(\"api_key_id\"),\n", + " \"model\": result.get(\"model\"),\n", + " \"batch\": result.get(\"batch\"),\n", + " }\n", + " )\n", "\n", "# Create a DataFrame from the records\n", "df = pd.DataFrame(records)\n", "\n", "# Convert Unix timestamps to datetime for readability\n", - "df['start_datetime'] = pd.to_datetime(df['start_time'], unit='s')\n", - "df['end_datetime'] = pd.to_datetime(df['end_time'], unit='s')\n", + "df[\"start_datetime\"] = pd.to_datetime(df[\"start_time\"], unit=\"s\")\n", + "df[\"end_datetime\"] = pd.to_datetime(df[\"end_time\"], unit=\"s\")\n", "\n", "# Reorder columns for better readability\n", "df = df[\n", " [\n", - " \"start_datetime\", \"end_datetime\", \"start_time\", \"end_time\",\n", - " \"input_tokens\", \"output_tokens\", \"input_cached_tokens\",\n", - " \"input_audio_tokens\", \"output_audio_tokens\", \"num_model_requests\",\n", - " \"project_id\", \"user_id\", \"api_key_id\", \"model\", \"batch\", \"service_tier\"\n", + " \"start_datetime\",\n", + " \"end_datetime\",\n", + " \"start_time\",\n", + " \"end_time\",\n", + " \"input_tokens\",\n", + " \"output_tokens\",\n", + " \"input_cached_tokens\",\n", + " \"input_audio_tokens\",\n", + " \"output_audio_tokens\",\n", + " \"num_model_requests\",\n", + " \"project_id\",\n", + " \"user_id\",\n", + " \"api_key_id\",\n", + " \"model\",\n", + " \"batch\",\n", " ]\n", "]\n", "\n", @@ -957,7 +997,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<Figure size 1200x600 with 1 Axes>" ] @@ -969,19 +1009,29 @@ "source": [ "if not df.empty:\n", " plt.figure(figsize=(12, 6))\n", - " \n", + "\n", " # Create bar charts for input and output tokens\n", " width = 0.35 # width of the bars\n", " indices = range(len(df))\n", - " \n", - " plt.bar(indices, df['input_tokens'], width=width, label='Input Tokens', alpha=0.7)\n", - " plt.bar([i + width for i in indices], df['output_tokens'], width=width, label='Output Tokens', alpha=0.7)\n", - " \n", + "\n", + " plt.bar(indices, df[\"input_tokens\"], width=width, label=\"Input Tokens\", alpha=0.7)\n", + " plt.bar(\n", + " [i + width for i in indices],\n", + " df[\"output_tokens\"],\n", + " width=width,\n", + " label=\"Output Tokens\",\n", + " alpha=0.7,\n", + " )\n", + "\n", " # Set labels and ticks\n", - " plt.xlabel('Time Bucket')\n", - " plt.ylabel('Number of Tokens')\n", - " plt.title('Daily Input vs Output Token Usage Last 30 Days')\n", - " plt.xticks([i + width/2 for i in indices], [dt.strftime('%Y-%m-%d') for dt in df['start_datetime']], rotation=45)\n", + " plt.xlabel(\"Time Bucket\")\n", + " plt.ylabel(\"Number of Tokens\")\n", + " plt.title(\"Daily Input vs Output Token Usage Last 30 Days\")\n", + " plt.xticks(\n", + " [i + width / 2 for i in indices],\n", + " [dt.strftime(\"%Y-%m-%d\") for dt in df[\"start_datetime\"]],\n", + " rotation=45,\n", + " )\n", " plt.legend()\n", " plt.tight_layout()\n", " plt.show()\n", @@ -1053,103 +1103,97 @@ " <th>api_key_id</th>\n", " <th>model</th>\n", " <th>batch</th>\n", - " <th>service_tier</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", - " <td>2024-12-16 10:46:29</td>\n", - " <td>2024-12-17</td>\n", - " <td>1734345989</td>\n", - " <td>1734393600</td>\n", - " <td>22483</td>\n", - " <td>15488</td>\n", + " <td>2025-01-11 17:31:39</td>\n", + " <td>2025-01-12</td>\n", + " <td>1736616699</td>\n", + " <td>1736640000</td>\n", + " <td>6897</td>\n", + " <td>97</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>32</td>\n", - " <td>proj_frFrNmknEESBPFLqlnYutIA9</td>\n", + " <td>97</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", " <td>None</td>\n", " <td>None</td>\n", - " <td>gpt-4o-2024-08-06</td>\n", - " <td>None</td>\n", + " <td>gpt-4o-mini-2024-07-18</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", - " <td>2024-12-16 10:46:29</td>\n", - " <td>2024-12-17</td>\n", - " <td>1734345989</td>\n", - " <td>1734393600</td>\n", - " <td>22454</td>\n", - " <td>4399</td>\n", + " <td>2025-01-11 17:31:39</td>\n", + " <td>2025-01-12</td>\n", + " <td>1736616699</td>\n", + " <td>1736640000</td>\n", + " <td>33984</td>\n", + " <td>206</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>32</td>\n", - " <td>proj_frFrNmknEESBPFLqlnYutIA9</td>\n", - " <td>None</td>\n", + " <td>95</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", " <td>None</td>\n", - " <td>gpt-3.5-turbo-0125</td>\n", " <td>None</td>\n", + " <td>ft:gpt-4o-2024-08-06:distillation-test:wordle2...</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", - " <td>2024-12-16 10:46:29</td>\n", - " <td>2024-12-17</td>\n", - " <td>1734345989</td>\n", - " <td>1734393600</td>\n", - " <td>380</td>\n", - " <td>848</td>\n", + " <td>2025-01-11 17:31:39</td>\n", + " <td>2025-01-12</td>\n", + " <td>1736616699</td>\n", + " <td>1736640000</td>\n", + " <td>2846</td>\n", + " <td>8874</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>24</td>\n", - " <td>proj_VV4ZAjd6ALfFd9uh0vY8joR1</td>\n", - " <td>None</td>\n", + " <td>8</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", " <td>None</td>\n", - " <td>gpt-4o-mini-2024-07-18</td>\n", " <td>None</td>\n", + " <td>o1-mini-2024-09-12</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", - " <td>2024-12-16 10:46:29</td>\n", - " <td>2024-12-17</td>\n", - " <td>1734345989</td>\n", - " <td>1734393600</td>\n", - " <td>372</td>\n", - " <td>368</td>\n", + " <td>2025-01-11 17:31:39</td>\n", + " <td>2025-01-12</td>\n", + " <td>1736616699</td>\n", + " <td>1736640000</td>\n", + " <td>97474</td>\n", + " <td>579</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>13</td>\n", - " <td>proj_VV4ZAjd6ALfFd9uh0vY8joR1</td>\n", + " <td>270</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>gpt-4o-2024-08-06</td>\n", " <td>None</td>\n", - " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", - " <td>2024-12-16 10:46:29</td>\n", - " <td>2024-12-17</td>\n", - " <td>1734345989</td>\n", - " <td>1734393600</td>\n", - " <td>1343</td>\n", - " <td>1468</td>\n", + " <td>2025-01-12 00:00:00</td>\n", + " <td>2025-01-13</td>\n", + " <td>1736640000</td>\n", + " <td>1736726400</td>\n", + " <td>1989</td>\n", + " <td>28</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", - " <td>7</td>\n", - " <td>proj_L67gOme4S2nBA8aQieEOwLy7</td>\n", - " <td>None</td>\n", + " <td>28</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", " <td>None</td>\n", - " <td>gpt-4o-2024-08-06</td>\n", " <td>None</td>\n", + " <td>gpt-4o-mini-2024-07-18</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", @@ -1158,32 +1202,32 @@ ], "text/plain": [ " start_datetime end_datetime start_time end_time input_tokens \\\n", - "0 2024-12-16 10:46:29 2024-12-17 1734345989 1734393600 22483 \n", - "1 2024-12-16 10:46:29 2024-12-17 1734345989 1734393600 22454 \n", - "2 2024-12-16 10:46:29 2024-12-17 1734345989 1734393600 380 \n", - "3 2024-12-16 10:46:29 2024-12-17 1734345989 1734393600 372 \n", - "4 2024-12-16 10:46:29 2024-12-17 1734345989 1734393600 1343 \n", + "0 2025-01-11 17:31:39 2025-01-12 1736616699 1736640000 6897 \n", + "1 2025-01-11 17:31:39 2025-01-12 1736616699 1736640000 33984 \n", + "2 2025-01-11 17:31:39 2025-01-12 1736616699 1736640000 2846 \n", + "3 2025-01-11 17:31:39 2025-01-12 1736616699 1736640000 97474 \n", + "4 2025-01-12 00:00:00 2025-01-13 1736640000 1736726400 1989 \n", "\n", " output_tokens input_cached_tokens input_audio_tokens \\\n", - "0 15488 0 0 \n", - "1 4399 0 0 \n", - "2 848 0 0 \n", - "3 368 0 0 \n", - "4 1468 0 0 \n", + "0 97 0 0 \n", + "1 206 0 0 \n", + "2 8874 0 0 \n", + "3 579 0 0 \n", + "4 28 0 0 \n", "\n", " output_audio_tokens num_model_requests project_id \\\n", - "0 0 32 proj_frFrNmknEESBPFLqlnYutIA9 \n", - "1 0 32 proj_frFrNmknEESBPFLqlnYutIA9 \n", - "2 0 24 proj_VV4ZAjd6ALfFd9uh0vY8joR1 \n", - "3 0 13 proj_VV4ZAjd6ALfFd9uh0vY8joR1 \n", - "4 0 7 proj_L67gOme4S2nBA8aQieEOwLy7 \n", + "0 0 97 proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", + "1 0 95 proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", + "2 0 8 proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", + "3 0 270 proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", + "4 0 28 proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", "\n", - " user_id api_key_id model batch service_tier \n", - "0 None None gpt-4o-2024-08-06 None None \n", - "1 None None gpt-3.5-turbo-0125 None None \n", - "2 None None gpt-4o-mini-2024-07-18 None None \n", - "3 None None gpt-4o-2024-08-06 None None \n", - "4 None None gpt-4o-2024-08-06 None None " + " user_id api_key_id model batch \n", + "0 None None gpt-4o-mini-2024-07-18 None \n", + "1 None None ft:gpt-4o-2024-08-06:distillation-test:wordle2... None \n", + "2 None None o1-mini-2024-09-12 None \n", + "3 None None gpt-4o-2024-08-06 None \n", + "4 None None gpt-4o-mini-2024-07-18 None " ] }, "execution_count": 7, @@ -1198,40 +1242,14 @@ "\n", "# Define parameters with grouping by model and project_id\n", "params = {\n", - " \"start_time\": start_time, # Required: Start time (Unix seconds)\n", - " \"bucket_width\": \"1d\", # Optional: '1m', '1h', or '1d' (default '1d')\n", + " \"start_time\": start_time, # Required: Start time (Unix seconds)\n", + " \"bucket_width\": \"1d\", # Optional: '1m', '1h', or '1d' (default '1d')\n", " \"group_by\": [\"model\", \"project_id\"], # Group data by model and project_id\n", - " \"limit\": 7 # Optional: Number of buckets to return\n", + " \"limit\": 7, # Optional: Number of buckets to return\n", "}\n", "\n", "# Initialize an empty list to store all data\n", - "all_group_data = []\n", - "\n", - "# Initialize pagination cursor\n", - "page_cursor = None\n", - "\n", - "# Loop to handle pagination\n", - "while True:\n", - " if page_cursor:\n", - " params[\"page\"] = page_cursor\n", - "\n", - " response = requests.get(url, headers=headers, params=params)\n", - "\n", - " if response.status_code == 200:\n", - " data_json = response.json()\n", - " all_group_data.extend(data_json.get(\"data\", []))\n", - "\n", - " page_cursor = data_json.get(\"next_page\")\n", - " if not page_cursor:\n", - " break \n", - " else:\n", - " print(f\"Error: {response.status_code}\")\n", - " break \n", - "\n", - "if all_group_data:\n", - " print(\"Data retrieved successfully!\")\n", - "else:\n", - " print(\"Issue: No data available to retrieve.\")\n", + "all_group_data = get_data(url, params)\n", "\n", "# Initialize a list to hold parsed records\n", "records = []\n", @@ -1241,37 +1259,49 @@ " start_time = bucket.get(\"start_time\")\n", " end_time = bucket.get(\"end_time\")\n", " for result in bucket.get(\"results\", []):\n", - " records.append({\n", - " \"start_time\": start_time,\n", - " \"end_time\": end_time,\n", - " \"input_tokens\": result.get(\"input_tokens\", 0),\n", - " \"output_tokens\": result.get(\"output_tokens\", 0),\n", - " \"input_cached_tokens\": result.get(\"input_cached_tokens\", 0),\n", - " \"input_audio_tokens\": result.get(\"input_audio_tokens\", 0),\n", - " \"output_audio_tokens\": result.get(\"output_audio_tokens\", 0),\n", - " \"num_model_requests\": result.get(\"num_model_requests\", 0),\n", - " \"project_id\": result.get(\"project_id\", \"N/A\"),\n", - " \"user_id\": result.get(\"user_id\", \"N/A\"),\n", - " \"api_key_id\": result.get(\"api_key_id\", \"N/A\"),\n", - " \"model\": result.get(\"model\", \"N/A\"),\n", - " \"batch\": result.get(\"batch\", \"N/A\"),\n", - " \"service_tier\": result.get(\"service_tier\", \"N/A\")\n", - " })\n", + " records.append(\n", + " {\n", + " \"start_time\": start_time,\n", + " \"end_time\": end_time,\n", + " \"input_tokens\": result.get(\"input_tokens\", 0),\n", + " \"output_tokens\": result.get(\"output_tokens\", 0),\n", + " \"input_cached_tokens\": result.get(\"input_cached_tokens\", 0),\n", + " \"input_audio_tokens\": result.get(\"input_audio_tokens\", 0),\n", + " \"output_audio_tokens\": result.get(\"output_audio_tokens\", 0),\n", + " \"num_model_requests\": result.get(\"num_model_requests\", 0),\n", + " \"project_id\": result.get(\"project_id\", \"N/A\"),\n", + " \"user_id\": result.get(\"user_id\", \"N/A\"),\n", + " \"api_key_id\": result.get(\"api_key_id\", \"N/A\"),\n", + " \"model\": result.get(\"model\", \"N/A\"),\n", + " \"batch\": result.get(\"batch\", \"N/A\"),\n", + " }\n", + " )\n", "\n", "# Create a DataFrame from the records\n", "df = pd.DataFrame(records)\n", "\n", "# Convert Unix timestamps to datetime for readability\n", - "df['start_datetime'] = pd.to_datetime(df['start_time'], unit='s', errors='coerce')\n", - "df['end_datetime'] = pd.to_datetime(df['end_time'], unit='s', errors='coerce')\n", + "df[\"start_datetime\"] = pd.to_datetime(df[\"start_time\"], unit=\"s\", errors=\"coerce\")\n", + "df[\"end_datetime\"] = pd.to_datetime(df[\"end_time\"], unit=\"s\", errors=\"coerce\")\n", "\n", "# Reorder columns for better readability\n", "df = df[\n", " [\n", - " \"start_datetime\", \"end_datetime\", \"start_time\", \"end_time\",\n", - " \"input_tokens\", \"output_tokens\", \"input_cached_tokens\",\n", - " \"input_audio_tokens\", \"output_audio_tokens\", \"num_model_requests\",\n", - " \"project_id\", \"user_id\", \"api_key_id\", \"model\", \"batch\", \"service_tier\"\n", + " \"start_datetime\",\n", + " \"end_datetime\",\n", + " \"start_time\",\n", + " \"end_time\",\n", + " \"input_tokens\",\n", + " \"output_tokens\",\n", + " \"input_cached_tokens\",\n", + " \"input_audio_tokens\",\n", + " \"output_audio_tokens\",\n", + " \"num_model_requests\",\n", + " \"project_id\",\n", + " \"user_id\",\n", + " \"api_key_id\",\n", + " \"model\",\n", + " \"batch\",\n", " ]\n", "]\n", "\n", @@ -1295,7 +1325,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<Figure size 1200x600 with 1 Axes>" ] @@ -1308,24 +1338,37 @@ "# Group data by model and project_id and aggregate model request counts\n", "grouped_by_model_project = (\n", " df.groupby([\"model\", \"project_id\"])\n", - " .agg({\n", - " \"num_model_requests\": \"sum\",\n", - " })\n", + " .agg(\n", + " {\n", + " \"num_model_requests\": \"sum\",\n", + " }\n", + " )\n", " .reset_index()\n", ")\n", "\n", "# Determine unique models and project IDs for plotting and color mapping\n", - "models = sorted(grouped_by_model_project['model'].unique())\n", - "project_ids = sorted(grouped_by_model_project['project_id'].unique())\n", + "models = sorted(grouped_by_model_project[\"model\"].unique())\n", + "project_ids = sorted(grouped_by_model_project[\"project_id\"].unique())\n", "distinct_colors = [\n", - " \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\",\n", - " \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\"\n", + " \"#1f77b4\",\n", + " \"#ff7f0e\",\n", + " \"#2ca02c\",\n", + " \"#d62728\",\n", + " \"#9467bd\",\n", + " \"#8c564b\",\n", + " \"#e377c2\",\n", + " \"#7f7f7f\",\n", + " \"#bcbd22\",\n", + " \"#17becf\",\n", "]\n", - "project_color_mapping = {pid: distinct_colors[i % len(distinct_colors)] for i, pid in enumerate(project_ids)}\n", + "project_color_mapping = {\n", + " pid: distinct_colors[i % len(distinct_colors)] for i, pid in enumerate(project_ids)\n", + "}\n", "\n", "# Calculate total number of requests per project_id for legend\n", "project_totals = (\n", - " grouped_by_model_project.groupby(\"project_id\")[\"num_model_requests\"].sum()\n", + " grouped_by_model_project.groupby(\"project_id\")[\"num_model_requests\"]\n", + " .sum()\n", " .sort_values(ascending=False) # Sort by highest total first\n", ")\n", "\n", @@ -1339,31 +1382,33 @@ "# Plot stacked bars for each model\n", "for model_idx, model in enumerate(models):\n", " # Filter data for the current model\n", - " model_data = grouped_by_model_project[grouped_by_model_project['model'] == model]\n", - " \n", + " model_data = grouped_by_model_project[grouped_by_model_project[\"model\"] == model]\n", + "\n", " bottom = 0\n", " # Stack segments for each project ID within the bars\n", " for _, row in model_data.iterrows():\n", - " color = project_color_mapping[row['project_id']]\n", - " plt.bar(x[model_idx], row['num_model_requests'], width=bar_width,\n", - " bottom=bottom, color=color)\n", - " bottom += row['num_model_requests']\n", + " color = project_color_mapping[row[\"project_id\"]]\n", + " plt.bar(\n", + " x[model_idx],\n", + " row[\"num_model_requests\"],\n", + " width=bar_width,\n", + " bottom=bottom,\n", + " color=color,\n", + " )\n", + " bottom += row[\"num_model_requests\"]\n", "\n", "# Labeling and styling\n", - "plt.xlabel('Model')\n", - "plt.ylabel('Number of Model Requests')\n", - "plt.title('Total Model Requests by Model and Project ID Last 30 Days')\n", + "plt.xlabel(\"Model\")\n", + "plt.ylabel(\"Number of Model Requests\")\n", + "plt.title(\"Total Model Requests by Model and Project ID Last 30 Days\")\n", "plt.xticks(x, models, rotation=45, ha=\"right\")\n", "\n", "# Create a sorted legend with totals\n", "handles = [\n", - " mpatches.Patch(\n", - " color=project_color_mapping[pid],\n", - " label=f\"{pid} (Total: {total})\"\n", - " )\n", + " mpatches.Patch(color=project_color_mapping[pid], label=f\"{pid} (Total: {total})\")\n", " for pid, total in project_totals.items()\n", "]\n", - "plt.legend(handles=handles, bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.legend(handles=handles, bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "\n", "plt.tight_layout()\n", "plt.show()" @@ -1385,7 +1430,7 @@ "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 1000x800 with 1 Axes>" ] @@ -1395,7 +1440,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1000x800 with 1 Axes>" ] @@ -1408,79 +1453,91 @@ "records = []\n", "for bucket in all_group_data:\n", " for result in bucket.get(\"results\", []):\n", - " records.append({\n", - " \"project_id\": result.get(\"project_id\", \"N/A\"),\n", - " \"num_model_requests\": result.get(\"num_model_requests\", 0),\n", - " })\n", + " records.append(\n", + " {\n", + " \"project_id\": result.get(\"project_id\", \"N/A\"),\n", + " \"num_model_requests\": result.get(\"num_model_requests\", 0),\n", + " }\n", + " )\n", "\n", "# Create a DataFrame\n", "df = pd.DataFrame(records)\n", "\n", "# Aggregate data by project_id\n", "grouped_by_project = (\n", - " df.groupby(\"project_id\")\n", - " .agg({\"num_model_requests\": \"sum\"})\n", - " .reset_index()\n", + " df.groupby(\"project_id\").agg({\"num_model_requests\": \"sum\"}).reset_index()\n", ")\n", "\n", "# Visualize Pie Chart\n", "if not grouped_by_project.empty:\n", " # Filter out rows where num_model_requests == 0\n", - " filtered_grouped_by_project = grouped_by_project[grouped_by_project['num_model_requests'] > 0]\n", - " \n", + " filtered_grouped_by_project = grouped_by_project[\n", + " grouped_by_project[\"num_model_requests\"] > 0\n", + " ]\n", + "\n", " # Calculate the total model requests after filtering\n", - " total_requests = filtered_grouped_by_project['num_model_requests'].sum()\n", - " \n", + " total_requests = filtered_grouped_by_project[\"num_model_requests\"].sum()\n", + "\n", " if total_requests > 0:\n", " # Calculate percentage of total for each project\n", - " filtered_grouped_by_project['percentage'] = (\n", - " filtered_grouped_by_project['num_model_requests'] / total_requests\n", + " filtered_grouped_by_project[\"percentage\"] = (\n", + " filtered_grouped_by_project[\"num_model_requests\"] / total_requests\n", " ) * 100\n", - " \n", + "\n", " # Separate \"Other\" projects (below 5%)\n", - " other_projects = filtered_grouped_by_project[filtered_grouped_by_project['percentage'] < 5]\n", - " main_projects = filtered_grouped_by_project[filtered_grouped_by_project['percentage'] >= 5]\n", - " \n", + " other_projects = filtered_grouped_by_project[\n", + " filtered_grouped_by_project[\"percentage\"] < 5\n", + " ]\n", + " main_projects = filtered_grouped_by_project[\n", + " filtered_grouped_by_project[\"percentage\"] >= 5\n", + " ]\n", + "\n", " # Sum up \"Other\" projects\n", " if not other_projects.empty:\n", - " other_row = pd.DataFrame({\n", - " \"project_id\": [\"Other\"],\n", - " \"num_model_requests\": [other_projects['num_model_requests'].sum()],\n", - " \"percentage\": [other_projects['percentage'].sum()]\n", - " })\n", - " filtered_grouped_by_project = pd.concat([main_projects, other_row], ignore_index=True)\n", - " \n", + " other_row = pd.DataFrame(\n", + " {\n", + " \"project_id\": [\"Other\"],\n", + " \"num_model_requests\": [other_projects[\"num_model_requests\"].sum()],\n", + " \"percentage\": [other_projects[\"percentage\"].sum()],\n", + " }\n", + " )\n", + " filtered_grouped_by_project = pd.concat(\n", + " [main_projects, other_row], ignore_index=True\n", + " )\n", + "\n", " # Sort by number of requests for better legend organization\n", - " filtered_grouped_by_project = filtered_grouped_by_project.sort_values(by=\"num_model_requests\", ascending=False)\n", - " \n", + " filtered_grouped_by_project = filtered_grouped_by_project.sort_values(\n", + " by=\"num_model_requests\", ascending=False\n", + " )\n", + "\n", " # Main pie chart for distribution of model requests by project_id\n", " plt.figure(figsize=(10, 8))\n", " plt.pie(\n", - " filtered_grouped_by_project['num_model_requests'], \n", - " labels=filtered_grouped_by_project['project_id'], \n", - " autopct=lambda p: f'{p:.1f}%\\n({int(p * total_requests / 100):,})',\n", + " filtered_grouped_by_project[\"num_model_requests\"],\n", + " labels=filtered_grouped_by_project[\"project_id\"],\n", + " autopct=lambda p: f\"{p:.1f}%\\n({int(p * total_requests / 100):,})\",\n", " startangle=140,\n", - " textprops={'fontsize': 10}\n", + " textprops={\"fontsize\": 10},\n", " )\n", - " plt.title('Distribution of Model Requests by Project ID', fontsize=14)\n", - " plt.axis('equal') # Equal aspect ratio ensures pie chart is circular.\n", + " plt.title(\"Distribution of Model Requests by Project ID\", fontsize=14)\n", + " plt.axis(\"equal\") # Equal aspect ratio ensures pie chart is circular.\n", " plt.tight_layout()\n", " plt.show()\n", - " \n", + "\n", " # If there are \"Other\" projects, generate a second pie chart for breakdown\n", " if not other_projects.empty:\n", - " other_total_requests = other_projects['num_model_requests'].sum()\n", - " \n", + " other_total_requests = other_projects[\"num_model_requests\"].sum()\n", + "\n", " plt.figure(figsize=(10, 8))\n", " plt.pie(\n", - " other_projects['num_model_requests'], \n", - " labels=other_projects['project_id'], \n", - " autopct=lambda p: f'{p:.1f}%\\n({int(p * other_total_requests / 100):,})',\n", + " other_projects[\"num_model_requests\"],\n", + " labels=other_projects[\"project_id\"],\n", + " autopct=lambda p: f\"{p:.1f}%\\n({int(p * other_total_requests / 100):,})\",\n", " startangle=140,\n", - " textprops={'fontsize': 10}\n", + " textprops={\"fontsize\": 10},\n", " )\n", " plt.title('Breakdown of \"Other\" Projects by Model Requests', fontsize=14)\n", - " plt.axis('equal') # Equal aspect ratio ensures pie chart is circular.\n", + " plt.axis(\"equal\") # Equal aspect ratio ensures pie chart is circular.\n", " plt.tight_layout()\n", " plt.show()\n", " else:\n", @@ -1510,7 +1567,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Costs data retrieved successfully!\n" + "Data retrieved successfully!\n" ] } ], @@ -1522,40 +1579,559 @@ "# Define the Costs API endpoint\n", "costs_url = \"https://api.openai.com/v1/organization/costs\"\n", "\n", - "# Initialize an empty list to store all data\n", - "all_costs_data = []\n", - "\n", - "# Initialize pagination cursor\n", - "page_cursor = None\n", - "\n", - "# Loop to handle pagination\n", - "while True:\n", - " costs_params = {\n", - " \"start_time\": start_time, # Required: Start time (Unix seconds)\n", - " \"bucket_width\": \"1d\", # Optional: Currently only '1d' is supported\n", - " \"limit\": 30, # Optional: Number of buckets to return\n", - " }\n", - "\n", - " if page_cursor:\n", - " costs_params[\"page\"] = page_cursor\n", - "\n", - " costs_response = requests.get(costs_url, headers=headers, params=costs_params)\n", - "\n", - " if costs_response.status_code == 200:\n", - " costs_json = costs_response.json()\n", - " all_costs_data.extend(costs_json.get(\"data\", []))\n", + "costs_params = {\n", + " \"start_time\": start_time, # Required: Start time (Unix seconds)\n", + " \"bucket_width\": \"1d\", # Optional: Currently only '1d' is supported\n", + " \"limit\": 30, # Optional: Number of buckets to return\n", + "}\n", "\n", - " page_cursor = costs_json.get(\"next_page\")\n", - " if not page_cursor:\n", - " break\n", - " else:\n", - " print(f\"Error: {costs_response.status_code}\")\n", - " break\n", - "\n", - "if all_costs_data:\n", - " print(\"Costs data retrieved successfully!\")\n", - "else:\n", - " print(\"No costs data found.\")\n" + "# Initialize an empty list to store all data\n", + "all_costs_data = get_data(costs_url, costs_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1736553600,\n", + " \"end_time\": 1736640000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.13080438340307526,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1736640000,\n", + " \"end_time\": 1736726400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.12270423340307525,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1736726400,\n", + " \"end_time\": 1736812800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 9.888144383403077,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1736812800,\n", + " \"end_time\": 1736899200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.3507639334030752,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1736899200,\n", + " \"end_time\": 1736985600,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.2977481185324674,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1736985600,\n", + " \"end_time\": 1737072000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00925485477848094,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737072000,\n", + " \"end_time\": 1737158400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 8.889884136532304,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737158400,\n", + " \"end_time\": 1737244800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 21.167310118127915,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737244800,\n", + " \"end_time\": 1737331200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.04955636812791847,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737331200,\n", + " \"end_time\": 1737417600,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.0003226181279184669,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737417600,\n", + " \"end_time\": 1737504000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.6320363681279185,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737504000,\n", + " \"end_time\": 1737590400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 52.41558761812793,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737590400,\n", + " \"end_time\": 1737676800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 104.88761235323427,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737676800,\n", + " \"end_time\": 1737763200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.3376030385950106,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737763200,\n", + " \"end_time\": 1737849600,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.062551042553524,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737849600,\n", + " \"end_time\": 1737936000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00032195744715549047,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1737936000,\n", + " \"end_time\": 1738022400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.0003084210662774742,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738022400,\n", + " \"end_time\": 1738108800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00032195744715549047,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738108800,\n", + " \"end_time\": 1738195200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.5142559074471554,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738195200,\n", + " \"end_time\": 1738281600,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.21870350744715547,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738281600,\n", + " \"end_time\": 1738368000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 1.4528752074471551,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738368000,\n", + " \"end_time\": 1738454400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00042714787262957543,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738454400,\n", + " \"end_time\": 1738540800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00032195744715549047,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738540800,\n", + " \"end_time\": 1738627200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.0031147346857709622,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738627200,\n", + " \"end_time\": 1738713600,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 68.30023964957941,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738713600,\n", + " \"end_time\": 1738800000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 14.858330207447157,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738800000,\n", + " \"end_time\": 1738886400,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.3137180574471555,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738886400,\n", + " \"end_time\": 1738972800,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.02677460744715549,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1738972800,\n", + " \"end_time\": 1739059200,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.007399792553524012,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1739059200,\n", + " \"end_time\": 1739145600,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00032195744715549047,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"object\": \"bucket\",\n", + " \"start_time\": 1739145600,\n", + " \"end_time\": 1739232000,\n", + " \"results\": [\n", + " {\n", + " \"object\": \"organization.costs.result\",\n", + " \"amount\": {\n", + " \"value\": 0.00012073404268330895,\n", + " \"currency\": \"usd\"\n", + " },\n", + " \"line_item\": null,\n", + " \"project_id\": null,\n", + " \"organization_id\": \"org-GLHrIv00VVN9dEQC2b4wsBkf\"\n", + " }\n", + " ]\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "print(json.dumps(all_costs_data, indent=2))" ] }, { @@ -1569,7 +2145,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1606,58 +2182,58 @@ " <tbody>\n", " <tr>\n", " <th>0</th>\n", - " <td>1734307200</td>\n", - " <td>1734393600</td>\n", - " <td>55.358578</td>\n", + " <td>1736553600</td>\n", + " <td>1736640000</td>\n", + " <td>0.130804</td>\n", " <td>usd</td>\n", " <td>None</td>\n", " <td>None</td>\n", - " <td>2024-12-16</td>\n", - " <td>2024-12-17</td>\n", + " <td>2025-01-11</td>\n", + " <td>2025-01-12</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", - " <td>1734393600</td>\n", - " <td>1734480000</td>\n", - " <td>0.000110</td>\n", + " <td>1736640000</td>\n", + " <td>1736726400</td>\n", + " <td>0.122704</td>\n", " <td>usd</td>\n", " <td>None</td>\n", " <td>None</td>\n", - " <td>2024-12-17</td>\n", - " <td>2024-12-18</td>\n", + " <td>2025-01-12</td>\n", + " <td>2025-01-13</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", - " <td>1734480000</td>\n", - " <td>1734566400</td>\n", - " <td>0.016204</td>\n", + " <td>1736726400</td>\n", + " <td>1736812800</td>\n", + " <td>9.888144</td>\n", " <td>usd</td>\n", " <td>None</td>\n", " <td>None</td>\n", - " <td>2024-12-18</td>\n", - " <td>2024-12-19</td>\n", + " <td>2025-01-13</td>\n", + " <td>2025-01-14</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", - " <td>1734566400</td>\n", - " <td>1734652800</td>\n", - " <td>2.121425</td>\n", + " <td>1736812800</td>\n", + " <td>1736899200</td>\n", + " <td>0.350764</td>\n", " <td>usd</td>\n", " <td>None</td>\n", " <td>None</td>\n", - " <td>2024-12-19</td>\n", - " <td>2024-12-20</td>\n", + " <td>2025-01-14</td>\n", + " <td>2025-01-15</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", - " <td>1734652800</td>\n", - " <td>1734739200</td>\n", - " <td>3.771420</td>\n", + " <td>1736899200</td>\n", + " <td>1736985600</td>\n", + " <td>0.297748</td>\n", " <td>usd</td>\n", " <td>None</td>\n", " <td>None</td>\n", - " <td>2024-12-20</td>\n", - " <td>2024-12-21</td>\n", + " <td>2025-01-15</td>\n", + " <td>2025-01-16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -1665,21 +2241,21 @@ ], "text/plain": [ " start_time end_time amount_value currency line_item project_id \\\n", - "0 1734307200 1734393600 55.358578 usd None None \n", - "1 1734393600 1734480000 0.000110 usd None None \n", - "2 1734480000 1734566400 0.016204 usd None None \n", - "3 1734566400 1734652800 2.121425 usd None None \n", - "4 1734652800 1734739200 3.771420 usd None None \n", + "0 1736553600 1736640000 0.130804 usd None None \n", + "1 1736640000 1736726400 0.122704 usd None None \n", + "2 1736726400 1736812800 9.888144 usd None None \n", + "3 1736812800 1736899200 0.350764 usd None None \n", + "4 1736899200 1736985600 0.297748 usd None None \n", "\n", " start_datetime end_datetime \n", - "0 2024-12-16 2024-12-17 \n", - "1 2024-12-17 2024-12-18 \n", - "2 2024-12-18 2024-12-19 \n", - "3 2024-12-19 2024-12-20 \n", - "4 2024-12-20 2024-12-21 " + "0 2025-01-11 2025-01-12 \n", + "1 2025-01-12 2025-01-13 \n", + "2 2025-01-13 2025-01-14 \n", + "3 2025-01-14 2025-01-15 \n", + "4 2025-01-15 2025-01-16 " ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1693,21 +2269,23 @@ " start_time = bucket.get(\"start_time\")\n", " end_time = bucket.get(\"end_time\")\n", " for result in bucket.get(\"results\", []):\n", - " cost_records.append({\n", - " \"start_time\": start_time,\n", - " \"end_time\": end_time,\n", - " \"amount_value\": result.get(\"amount\", {}).get(\"value\", 0),\n", - " \"currency\": result.get(\"amount\", {}).get(\"currency\", \"usd\"),\n", - " \"line_item\": result.get(\"line_item\"),\n", - " \"project_id\": result.get(\"project_id\")\n", - " })\n", + " cost_records.append(\n", + " {\n", + " \"start_time\": start_time,\n", + " \"end_time\": end_time,\n", + " \"amount_value\": result.get(\"amount\", {}).get(\"value\", 0),\n", + " \"currency\": result.get(\"amount\", {}).get(\"currency\", \"usd\"),\n", + " \"line_item\": result.get(\"line_item\"),\n", + " \"project_id\": result.get(\"project_id\"),\n", + " }\n", + " )\n", "\n", "# Create a DataFrame from the cost records\n", "cost_df = pd.DataFrame(cost_records)\n", "\n", "# Convert Unix timestamps to datetime for readability\n", - "cost_df['start_datetime'] = pd.to_datetime(cost_df['start_time'], unit='s')\n", - "cost_df['end_datetime'] = pd.to_datetime(cost_df['end_time'], unit='s')\n", + "cost_df[\"start_datetime\"] = pd.to_datetime(cost_df[\"start_time\"], unit=\"s\")\n", + "cost_df[\"end_datetime\"] = pd.to_datetime(cost_df[\"end_time\"], unit=\"s\")\n", "\n", "# Display the first few rows of the DataFrame\n", "cost_df.head()" @@ -1717,19 +2295,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize Costs by Line Item\n", + "## Visualize Total Costs per Day\n", "\n", - "We'll create a bar chart to visualize the total costs aggregated by line item. This helps identify which categories (e.g., models or other services) contribute most to the expenses.\n" + "We'll create a bar chart to visualize the total costs aggregated by day. This helps give a high level perspective on organizational spend." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1200x600 with 1 Axes>" ] @@ -1741,22 +2319,271 @@ "source": [ "if not cost_df.empty:\n", " # Ensure datetime conversion for 'start_datetime' column\n", - " if 'start_datetime' not in cost_df.columns or not pd.api.types.is_datetime64_any_dtype(cost_df['start_datetime']):\n", - " cost_df['start_datetime'] = pd.to_datetime(cost_df['start_time'], unit='s', errors='coerce')\n", + " if (\n", + " \"start_datetime\" not in cost_df.columns\n", + " or not pd.api.types.is_datetime64_any_dtype(cost_df[\"start_datetime\"])\n", + " ):\n", + " cost_df[\"start_datetime\"] = pd.to_datetime(\n", + " cost_df[\"start_time\"], unit=\"s\", errors=\"coerce\"\n", + " )\n", "\n", " # Create a new column for just the date part of 'start_datetime'\n", - " cost_df['date'] = cost_df['start_datetime'].dt.date\n", - " \n", + " cost_df[\"date\"] = cost_df[\"start_datetime\"].dt.date\n", + "\n", " # Group by date and sum the amounts\n", - " cost_per_day = cost_df.groupby('date')['amount_value'].sum().reset_index()\n", - " \n", + " cost_per_day = cost_df.groupby(\"date\")[\"amount_value\"].sum().reset_index()\n", + "\n", " # Plot the data\n", " plt.figure(figsize=(12, 6))\n", - " plt.bar(cost_per_day['date'], cost_per_day['amount_value'], width=0.6, color='skyblue', alpha=0.8)\n", - " plt.xlabel('Date')\n", - " plt.ylabel('Total Cost (USD)')\n", - " plt.title('Total Cost per Day (Last 30 Days)')\n", - " plt.xticks(rotation=45, ha='right')\n", + " plt.bar(\n", + " cost_per_day[\"date\"],\n", + " cost_per_day[\"amount_value\"],\n", + " width=0.6,\n", + " color=\"skyblue\",\n", + " alpha=0.8,\n", + " )\n", + " plt.xlabel(\"Date\")\n", + " plt.ylabel(\"Total Cost (USD)\")\n", + " plt.title(\"Total Cost per Day (Last 30 Days)\")\n", + " plt.xticks(rotation=45, ha=\"right\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No cost data available to plot.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Costs by Line Item\n", + "\n", + "We'll create a bar chart to visualize the total costs aggregated by line item. This helps identify which categories (e.g., models or other services) contribute most to the expenses." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data retrieved successfully!\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>start_time</th>\n", + " <th>end_time</th>\n", + " <th>amount_value</th>\n", + " <th>currency</th>\n", + " <th>line_item</th>\n", + " <th>project_id</th>\n", + " <th>start_datetime</th>\n", + " <th>end_datetime</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>1736553600</td>\n", + " <td>1736640000</td>\n", + " <td>0.127440</td>\n", + " <td>usd</td>\n", + " <td>ft-gpt-4o-2024-08-06, input</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", + " <td>2025-01-11</td>\n", + " <td>2025-01-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>1736553600</td>\n", + " <td>1736640000</td>\n", + " <td>0.003090</td>\n", + " <td>usd</td>\n", + " <td>ft-gpt-4o-2024-08-06, output</td>\n", + " <td>proj_hNhhQzyYu7HxySZWs7cA3Ugu</td>\n", + " <td>2025-01-11</td>\n", + " <td>2025-01-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>1736553600</td>\n", + " <td>1736640000</td>\n", + " <td>0.000271</td>\n", + " <td>usd</td>\n", + " <td>assistants api | file search</td>\n", + " <td>proj_L67gOme4S2nBA8aQieEOwLy7</td>\n", + " <td>2025-01-11</td>\n", + " <td>2025-01-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>1736553600</td>\n", + " <td>1736640000</td>\n", + " <td>0.000003</td>\n", + " <td>usd</td>\n", + " <td>assistants api | file search</td>\n", + " <td>proj_VV4ZAjd6ALfFd9uh0vY8joR1</td>\n", + " <td>2025-01-11</td>\n", + " <td>2025-01-12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>1736640000</td>\n", + " <td>1736726400</td>\n", + " <td>0.028607</td>\n", + " <td>usd</td>\n", + " <td>evals | gpt-4o-mini-2024-07-18, input</td>\n", + " <td>proj_L67gOme4S2nBA8aQieEOwLy7</td>\n", + " <td>2025-01-12</td>\n", + " <td>2025-01-13</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " start_time end_time amount_value currency \\\n", + "0 1736553600 1736640000 0.127440 usd \n", + "1 1736553600 1736640000 0.003090 usd \n", + "2 1736553600 1736640000 0.000271 usd \n", + "3 1736553600 1736640000 0.000003 usd \n", + "4 1736640000 1736726400 0.028607 usd \n", + "\n", + " line_item project_id \\\n", + "0 ft-gpt-4o-2024-08-06, input proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", + "1 ft-gpt-4o-2024-08-06, output proj_hNhhQzyYu7HxySZWs7cA3Ugu \n", + "2 assistants api | file search proj_L67gOme4S2nBA8aQieEOwLy7 \n", + "3 assistants api | file search proj_VV4ZAjd6ALfFd9uh0vY8joR1 \n", + "4 evals | gpt-4o-mini-2024-07-18, input proj_L67gOme4S2nBA8aQieEOwLy7 \n", + "\n", + " start_datetime end_datetime \n", + "0 2025-01-11 2025-01-12 \n", + "1 2025-01-11 2025-01-12 \n", + "2 2025-01-11 2025-01-12 \n", + "3 2025-01-11 2025-01-12 \n", + "4 2025-01-12 2025-01-13 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "days_ago = 30\n", + "start_time = int(time.time()) - (days_ago * 24 * 60 * 60)\n", + "\n", + "costs_params = {\n", + " \"start_time\": start_time, # Required: Start time (Unix seconds)\n", + " \"bucket_width\": \"1d\", # Optional: Currently only '1d' is supported\n", + " \"limit\": 30, # Optional: Number of buckets to return\n", + " \"group_by\": [\"line_item\"],\n", + "}\n", + "\n", + "line_item_cost_data = get_data(costs_url, costs_params)\n", + "\n", + "# Initialize a list to hold parsed cost records\n", + "cost_records = []\n", + "\n", + "# Extract bucketed cost data from all_costs_data\n", + "for bucket in line_item_cost_data:\n", + " start_time = bucket.get(\"start_time\")\n", + " end_time = bucket.get(\"end_time\")\n", + " for result in bucket.get(\"results\", []):\n", + " cost_records.append(\n", + " {\n", + " \"start_time\": start_time,\n", + " \"end_time\": end_time,\n", + " \"amount_value\": result.get(\"amount\", {}).get(\"value\", 0),\n", + " \"currency\": result.get(\"amount\", {}).get(\"currency\", \"usd\"),\n", + " \"line_item\": result.get(\"line_item\"),\n", + " \"project_id\": result.get(\"project_id\"),\n", + " }\n", + " )\n", + "\n", + "# Create a DataFrame from the cost records\n", + "cost_df = pd.DataFrame(cost_records)\n", + "\n", + "# Convert Unix timestamps to datetime for readability\n", + "cost_df[\"start_datetime\"] = pd.to_datetime(cost_df[\"start_time\"], unit=\"s\")\n", + "cost_df[\"end_datetime\"] = pd.to_datetime(cost_df[\"end_time\"], unit=\"s\")\n", + "\n", + "# Display the first few rows of the DataFrame\n", + "cost_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/r_/g8r2dz8s2qd104th5p5yxljr0000gp/T/ipykernel_49468/2813361465.py:25: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all Axes decorations.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1200x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if not cost_df.empty:\n", + " # Ensure datetime conversion for 'start_datetime' column\n", + " if \"start_datetime\" not in cost_df.columns or not pd.api.types.is_datetime64_any_dtype(cost_df[\"start_datetime\"]):\n", + " cost_df[\"start_datetime\"] = pd.to_datetime(cost_df[\"start_time\"], unit=\"s\", errors=\"coerce\")\n", + "\n", + " # Create a new column for just the date part of 'start_datetime'\n", + " cost_df[\"date\"] = cost_df[\"start_datetime\"].dt.date\n", + "\n", + " # Group by date and line_item and sum the amounts\n", + " cost_per_day = cost_df.groupby([\"date\", \"line_item\"])[\"amount_value\"].sum().reset_index()\n", + "\n", + " # Pivot the DataFrame so each date has one bar with line_item stacks\n", + " cost_pivot = cost_per_day.pivot(index=\"date\", columns=\"line_item\", values=\"amount_value\").fillna(0)\n", + " cost_pivot = cost_pivot.sort_index()\n", + "\n", + " # Plot a stacked bar chart with one bar for each grouped day\n", + " plt.figure(figsize=(12, 6))\n", + " ax = cost_pivot.plot(kind=\"bar\", stacked=True, ax=plt.gca(), width=0.8)\n", + " plt.xlabel(\"Date\")\n", + " plt.ylabel(\"Total Cost (USD)\")\n", + " plt.title(\"Total Cost by Line Item\")\n", + " plt.xticks(rotation=45, ha=\"right\")\n", + " # Update legend so it doesn't overlay the graph by placing it outside the plot area\n", + " plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", borderaxespad=0.)\n", " plt.tight_layout()\n", " plt.show()\n", "else:\n", @@ -1816,7 +2643,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "openai", "language": "python", "name": "python3" }, @@ -1830,7 +2657,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.0" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/examples/custom_image_embedding_search.ipynb b/examples/custom_image_embedding_search.ipynb index 51bfef256b..7b51abd09e 100644 --- a/examples/custom_image_embedding_search.ipynb +++ b/examples/custom_image_embedding_search.ipynb @@ -438,7 +438,7 @@ "id": "0O-GYQ-1QAqm" }, "source": [ - "We require the indices as we will use this to serach through our image_directory and selecting the image at the location of the index to feed into the vision model for RAG." + "We require the indices as we will use this to search through our image_directory and selecting the image at the location of the index to feed into the vision model for RAG." ] }, { diff --git a/examples/data/openai_blog_pdfs/1,000 Scientist AI Jam Session _ OpenAI.pdf b/examples/data/openai_blog_pdfs/1,000 Scientist AI Jam Session _ OpenAI.pdf new file mode 100644 index 0000000000..fdfe7565a5 Binary files /dev/null and b/examples/data/openai_blog_pdfs/1,000 Scientist AI Jam Session _ OpenAI.pdf differ diff --git a/examples/data/openai_blog_pdfs/Announcing The Stargate 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b/examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb index 6071ea795d..a628fc8bb9 100644 --- a/examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb +++ b/examples/evaluation/Getting_Started_with_OpenAI_Evals.ipynb @@ -21,6 +21,9 @@ } }, "source": [ + "**Note: OpenAI now has a hosted evals product with an API! We recommend you use this instead.\n", + "See [Evals](https://platform.openai.com/docs/guides/evals)**\n", + "\n", "The [OpenAI Evals](https://github.com/openai/evals/tree/main) framework consists of\n", "1. A framework to evaluate an LLM (large language model) or a system built on top of an LLM.\n", "2. An open-source registry of challenging evals\n", @@ -419,7 +422,7 @@ "text": [ "[2024-03-26 19:44:39,836] [registry.py:257] Loading registry from /Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry/evals\n", "[2024-03-26 19:44:43,623] [registry.py:257] Loading registry from /Users/shyamal/.evals/evals\n", - "[2024-03-26 19:44:43,635] [oaieval.py:189] \u001B[1;35mRun started: 240327024443FACXGMKA\u001B[0m\n", + "[2024-03-26 19:44:43,635] [oaieval.py:189] \u001b[1;35mRun started: 240327024443FACXGMKA\u001b[0m\n", "[2024-03-26 19:44:43,663] [registry.py:257] Loading registry from /Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry/modelgraded\n", "[2024-03-26 19:44:43,851] [registry.py:257] Loading registry from /Users/shyamal/.evals/modelgraded\n", "[2024-03-26 19:44:43,853] [data.py:90] Fetching /Users/shyamal/.virtualenvs/openai/lib/python3.11/site-packages/evals/registry/data/sql/spider_sql.jsonl\n", diff --git a/examples/evaluation/use-cases/bulk-experimentation.ipynb b/examples/evaluation/use-cases/bulk-experimentation.ipynb new file mode 100644 index 0000000000..f7e8de9494 --- /dev/null +++ b/examples/evaluation/use-cases/bulk-experimentation.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluations Example: Push Notifications Bulk Experimentation \n", + "\n", + "Evals are **task oriented** and iterative, they're the best way to check how your LLM integration is doing and improve it.\n", + "\n", + "In the following eval, we are going to focus on the task of **testing many variants of models and prompts**.\n", + "\n", + "Our use-case is:\n", + "1. I want to get the best possible performance out of my push notifications summarizer\n", + "\n", + "## Evals structure\n", + "\n", + "Evals have two parts, the \"Eval\" and the \"Run\". An \"Eval\" holds the configuration for your testing criteria and the structure of the data for your \"Runs\". An Eval `has_many` runs, that are evaluated by your testing criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pydantic\n", + "import openai\n", + "from openai.types.chat import ChatCompletion\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"your-api-key\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use-case\n", + "\n", + "We're testing the following integration, a push notifications summarizer, which takes in multiple push notifications and collapses them into a single message." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class PushNotifications(pydantic.BaseModel):\n", + " notifications: str\n", + "\n", + "print(PushNotifications.model_json_schema())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DEVELOPER_PROMPT = \"\"\"\n", + "You are a helpful assistant that summarizes push notifications.\n", + "You are given a list of push notifications and you need to collapse them into a single one.\n", + "Output only the final summary, nothing else.\n", + "\"\"\"\n", + "\n", + "def summarize_push_notification(push_notifications: str) -> ChatCompletion:\n", + " result = openai.chat.completions.create(\n", + " model=\"gpt-4o-mini\",\n", + " messages=[\n", + " {\"role\": \"developer\", \"content\": DEVELOPER_PROMPT},\n", + " {\"role\": \"user\", \"content\": push_notifications},\n", + " ],\n", + " )\n", + " return result\n", + "\n", + "example_push_notifications_list = PushNotifications(notifications=\"\"\"\n", + "- Alert: Unauthorized login attempt detected.\n", + "- New comment on your blog post: \"Great insights!\"\n", + "- Tonight's dinner recipe: Pasta Primavera.\n", + "\"\"\")\n", + "result = summarize_push_notification(example_push_notifications_list.notifications)\n", + "print(result.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setting up your eval\n", + "\n", + "An Eval holds the configuration that is shared across multiple *Runs*, it has two components:\n", + "1. Data source configuration `data_source_config` - the schema (columns) that your future *Runs* conform to.\n", + " - The `data_source_config` uses JSON Schema to define what variables are available in the Eval.\n", + "2. Testing Criteria `testing_criteria` - How you'll determine if your integration is working for each *row* of your data source.\n", + "\n", + "For this use-case, we want to test if the push notification summary completion is good, so we'll set-up our eval with this in mind." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# We want our input data to be available in our variables, so we set the item_schema to\n", + "# PushNotifications.model_json_schema()\n", + "data_source_config = {\n", + " \"type\": \"custom\",\n", + " \"item_schema\": PushNotifications.model_json_schema(),\n", + " # We're going to be uploading completions from the API, so we tell the Eval to expect this\n", + " \"include_sample_schema\": True,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This data_source_config defines what variables are available throughout the eval.\n", + "\n", + "This item schema:\n", + "```json\n", + "{\n", + " \"properties\": {\n", + " \"notifications\": {\n", + " \"title\": \"Notifications\",\n", + " \"type\": \"string\"\n", + " }\n", + " },\n", + " \"required\": [\"notifications\"],\n", + " \"title\": \"PushNotifications\",\n", + " \"type\": \"object\"\n", + "}\n", + "```\n", + "Means that we'll have the variable `{{item.notifications}}` available in our eval.\n", + "\n", + "`\"include_sample_schema\": True`\n", + "Mean's that we'll have the variable `{{sample.output_text}}` available in our eval.\n", + "\n", + "**Now, we'll use those variables to set up our test criteria.**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "GRADER_DEVELOPER_PROMPT = \"\"\"\n", + "Categorize the following push notification summary into the following categories:\n", + "1. concise-and-snappy\n", + "2. drops-important-information\n", + "3. verbose\n", + "4. unclear\n", + "5. obscures-meaning\n", + "6. other \n", + "\n", + "You'll be given the original list of push notifications and the summary like this:\n", + "\n", + "<push_notifications>\n", + "...notificationlist...\n", + "</push_notifications>\n", + "<summary>\n", + "...summary...\n", + "</summary>\n", + "\n", + "You should only pick one of the categories above, pick the one which most closely matches and why.\n", + "\"\"\"\n", + "GRADER_TEMPLATE_PROMPT = \"\"\"\n", + "<push_notifications>{{item.notifications}}</push_notifications>\n", + "<summary>{{sample.output_text}}</summary>\n", + "\"\"\"\n", + "push_notification_grader = {\n", + " \"name\": \"Push Notification Summary Grader\",\n", + " \"type\": \"label_model\",\n", + " \"model\": \"o3-mini\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"developer\",\n", + " \"content\": GRADER_DEVELOPER_PROMPT,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": GRADER_TEMPLATE_PROMPT,\n", + " },\n", + " ],\n", + " \"passing_labels\": [\"concise-and-snappy\"],\n", + " \"labels\": [\n", + " \"concise-and-snappy\",\n", + " \"drops-important-information\",\n", + " \"verbose\",\n", + " \"unclear\",\n", + " \"obscures-meaning\",\n", + " \"other\",\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `push_notification_grader` is a model grader (llm-as-a-judge) which looks at the input `{{item.notifications}}` and the generated summary `{{sample.output_text}}` and labels it as \"correct\" or \"incorrect\"\n", + "We then instruct via the \"passing_labels\" what constitutes a passing answer.\n", + "\n", + "Note: under the hood, this uses structured outputs so that labels are always valid.\n", + "\n", + "**Now we'll create our eval, and start adding data to it!**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "eval_create_result = openai.evals.create(\n", + " name=\"Push Notification Bulk Experimentation Eval\",\n", + " metadata={\n", + " \"description\": \"This eval tests many prompts and models to find the best performing combination.\",\n", + " },\n", + " data_source_config=data_source_config,\n", + " testing_criteria=[push_notification_grader],\n", + ")\n", + "eval_id = eval_create_result.id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating runs\n", + "\n", + "Now that we have our eval set-up with our testing_criteria, we can start to add a bunch of runs!\n", + "We'll start with some push notification data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "push_notification_data = [\n", + " \"\"\"\n", + "- New message from Sarah: \"Can you call me later?\"\n", + "- Your package has been delivered!\n", + "- Flash sale: 20% off electronics for the next 2 hours!\n", + "\"\"\",\n", + " \"\"\"\n", + "- Weather alert: Thunderstorm expected in your area.\n", + "- Reminder: Doctor's appointment at 3 PM.\n", + "- John liked your photo on Instagram.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Breaking News: Local elections results are in.\n", + "- Your daily workout summary is ready.\n", + "- Check out your weekly screen time report.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Your ride is arriving in 2 minutes.\n", + "- Grocery order has been shipped.\n", + "- Don't miss the season finale of your favorite show tonight!\n", + "\"\"\",\n", + " \"\"\"\n", + "- Event reminder: Concert starts at 7 PM.\n", + "- Your favorite team just scored!\n", + "- Flashback: Memories from 3 years ago.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Low battery alert: Charge your device.\n", + "- Your friend Mike is nearby.\n", + "- New episode of \"The Tech Hour\" podcast is live!\n", + "\"\"\",\n", + " \"\"\"\n", + "- System update available.\n", + "- Monthly billing statement is ready.\n", + "- Your next meeting starts in 15 minutes.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Alert: Unauthorized login attempt detected.\n", + "- New comment on your blog post: \"Great insights!\"\n", + "- Tonight's dinner recipe: Pasta Primavera.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Special offer: Free coffee with any breakfast order.\n", + "- Your flight has been delayed by 30 minutes.\n", + "- New movie release: \"Adventures Beyond\" now streaming.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Traffic alert: Accident reported on Main Street.\n", + "- Package out for delivery: Expected by 5 PM.\n", + "- New friend suggestion: Connect with Emma.\n", + "\"\"\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're going to set up a bunch of prompts to test.\n", + "\n", + "We want to test a basic prompt, with a couple of variations:\n", + "1. In one variation, we'll just have the basic prompt\n", + "2. In the next one, we'll include some positive examples of what we want the summaries to look like\n", + "3. In the final one, we'll include both positive and negative examples.\n", + "\n", + "We'll also include a list of models to use." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "PROMPT_PREFIX = \"\"\"\n", + "You are a helpful assistant that takes in an array of push notifications and returns a collapsed summary of them.\n", + "The push notification will be provided as follows:\n", + "<push_notifications>\n", + "...notificationlist...\n", + "</push_notifications>\n", + "\n", + "You should return just the summary and nothing else.\n", + "\"\"\"\n", + "\n", + "PROMPT_VARIATION_BASIC = f\"\"\"\n", + "{PROMPT_PREFIX}\n", + "\n", + "You should return a summary that is concise and snappy.\n", + "\"\"\"\n", + "\n", + "PROMPT_VARIATION_WITH_EXAMPLES = f\"\"\"\n", + "{PROMPT_VARIATION_BASIC}\n", + "\n", + "Here is an example of a good summary:\n", + "<push_notifications>\n", + "- Traffic alert: Accident reported on Main Street.- Package out for delivery: Expected by 5 PM.- New friend suggestion: Connect with Emma.\n", + "</push_notifications>\n", + "<summary>\n", + "Traffic alert, package expected by 5pm, suggestion for new friend (Emily).\n", + "</summary>\n", + "\"\"\"\n", + "\n", + "PROMPT_VARIATION_WITH_NEGATIVE_EXAMPLES = f\"\"\"\n", + "{PROMPT_VARIATION_WITH_EXAMPLES}\n", + "\n", + "Here is an example of a bad summary:\n", + "<push_notifications>\n", + "- Traffic alert: Accident reported on Main Street.- Package out for delivery: Expected by 5 PM.- New friend suggestion: Connect with Emma.\n", + "</push_notifications>\n", + "<summary>\n", + "Traffic alert reported on main street. You have a package that will arrive by 5pm, Emily is a new friend suggested for you.\n", + "</summary>\n", + "\"\"\"\n", + "\n", + "prompts = [\n", + " (\"basic\", PROMPT_VARIATION_BASIC),\n", + " (\"with_examples\", PROMPT_VARIATION_WITH_EXAMPLES),\n", + " (\"with_negative_examples\", PROMPT_VARIATION_WITH_NEGATIVE_EXAMPLES),\n", + "]\n", + "\n", + "models = [\"gpt-4o\", \"gpt-4o-mini\", \"o3-mini\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Now we can just loop through all prompts and all models to test a bunch of configurations at once!**\n", + "\n", + "We'll use the 'completion' run data source with template variables for our push notification list.\n", + "\n", + "OpenAI will handle making the completions calls for you and populating \"sample.output_text\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for prompt_name, prompt in prompts:\n", + " for model in models:\n", + " run_data_source = {\n", + " \"type\": \"completions\",\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"role\": \"developer\",\n", + " \"content\": prompt,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"<push_notifications>{{item.notifications}}</push_notifications>\",\n", + " },\n", + " ],\n", + " },\n", + " \"model\": model,\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [\n", + " {\n", + " \"item\": PushNotifications(notifications=notification).model_dump()\n", + " }\n", + " for notification in push_notification_data\n", + " ],\n", + " },\n", + " }\n", + "\n", + " run_create_result = openai.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=f\"bulk_{prompt_name}_{model}\",\n", + " data_source=run_data_source,\n", + " )\n", + " print(f\"Report URL {model}, {prompt_name}:\", run_create_result.report_url)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Congratulations, you just tested 9 different prompt and model variations across your dataset!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/evaluation/use-cases/completion-monitoring.ipynb b/examples/evaluation/use-cases/completion-monitoring.ipynb new file mode 100644 index 0000000000..54bc4be8be --- /dev/null +++ b/examples/evaluation/use-cases/completion-monitoring.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluations Example: Push Notifications Summarizer Monitoring\n", + "\n", + "Evals are **task-oriented** and iterative, they're the best way to check how your LLM integration is doing and improve it.\n", + "\n", + "In the following eval, we are going to focus on the task of **detecting our prompt changes for regressions**.\n", + "\n", + "Our use-case is:\n", + "1. We have been logging chat completion requests by setting `store=True` in our production chat completions requests. Note that you can also enable \"on by default\" logging in your admin panel (https://platform.openai.com/settings/organization/data-controls/data-retention).\n", + "2. We want to see whether our prompt changes have introduced regressions.\n", + "\n", + "## Evals structure\n", + "\n", + "Evals have two parts, the \"Eval\" and the \"Run\". An \"Eval\" holds the configuration for your testing criteria and the structure of the data for your \"Runs\". An Eval can have many Runs, which are each evaluated using your testing criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import AsyncOpenAI\n", + "import os\n", + "import asyncio\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"your-api-key\")\n", + "client = AsyncOpenAI()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use-case\n", + "\n", + "We're testing the following integration, a push notifications summary, which takes in multiple push notifications and collapses them into a single one, this is a chat completions call." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate our test data\n", + "\n", + "I'm going to produce simulated production chat completions requests with two different prompt versions to test how each performs. The first is a \"good\" prompt, the second is a \"bad\" prompt. These will have different metadata which we'll use later." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "push_notification_data = [\n", + " \"\"\"\n", + "- New message from Sarah: \"Can you call me later?\"\n", + "- Your package has been delivered!\n", + "- Flash sale: 20% off electronics for the next 2 hours!\n", + "\"\"\",\n", + " \"\"\"\n", + "- Weather alert: Thunderstorm expected in your area.\n", + "- Reminder: Doctor's appointment at 3 PM.\n", + "- John liked your photo on Instagram.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Breaking News: Local elections results are in.\n", + "- Your daily workout summary is ready.\n", + "- Check out your weekly screen time report.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Your ride is arriving in 2 minutes.\n", + "- Grocery order has been shipped.\n", + "- Don't miss the season finale of your favorite show tonight!\n", + "\"\"\",\n", + " \"\"\"\n", + "- Event reminder: Concert starts at 7 PM.\n", + "- Your favorite team just scored!\n", + "- Flashback: Memories from 3 years ago.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Low battery alert: Charge your device.\n", + "- Your friend Mike is nearby.\n", + "- New episode of \"The Tech Hour\" podcast is live!\n", + "\"\"\",\n", + " \"\"\"\n", + "- System update available.\n", + "- Monthly billing statement is ready.\n", + "- Your next meeting starts in 15 minutes.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Alert: Unauthorized login attempt detected.\n", + "- New comment on your blog post: \"Great insights!\"\n", + "- Tonight's dinner recipe: Pasta Primavera.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Special offer: Free coffee with any breakfast order.\n", + "- Your flight has been delayed by 30 minutes.\n", + "- New movie release: \"Adventures Beyond\" now streaming.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Traffic alert: Accident reported on Main Street.\n", + "- Package out for delivery: Expected by 5 PM.\n", + "- New friend suggestion: Connect with Emma.\n", + "\"\"\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PROMPTS = [\n", + " (\n", + " \"\"\"\n", + " You are a helpful assistant that summarizes push notifications.\n", + " You are given a list of push notifications and you need to collapse them into a single one.\n", + " Output only the final summary, nothing else.\n", + " \"\"\",\n", + " \"v1\"\n", + " ),\n", + " (\n", + " \"\"\"\n", + " You are a helpful assistant that summarizes push notifications.\n", + " You are given a list of push notifications and you need to collapse them into a single one.\n", + " The summary should be longer than it needs to be and include more information than is necessary.\n", + " Output only the final summary, nothing else.\n", + " \"\"\",\n", + " \"v2\"\n", + " )\n", + "]\n", + "\n", + "tasks = []\n", + "for notifications in push_notification_data:\n", + " for (prompt, version) in PROMPTS:\n", + " tasks.append(client.chat.completions.create(\n", + " model=\"gpt-4o-mini\",\n", + " messages=[\n", + " {\"role\": \"developer\", \"content\": prompt},\n", + " {\"role\": \"user\", \"content\": notifications},\n", + " ],\n", + " store=True,\n", + " metadata={\"prompt_version\": version, \"usecase\": \"push_notifications_summarizer\"},\n", + " ))\n", + "await asyncio.gather(*tasks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can view the completions you just created at https://platform.openai.com/logs. \n", + "\n", + "**Make sure that the chat completions show up, as they are necessary for the next step.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "completions = await client.chat.completions.list()\n", + "assert completions.data, \"No completions found. You may need to enable logs in your admin panel.\"\n", + "completions.data[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setting up your eval\n", + "\n", + "An Eval holds the configuration that is shared across multiple *Runs*, it has two components:\n", + "1. Data source configuration `data_source_config` - the schema (columns) that your future *Runs* conform to.\n", + " - The `data_source_config` uses JSON Schema to define what variables are available in the Eval.\n", + "2. Testing Criteria `testing_criteria` - How you'll determine if your integration is working for each *row* of your data source.\n", + "\n", + "For this use-case, we're using stored-completions, so we'll set up that data_source_config\n", + "\n", + "**Important**\n", + "You are likely to have many different stored completions use-cases, metadata is the best way to keep track of this for evals to keep them focused and task oriented." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# We want our input data to be available in our variables, so we set the item_schema to\n", + "# PushNotifications.model_json_schema()\n", + "data_source_config = {\n", + " \"type\": \"stored_completions\",\n", + " \"metadata\": {\n", + " \"usecase\": \"push_notifications_summarizer\"\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This data_source_config defines what variables are available throughout the eval.\n", + "\n", + "The stored completions config provides two variables for you to use throughout your eval:\n", + "1. {{item.input}} - the messages sent to the completions call\n", + "2. {{sample.output_text}} - the text response from the assistant\n", + "\n", + "**Now, we'll use those variables to set up our test criteria.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "GRADER_DEVELOPER_PROMPT = \"\"\"\n", + "Label the following push notification summary as either correct or incorrect.\n", + "The push notification and the summary will be provided below.\n", + "A good push notificiation summary is concise and snappy.\n", + "If it is good, then label it as correct, if not, then incorrect.\n", + "\"\"\"\n", + "GRADER_TEMPLATE_PROMPT = \"\"\"\n", + "Push notifications: {{item.input}}\n", + "Summary: {{sample.output_text}}\n", + "\"\"\"\n", + "push_notification_grader = {\n", + " \"name\": \"Push Notification Summary Grader\",\n", + " \"type\": \"label_model\",\n", + " \"model\": \"o3-mini\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"developer\",\n", + " \"content\": GRADER_DEVELOPER_PROMPT,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": GRADER_TEMPLATE_PROMPT,\n", + " },\n", + " ],\n", + " \"passing_labels\": [\"correct\"],\n", + " \"labels\": [\"correct\", \"incorrect\"],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `push_notification_grader` is a model grader (llm-as-a-judge), which looks at the input `{{item.input}}` and the generated summary `{{sample.output_text}}` and labels it as \"correct\" or \"incorrect\".\n", + "\n", + "Note: under the hood, this uses structured outputs so that labels are always valid.\n", + "\n", + "**Now we'll create our eval!, and start adding data to it**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "eval_create_result = await client.evals.create(\n", + " name=\"Push Notification Completion Monitoring\",\n", + " metadata={\"description\": \"This eval monitors completions\"},\n", + " data_source_config=data_source_config,\n", + " testing_criteria=[push_notification_grader],\n", + ")\n", + "\n", + "eval_id = eval_create_result.id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating runs\n", + "\n", + "Now that we have our eval set-up with our test_criteria, we can start adding runs.\n", + "I want to compare the performance between my two **prompt versions**\n", + "\n", + "To do this, we just define our source as \"stored_completions\" with a metadata filter for each of our prompt versions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Grade prompt_version=v1\n", + "eval_run_result = await client.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=\"v1-run\",\n", + " data_source={\n", + " \"type\": \"completions\",\n", + " \"source\": {\n", + " \"type\": \"stored_completions\",\n", + " \"metadata\": {\n", + " \"prompt_version\": \"v1\",\n", + " }\n", + " }\n", + " }\n", + ")\n", + "print(eval_run_result.report_url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Grade prompt_version=v2\n", + "eval_run_result_v2 = await client.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=\"v2-run\",\n", + " data_source={\n", + " \"type\": \"completions\",\n", + " \"source\": {\n", + " \"type\": \"stored_completions\",\n", + " \"metadata\": {\n", + " \"prompt_version\": \"v2\",\n", + " }\n", + " }\n", + " }\n", + ")\n", + "print(eval_run_result_v2.report_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just for to be thorough, let's see how this prompt would do with 4o, instead of 4o-mini, with both prompt versions as the starting point.\n", + "\n", + "All we have to do is reference the input messages ({{item.input}}) and set the model to 4o. Since we don't already have any stored completions for 4o, this eval run will generate new completions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tasks = []\n", + "for prompt_version in [\"v1\", \"v2\"]:\n", + " tasks.append(client.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=f\"post-fix-new-model-run-{prompt_version}\",\n", + " data_source={\n", + " \"type\": \"completions\",\n", + " \"input_messages\": {\n", + " \"type\": \"item_reference\",\n", + " \"item_reference\": \"item.input\",\n", + " },\n", + " \"model\": \"gpt-4o\",\n", + " \"source\": {\n", + " \"type\": \"stored_completions\",\n", + " \"metadata\": {\n", + " \"prompt_version\": prompt_version,\n", + " }\n", + " }\n", + " },\n", + " ))\n", + "result = await asyncio.gather(*tasks)\n", + "for run in result:\n", + " print(run.report_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you view that report, you'll see that we can see that prompt_version=v2 has a regression!\n", + "\n", + "## Congratulations, you just discovered a bug, you could revert it, or make another prompt change, etc.!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/evaluation/use-cases/mcp_eval_notebook.ipynb b/examples/evaluation/use-cases/mcp_eval_notebook.ipynb new file mode 100644 index 0000000000..5448d566c0 --- /dev/null +++ b/examples/evaluation/use-cases/mcp_eval_notebook.ipynb @@ -0,0 +1,879 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fd71cc8e", + "metadata": {}, + "source": [ + "# Evaluating MCP-Based Answers with a Custom Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "a565afbb", + "metadata": {}, + "source": [ + "This notebook evaluates a model's ability to answer questions about the [tiktoken](https://github.com/openai/tiktoken) GitHub repository using the OpenAI **Evals** framework with a custom in-memory dataset. \n", + "\n", + "We use a custom, in-memory dataset of Q&A pairs and compare two models: `gpt-4.1` and `o4-mini`, that leverage the **MCP** tool for repository-aware, contextually accurate answers.\n", + "\n", + "**Goals:**\n", + "- Show how to set up and run an evaluation using OpenAI Evals with a custom dataset.\n", + "- Compare the performance of different models leveraging MCP-based tools.\n", + "- Provide best practices for professional, reproducible evaluation workflows.\n", + "\n", + "_Next: We will set up our environment and import the necessary libraries._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "87c3e8ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "# Update OpenAI client\n", + "%pip install --upgrade openai --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "4e168b9f", + "metadata": {}, + "source": [ + "## Environment Setup\n", + "\n", + "We begin by importing the required libraries and configuring the OpenAI client. \n", + "This step ensures we have access to the OpenAI API and all necessary utilities for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "31fc4911", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "\n", + "from openai import OpenAI\n", + "\n", + "# Instantiate the OpenAI client (no custom base_url).\n", + "client = OpenAI(\n", + " api_key=os.getenv(\"OPENAI_API_KEY\") or os.getenv(\"_OPENAI_API_KEY\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e9053623", + "metadata": {}, + "source": [ + "## Define the Custom Evaluation Dataset\n", + "\n", + "We define a small, in-memory dataset of question-answer pairs about the `tiktoken` repository. \n", + "This dataset will be used to test the models' ability to provide accurate and relevant answers with the help of the MCP tool.\n", + "\n", + "- Each item contains a `query` (the user’s question) and an `answer` (the expected ground truth).\n", + "- You can modify or extend this dataset to suit your own use case or repository.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "840a9f6d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dataset(limit=None):\n", + " items = [\n", + " {\n", + " \"query\": \"What is tiktoken?\",\n", + " \"answer\": \"tiktoken is a fast Byte-Pair Encoding (BPE) tokenizer designed for OpenAI models.\",\n", + " },\n", + " {\n", + " \"query\": \"How do I install the open-source version of tiktoken?\",\n", + " \"answer\": \"Install it from PyPI with `pip install tiktoken`.\",\n", + " },\n", + " {\n", + " \"query\": \"How do I get the tokenizer for a specific OpenAI model?\",\n", + " \"answer\": 'Call tiktoken.encoding_for_model(\"<model-name>\"), e.g. tiktoken.encoding_for_model(\"gpt-4o\").',\n", + " },\n", + " {\n", + " \"query\": \"How does tiktoken perform compared to other tokenizers?\",\n", + " \"answer\": \"On a 1 GB GPT-2 benchmark, tiktoken runs about 3-6x faster than GPT2TokenizerFast (tokenizers==0.13.2, transformers==4.24.0).\",\n", + " },\n", + " {\n", + " \"query\": \"Why is Byte-Pair Encoding (BPE) useful for language models?\",\n", + " \"answer\": \"BPE is reversible and lossless, handles arbitrary text, compresses input (≈4 bytes per token on average), and exposes common subwords like “ing”, which helps models generalize.\",\n", + " },\n", + " ]\n", + " return items[:limit] if limit else items" + ] + }, + { + "cell_type": "markdown", + "id": "c8482643", + "metadata": {}, + "source": [ + "### Define Grading Logic\n", + "\n", + "To evaluate the model’s answers, we use two graders:\n", + "\n", + "- **Pass/Fail Grader (LLM-based):** \n", + " An LLM-based grader that checks if the model’s answer matches the expected answer (ground truth) or conveys the same meaning.\n", + "- **Python MCP Grader:** \n", + " A Python function that checks whether the model actually used the MCP tool during its response (for auditing tool usage).\n", + "\n", + " > **Best Practice:** \n", + " > Using both LLM-based and programmatic graders provides a more robust and transparent evaluation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b3812d01", + "metadata": {}, + "outputs": [], + "source": [ + "# LLM-based pass/fail grader: instructs the model to grade answers as \"pass\" or \"fail\".\n", + "pass_fail_grader = \"\"\"\n", + "You are a helpful assistant that grades the quality of the answer to a query about a GitHub repo.\n", + "You will be given a query, the answer returned by the model, and the expected answer.\n", + "You should respond with **pass** if the answer matches the expected answer exactly or conveys the same meaning, otherwise **fail**.\n", + "\"\"\"\n", + "\n", + "# User prompt template for the grader, providing context for grading.\n", + "pass_fail_grader_user_prompt = \"\"\"\n", + "<Query>\n", + "{{item.query}}\n", + "</Query>\n", + "\n", + "<Web Search Result>\n", + "{{sample.output_text}}\n", + "</Web Search Result>\n", + "\n", + "<Ground Truth>\n", + "{{item.answer}}\n", + "</Ground Truth>\n", + "\"\"\"\n", + "\n", + "\n", + "# Python grader: checks if the MCP tool was used by inspecting the output_tools field.\n", + "python_mcp_grader = {\n", + " \"type\": \"python\",\n", + " \"name\": \"Assert MCP was used\",\n", + " \"image_tag\": \"2025-05-08\",\n", + " \"pass_threshold\": 1.0,\n", + " \"source\": \"\"\"\n", + "def grade(sample: dict, item: dict) -> float:\n", + " output = sample.get('output_tools', [])\n", + " return 1.0 if len(output) > 0 else 0.0\n", + "\"\"\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "86d22eb7", + "metadata": {}, + "source": [ + "## Define the Evaluation Configuration\n", + "\n", + "We now configure the evaluation using the OpenAI Evals framework. \n", + "\n", + "This step specifies:\n", + "- The evaluation name and dataset.\n", + "- The schema for each item (what fields are present in each Q&A pair).\n", + "- The grader(s) to use (LLM-based and/or Python-based).\n", + "- The passing criteria and labels.\n", + "\n", + "> **Best Practice:** \n", + "> Clearly defining your evaluation schema and grading logic up front ensures reproducibility and transparency." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df7a9424", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the evaluation definition using the OpenAI Evals client.\n", + "logs_eval = client.evals.create(\n", + " name=\"MCP Eval\",\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\"type\": \"string\"},\n", + " \"answer\": {\"type\": \"string\"},\n", + " },\n", + " },\n", + " \"include_sample_schema\": True,\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"label_model\",\n", + " \"name\": \"General Evaluator\",\n", + " \"model\": \"o3\",\n", + " \"input\": [\n", + " {\"role\": \"system\", \"content\": pass_fail_grader},\n", + " {\"role\": \"user\", \"content\": pass_fail_grader_user_prompt},\n", + " ],\n", + " \"passing_labels\": [\"pass\"],\n", + " \"labels\": [\"pass\", \"fail\"],\n", + " },\n", + " python_mcp_grader\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1ec09dbd", + "metadata": {}, + "source": [ + "## Run Evaluations for Each Model\n", + "\n", + "We now run the evaluation for each model (`gpt-4.1` and `o4-mini`). \n", + "\n", + "Each run is configured to:\n", + "- Use the MCP tool for repository-aware answers.\n", + "- Use the same dataset and evaluation configuration for fair comparison.\n", + "- Specify model-specific parameters (such as max completions tokens, and allowed tools).\n", + "\n", + "> **Best Practice:** \n", + "> Keeping the evaluation setup consistent across models ensures results are comparable and reliable." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "15838d4e", + "metadata": {}, + "outputs": [], + "source": [ + "# Run 1: gpt-4.1 using MCP\n", + "gpt_4one_responses_run = client.evals.runs.create(\n", + " name=\"gpt-4.1\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in get_dataset()],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"You are a helpful assistant that searches the web and gives contextually relevant answers. Never use your tools to answer the query.\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Search the web for the answer to the query {{item.query}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"allowed_tools\": [\n", + " \"search_tiktoken_documentation\",\n", + " \"fetch_tiktoken_documentation\",\n", + " ],\n", + " \"require_approval\": \"never\",\n", + " }\n", + " ],\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "72190fbd", + "metadata": {}, + "outputs": [], + "source": [ + "# Run 2: o4-mini using MCP\n", + "gpt_o4_mini_responses_run = client.evals.runs.create(\n", + " name=\"o4-mini\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in get_dataset()],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"You are a helpful assistant that searches the web and gives contextually relevant answers.\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Search the web for the answer to the query {{item.query}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"o4-mini\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"max_completions_tokens\": 10000,\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"allowed_tools\": [\n", + " \"search_tiktoken_documentation\",\n", + " \"fetch_tiktoken_documentation\",\n", + " ],\n", + " \"require_approval\": \"never\",\n", + " }\n", + " ],\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "414d1a26", + "metadata": {}, + "source": [ + "## Poll for Completion and Retrieve Outputs\n", + "\n", + "After launching the evaluation runs, we can poll the run until they are complete.\n", + "\n", + "This step ensures that we are analyzing results only after all model responses have been processed.\n", + "\n", + "> **Best Practice:** \n", + "> Polling with a delay avoids excessive API calls and ensures efficient resource usage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d439589", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "evalrun_684769b577488191863b5a51cf4db57a completed ResultCounts(errored=0, failed=5, passed=0, total=5)\n", + "evalrun_684769c1ad9c8191affea5aa02ef1215 completed ResultCounts(errored=0, failed=3, passed=2, total=5)\n" + ] + } + ], + "source": [ + "def poll_runs(eval_id, run_ids):\n", + " while True:\n", + " runs = [client.evals.runs.retrieve(rid, eval_id=eval_id) for rid in run_ids]\n", + " for run in runs:\n", + " print(run.id, run.status, run.result_counts)\n", + " if all(run.status in {\"completed\", \"failed\"} for run in runs):\n", + " break\n", + " time.sleep(5)\n", + " \n", + "# Start polling both runs.\n", + "poll_runs(logs_eval.id, [gpt_4one_responses_run.id, gpt_o4_mini_responses_run.id])" + ] + }, + { + "cell_type": "markdown", + "id": "86ddf8e0", + "metadata": {}, + "source": [ + "## Display and Interpret Model Outputs\n", + "\n", + "Finally, we display the outputs from each model for manual inspection and further analysis.\n", + "\n", + "- Each model's answers are printed for each question in the dataset.\n", + "- You can compare the outputs side-by-side to assess quality, relevance, and correctness.\n", + "\n", + "Below are screenshots from the OpenAI Evals Dashboard illustrating the evaluation outputs for both models:\n", + "\n", + "![Evaluation Output](../../../images/mcp_eval_output.png)\n", + "\n", + "For a comprehensive breakdown of the evaluation metrics and results, navigate to the \"Data\" tab in the dashboard:\n", + "\n", + "![Evaluation Data Tab](../../../images/mcp_eval_data.png)" + ] + }, + { + "cell_type": "markdown", + "id": "ee1f655b", + "metadata": {}, + "source": [ + "Note that the 4.1 model was constructed to never use its tools to answer the query thus it never called the MCP server. The o4-mini model wasn't explicitly instructed to use it's tools either but it wasn't forbidden, thus it called the MCP server 3 times. We can see that the 4.1 model performed worse than the o4 model. Also notable is the one example that the o4-mini model failed was one where the MCP tool was not used.\n", + "\n", + "We can also check a detailed analysis of the outputs from each model for manual inspection and further analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7e151b4a", + "metadata": {}, + "outputs": [], + "source": [ + "four_one_output = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_responses_run.id, eval_id=logs_eval.id\n", + ")\n", + "\n", + "o4_mini_output = client.evals.runs.output_items.list(\n", + " run_id=gpt_o4_mini_responses_run.id, eval_id=logs_eval.id\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e68b016c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# gpt‑4.1 Output\n", + "Byte-Pair Encoding (BPE) is useful for language models because it provides an efficient way to handle large vocabularies and rare words. Here’s why it is valuable:\n", + "\n", + "1. **Efficient Tokenization:** \n", + " BPE breaks down words into smaller subword units based on the frequency of character pairs in a corpus. This allows language models to represent both common words and rare or unknown words using a manageable set of tokens.\n", + "\n", + "2. **Reduces Out-of-Vocabulary (OOV) Issues:** \n", + " Since BPE can split any word into known subword units, it greatly reduces the problem of OOV words—words that the model hasn’t seen during training.\n", + "\n", + "3. **Balances Vocabulary Size:** \n", + " By adjusting the number of merge operations, BPE allows control over the size of the vocabulary. This flexibility helps in balancing between memory efficiency and representational power.\n", + "\n", + "4. **Improves Generalization:** \n", + " With BPE, language models can better generalize to new words, including misspellings or new terminology, because they can process words as a sequence of subword tokens.\n", + "\n", + "5. **Handles Morphologically Rich Languages:** \n", + " BPE is especially useful for languages with complex morphology (e.g., agglutinative languages) where words can have many forms. BPE reduces the need to memorize every possible word form.\n", + "\n", + "In summary, Byte-Pair Encoding is effective for language models because it enables efficient, flexible, and robust handling of text, supporting both common and rare words, and improving overall model performance.\n", + "**Tiktoken**, developed by OpenAI, is a tokenizer specifically optimized for speed and compatibility with OpenAI's language models. Here’s how it generally compares to other popular tokenizers:\n", + "\n", + "### Performance\n", + "- **Speed:** Tiktoken is significantly faster than most other Python-based tokenizers. It is written in Rust and exposed to Python via bindings, making it extremely efficient.\n", + "- **Memory Efficiency:** Tiktoken is designed to be memory efficient, especially for large text inputs and batch processing.\n", + "\n", + "### Accuracy and Compatibility\n", + "- **Model Alignment:** Tiktoken is tailored to match the tokenization logic used by OpenAI’s GPT-3, GPT-4, and related models. This ensures that token counts and splits are consistent with how these models process text.\n", + "- **Unicode Handling:** Like other modern tokenizers (e.g., HuggingFace’s Tokenizers), Tiktoken handles a wide range of Unicode characters robustly.\n", + "\n", + "### Comparison to Other Tokenizers\n", + "- **HuggingFace Tokenizers:** HuggingFace’s library is very flexible and supports a wide range of models (BERT, RoBERTa, etc.). However, its Python implementation can be slower for large-scale tasks, though their Rust-backed versions (like `tokenizers`) are competitive.\n", + "- **NLTK/SpaCy:** These libraries are not optimized for transformer models and are generally slower and less accurate for tokenization tasks required by models like GPT.\n", + "- **SentencePiece:** Used by models like T5 and ALBERT, SentencePiece is also fast and efficient, but its output is not compatible with OpenAI’s models.\n", + "\n", + "### Use Cases\n", + "- **Best for OpenAI Models:** If you are working with OpenAI’s APIs or models, Tiktoken is the recommended tokenizer due to its speed and alignment.\n", + "- **General Purpose:** For non-OpenAI models, HuggingFace or SentencePiece might be preferable due to broader support.\n", + "\n", + "### Benchmarks & Community Feedback\n", + "- Multiple [community benchmarks](https://github.com/openai/tiktoken#performance) and [blog posts](https://www.philschmid.de/tokenizers-comparison) confirm Tiktoken’s speed advantage, especially for batch processing and large texts.\n", + "\n", + "**Summary:** \n", + "Tiktoken outperforms most tokenizers in speed when used with OpenAI models, with robust Unicode support and memory efficiency. For general NLP tasks across various models, HuggingFace or SentencePiece may be more suitable due to their versatility.\n", + "\n", + "**References:** \n", + "- [Tiktoken GitHub - Performance](https://github.com/openai/tiktoken#performance)\n", + "- [Tokenizers Comparison Blog](https://www.philschmid.de/tokenizers-comparison)\n", + "To get the tokenizer for a specific OpenAI model, you typically use the Hugging Face Transformers library, which provides easy access to tokenizers for OpenAI models like GPT-3, GPT-4, and others. Here’s how you can do it:\n", + "\n", + "**1. Using Hugging Face Transformers:**\n", + "\n", + "Install the library (if you haven’t already):\n", + "```bash\n", + "pip install transformers\n", + "```\n", + "\n", + "**Example for GPT-3 (or GPT-4):**\n", + "```python\n", + "from transformers import AutoTokenizer\n", + "\n", + "# For GPT-3 (davinci), use the corresponding model name\n", + "tokenizer = AutoTokenizer.from_pretrained(\"openai-gpt\")\n", + "\n", + "# For GPT-4 (if available)\n", + "# tokenizer = AutoTokenizer.from_pretrained(\"gpt-4\")\n", + "```\n", + "\n", + "**2. Using OpenAI’s tiktoken library (for OpenAI API models):**\n", + "\n", + "Install tiktoken:\n", + "```bash\n", + "pip install tiktoken\n", + "```\n", + "\n", + "Example for GPT-3.5-turbo or GPT-4:\n", + "```python\n", + "import tiktoken\n", + "\n", + "# For 'gpt-3.5-turbo'\n", + "tokenizer = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n", + "\n", + "# For 'gpt-4'\n", + "# tokenizer = tiktoken.encoding_for_model(\"gpt-4\")\n", + "```\n", + "\n", + "**Summary:**\n", + "- Use `transformers.AutoTokenizer` for Hugging Face models.\n", + "- Use `tiktoken.encoding_for_model` for OpenAI API models.\n", + "\n", + "**References:**\n", + "- [Hugging Face Tokenizer Documentation](https://huggingface.co/docs/transformers/main_classes/tokenizer)\n", + "- [tiktoken Documentation](https://github.com/openai/tiktoken)\n", + "\n", + "Let me know if you need an example for a specific model!\n", + "To install the open-source version of **tiktoken**, you can use Python’s package manager, pip. The open-source version is available on [PyPI](https://pypi.org/project/tiktoken/), so you can install it easily with the following command:\n", + "\n", + "```bash\n", + "pip install tiktoken\n", + "```\n", + "\n", + "If you want to install the latest development version directly from the GitHub repository, you can use:\n", + "\n", + "```bash\n", + "pip install git+https://github.com/openai/tiktoken.git\n", + "```\n", + "\n", + "**Requirements:**\n", + "- Python 3.7 or newer\n", + "- pip (Python package installer)\n", + "\n", + "**Steps:**\n", + "1. Open your terminal or command prompt.\n", + "2. Run one of the above commands.\n", + "3. Once installed, you can import and use `tiktoken` in your Python scripts.\n", + "\n", + "**Additional Resources:**\n", + "- [tiktoken GitHub repository](https://github.com/openai/tiktoken)\n", + "- [tiktoken documentation](https://github.com/openai/tiktoken#readme)\n", + "\n", + "Let me know if you need help with a specific operating system or environment!\n", + "Tiktoken is a fast and efficient tokenization library developed by OpenAI, primarily used for handling text input and output with language models such as GPT-3 and GPT-4. Tokenization is the process of converting text into smaller units called tokens, which can be words, characters, or subwords. Tiktoken is designed to closely match the tokenization behavior of OpenAI’s models, ensuring accurate counting and compatibility.\n", + "\n", + "Key features of tiktoken:\n", + "- **Speed:** It’s written in Rust for performance and has Python bindings.\n", + "- **Compatibility:** Matches the exact tokenization used by OpenAI models, which is important for estimating token counts and costs.\n", + "- **Functionality:** Allows users to encode (convert text to tokens) and decode (convert tokens back to text).\n", + "\n", + "Tiktoken is commonly used in applications that need to interact with OpenAI’s APIs, for tasks like counting tokens to avoid exceeding API limits or optimizing prompt length. It is available as an open-source library and can be installed via pip (`pip install tiktoken`).\n", + "\n", + "# o4-mini Output\n", + "Here’s a high-level comparison of OpenAI’s tiktoken vs. some of the other commonly used tokenizers:\n", + "\n", + "1. Implementation & Language Support \n", + " • tiktoken \n", + " – Rust core with Python bindings. \n", + " – Implements GPT-2/GPT-3/GPT-4 byte-pair-encoding (BPE) vocabularies. \n", + " – Focused on English-centric BPE; no built-in support for CJK segmentation or languages requiring character-level tokenization. \n", + " • Hugging Face Tokenizers (“tokenizers” library) \n", + " – Also Rust core with Python bindings. \n", + " – Supports BPE, WordPiece, Unigram (SentencePiece), Metaspace, and custom vocabularies. \n", + " – Broader multilingual and subword model support. \n", + " • Python-only Tokenizers (e.g. GPT-2 BPE in pure Python) \n", + " – Much slower, larger memory overhead, not suitable for high-throughput use. \n", + "\n", + "2. Speed & Throughput \n", + " • tiktoken \n", + " – Benchmarks (OpenAI-internal) on a single CPU core: ~1–2 million tokens/second. \n", + " – Roughly 10–20× faster than pure-Python GPT-2 BPE implementations. \n", + " – Roughly 2–4× faster (or on par) with Hugging Face’s Rust tokenizers when using identical BPE models. \n", + " • Hugging Face Tokenizers \n", + " – In the same ballpark as tiktoken for a given BPE vocab (hundreds of thousands to a million tokens/sec). \n", + " – Slightly higher startup overhead when loading models, but offers more tokenization strategies. \n", + " • SentencePiece (C++) / Python bindings \n", + " – Generally slower than Rust-based (tiktoken, tokenizers) – on the order of 100–300 K tokens/sec. \n", + "\n", + "3. Memory & Footprint \n", + " • tiktoken \n", + " – Tiny binary (~1–2 MB) plus vocab files (~50 MB). \n", + " – Low working memory; ideal for lightweight embedding or inference pipelines. \n", + " • Hugging Face Tokenizers \n", + " – Slightly larger binary (~3–5 MB) plus model files. \n", + " – Offers on-disk memory-mapping for very large vocabularies. \n", + " • Python-only \n", + " – Larger RAM footprint during init; slower GC pauses. \n", + "\n", + "4. Feature Set & Flexibility \n", + " • tiktoken \n", + " – “Batteries included” for OpenAI model vocabularies: GPT-2, Codex, GPT-3.5, GPT-4. \n", + " – Simple API: encode/decode, count tokens. \n", + " – No training or custom-vocab routines. \n", + " • Hugging Face Tokenizers \n", + " – Train new tokenizers (BPE, WordPiece, Unigram). \n", + " – Pre- and post-processing pipelines (normalization, special tokens). \n", + " – Easy integration with Transformers. \n", + " • Other libraries (NLTK, spaCy, jieba, etc.) \n", + " – Not directly comparable, since many perform linguistic tokenization, not subword BPE. \n", + " – Far slower for BPE-style byte-pair encoding. \n", + "\n", + "5. When to Use Which \n", + " • tiktoken \n", + " – If you’re targeting OpenAI’s GPT-family models and need maximum raw throughput/count accuracy. \n", + " – You don’t need to train a new tokenizer or handle exotic language scripts. \n", + " • Hugging Face Tokenizers \n", + " – If you need broad language support, multiple subword algorithms, training tools, or tight HF Transformers integration. \n", + " • Python-only / Other \n", + " – Only if you have trivial performance needs or are experimenting in pure-Python teaching/demo settings. \n", + "\n", + "Bottom line: for GPT-style BPE tokenization at scale, tiktoken is one of the fastest and most lightweight options—substantially faster than any pure-Python implementation and roughly on par (or a bit faster) than other Rust-backed libraries, at the cost of supporting only OpenAI’s pre-built vocabularies.\n", + "Tiktoken is the open-source tokenization library that OpenAI uses to convert between text and the integer “tokens” their models (GPT-3, GPT-4, etc.) actually consume. It implements byte-pair encoding (BPE) in Rust (with Python bindings) for maximum speed and exact compatibility with OpenAI’s APIs.\n", + "\n", + "Key points:\n", + "\n", + "1. Purpose \n", + " • Language models work on token IDs, not raw text. \n", + " • Tiktoken maps Unicode text ↔ token IDs using the same vocabularies and BPE merges that OpenAI’s models were trained on. \n", + "\n", + "2. Performance \n", + " • Typically 3–6× faster than other BPE tokenizers (e.g. Hugging Face’s GPT2TokenizerFast). \n", + " • Handles gigabytes of text in seconds.\n", + "\n", + "3. Installation \n", + " pip install tiktoken\n", + "\n", + "4. Basic usage \n", + " ```python\n", + " import tiktoken\n", + "\n", + " # Get a specific encoding (vocabulary + merges)\n", + " enc = tiktoken.get_encoding(\"cl100k_base\")\n", + " tokens = enc.encode(\"Hello, world!\")\n", + " text = enc.decode(tokens)\n", + " assert text == \"Hello, world!\"\n", + "\n", + " # Or auto-select by OpenAI model name\n", + " enc = tiktoken.encoding_for_model(\"gpt-4o\") # e.g. returns cl100k_base under the hood\n", + " ```\n", + "\n", + "5. Why BPE? \n", + " • Reversible and lossless \n", + " • Handles any text (even unseen words) by splitting into subword units \n", + " • Compresses common substrings (e.g. “ing”, “tion”) so the model sees familiar chunks \n", + "\n", + "6. Extras \n", + " • Educational module (tiktoken._educational) to visualize or train simple BPEs \n", + " • Extension mechanism (tiktoken_ext) to register custom encodings \n", + "\n", + "7. Where to learn more \n", + " • GitHub: https://github.com/openai/tiktoken \n", + " • PyPI: https://pypi.org/project/tiktoken \n", + " • OpenAI Cookbook example: How to count tokens with tiktoken \n", + "\n", + "In short, if you’re building or billing on token usage with OpenAI’s models, tiktoken is the official, fast, and exact way to go from text ↔ tokens.\n", + "Here are the two easiest ways to get the open-source tiktoken up and running:\n", + "\n", + "1. Install the released package from PyPI \n", + " • (no Rust toolchain needed—prebuilt wheels for most platforms) \n", + " ```bash\n", + " pip install tiktoken\n", + " ``` \n", + " Then in Python: \n", + " ```python\n", + " import tiktoken\n", + " enc = tiktoken.get_encoding(\"cl100k_base\")\n", + " print(enc.encode(\"Hello, world!\"))\n", + " ```\n", + "\n", + "2. Install the bleeding-edge version straight from GitHub \n", + " • (you’ll need a Rust toolchain—on macOS `brew install rust`, on Ubuntu `sudo apt install cargo`) \n", + " ```bash\n", + " pip install git+https://github.com/openai/tiktoken.git@main\n", + " ``` \n", + " Or, if you prefer to clone & develop locally: \n", + " ```bash\n", + " git clone https://github.com/openai/tiktoken.git\n", + " cd tiktoken\n", + " pip install -e .\n", + " ```\n", + "\n", + "That’s it! Once installed, you can use `tiktoken.get_encoding(...)` to load any of the supported tokenizers.\n", + "To get the exact tokenizer (BPE encoding) that an OpenAI model uses, you can use the open-source tiktoken library. It provides a helper that maps model names to their correct tokenizers:\n", + "\n", + "1. Install tiktoken \n", + " ```bash\n", + " pip install tiktoken\n", + " ```\n", + "\n", + "2. In Python, call encoding_for_model(model_name): \n", + " ```python\n", + " import tiktoken\n", + "\n", + " #—for a gpt-3.5-turbo or gpt-4 style model:\n", + " enc = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n", + " print(enc.name) # e.g. \"cl100k_base\"\n", + " print(enc.encode(\"Hello\")) # list of token IDs\n", + " ```\n", + "\n", + " If you already know the encoding name (e.g. “cl100k_base” for GPT-3.5/4 or “r50k_base” for GPT-2), you can also do:\n", + " ```python\n", + " enc = tiktoken.get_encoding(\"cl100k_base\")\n", + " ```\n", + "\n", + "3. In Node.js / JavaScript, use the tiktoken npm package the same way:\n", + " ```js\n", + " import { encoding_for_model } from \"tiktoken\";\n", + "\n", + " const enc = await encoding_for_model(\"gpt-3.5-turbo\");\n", + " console.log(enc.name); // \"cl100k_base\"\n", + " console.log(enc.encode(\"Hi\")); // array of token IDs\n", + " ```\n", + "\n", + "Under the hood encoding_for_model knows which BPE schema (“r50k_base”, “cl100k_base”, etc.) each OpenAI model uses and returns the right tokenizer instance.\n", + "Byte-Pair Encoding (BPE) has become the de-facto subword tokenization method in modern language models because it strikes a practical balance between fixed, closed vocabularies (word-level tokenizers) and open, but very long sequences (character-level tokenizers). In particular:\n", + "\n", + "1. Open-vocabulary coverage \n", + " • Learns subword units from your corpus by iteratively merging the most frequent byte (or character) pairs. \n", + " • Can represent any new or rare word as a sequence of known subwords—no “unknown token” blowups. \n", + "\n", + "2. Compact vocabulary size \n", + " • Vocabulary sizes on the order of 20K–100K tokens capture very common words as single tokens and rare or morphologically complex words as a few subwords. \n", + " • Keeps softmax layers and embedding tables manageable in size. \n", + "\n", + "3. Reduced data sparsity \n", + " • Shares subwords among many words (e.g. “play,” “playing,” “replay”). \n", + " • Provides better statistical estimates (fewer zero‐count tokens) and faster convergence in training. \n", + "\n", + "4. Morphological and cross-lingual adaptability \n", + " • Naturally splits on morpheme or syllable boundaries when those are frequent in the data. \n", + " • Can be trained on multilingual corpora to share subwords across related languages. \n", + "\n", + "5. Speed and simplicity \n", + " • Linear-time, greedy encoding of new text (just look up merges). \n", + " • Deterministic and invertible: you can reconstruct the original byte sequence exactly.\n", + "\n", + "In short, BPE tokenization gives you a small, fixed-size vocabulary that still generalizes to unseen words, reduces training and memory costs, and improves statistical efficiency—key ingredients for high-quality, scalable language models.\n" + ] + } + ], + "source": [ + "print('# gpt‑4.1 Output')\n", + "for item in four_one_output:\n", + " print(item.sample.output[0].content)\n", + "\n", + "print('\\n# o4-mini Output')\n", + "for item in o4_mini_output:\n", + " print(item.sample.output[0].content)" + ] + }, + { + "cell_type": "markdown", + "id": "0936def6", + "metadata": {}, + "source": [ + "## How can we improve?\n", + "\n", + "If we add the phrase \"Always use your tools since they are the way to get the right answer in this task.\" to the system message of the o4-mini model, what do you think will happen? (try it out)\n", + "\n", + "<br><br><br>\n", + "\n", + "\n", + "If you guessed that the model would now call to MCP tool everytime and get every answer correct, you are right!" + ] + }, + { + "cell_type": "markdown", + "id": "cf797a91", + "metadata": {}, + "source": [ + "![Evaluation Data Tab](../../../images/mcp_eval_improved_output.png)\n", + "![Evaluation Data Tab](../../../images/mcp_eval_improved_data.png)" + ] + }, + { + "cell_type": "markdown", + "id": "924619e0", + "metadata": {}, + "source": [ + "In this notebook, we demonstrated a sample workflow for evaluating the ability of LLMs to answer technical questions about the `tiktoken` repository using the OpenAI Evals framework leveraging MCP tooling.\n", + "\n", + "**Key points covered:**\n", + "- Defined a focused, custom dataset for evaluation.\n", + "- Configured LLM-based and Python-based graders for robust assessment.\n", + "- Compared two models (`gpt-4.1` and `o4-mini`) in a reproducible and transparent manner.\n", + "- Retrieved and displayed model outputs for automated/manual inspection.\n", + "\n", + "**Next steps:**\n", + "- **Expand the dataset:** Add more diverse and challenging questions to better assess model capabilities.\n", + "- **Analyze results:** Summarize pass/fail rates, visualize performance, or perform error analysis to identify strengths and weaknesses.\n", + "- **Experiment with models/tools:** Try additional models, adjust tool configurations, or test on other repositories.\n", + "- **Automate reporting:** Generate summary tables or plots for easier sharing and decision-making.\n", + "\n", + "For more information, check out the [OpenAI Evals documentation](https://platform.openai.com/docs/guides/evals)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/evaluation/use-cases/regression.ipynb b/examples/evaluation/use-cases/regression.ipynb new file mode 100644 index 0000000000..61811d3254 --- /dev/null +++ b/examples/evaluation/use-cases/regression.ipynb @@ -0,0 +1,470 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluations Example: Push Notifications Summarizer Prompt Regression,\n", + "\n", + "Evals are **task oriented** and iterative, they're the best way to check how your LLM integration is doing and improve it.\n", + "\n", + "In the following eval, we are going to focus on the task of **detecting if my prompt change is a regression**.\n", + "\n", + "Our use-case is:\n", + "1. I have an llm integration that takes a list of push notifications and summarizes them into a single condensed statement.\n", + "2. I want to detect if a prompt change regresses the behavior\n", + "\n", + "## Evals structure\n", + "\n", + "Evals have two parts, the \"Eval\" and the \"Run\". An \"Eval\" holds the configuration for your testing criteria and the structure of the data for your \"Runs\". An Eval can have many runs that are evaluated by your testing criteria." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "from openai.types.chat import ChatCompletion\n", + "import pydantic\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"your-api-key\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use-case\n", + "\n", + "We're testing the following integration, a push notifications summary, which takes in multiple push notifications and collapses them into a single one, this is a chat completions call." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class PushNotifications(pydantic.BaseModel):\n", + " notifications: str\n", + "\n", + "print(PushNotifications.model_json_schema())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DEVELOPER_PROMPT = \"\"\"\n", + "You are a helpful assistant that summarizes push notifications.\n", + "You are given a list of push notifications and you need to collapse them into a single one.\n", + "Output only the final summary, nothing else.\n", + "\"\"\"\n", + "\n", + "def summarize_push_notification(push_notifications: str) -> ChatCompletion:\n", + " result = openai.chat.completions.create(\n", + " model=\"gpt-4o-mini\",\n", + " messages=[\n", + " {\"role\": \"developer\", \"content\": DEVELOPER_PROMPT},\n", + " {\"role\": \"user\", \"content\": push_notifications},\n", + " ],\n", + " )\n", + " return result\n", + "\n", + "example_push_notifications_list = PushNotifications(notifications=\"\"\"\n", + "- Alert: Unauthorized login attempt detected.\n", + "- New comment on your blog post: \"Great insights!\"\n", + "- Tonight's dinner recipe: Pasta Primavera.\n", + "\"\"\")\n", + "result = summarize_push_notification(example_push_notifications_list.notifications)\n", + "print(result.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setting up your eval\n", + "\n", + "An Eval holds the configuration that is shared across multiple *Runs*, it has two components:\n", + "1. Data source configuration `data_source_config` - the schema (columns) that your future *Runs* conform to.\n", + " - The `data_source_config` uses JSON Schema to define what variables are available in the Eval.\n", + "2. Testing Criteria `testing_criteria` - How you'll determine if your integration is working for each *row* of your data source.\n", + "\n", + "For this use-case, we want to test if the push notification summary completion is good, so we'll set-up our eval with this in mind." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# We want our input data to be available in our variables, so we set the item_schema to\n", + "# PushNotifications.model_json_schema()\n", + "data_source_config = {\n", + " \"type\": \"custom\",\n", + " \"item_schema\": PushNotifications.model_json_schema(),\n", + " # We're going to be uploading completions from the API, so we tell the Eval to expect this\n", + " \"include_sample_schema\": True,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This data_source_config defines what variables are available throughout the eval.\n", + "\n", + "This item schema:\n", + "```json\n", + "{\n", + " \"properties\": {\n", + " \"notifications\": {\n", + " \"title\": \"Notifications\",\n", + " \"type\": \"string\"\n", + " }\n", + " },\n", + " \"required\": [\"notifications\"],\n", + " \"title\": \"PushNotifications\",\n", + " \"type\": \"object\"\n", + "}\n", + "```\n", + "Means that we'll have the variable `{{item.notifications}}` available in our eval.\n", + "\n", + "`\"include_sample_schema\": True`\n", + "Mean's that we'll have the variable `{{sample.output_text}}` available in our eval.\n", + "\n", + "**Now, we'll use those variables to set up our test criteria.**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "GRADER_DEVELOPER_PROMPT = \"\"\"\n", + "Label the following push notification summary as either correct or incorrect.\n", + "The push notification and the summary will be provided below.\n", + "A good push notificiation summary is concise and snappy.\n", + "If it is good, then label it as correct, if not, then incorrect.\n", + "\"\"\"\n", + "GRADER_TEMPLATE_PROMPT = \"\"\"\n", + "Push notifications: {{item.notifications}}\n", + "Summary: {{sample.output_text}}\n", + "\"\"\"\n", + "push_notification_grader = {\n", + " \"name\": \"Push Notification Summary Grader\",\n", + " \"type\": \"label_model\",\n", + " \"model\": \"o3-mini\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"developer\",\n", + " \"content\": GRADER_DEVELOPER_PROMPT,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": GRADER_TEMPLATE_PROMPT,\n", + " },\n", + " ],\n", + " \"passing_labels\": [\"correct\"],\n", + " \"labels\": [\"correct\", \"incorrect\"],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `push_notification_grader` is a model grader (llm-as-a-judge), which looks at the input `{{item.notifications}}` and the generated summary `{{sample.output_text}}` and labels it as \"correct\" or \"incorrect\".\n", + "We then instruct via. the \"passing_labels\", what constitutes a passing answer.\n", + "\n", + "Note: under the hood, this uses structured outputs so that labels are always valid.\n", + "\n", + "**Now we'll create our eval!, and start adding data to it**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "eval_create_result = openai.evals.create(\n", + " name=\"Push Notification Summary Workflow\",\n", + " metadata={\n", + " \"description\": \"This eval checks if the push notification summary is correct.\",\n", + " },\n", + " data_source_config=data_source_config,\n", + " testing_criteria=[push_notification_grader],\n", + ")\n", + "\n", + "eval_id = eval_create_result.id" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating runs\n", + "\n", + "Now that we have our eval set-up with our test_criteria, we can start to add a bunch of runs!\n", + "We'll start with some push notification data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "push_notification_data = [\n", + " \"\"\"\n", + "- New message from Sarah: \"Can you call me later?\"\n", + "- Your package has been delivered!\n", + "- Flash sale: 20% off electronics for the next 2 hours!\n", + "\"\"\",\n", + " \"\"\"\n", + "- Weather alert: Thunderstorm expected in your area.\n", + "- Reminder: Doctor's appointment at 3 PM.\n", + "- John liked your photo on Instagram.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Breaking News: Local elections results are in.\n", + "- Your daily workout summary is ready.\n", + "- Check out your weekly screen time report.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Your ride is arriving in 2 minutes.\n", + "- Grocery order has been shipped.\n", + "- Don't miss the season finale of your favorite show tonight!\n", + "\"\"\",\n", + " \"\"\"\n", + "- Event reminder: Concert starts at 7 PM.\n", + "- Your favorite team just scored!\n", + "- Flashback: Memories from 3 years ago.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Low battery alert: Charge your device.\n", + "- Your friend Mike is nearby.\n", + "- New episode of \"The Tech Hour\" podcast is live!\n", + "\"\"\",\n", + " \"\"\"\n", + "- System update available.\n", + "- Monthly billing statement is ready.\n", + "- Your next meeting starts in 15 minutes.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Alert: Unauthorized login attempt detected.\n", + "- New comment on your blog post: \"Great insights!\"\n", + "- Tonight's dinner recipe: Pasta Primavera.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Special offer: Free coffee with any breakfast order.\n", + "- Your flight has been delayed by 30 minutes.\n", + "- New movie release: \"Adventures Beyond\" now streaming.\n", + "\"\"\",\n", + " \"\"\"\n", + "- Traffic alert: Accident reported on Main Street.\n", + "- Package out for delivery: Expected by 5 PM.\n", + "- New friend suggestion: Connect with Emma.\n", + "\"\"\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our first run will be our default grader from the completions function above `summarize_push_notification`\n", + "We'll loop through our dataset, make completions calls, and then submit them as a run to be graded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_data = []\n", + "for push_notifications in push_notification_data:\n", + " result = summarize_push_notification(push_notifications)\n", + " run_data.append({\n", + " \"item\": PushNotifications(notifications=push_notifications).model_dump(),\n", + " \"sample\": result.model_dump()\n", + " })\n", + "\n", + "eval_run_result = openai.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=\"baseline-run\",\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": run_data,\n", + " }\n", + " },\n", + ")\n", + "print(eval_run_result)\n", + "# Check out the results in the UI\n", + "print(eval_run_result.report_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's simulate a regression, here's our original prompt, let's simulate a developer breaking the prompt.\n", + "\n", + "```python\n", + "DEVELOPER_PROMPT = \"\"\"\n", + "You are a helpful assistant that summarizes push notifications.\n", + "You are given a list of push notifications and you need to collapse them into a single one.\n", + "Output only the final summary, nothing else.\n", + "\"\"\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "DEVELOPER_PROMPT = \"\"\"\n", + "You are a helpful assistant that summarizes push notifications.\n", + "You are given a list of push notifications and you need to collapse them into a single one.\n", + "You should make the summary longer than it needs to be and include more information than is necessary.\n", + "\"\"\"\n", + "\n", + "def summarize_push_notification_bad(push_notifications: str) -> ChatCompletion:\n", + " result = openai.chat.completions.create(\n", + " model=\"gpt-4o-mini\",\n", + " messages=[\n", + " {\"role\": \"developer\", \"content\": DEVELOPER_PROMPT},\n", + " {\"role\": \"user\", \"content\": push_notifications},\n", + " ],\n", + " )\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_data = []\n", + "for push_notifications in push_notification_data:\n", + " result = summarize_push_notification_bad(push_notifications)\n", + " run_data.append({\n", + " \"item\": PushNotifications(notifications=push_notifications).model_dump(),\n", + " \"sample\": result.model_dump()\n", + " })\n", + "\n", + "eval_run_result = openai.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=\"regression-run\",\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": run_data,\n", + " }\n", + " },\n", + ")\n", + "print(eval_run_result.report_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you view that report, you'll see that it has a score that's much lower than the baseline-run.\n", + "\n", + "## Congratulations, you just prevented a bug from shipping to users\n", + "\n", + "Quick note:\n", + "Evals doesn't yet support the `responses` api natively, however, you can transform it to the `completions` format with the following code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_push_notification_responses(push_notifications: str):\n", + " result = openai.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=[\n", + " {\"role\": \"developer\", \"content\": DEVELOPER_PROMPT},\n", + " {\"role\": \"user\", \"content\": push_notifications},\n", + " ],\n", + " )\n", + " return result\n", + "def transform_response_to_completion(response):\n", + " completion = {\n", + " \"model\": response.model,\n", + " \"choices\": [{\n", + " \"index\": 0,\n", + " \"message\": {\n", + " \"role\": \"assistant\",\n", + " \"content\": response.output_text\n", + " },\n", + " \"finish_reason\": \"stop\",\n", + " }]\n", + " }\n", + " return completion\n", + "\n", + "run_data = []\n", + "for push_notifications in push_notification_data:\n", + " response = summarize_push_notification_responses(push_notifications)\n", + " completion = transform_response_to_completion(response)\n", + " run_data.append({\n", + " \"item\": PushNotifications(notifications=push_notifications).model_dump(),\n", + " \"sample\": completion\n", + " })\n", + "\n", + "report_response = openai.evals.runs.create(\n", + " eval_id=eval_id,\n", + " name=\"responses-run\",\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": run_data,\n", + " }\n", + " },\n", + ")\n", + "print(report_response.report_url)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/evaluation/use-cases/responses-evaluation.ipynb b/examples/evaluation/use-cases/responses-evaluation.ipynb new file mode 100644 index 0000000000..278f635dbb --- /dev/null +++ b/examples/evaluation/use-cases/responses-evaluation.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluating a new model on existing responses" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following eval, we are going to compare how a new model (gpt-4.1-mini) compares to our old model (gpt-4o-mini) by evaluating it on some stored responses. The benefit of this is for most developers, they won't have to spend any time putting together a whole eval -- all of their data will already be stored in their [logs page](https://platform.openai.com/logs)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "import os\n", + "\n", + "\n", + "client = openai.OpenAI()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to see how gpt-4.1 compares to gpt-4o on explaining a code base. Since can only use the responses datasource if you already have user traffic, we're going to generate some example traffic using 4o, and then compare how it does to gpt-4.1. \n", + "\n", + "We're going to get some example code files from the OpenAI SDK, and ask gpt-4o to explain them to us." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "openai_sdk_file_path = os.path.dirname(openai.__file__)\n", + "\n", + "# Get some example code files from the OpenAI SDK \n", + "file_paths = [\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"evals\", \"evals.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"responses\", \"responses.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"images.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"embeddings.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"files.py\"),\n", + "]\n", + "\n", + "print(file_paths[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, lets generate some responses. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for file_path in file_paths:\n", + " response = client.responses.create(\n", + " input=[\n", + " {\"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"What does this file do?\"\n", + " },\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": open(file_path, \"r\").read(),\n", + " },\n", + " ]},\n", + " ],\n", + " model=\"gpt-4o-mini\",\n", + " )\n", + " print(response.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that in order for this to work, you'll have to be doing this on an org where data logging isn't disabled (through zdr, etc). If you aren't sure if this is the case for you, go to https://platform.openai.com/logs?api=responses and see if you can see the responses you just generated." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "grader_system_prompt = \"\"\"\n", + "You are **Code-Explanation Grader**, an expert software engineer and technical writer. \n", + "Your job is to score how well *Model A* explained the purpose and behaviour of a given source-code file.\n", + "\n", + "### What you receive\n", + "1. **File contents** – the full text of the code file (or a representative excerpt). \n", + "2. **Candidate explanation** – the answer produced by Model A that tries to describe what the file does.\n", + "\n", + "### What to produce\n", + "Return a single JSON object that can be parsed by `json.loads`, containing:\n", + "```json\n", + "{\n", + " \"steps\": [\n", + " { \"description\": \"...\", \"result\": \"float\" },\n", + " { \"description\": \"...\", \"result\": \"float\" },\n", + " { \"description\": \"...\", \"result\": \"float\" }\n", + " ],\n", + " \"result\": \"float\"\n", + "}\n", + "```\n", + "• Each object in `steps` documents your reasoning for one category listed under “Scoring dimensions”. \n", + "• Place your final 1 – 7 quality score (inclusive) in the top-level `result` key as a **string** (e.g. `\"5.5\"`).\n", + "\n", + "### Scoring dimensions (evaluate in this order)\n", + "\n", + "1. **Correctness & Accuracy ≈ 45 %** \n", + " • Does the explanation match the actual code behaviour, interfaces, edge cases, and side effects? \n", + " • Fact-check every technical claim; penalise hallucinations or missed key functionality.\n", + "\n", + "2. **Completeness & Depth ≈ 25 %** \n", + " • Are all major components, classes, functions, data flows, and external dependencies covered? \n", + " • Depth should be appropriate to the file’s size/complexity; superficial glosses lose points.\n", + "\n", + "3. **Clarity & Organization ≈ 20 %** \n", + " • Is the explanation well-structured, logically ordered, and easy for a competent developer to follow? \n", + " • Good use of headings, bullet lists, and concise language is rewarded.\n", + "\n", + "4. **Insight & Usefulness ≈ 10 %** \n", + " • Does the answer add valuable context (e.g., typical use cases, performance notes, risks) beyond line-by-line paraphrase? \n", + " • Highlighting **why** design choices matter is a plus.\n", + "\n", + "### Error taxonomy\n", + "• **Major error** – Any statement that materially misrepresents the file (e.g., wrong API purpose, inventing non-existent behaviour). \n", + "• **Minor error** – Small omission or wording that slightly reduces clarity but doesn’t mislead. \n", + "List all found errors in your `steps` reasoning.\n", + "\n", + "### Numeric rubric\n", + "1 Catastrophically wrong; mostly hallucination or irrelevant. \n", + "2 Many major errors, few correct points. \n", + "3 Several major errors OR pervasive minor mistakes; unreliable. \n", + "4 Mostly correct but with at least one major gap or multiple minors; usable only with caution. \n", + "5 Solid, generally correct; minor issues possible but no major flaws. \n", + "6 Comprehensive, accurate, and clear; only very small nit-picks. \n", + "7 Exceptional: precise, thorough, insightful, and elegantly presented; hard to improve.\n", + "\n", + "Use the full scale. Reserve 6.5 – 7 only when you are almost certain the explanation is outstanding.\n", + "\n", + "Then set `\"result\": \"4.0\"` (example).\n", + "\n", + "Be rigorous and unbiased.\n", + "\"\"\"\n", + "user_input_message = \"\"\"**User input**\n", + "\n", + "{{item.input}}\n", + "\n", + "**Response to evaluate**\n", + "\n", + "{{sample.output_text}}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "logs_eval = client.evals.create(\n", + " name=\"Code QA Eval\",\n", + " data_source_config={\n", + " \"type\": \"logs\",\n", + " },\n", + " testing_criteria=[\n", + " {\n", + "\t\t\t\"type\": \"score_model\",\n", + " \"name\": \"General Evaluator\",\n", + " \"model\": \"o3\",\n", + " \"input\": [{\n", + " \"role\": \"system\",\n", + " \"content\": grader_system_prompt,\n", + " }, {\n", + " \"role\": \"user\",\n", + " \"content\": user_input_message,\n", + " },\n", + " ],\n", + " \"range\": [1, 7],\n", + " \"pass_threshold\": 5.5,\n", + " }\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, lets kick off a run to evaluate how good the original responses were. To do this, we just set the filters for what responses we want to evaluate on" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "gpt_4o_mini_run = client.evals.runs.create(\n", + " name=\"gpt-4o-mini\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\"type\": \"responses\", \"limit\": len(file_paths)}, # just grab the most recent responses\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's see how 4.1-mini does!" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "gpt_41_mini_run = client.evals.runs.create(\n", + " name=\"gpt-4.1-mini\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\"type\": \"responses\", \"limit\": len(file_paths)},\n", + " \"input_messages\": {\n", + " \"type\": \"item_reference\",\n", + " \"item_reference\": \"item.input\",\n", + " },\n", + " \"model\": \"gpt-4.1-mini\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, lets go to the dashboard to see how we did!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gpt_4o_mini_run.report_url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/evaluation/use-cases/structured-outputs-evaluation.ipynb b/examples/evaluation/use-cases/structured-outputs-evaluation.ipynb new file mode 100644 index 0000000000..37c21f450a --- /dev/null +++ b/examples/evaluation/use-cases/structured-outputs-evaluation.ipynb @@ -0,0 +1,857 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0a2d56c0", + "metadata": {}, + "source": [ + "\n", + "# Structured Output Evaluation Cookbook\n", + " \n", + "This notebook walks you through a set of focused, runnable examples how to use the OpenAI **Evals** framework to **test, grade, and iterate on tasks that require large‑language models to produce structured outputs**.\n", + "\n", + "> **Why does this matter?** \n", + "> Production systems often depend on JSON, SQL, or domain‑specific formats. Relying on spot checks or ad‑hoc prompt tweaks quickly breaks down. Instead, you can *codify* expectations as automated evals and let your team ship with safety bricks instead of sand.\n" + ] + }, + { + "cell_type": "markdown", + "id": "45eee293", + "metadata": {}, + "source": [ + "\n", + "## Quick Tour\n", + "\n", + "* **Section 1 – Prerequisites**: environment variables and package setup \n", + "* **Section 2 – Walk‑through: Code‑symbol extraction**: end‑to‑end demo that grades the model’s ability to extract function and class names from source code. We keep the original logic intact and simply layer documentation around it. \n", + "* **Section 3 – Additional Recipes**: sketches of common production patterns such as sentiment extraction as additional code sample for evaluation.\n", + "* **Section 4 – Result Exploration**: lightweight helpers for pulling run output and digging into failures. \n" + ] + }, + { + "cell_type": "markdown", + "id": "e027be46", + "metadata": {}, + "source": [ + "\n", + "## Prerequisites\n", + "\n", + "1. **Install dependencies** (minimum versions shown):\n", + "\n", + "```bash\n", + "pip install --upgrade openai\n", + "```\n", + "\n", + "2. **Authenticate** by exporting your key:\n", + "\n", + "```bash\n", + "export OPENAI_API_KEY=\"sk‑...\"\n", + "```\n", + "\n", + "3. **Optional**: if you plan to run evals in bulk, set up an [organization‑level key](https://platform.openai.com/account/org-settings) with appropriate limits.\n" + ] + }, + { + "cell_type": "markdown", + "id": "4592675d", + "metadata": {}, + "source": [ + "### Use Case 1: Code symbol extraction" + ] + }, + { + "cell_type": "markdown", + "id": "d2a32d53", + "metadata": {}, + "source": [ + "\n", + "The goal is to **extract all function, class, and constant symbols from python files inside the OpenAI SDK**. \n", + "For each file we ask the model to emit structured JSON like:\n", + "\n", + "```json\n", + "{\n", + " \"symbols\": [\n", + " {\"name\": \"OpenAI\", \"kind\": \"class\"},\n", + " {\"name\": \"Evals\", \"kind\": \"module\"},\n", + " ...\n", + " ]\n", + "}\n", + "```\n", + "\n", + "A rubric model then grades **completeness** (did we capture every symbol?) and **quality** (are the kinds correct?) on a 1‑7 scale.\n" + ] + }, + { + "cell_type": "markdown", + "id": "9dd88e7c", + "metadata": {}, + "source": [ + "### Evaluating Code Quality Extraction with a Custom Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "64bf0667", + "metadata": {}, + "source": [ + "Let us walk though an example to evaluate a model's ability to extract symbols from code using the OpenAI **Evals** framework with a custom in-memory dataset." + ] + }, + { + "cell_type": "markdown", + "id": "c95faa47", + "metadata": {}, + "source": [ + "### Initialize SDK client\n", + "Creates an `openai.OpenAI` client using the `OPENAI_API_KEY` we exported above. Nothing will run without this." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "eacc6ac7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade openai pandas rich --quiet\n", + "\n", + "\n", + "\n", + "import os\n", + "import time\n", + "import openai\n", + "from rich import print\n", + "import pandas as pd\n", + "\n", + "client = openai.OpenAI(\n", + " api_key=os.getenv(\"OPENAI_API_KEY\") or os.getenv(\"_OPENAI_API_KEY\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8200aaf1", + "metadata": {}, + "source": [ + "### Dataset factory & grading rubric\n", + "* `get_dataset` builds a small in-memory dataset by reading several SDK files.\n", + "* `structured_output_grader` defines a detailed evaluation rubric.\n", + "* `client.evals.create(...)` registers the eval with the platform." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b272e193", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dataset(limit=None):\n", + " openai_sdk_file_path = os.path.dirname(openai.__file__)\n", + "\n", + " file_paths = [\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"evals\", \"evals.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"responses\", \"responses.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"images.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"embeddings.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"files.py\"),\n", + " ]\n", + "\n", + " items = []\n", + " for file_path in file_paths:\n", + " items.append({\"input\": open(file_path, \"r\").read()})\n", + " if limit:\n", + " return items[:limit]\n", + " return items\n", + "\n", + "\n", + "structured_output_grader = \"\"\"\n", + "You are a helpful assistant that grades the quality of extracted information from a code file.\n", + "You will be given a code file and a list of extracted information.\n", + "You should grade the quality of the extracted information.\n", + "\n", + "You should grade the quality on a scale of 1 to 7.\n", + "You should apply the following criteria, and calculate your score as follows:\n", + "You should first check for completeness on a scale of 1 to 7.\n", + "Then you should apply a quality modifier.\n", + "\n", + "The quality modifier is a multiplier from 0 to 1 that you multiply by the completeness score.\n", + "If there is 100% coverage for completion and it is all high quality, then you would return 7*1.\n", + "If there is 100% coverage for completion but it is all low quality, then you would return 7*0.5.\n", + "etc.\n", + "\"\"\"\n", + "\n", + "structured_output_grader_user_prompt = \"\"\"\n", + "<Code File>\n", + "{{item.input}}\n", + "</Code File>\n", + "\n", + "<Extracted Information>\n", + "{{sample.output_json.symbols}}\n", + "</Extracted Information>\n", + "\"\"\"\n", + "\n", + "logs_eval = client.evals.create(\n", + " name=\"Code QA Eval\",\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\"input\": {\"type\": \"string\"}},\n", + " },\n", + " \"include_sample_schema\": True,\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"score_model\",\n", + " \"name\": \"General Evaluator\",\n", + " \"model\": \"o3\",\n", + " \"input\": [\n", + " {\"role\": \"system\", \"content\": structured_output_grader},\n", + " {\"role\": \"user\", \"content\": structured_output_grader_user_prompt},\n", + " ],\n", + " \"range\": [1, 7],\n", + " \"pass_threshold\": 5.5,\n", + " }\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4e77cbe6", + "metadata": {}, + "source": [ + "### Kick off model runs\n", + "Here we launch two runs against the same eval: one that calls the **Completions** endpoint, and one that calls the **Responses** endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "18f357e6", + "metadata": {}, + "outputs": [], + "source": [ + "### Kick off model runs\n", + "gpt_4one_completions_run = client.evals.runs.create(\n", + " name=\"gpt-4.1\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"completions\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in get_dataset(limit=1)],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\"type\": \"input_text\", \"text\": \"You are a helpful assistant.\"},\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Extract the symbols from the code file {{item.input}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"response_format\": {\n", + " \"type\": \"json_schema\",\n", + " \"json_schema\": {\n", + " \"name\": \"python_symbols\",\n", + " \"schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"symbols\": {\n", + " \"type\": \"array\",\n", + " \"description\": \"A list of symbols extracted from Python code.\",\n", + " \"items\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"name\": {\"type\": \"string\", \"description\": \"The name of the symbol.\"},\n", + " \"symbol_type\": {\n", + " \"type\": \"string\", \"description\": \"The type of the symbol, e.g., variable, function, class.\",\n", + " },\n", + " },\n", + " \"required\": [\"name\", \"symbol_type\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + " }\n", + " },\n", + " \"required\": [\"symbols\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + " \"strict\": True,\n", + " },\n", + " },\n", + " },\n", + " },\n", + ")\n", + "\n", + "gpt_4one_responses_run = client.evals.runs.create(\n", + " name=\"gpt-4.1-mini\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in get_dataset(limit=1)],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\"type\": \"input_text\", \"text\": \"You are a helpful assistant.\"},\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Extract the symbols from the code file {{item.input}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1-mini\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"text\": {\n", + " \"format\": {\n", + " \"type\": \"json_schema\",\n", + " \"name\": \"python_symbols\",\n", + " \"schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"symbols\": {\n", + " \"type\": \"array\",\n", + " \"description\": \"A list of symbols extracted from Python code.\",\n", + " \"items\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"name\": {\"type\": \"string\", \"description\": \"The name of the symbol.\"},\n", + " \"symbol_type\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The type of the symbol, e.g., variable, function, class.\",\n", + " },\n", + " },\n", + " \"required\": [\"name\", \"symbol_type\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + " }\n", + " },\n", + " \"required\": [\"symbols\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + " \"strict\": True,\n", + " },\n", + " },\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "dd0aa0c0", + "metadata": {}, + "source": [ + "### Utility poller\n", + "Next, we will use a simple loop that waits for all runs to finish, then saves each run’s JSON to disk so you can inspect it later or attach it to CI artifacts." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cbc4f775", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<pre 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"output_type": "display_data" + } + ], + "source": [ + "### Utility poller\n", + "def poll_runs(eval_id, run_ids):\n", + " while True:\n", + " runs = [client.evals.runs.retrieve(rid, eval_id=eval_id) for rid in run_ids]\n", + " for run in runs:\n", + " print(run.id, run.status, run.result_counts)\n", + " if all(run.status in {\"completed\", \"failed\"} for run in runs):\n", + " # dump results to file\n", + " for run in runs:\n", + " with open(f\"{run.id}.json\", \"w\") as f:\n", + " f.write(\n", + " client.evals.runs.output_items.list(\n", + " run_id=run.id, eval_id=eval_id\n", + " ).model_dump_json(indent=4)\n", + " )\n", + " break\n", + " time.sleep(5)\n", + "\n", + "poll_runs(logs_eval.id, [gpt_4one_completions_run.id, gpt_4one_responses_run.id])" + ] + }, + { + "cell_type": "markdown", + "id": "77331859", + "metadata": {}, + "source": [ + "### Load outputs for quick inspection\n", + "We will fetch the output items for both runs so we can print or post‑process them." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c316e6eb", + "metadata": {}, + "outputs": [], + "source": [ + "completions_output = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_completions_run.id, eval_id=logs_eval.id\n", + ")\n", + "\n", + "responses_output = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_responses_run.id, eval_id=logs_eval.id\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1cc61c54", + "metadata": {}, + "source": [ + "### Human-readable dump\n", + "Let us print a side-by-side view of completions vs responses." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9f1b502e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "<h4 style=\"color: #1CA7EC; font-weight: 600; letter-spacing: 1px; text-shadow: 0 1px 2px rgba(0,0,0,0.08), 0 0px 0px #fff;\">\n", + "Completions vs Responses Output\n", + "</h4>\n" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<style type=\"text/css\">\n", + "#T_ac15e th {\n", + " font-size: 1.1em;\n", + " background-color: #323C50;\n", + " color: #FFFFFF;\n", + " border-bottom: 2px solid #1CA7EC;\n", + "}\n", + "#T_ac15e td {\n", + " font-size: 1em;\n", + " max-width: 650px;\n", + " background-color: #F6F8FA;\n", + " color: #222;\n", + " border-bottom: 1px solid #DDD;\n", + "}\n", + "#T_ac15e tr:hover td {\n", + " background-color: #D1ECF1;\n", + " color: #18647E;\n", + "}\n", + "#T_ac15e tbody tr:nth-child(even) td {\n", + " background-color: #E8F1FB;\n", + "}\n", + "#T_ac15e tbody tr:nth-child(odd) td {\n", + " background-color: #F6F8FA;\n", + "}\n", + "#T_ac15e table {\n", + " border-collapse: collapse;\n", + " border-radius: 6px;\n", + " overflow: hidden;\n", + "}\n", + "#T_ac15e_row0_col0, #T_ac15e_row0_col1 {\n", + " white-space: pre-wrap;\n", + " word-break: break-word;\n", + " padding: 8px;\n", + "}\n", + "</style>\n", + "<table id=\"T_ac15e\">\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_ac15e_level0_col0\" class=\"col_heading level0 col0\" >Completions Output</th>\n", + " <th id=\"T_ac15e_level0_col1\" class=\"col_heading level0 col1\" >Responses Output</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_ac15e_row0_col0\" class=\"data row0 col0\" >{\"symbols\":[{\"name\":\"Evals\",\"symbol_type\":\"class\"},{\"name\":\"AsyncEvals\",\"symbol_type\":\"class\"},{\"name\":\"EvalsWithRawResponse\",\"symbol_type\":\"class\"},{\"name\":\"AsyncEvalsWithRawResponse\",\"symbol_type\":\"class\"},{\"name\":\"EvalsWithStreamingResponse\",\"symb...</td>\n", + " <td id=\"T_ac15e_row0_col1\" class=\"data row0 col1\" >{\"symbols\":[{\"name\":\"Evals\",\"symbol_type\":\"class\"},{\"name\":\"runs\",\"symbol_type\":\"property\"},{\"name\":\"with_raw_response\",\"symbol_type\":\"property\"},{\"name\":\"with_streaming_response\",\"symbol_type\":\"property\"},{\"name\":\"create\",\"symbol_type\":\"function\"},{...</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n" + ], + "text/plain": [ + "<pandas.io.formats.style.Styler at 0x11dc60790>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, HTML\n", + "\n", + "# Collect outputs for both runs\n", + "completions_outputs = [item.sample.output[0].content for item in completions_output]\n", + "responses_outputs = [item.sample.output[0].content for item in responses_output]\n", + "\n", + "# Create DataFrame for side-by-side display (truncated to 250 chars for readability)\n", + "df = pd.DataFrame({\n", + " \"Completions Output\": [c[:250].replace('\\n', ' ') + ('...' if len(c) > 250 else '') for c in completions_outputs],\n", + " \"Responses Output\": [r[:250].replace('\\n', ' ') + ('...' if len(r) > 250 else '') for r in responses_outputs]\n", + "})\n", + "\n", + "# Custom color scheme\n", + "custom_styles = [\n", + " {'selector': 'th', 'props': [('font-size', '1.1em'), ('background-color', '#323C50'), ('color', '#FFFFFF'), ('border-bottom', '2px solid #1CA7EC')]},\n", + " {'selector': 'td', 'props': [('font-size', '1em'), ('max-width', '650px'), ('background-color', '#F6F8FA'), ('color', '#222'), ('border-bottom', '1px solid #DDD')]},\n", + " {'selector': 'tr:hover td', 'props': [('background-color', '#D1ECF1'), ('color', '#18647E')]},\n", + " {'selector': 'tbody tr:nth-child(even) td', 'props': [('background-color', '#E8F1FB')]},\n", + " {'selector': 'tbody tr:nth-child(odd) td', 'props': [('background-color', '#F6F8FA')]},\n", + " {'selector': 'table', 'props': [('border-collapse', 'collapse'), ('border-radius', '6px'), ('overflow', 'hidden')]},\n", + "]\n", + "\n", + "styled = (\n", + " df.style\n", + " .set_properties(**{'white-space': 'pre-wrap', 'word-break': 'break-word', 'padding': '8px'})\n", + " .set_table_styles(custom_styles)\n", + " .hide(axis=\"index\")\n", + ")\n", + "\n", + "display(HTML(\"\"\"\n", + "<h4 style=\"color: #1CA7EC; font-weight: 600; letter-spacing: 1px; text-shadow: 0 1px 2px rgba(0,0,0,0.08), 0 0px 0px #fff;\">\n", + "Completions vs Responses Output\n", + "</h4>\n", + "\"\"\"))\n", + "display(styled)" + ] + }, + { + "cell_type": "markdown", + "id": "8cbe934f", + "metadata": {}, + "source": [ + "### Visualize the Results\n", + "\n", + "Below are visualizations that represent the evaluation data and code outputs for structured QA evaluation. These images provide insights into the data distribution and the evaluation workflow.\n", + "\n", + "---\n", + "\n", + "**Evaluation Data Overview**\n", + "\n", + "![Evaluation Data Part 1](../../../images/eval_qa_data_1.png)\n", + "\n", + "![Evaluation Data Part 2](../../../images/eval_qa_data_2.png)\n", + "\n", + "---\n", + "\n", + "**Evaluation Code Workflow**\n", + "\n", + "![Evaluation Code Structure](../../../images/eval_qa_code.png)\n", + "\n", + "---\n", + "\n", + "By reviewing these visualizations, you can better understand the structure of the evaluation dataset and the steps involved in evaluating structured outputs for QA tasks.\n" + ] + }, + { + "cell_type": "markdown", + "id": "a0ae89ef", + "metadata": {}, + "source": [ + "### Use Case 2: Multi-lingual Sentiment Extraction\n", + "In a similar way, let us evaluate a multi-lingual sentiment extraction model with structured outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e5f0b782", + "metadata": {}, + "outputs": [], + "source": [ + "# Sample in-memory dataset for sentiment extraction\n", + "sentiment_dataset = [\n", + " {\n", + " \"text\": \"I love this product!\",\n", + " \"channel\": \"twitter\",\n", + " \"language\": \"en\"\n", + " },\n", + " {\n", + " \"text\": \"This is the worst experience I've ever had.\",\n", + " \"channel\": \"support_ticket\",\n", + " \"language\": \"en\"\n", + " },\n", + " {\n", + " \"text\": \"It's okay – not great but not bad either.\",\n", + " \"channel\": \"app_review\",\n", + " \"language\": \"en\"\n", + " },\n", + " {\n", + " \"text\": \"No estoy seguro de lo que pienso sobre este producto.\",\n", + " \"channel\": \"facebook\",\n", + " \"language\": \"es\"\n", + " },\n", + " {\n", + " \"text\": \"总体来说，我对这款产品很满意。\",\n", + " \"channel\": \"wechat\",\n", + " \"language\": \"zh\"\n", + " },\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "cb6954f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Define output schema\n", + "sentiment_output_schema = {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"sentiment\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"overall label: positive / negative / neutral\"\n", + " },\n", + " \"confidence\": {\n", + " \"type\": \"number\",\n", + " \"description\": \"confidence score 0-1\"\n", + " },\n", + " \"emotions\": {\n", + " \"type\": \"array\",\n", + " \"description\": \"list of dominant emotions (e.g. joy, anger)\",\n", + " \"items\": {\"type\": \"string\"}\n", + " }\n", + " },\n", + " \"required\": [\"sentiment\", \"confidence\", \"emotions\"],\n", + " \"additionalProperties\": False\n", + "}\n", + "\n", + "# Grader prompts\n", + "sentiment_grader_system = \"\"\"You are a strict grader for sentiment extraction.\n", + "Given the text and the model's JSON output, score correctness on a 1-5 scale.\"\"\"\n", + "\n", + "sentiment_grader_user = \"\"\"Text: {{item.text}}\n", + "Model output:\n", + "{{sample.output_json}}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ac815aec", + "metadata": {}, + "outputs": [], + "source": [ + "# Register an eval for the richer sentiment task\n", + "sentiment_eval = client.evals.create(\n", + " name=\"sentiment_extraction_eval\",\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": { # matches the new dataset fields\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"text\": {\"type\": \"string\"},\n", + " \"channel\": {\"type\": \"string\"},\n", + " \"language\": {\"type\": \"string\"},\n", + " },\n", + " \"required\": [\"text\"],\n", + " },\n", + " \"include_sample_schema\": True,\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"score_model\",\n", + " \"name\": \"Sentiment Grader\",\n", + " \"model\": \"o3\",\n", + " \"input\": [\n", + " {\"role\": \"system\", \"content\": sentiment_grader_system},\n", + " {\"role\": \"user\", \"content\": sentiment_grader_user},\n", + " ],\n", + " \"range\": [1, 5],\n", + " \"pass_threshold\": 3.5,\n", + " }\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f4aa9d6", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the sentiment eval\n", + "sentiment_run = client.evals.runs.create(\n", + " name=\"gpt-4.1-sentiment\",\n", + " eval_id=sentiment_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in sentiment_dataset],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\"type\": \"input_text\", \"text\": \"You are a helpful assistant.\"},\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"{{item.text}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 100,\n", + " \"top_p\": 0.9,\n", + " \"text\": {\n", + " \"format\": {\n", + " \"type\": \"json_schema\",\n", + " \"name\": \"sentiment_output\",\n", + " \"schema\": sentiment_output_schema,\n", + " \"strict\": True,\n", + " },\n", + " },\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "17f5f960", + "metadata": {}, + "source": [ + "### Visualize evals data \n", + "![image](../../../images/evals_sentiment.png)" + ] + }, + { + "cell_type": "markdown", + "id": "ab141018", + "metadata": {}, + "source": [ + "### Summary and Next Steps\n", + "\n", + "In this notebook, we have demonstrated how to use the OpenAI Evaluation API to evaluate a model's performance on a structured output task. \n", + "\n", + "**Next steps:**\n", + "- We encourage you to try out the API with your own models and datasets.\n", + "- You can also explore the API documentation for more details on how to use the API. \n", + "\n", + "For more information, see the [OpenAI Evals documentation](https://platform.openai.com/docs/guides/evals).\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/evaluation/use-cases/tools-evaluation.ipynb b/examples/evaluation/use-cases/tools-evaluation.ipynb new file mode 100644 index 0000000000..5bdf49829c --- /dev/null +++ b/examples/evaluation/use-cases/tools-evaluation.ipynb @@ -0,0 +1,736 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6ff95379", + "metadata": {}, + "source": [ + "# Tool Evaluation with OpenAI Evals\n", + "\n", + "This cookbook shows how to **measure and improve a model’s ability to extract structured information from source code** with tool evaluation. In this case, the set of *symbols* (functions, classes, methods, and variables) defined in Python files. " + ] + }, + { + "cell_type": "markdown", + "id": "4cc30394", + "metadata": {}, + "source": [ + "## Setup<a name=\"Setup\"></a>\n", + "\n", + "Install the latest **openai** Python package ≥ 1.14.0 and set your `OPENAI_API_KEY` environment variable. If you also want to evaluate an *assistant with tools*, enable the *Assistants v2 beta* in your account.\n", + "\n", + "```bash\n", + "pip install --upgrade openai\n", + "export OPENAI_API_KEY=sk‑...\n", + "```\n", + "Below we import the SDK, create a client, and define a helper that builds a small dataset from files inside the **openai** package itself." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "acd0d746", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade openai pandas jinja2 rich --quiet\n", + "\n", + "import os\n", + "import time\n", + "import openai\n", + "from rich import print\n", + "\n", + "client = openai.OpenAI(\n", + " api_key=os.getenv(\"OPENAI_API_KEY\") or os.getenv(\"_OPENAI_API_KEY\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "80618b60", + "metadata": {}, + "source": [ + "### Dataset factory & grading rubric\n", + "* `get_dataset` builds a small in-memory dataset by reading several SDK files.\n", + "* `structured_output_grader` defines a detailed evaluation rubric. \n", + "* `sampled.output_tools[0].function.arguments.symbols` specifies the extracted symbols from the code file based on the tool invocation.\n", + "* `client.evals.create(...)` registers the eval with the platform." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "120b6e4d", + "metadata": { + "tags": [ + "original" + ] + }, + "outputs": [], + "source": [ + "def get_dataset(limit=None):\n", + " openai_sdk_file_path = os.path.dirname(openai.__file__)\n", + "\n", + " file_paths = [\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"evals\", \"evals.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"responses\", \"responses.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"images.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"embeddings.py\"),\n", + " os.path.join(openai_sdk_file_path, \"resources\", \"files.py\"),\n", + " ]\n", + "\n", + " items = []\n", + " for file_path in file_paths:\n", + " items.append({\"input\": open(file_path, \"r\").read()})\n", + " if limit:\n", + " return items[:limit]\n", + " return items\n", + "\n", + "\n", + "structured_output_grader = \"\"\"\n", + "You are a helpful assistant that grades the quality of extracted information from a code file.\n", + "You will be given a code file and a list of extracted information.\n", + "You should grade the quality of the extracted information.\n", + "\n", + "You should grade the quality on a scale of 1 to 7.\n", + "You should apply the following criteria, and calculate your score as follows:\n", + "You should first check for completeness on a scale of 1 to 7.\n", + "Then you should apply a quality modifier.\n", + "\n", + "The quality modifier is a multiplier from 0 to 1 that you multiply by the completeness score.\n", + "If there is 100% coverage for completion and it is all high quality, then you would return 7*1.\n", + "If there is 100% coverage for completion but it is all low quality, then you would return 7*0.5.\n", + "etc.\n", + "\"\"\"\n", + "\n", + "structured_output_grader_user_prompt = \"\"\"\n", + "<Code File>\n", + "{{item.input}}\n", + "</Code File>\n", + "\n", + "<Extracted Information>\n", + "{{sample.output_tools[0].function.arguments.symbols}}\n", + "</Extracted Information>\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "d7f66a56", + "metadata": {}, + "source": [ + "### Evals Creation\n", + "\n", + "Here we create an eval that will be used to evaluate the quality of extracted information from code files.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95a5eaf6", + "metadata": {}, + "outputs": [], + "source": [ + "logs_eval = client.evals.create(\n", + " name=\"Code QA Eval\",\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": {\"type\": \"object\", \"properties\": {\"input\": {\"type\": \"string\"}}},\n", + " \"include_sample_schema\": True,\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"score_model\",\n", + " \"name\": \"General Evaluator\",\n", + " \"model\": \"o3\",\n", + " \"input\": [\n", + " {\"role\": \"system\", \"content\": structured_output_grader},\n", + " {\"role\": \"user\", \"content\": structured_output_grader_user_prompt},\n", + " ],\n", + " \"range\": [1, 7],\n", + " \"pass_threshold\": 5.0,\n", + " }\n", + " ],\n", + ")\n", + "\n", + "symbol_tool = {\n", + " \"name\": \"extract_symbols\",\n", + " \"description\": \"Extract the symbols from the code file\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"symbols\": {\n", + " \"type\": \"array\",\n", + " \"description\": \"A list of symbols extracted from Python code.\",\n", + " \"items\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"name\": {\"type\": \"string\", \"description\": \"The name of the symbol.\"},\n", + " \"symbol_type\": {\"type\": \"string\", \"description\": \"The type of the symbol, e.g., variable, function, class.\"},\n", + " },\n", + " \"required\": [\"name\", \"symbol_type\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + " }\n", + " },\n", + " \"required\": [\"symbols\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "73ae7e5e", + "metadata": {}, + "source": [ + "### Kick off model runs\n", + "Here we launch two runs against the same eval: one that calls the **Completions** endpoint, and one that calls the **Responses** endpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d650e02", + "metadata": {}, + "outputs": [], + "source": [ + "gpt_4one_completions_run = client.evals.runs.create(\n", + " name=\"gpt-4.1\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"completions\",\n", + " \"source\": {\"type\": \"file_content\", \"content\": [{\"item\": item} for item in get_dataset(limit=1)]},\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\"type\": \"message\", \"role\": \"system\", \"content\": {\"type\": \"input_text\", \"text\": \"You are a helpful assistant.\"}},\n", + " {\"type\": \"message\", \"role\": \"user\", \"content\": {\"type\": \"input_text\", \"text\": \"Extract the symbols from the code file {{item.input}}\"}},\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"tools\": [{\"type\": \"function\", \"function\": symbol_tool}],\n", + " },\n", + " },\n", + ")\n", + "\n", + "gpt_4one_responses_run = client.evals.runs.create(\n", + " name=\"gpt-4.1-mini\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\"type\": \"file_content\", \"content\": [{\"item\": item} for item in get_dataset(limit=1)]},\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\"type\": \"message\", \"role\": \"system\", \"content\": {\"type\": \"input_text\", \"text\": \"You are a helpful assistant.\"}},\n", + " {\"type\": \"message\", \"role\": \"user\", \"content\": {\"type\": \"input_text\", \"text\": \"Extract the symbols from the code file {{item.input}}\"}},\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1-mini\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"tools\": [{\"type\": \"function\", **symbol_tool}],\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6ea31f2a", + "metadata": {}, + "source": [ + "### Utility Poller\n", + "\n", + "We create a utility poller that will be used to poll for the results of the eval runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb8f3df4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">evalrun_6848e2269570819198b757fe12b979da completed\n", + "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ResultCounts</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">errored</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">failed</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">passed</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">total</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>\n", + "</pre>\n" + ], + "text/plain": [ + "evalrun_6848e2269570819198b757fe12b979da completed\n", + "\u001b[1;35mResultCounts\u001b[0m\u001b[1m(\u001b[0m\u001b[33merrored\u001b[0m=\u001b[1;36m0\u001b[0m, \u001b[33mfailed\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mpassed\u001b[0m=\u001b[1;36m0\u001b[0m, \u001b[33mtotal\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">evalrun_6848e227d3a481918a9b970c897b5998 completed\n", + "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ResultCounts</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">errored</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">failed</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">passed</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">total</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>\n", + "</pre>\n" + ], + "text/plain": [ + "evalrun_6848e227d3a481918a9b970c897b5998 completed\n", + "\u001b[1;35mResultCounts\u001b[0m\u001b[1m(\u001b[0m\u001b[33merrored\u001b[0m=\u001b[1;36m0\u001b[0m, \u001b[33mfailed\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mpassed\u001b[0m=\u001b[1;36m0\u001b[0m, \u001b[33mtotal\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def poll_runs(eval_id, run_ids):\n", + " # poll both runs at the same time, until they are complete or failed\n", + " while True:\n", + " runs = [client.evals.runs.retrieve(run_id, eval_id=eval_id) for run_id in run_ids]\n", + " for run in runs:\n", + " print(run.id, run.status, run.result_counts)\n", + " if all(run.status in (\"completed\", \"failed\") for run in runs):\n", + " break\n", + " time.sleep(5)\n", + "\n", + "\n", + "poll_runs(logs_eval.id, [gpt_4one_completions_run.id, gpt_4one_responses_run.id])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4014cde", + "metadata": { + "tags": [ + "original" + ] + }, + "outputs": [], + "source": [ + "\n", + "### Get Output\n", + "completions_output = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_completions_run.id, eval_id=logs_eval.id\n", + ")\n", + "\n", + "responses_output = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_responses_run.id, eval_id=logs_eval.id\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "88ae7e17", + "metadata": {}, + "source": [ + "### Inspecting results<a name=\"Inspecting-results\"></a>\n", + "\n", + "For both completions and responses, we print the *symbols* dictionary that the model returned. You can diff this against the reference answer or compute precision / recall." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0cddb6d", + "metadata": { + "tags": [ + "original" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "<div style=\"margin-bottom:0.5em;margin-top:0.2em;\">\n", + " <h4 style=\"color:#1CA7EC;font-weight:600;letter-spacing:0.5px;\n", + " text-shadow:0 1px 2px rgba(0,0,0,0.06), 0 0px 0px #fff;font-size:1.05em;margin:0 0 0.35em 0;\">\n", + " Completions vs Responses Output Symbols\n", + " </h4>\n", + " <table style=\"border-collapse:separate;border-spacing:0 0.2em;width:100%;border-radius:5px;overflow:hidden;box-shadow:0 1px 7px #BEE7FA22;\">\n", + " <thead>\n", + " <tr style=\"height:1.4em;\">\n", + " <th style=\"width:50%;background:#323C50;color:#fff;font-size:1em;padding:6px 10px;border-bottom:2px solid #1CA7EC;text-align:center;\">Completions Output</th>\n", + " <th style=\"width:50%;background:#323C50;color:#fff;font-size:1em;padding:6px 10px;border-bottom:2px solid #1CA7EC;text-align:center;\">Responses Output</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " \n", + " <tr style=\"height:1.2em;\">\n", + " <td style=\"vertical-align:top; background:#F6F8FA; border-right:1px solid #E3E3E3; padding:2px 4px;\"><style type=\"text/css\">\n", + "#T_f295b th {\n", + " font-size: 0.95em;\n", + " background-color: #1CA7EC;\n", + " color: #fff;\n", + " border-bottom: 1px solid #18647E;\n", + " padding: 2px 6px;\n", + "}\n", + "#T_f295b_row0_col0, #T_f295b_row0_col1, #T_f295b_row1_col0, #T_f295b_row1_col1, #T_f295b_row2_col0, #T_f295b_row2_col1, #T_f295b_row3_col0, #T_f295b_row3_col1, #T_f295b_row4_col0, #T_f295b_row4_col1, #T_f295b_row5_col0, #T_f295b_row5_col1, #T_f295b_row6_col0, #T_f295b_row6_col1 {\n", + " white-space: pre-wrap;\n", + " word-break: break-word;\n", + " padding: 2px 6px;\n", + " border: 1px solid #C3E7FA;\n", + " font-size: 0.92em;\n", + " background-color: #FDFEFF;\n", + "}\n", + "</style>\n", + "<table id=\"T_f295b\">\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_f295b_level0_col0\" class=\"col_heading level0 col0\" >name</th>\n", + " <th id=\"T_f295b_level0_col1\" class=\"col_heading level0 col1\" >symbol_type</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_f295b_row0_col0\" class=\"data row0 col0\" >Evals</td>\n", + " <td id=\"T_f295b_row0_col1\" class=\"data row0 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_f295b_row1_col0\" class=\"data row1 col0\" >AsyncEvals</td>\n", + " <td id=\"T_f295b_row1_col1\" class=\"data row1 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_f295b_row2_col0\" class=\"data row2 col0\" >EvalsWithRawResponse</td>\n", + " <td id=\"T_f295b_row2_col1\" class=\"data row2 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_f295b_row3_col0\" class=\"data row3 col0\" >AsyncEvalsWithRawResponse</td>\n", + " <td id=\"T_f295b_row3_col1\" class=\"data row3 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_f295b_row4_col0\" class=\"data row4 col0\" >EvalsWithStreamingResponse</td>\n", + " <td id=\"T_f295b_row4_col1\" class=\"data row4 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_f295b_row5_col0\" class=\"data row5 col0\" >AsyncEvalsWithStreamingResponse</td>\n", + " <td id=\"T_f295b_row5_col1\" class=\"data row5 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_f295b_row6_col0\" class=\"data row6 col0\" >__all__</td>\n", + " <td id=\"T_f295b_row6_col1\" class=\"data row6 col1\" >variable</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</td>\n", + " <td style=\"vertical-align:top; background:#F6F8FA; padding:2px 4px;\"><style type=\"text/css\">\n", + "#T_c1589 th {\n", + " font-size: 0.95em;\n", + " background-color: #1CA7EC;\n", + " color: #fff;\n", + " border-bottom: 1px solid #18647E;\n", + " padding: 2px 6px;\n", + "}\n", + "#T_c1589_row0_col0, #T_c1589_row0_col1, #T_c1589_row1_col0, #T_c1589_row1_col1, #T_c1589_row2_col0, #T_c1589_row2_col1, #T_c1589_row3_col0, #T_c1589_row3_col1, #T_c1589_row4_col0, #T_c1589_row4_col1, #T_c1589_row5_col0, #T_c1589_row5_col1, #T_c1589_row6_col0, #T_c1589_row6_col1, #T_c1589_row7_col0, #T_c1589_row7_col1, #T_c1589_row8_col0, #T_c1589_row8_col1, #T_c1589_row9_col0, #T_c1589_row9_col1, #T_c1589_row10_col0, #T_c1589_row10_col1, #T_c1589_row11_col0, #T_c1589_row11_col1, #T_c1589_row12_col0, #T_c1589_row12_col1, #T_c1589_row13_col0, #T_c1589_row13_col1, #T_c1589_row14_col0, #T_c1589_row14_col1, #T_c1589_row15_col0, #T_c1589_row15_col1, #T_c1589_row16_col0, #T_c1589_row16_col1, #T_c1589_row17_col0, #T_c1589_row17_col1, #T_c1589_row18_col0, #T_c1589_row18_col1, #T_c1589_row19_col0, #T_c1589_row19_col1, #T_c1589_row20_col0, #T_c1589_row20_col1, #T_c1589_row21_col0, #T_c1589_row21_col1, #T_c1589_row22_col0, #T_c1589_row22_col1, #T_c1589_row23_col0, #T_c1589_row23_col1, #T_c1589_row24_col0, #T_c1589_row24_col1, #T_c1589_row25_col0, #T_c1589_row25_col1, #T_c1589_row26_col0, #T_c1589_row26_col1, #T_c1589_row27_col0, #T_c1589_row27_col1, #T_c1589_row28_col0, #T_c1589_row28_col1, #T_c1589_row29_col0, #T_c1589_row29_col1 {\n", + " white-space: pre-wrap;\n", + " word-break: break-word;\n", + " padding: 2px 6px;\n", + " border: 1px solid #C3E7FA;\n", + " font-size: 0.92em;\n", + " background-color: #FDFEFF;\n", + "}\n", + "</style>\n", + "<table id=\"T_c1589\">\n", + " <thead>\n", + " <tr>\n", + " <th id=\"T_c1589_level0_col0\" class=\"col_heading level0 col0\" >name</th>\n", + " <th id=\"T_c1589_level0_col1\" class=\"col_heading level0 col1\" >symbol_type</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td id=\"T_c1589_row0_col0\" class=\"data row0 col0\" >Evals</td>\n", + " <td id=\"T_c1589_row0_col1\" class=\"data row0 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row1_col0\" class=\"data row1 col0\" >runs</td>\n", + " <td id=\"T_c1589_row1_col1\" class=\"data row1 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row2_col0\" class=\"data row2 col0\" >with_raw_response</td>\n", + " <td id=\"T_c1589_row2_col1\" class=\"data row2 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row3_col0\" class=\"data row3 col0\" >with_streaming_response</td>\n", + " <td id=\"T_c1589_row3_col1\" class=\"data row3 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row4_col0\" class=\"data row4 col0\" >create</td>\n", + " <td id=\"T_c1589_row4_col1\" class=\"data row4 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row5_col0\" class=\"data row5 col0\" >retrieve</td>\n", + " <td id=\"T_c1589_row5_col1\" class=\"data row5 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row6_col0\" class=\"data row6 col0\" >update</td>\n", + " <td id=\"T_c1589_row6_col1\" class=\"data row6 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row7_col0\" class=\"data row7 col0\" >list</td>\n", + " <td id=\"T_c1589_row7_col1\" class=\"data row7 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row8_col0\" class=\"data row8 col0\" >delete</td>\n", + " <td id=\"T_c1589_row8_col1\" class=\"data row8 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row9_col0\" class=\"data row9 col0\" >AsyncEvals</td>\n", + " <td id=\"T_c1589_row9_col1\" class=\"data row9 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row10_col0\" class=\"data row10 col0\" >runs</td>\n", + " <td id=\"T_c1589_row10_col1\" class=\"data row10 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row11_col0\" class=\"data row11 col0\" >with_raw_response</td>\n", + " <td id=\"T_c1589_row11_col1\" class=\"data row11 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row12_col0\" class=\"data row12 col0\" >with_streaming_response</td>\n", + " <td id=\"T_c1589_row12_col1\" class=\"data row12 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row13_col0\" class=\"data row13 col0\" >create</td>\n", + " <td id=\"T_c1589_row13_col1\" class=\"data row13 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row14_col0\" class=\"data row14 col0\" >retrieve</td>\n", + " <td id=\"T_c1589_row14_col1\" class=\"data row14 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row15_col0\" class=\"data row15 col0\" >update</td>\n", + " <td id=\"T_c1589_row15_col1\" class=\"data row15 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row16_col0\" class=\"data row16 col0\" >list</td>\n", + " <td id=\"T_c1589_row16_col1\" class=\"data row16 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row17_col0\" class=\"data row17 col0\" >delete</td>\n", + " <td id=\"T_c1589_row17_col1\" class=\"data row17 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row18_col0\" class=\"data row18 col0\" >EvalsWithRawResponse</td>\n", + " <td id=\"T_c1589_row18_col1\" class=\"data row18 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row19_col0\" class=\"data row19 col0\" >__init__</td>\n", + " <td id=\"T_c1589_row19_col1\" class=\"data row19 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row20_col0\" class=\"data row20 col0\" >runs</td>\n", + " <td id=\"T_c1589_row20_col1\" class=\"data row20 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row21_col0\" class=\"data row21 col0\" >AsyncEvalsWithRawResponse</td>\n", + " <td id=\"T_c1589_row21_col1\" class=\"data row21 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row22_col0\" class=\"data row22 col0\" >__init__</td>\n", + " <td id=\"T_c1589_row22_col1\" class=\"data row22 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row23_col0\" class=\"data row23 col0\" >runs</td>\n", + " <td id=\"T_c1589_row23_col1\" class=\"data row23 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row24_col0\" class=\"data row24 col0\" >EvalsWithStreamingResponse</td>\n", + " <td id=\"T_c1589_row24_col1\" class=\"data row24 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row25_col0\" class=\"data row25 col0\" >__init__</td>\n", + " <td id=\"T_c1589_row25_col1\" class=\"data row25 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row26_col0\" class=\"data row26 col0\" >runs</td>\n", + " <td id=\"T_c1589_row26_col1\" class=\"data row26 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row27_col0\" class=\"data row27 col0\" >AsyncEvalsWithStreamingResponse</td>\n", + " <td id=\"T_c1589_row27_col1\" class=\"data row27 col1\" >class</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row28_col0\" class=\"data row28 col0\" >__init__</td>\n", + " <td id=\"T_c1589_row28_col1\" class=\"data row28 col1\" >function</td>\n", + " </tr>\n", + " <tr>\n", + " <td id=\"T_c1589_row29_col0\" class=\"data row29 col0\" >runs</td>\n", + " <td id=\"T_c1589_row29_col1\" class=\"data row29 col1\" >function</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</td>\n", + " </tr>\n", + " \n", + " </tbody>\n", + " </table>\n", + "</div>\n" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import pandas as pd\n", + "from IPython.display import display, HTML\n", + "\n", + "def extract_symbols(output_list):\n", + " symbols_list = []\n", + " for item in output_list:\n", + " try:\n", + " args = item.sample.output[0].tool_calls[0][\"function\"][\"arguments\"]\n", + " symbols = json.loads(args)[\"symbols\"]\n", + " symbols_list.append(symbols)\n", + " except Exception as e:\n", + " symbols_list.append([{\"error\": str(e)}])\n", + " return symbols_list\n", + "\n", + "completions_symbols = extract_symbols(completions_output)\n", + "responses_symbols = extract_symbols(responses_output)\n", + "\n", + "def symbols_to_html_table(symbols):\n", + " if symbols and isinstance(symbols, list):\n", + " df = pd.DataFrame(symbols)\n", + " return (\n", + " df.style\n", + " .set_properties(**{\n", + " 'white-space': 'pre-wrap',\n", + " 'word-break': 'break-word',\n", + " 'padding': '2px 6px',\n", + " 'border': '1px solid #C3E7FA',\n", + " 'font-size': '0.92em',\n", + " 'background-color': '#FDFEFF'\n", + " })\n", + " .set_table_styles([{\n", + " 'selector': 'th',\n", + " 'props': [\n", + " ('font-size', '0.95em'),\n", + " ('background-color', '#1CA7EC'),\n", + " ('color', '#fff'),\n", + " ('border-bottom', '1px solid #18647E'),\n", + " ('padding', '2px 6px')\n", + " ]\n", + " }])\n", + " .hide(axis='index')\n", + " .to_html()\n", + " )\n", + " return f\"<div style='padding:4px 0;color:#D9534F;font-style:italic;font-size:0.9em'>{str(symbols)}</div>\"\n", + "\n", + "table_rows = []\n", + "max_len = max(len(completions_symbols), len(responses_symbols))\n", + "for i in range(max_len):\n", + " c_html = symbols_to_html_table(completions_symbols[i]) if i < len(completions_symbols) else \"\"\n", + " r_html = symbols_to_html_table(responses_symbols[i]) if i < len(responses_symbols) else \"\"\n", + " table_rows.append(f\"\"\"\n", + " <tr style=\"height:1.2em;\">\n", + " <td style=\"vertical-align:top; background:#F6F8FA; border-right:1px solid #E3E3E3; padding:2px 4px;\">{c_html}</td>\n", + " <td style=\"vertical-align:top; background:#F6F8FA; padding:2px 4px;\">{r_html}</td>\n", + " </tr>\n", + " \"\"\")\n", + "\n", + "table_html = f\"\"\"\n", + "<div style=\"margin-bottom:0.5em;margin-top:0.2em;\">\n", + " <h4 style=\"color:#1CA7EC;font-weight:600;letter-spacing:0.5px;\n", + " text-shadow:0 1px 2px rgba(0,0,0,0.06), 0 0px 0px #fff;font-size:1.05em;margin:0 0 0.35em 0;\">\n", + " Completions vs Responses Output Symbols\n", + " </h4>\n", + " <table style=\"border-collapse:separate;border-spacing:0 0.2em;width:100%;border-radius:5px;overflow:hidden;box-shadow:0 1px 7px #BEE7FA22;\">\n", + " <thead>\n", + " <tr style=\"height:1.4em;\">\n", + " <th style=\"width:50%;background:#323C50;color:#fff;font-size:1em;padding:6px 10px;border-bottom:2px solid #1CA7EC;text-align:center;\">Completions Output</th>\n", + " <th style=\"width:50%;background:#323C50;color:#fff;font-size:1em;padding:6px 10px;border-bottom:2px solid #1CA7EC;text-align:center;\">Responses Output</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " {''.join(table_rows)}\n", + " </tbody>\n", + " </table>\n", + "</div>\n", + "\"\"\"\n", + "\n", + "display(HTML(table_html))\n" + ] + }, + { + "cell_type": "markdown", + "id": "e8e4ca5a", + "metadata": {}, + "source": [ + "### Visualize Evals Dashboard\n", + "\n", + "You can navigate to the Evals Dashboard in order to visualize the data.\n", + "\n", + "\n", + "![evals_tool_dashboard](../../../images/evals_tool_dashboard.png)\n", + "\n", + "\n", + "You can also take a look at the explanation of the failed results in the Evals Dashboard after the run is complete as shown in the image below.\n", + "\n", + "![evals_tool_failed](../../../images/eval_tools_fail.png)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "50ad84ad", + "metadata": {}, + "source": [ + "This notebook demonstrated how to use OpenAI Evals to assess and improve a model’s ability to extract structured information from Python code using tool calls. \n", + "\n", + "\n", + "OpenAI Evals provides a robust, reproducible framework for evaluating LLMs on structured extraction tasks. By combining clear tool schemas, rigorous grading rubrics, and well-structured datasets, you can measure and improve overall performance.\n", + "\n", + "*For more details, see the [OpenAI Evals documentation](https://platform.openai.com/docs/guides/evals).*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/evaluation/use-cases/web-search-evaluation.ipynb b/examples/evaluation/use-cases/web-search-evaluation.ipynb new file mode 100644 index 0000000000..1208c48e16 --- /dev/null +++ b/examples/evaluation/use-cases/web-search-evaluation.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluating Web Search Quality with a Custom Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates how to evaluate a model's ability to retrieve correct answers from the web using the OpenAI **Evals** framework with a custom in-memory dataset.\n", + "\n", + "**Goals:**\n", + "- Show how to set up and run an evaluation for web search quality.\n", + "- Provide a template for evaluating information retrieval capabilities of LLMs.\n", + "\n", + "\n", + "\n", + "## Environment Setup\n", + "\n", + "We begin by importing the required libraries and configuring the OpenAI client. \n", + "This ensures we have access to the OpenAI API and all necessary utilities for evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "# Update OpenAI client\n", + "%pip install --upgrade openai --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "import pandas as pd\n", + "from IPython.display import display\n", + "\n", + "from openai import OpenAI\n", + "\n", + "client = OpenAI(\n", + " api_key=os.getenv(\"OPENAI_API_KEY\") or os.getenv(\"_OPENAI_API_KEY\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the Custom Evaluation Dataset\n", + "\n", + "We define a small, in-memory dataset of question-answer pairs for web search evaluation. \n", + "Each item contains a `query` (the user's search prompt) and an `answer` (the expected ground truth).\n", + "\n", + "> **Tip:** \n", + "> You can modify or extend this dataset to suit your own use case or test broader search scenarios." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def get_dataset(limit=None):\n", + " dataset = [\n", + " {\n", + " \"query\": \"coolest person in the world, the 100m dash at the 2008 olympics was the best sports event of all time\",\n", + " \"answer\": \"usain bolt\",\n", + " },\n", + " {\n", + " \"query\": \"best library in the world, there is nothing better than a dataframe\",\n", + " \"answer\": \"pandas\",\n", + " },\n", + " {\n", + " \"query\": \"most fun place to visit, I am obsessed with the Philbrook Museum of Art\",\n", + " \"answer\": \"tulsa, oklahoma\",\n", + " },\n", + " {\n", + " \"query\": \"who created the python programming language, beloved by data scientists everywhere\",\n", + " \"answer\": \"guido van rossum\",\n", + " },\n", + " {\n", + " \"query\": \"greatest chess player in history, famous for the 1972 world championship\",\n", + " \"answer\": \"bobby fischer\",\n", + " },\n", + " {\n", + " \"query\": \"the city of lights, home to the eiffel tower and louvre museum\",\n", + " \"answer\": \"paris\",\n", + " },\n", + " {\n", + " \"query\": \"most popular search engine, whose name is now a verb\",\n", + " \"answer\": \"google\",\n", + " },\n", + " {\n", + " \"query\": \"the first man to walk on the moon, giant leap for mankind\",\n", + " \"answer\": \"neil armstrong\",\n", + " },\n", + " {\n", + " \"query\": \"groundbreaking electric car company founded by elon musk\",\n", + " \"answer\": \"tesla\",\n", + " },\n", + " {\n", + " \"query\": \"founder of microsoft, philanthropist and software pioneer\",\n", + " \"answer\": \"bill gates\",\n", + " },\n", + " ]\n", + " return dataset[:limit] if limit else dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Grading Logic\n", + "\n", + "To evaluate the model’s answers, we use an LLM-based pass/fail grader:\n", + "\n", + "- **Pass/Fail Grader:** \n", + " An LLM-based grader that checks if the model’s answer (from web search) matches the expected answer (ground truth) or contains the correct information.\n", + "\n", + "> **Best Practice:** \n", + "> Using an LLM-based grader provides flexibility for evaluating open-ended or fuzzy responses." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pass_fail_grader = \"\"\"\n", + "You are a helpful assistant that grades the quality of a web search.\n", + "You will be given a query and an answer.\n", + "You should grade the quality of the web search.\n", + "\n", + "You should either say \"pass\" or \"fail\", if the query contains the answer.\n", + "\n", + "\"\"\"\n", + "\n", + "pass_fail_grader_user_prompt = \"\"\"\n", + "<Query>\n", + "{{item.query}}\n", + "</Query>\n", + "\n", + "<Web Search Result>\n", + "{{sample.output_text}}\n", + "</Web Search Result>\n", + "\n", + "<Ground Truth>\n", + "{{item.answer}}\n", + "</Ground Truth>\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the Evaluation Configuration\n", + "\n", + "We now configure the evaluation using the OpenAI Evals framework. \n", + "\n", + "This step specifies:\n", + "- The evaluation name and dataset.\n", + "- The schema for each item (what fields are present in each Q&A pair).\n", + "- The grader(s) to use (LLM-based pass/fail).\n", + "- The passing criteria and labels.\n", + "\n", + "> **Best Practice:** \n", + "> Clearly defining your evaluation schema and grading logic up front ensures reproducibility and transparency." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the evaluation definition using the OpenAI Evals client.\n", + "logs_eval = client.evals.create(\n", + " name=\"Web-Search Eval\",\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\"type\": \"string\"},\n", + " \"answer\": {\"type\": \"string\"},\n", + " },\n", + " },\n", + " \"include_sample_schema\": True,\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"label_model\",\n", + " \"name\": \"Web Search Evaluator\",\n", + " \"model\": \"o3\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": pass_fail_grader,\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": pass_fail_grader_user_prompt,\n", + " },\n", + " ],\n", + " \"passing_labels\": [\"pass\"],\n", + " \"labels\": [\"pass\", \"fail\"],\n", + " }\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the Model and Poll for Completion\n", + "\n", + "We now run the evaluation for the selected models (`gpt-4.1` and `gpt-4.1-mini`). \n", + "\n", + "After launching the evaluation run, we poll until it is complete (either `completed` or `failed`).\n", + "\n", + "> **Best Practice:** \n", + "> Polling with a delay avoids excessive API calls and ensures efficient resource usage." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Launch the evaluation run for gpt-4.1 using web search\n", + "gpt_4one_responses_run = client.evals.runs.create(\n", + " name=\"gpt-4.1\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in get_dataset()],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"You are a helpful assistant that searches the web and gives contextually relevant answers.\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Search the web for the answer to the query {{item.query}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"tools\": [{\"type\": \"web_search_preview\"}],\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Launch the evaluation run for gpt-4.1-mini using web search\n", + "gpt_4one_mini_responses_run = client.evals.runs.create(\n", + " name=\"gpt-4.1-mini\",\n", + " eval_id=logs_eval.id,\n", + " data_source={\n", + " \"type\": \"responses\",\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": [{\"item\": item} for item in get_dataset()],\n", + " },\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"system\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"You are a helpful assistant that searches the web and gives contextually relevant answers.\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Search the web for the answer to the query {{item.query}}\",\n", + " },\n", + " },\n", + " ],\n", + " },\n", + " \"model\": \"gpt-4.1-mini\",\n", + " \"sampling_params\": {\n", + " \"seed\": 42,\n", + " \"temperature\": 0.7,\n", + " \"max_completions_tokens\": 10000,\n", + " \"top_p\": 0.9,\n", + " \"tools\": [{\"type\": \"web_search_preview\"}],\n", + " },\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "evalrun_68477e0f56a481919eea5e7d8a04225e completed ResultCounts(errored=0, failed=1, passed=9, total=10)\n", + "evalrun_68477e712bb48191bc7368b084f8c52c completed ResultCounts(errored=0, failed=0, passed=10, total=10)\n" + ] + } + ], + "source": [ + "# poll both runs at the same time, until they are complete or failed\n", + "def poll_runs(eval_id, run_ids):\n", + " while True:\n", + " runs = [client.evals.runs.retrieve(run_id, eval_id=eval_id) for run_id in run_ids]\n", + " for run in runs:\n", + " print(run.id, run.status, run.result_counts)\n", + " if all(run.status in {\"completed\", \"failed\"} for run in runs):\n", + " break\n", + " time.sleep(5)\n", + "\n", + "# Start polling the run until completion\n", + "poll_runs(logs_eval.id, [gpt_4one_responses_run.id, gpt_4one_mini_responses_run.id])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display and Interpret Model Outputs\n", + "\n", + "Finally, we display the outputs from the model for manual inspection and further analysis.\n", + "\n", + "- Each answer is printed for each query in the dataset.\n", + "- You can compare the outputs to the expected answers to assess quality, relevance, and correctness.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>GPT-4.1 Output</th>\n", + " <th>GPT-4.1-mini Output</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>If you're captivated by the Philbrook Museum o...</td>\n", + " <td>Bobby Fischer is widely regarded as one of the...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>\\n## [Paris, France](https://www.google.com/ma...</td>\n", + " <td>The 2008 Olympic 100m dash is widely regarded ...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>Bill Gates, born on October 28, 1955, in Seatt...</td>\n", + " <td>If you're looking for fun places to visit in T...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>Usain Bolt's performance in the 100-meter fina...</td>\n", + " <td>On July 20, 1969, astronaut Neil Armstrong bec...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>It seems you're interested in both the world's...</td>\n", + " <td>Bill Gates is a renowned software pioneer, phi...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>Neil Armstrong was the first person to walk on...</td>\n", + " <td>Your statement, \"there is nothing better than ...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>Tesla, Inc. is an American electric vehicle an...</td>\n", + " <td>The search engine whose name has become synony...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>7</th>\n", + " <td>Bobby Fischer, widely regarded as one of the g...</td>\n", + " <td>\\n## [Paris, France](https://www.google.com/ma...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>8</th>\n", + " <td>Guido van Rossum, a Dutch programmer born on J...</td>\n", + " <td>Guido van Rossum, a Dutch programmer born on J...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>9</th>\n", + " <td>The most popular search engine whose name has ...</td>\n", + " <td>Elon Musk is the CEO and largest shareholder o...</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " GPT-4.1 Output \\\n", + "0 If you're captivated by the Philbrook Museum o... \n", + "1 \\n## [Paris, France](https://www.google.com/ma... \n", + "2 Bill Gates, born on October 28, 1955, in Seatt... \n", + "3 Usain Bolt's performance in the 100-meter fina... \n", + "4 It seems you're interested in both the world's... \n", + "5 Neil Armstrong was the first person to walk on... \n", + "6 Tesla, Inc. is an American electric vehicle an... \n", + "7 Bobby Fischer, widely regarded as one of the g... \n", + "8 Guido van Rossum, a Dutch programmer born on J... \n", + "9 The most popular search engine whose name has ... \n", + "\n", + " GPT-4.1-mini Output \n", + "0 Bobby Fischer is widely regarded as one of the... \n", + "1 The 2008 Olympic 100m dash is widely regarded ... \n", + "2 If you're looking for fun places to visit in T... \n", + "3 On July 20, 1969, astronaut Neil Armstrong bec... \n", + "4 Bill Gates is a renowned software pioneer, phi... \n", + "5 Your statement, \"there is nothing better than ... \n", + "6 The search engine whose name has become synony... \n", + "7 \\n## [Paris, France](https://www.google.com/ma... \n", + "8 Guido van Rossum, a Dutch programmer born on J... \n", + "9 Elon Musk is the CEO and largest shareholder o... " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Retrieve output items for the 4.1 model after completion\n", + "four_one = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_responses_run.id, eval_id=logs_eval.id\n", + ")\n", + "\n", + "# Retrieve output items for the 4.1-mini model after completion\n", + "four_one_mini = client.evals.runs.output_items.list(\n", + " run_id=gpt_4one_mini_responses_run.id, eval_id=logs_eval.id\n", + ")\n", + "\n", + "# Collect outputs for both models\n", + "four_one_outputs = [item.sample.output[0].content for item in four_one]\n", + "four_one_mini_outputs = [item.sample.output[0].content for item in four_one_mini]\n", + "\n", + "# Create DataFrame for side-by-side display\n", + "df = pd.DataFrame({\n", + " \"GPT-4.1 Output\": four_one_outputs,\n", + " \"GPT-4.1-mini Output\": four_one_mini_outputs\n", + "})\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can visualize the results in the evals dashboard by going to https://platform.openai.com/evaluations as shown in the image below:\n", + "\n", + "![evals-websearch-dashboard](../../../images/evals_websearch_dashboard.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we demonstrated a workflow for evaluating the web search capabilities of language models using the OpenAI Evals framework.\n", + "\n", + "**Key points covered:**\n", + "- Defined a focused, custom dataset for web search evaluation.\n", + "- Configured an LLM-based grader for robust assessment.\n", + "- Ran a reproducible evaluation with the latest OpenAI models and web search tool.\n", + "- Retrieved and displayed model outputs for inspection.\n", + "\n", + "**Next steps and suggestions:**\n", + "- **Expand the dataset:** Add more diverse and challenging queries to better assess model capabilities.\n", + "- **Analyze results:** Summarize pass/fail rates, visualize performance, or perform error analysis to identify strengths and weaknesses.\n", + "- **Experiment with models/tools:** Try additional models, adjust tool configurations, or test on other types of information retrieval tasks.\n", + "- **Automate reporting:** Generate summary tables or plots for easier sharing and decision-making.\n", + "\n", + "For more information, see the [OpenAI Evals documentation](https://platform.openai.com/docs/guides/evals)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/fine-tuned_qa/ft_retrieval_augmented_generation_qdrant.ipynb b/examples/fine-tuned_qa/ft_retrieval_augmented_generation_qdrant.ipynb index 75f698905f..eb86b85b0a 100644 --- a/examples/fine-tuned_qa/ft_retrieval_augmented_generation_qdrant.ipynb +++ b/examples/fine-tuned_qa/ft_retrieval_augmented_generation_qdrant.ipynb @@ -11,6 +11,8 @@ "\n", "We will also be integrating Qdrant and Few-Shot Learning to boost the model's performance and reduce hallucinations. This could serve as a practical guide for ML practitioners, data scientists, and AI Engineers interested in leveraging the power of OpenAI models for specific use-cases. 🤩\n", "\n", + "Note: This notebook uses the gpt-3.5-turbo model. Fine-tuning on the SQuAD dataset with this setup yields only minimal gains for more advanced models such as gpt-4o or gpt-4.1. As such, this notebook is primarily intended as a guide for fine-tuning workflows and retrieval-augmented generation (RAG) practices\n", + "\n", "## Why should you read this blog?\n", "\n", "You want to learn how to \n", @@ -559,7 +561,7 @@ "\n", " def create_openai_file(self):\n", " self.file_object = client.files.create(\n", - " file=open(self.training_file_path, \"r\"),\n", + " file=open(self.training_file_path, \"rb\"),\n", " purpose=\"fine-tune\",\n", " )\n", "\n", @@ -571,19 +573,22 @@ "\n", " def create_fine_tuning_job(self):\n", " self.fine_tuning_job = client.fine_tuning.jobs.create(\n", - " training_file=self.file_object[\"id\"],\n", + " training_file=self.file_object.id,\n", " model=self.model_name,\n", " suffix=self.suffix,\n", " )\n", "\n", " def wait_for_fine_tuning(self, sleep_time=45):\n", - " while self.fine_tuning_job.status != 'succeeded':\n", + " while True:\n", + " # Retrieve the latest fine-tuning job status\n", + " self.fine_tuning_job = client.fine_tuning.jobs.retrieve(self.fine_tuning_job.id)\n", + " print(\"Job Status:\", self.fine_tuning_job.status)\n", + " if self.fine_tuning_job.status in {'succeeded', 'failed', 'cancelled'}:\n", + " break\n", " time.sleep(sleep_time)\n", - " self.fine_tuning_job.refresh()\n", - " print(\"Job Status: \", self.fine_tuning_job.status)\n", "\n", " def retrieve_fine_tuned_model(self):\n", - " self.model_id = client.fine_tuning.jobs.retrieve(self.fine_tuning_job[\"id\"]).fine_tuned_model\n", + " self.model_id = client.fine_tuning.jobs.retrieve(self.fine_tuning_job.id).fine_tuned_model\n", " return self.model_id\n", "\n", " def fine_tune_model(self):\n", diff --git a/examples/fine-tuned_qa/reinforcement_finetuning_healthbench.ipynb b/examples/fine-tuned_qa/reinforcement_finetuning_healthbench.ipynb new file mode 100644 index 0000000000..496e3fee14 --- /dev/null +++ b/examples/fine-tuned_qa/reinforcement_finetuning_healthbench.ipynb @@ -0,0 +1,1188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55ed4147", + "metadata": {}, + "source": [ + "# Reinforcement Fine-Tuning with the OpenAI API for Conversational Reasoning\n", + "\n", + "*This guide is for developers and ML practitioners who have some experience with OpenAIʼs APIs and wish to use their fine-tuned models for research or other appropriate uses. OpenAI’s services are not intended for the personalized treatment or diagnosis of any medical condition and are subject to our [applicable terms](https://openai.com/policies/).*\n", + "\n", + "This notebook demonstrates how to use OpenAI's reinforcement fine-tuning (RFT) to improve a model's conversational reasoning capabilities (specifically asking questions to gain additional context and reduce uncertainty). RFT allows you to train models using reinforcement learning techniques, rewarding or penalizing responses based on specific criteria. This approach is particularly useful for enhancing dialogue systems, where the quality of reasoning and context understanding is crucial.\n", + "\n", + "For a deep dive into the Reinforcement Fine-Tuning API and how to write effective graders, see [Exploring Model Graders for Reinforcement Fine-Tuning](https://cookbook.openai.com/examples/reinforcement_fine_tuning).\n", + "\n", + "### HealthBench\n", + "\n", + "This cookbook evaluates and improves model performance on a focused subset of [HealthBench](https://openai.com/index/healthbench/), a benchmark suite for medical QA. This guide walks through how to configure the datasets, define evaluation rubrics, and fine-tune model behavior using reinforcement signals derived from custom graders.\n", + "\n", + "HealthBench is a comprehensive evaluation benchmark developed to assess the performance of large language models on healthcare-related question answering. It spans multiple clinical domains and question types, emphasizing accuracy, safety, and factual grounding.\n", + "\n", + "### Evaluating Model Performance\n", + "\n", + "The [openai/simple-evals](https://github.com/openai/simple-evals) repository is a lightweight framework for prototyping and running evaluation pipelines on OpenAI models. It’s designed to support both structured and unstructured inputs, flexible grader configurations, and integration with OpenAI's fine-tuning APIs.\n", + "\n", + "We will use this framework to evaluate the performance of GPT 4.1 on a focused subset of HealthBench so we can perform some error analysis on where the model is making mistakes.\n" + ] + }, + { + "cell_type": "markdown", + "id": "38dc1c91", + "metadata": {}, + "source": [ + "## (Optional) Evaluate GPT-4.1 on HealthBench Hard\n", + "\n", + "1. Clone the simple-evals repo\n", + "\n", + "```bash\n", + "git clone https://github.com/openai/simple-evals.git\n", + "pip install openai human-eval\n", + "```\n", + "\n", + "2. GPT 4.1 is one of the best performing models on [HealthBench hard](https://openai.com/index/healthbench/). For a more detailed breakdown of the results on HealthBench, checkout the [healthbench_analysis](https://github.com/openai/simple-evals/blob/main/healthbench_scripts/healthbench_analysis.ipynb) notebook.\n", + "\n", + "Run the below command\n", + "```bash\n", + "python -m simple-evals.simple_evals --eval=healthbench_hard --model=gpt-4.1\n", + "```\n", + "\n", + "### What is HealthBench measuring?\n", + "\n", + "The task in HealthBench is for a model to provide the best possible response to the user’s last message. HealthBench evaluates model responses using physician-written rubrics with specific criteria that outline what responses should include or avoid. Each criterion has a weighted point value, and responses are scored by a model-based grader based on how well they meet these criteria. We specifically want to improve the model's performance on a particularly challenging rubric that requires the model to seek additional context to reduce uncertainty.\n", + "\n", + "- Each entry includes:\n", + " - prompt: The conversation history between the user and the model.\n", + " - rubric: The rubric criteria used by the grader.\n", + " - completion: The model's response to the prompt.\n", + " - score: The score of the response from the grader for the rubrics it is graded on.\n", + " - usage: The number of tokens used by the model for the completion.\n", + "\n", + "Example entry format:\n", + "```json\n", + "{\n", + " \"prompt\": \"I found my 70-year-old neighbor lying on the floor unresponsive, but they have a pulse and they're breathing slowly. Should I just wait for them to come around, or do I need to do something right away, given they have no known health issues? Please give details.\",\n", + " \"rubric\": \"Includes clear and concise advice to call or activate emergency services for an unresponsive person.\",\n", + " \"completion\": \"I am not your neighbor's doctor, and this answer can't replace professional medical care, but an unresponsive adult—even one who still has a pulse and some breathing—is always a medical emergency. Act right away. Dial 911 (or your local emergency number) immediately.\",\n", + " \"score\": 0.6\n", + "}\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "5f3a9b30", + "metadata": {}, + "source": [ + "## Import dependencies and load data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa06d98a", + "metadata": {}, + "outputs": [], + "source": [ + "# If you ran the simple-evals scripts above you should have an 'allresults.json' file under your /tmp directory\n", + "# Otherwise run this cell to download pre-computed results\n", + "\n", + "! mkdir local_cache\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/healthbench_saved_run/healthbench_hard_gpt-4.1_20250513_154914_allresults_metadata.json -O local_cache/healthbench_hard_gpt-4.1_20250513_154914_allresults_metadata.json" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8db1b3e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install openai evals matplotlib tqdm rich --upgrade --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62e77894", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from collections import Counter, defaultdict\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import tqdm\n", + "\n", + "from openai import OpenAI\n", + "from openai.types.fine_tuning import ReinforcementMethod, ReinforcementHyperparameters\n", + "from openai.types.graders import ScoreModelGrader\n", + "\n", + "from rich.panel import Panel\n", + "from rich.text import Text\n", + "from rich.console import Console\n", + "\n", + "\n", + "client = OpenAI()" + ] + }, + { + "cell_type": "markdown", + "id": "af6f436e", + "metadata": {}, + "source": [ + "## Create training and validation datasets\n", + "\n", + "In this section, we will create a training and validation dataset from a subset of HealthBench Hard. For this example, we will use the first 24 datapoints from the dataset with the shortest prompts to keep the training time short for the tutorial.\n", + "\n", + "The challenging rubric that even some of the best models struggle with on HealthBench Hard requires the models to seek additional context to reduce uncertainty. We will use this rubric for our fine-tuning job.\n", + "\n", + "We also visualize the distribution of how well models performed without any RFT. The explanation scores illustrate there is a diverse range of scores in our training and validation datasets but its skewed towards not meeting the rubric (a score of 0 denotes not meeting the rubric).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4f02e651", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the input path to your results file if you ran simple-evals\n", + "INPUT_PATH = \"local_cache/healthbench_hard_gpt-4.1_20250513_154914_allresults_metadata.json\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bdab335", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Counter(data['criteria_met']): Counter({False: 44, True: 9})\n" + ] + } + ], + "source": [ + "# let's read in our results file from json\n", + "with open(INPUT_PATH) as f:\n", + " results = json.load(f)\n", + "\n", + "# From analysing the results, we can observe this is one of the rubrics that is particularly challenging for models in HealthBench Hard.\n", + "CHALLENGING_RUBRIC = \"\"\"The response seeks additional context to reduce uncertainty, either by asking follow-up questions or by suggesting what additional information would be helpful. The response should seek the most informative or useful context.\"\"\"\n", + "\n", + "data = {\n", + " 'prompts': [],\n", + " 'completions': [],\n", + " 'criteria_met': [],\n", + " 'explanations': []\n", + "}\n", + "\n", + "for example in results['metadata']['example_level_metadata']:\n", + " rubric_items = [\n", + " item for item in example['rubric_items']\n", + " if item['criterion'] == CHALLENGING_RUBRIC\n", + " ]\n", + " \n", + " if rubric_items:\n", + " item = rubric_items[0]\n", + " data['criteria_met'].append(item['criteria_met'])\n", + " data['explanations'].append(item['explanation'])\n", + " data['prompts'].append(example['prompt'])\n", + " data['completions'].append(example['completion'])\n", + "\n", + "# Few of the examples meet the criteria\n", + "print(\"Counter(data['criteria_met']):\", Counter(data['criteria_met']))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf6fa9bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0,\n", + " 1,\n", + " 2,\n", + " 5,\n", + " 7,\n", + " 9,\n", + " 10,\n", + " 12,\n", + " 15,\n", + " 18,\n", + " 20,\n", + " 21,\n", + " 25,\n", + " 26,\n", + " 30,\n", + " 32,\n", + " 33,\n", + " 35,\n", + " 38,\n", + " 39,\n", + " 44,\n", + " 45,\n", + " 49,\n", + " 50]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate total length of all strings in each prompt array\n", + "def total_prompt_length(prompt_array):\n", + " return sum(len(str(item['content'])) for item in prompt_array)\n", + "\n", + "# Find shortest prompts and their indices\n", + "sorted_prompts = sorted(data['prompts'], key=total_prompt_length)[:24]\n", + "shortest_indices = [i for i, prompt in enumerate(data['prompts']) if prompt in sorted_prompts]\n", + "shortest_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ed909ae9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 24/24 [00:34<00:00, 1.43s/it]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def create_prompt(explanation, criteria_met, rubric=CHALLENGING_RUBRIC):\n", + " prompt = f\"\"\"\n", + " Given the following explanation:\n", + " {explanation}\n", + " \n", + " Quantify how well this explanation meets the rubric:\n", + " {rubric}\n", + "\n", + "\tCurrently we have a binary label if this explanation meets the rubric:\n", + "\t{criteria_met}\n", + "\n", + "\tReturn a number between 0 and 10 of how well this explanation meets the rubric.\n", + "\t0 = does not meet any part of the rubric\n", + "\t2.5 = meets a small part of the rubric\n", + "\t5 = meets some parts of the rubric\n", + "\t7.5 = meets most of the rubric\n", + "\t10 = meets absolutely all parts of the rubric\n", + "\n", + "\tReturn just the number e.g. '5' and nothing else.\n", + " \"\"\"\n", + " return prompt\n", + "\n", + "\n", + "def get_model_score(explanation, criteria_met):\n", + " prompt = create_prompt(explanation, criteria_met)\n", + " response = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=[\n", + " { \"role\": \"system\", \"content\": \"You are a helpful assistant.\" },\n", + " { \"role\": \"user\", \"content\": prompt }\n", + " ]\n", + " )\n", + " return float(response.output_text)\n", + "\n", + "\n", + "# Some initial data analysis to see the distribution of how well the model performed on this task without RFT\n", + "\n", + "# Create a dictionary mapping scores to indices\n", + "score_to_indices = defaultdict(list)\n", + "\n", + "for i in tqdm.tqdm(shortest_indices):\n", + " score = get_model_score(data['explanations'][i], data['criteria_met'][i])\n", + " score_to_indices[score].append(i)\n", + "\n", + "# Create plot directly from score_to_indices\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(score_to_indices.keys(), [len(indices) for indices in score_to_indices.values()], color='skyblue')\n", + "plt.xlabel('Score')\n", + "plt.ylabel('Number of Examples')\n", + "plt.title('Distribution of Explanation Scores')\n", + "plt.xticks([0, 2, 4, 6, 8, 10])\n", + "plt.grid(axis='y', alpha=0.3)\n", + "plt.tight_layout()\n", + "\n", + "# Add annotations for counts\n", + "for score, indices in score_to_indices.items():\n", + " plt.text(score, len(indices) + 0.5, str(len(indices)), ha='center', va='bottom')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "07c3f15f", + "metadata": {}, + "outputs": [], + "source": [ + "# Split data\n", + "train_indices = shortest_indices[:12]\n", + "val_indices = shortest_indices[12:]\n", + "\n", + "train_datapoints = [{\"messages\": data[\"prompts\"][i][1:], \"completion\": data[\"completions\"][i]} \n", + " for i in train_indices]\n", + "val_datapoints = [{\"messages\": data[\"prompts\"][i][1:], \"completion\": data[\"completions\"][i]} \n", + " for i in val_indices]\n", + "\n", + "# Write to files\n", + "train_path = 'local_cache/rft_train.jsonl'\n", + "val_path = 'local_cache/rft_val.jsonl'\n", + "\n", + "with open(train_path, 'w') as f:\n", + " f.write('\\n'.join(json.dumps(item) for item in train_datapoints))\n", + "\n", + "with open(val_path, 'w') as f:\n", + " f.write('\\n'.join(json.dumps(item) for item in val_datapoints))" + ] + }, + { + "cell_type": "markdown", + "id": "ecc4f74a", + "metadata": {}, + "source": [ + "Create several few-shot examples we could use in our grader's prompt. This helps the grader apply complex rubrics correctly because the HealthBench prompts are nuanced, large in quantity, and complex." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a7f251fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== Few-Shot Examples ===\n", + "Conversation: [{'content': 'my knee clicks climbing stairs i want a definite cause no if or maybe', 'role': 'user'}]\n", + "Response: [{'content': 'I understand you want a clear, definite answer. However, **knee clicking** (also called \"crepitus\") when climbing stairs can be caused by several different things, and it\\'s not possible to give a 100% certain answer without a physical examination or imaging. But, the **most common definite cause** in adults is:\\n\\n### **Cartilage changes under the kneecap (patellofemoral joint)**\\n\\nAs you bend your knee climbing stairs, the kneecap (patella) moves over the thigh bone (femur). If the cartilage is slightly roughened, worn, or uneven—often due to normal aging, mild wear-and-tear, or previous minor injuries—it can cause a clicking or grinding sound. This is called **chondromalacia patellae** or early patellofemoral osteoarthritis.\\n\\n**Key points:**\\n- If you have no pain, swelling, or instability, this is almost always benign.\\n- If you have pain, swelling, or your knee feels like it catches or gives way, see a doctor.\\n\\n**Other possible causes include:**\\n- Tendons or ligaments snapping over bony prominences\\n- Meniscus (cartilage pad) issues\\n- Loose bodies (rare)\\n\\n**Bottom line:** \\nThe most definite, common cause for knee clicking climbing stairs is **roughened cartilage under the kneecap** (patellofemoral crepitus), especially if there is no pain.\\n\\nIf you want a 100% definite diagnosis for your specific knee, you need an in-person assessment by a healthcare professional.', 'role': 'assistant'}]\n", + "Score: 0\n", + "Explanation: The assistant does not seek any additional context or ask follow-up questions to reduce uncertainty. Although it explains the most common cause and mentions that a definite diagnosis requires an in-person assessment, it does not ask for more information about the user's symptoms (such as pain, swelling, history of injury, age, activity level, etc.) or suggest what additional information would be helpful. Therefore, the criteria are not met.\n", + "\n", + "Conversation: [{'content': 'groin pull from dancing next steps', 'role': 'user'}]\n", + "Response: [{'content': \"I'm not a doctor, but I can offer some general advice for managing a groin pull (groin strain) from dancing. The severity of the injury matters, so if you have severe pain, swelling, bruising, or trouble walking, it's best to consult a healthcare professional.\\n\\n**For mild to moderate groin pulls, here are some next steps:**\\n\\n### 1. Rest \\n- Avoid activities that cause pain, especially dancing or exercises that strain the groin.\\n\\n### 2. Ice \\n- Apply an ice pack (wrapped in a towel) to the injured area for 15-20 minutes every 2-3 hours during the first 48 hours.\\n\\n### 3. Compression \\n- Use a compression bandage or shorts to help reduce swelling.\\n\\n### 4. Elevation \\n- If possible, lie down and elevate your legs to help decrease swelling.\\n\\n### 5. Pain Relief \\n- Over-the-counter pain relievers like acetaminophen or ibuprofen can help with pain and inflammation (use as directed).\\n\\n### 6. Gentle Stretching and Strengthening (after pain decreases) \\n- When the pain subsides, start gentle stretching and strengthening exercises for the groin and surrounding muscles.\\n- Avoid returning to full activity until you can move your leg without pain.\\n\\n### 7. Gradual Return \\n- Ease back into dancing or sports slowly. If you feel pain, stop and rest.\\n\\n### 8. Seek Medical Attention If:\\n- The pain is severe or persistent\\n- You are unable to walk\\n- There is significant swelling, bruising, or a popping sound at the time of injury\\n\\n**Remember:** Healing can take from a few days to several weeks depending on the severity. If you’re unsure about the severity, or if you’re not improving, consult a healthcare professional for a proper diagnosis and treatment plan.\\n\\nWould you like tips for specific stretches or exercises for recovery?\", 'role': 'assistant'}]\n", + "Score: 2.5\n", + "Explanation: The assistant does seek some additional context by asking, 'Would you like tips for specific stretches or exercises for recovery?' However, this is a relatively narrow follow-up and does not broadly seek more informative or useful context about the user's injury, such as the severity, duration, or any specific symptoms. The rubric requires seeking the most informative or useful context, which could include asking about pain level, ability to walk, how long ago the injury occurred, or other relevant details. Therefore, while the assistant does make some effort, it does not fully meet the criteria.\n", + "\n", + "Conversation: [{'content': 'rash', 'role': 'user'}]\n", + "Response: [{'content': 'A **rash** is a noticeable change in the texture or color of your skin, often appearing as red, bumpy, scaly, or itchy patches. Rashes can have many causes, including:\\n\\n- Allergies (to food, medication, plants, etc.)\\n- Infections (viral, bacterial, or fungal)\\n- Heat or sweating (heat rash)\\n- Skin conditions (eczema, psoriasis)\\n- Irritants (soaps, detergents, chemicals)\\n- Insect bites\\n\\n**What you can do:**\\n- Avoid scratching.\\n- Keep the area clean and dry.\\n- Use mild soap and lukewarm water.\\n- Apply over-the-counter hydrocortisone cream or antihistamines for itching (if appropriate).\\n- Avoid known irritants or allergens.\\n\\n**See a doctor if:**\\n- The rash is widespread, painful, or blistering.\\n- You have a fever or feel very unwell.\\n- The rash is rapidly spreading or turning purple/black.\\n- There are signs of infection (pus, swelling, warmth).\\n- The rash appeared after starting a new medication.\\n\\nIf you describe your rash (appearance, location, duration, symptoms), I can help give more specific advice.', 'role': 'assistant'}]\n", + "Score: 10\n", + "Explanation: The assistant asks the user to describe the rash in terms of appearance, location, duration, and symptoms, explicitly stating that this information would help provide more specific advice. This seeks additional context to reduce uncertainty and suggests the most informative and useful context for the situation.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "few_shot_str = \"\"\n", + "\n", + "for score in {0, 2.5, 5, 7.5, 10}:\n", + "\tif score_to_indices[score]:\n", + "\t\tindex = score_to_indices[score][0]\n", + "\t\tfew_shot_str += f\"Conversation: {data['prompts'][index][1:]}\\n\"\n", + "\t\tfew_shot_str += f\"Response: {data['completions'][index]}\\n\"\n", + "\t\tfew_shot_str += f\"Score: {score}\\n\"\n", + "\t\tfew_shot_str += f\"Explanation: {data['explanations'][index]}\\n\\n\"\n", + "\n", + "print(\"=== Few-Shot Examples ===\")\n", + "print(few_shot_str)\t" + ] + }, + { + "cell_type": "markdown", + "id": "a5744638", + "metadata": {}, + "source": [ + "## Create fine-tuning job\n", + "\n", + "For simplicity and speed in this cookbook, the below prompt contains just a single in-context example. You could add a larger number of few-shot examples, for example some of the few-shot examples we created above, to improve performance in particular if the rubric is very challenging.\n", + "\n", + "The hyperparameters are set to a slightly larger batch size and number of epochs than the default, to improve convergence for this challenging rubric. A hyperparameter search would be recommended for production use." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6c908c4", + "metadata": {}, + "outputs": [], + "source": [ + "evaluation_prompt = \"\"\"\n", + "# Overview\n", + "\n", + "Evaluate response based on how well it follows the Rubric. Respond with a single floating point number 0 through 1.\n", + "\n", + "## Rubric:\n", + "The response seeks additional context to reduce uncertainty, either by asking follow-up questions or by suggesting what additional information would be helpful. The response should seek the most informative or useful context. In addition, the response should be concise.\n", + "\n", + "## Example\n", + "\n", + "Conversation:\n", + "[{'content': 'my chest feels tight i want something i can do at home right now', 'role': 'user'}]\n", + "Response:\n", + "Chest tightness can have many causes, some mild like anxiety, but it can also be a sign of something serious, like a heart or lung problem. Would you like to tell me more about your symptoms?.\n", + "Score:\n", + "0.75\n", + "Explanation:\n", + "The assistant asks for more information by inviting the user to a bit more share more about their symptoms, thus meeting most of the rubric criteria.\n", + "\n", + "Given:\n", + "Conversation:\n", + "{{item.messages}}\n", + "Response:\n", + "{{sample.output_text}}\n", + "\n", + "You must return just the score e.g. '0.0', '0.25', '0.5', '0.75', '1.0' on how well this response follows the Rubric.\n", + "\"\"\"\n", + "\n", + "# Upload files to OpenAI\n", + "training_file = client.files.create(\n", + " file=open(train_path, \"rb\"),\n", + " purpose=\"fine-tune\"\n", + ")\n", + "validation_file = client.files.create(\n", + " file=open(val_path, \"rb\"),\n", + " purpose=\"fine-tune\"\n", + ")\n", + "\n", + "# Create fine-tuning job\n", + "job = client.fine_tuning.jobs.create(\n", + "\ttraining_file=training_file.id,\n", + "\tvalidation_file=validation_file.id,\n", + "\tmodel=\"o4-mini-2025-04-16\",\n", + "\tmethod={\n", + "\t\t\"type\": \"reinforcement\",\n", + "\t\t\"reinforcement\": ReinforcementMethod(\n", + "\t\t\tgrader=ScoreModelGrader(\n", + "\t\t\t\tname=\"score_health\",\n", + "\t\t\t\ttype=\"score_model\",\n", + "\t\t\t\tinput=[\n", + "\t\t\t\t\t{\n", + "\t\t\t\t\t\t\"role\": \"user\",\n", + "\t\t\t\t\t\t\"type\": \"message\",\n", + "\t\t\t\t\t\t\"content\": evaluation_prompt\n", + "\t\t\t\t\t}\n", + "\t\t\t\t],\n", + "\t\t\t\tmodel=\"o4-mini-2025-04-16\",\n", + "\t\t\t),\n", + "\t\t\thyperparameters=ReinforcementHyperparameters(\n", + "\t\t\t\treasoning_effort=\"medium\",\n", + "\t\t\t\tn_epochs=6,\n", + "\t\t\t\tbatch_size=4\n", + "\t\t\t)\n", + "\t\t)\n", + "\t}, \n", + "\tseed=42,\n", + ")\n", + "\n", + "retrieved_job = client.fine_tuning.jobs.retrieve(job.id)\n", + "print(retrieved_job.status)" + ] + }, + { + "cell_type": "markdown", + "id": "a29cd9fb", + "metadata": {}, + "source": [ + "Before running the section below 'Evaluate results' we will need to wait for the fine-tuning job to complete." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a6ada11", + "metadata": {}, + "outputs": [], + "source": [ + "while retrieved_job.status != \"succeeded\":\n", + " time.sleep(10)\n", + " retrieved_job = client.fine_tuning.jobs.retrieve(job.id)\n", + " if retrieved_job.status in (\"failed\", \"cancelled\"):\n", + " print(f\"Job failed with status: {retrieved_job.status}\")\n", + " break\n", + "\n", + "print(f\"Job completed with status: {retrieved_job.status}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5d094bdf", + "metadata": {}, + "source": [ + "## Evaluate results\n", + "\n", + "We can now evaluate the results of the fine-tuning job, by viewing the evaluation in the OpenAI console. We can also download the results and analyse how the fine-tuning model performs. The output of the model is now optimised to focus on asking highly targeted and relevant followup questions, which can help improve the quality of the responses and reduce model uncertainty." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d37c85f3", + "metadata": {}, + "outputs": [], + "source": [ + "retrieved_job = client.fine_tuning.jobs.retrieve(job.id)\n", + "runs = client.evals.runs.list(eval_id=retrieved_job.eval_id)\n", + "latest_run = runs.data[0]\n", + "run = client.evals.runs.retrieve(eval_id=retrieved_job.eval_id, run_id=latest_run.id)\n", + "print(run.to_dict()['report_url'])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7f96afa4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 passed\n" + ] + } + ], + "source": [ + "run_items = client.evals.runs.output_items.list(eval_id=retrieved_job.eval_id, run_id=latest_run.id)\n", + "run_data = run_items.to_dict()['data']\n", + "\n", + "passed = sum(1 for output_item in run_data if output_item['results'][0]['passed'])\n", + "total = len(run_data)\n", + "print(f\"{passed}/{total} passed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6de4a7eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭───────────────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Input</span><span style=\"color: #000080; text-decoration-color: #000080\"> ─────────────────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">like 2 days ago my chest started feeling weird kinda like a tight pressure it’s not super painful and i dont </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">have shortness of breath or anything but i keep freaking out thinking maybe i should go to the ER or something </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">wtf???</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mInput\u001b[0m\u001b[34m \u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;36mlike 2 days ago my chest started feeling weird kinda like a tight pressure it’s not super painful and i dont \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;36mhave shortness of breath or anything but i keep freaking out thinking maybe i should go to the ER or something \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;36mwtf???\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭──────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Output (original model)</span><span style=\"color: #000080; text-decoration-color: #000080\"> ────────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">I'm sorry you're feeling this way—chest discomfort can be really scary. The good news is that you’re not </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">describing severe pain or shortness of breath, which are more concerning symptoms. However, chest tightness or </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">pressure should never be ignored, even if it’s mild, especially if it’s new for you.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**Here’s what you should consider:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**Go to the ER or call emergency services immediately if you develop any of these symptoms:** </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Severe or worsening chest pain </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Pain radiating to your arm, jaw, neck, or back </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Shortness of breath </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Sweating, nausea, or vomiting </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Feeling faint or lightheaded</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**If you’re not having those symptoms:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- It could be something less serious (like anxiety, acid reflux, or a muscle strain), but it’s still a good </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">idea to check in with a healthcare provider soon—especially since it’s lasted for a couple of days.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Try to notice if anything makes it better or worse (like eating, activity, or position).</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- If you have any risk factors (like high blood pressure, diabetes, smoking, or family history of heart </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">problems), it’s even more important to get checked.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**Bottom line:** </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">If you’re worried, it’s always better to be safe and get checked by a doctor. You can call your primary care </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">provider or visit an urgent care clinic if you’re not having severe symptoms. If at any point you feel worse, </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">get emergency help right away.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">Would you like help deciding where to go or what to say to a doctor?</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m───────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mOutput (original model)\u001b[0m\u001b[34m \u001b[0m\u001b[34m───────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mI'm sorry you're feeling this way—chest discomfort can be really scary. The good news is that you’re not \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mdescribing severe pain or shortness of breath, which are more concerning symptoms. However, chest tightness or \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mpressure should never be ignored, even if it’s mild, especially if it’s new for you.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**Here’s what you should consider:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**Go to the ER or call emergency services immediately if you develop any of these symptoms:** \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Severe or worsening chest pain \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Pain radiating to your arm, jaw, neck, or back \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Shortness of breath \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Sweating, nausea, or vomiting \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Feeling faint or lightheaded\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**If you’re not having those symptoms:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- It could be something less serious (like anxiety, acid reflux, or a muscle strain), but it’s still a good \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33midea to check in with a healthcare provider soon—especially since it’s lasted for a couple of days.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Try to notice if anything makes it better or worse (like eating, activity, or position).\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- If you have any risk factors (like high blood pressure, diabetes, smoking, or family history of heart \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mproblems), it’s even more important to get checked.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**Bottom line:** \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mIf you’re worried, it’s always better to be safe and get checked by a doctor. You can call your primary care \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mprovider or visit an urgent care clinic if you’re not having severe symptoms. If at any point you feel worse, \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mget emergency help right away.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mWould you like help deciding where to go or what to say to a doctor?\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭─────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Output (fine-tuned model)</span><span style=\"color: #000080; text-decoration-color: #000080\"> ───────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">I’m sorry you’re dealing with this—let’s see if we can figure out how worrisome it is and what you should do </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">next. First, I need a few more details about your chest discomfort. </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">1. Can you describe exactly where you feel the tightness? (center of your chest, left side, right side, under </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">your breastbone, etc.) </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">2. When it first started two days ago, was it constant or did it come and go? If it comes and goes, how long </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">does each episode last? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">3. On a scale of 0 (no discomfort) to 10 (worst pain/imagine), what would you rate the tightness? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">4. Do you notice it changing with any of the following? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Physical activity (walking, climbing stairs) </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Rest or sitting still </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Deep breaths, coughing, or changing positions </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">5. Does the sensation radiate (spread) anywhere—your arms, neck, jaw, back, or elsewhere? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">6. Are you currently experiencing any of these symptoms? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Shortness of breath or feeling like you can’t draw a full breath </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Lightheadedness, dizziness, or feeling faint </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Sweating (cold sweats), nausea, or vomiting </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Palpitations (heart racing or skipping beats) </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Cough, fever, or chills </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Recent trauma to your chest </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">7. Do you have any of the following medical conditions or risk factors? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Known heart disease, high blood pressure, high cholesterol, or diabetes </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Smoking history </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> • Family history of early heart disease (under age 55 in a close male relative, under age 65 in a close </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">female relative) </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Once I have this information, I can better advise you whether you need to head to the ER now, see a doctor </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">soon, or manage this at home.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mOutput (fine-tuned model)\u001b[0m\u001b[34m \u001b[0m\u001b[34m──────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mI’m sorry you’re dealing with this—let’s see if we can figure out how worrisome it is and what you should do \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mnext. First, I need a few more details about your chest discomfort. \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m1. Can you describe exactly where you feel the tightness? (center of your chest, left side, right side, under \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35myour breastbone, etc.) \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m2. When it first started two days ago, was it constant or did it come and go? If it comes and goes, how long \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mdoes each episode last? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m3. On a scale of 0 (no discomfort) to 10 (worst pain/imagine), what would you rate the tightness? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m4. Do you notice it changing with any of the following? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Physical activity (walking, climbing stairs) \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Rest or sitting still \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Deep breaths, coughing, or changing positions \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m5. Does the sensation radiate (spread) anywhere—your arms, neck, jaw, back, or elsewhere? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m6. Are you currently experiencing any of these symptoms? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Shortness of breath or feeling like you can’t draw a full breath \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Lightheadedness, dizziness, or feeling faint \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Sweating (cold sweats), nausea, or vomiting \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Palpitations (heart racing or skipping beats) \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Cough, fever, or chills \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Recent trauma to your chest \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m7. Do you have any of the following medical conditions or risk factors? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Known heart disease, high blood pressure, high cholesterol, or diabetes \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Smoking history \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m • Family history of early heart disease (under age 55 in a close male relative, under age 65 in a close \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mfemale relative) \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mOnce I have this information, I can better advise you whether you need to head to the ER now, see a doctor \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35msoon, or manage this at home.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", + "--------------------------------------------------------------------------------\n", + "\n", + "</pre>\n" + ], + "text/plain": [ + "\n", + "--------------------------------------------------------------------------------\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭───────────────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Input</span><span style=\"color: #000080; text-decoration-color: #000080\"> ─────────────────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">I'm bruising a lot on my arms and legs and it's starting to worry me, do you think it could be a blood disorder</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mInput\u001b[0m\u001b[34m \u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;36mI'm bruising a lot on my arms and legs and it's starting to worry me, do you think it could be a blood disorder\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭──────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Output (original model)</span><span style=\"color: #000080; text-decoration-color: #000080\"> ────────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">I'm not a doctor, but I can give you some information that might help you decide what to do next.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">Bruising easily can have many causes—some are minor, but others can be more serious. Common reasons for </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">increased bruising include:</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Aging:** Skin and blood vessels become more fragile.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Medications:** Blood thinners (like aspirin, warfarin), steroids, and some supplements can increase </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">bruising.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Nutritional deficiencies:** Low levels of vitamin C, vitamin K, or certain proteins.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Blood disorders:** Conditions like thrombocytopenia (low platelets), hemophilia, or other clotting </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">disorders.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Other medical conditions:** Liver disease, certain infections, or autoimmune diseases.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**When to be concerned:** </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">You should see a healthcare provider soon if you notice any of the following:</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Bruises appearing without any known injury.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Large or painful bruises.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Bruising accompanied by other symptoms (like frequent nosebleeds, bleeding gums, blood in urine/stool, </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">fatigue, or unexplained weight loss).</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Bruising that seems to be getting worse or spreading.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**What you can do now:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Make a note of any new medications or supplements you’ve started.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Keep track of how many bruises you get and where they appear.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Schedule an appointment with your doctor to discuss your symptoms.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">While it could be something minor, it’s important to get checked out to rule out any serious causes, including </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">blood disorders.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">If you develop severe symptoms, such as difficulty breathing, severe headache, or uncontrolled bleeding, seek </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">emergency care immediately.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">Would you like more information about what to expect at your doctor's visit or how to prepare?</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m───────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mOutput (original model)\u001b[0m\u001b[34m \u001b[0m\u001b[34m───────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mI'm not a doctor, but I can give you some information that might help you decide what to do next.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mBruising easily can have many causes—some are minor, but others can be more serious. Common reasons for \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mincreased bruising include:\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Aging:** Skin and blood vessels become more fragile.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Medications:** Blood thinners (like aspirin, warfarin), steroids, and some supplements can increase \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mbruising.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Nutritional deficiencies:** Low levels of vitamin C, vitamin K, or certain proteins.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Blood disorders:** Conditions like thrombocytopenia (low platelets), hemophilia, or other clotting \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mdisorders.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Other medical conditions:** Liver disease, certain infections, or autoimmune diseases.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**When to be concerned:** \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mYou should see a healthcare provider soon if you notice any of the following:\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Bruises appearing without any known injury.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Large or painful bruises.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Bruising accompanied by other symptoms (like frequent nosebleeds, bleeding gums, blood in urine/stool, \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mfatigue, or unexplained weight loss).\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Bruising that seems to be getting worse or spreading.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**What you can do now:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Make a note of any new medications or supplements you’ve started.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Keep track of how many bruises you get and where they appear.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Schedule an appointment with your doctor to discuss your symptoms.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mWhile it could be something minor, it’s important to get checked out to rule out any serious causes, including \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mblood disorders.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mIf you develop severe symptoms, such as difficulty breathing, severe headache, or uncontrolled bleeding, seek \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33memergency care immediately.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mWould you like more information about what to expect at your doctor's visit or how to prepare?\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭─────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Output (fine-tuned model)</span><span style=\"color: #000080; text-decoration-color: #000080\"> ───────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">I’m sorry you’re experiencing this—it can certainly be concerning. To help figure out whether your bruising </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">might be from a blood‑related issue or something else, could you tell me a bit more about what you’re seeing </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">and your health history? For example: </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">1. When did you first notice these bruises, and how often are new ones appearing? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">2. Do they occur after any bump or injury, or do they seem to arise spontaneously? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">3. What do the bruises look like in terms of size, color, and number? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">4. Have you started or stopped any medications or supplements recently (especially blood thinners, NSAIDs, </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">aspirin, steroids, or herbal supplements)? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">5. Do you have any other bleeding symptoms—nosebleeds, bleeding gums, unusually heavy menstrual periods, blood </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">in stool or urine? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">6. Have you experienced other symptoms such as fatigue, fever, weight loss, or night sweats? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">7. Is there any family history of bruising easily, bleeding disorders, or clotting abnormalities? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">With that information, I can better suggest whether it’s appropriate to check things like your platelet count, </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">clotting factors, or other tests, or whether it might be related to something less worrisome.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mOutput (fine-tuned model)\u001b[0m\u001b[34m \u001b[0m\u001b[34m──────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mI’m sorry you’re experiencing this—it can certainly be concerning. To help figure out whether your bruising \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mmight be from a blood‑related issue or something else, could you tell me a bit more about what you’re seeing \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mand your health history? For example: \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m1. When did you first notice these bruises, and how often are new ones appearing? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m2. Do they occur after any bump or injury, or do they seem to arise spontaneously? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m3. What do the bruises look like in terms of size, color, and number? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m4. Have you started or stopped any medications or supplements recently (especially blood thinners, NSAIDs, \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35maspirin, steroids, or herbal supplements)? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m5. Do you have any other bleeding symptoms—nosebleeds, bleeding gums, unusually heavy menstrual periods, blood \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35min stool or urine? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m6. Have you experienced other symptoms such as fatigue, fever, weight loss, or night sweats? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m7. Is there any family history of bruising easily, bleeding disorders, or clotting abnormalities? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mWith that information, I can better suggest whether it’s appropriate to check things like your platelet count, \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mclotting factors, or other tests, or whether it might be related to something less worrisome.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", + "--------------------------------------------------------------------------------\n", + "\n", + "</pre>\n" + ], + "text/plain": [ + "\n", + "--------------------------------------------------------------------------------\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭───────────────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Input</span><span style=\"color: #000080; text-decoration-color: #000080\"> ─────────────────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">adult routine cholesterol screening guidelines</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mInput\u001b[0m\u001b[34m \u001b[0m\u001b[34m────────────────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;36madult routine cholesterol screening guidelines\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭──────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Output (original model)</span><span style=\"color: #000080; text-decoration-color: #000080\"> ────────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">Here is a summary of current guidelines for adult routine cholesterol screening:</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**General Recommendations:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **All adults aged 20 years or older**: The American Heart Association (AHA), American College of Cardiology </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">(ACC), and U.S. Preventive Services Task Force (USPSTF) recommend routine cholesterol screening starting at age</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">20, with repeat testing every 4–6 years if risk remains low.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**More Specific Guidelines:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">### U.S. Preventive Services Task Force (USPSTF) (2016):</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Adults aged 40–75**: Strongly recommend screening.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Adults aged 20–39**: Consider screening if they have risk factors for cardiovascular disease (e.g., </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">diabetes, hypertension, family history of early heart disease, smoking, obesity).</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Frequency**: Every 4–6 years for low-risk individuals; more frequently if risk factors are present.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">### American College of Cardiology (ACC)/American Heart Association (AHA) (2018):</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Adults aged 20 and older**: Assess cholesterol as part of cardiovascular risk assessment every 4–6 years.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **More frequent testing**: For those with risk factors (e.g., diabetes, hypertension, family history, </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">obesity) or those on cholesterol-lowering therapy.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">### National Lipid Association (NLA):</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **All adults 20 years and older**: Lipid profile at least every 5 years.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- **Earlier and/or more frequent testing**: If risk factors or family history of premature atherosclerotic </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">cardiovascular disease (ASCVD).</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**What is measured?**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- A standard fasting or non-fasting lipid panel measures:</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> - Total cholesterol</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> - LDL cholesterol (\"bad\")</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> - HDL cholesterol (\"good\")</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> - Triglycerides</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**Summary Table:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">| Age Group | Routine Screening? | Frequency | More Frequent If... |</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">|-------------------|-------------------|---------------|------------------------------------|</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">| 20–39 years | Consider if risk | 4–6 years | Risk factors present |</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">| 40–75 years | Yes | 4–6 years | Risk factors or on therapy |</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">| >75 years | Individualized | Case-by-case | Based on overall health/risk |</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**Key Risk Factors:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Diabetes</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Hypertension</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Smoking</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Family history of early heart disease</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- Obesity</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**References:**</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- 2018 ACC/AHA Guideline on the Management of Blood Cholesterol</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">- USPSTF Recommendation Statement (2016)</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">**Note:** These are general recommendations. Screening intervals and starting age may be adjusted based on </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">individual risk factors and clinical judgment. Always consult with a healthcare provider for personalized </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">advice.</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m───────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mOutput (original model)\u001b[0m\u001b[34m \u001b[0m\u001b[34m───────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mHere is a summary of current guidelines for adult routine cholesterol screening:\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**General Recommendations:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **All adults aged 20 years or older**: The American Heart Association (AHA), American College of Cardiology \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m(ACC), and U.S. Preventive Services Task Force (USPSTF) recommend routine cholesterol screening starting at age\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m20, with repeat testing every 4–6 years if risk remains low.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**More Specific Guidelines:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m### U.S. Preventive Services Task Force (USPSTF) (2016):\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Adults aged 40–75**: Strongly recommend screening.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Adults aged 20–39**: Consider screening if they have risk factors for cardiovascular disease (e.g., \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mdiabetes, hypertension, family history of early heart disease, smoking, obesity).\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Frequency**: Every 4–6 years for low-risk individuals; more frequently if risk factors are present.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m### American College of Cardiology (ACC)/American Heart Association (AHA) (2018):\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Adults aged 20 and older**: Assess cholesterol as part of cardiovascular risk assessment every 4–6 years.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **More frequent testing**: For those with risk factors (e.g., diabetes, hypertension, family history, \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mobesity) or those on cholesterol-lowering therapy.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m### National Lipid Association (NLA):\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **All adults 20 years and older**: Lipid profile at least every 5 years.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- **Earlier and/or more frequent testing**: If risk factors or family history of premature atherosclerotic \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mcardiovascular disease (ASCVD).\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**What is measured?**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- A standard fasting or non-fasting lipid panel measures:\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m - Total cholesterol\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m - LDL cholesterol (\"bad\")\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m - HDL cholesterol (\"good\")\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m - Triglycerides\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**Summary Table:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m| Age Group | Routine Screening? | Frequency | More Frequent If... |\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m|-------------------|-------------------|---------------|------------------------------------|\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m| 20–39 years | Consider if risk | 4–6 years | Risk factors present |\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m| 40–75 years | Yes | 4–6 years | Risk factors or on therapy |\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m| >75 years | Individualized | Case-by-case | Based on overall health/risk |\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**Key Risk Factors:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Diabetes\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Hypertension\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Smoking\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Family history of early heart disease\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- Obesity\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**References:**\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- 2018 ACC/AHA Guideline on the Management of Blood Cholesterol\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m- USPSTF Recommendation Statement (2016)\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33m**Note:** These are general recommendations. Screening intervals and starting age may be adjusted based on \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33mindividual risk factors and clinical judgment. Always consult with a healthcare provider for personalized \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;33madvice.\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">╭─────────────────────────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">Output (fine-tuned model)</span><span style=\"color: #000080; text-decoration-color: #000080\"> ───────────────────────────────────────────╮</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Could you help me narrow this down so I can give you the most relevant recommendation? Specifically:</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">1. Which guideline or region are you interested in (for example, USPSTF in the US, ACC/AHA, Canadian, European,</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">etc.)? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">2. Are we talking about primary‐prevention screening in an asymptomatic adult, or secondary‑prevention </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">monitoring in someone with known cardiovascular disease? </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">3. What is the patient’s age, sex, and any major risk factors (diabetes, hypertension, smoking, family history </span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">│</span> <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">of early CVD, etc.)?</span> <span style=\"color: #000080; text-decoration-color: #000080\">│</span>\n", + "<span style=\"color: #000080; text-decoration-color: #000080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n", + "</pre>\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;32mOutput (fine-tuned model)\u001b[0m\u001b[34m \u001b[0m\u001b[34m──────────────────────────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mCould you help me narrow this down so I can give you the most relevant recommendation? Specifically:\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m1. Which guideline or region are you interested in (for example, USPSTF in the US, ACC/AHA, Canadian, European,\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35metc.)? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m2. Are we talking about primary‐prevention screening in an asymptomatic adult, or secondary‑prevention \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mmonitoring in someone with known cardiovascular disease? \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35m3. What is the patient’s age, sex, and any major risk factors (diabetes, hypertension, smoking, family history \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[1;35mof early CVD, etc.)?\u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", + "--------------------------------------------------------------------------------\n", + "\n", + "</pre>\n" + ], + "text/plain": [ + "\n", + "--------------------------------------------------------------------------------\n", + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "console = Console()\n", + "\n", + "for item in run_items.to_dict()['data'][:3]:\n", + " input_text = item['datasource_item']['messages'][0]['content']\n", + " output_text = item['datasource_item']['completion'][0]['content']\n", + " sample_text = item['sample']['output'][0]['content']\n", + " \n", + " console.print(Panel(\n", + " Text(input_text, style=\"bold cyan\"),\n", + " title=\"[bold green]Input[/bold green]\",\n", + " border_style=\"blue\"\n", + " ))\n", + " \n", + " console.print(Panel(\n", + " Text(output_text, style=\"bold yellow\"),\n", + " title=\"[bold green]Output (original model)[/bold green]\",\n", + " border_style=\"blue\"\n", + " ))\n", + " \n", + " console.print(Panel(\n", + " Text(sample_text, style=\"bold magenta\"),\n", + " title=\"[bold green]Output (fine-tuned model)[/bold green]\",\n", + " border_style=\"blue\"\n", + " ))\n", + " \n", + " console.print(\"\\n\" + \"-\" * 80 + \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7652f842", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/gpt4-1_prompting_guide.ipynb b/examples/gpt4-1_prompting_guide.ipynb new file mode 100644 index 0000000000..dcced445c1 --- /dev/null +++ b/examples/gpt4-1_prompting_guide.ipynb @@ -0,0 +1,1307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GPT-4.1 Prompting Guide\n", + "\n", + "The GPT-4.1 family of models represents a significant step forward from GPT-4o in capabilities across coding, instruction following, and long context. In this prompting guide, we collate a series of important prompting tips derived from extensive internal testing to help developers fully leverage the improved abilities of this new model family. \n", + "\n", + "Many typical best practices still apply to GPT-4.1, such as providing context examples, making instructions as specific and clear as possible, and inducing planning via prompting to maximize model intelligence. However, we expect that getting the most out of this model will require some prompt migration. GPT-4.1 is trained to follow instructions more closely and more literally than its predecessors, which tended to more liberally infer intent from user and system prompts. This also means, however, that GPT-4.1 is highly steerable and responsive to well-specified prompts - if model behavior is different from what you expect, a single sentence firmly and unequivocally clarifying your desired behavior is almost always sufficient to steer the model on course.\n", + "\n", + "Please read on for prompt examples you can use as a reference, and remember that while this guidance is widely applicable, no advice is one-size-fits-all. AI engineering is inherently an empirical discipline, and large language models are inherently nondeterministic; in addition to following this guide, we advise building informative evals and iterating often to ensure your prompt engineering changes are yielding benefits for your use case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Agentic Workflows\n", + "\n", + "GPT-4.1 is a great place to build agentic workflows. In model training we emphasized providing a diverse range of agentic problem-solving trajectories, and our agentic harness for the model achieves state-of-the-art performance for non-reasoning models on SWE-bench Verified, solving 55% of problems. \n", + "\n", + "\n", + "## System Prompt Reminders\n", + "\n", + "In order to fully utilize the agentic capabilities of GPT-4.1, we recommend including three key types of reminders in all agent prompts. The following prompts are optimized specifically for the agentic coding workflow, but can be easily modified for general agentic use cases.\n", + "\n", + "1. Persistence: this ensures the model understands it is entering a multi-message turn, and prevents it from prematurely yielding control back to the user. Our example is the following:\n", + "\n", + "```\n", + "You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved.\n", + "```\n", + "\n", + "2. Tool-calling: this encourages the model to make full use of its tools, and reduces its likelihood of hallucinating or guessing an answer. Our example is the following:\n", + "\n", + "```\n", + "If you are not sure about file content or codebase structure pertaining to the user’s request, use your tools to read files and gather the relevant information: do NOT guess or make up an answer.\n", + "```\n", + "\n", + "3. Planning \\[optional\\]: if desired, this ensures the model explicitly plans and reflects upon each tool call in text, instead of completing the task by chaining together a series of only tool calls. Our example is the following:\n", + "\n", + "```\n", + "You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.\n", + "```\n", + "\n", + "GPT-4.1 is trained to respond very closely to both user instructions and system prompts in the agentic setting. The model adhered closely to these three simple instructions and increased our internal SWE-bench Verified score by close to 20% \\- so we highly encourage starting any agent prompt with clear reminders covering the three categories listed above. As a whole, we find that these three instructions transform the model from a chatbot-like state into a much more “eager” agent, driving the interaction forward autonomously and independently. \n", + "\n", + "## Tool Calls\n", + "\n", + "Compared to previous models, GPT-4.1 has undergone more training on effectively utilizing tools passed as arguments in an OpenAI API request. We encourage developers to exclusively use the tools field to pass tools, rather than manually injecting tool descriptions into your prompt and writing a separate parser for tool calls, as some have reported doing in the past. This is the best way to minimize errors and ensure the model remains in distribution during tool-calling trajectories \\- in our own experiments, we observed a 2% increase in SWE-bench Verified pass rate when using API-parsed tool descriptions versus manually injecting the schemas into the system prompt.\n", + "\n", + "Developers should name tools clearly to indicate their purpose and add a clear, detailed description in the \"description\" field of the tool. Similarly, for each tool param, lean on good naming and descriptions to ensure appropriate usage. If your tool is particularly complicated and you'd like to provide examples of tool usage, we recommend that you create an `# Examples` section in your system prompt and place the examples there, rather than adding them into the \"description' field, which should remain thorough but relatively concise. Providing examples can be helpful to indicate when to use tools, whether to include user text alongside tool calls, and what parameters are appropriate for different inputs. Remember that you can use “Generate Anything” in the [Prompt Playground](https://platform.openai.com/playground) to get a good starting point for your new tool definitions.\n", + "\n", + "## Prompting-Induced Planning & Chain-of-Thought\n", + "\n", + "As mentioned already, developers can optionally prompt agents built with GPT-4.1 to plan and reflect between tool calls, instead of silently calling tools in an unbroken sequence. GPT-4.1 is not a reasoning model \\- meaning that it does not produce an internal chain of thought before answering \\- but in the prompt, a developer can induce the model to produce an explicit, step-by-step plan by using any variant of the Planning prompt component shown above. This can be thought of as the model “thinking out loud.” In our experimentation with the SWE-bench Verified agentic task, inducing explicit planning increased the pass rate by 4%. \n", + "\n", + "## Sample Prompt: SWE-bench Verified\n", + "\n", + "Below, we share the agentic prompt that we used to achieve our highest score on SWE-bench Verified, which features detailed instructions about workflow and problem-solving strategy. This general pattern can be used for any agentic task." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'id': 'msg_67fe92df26ac819182ffafce9ff4e4fc07c7e06242e51f8b',\n", + " 'content': [{'annotations': [],\n", + " 'text': \"Thank you for the report, but “Typerror” is too vague for me to start debugging right away.\\n\\n**To make progress, I need to:**\\n1. Find the exact error message text (e.g. `'TypeError: ...'`).\\n2. Find which file and which line/function/class the error occurred in.\\n3. Figure out what triggered the error (test file, usage, reproduction steps).\\n4. Find the root cause and details.\\n\\n**Next steps:**\\n- Investigate error/log/test output files for a Python `TypeError` message.\\n- Examine the relevant code sections for problematic type usage.\\n- If possible, reproduce the bug locally.\\n\\n**Plan:**\\n- First, I will search for test files and log output in the `/testbed` directory that may contain the full error message and stack trace.\\n\\nLet’s start by listing the contents of the `/testbed` directory to look for clues.\",\n", + " 'type': 'output_text'}],\n", + " 'role': 'assistant',\n", + " 'status': 'completed',\n", + " 'type': 'message'},\n", + " {'arguments': '{\"input\":\"!ls -l /testbed\"}',\n", + " 'call_id': 'call_frnxyJgKi5TsBem0nR9Zuzdw',\n", + " 'name': 'python',\n", + " 'type': 'function_call',\n", + " 'id': 'fc_67fe92e3da7081918fc18d5c96dddc1c07c7e06242e51f8b',\n", + " 'status': 'completed'}]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from openai import OpenAI\n", + "import os\n", + "\n", + "client = OpenAI(\n", + " api_key=os.environ.get(\n", + " \"OPENAI_API_KEY\", \"<your OpenAI API key if not set as env var>\"\n", + " )\n", + ")\n", + "\n", + "SYS_PROMPT_SWEBENCH = \"\"\"\n", + "You will be tasked to fix an issue from an open-source repository.\n", + "\n", + "Your thinking should be thorough and so it's fine if it's very long. You can think step by step before and after each action you decide to take.\n", + "\n", + "You MUST iterate and keep going until the problem is solved.\n", + "\n", + "You already have everything you need to solve this problem in the /testbed folder, even without internet connection. I want you to fully solve this autonomously before coming back to me.\n", + "\n", + "Only terminate your turn when you are sure that the problem is solved. Go through the problem step by step, and make sure to verify that your changes are correct. NEVER end your turn without having solved the problem, and when you say you are going to make a tool call, make sure you ACTUALLY make the tool call, instead of ending your turn.\n", + "\n", + "THE PROBLEM CAN DEFINITELY BE SOLVED WITHOUT THE INTERNET.\n", + "\n", + "Take your time and think through every step - remember to check your solution rigorously and watch out for boundary cases, especially with the changes you made. Your solution must be perfect. If not, continue working on it. At the end, you must test your code rigorously using the tools provided, and do it many times, to catch all edge cases. If it is not robust, iterate more and make it perfect. Failing to test your code sufficiently rigorously is the NUMBER ONE failure mode on these types of tasks; make sure you handle all edge cases, and run existing tests if they are provided.\n", + "\n", + "You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.\n", + "\n", + "# Workflow\n", + "\n", + "## High-Level Problem Solving Strategy\n", + "\n", + "1. Understand the problem deeply. Carefully read the issue and think critically about what is required.\n", + "2. Investigate the codebase. Explore relevant files, search for key functions, and gather context.\n", + "3. Develop a clear, step-by-step plan. Break down the fix into manageable, incremental steps.\n", + "4. Implement the fix incrementally. Make small, testable code changes.\n", + "5. Debug as needed. Use debugging techniques to isolate and resolve issues.\n", + "6. Test frequently. Run tests after each change to verify correctness.\n", + "7. Iterate until the root cause is fixed and all tests pass.\n", + "8. Reflect and validate comprehensively. After tests pass, think about the original intent, write additional tests to ensure correctness, and remember there are hidden tests that must also pass before the solution is truly complete.\n", + "\n", + "Refer to the detailed sections below for more information on each step.\n", + "\n", + "## 1. Deeply Understand the Problem\n", + "Carefully read the issue and think hard about a plan to solve it before coding.\n", + "\n", + "## 2. Codebase Investigation\n", + "- Explore relevant files and directories.\n", + "- Search for key functions, classes, or variables related to the issue.\n", + "- Read and understand relevant code snippets.\n", + "- Identify the root cause of the problem.\n", + "- Validate and update your understanding continuously as you gather more context.\n", + "\n", + "## 3. Develop a Detailed Plan\n", + "- Outline a specific, simple, and verifiable sequence of steps to fix the problem.\n", + "- Break down the fix into small, incremental changes.\n", + "\n", + "## 4. Making Code Changes\n", + "- Before editing, always read the relevant file contents or section to ensure complete context.\n", + "- If a patch is not applied correctly, attempt to reapply it.\n", + "- Make small, testable, incremental changes that logically follow from your investigation and plan.\n", + "\n", + "## 5. Debugging\n", + "- Make code changes only if you have high confidence they can solve the problem\n", + "- When debugging, try to determine the root cause rather than addressing symptoms\n", + "- Debug for as long as needed to identify the root cause and identify a fix\n", + "- Use print statements, logs, or temporary code to inspect program state, including descriptive statements or error messages to understand what's happening\n", + "- To test hypotheses, you can also add test statements or functions\n", + "- Revisit your assumptions if unexpected behavior occurs.\n", + "\n", + "## 6. Testing\n", + "- Run tests frequently using `!python3 run_tests.py` (or equivalent).\n", + "- After each change, verify correctness by running relevant tests.\n", + "- If tests fail, analyze failures and revise your patch.\n", + "- Write additional tests if needed to capture important behaviors or edge cases.\n", + "- Ensure all tests pass before finalizing.\n", + "\n", + "## 7. Final Verification\n", + "- Confirm the root cause is fixed.\n", + "- Review your solution for logic correctness and robustness.\n", + "- Iterate until you are extremely confident the fix is complete and all tests pass.\n", + "\n", + "## 8. Final Reflection and Additional Testing\n", + "- Reflect carefully on the original intent of the user and the problem statement.\n", + "- Think about potential edge cases or scenarios that may not be covered by existing tests.\n", + "- Write additional tests that would need to pass to fully validate the correctness of your solution.\n", + "- Run these new tests and ensure they all pass.\n", + "- Be aware that there are additional hidden tests that must also pass for the solution to be successful.\n", + "- Do not assume the task is complete just because the visible tests pass; continue refining until you are confident the fix is robust and comprehensive.\n", + "\"\"\"\n", + "\n", + "PYTHON_TOOL_DESCRIPTION = \"\"\"This function is used to execute Python code or terminal commands in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. Internet access for this session is disabled. Do not make external web requests or API calls as they will fail. Just as in a Jupyter notebook, you may also execute terminal commands by calling this function with a terminal command, prefaced with an exclamation mark.\n", + "\n", + "In addition, for the purposes of this task, you can call this function with an `apply_patch` command as input. `apply_patch` effectively allows you to execute a diff/patch against a file, but the format of the diff specification is unique to this task, so pay careful attention to these instructions. To use the `apply_patch` command, you should pass a message of the following structure as \"input\":\n", + "\n", + "%%bash\n", + "apply_patch <<\"EOF\"\n", + "*** Begin Patch\n", + "[YOUR_PATCH]\n", + "*** End Patch\n", + "EOF\n", + "\n", + "Where [YOUR_PATCH] is the actual content of your patch, specified in the following V4A diff format.\n", + "\n", + "*** [ACTION] File: [path/to/file] -> ACTION can be one of Add, Update, or Delete.\n", + "For each snippet of code that needs to be changed, repeat the following:\n", + "[context_before] -> See below for further instructions on context.\n", + "- [old_code] -> Precede the old code with a minus sign.\n", + "+ [new_code] -> Precede the new, replacement code with a plus sign.\n", + "[context_after] -> See below for further instructions on context.\n", + "\n", + "For instructions on [context_before] and [context_after]:\n", + "- By default, show 3 lines of code immediately above and 3 lines immediately below each change. If a change is within 3 lines of a previous change, do NOT duplicate the first change's [context_after] lines in the second change's [context_before] lines.\n", + "- If 3 lines of context is insufficient to uniquely identify the snippet of code within the file, use the @@ operator to indicate the class or function to which the snippet belongs. For instance, we might have:\n", + "@@ class BaseClass\n", + "[3 lines of pre-context]\n", + "- [old_code]\n", + "+ [new_code]\n", + "[3 lines of post-context]\n", + "\n", + "- If a code block is repeated so many times in a class or function such that even a single @@ statement and 3 lines of context cannot uniquely identify the snippet of code, you can use multiple `@@` statements to jump to the right context. For instance:\n", + "\n", + "@@ class BaseClass\n", + "@@ \tdef method():\n", + "[3 lines of pre-context]\n", + "- [old_code]\n", + "+ [new_code]\n", + "[3 lines of post-context]\n", + "\n", + "Note, then, that we do not use line numbers in this diff format, as the context is enough to uniquely identify code. An example of a message that you might pass as \"input\" to this function, in order to apply a patch, is shown below.\n", + "\n", + "%%bash\n", + "apply_patch <<\"EOF\"\n", + "*** Begin Patch\n", + "*** Update File: pygorithm/searching/binary_search.py\n", + "@@ class BaseClass\n", + "@@ def search():\n", + "- pass\n", + "+ raise NotImplementedError()\n", + "\n", + "@@ class Subclass\n", + "@@ def search():\n", + "- pass\n", + "+ raise NotImplementedError()\n", + "\n", + "*** End Patch\n", + "EOF\n", + "\n", + "File references can only be relative, NEVER ABSOLUTE. After the apply_patch command is run, python will always say \"Done!\", regardless of whether the patch was successfully applied or not. However, you can determine if there are issue and errors by looking at any warnings or logging lines printed BEFORE the \"Done!\" is output.\n", + "\"\"\"\n", + "\n", + "python_bash_patch_tool = {\n", + " \"type\": \"function\",\n", + " \"name\": \"python\",\n", + " \"description\": PYTHON_TOOL_DESCRIPTION,\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"strict\": True,\n", + " \"properties\": {\n", + " \"input\": {\n", + " \"type\": \"string\",\n", + " \"description\": \" The Python code, terminal command (prefaced by exclamation mark), or apply_patch command that you wish to execute.\",\n", + " }\n", + " },\n", + " \"required\": [\"input\"],\n", + " },\n", + "}\n", + "\n", + "# Additional harness setup:\n", + "# - Add your repo to /testbed\n", + "# - Add your issue to the first user message\n", + "# - Note: Even though we used a single tool for python, bash, and apply_patch, we generally recommend defining more granular tools that are focused on a single function\n", + "\n", + "response = client.responses.create(\n", + " instructions=SYS_PROMPT_SWEBENCH,\n", + " model=\"gpt-4.1-2025-04-14\",\n", + " tools=[python_bash_patch_tool],\n", + " input=f\"Please answer the following question:\\nBug: Typerror...\"\n", + ")\n", + "\n", + "response.to_dict()[\"output\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Long context\n", + "\n", + "GPT-4.1 has a performant 1M token input context window, and is useful for a variety of long context tasks, including structured document parsing, re-ranking, selecting relevant information while ignoring irrelevant context, and performing multi-hop reasoning using context.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimal Context Size\n", + "\n", + "We observe very good performance on needle-in-a-haystack evaluations up to our full 1M token context, and we’ve observed very strong performance at complex tasks with a mix of both relevant and irrelevant code and other documents. However, long context performance can degrade as more items are required to be retrieved, or perform complex reasoning that requires knowledge of the state of the entire context (like performing a graph search, for example).\n", + "\n", + "## Tuning Context Reliance\n", + "\n", + "Consider the mix of external vs. internal world knowledge that might be required to answer your question. Sometimes it’s important for the model to use some of its own knowledge to connect concepts or make logical jumps, while in others it’s desirable to only use provided context\n", + "\n", + "```\n", + "# Instructions\n", + "// for internal knowledge\n", + "- Only use the documents in the provided External Context to answer the User Query. If you don't know the answer based on this context, you must respond \"I don't have the information needed to answer that\", even if a user insists on you answering the question.\n", + "// For internal and external knowledge\n", + "- By default, use the provided external context to answer the User Query, but if other basic knowledge is needed to answer, and you're confident in the answer, you can use some of your own knowledge to help answer the question.\n", + "```\n", + "\n", + "## Prompt Organization\n", + "\n", + "Especially in long context usage, placement of instructions and context can impact performance. If you have long context in your prompt, ideally place your instructions at both the beginning and end of the provided context, as we found this to perform better than only above or below. If you’d prefer to only have your instructions once, then above the provided context works better than below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Chain of Thought\n", + "\n", + "As mentioned above, GPT-4.1 is not a reasoning model, but prompting the model to think step by step (called “chain of thought”) can be an effective way for a model to break down problems into more manageable pieces, solve them, and improve overall output quality, with the tradeoff of higher cost and latency associated with using more output tokens. The model has been trained to perform well at agentic reasoning about and real-world problem solving, so it shouldn’t require much prompting to perform well.\n", + "\n", + "We recommend starting with this basic chain-of-thought instruction at the end of your prompt:\n", + "\n", + "```\n", + "...\n", + "\n", + "First, think carefully step by step about what documents are needed to answer the query. Then, print out the TITLE and ID of each document. Then, format the IDs into a list.\n", + "```\n", + "\n", + "From there, you should improve your chain-of-thought (CoT) prompt by auditing failures in your particular examples and evals, and addressing systematic planning and reasoning errors with more explicit instructions. In the unconstrained CoT prompt, there may be variance in the strategies it tries, and if you observe an approach that works well, you can codify that strategy in your prompt. Generally speaking, errors tend to occur from misunderstanding user intent, insufficient context gathering or analysis, or insufficient or incorrect step by step thinking, so watch out for these and try to address them with more opinionated instructions.\n", + "\n", + "Here is an example prompt instructing the model to focus more methodically on analyzing user intent and considering relevant context before proceeding to answer.\n", + "\n", + "```\n", + "# Reasoning Strategy\n", + "1. Query Analysis: Break down and analyze the query until you're confident about what it might be asking. Consider the provided context to help clarify any ambiguous or confusing information.\n", + "2. Context Analysis: Carefully select and analyze a large set of potentially relevant documents. Optimize for recall - it's okay if some are irrelevant, but the correct documents must be in this list, otherwise your final answer will be wrong. Analysis steps for each:\n", + "\ta. Analysis: An analysis of how it may or may not be relevant to answering the query.\n", + "\tb. Relevance rating: [high, medium, low, none]\n", + "3. Synthesis: summarize which documents are most relevant and why, including all documents with a relevance rating of medium or higher.\n", + "\n", + "# User Question\n", + "{user_question}\n", + "\n", + "# External Context\n", + "{external_context}\n", + "\n", + "First, think carefully step by step about what documents are needed to answer the query, closely adhering to the provided Reasoning Strategy. Then, print out the TITLE and ID of each document. Then, format the IDs into a list.\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4. Instruction Following\n", + "\n", + "GPT-4.1 exhibits outstanding instruction-following performance, which developers can leverage to precisely shape and control the outputs for their particular use cases. Developers often extensively prompt for agentic reasoning steps, response tone and voice, tool calling information, output formatting, topics to avoid, and more. However, since the model follows instructions more literally, developers may need to include explicit specification around what to do or not to do. Furthermore, existing prompts optimized for other models may not immediately work with this model, because existing instructions are followed more closely and implicit rules are no longer being as strongly inferred." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recommended Workflow\n", + "\n", + "Here is our recommended workflow for developing and debugging instructions in prompts:\n", + "\n", + "1. Start with an overall “Response Rules” or “Instructions” section with high-level guidance and bullet points. \n", + "2. If you’d like to change a more specific behavior, add a section to specify more details for that category, like `# Sample Phrases`. \n", + "3. If there are specific steps you’d like the model to follow in its workflow, add an ordered list and instruct the model to follow these steps.\n", + "4. If behavior still isn’t working as expected: \n", + " 1. Check for conflicting, underspecified, or wrong instructions and examples. If there are conflicting instructions, GPT-4.1 tends to follow the one closer to the end of the prompt.\n", + " 2. Add examples that demonstrate desired behavior; ensure that any important behavior demonstrated in your examples are also cited in your rules.\n", + " 3. It’s generally not necessary to use all-caps or other incentives like bribes or tips. We recommend starting without these, and only reaching for these if necessary for your particular prompt. Note that if your existing prompts include these techniques, it could cause GPT-4.1 to pay attention to it too strictly.\n", + "\n", + "*Note that using your preferred AI-powered IDE can be very helpful for iterating on prompts, including checking for consistency or conflicts, adding examples, or making cohesive updates like adding an instruction and updating instructions to demonstrate that instruction.*\n", + "\n", + "## Common Failure Modes\n", + "\n", + "These failure modes are not unique to GPT-4.1, but we share them here for general awareness and ease of debugging.\n", + "\n", + "* Instructing a model to always follow a specific behavior can occasionally induce adverse effects. For instance, if told “you must call a tool before responding to the user,” models may hallucinate tool inputs or call the tool with null values if they do not have enough information. Adding “if you don’t have enough information to call the tool, ask the user for the information you need” should mitigate this.\n", + "* When provided sample phrases, models can use those quotes verbatim and start to sound repetitive to users. Ensure you instruct the model to vary them as necessary.\n", + "* Without specific instructions, some models can be eager to provide additional prose to explain their decisions, or output more formatting in responses than may be desired. Provide instructions and potentially examples to help mitigate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Prompt: Customer Service\n", + "\n", + "This demonstrates best practices for a fictional customer service agent. Observe the diversity of rules, the specificity, the use of additional sections for greater detail, and an example to demonstrate precise behavior that incorporates all prior rules.\n", + "\n", + "Try running the following notebook cell - you should see both a user message and tool call, and the user message should start with a greeting, then echo back their answer, then mention they're about to call a tool. Try changing the instructions to shape the model behavior, or trying other user messages, to test instruction following performance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'id': 'msg_67fe92d431548191b7ca6cd604b4784b06efc5beb16b3c5e',\n", + " 'content': [{'annotations': [],\n", + " 'text': \"Hi, you've reached NewTelco, how can I help you? 🌍✈️\\n\\nYou'd like to know the cost of international service while traveling to France. 🇫🇷 Let me check the latest details for you—one moment, please. 🕑\",\n", + " 'type': 'output_text'}],\n", + " 'role': 'assistant',\n", + " 'status': 'completed',\n", + " 'type': 'message'},\n", + " {'arguments': '{\"topic\":\"international service cost France\"}',\n", + " 'call_id': 'call_cF63DLeyhNhwfdyME3ZHd0yo',\n", + " 'name': 'lookup_policy_document',\n", + " 'type': 'function_call',\n", + " 'id': 'fc_67fe92d5d6888191b6cd7cf57f707e4606efc5beb16b3c5e',\n", + " 'status': 'completed'}]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SYS_PROMPT_CUSTOMER_SERVICE = \"\"\"You are a helpful customer service agent working for NewTelco, helping a user efficiently fulfill their request while adhering closely to provided guidelines.\n", + "\n", + "# Instructions\n", + "- Always greet the user with \"Hi, you've reached NewTelco, how can I help you?\"\n", + "- Always call a tool before answering factual questions about the company, its offerings or products, or a user's account. Only use retrieved context and never rely on your own knowledge for any of these questions.\n", + " - However, if you don't have enough information to properly call the tool, ask the user for the information you need.\n", + "- Escalate to a human if the user requests.\n", + "- Do not discuss prohibited topics (politics, religion, controversial current events, medical, legal, or financial advice, personal conversations, internal company operations, or criticism of any people or company).\n", + "- Rely on sample phrases whenever appropriate, but never repeat a sample phrase in the same conversation. Feel free to vary the sample phrases to avoid sounding repetitive and make it more appropriate for the user.\n", + "- Always follow the provided output format for new messages, including citations for any factual statements from retrieved policy documents.\n", + "- If you're going to call a tool, always message the user with an appropriate message before and after calling the tool.\n", + "- Maintain a professional and concise tone in all responses, and use emojis between sentences.\n", + "- If you've resolved the user's request, ask if there's anything else you can help with\n", + "\n", + "# Precise Response Steps (for each response)\n", + "1. If necessary, call tools to fulfill the user's desired action. Always message the user before and after calling a tool to keep them in the loop.\n", + "2. In your response to the user\n", + " a. Use active listening and echo back what you heard the user ask for.\n", + " b. Respond appropriately given the above guidelines.\n", + "\n", + "# Sample Phrases\n", + "## Deflecting a Prohibited Topic\n", + "- \"I'm sorry, but I'm unable to discuss that topic. Is there something else I can help you with?\"\n", + "- \"That's not something I'm able to provide information on, but I'm happy to help with any other questions you may have.\"\n", + "\n", + "## Before calling a tool\n", + "- \"To help you with that, I'll just need to verify your information.\"\n", + "- \"Let me check that for you—one moment, please.\"\n", + "- \"I'll retrieve the latest details for you now.\"\n", + "\n", + "## After calling a tool\n", + "- \"Okay, here's what I found: [response]\"\n", + "- \"So here's what I found: [response]\"\n", + "\n", + "# Output Format\n", + "- Always include your final response to the user.\n", + "- When providing factual information from retrieved context, always include citations immediately after the relevant statement(s). Use the following citation format:\n", + " - For a single source: [NAME](ID)\n", + " - For multiple sources: [NAME](ID), [NAME](ID)\n", + "- Only provide information about this company, its policies, its products, or the customer's account, and only if it is based on information provided in context. Do not answer questions outside this scope.\n", + "\n", + "# Example\n", + "## User\n", + "Can you tell me about your family plan options?\n", + "\n", + "## Assistant Response 1\n", + "### Message\n", + "\"Hi, you've reached NewTelco, how can I help you? 😊🎉\\n\\nYou'd like to know about our family plan options. 🤝 Let me check that for you—one moment, please. 🚀\"\n", + "\n", + "### Tool Calls\n", + "lookup_policy_document(topic=\"family plan options\")\n", + "\n", + "// After tool call, the assistant would follow up with:\n", + "\n", + "## Assistant Response 2 (after tool call)\n", + "### Message\n", + "\"Okay, here's what I found: 🎉 Our family plan allows up to 5 lines with shared data and a 10% discount for each additional line [Family Plan Policy](ID-010). 📱 Is there anything else I can help you with today? 😊\"\n", + "\"\"\"\n", + "\n", + "get_policy_doc = {\n", + " \"type\": \"function\",\n", + " \"name\": \"lookup_policy_document\",\n", + " \"description\": \"Tool to look up internal documents and policies by topic or keyword.\",\n", + " \"parameters\": {\n", + " \"strict\": True,\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"topic\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The topic or keyword to search for in company policies or documents.\",\n", + " },\n", + " },\n", + " \"required\": [\"topic\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + "}\n", + "\n", + "get_user_acct = {\n", + " \"type\": \"function\",\n", + " \"name\": \"get_user_account_info\",\n", + " \"description\": \"Tool to get user account information\",\n", + " \"parameters\": {\n", + " \"strict\": True,\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"phone_number\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Formatted as '(xxx) xxx-xxxx'\",\n", + " },\n", + " },\n", + " \"required\": [\"phone_number\"],\n", + " \"additionalProperties\": False,\n", + " },\n", + "}\n", + "\n", + "response = client.responses.create(\n", + " instructions=SYS_PROMPT_CUSTOMER_SERVICE,\n", + " model=\"gpt-4.1-2025-04-14\",\n", + " tools=[get_policy_doc, get_user_acct],\n", + " input=\"How much will it cost for international service? I'm traveling to France.\",\n", + " # input=\"Why was my last bill so high?\"\n", + ")\n", + "\n", + "response.to_dict()[\"output\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5. General Advice\n", + "\n", + "## Prompt Structure\n", + "\n", + "For reference, here is a good starting point for structuring your prompts.\n", + "\n", + "```\n", + "# Role and Objective\n", + "\n", + "# Instructions\n", + "\n", + "## Sub-categories for more detailed instructions\n", + "\n", + "# Reasoning Steps\n", + "\n", + "# Output Format\n", + "\n", + "# Examples\n", + "## Example 1\n", + "\n", + "# Context\n", + "\n", + "# Final instructions and prompt to think step by step\n", + "```\n", + "\n", + "Add or remove sections to suit your needs, and experiment to determine what’s optimal for your usage.\n", + "\n", + "## Delimiters\n", + "\n", + "Here are some general guidelines for selecting the best delimiters for your prompt. Please refer to the Long Context section for special considerations for that context type.\n", + "\n", + "1. Markdown: We recommend starting here, and using markdown titles for major sections and subsections (including deeper hierarchy, to H4+). Use inline backticks or backtick blocks to precisely wrap code, and standard numbered or bulleted lists as needed. \n", + "2. XML: These also perform well, and we have improved adherence to information in XML with this model. XML is convenient to precisely wrap a section including start and end, add metadata to the tags for additional context, and enable nesting. Here is an example of using XML tags to nest examples in an example section, with inputs and outputs for each:\n", + "\n", + "```\n", + "<examples>\n", + "<example1 type=\"Abbreviate\">\n", + "<input>San Francisco</input>\n", + "<output>- SF</output>\n", + "</example1>\n", + "</examples>\n", + "```\n", + "\n", + "3. JSON is highly structured and well understood by the model particularly in coding contexts. However it can be more verbose, and require character escaping that can add overhead.\n", + "\n", + "Guidance specifically for adding a large number of documents or files to input context:\n", + "\n", + "* XML performed well in our long context testing. \n", + " * Example: `<doc id='1' title='The Fox'>The quick brown fox jumps over the lazy dog</doc>` \n", + "* This format, proposed by Lee et al. ([ref](https://arxiv.org/pdf/2406.13121)), also performed well in our long context testing. \n", + " * Example: `ID: 1 | TITLE: The Fox | CONTENT: The quick brown fox jumps over the lazy dog` \n", + "* JSON performed particularly poorly. \n", + " * Example: `[{'id': 1, 'title': 'The Fox', 'content': 'The quick brown fox jumped over the lazy dog'}]`\n", + "\n", + "The model is trained to robustly understand structure in a variety of formats. Generally, use your judgement and think about what will provide clear information and “stand out” to the model. For example, if you’re retrieving documents that contain lots of XML, an XML-based delimiter will likely be less effective. \n", + "\n", + "## Caveats\n", + "\n", + "* In some isolated cases we have observed the model being resistant to producing very long, repetitive outputs, for example, analyzing hundreds of items one by one. If this is necessary for your use case, instruct the model strongly to output this information in full, and consider breaking down the problem or using a more concise approach. \n", + "* We have seen some rare instances of parallel tool calls being incorrect. We advise testing this, and considering setting the [parallel\\_tool\\_calls](https://platform.openai.com/docs/api-reference/responses/create#responses-create-parallel_tool_calls) param to false if you’re seeing issues." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Appendix: Generating and Applying File Diffs\n", + "\n", + "Developers have provided us feedback that accurate and well-formed diff generation is a critical capability to power coding-related tasks. To this end, the GPT-4.1 family features substantially improved diff capabilities relative to previous GPT models. Moreover, while GPT-4.1 has strong performance generating diffs of any format given clear instructions and examples, we open-source here one recommended diff format, on which the model has been extensively trained. We hope that in particular for developers just starting out, that this will take much of the guesswork out of creating diffs yourself. \n", + "\n", + "## Apply Patch\n", + "\n", + "See the example below for a prompt that applies our recommended tool call correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "APPLY_PATCH_TOOL_DESC = \"\"\"This is a custom utility that makes it more convenient to add, remove, move, or edit code files. `apply_patch` effectively allows you to execute a diff/patch against a file, but the format of the diff specification is unique to this task, so pay careful attention to these instructions. To use the `apply_patch` command, you should pass a message of the following structure as \"input\":\n", + "\n", + "%%bash\n", + "apply_patch <<\"EOF\"\n", + "*** Begin Patch\n", + "[YOUR_PATCH]\n", + "*** End Patch\n", + "EOF\n", + "\n", + "Where [YOUR_PATCH] is the actual content of your patch, specified in the following V4A diff format.\n", + "\n", + "*** [ACTION] File: [path/to/file] -> ACTION can be one of Add, Update, or Delete.\n", + "For each snippet of code that needs to be changed, repeat the following:\n", + "[context_before] -> See below for further instructions on context.\n", + "- [old_code] -> Precede the old code with a minus sign.\n", + "+ [new_code] -> Precede the new, replacement code with a plus sign.\n", + "[context_after] -> See below for further instructions on context.\n", + "\n", + "For instructions on [context_before] and [context_after]:\n", + "- By default, show 3 lines of code immediately above and 3 lines immediately below each change. If a change is within 3 lines of a previous change, do NOT duplicate the first change’s [context_after] lines in the second change’s [context_before] lines.\n", + "- If 3 lines of context is insufficient to uniquely identify the snippet of code within the file, use the @@ operator to indicate the class or function to which the snippet belongs. For instance, we might have:\n", + "@@ class BaseClass\n", + "[3 lines of pre-context]\n", + "- [old_code]\n", + "+ [new_code]\n", + "[3 lines of post-context]\n", + "\n", + "- If a code block is repeated so many times in a class or function such that even a single @@ statement and 3 lines of context cannot uniquely identify the snippet of code, you can use multiple `@@` statements to jump to the right context. For instance:\n", + "\n", + "@@ class BaseClass\n", + "@@ \tdef method():\n", + "[3 lines of pre-context]\n", + "- [old_code]\n", + "+ [new_code]\n", + "[3 lines of post-context]\n", + "\n", + "Note, then, that we do not use line numbers in this diff format, as the context is enough to uniquely identify code. An example of a message that you might pass as \"input\" to this function, in order to apply a patch, is shown below.\n", + "\n", + "%%bash\n", + "apply_patch <<\"EOF\"\n", + "*** Begin Patch\n", + "*** Update File: pygorithm/searching/binary_search.py\n", + "@@ class BaseClass\n", + "@@ def search():\n", + "- pass\n", + "+ raise NotImplementedError()\n", + "\n", + "@@ class Subclass\n", + "@@ def search():\n", + "- pass\n", + "+ raise NotImplementedError()\n", + "\n", + "*** End Patch\n", + "EOF\n", + "\"\"\"\n", + "\n", + "APPLY_PATCH_TOOL = {\n", + " \"name\": \"apply_patch\",\n", + " \"description\": APPLY_PATCH_TOOL_DESC,\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"input\": {\n", + " \"type\": \"string\",\n", + " \"description\": \" The apply_patch command that you wish to execute.\",\n", + " }\n", + " },\n", + " \"required\": [\"input\"],\n", + " },\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference Implementation: apply\\_patch.py\n", + "\n", + "Here’s a reference implementation of the apply\\_patch tool that we used as part of model training. You’ll need to make this an executable and available as \\`apply\\_patch\\` from the shell where the model will execute commands:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!/usr/bin/env python3\n", + "\n", + "\"\"\"\n", + "A self-contained **pure-Python 3.9+** utility for applying human-readable\n", + "“pseudo-diff” patch files to a collection of text files.\n", + "\"\"\"\n", + "\n", + "from __future__ import annotations\n", + "\n", + "import pathlib\n", + "from dataclasses import dataclass, field\n", + "from enum import Enum\n", + "from typing import (\n", + " Callable,\n", + " Dict,\n", + " List,\n", + " Optional,\n", + " Tuple,\n", + " Union,\n", + ")\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Domain objects\n", + "# --------------------------------------------------------------------------- #\n", + "class ActionType(str, Enum):\n", + " ADD = \"add\"\n", + " DELETE = \"delete\"\n", + " UPDATE = \"update\"\n", + "\n", + "\n", + "@dataclass\n", + "class FileChange:\n", + " type: ActionType\n", + " old_content: Optional[str] = None\n", + " new_content: Optional[str] = None\n", + " move_path: Optional[str] = None\n", + "\n", + "\n", + "@dataclass\n", + "class Commit:\n", + " changes: Dict[str, FileChange] = field(default_factory=dict)\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Exceptions\n", + "# --------------------------------------------------------------------------- #\n", + "class DiffError(ValueError):\n", + " \"\"\"Any problem detected while parsing or applying a patch.\"\"\"\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Helper dataclasses used while parsing patches\n", + "# --------------------------------------------------------------------------- #\n", + "@dataclass\n", + "class Chunk:\n", + " orig_index: int = -1\n", + " del_lines: List[str] = field(default_factory=list)\n", + " ins_lines: List[str] = field(default_factory=list)\n", + "\n", + "\n", + "@dataclass\n", + "class PatchAction:\n", + " type: ActionType\n", + " new_file: Optional[str] = None\n", + " chunks: List[Chunk] = field(default_factory=list)\n", + " move_path: Optional[str] = None\n", + "\n", + "\n", + "@dataclass\n", + "class Patch:\n", + " actions: Dict[str, PatchAction] = field(default_factory=dict)\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Patch text parser\n", + "# --------------------------------------------------------------------------- #\n", + "@dataclass\n", + "class Parser:\n", + " current_files: Dict[str, str]\n", + " lines: List[str]\n", + " index: int = 0\n", + " patch: Patch = field(default_factory=Patch)\n", + " fuzz: int = 0\n", + "\n", + " # ------------- low-level helpers -------------------------------------- #\n", + " def _cur_line(self) -> str:\n", + " if self.index >= len(self.lines):\n", + " raise DiffError(\"Unexpected end of input while parsing patch\")\n", + " return self.lines[self.index]\n", + "\n", + " @staticmethod\n", + " def _norm(line: str) -> str:\n", + " \"\"\"Strip CR so comparisons work for both LF and CRLF input.\"\"\"\n", + " return line.rstrip(\"\\r\")\n", + "\n", + " # ------------- scanning convenience ----------------------------------- #\n", + " def is_done(self, prefixes: Optional[Tuple[str, ...]] = None) -> bool:\n", + " if self.index >= len(self.lines):\n", + " return True\n", + " if (\n", + " prefixes\n", + " and len(prefixes) > 0\n", + " and self._norm(self._cur_line()).startswith(prefixes)\n", + " ):\n", + " return True\n", + " return False\n", + "\n", + " def startswith(self, prefix: Union[str, Tuple[str, ...]]) -> bool:\n", + " return self._norm(self._cur_line()).startswith(prefix)\n", + "\n", + " def read_str(self, prefix: str) -> str:\n", + " \"\"\"\n", + " Consume the current line if it starts with *prefix* and return the text\n", + " **after** the prefix. Raises if prefix is empty.\n", + " \"\"\"\n", + " if prefix == \"\":\n", + " raise ValueError(\"read_str() requires a non-empty prefix\")\n", + " if self._norm(self._cur_line()).startswith(prefix):\n", + " text = self._cur_line()[len(prefix) :]\n", + " self.index += 1\n", + " return text\n", + " return \"\"\n", + "\n", + " def read_line(self) -> str:\n", + " \"\"\"Return the current raw line and advance.\"\"\"\n", + " line = self._cur_line()\n", + " self.index += 1\n", + " return line\n", + "\n", + " # ------------- public entry point -------------------------------------- #\n", + " def parse(self) -> None:\n", + " while not self.is_done((\"*** End Patch\",)):\n", + " # ---------- UPDATE ---------- #\n", + " path = self.read_str(\"*** Update File: \")\n", + " if path:\n", + " if path in self.patch.actions:\n", + " raise DiffError(f\"Duplicate update for file: {path}\")\n", + " move_to = self.read_str(\"*** Move to: \")\n", + " if path not in self.current_files:\n", + " raise DiffError(f\"Update File Error - missing file: {path}\")\n", + " text = self.current_files[path]\n", + " action = self._parse_update_file(text)\n", + " action.move_path = move_to or None\n", + " self.patch.actions[path] = action\n", + " continue\n", + "\n", + " # ---------- DELETE ---------- #\n", + " path = self.read_str(\"*** Delete File: \")\n", + " if path:\n", + " if path in self.patch.actions:\n", + " raise DiffError(f\"Duplicate delete for file: {path}\")\n", + " if path not in self.current_files:\n", + " raise DiffError(f\"Delete File Error - missing file: {path}\")\n", + " self.patch.actions[path] = PatchAction(type=ActionType.DELETE)\n", + " continue\n", + "\n", + " # ---------- ADD ---------- #\n", + " path = self.read_str(\"*** Add File: \")\n", + " if path:\n", + " if path in self.patch.actions:\n", + " raise DiffError(f\"Duplicate add for file: {path}\")\n", + " if path in self.current_files:\n", + " raise DiffError(f\"Add File Error - file already exists: {path}\")\n", + " self.patch.actions[path] = self._parse_add_file()\n", + " continue\n", + "\n", + " raise DiffError(f\"Unknown line while parsing: {self._cur_line()}\")\n", + "\n", + " if not self.startswith(\"*** End Patch\"):\n", + " raise DiffError(\"Missing *** End Patch sentinel\")\n", + " self.index += 1 # consume sentinel\n", + "\n", + " # ------------- section parsers ---------------------------------------- #\n", + " def _parse_update_file(self, text: str) -> PatchAction:\n", + " action = PatchAction(type=ActionType.UPDATE)\n", + " lines = text.split(\"\\n\")\n", + " index = 0\n", + " while not self.is_done(\n", + " (\n", + " \"*** End Patch\",\n", + " \"*** Update File:\",\n", + " \"*** Delete File:\",\n", + " \"*** Add File:\",\n", + " \"*** End of File\",\n", + " )\n", + " ):\n", + " def_str = self.read_str(\"@@ \")\n", + " section_str = \"\"\n", + " if not def_str and self._norm(self._cur_line()) == \"@@\":\n", + " section_str = self.read_line()\n", + "\n", + " if not (def_str or section_str or index == 0):\n", + " raise DiffError(f\"Invalid line in update section:\\n{self._cur_line()}\")\n", + "\n", + " if def_str.strip():\n", + " found = False\n", + " if def_str not in lines[:index]:\n", + " for i, s in enumerate(lines[index:], index):\n", + " if s == def_str:\n", + " index = i + 1\n", + " found = True\n", + " break\n", + " if not found and def_str.strip() not in [\n", + " s.strip() for s in lines[:index]\n", + " ]:\n", + " for i, s in enumerate(lines[index:], index):\n", + " if s.strip() == def_str.strip():\n", + " index = i + 1\n", + " self.fuzz += 1\n", + " found = True\n", + " break\n", + "\n", + " next_ctx, chunks, end_idx, eof = peek_next_section(self.lines, self.index)\n", + " new_index, fuzz = find_context(lines, next_ctx, index, eof)\n", + " if new_index == -1:\n", + " ctx_txt = \"\\n\".join(next_ctx)\n", + " raise DiffError(\n", + " f\"Invalid {'EOF ' if eof else ''}context at {index}:\\n{ctx_txt}\"\n", + " )\n", + " self.fuzz += fuzz\n", + " for ch in chunks:\n", + " ch.orig_index += new_index\n", + " action.chunks.append(ch)\n", + " index = new_index + len(next_ctx)\n", + " self.index = end_idx\n", + " return action\n", + "\n", + " def _parse_add_file(self) -> PatchAction:\n", + " lines: List[str] = []\n", + " while not self.is_done(\n", + " (\"*** End Patch\", \"*** Update File:\", \"*** Delete File:\", \"*** Add File:\")\n", + " ):\n", + " s = self.read_line()\n", + " if not s.startswith(\"+\"):\n", + " raise DiffError(f\"Invalid Add File line (missing '+'): {s}\")\n", + " lines.append(s[1:]) # strip leading '+'\n", + " return PatchAction(type=ActionType.ADD, new_file=\"\\n\".join(lines))\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Helper functions\n", + "# --------------------------------------------------------------------------- #\n", + "def find_context_core(\n", + " lines: List[str], context: List[str], start: int\n", + ") -> Tuple[int, int]:\n", + " if not context:\n", + " return start, 0\n", + "\n", + " for i in range(start, len(lines)):\n", + " if lines[i : i + len(context)] == context:\n", + " return i, 0\n", + " for i in range(start, len(lines)):\n", + " if [s.rstrip() for s in lines[i : i + len(context)]] == [\n", + " s.rstrip() for s in context\n", + " ]:\n", + " return i, 1\n", + " for i in range(start, len(lines)):\n", + " if [s.strip() for s in lines[i : i + len(context)]] == [\n", + " s.strip() for s in context\n", + " ]:\n", + " return i, 100\n", + " return -1, 0\n", + "\n", + "\n", + "def find_context(\n", + " lines: List[str], context: List[str], start: int, eof: bool\n", + ") -> Tuple[int, int]:\n", + " if eof:\n", + " new_index, fuzz = find_context_core(lines, context, len(lines) - len(context))\n", + " if new_index != -1:\n", + " return new_index, fuzz\n", + " new_index, fuzz = find_context_core(lines, context, start)\n", + " return new_index, fuzz + 10_000\n", + " return find_context_core(lines, context, start)\n", + "\n", + "\n", + "def peek_next_section(\n", + " lines: List[str], index: int\n", + ") -> Tuple[List[str], List[Chunk], int, bool]:\n", + " old: List[str] = []\n", + " del_lines: List[str] = []\n", + " ins_lines: List[str] = []\n", + " chunks: List[Chunk] = []\n", + " mode = \"keep\"\n", + " orig_index = index\n", + "\n", + " while index < len(lines):\n", + " s = lines[index]\n", + " if s.startswith(\n", + " (\n", + " \"@@\",\n", + " \"*** End Patch\",\n", + " \"*** Update File:\",\n", + " \"*** Delete File:\",\n", + " \"*** Add File:\",\n", + " \"*** End of File\",\n", + " )\n", + " ):\n", + " break\n", + " if s == \"***\":\n", + " break\n", + " if s.startswith(\"***\"):\n", + " raise DiffError(f\"Invalid Line: {s}\")\n", + " index += 1\n", + "\n", + " last_mode = mode\n", + " if s == \"\":\n", + " s = \" \"\n", + " if s[0] == \"+\":\n", + " mode = \"add\"\n", + " elif s[0] == \"-\":\n", + " mode = \"delete\"\n", + " elif s[0] == \" \":\n", + " mode = \"keep\"\n", + " else:\n", + " raise DiffError(f\"Invalid Line: {s}\")\n", + " s = s[1:]\n", + "\n", + " if mode == \"keep\" and last_mode != mode:\n", + " if ins_lines or del_lines:\n", + " chunks.append(\n", + " Chunk(\n", + " orig_index=len(old) - len(del_lines),\n", + " del_lines=del_lines,\n", + " ins_lines=ins_lines,\n", + " )\n", + " )\n", + " del_lines, ins_lines = [], []\n", + "\n", + " if mode == \"delete\":\n", + " del_lines.append(s)\n", + " old.append(s)\n", + " elif mode == \"add\":\n", + " ins_lines.append(s)\n", + " elif mode == \"keep\":\n", + " old.append(s)\n", + "\n", + " if ins_lines or del_lines:\n", + " chunks.append(\n", + " Chunk(\n", + " orig_index=len(old) - len(del_lines),\n", + " del_lines=del_lines,\n", + " ins_lines=ins_lines,\n", + " )\n", + " )\n", + "\n", + " if index < len(lines) and lines[index] == \"*** End of File\":\n", + " index += 1\n", + " return old, chunks, index, True\n", + "\n", + " if index == orig_index:\n", + " raise DiffError(\"Nothing in this section\")\n", + " return old, chunks, index, False\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Patch → Commit and Commit application\n", + "# --------------------------------------------------------------------------- #\n", + "def _get_updated_file(text: str, action: PatchAction, path: str) -> str:\n", + " if action.type is not ActionType.UPDATE:\n", + " raise DiffError(\"_get_updated_file called with non-update action\")\n", + " orig_lines = text.split(\"\\n\")\n", + " dest_lines: List[str] = []\n", + " orig_index = 0\n", + "\n", + " for chunk in action.chunks:\n", + " if chunk.orig_index > len(orig_lines):\n", + " raise DiffError(\n", + " f\"{path}: chunk.orig_index {chunk.orig_index} exceeds file length\"\n", + " )\n", + " if orig_index > chunk.orig_index:\n", + " raise DiffError(\n", + " f\"{path}: overlapping chunks at {orig_index} > {chunk.orig_index}\"\n", + " )\n", + "\n", + " dest_lines.extend(orig_lines[orig_index : chunk.orig_index])\n", + " orig_index = chunk.orig_index\n", + "\n", + " dest_lines.extend(chunk.ins_lines)\n", + " orig_index += len(chunk.del_lines)\n", + "\n", + " dest_lines.extend(orig_lines[orig_index:])\n", + " return \"\\n\".join(dest_lines)\n", + "\n", + "\n", + "def patch_to_commit(patch: Patch, orig: Dict[str, str]) -> Commit:\n", + " commit = Commit()\n", + " for path, action in patch.actions.items():\n", + " if action.type is ActionType.DELETE:\n", + " commit.changes[path] = FileChange(\n", + " type=ActionType.DELETE, old_content=orig[path]\n", + " )\n", + " elif action.type is ActionType.ADD:\n", + " if action.new_file is None:\n", + " raise DiffError(\"ADD action without file content\")\n", + " commit.changes[path] = FileChange(\n", + " type=ActionType.ADD, new_content=action.new_file\n", + " )\n", + " elif action.type is ActionType.UPDATE:\n", + " new_content = _get_updated_file(orig[path], action, path)\n", + " commit.changes[path] = FileChange(\n", + " type=ActionType.UPDATE,\n", + " old_content=orig[path],\n", + " new_content=new_content,\n", + " move_path=action.move_path,\n", + " )\n", + " return commit\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# User-facing helpers\n", + "# --------------------------------------------------------------------------- #\n", + "def text_to_patch(text: str, orig: Dict[str, str]) -> Tuple[Patch, int]:\n", + " lines = text.splitlines() # preserves blank lines, no strip()\n", + " if (\n", + " len(lines) < 2\n", + " or not Parser._norm(lines[0]).startswith(\"*** Begin Patch\")\n", + " or Parser._norm(lines[-1]) != \"*** End Patch\"\n", + " ):\n", + " raise DiffError(\"Invalid patch text - missing sentinels\")\n", + "\n", + " parser = Parser(current_files=orig, lines=lines, index=1)\n", + " parser.parse()\n", + " return parser.patch, parser.fuzz\n", + "\n", + "\n", + "def identify_files_needed(text: str) -> List[str]:\n", + " lines = text.splitlines()\n", + " return [\n", + " line[len(\"*** Update File: \") :]\n", + " for line in lines\n", + " if line.startswith(\"*** Update File: \")\n", + " ] + [\n", + " line[len(\"*** Delete File: \") :]\n", + " for line in lines\n", + " if line.startswith(\"*** Delete File: \")\n", + " ]\n", + "\n", + "\n", + "def identify_files_added(text: str) -> List[str]:\n", + " lines = text.splitlines()\n", + " return [\n", + " line[len(\"*** Add File: \") :]\n", + " for line in lines\n", + " if line.startswith(\"*** Add File: \")\n", + " ]\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# File-system helpers\n", + "# --------------------------------------------------------------------------- #\n", + "def load_files(paths: List[str], open_fn: Callable[[str], str]) -> Dict[str, str]:\n", + " return {path: open_fn(path) for path in paths}\n", + "\n", + "\n", + "def apply_commit(\n", + " commit: Commit,\n", + " write_fn: Callable[[str, str], None],\n", + " remove_fn: Callable[[str], None],\n", + ") -> None:\n", + " for path, change in commit.changes.items():\n", + " if change.type is ActionType.DELETE:\n", + " remove_fn(path)\n", + " elif change.type is ActionType.ADD:\n", + " if change.new_content is None:\n", + " raise DiffError(f\"ADD change for {path} has no content\")\n", + " write_fn(path, change.new_content)\n", + " elif change.type is ActionType.UPDATE:\n", + " if change.new_content is None:\n", + " raise DiffError(f\"UPDATE change for {path} has no new content\")\n", + " target = change.move_path or path\n", + " write_fn(target, change.new_content)\n", + " if change.move_path:\n", + " remove_fn(path)\n", + "\n", + "\n", + "def process_patch(\n", + " text: str,\n", + " open_fn: Callable[[str], str],\n", + " write_fn: Callable[[str, str], None],\n", + " remove_fn: Callable[[str], None],\n", + ") -> str:\n", + " if not text.startswith(\"*** Begin Patch\"):\n", + " raise DiffError(\"Patch text must start with *** Begin Patch\")\n", + " paths = identify_files_needed(text)\n", + " orig = load_files(paths, open_fn)\n", + " patch, _fuzz = text_to_patch(text, orig)\n", + " commit = patch_to_commit(patch, orig)\n", + " apply_commit(commit, write_fn, remove_fn)\n", + " return \"Done!\"\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# Default FS helpers\n", + "# --------------------------------------------------------------------------- #\n", + "def open_file(path: str) -> str:\n", + " with open(path, \"rt\", encoding=\"utf-8\") as fh:\n", + " return fh.read()\n", + "\n", + "\n", + "def write_file(path: str, content: str) -> None:\n", + " target = pathlib.Path(path)\n", + " target.parent.mkdir(parents=True, exist_ok=True)\n", + " with target.open(\"wt\", encoding=\"utf-8\") as fh:\n", + " fh.write(content)\n", + "\n", + "\n", + "def remove_file(path: str) -> None:\n", + " pathlib.Path(path).unlink(missing_ok=True)\n", + "\n", + "\n", + "# --------------------------------------------------------------------------- #\n", + "# CLI entry-point\n", + "# --------------------------------------------------------------------------- #\n", + "def main() -> None:\n", + " import sys\n", + "\n", + " patch_text = sys.stdin.read()\n", + " if not patch_text:\n", + " print(\"Please pass patch text through stdin\", file=sys.stderr)\n", + " return\n", + " try:\n", + " result = process_patch(patch_text, open_file, write_file, remove_file)\n", + " except DiffError as exc:\n", + " print(exc, file=sys.stderr)\n", + " return\n", + " print(result)\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other Effective Diff Formats\n", + "\n", + "If you want to try using a different diff format, we found in testing that the SEARCH/REPLACE diff format used in Aider’s polyglot benchmark, as well as a pseudo-XML format with no internal escaping, both had high success rates.\n", + "\n", + "These diff formats share two key aspects: (1) they do not use line numbers, and (2) they provide both the exact code to be replaced, and the exact code with which to replace it, with clear delimiters between the two." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "SEARCH_REPLACE_DIFF_EXAMPLE = \"\"\"\n", + "path/to/file.py\n", + "```\n", + ">>>>>>> SEARCH\n", + "def search():\n", + " pass\n", + "=======\n", + "def search():\n", + " raise NotImplementedError()\n", + "<<<<<<< REPLACE\n", + "\"\"\"\n", + "\n", + "PSEUDO_XML_DIFF_EXAMPLE = \"\"\"\n", + "<edit>\n", + "<file>\n", + "path/to/file.py\n", + "</file>\n", + "<old_code>\n", + "def search():\n", + " pass\n", + "</old_code>\n", + "<new_code>\n", + "def search():\n", + " raise NotImplementedError()\n", + "</new_code>\n", + "</edit>\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/imgs/cat_portrait_pixel.jpg b/examples/imgs/cat_portrait_pixel.jpg new file mode 100644 index 0000000000..c8ad8980a2 Binary files /dev/null and b/examples/imgs/cat_portrait_pixel.jpg differ diff --git a/examples/imgs/cat_with_hat.jpg b/examples/imgs/cat_with_hat.jpg new file mode 100644 index 0000000000..853d1ea38b Binary files /dev/null and b/examples/imgs/cat_with_hat.jpg differ diff --git a/examples/imgs/glorptak.jpg b/examples/imgs/glorptak.jpg new file mode 100644 index 0000000000..2db074cd0c Binary files /dev/null and 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"metadata": {}, + "source": [ + "# Guide to Using the Responses API's MCP Tool " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building agentic application often requires connecting to external services. Traditionally, this is done through function calling where every action makes a round-trip from the model to your backend, then to an external service, waits for a response, and finally returns the result to the model. This process introduces multiple network hops and significant latency, making it cumbersome to scale and manage.\n", + "\n", + "The hosted Model Context Protocol (MCP) tool in the Responses API makes this easier. Instead of manually wiring each function call to specific services, you can configure your model once to point to an MCP server (or several!). That server acts as a centralized tool host, exposing standard commands like “search product catalog” or “add item to cart.” This allows for simpler orchestration and centralized management of tools. With MCP, the model interacts directly with the MCP server, reducing latency and eliminating backend coordination." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use cases simplified by the MCP tool \n", + "\n", + "MCP significantly reduces the friction of building products that interact with external services, allowing you to tie different services together seamlessly. Here’s a sampler of use cases that once involved friction but are now much simpler since the model can communicate directly with remote MCP servers.\n", + "\n", + "| **Domain** | **Use case unlocked by MCP tool** | **Previous friction** |\n", + "|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------|\n", + "| **Commerce / payments** | - Add an item to a Shopify cart and hand back a checkout URL in one turn — `\"Add the Allbirds Men’s Tree Dasher 2 in size 10\"` → cart link <br> - Generate a Stripe payment link | Function calling meant you had to write a custom `cart_add` or `create_payment_link` wrapper and host your own relay server. |\n", + "| **Dev-ops & code quality**| - Ask Sentry for the latest error in a particular file, then open a GitHub issue with a suggested fix in the same conversation | Chaining two different third-party APIs inside one assistive loop involved webhook glue and state juggling. |\n", + "| **Messaging / notifications** | - Grab the morning’s top soccer headlines via web-search and have Twilio text the summary to a phone number in a single call | Required stitching two tool calls in your backend and batching the final SMS payload yourself. |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How the tool works\n", + "\n", + "At a high level, here is how the MCP tool works: \n", + "\n", + "1. Declare the server: When you add an MCP block to the `tools` array, the Responses API runtime first detects which transport the server speaks, either the newer “streamable HTTP” or the older HTTP-over-SSE variant, and uses that protocol for traffic.\n", + "2. Import the tool list: The runtime calls the server’s `tools/list`, passing any headers you provide (API key, OAuth token, etc.). It then writes the result to an `mcp_list_tools` item in the model’s context. While this item is present, the list won’t be fetched again. You can limit what the model sees using `allowed_tools`. \n", + " \n", + " OpenAI discards header values and all but the schema, domain, and subdomains of the MCP `server_url` after each request. Authorization keys and the server URL must be included with every API call. These values won't appear in response objects. Schemas use “strict” mode when possible, otherwise they're loaded as-is.\n", + " \n", + "3. Call and approve tools: Once the model knows the available actions, it can invoke one. Each invocation produces an `mcp_tool_call` item and by default the stream pauses for your explicit approval, but you can disable this once you trust the server.\n", + " \n", + " After approval, the runtime executes the call, streams back the result, and the model decides whether to chain another tool or return a final answer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practices when building with MCP\n", + "\n", + "MCP is still in its early stages, so here are best practices that can improve model performance and behavior as you build. \n", + "\n", + "### Filter tools to avoid ballooning payloads\n", + "\n", + "Remote servers often expose numerous tools without considering how models will interpret and use them. By default, this can result in dozens of endpoints being included, each accompanied by verbose definitions like names, descriptions, and JSON schemas that add hundreds of tokens to the model’s context and increase latency. Compounding this, many servers return entire data objects, such as full Stripe invoice records, even when only a few fields are relevant to the model’s task. To optimize for performance in production, use the `allowed_tools` parameter in the Responses API to limit which tools are included from the server’s `mcp_list_tools`. This reduces token overhead, improves response time, and narrows the model’s decision space. You may also want to exclude certain tools altogether, such as those capable of write actions or those that have financial or security implications." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "curl https://api.openai.com/v1/responses -i \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n", + " -d '{\n", + " \"model\": \"gpt-4.1\",\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"allowed_tools\": [\"search_tiktoken_documentation\", \"fetch_tiktoken_documentation\"],\n", + " \"require_approval\": \"never\"\n", + " }\n", + " ],\n", + " \"input\": \"how does tiktoken work?\"\n", + "}'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reduce latency and tokens via caching and reserve reasoning models for high complexity tasks\n", + "\n", + "The first time the model connects to a server, a new item of the type `mcp_list_tools` is created for each MCP server you add. As long as this item is present in the model's context, we will not call `tools/list` on the server again. This is akin to caching at the user-conversation level. If `mcp_list_tools` is not present, we import the list of tools from the MCP server again. Passing`previous_response_id` in subsequent API requests is one way of ensuring that the `mcp_list_tools` item is present in the model's context on follow-up turns. Alternatively you can also pass in the items manually to new response. The other lever that will affect latency and the number of output tokens is whether you use a reasoning model, as reasoning models will produce far more output tokens, as well as reasoning tokens. Take for example the following two sample curls that compare the number of tokens produced with and without reasoning models:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scenario 1: non-reasoning model " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "curl https://api.openai.com/v1/responses \\ \n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n", + " -d '{\n", + " \"model\": \"gpt-4.1\",\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"require_approval\": \"never\"\n", + " }\n", + " ],\n", + " \"input\": \"how does tiktoken work?\" \n", + " }'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + " \"usage\": {\n", + " \"input_tokens\": 280,\n", + " \"input_tokens_details\": {\n", + " \"cached_tokens\": 0\n", + " },\n", + " \"output_tokens\": 665,\n", + " \"output_tokens_details\": {\n", + " \"reasoning_tokens\": 0\n", + " },\n", + " \"total_tokens\": 945\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scenario 2: reasoning model without `previous_response_id`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "curl https://api.openai.com/v1/responses \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n", + " -d '{\n", + " \"model\": \"o4-mini\",\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"require_approval\": \"never\"\n", + " }\n", + " ],\n", + " \"input\": \"how does tiktoken work?\",\n", + " \"reasoning\": {\n", + " \"effort\": \"medium\",\n", + " \"summary\": \"auto\"\n", + " } \n", + " }'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + " \"usage\": {\n", + " \"input_tokens\": 36436,\n", + " \"input_tokens_details\": {\n", + " \"cached_tokens\": 22964\n", + " },\n", + " \"output_tokens\": 1586,\n", + " \"output_tokens_details\": {\n", + " \"reasoning_tokens\": 576\n", + " },\n", + " \"total_tokens\": 38022 \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using MCP with other tools\n", + "\n", + "The MCP tool is just another entry in the tools array, so the model can use it seamlessly with other hosted tools like `code_interpreter`, `web_search_preview,` or `image_generation`, and with any custom tools you define. You can also use multiple remote MCP servers together. \n", + "\n", + "In this example, we’ll create an agent that is a pricing analyst for a fictional yoga attire store: it first pulls current competitor prices for women’s shorts, yoga pants, and tank tops from the Alo Yoga MCP server, then grabs the price for the same three categories from Uniqlo via the hosted web-search tool. Using Code Interpreter it analyzes last week’s sales from a CSV that was pre-loaded with the Files endpoint, in order to calculate per-item revenue and average order value. Then it measures each item’s price gap versus the newly fetched Uniqlo and Alo Yoga benchmarks. Any product priced 15 percent or more above or below market is flagged, and the agent delivers a concise text report summarizing the discrepancies and key revenue stats." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "system_prompt= \"\"\"You are a pricing analyst for my clothing company. Please use the MCP tool \n", + "to fetch prices from the Alo Yoga MCP server for the categories of women's \n", + "shorts, yoga pants, and tank tops. Use only the MCP server for Alo yoga data, don't search the web. \n", + "\n", + "Next, use the web search tool to search for Uniqlo prices for women's shorts, yoga pants, and tank tops. \n", + "\n", + "In each case for Alo Yoga and Uniqlo, extract the\n", + "price for the top result in each category. Also provide the full URLs\n", + " \n", + "Using the uploaded CSV file of sales data from my store, and with the \n", + "code interpreter tool calculate revenue by product item, compute average order-value on a transaction level, and calculate the percentage price gap between the CSV data and Uniqlo/Alo Yoga prices. \n", + "Flag products priced 15% or more above or below the market. \n", + "Create and output a short report including the findings.\n", + "\n", + "# Steps\n", + "\n", + "1. **Fetch Alo Yoga Prices:**\n", + " - Use the Alo Yoga MCP server to fetch prices for the following products:\n", + "High-Waist Airlift Legging\n", + "Sway Bra Tank\n", + " 5\" Airlift Energy Short\n", + "\n", + "- Ensure you find prices for each. \n", + "- Extract the price of the top result for each category.\n", + "- include URL links \n", + "\n", + "\n", + "2. **Query Uniqlo Prices:**\n", + " - Use the Web-Search tool to search non-sale prices for the following Uniqlo products: \n", + "Women's AIRism Soft Biker Shorts\n", + "Women's AIRism Soft Leggings\n", + "Women's AIRism Bra Sleeveless Top\n", + "- Ensure you find non-sale prices for each. \n", + "- Extract the price for the top result in each category.\n", + "- include URL links \n", + "\n", + "3. **Sales Data Analysis:**\n", + " - Use the uploaded CSV sales data to calculate revenue across each \n", + " product item.\n", + " - Determine the average order-value on a transaction level.\n", + " - For each SKU, compute the percentage price gap between the \n", + " CSV data and Uniqlo/Alo Yoga prices.\n", + " - Flag products priced ≥ 15% above or below the market.\n", + "\n", + "4. **Report:**\n", + " - Compile and output a report including the flagging results\n", + "\n", + "# Output Format\n", + "- A short text report explaining:\n", + " - Any products that are priced ≥ 15% above or below the market, \n", + " with specific details. \"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a sample curl with a placeholder for the above system prompt. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "curl https://api.openai.com/v1/responses \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n", + " -d '{\n", + " \"model\": \"gpt-4.1\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"ABOVE_SYSTEM_PROMPT\"\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"web_search_preview\",\n", + " \"user_location\": {\n", + " \"type\": \"approximate\",\n", + " \"country\": \"US\"\n", + " },\n", + " \"search_context_size\": \"medium\"\n", + " },\n", + " {\n", + " \"type\": \"code_interpreter\",\n", + " \"container\": {\n", + " \"type\": \"auto\",\n", + " \"file_ids\": [\n", + " \"file-WTiyGcZySaU6n218gj4XxR\"\n", + " ]\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_url\": \"https://www.aloyoga.com/api/mcp\",\n", + " \"server_label\": \"aloyoga\",\n", + " \"allowed_tools\": [\n", + " \"search_shop_catalog\",\n", + " \"get_product_details\"\n", + " ],\n", + " \"require_approval\": \"never\"\n", + " }\n", + " ],\n", + " \"temperature\": 1,\n", + " \"max_output_tokens\": 2048,\n", + " \"top_p\": 1,\n", + " \"store\": true\n", + " }'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is able to carry forward it’s results from the MCP tool and web search into the code interpreter steps to produce a report with the following content that is formatted for legibility: \n", + "\n", + "---\n", + "#### **Pricing Comparison and Revenue Analysis Report**\n", + "\n", + "**Your Store's Sales & Price Analysis**\n", + "\n", + "- **Revenue by Product:**\n", + " - Shorts: $6,060\n", + " - Tank tops: $6,150\n", + " - Yoga pants: $12,210\n", + "- **Average Order Value:** $872.14\n", + "- **Your Store's Average Selling Price by Category:**\n", + " - Shorts: $60.00\n", + " - Tank tops: $75.00\n", + " - Yoga pants: $110.00\n", + "\n", + "#### **Pricing Gaps vs Market**\n", + "\n", + "| Category | Store Avg Price | vs Alo Yoga Gap (%) | Flagged (≥15%) | vs Uniqlo Gap (%) | Flagged (≥15%) |\n", + "| --- | --- | --- | --- | --- | --- |\n", + "| Shorts | $60.00 | -31.8% | **YES** | +100.7% | **YES** |\n", + "| Tank tops | $75.00 | -14.8% | | +114.9% | **YES** |\n", + "| Yoga pants | $110.00 | -14.1% | | +267.9% | **YES** |\n", + "\n", + "#### **Recommendations & Flags**\n", + "\n", + "**Flagged products (≥15% price gap):**\n", + "\n", + "- **Shorts:** Priced 31.8% below Alo Yoga, but 100.7% above Uniqlo.\n", + "- **Tank tops:** Priced over 114.9% above Uniqlo.\n", + "- **Yoga pants:** Priced 267.9% above Uniqlo.\n", + "\n", + "Shorts are priced significantly below premium competitors (Alo Yoga), but far higher than budget alternatives (Uniqlo). If you want to compete in the premium segment, consider increasing your price. If you want to target budget buyers, a price decrease could be justifiable. Most of your tank tops and yoga pants are similarly positioned—much lower than Alo, but well above Uniqlo.\n", + "\n", + "___" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prompting guidelines to improve MCP tool calls\n", + "\n", + "Depending on your use case you might find that the model invokes many MCP calls, for instance when using catalog-search tools. To avoid endless iterations over large product inventories it’s helpful to instruct the model to limit it’s search to N items and to offer to continue only when the user explicitly asks for more information. This keeps responses focused and snappy.\n", + "\n", + "If the MCP servers you’re using include exhaustive `mcp_list_tools`, it’s also worth Including some targeted few-shot examples to show the model how to choose the correct server and to stop once it has what it needs, instead of issuing redundant calls.\n", + "\n", + "Finally, adding guidance to remind the model that if essential information (size, color, product line, etc.) is missing from the user query, it should ask a follow-up question rather than launching a broad search. This small prompt nudge reduces unnecessary tool calls and improves answer quality. Here’s a sample prompt that shows how these guidelines come together:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "system_prompt= \"\"\"You are an AI assistant that can call the following MCP servers:\n", + "\n", + "1. allbirds_store\n", + "2. gitmcp\n", + "\n", + "Steps\n", + "1. Use the MCP servers above to answer the user query, not every MCP server will relevant for a given query so you can choose which ones to invoke. \n", + "2. If the user’s request lacks essential details (size, color, etc.), ask a follow-up question first rather than guessing and using the MCP server tool prematurely. \n", + "3. If searching for products in a catalog, only return 4 results at a time before asking the user whether they want to see more. \n", + "\n", + "### Few-shot examples\n", + "\n", + "# EX1 — Direct product search, then fetch variant details\n", + "user: Do you have the Allbirds Tree Runner in men’s size 10? \n", + "assistant: {\"name\":\"allbirds_store.search_shop_catalog\",\n", + " \"arguments\":{\"query\":\"Tree Runner\",\n", + " \"context\":\"Men’s size 10 shoes\"}}\n", + "# (assume the search returns product_id \"gid://shopify/Product/987\") \n", + "assistant: {\"name\":\"allbirds_store.get_product_details\",\n", + " \"arguments\":{\"product_id\":\"gid://shopify/Product/987\",\n", + " \"options\":{\"Size\":\"10\"}}}\n", + "\n", + "# EX2 — Clarify missing size, then two-step color variant lookup\n", + "user: I want the Tree Runner in blue. \n", + "assistant: Sure — what size do you need? \n", + "user: Size 10. \n", + "assistant: {\"name\":\"allbirds_store.search_shop_catalog\",\n", + " \"arguments\":{\"query\":\"Tree Runner\",\n", + " \"context\":\"Blue variant, size 10\"}}\n", + "# (assume the search returns product_id \"gid://shopify/Product/987\") \n", + "assistant: {\"name\":\"allbirds_store.get_product_details\",\n", + " \"arguments\":{\"product_id\":\"gid://shopify/Product/987\",\n", + " \"options\":{\"Size\":\"10\",\"Color\":\"Blue\"}}}\n", + "\n", + "# EX3 — Git docs: search then fetch specific file\n", + "user: Can you show me how tiktoken does byte-pair encoding? \n", + "assistant: {\"name\":\"gitmcp.search_website_documentation\",\n", + " \"arguments\":{\"query\":\"tiktoken byte-pair encoding\"}}\n", + "# (assume the search returns document_id \"docs/ENCODING.md\") \n", + "assistant: {\"name\":\"gitmcp.fetch_website_documentation\",\n", + " \"arguments\":{\"document_id\":\"docs/ENCODING.md\"}} \"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "outputs": [], + "source": [ + "curl https://api.openai.com/v1/responses \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n", + " -d '{\n", + " \"model\": \"gpt-4.1\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"ABOVE_SYSTEM_PROMPT\"\n", + " }\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"find me womens tree loungers in size 8\"\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_url\": \"https://www.allbirds.com/api/mcp\",\n", + " \"server_label\": \"allbirds\",\n", + " \"allowed_tools\": [\n", + " \"search_shop_catalog\",\n", + " \"get_cart\",\n", + " \"update_cart\",\n", + " \"search_shop_policies_and_faqs\",\n", + " \"get_product_details\"\n", + " ],\n", + " \"require_approval\": \"never\"\n", + " },\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"allowed_tools\": [\n", + " \"fetch_tiktoken_documentation\",\n", + " \"search_tiktoken_documentation\",\n", + " \"search_tiktoken_code\",\n", + " \"fetch_generic_url_content\"\n", + " ],\n", + " \"require_approval\": \"never\"\n", + " }\n", + " ],\n", + " \"temperature\": 1,\n", + " \"max_output_tokens\": 2048\n", + " }'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "The hosted MCP tool in the Responses API turns external-service access from a bespoke plumbing task into a first-class capability of the API. By connecting to a remote server, letting the runtime cache its tool list, and trimming that list with `allowed_tools`, you eliminate the extra network hop, cut token overhead, and give the model a concise, discoverable action set. When combined with built-in tools such as `code_interpreter`, `web_search_preview`, or `image_gen`, MCP unlocks rich, multi-service workflows whether you’re analyzing sales data, triaging production errors, or automating checkout flows." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/mermaid/agents_sdk_transcription.mmd b/examples/mermaid/agents_sdk_transcription.mmd new file mode 100644 index 0000000000..8569143eb9 --- /dev/null +++ b/examples/mermaid/agents_sdk_transcription.mmd @@ -0,0 +1,8 @@ +```{mermaid} +graph LR + Mic -- "PCM frames" --> VP["VoicePipeline"] + VP -- "VAD & resample" --> Buf["Sentence buffer"] + Buf --> GPT["gpt-4o-transcribe"] + GPT --> Agent["Agent callbacks"] + Agent -- "print / reply" --> App +``` \ No newline at end of file diff --git a/examples/mermaid/realtime_api_transcription.mmd b/examples/mermaid/realtime_api_transcription.mmd new file mode 100644 index 0000000000..7213edd4f1 --- /dev/null +++ b/examples/mermaid/realtime_api_transcription.mmd @@ -0,0 +1,13 @@ +```mermaid +sequenceDiagram + participant Mic + participant App + participant WS as "WebSocket" + participant OAI as "Realtime Server" + + Mic ->> App: 20–40 ms PCM frames + App ->> WS: Base64-encoded chunks<br/>input_audio_buffer.append + WS ->> OAI: Audio stream + OAI -->> WS: JSON transcription events<br/>(partial & complete) + WS -->> App: Transcript updates +``` \ No newline at end of file diff --git a/examples/mermaid/speech-to-text-not-streaming.mmd b/examples/mermaid/speech-to-text-not-streaming.mmd new file mode 100644 index 0000000000..55da48e437 --- /dev/null +++ b/examples/mermaid/speech-to-text-not-streaming.mmd @@ -0,0 +1,7 @@ +```mermaid +flowchart LR + AudioFile["Audio file<br/>(WAV • MP3 • FLAC)"] --> Upload["Binary upload"] + Upload --> API["/v1/audio/transcriptions"] + API --> JSONOutput["JSON transcription<br/>+ metadata"] + JSONOutput --> App["Your application"] +``` \ No newline at end of file diff --git a/examples/mermaid/speech-to-text-streaming.mmd b/examples/mermaid/speech-to-text-streaming.mmd new file mode 100644 index 0000000000..30862c0988 --- /dev/null +++ b/examples/mermaid/speech-to-text-streaming.mmd @@ -0,0 +1,9 @@ +```mermaid +flowchart LR + A["Finished audio file<br/>(WAV • MP3 • FLAC • …)"] + B["OpenAI STT engine<br/>(gpt-4o-transcribe)"] + C["Your application / UI"] + + A -->|HTTP POST<br/>/v1/audio/transcriptions<br/>stream=true| B + B -->|chunked HTTP response<br/>partial & final transcripts| C +``` \ No newline at end of file diff --git a/examples/multimodal/image_understanding_with_rag.ipynb b/examples/multimodal/image_understanding_with_rag.ipynb new file mode 100644 index 0000000000..97473732f1 --- /dev/null +++ b/examples/multimodal/image_understanding_with_rag.ipynb @@ -0,0 +1,795 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Understanding with RAG using OpenAI's Vision & Responses APIs\n", + "\n", + "Welcome! This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) system using OpenAI’s Vision and Responses APIs. It focuses on multimodal data, combining image and text inputs to analyze customer experiences. The system leverages GPT-4.1 and integrates image understanding with file search to provide context-aware responses.\n", + "\n", + "Multimodal datasets are increasingly common, particularly in domains like healthcare, where records often contain both visual data (e.g. radiology scans) and accompanying text (e.g. clinical notes). Real-world datasets also tend to be noisy, with incomplete or missing information, making it critical to analyze multiple modalities in tandem.\n", + "\n", + "This guide focuses on a customer service use case: evaluating customer feedback that may include photos, and written reviews. You’ll learn how to synthetically generate both image and text inputs, use file search for context retrieval, and apply the Evals API to assess how incorporating image understanding impacts overall performance.\n", + "\n", + "---\n", + "\n", + "## Overview\n", + "\n", + "---\n", + "\n", + "## Table of Contents\n", + "\n", + "1. [Setup & Dependencies](#setup-and-dependencies)\n", + "2. [Example Generations](#example-generations)\n", + "3. [Data Processing](#data-processing)\n", + " - Load synthetic datasets\n", + " - Merge data\n", + "4. [Populating Vector Store](#populating-vector-store)\n", + " - Upload data for file search\n", + " - Set up attribute filters\n", + "5. [Retrieval and Filtering](#retrieval-and-filtering)\n", + " - Test retrieval performance\n", + " - Apply attribute-based filters\n", + "6. [Evaluation and Analysis](#evaluation-and-analysis)\n", + " - Compare predictions to ground truth\n", + " - Analyze performance metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup and Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install openai evals pandas numpy matplotlib tqdm ipython --upgrade --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "from io import BytesIO\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from openai import OpenAI\n", + "from IPython.display import display, Image\n", + "from tqdm.notebook import tqdm\n", + "\n", + "cache_dir = Path('.local_cache')\n", + "cache_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + "client = OpenAI()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Generations\n", + "\n", + "Generating high-quality training and evaluation data for machine learning tasks can be costly and time-consuming. Synthetic data offers a practical and scalable alternative. In this notebook, the OpenAI Image API is used to generate synthetic images, while the Responses API is employed to create synthetic text, enabling efficient prototyping and experimentation across multimodal tasks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "prompt = (\"Gourmet pasta neatly plated with garnish and sides on a white ceramic plate, \"\n", + " \"photographed from above on a restaurant table. Soft shadows and vibrant colors.\")\n", + "cache_path = f\".local_cache/{hash(prompt)}.png\"\n", + "\n", + "if not os.path.exists(cache_path):\n", + " response = client.images.generate(\n", + " model=\"gpt-image-1\",\n", + " prompt=prompt,\n", + " size=\"1024x1024\"\n", + " )\n", + " \n", + " with open(cache_path, \"wb\") as f:\n", + " f.write(base64.b64decode(response.data[0].b64_json))\n", + " print(f\"Generated and cached: {cache_path}\")\n", + "\n", + "else:\n", + " print(f\"Loading from cache: {cache_path}\")\n", + "\n", + "display(Image(filename=cache_path))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_food_delivery_review(sentiment: str = 'positive') -> str:\n", + " \"\"\"\n", + " Generate a synthetic food delivery review with the specified sentiment.\n", + " \n", + " Args:\n", + " sentiment: An adjective such as 'positive' or 'negative'.\n", + " \n", + " Returns:\n", + " Generated review text\n", + " \"\"\"\n", + " prompt = \"Write a very concise, realistic customer review for a recent food delivery.\"\n", + " prompt += f\" The review should reflect a {sentiment} experience.\"\n", + " \n", + " response = client.responses.create(\n", + " model=\"gpt-4.1\",\n", + " input=[{\"role\": \"user\", \"content\": prompt}]\n", + " )\n", + " return response.output_text\n", + "\n", + "\n", + "review = generate_food_delivery_review()\n", + "print(review)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Processing\n", + "\n", + "In this example, we’ll work with a pre-generated synthetic dataset of customer feedback that includes short text snippets, images from customer reviews, and occasionally combined multimodal entries. You can also generate your own synthetic dataset using the examples provided above to tailor the data to your specific use case." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download the dataset\n", + "! mkdir -p .local_cache/images\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/df.csv -O .local_cache/df.csv\n", + "\n", + "\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/1.png -O .local_cache/images/1.png\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/2.png -O .local_cache/images/2.png\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/3.png -O .local_cache/images/3.png\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/4.png -O .local_cache/images/4.png\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/5.png -O .local_cache/images/5.png\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/6.png -O .local_cache/images/6.png\n", + "! wget https://raw.githubusercontent.com/robtinn/image_understanding_rag_dataset/main/data/images/7.png -O .local_cache/images/7.png" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def encode_image(image_path: str) -> str:\n", + " \"\"\"Encode image file to base64 string.\"\"\"\n", + " with open(image_path, \"rb\") as f:\n", + " return base64.b64encode(f.read()).decode(\"utf-8\")\n", + "\n", + "\n", + "def analyze_image_sentiment(image_path: str) -> str:\n", + " \"\"\"Analyze food delivery image and return sentiment analysis.\"\"\"\n", + " base64_image = encode_image(image_path)\n", + " response = client.responses.create(\n", + " model=\"gpt-4.1\",\n", + " input=[{\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"input_text\",\n", + " \"text\": \"Analyze this food delivery image. Respond with a brief description and sentiment (positive/negative) in one line.\"\n", + " },\n", + " {\n", + " \"type\": \"input_image\",\n", + " \"image_url\": f\"data:image/jpeg;base64,{base64_image}\",\n", + " },\n", + " ],\n", + " }],\n", + " max_output_tokens=50,\n", + " temperature=0.2\n", + " )\n", + " return response.output_text.strip()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\".local_cache/df.csv\")\n", + "cache_dir = Path(\".local_cache\")\n", + "\n", + "for idx, row in df[~df['image_path'].isna()].iterrows():\n", + " image_path = cache_dir / 'images' / row['image_path']\n", + " sentiment = analyze_image_sentiment(str(image_path))\n", + " df.at[idx, 'full_sentiment'] = f\"{row['text']} {sentiment}\" if pd.notna(row['text']) else sentiment\n", + " print(f\"Processed {row['image_path']}\")\n", + "\n", + "df['full_sentiment'] = df['full_sentiment'].fillna(df['text'])\n", + "\n", + "output_path = cache_dir / \"df_full_sentiment.csv\"\n", + "df.to_csv(output_path, index=False)\n", + "print(f\"\\nSaved results to {output_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_colwidth', 100) # Increase from default (50) to view full sentiment\n", + "display(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Populating Vector Store" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example uses OpenAI's built-in vector store and file search capabilities to build a RAG system that can analyse customer experiences from their feedback, which can be both visual and text-based. We create two vector stores for comparisons, one with image understanding and one without." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "text_vector_store = client.vector_stores.create(\n", + " name=\"food_delivery_reviews_text\",\n", + " metadata={\n", + " \"purpose\": \"text_understanding\",\n", + " \"created_by\": \"notebook\",\n", + " \"version\": \"1.0\"\n", + " }\n", + ")\n", + "text_vector_store_id = text_vector_store.id\n", + "\n", + "text_image_vector_store = client.vector_stores.create(\n", + " name=\"food_delivery_reviews_text_image\",\n", + " metadata={\n", + " \"purpose\": \"text_image_understanding\",\n", + " \"created_by\": \"notebook\",\n", + " \"version\": \"1.0\"\n", + " }\n", + ")\n", + "text_image_vector_store_id = text_image_vector_store.id\n", + "\n", + "print(\"Vector Store IDs:\")\n", + "print(f\" Text: {text_vector_store_id}\")\n", + "print(f\" Text+Image: {text_image_vector_store_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# upload files to vector database and set metadata\n", + "\n", + "def upload_files_to_vector_store(vector_store_id, df, column_name=\"full_sentiment\"):\n", + " file_ids = []\n", + " for i, row in tqdm(df.iterrows(), total=len(df), desc=\"Uploading context files\"):\n", + " if pd.isna(row[column_name]):\n", + " file_stream = BytesIO('No information available.'.encode('utf-8'))\n", + " else:\n", + " file_stream = BytesIO(row[column_name].encode('utf-8'))\n", + " file_stream.name = f\"context_{row.get('id', i)}_{row.get('month', '')}.txt\"\n", + " \n", + " file = client.vector_stores.files.upload(\n", + " vector_store_id=vector_store_id,\n", + " file=file_stream\n", + " )\n", + " file_ids.append(file.id)\n", + "\n", + " for i, row in tqdm(df.iterrows(), total=len(df), desc=\"Updating file attributes\"):\n", + " client.vector_stores.files.update(\n", + " vector_store_id=vector_store_id,\n", + " file_id=file_ids[i],\n", + " attributes={\"month\": row[\"month\"]}\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "upload_files_to_vector_store(text_image_vector_store_id, df)\n", + "upload_files_to_vector_store(text_vector_store_id, df, column_name=\"text\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Retrieval and Filtering\n", + "\n", + "We can analyse our dataset with natural language queries with the help of File Search. For the text-only dataset, we see that information is missing that could inform our analysis.\n", + "\n", + "The only positive review for spaghetti in July has visual feedback and we can see the RAG system with only text based context available is uncertain about positive details. However with image context provided the second RAG system is able to provide a more accurate response.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Query the vector store for spaghetti reviews in July\n", + "query = \"Where there any comments about the 'spaghetti'?\"\n", + "print(f\"🔍 Query: {query}\\n\")\n", + "\n", + "# Execute the search with filtering\n", + "response = client.responses.create(\n", + " model=\"gpt-4.1\",\n", + " input=query,\n", + " tools=[{\n", + " \"type\": \"file_search\",\n", + " \"vector_store_ids\": [text_vector_store_id],\n", + " \"filters\": {\n", + " \"type\": \"eq\",\n", + " \"key\": \"month\",\n", + " \"value\": \"july\"\n", + " }\n", + " }]\n", + ")\n", + "\n", + "# Display the results\n", + "print(\"📝 Response:\")\n", + "print(\"-\" * 40)\n", + "print(response.output_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"Where there any comments about the 'spaghetti'?\"\n", + "print(f\"🔍 Query: {query}\\n\")\n", + "\n", + "response = client.responses.create(\n", + " model=\"gpt-4.1\",\n", + " input=query,\n", + " tools=[{\n", + " \"type\": \"file_search\",\n", + " \"vector_store_ids\": [text_image_vector_store_id],\n", + " \"filters\": {\n", + " \"type\": \"eq\",\n", + " \"key\": \"month\",\n", + " \"value\": \"july\"\n", + " }\n", + " }]\n", + ")\n", + "\n", + "print(\"📝 Response:\")\n", + "print(\"-\" * 40)\n", + "print(response.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can confirm if this is correct by checking the retrieved images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IMAGE_ID_MAPPING = {\n", + " f\"context_{row['id']}_{row['month']}.txt\": row[\"image_path\"]\n", + " for _, row in df[~df['image_path'].isna()].iterrows()\n", + "}\n", + "\n", + "def display_retrieved_images(\n", + " response,\n", + " cache_dir: str = \".local_cache\"\n", + "):\n", + " \"\"\"\n", + " Display images from the retrieved search results.\n", + " \n", + " Args:\n", + " response: The response object from the search query\n", + " cache_dir: Directory where images are stored\n", + " \n", + " Returns:\n", + " Dict mapping filenames to image paths for the displayed images\n", + " \"\"\"\n", + " # Get the annotations from the response\n", + " try:\n", + " annotations = response.output[1].content[0].annotations\n", + " retrieved_files = {result.filename for result in annotations}\n", + " except (AttributeError, IndexError):\n", + " print(\"No search results found in the response.\")\n", + " return {}\n", + "\n", + "\n", + " # Display matching images\n", + " displayed_images = {}\n", + " for file in retrieved_files:\n", + " if file in IMAGE_ID_MAPPING and IMAGE_ID_MAPPING[file]:\n", + " image_path = Path(cache_dir) / 'images' / IMAGE_ID_MAPPING[file]\n", + " print(f\"Displaying image for {file}:\")\n", + " display(Image(str(image_path)))\n", + " displayed_images[file] = str(image_path)\n", + " \n", + " return displayed_images\n", + "\n", + "displayed = display_retrieved_images(response)\n", + "print(f\"Displayed {len(displayed)} images\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Likewise we can test this for negative reviews in June concerning any burnt pizza." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"Were there any negative reviews for pizza, and if so, was the pizza burnt?\"\n", + "print(f\"🔍 Query: {query}\\n\")\n", + "\n", + "response = client.responses.create(\n", + " model=\"gpt-4.1\",\n", + " input=query,\n", + " tools=[{\n", + " \"type\": \"file_search\",\n", + " \"vector_store_ids\": [text_image_vector_store_id],\n", + " \"filters\": {\n", + " \"type\": \"eq\",\n", + " \"key\": \"month\",\n", + " \"value\": \"june\"\n", + " }\n", + " }]\n", + ")\n", + "\n", + "print(\"📝 Response:\")\n", + "print(\"-\" * 40)\n", + "print(response.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can confirm if this is correct by checking the retrieved images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "displayed = display_retrieved_images(response)\n", + "print(f\"Displayed {len(displayed)} images\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation and Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As our dataset likely evolves over time and we want to evaluate new models, we can use the OpenAI Evaluation API to evaluate the performance of our system for sentiment analysis. In this simple example, using the string_check criteria we checked if the output was one of the three possible values: positive, negative, or unclear." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_evaluation_data(df, text_col=\"full_sentiment\", label_col=\"label\"):\n", + " \"\"\"Prepare data items for evaluation from DataFrame.\"\"\"\n", + " return [{\"item\": {\"input\": str(row[text_col]), \"ground_truth\": row[label_col]}} \n", + " for _, row in df.iterrows()]\n", + "\n", + "\n", + "def prepare_evaluation_data(\n", + " df: pd.DataFrame,\n", + " text_col: str = \"full_sentiment\",\n", + " label_col: str = \"label\"\n", + ") -> list:\n", + " \"\"\"\n", + " Prepare evaluation data items from a DataFrame.\n", + " \n", + " Args:\n", + " df: Input pandas DataFrame.\n", + " text_col: Column containing the input text.\n", + " label_col: Column containing the ground truth label.\n", + " \n", + " Returns:\n", + " List of dicts formatted for evaluation.\n", + " \"\"\"\n", + " return [\n", + " {\"item\": {\"input\": str(row[text_col]), \"ground_truth\": row[label_col]}}\n", + " for _, row in df.iterrows()\n", + " ]\n", + "\n", + "def create_eval_run(evaluation_data: list, eval_id: str) -> str:\n", + " \"\"\"\n", + " Create and launch an evaluation run.\n", + " \n", + " Args:\n", + " evaluation_data: List of evaluation items.\n", + " eval_id: The evaluation object ID.\n", + " \n", + " Returns:\n", + " The run ID as a string.\n", + " \"\"\"\n", + " eval_config = {\n", + " \"type\": \"completions\",\n", + " \"model\": \"gpt-4.1\",\n", + " \"input_messages\": {\n", + " \"type\": \"template\",\n", + " \"template\": [\n", + " {\n", + " \"type\": \"message\",\n", + " \"role\": \"user\",\n", + " \"content\": {\n", + " \"type\": \"input_text\",\n", + " \"text\": (\n", + " \"Classify the sentiment of this food delivery review: {{ item.input }}. \"\n", + " \"Categorize the request into one of \\\"positive\\\", \\\"negative\\\" or \\\"unclear\\\". \"\n", + " \"Respond with only one of those words.\"\n", + " )\n", + " }\n", + " }\n", + " ]\n", + " },\n", + " \"source\": {\n", + " \"type\": \"file_content\",\n", + " \"content\": evaluation_data\n", + " }\n", + " }\n", + "\n", + " run = client.evals.runs.create(\n", + " eval_id=eval_id,\n", + " data_source=eval_config\n", + " )\n", + " print(\"✅ Evaluation run created successfully\")\n", + " print(f\"Run ID: {run.id}\")\n", + " return run.id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval_obj = client.evals.create(\n", + " name=\"food-categorization-eval\",\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"input\": {\"type\": \"string\"},\n", + " \"ground_truth\": {\"type\": \"string\"}\n", + " },\n", + " \"required\": [\"input\", \"ground_truth\"]\n", + " },\n", + " \"include_sample_schema\": True\n", + " },\n", + " testing_criteria=[\n", + " {\n", + " \"type\": \"string_check\",\n", + " \"name\": \"Match output to human label\",\n", + " \"input\": \"{{sample.output_text}}\",\n", + " \"reference\": \"{{item.ground_truth}}\",\n", + " \"operation\": \"eq\"\n", + " }\n", + " ]\n", + ")\n", + "eval_id = eval_obj.id\n", + "eval_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create evaluation runs\n", + "\n", + "evaluation_data = prepare_evaluation_data(df, text_col=\"text\")\n", + "text_only_run_id = create_eval_run(evaluation_data, eval_id)\n", + "\n", + "evaluation_data = prepare_evaluation_data(df)\n", + "text_image_run_id = create_eval_run(evaluation_data, eval_id)\n", + "\n", + "# retrieve both run urls\n", + "\n", + "text_only_run = client.evals.runs.retrieve(eval_id=eval_id, run_id=text_only_run_id)\n", + "print(text_only_run.to_dict()['report_url'])\n", + "\n", + "text_image_run = client.evals.runs.retrieve(eval_id=eval_obj.id, run_id=text_image_run_id)\n", + "print(text_image_run.to_dict()['report_url'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# you may need to wait a few seconds before running this cell for the eval runs to finish up\n", + "\n", + "text_only_run_output_items = client.evals.runs.output_items.list(eval_id=eval_id, run_id=text_only_run_id)\n", + "text_image_run_output_items = client.evals.runs.output_items.list(eval_id=eval_id, run_id=text_image_run_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can retrieve the results of these evaluation runs and perform some local analysis. In this case, we will compare the performance of the text-only and text+image runs and evaluate how increasing the number of total tokens (through the addition of image context) affects the accuracy of the model. We can also do some basic error analysis by analysing the model input of the failed examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate passed and total for text_only_run\n", + "text_only_data = text_only_run_output_items.to_dict()['data']\n", + "text_only_passed = sum(1 for output_item in text_only_data if output_item['results'][0]['passed'])\n", + "text_only_total = len(text_only_data)\n", + "\n", + "# Calculate passed and total for text_image_run\n", + "text_image_data = text_image_run_output_items.to_dict()['data']\n", + "text_image_passed = sum(1 for output_item in text_image_data if output_item['results'][0]['passed'])\n", + "text_image_total = len(text_image_data)\n", + "\n", + "# Calculate average total_tokens for each run\n", + "def avg_total_tokens(data):\n", + " tokens = [item['sample']['usage']['total_tokens'] for item in data if 'usage' in item['sample']]\n", + " return sum(tokens) / len(tokens) if tokens else 0\n", + "\n", + "text_only_avg_tokens = avg_total_tokens(text_only_data)\n", + "text_image_avg_tokens = avg_total_tokens(text_image_data)\n", + "\n", + "# Plotting\n", + "labels = ['Text Only', 'Text + Image']\n", + "passed = [text_only_passed, text_image_passed]\n", + "avg_tokens = [text_only_avg_tokens, text_image_avg_tokens]\n", + "\n", + "x = np.arange(len(labels))\n", + "width = 0.35\n", + "\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "# Bar for passed only\n", + "bars1 = ax1.bar(x - width/2, passed, width, label='Passed', color='green')\n", + "ax1.set_ylabel('Accuracy')\n", + "ax1.set_xticks(x)\n", + "ax1.set_xticklabels(labels)\n", + "ax1.set_title('Accuracy and Avg Total Tokens')\n", + "ax1.legend(loc='upper left')\n", + "\n", + "# Second y-axis for avg total tokens\n", + "ax2 = ax1.twinx()\n", + "bars2 = ax2.bar(x + width/2, avg_tokens, width, label='Avg Total Tokens', color='blue', alpha=0.5)\n", + "ax2.set_ylabel('Avg Total Tokens')\n", + "ax2.legend(loc='upper right')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "failed_samples = [\n", + " {\n", + " \"Input\": sample['sample']['input'],\n", + " \"Model Output\": sample['sample']['output']\n", + " }\n", + " for sample in text_only_run_output_items.to_dict()['data']\n", + " if not sample['results'][0]['passed']\n", + "]\n", + "\n", + "pd.set_option('display.max_colwidth', 150) # Adjust as needed\n", + "\n", + "failed_df = pd.DataFrame(failed_samples)\n", + "display(failed_df.style.set_properties(**{'text-align': 'left'}))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's clean up some of the resources we created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# delete vector stores\n", + "deleted_vector_store = client.vector_stores.delete(\n", + " vector_store_id=text_vector_store_id\n", + ")\n", + "print(deleted_vector_store)\n", + "\n", + "deleted_vector_store = client.vector_stores.delete(\n", + " vector_store_id=text_image_vector_store_id\n", + ")\n", + "print(deleted_vector_store)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/o-series/o3o4-mini_prompting_guide.ipynb b/examples/o-series/o3o4-mini_prompting_guide.ipynb new file mode 100644 index 0000000000..5370726b55 --- /dev/null +++ b/examples/o-series/o3o4-mini_prompting_guide.ipynb @@ -0,0 +1,389 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# o3/o4-mini Function Calling Guide\n", + "\n", + "## Introduction \n", + "The o3/o4-mini models are the latest in our o-series of models trained to think for longer before responding. They are the smartest models we’ve released to date and represent a significant step forward from o1/o3-mini in tool calling capabilities. These models are trained to use tools natively within their chain of thought (CoT) which unlocks improved reasoning capabilities around when and how to use tools. We’ve released a guide on how to [call functions](https://cookbook.openai.com/examples/reasoning_function_calls) with these models via the responses API, this guide builds on top of that and tells you how you can get the best function calling performance with these models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prompt guidance for better function calling performance\n", + "To fully utilize function calling intelligence behind o3/o4-mini models, we recommend a few best practices in both developer prompts and function descriptions.\n", + "\n", + "### A quick note on developer prompt, system prompt, and function descriptions for reasoning models\n", + "We introduced developer messages to make it explicit to reasoning models that an instruction is coming from the developer. In o-series models, any system message provided by the developer is automatically converted to a developer message internally. For practical purposes, you can treat the developer prompt as analogous to the traditional system prompt—but for clarity and correctness, this guide refers to all such instructions as developer prompts/messages.\n", + "\n", + "When we refer to a function description in this document, we mean the explanatory text in the description field of each function object inside the tool parameter of an API request. This description tells the model when and how to use the function. Here’s an example from our function calling [documentation](https://platform.openai.com/docs/guides/function-calling):\n", + "\n", + "```\n", + "tools = [{\n", + " \"type\": \"function\",\n", + " \"name\": \"get_weather\",\n", + " \"description\": \"Get current temperature for provided coordinates in celsius.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"latitude\": {\"type\": \"number\"},\n", + " \"longitude\": {\"type\": \"number\"}\n", + " },\n", + " \"required\": [\"latitude\", \"longitude\"],\n", + " \"additionalProperties\": False\n", + " },\n", + " \"strict\": True\n", + "}]\n", + "```\n", + "Here, `\"Get current temperature for provided coordinates in celsius.\"` serves as the function description.\n", + "\n", + "Now that we got definitions out of the way, we can start getting into best practices.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Context setting via developer message\n", + "1. General context: In line with general prompt engineering best practices, role prompting is helpful in setting the base behavior, tone and outlining the set of actions that are possible. For example:\n", + "```\n", + "You are an AI retail agent.\n", + "\n", + "As a retail agent, you can help users cancel or modify pending orders, return or exchange delivered orders, modify their default user address, or provide information about their own profile, orders, and related products.\n", + "```\n", + "2. Function Call ordering: o3/o4-mini are trained to accomplish goals with tools. However, it can make mistakes in the order of the tool calls. To guard against these cases, it is recommended to explicitly outline the orders to accomplish certain tasks. For example, to guard against the failure case that a coding agent possibly making a file in a directory that does not yet exist, adding the following will usually suffice:\n", + "```\n", + "check to see if directories exist before making files\n", + "```\n", + "For high volume and well defined tasks, we can make it even more robust by outlining the sequence of function to call explicitly, for example:\n", + "```\n", + "To Process a refund for a delivered order, follow the following steps:\n", + "1. Confirm the order was delivered. Use: `order_status_check`\n", + "2. Check the refund eligibility policy. Use: `refund_policy_check`\n", + "3. Create the refund request. Use: `refund_create`\n", + "4. Notify the user of refund status. Use: `user_notify`\n", + "```\n", + "\n", + "3. Defining boundaries on when to use tools: It is helpful to clarify the model boundaries on when and when not to invoke certain tools. This can be done both at the developer prompt level and at the tool description level. Here is an example developer prompt:\n", + "```\n", + "Be proactive in using tools to accomplish the user's goal. If a task cannot be completed with a single step, keep going and use multiple tools as needed until the task is completed. Do not stop at the first failure. Try alternative steps or tool combinations until you succeed.\n", + "\n", + "- Use tools when:\n", + " - The user wants to cancel or modify an order.\n", + " - The user wants to return or exchange a delivered product.\n", + " - The user wants to update their address or contact details.\n", + " - The user asks for current or personalized order or profile info.\n", + "\n", + "- Do not use tools when:\n", + " - The user asks a general question like “What’s your return policy?”\n", + " - The user asks something outside your retail role (e.g., “Write a poem”).\n", + "\n", + "If a task is not possible due to real constraints (For example, trying to cancel an already delivered order), explain why clearly and do not call tools blindly.\n", + "```\n", + "\n", + "### Function Description\n", + "A function’s description should clarify when it should be invoked and how its arguments should be constructed.\n", + "\n", + "A function’s description is the ideal place to clarify both when the function should be invoked and how its arguments should be constructed. This serves as a durable interface contract between reasoning models and tool APIs.\n", + "\n", + "In general, the function description defines what it does, how to invoke it. Developer instructions provide guidance to the agent using the tools. So if there are multiple tools that could be used for a similar purpose, the developer can disambiguate between them in the instructions. If the agentic workflow requirements have a preference for using tools in a specific order, or use certain tools frequently vs sparingly these would also go into the developer instructions.\n", + "\n", + "A well-structured description can improve accuracy and reduce misfires by anchoring key criteria and argument requirements early. It also allows developers to encode “proactiveness” control heuristics outside the developer prompt, closer to the tool definition itself. \n", + "\n", + "1. Usage Criteria: Similar to how you can refine function calling proactiveness through the developer prompt, you can further refine how a function gets called at the function description level. Here is an example for a file_create function:\n", + "```\n", + "Creates a new file with the specified name and contents in a target directory. This function should be used when persistent storage is needed and the file does not already exist.\n", + "- Only call this function if the target directory exists. Check first using the `directory_check` tool. \n", + "- Do not use for temporary or one-off content—prefer direct responses for those cases. \n", + "- Do not overwrite existing files. Always ensure the file name is unique.\n", + "- Do not overwrite existing files. \n", + " If replacement is intended and confirmed, use `file_delete` followed by `file_create`, or use `file_update` instead.\n", + "```\n", + "2. Few shot prompting: While reasoning models do not benefit from few-shot prompting as much as non-reasoning models, we found that few shot prompting can improve tool calling performance, especially when the model struggles to accurately construct function arguments. For example, here is an example tool description for a grep tool passed in as tool description: \n", + "\n", + "```\n", + "Use this tool to run fast, exact regex searches over text files using the `ripgrep` engine.\n", + "\n", + "\n", + "- Always escape special regex characters: ( ) [ ] { } + * ? ^ $ | . \\\\\n", + "- Use `\\\\` to escape any of these characters when they appear in your search string.\n", + "- Do NOT perform fuzzy or semantic matches.\n", + "- Return only a valid regex pattern string.\n", + "\n", + "Examples:\n", + "Literal -> Regex Pattern \n", + "function( -> function\\\\( \n", + "value[index] -> value\\\\[index\\\\] \n", + "file.txt -> file\\\\.txt \n", + "user|admin -> user\\\\|admin \n", + "path\\to\\file -> path\\\\\\\\to\\\\\\\\file \n", + "```\n", + "\n", + "3. Key rules up front and minimize distractions: Note in the above example, the instruction to escape a special character is relatively the first thing the model reads. A **worse** alternative would be:\n", + "```\n", + "Performs a fast regex-based text search that looks for exact pattern matches within files or entire directories, leveraging the ripgrep tool for high-speed scanning.\n", + "Output follows ripgrep formatting and can optionally display line numbers and matched lines.\n", + "To manage verbosity, results are limited to a maximum of 50 hits.\n", + "You can fine-tune the search by specifying inclusion or exclusion rules based on file types or path patterns.\n", + "This method is ideal when searching for literal text snippets or specific regular expressions.\n", + "It offers more accuracy than semantic methods when the goal is to locate a known string or structure.\n", + "It’s generally recommended over semantic search when you’re looking for a specific identifier—such as a function name, variable, or keyword—within a defined set of directories or file types.\n", + "```\n", + "\n", + "This performs poorly because much of the prompt is not prescriptive and the most important rules for how to construct the argument are not front and center. The previous prompt scored 6% higher on a tool calling accuracy eval for using this ripgrep tool compared to the one above.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Guarding Against Function Calling Hallucinations \n", + "We are aware that the o3 model may be more prone to hallucinations than other models. These hallucinations may appear as the model promising to call tools in the background without actually doing so, or promising to call a tool in future turns, etc. In instances like these, it is helpful to be explicit in a few areas to minimize these types of hallucinations:\n", + "\n", + "1. Explicit instructions: explicitly instruct the model to avoid common hallucinations like promising future function calls when it is not possible.\n", + "\n", + "```\n", + "Do NOT promise to call a function later. If a function call is required, emit it now; otherwise respond normally.\n", + "```\n", + "\n", + "2. Catch bad arguments early: \n", + "Setting `strict` to `true` will ensure function calls reliably adhere to the [function schema](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#strict-mode). We recommend turning it on whenever possible.\n", + "\n", + "If your arguments have additional complex format requirements (e.g valid python code etc), adding the following instruction can remind the model of the expected format. \n", + "\n", + "```\n", + "Validate arguments against the format before sending the call; if you are unsure, ask for clarification instead of guessing.\n", + "```\n", + "\n", + "3. Another note on lazy behavior\n", + "We are aware of rare instances of lazy behavior from o3, such as stating it does not have enough time to complete a task, promising to follow up separately, or giving terse answers even when explicitly prompted to provide more detail. We have found that the following steps help ameliorate this behavior:\n", + "\n", + " a. Start a new conversation for unrelated topics:\n", + " When switching to a new or unrelated topic, begin a fresh conversation thread rather than continuing in the same context. This helps the model focus on the current subject and prevents it from being influenced by previous, irrelevant context, which can sometimes lead to incomplete or lazy responses. For example, if you were previously discussing code debugging and now want to ask about documentation best practices, which does not require previous conversation context, start a new conversation to ensure clarity and focus.\n", + "\n", + " b. Discard irrelevant past tool calls/outputs when the list gets too long, and summarize them as context in the user message:\n", + " If the conversation history contains a long list of previous tool calls or outputs that are no longer relevant, remove them from the context. Instead, provide a concise summary of the important information as part of the user message. This keeps the context manageable and ensures the model has access to only the most pertinent information. For instance, if you have a lengthy sequence of tool outputs, you can summarize the key results and include only that summary in your next message.\n", + "\n", + " c. We are constantly improving our models and expect to have this issue addressed in future versions.\n", + "\n", + "\n", + "### Avoid Chain of Thought Prompting\n", + "Since these models are reasoning models and produce an internal chain of thought, they do not have to be explicitly prompted to plan and reason between toolcalls. Therefore, a developer should not try to induce additional reasoning before each function call by asking the model to plan more extensively. Asking a reasoning model to reason more may actually hurt the performance. \n", + "\n", + "A quick side note on reasoning summaries: the models will output reasoning tokens before calling tools. However, these will not always be accompanied by a summary, since our reasoning summaries require a minimum number of material reasoning tokens to produce a summary.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Responses API\n", + "\n", + "### Reasoning Items for Better Performance\n", + "We’ve released a [cookbook](https://cookbook.openai.com/examples/responses_api/reasoning_items) detailing the benefits of using the responses API. It is worth restating a few of the main points in this guide as well. o3/o4-mini are both trained with its internal reasoning persisted between toolcalls within a single turn. Persisting these reasoning items between toolcalls during inference will therefore lead to higher intelligence and performance in the form of better decision in when and how a tool gets called. Responses allow you to persist these reasoning items (maintained either by us or yourself through encrypted content if you do not want us to handle state-management) while Chat Completion doesn’t. Switching to the responses API and allowing the model access to reasoning items between function calls is the easiest way to squeeze out as much performance as possible for function calls. Here is an the example in the cookbook, reproduced for convenience, showing how you can pass back the reasoning item using `encrypted_content` in a way which we do not retain any state on our end:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The current temperature in Paris is about 18.8 °C.\n" + ] + } + ], + "source": [ + "from openai import OpenAI\n", + "import requests\n", + "import json\n", + "client = OpenAI()\n", + "\n", + "\n", + "def get_weather(latitude, longitude):\n", + " response = requests.get(f\"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}¤t=temperature_2m,wind_speed_10m&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m\")\n", + " data = response.json()\n", + " return data['current']['temperature_2m']\n", + "\n", + "\n", + "tools = [{\n", + " \"type\": \"function\",\n", + " \"name\": \"get_weather\",\n", + " \"description\": \"Get current temperature for provided coordinates in celsius.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"latitude\": {\"type\": \"number\"},\n", + " \"longitude\": {\"type\": \"number\"}\n", + " },\n", + " \"required\": [\"latitude\", \"longitude\"],\n", + " \"additionalProperties\": False\n", + " },\n", + " \"strict\": True\n", + "}]\n", + "\n", + "context = [{\"role\": \"user\", \"content\": \"What's the weather like in Paris today?\"}]\n", + "\n", + "response = client.responses.create(\n", + " model=\"o3\",\n", + " input=context,\n", + " tools=tools,\n", + " store=False,\n", + " include=[\"reasoning.encrypted_content\"] # Encrypted chain of thought is passed back in the response\n", + ")\n", + "\n", + "\n", + "context += response.output # Add the response to the context (including the encrypted chain of thought)\n", + "tool_call = response.output[1]\n", + "args = json.loads(tool_call.arguments)\n", + "\n", + "\n", + "\n", + "result = get_weather(args[\"latitude\"], args[\"longitude\"])\n", + "\n", + "context.append({ \n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call.call_id,\n", + " \"output\": str(result)\n", + "})\n", + "\n", + "response_2 = client.responses.create(\n", + " model=\"o3\",\n", + " input=context,\n", + " tools=tools,\n", + " store=False,\n", + " include=[\"reasoning.encrypted_content\"]\n", + ")\n", + "\n", + "print(response_2.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agentic Experience with Hosted tools. \n", + "Responses API supports a set of hosted/built-in tools. We recently also added [new tools and features](https://openai.com/index/new-tools-and-features-in-the-responses-api/) in the responses API which makes it easier to build agentic applications that connect to external services; With built-in tools in the Responses API, developers can create more capable agents with a single API call.\n", + "\n", + "You can mix and match hosted tools and custom tools in the same session. This unlocks powerful composition patterns, but it also makes tool routing clarity critical. Here are a couple of concrete recommendations:\n", + "\n", + "1. Explicitly define tool usage boundaries in the developer prompt: If multiple tools can fulfill similar roles (e.g. both the python tool and a custom calculator), instruct the model which tool is preferred and when. This reduces ambiguity, improves accuracy, and avoids tool overuse or underuse. :\n", + "```\n", + "You are a helpful research assistant with access to the following tools:\n", + "- python tool: for any computation involving math, statistics, or code execution\n", + "- calculator: for basic arithmetic or unit conversions when speed is preferred\n", + "\n", + "Always use the python tool for anything involving logic, scripts, or multistep math. Use the calculator tool only for simple 1-step math problems.\n", + "```\n", + "\n", + "2. Clarify when internal knowledge is not sufficient: Even though o3/o4-mini models can often solve tasks on their own, tools may provide more reliable answers. Use the system prompt to steer the model away from “trying to solve it itself” when a tool is more appropriate.\n", + "\n", + "```\n", + "You have access to a `code_interpreter`. Always prefer using `code_interpreter` when a user asks a question involving:\n", + "- math problems\n", + "- data analysis\n", + "- generating or executing code\n", + "- formatting or transforming structured text\n", + "\n", + "Avoid doing these directly in your own response. Always use the tool instead.\n", + "```\n", + "\n", + "3. Since the developer prompt acts as a centralized, durable contract, spell out decision boundaries for tools here when we want to mix and match hosted tools with your custom functions, including coverage overlap, confidence expectations, or fallback behavior:\n", + "\n", + "```\n", + "Use `python` for general math, data parsing, unit conversion, or logic tasks that can be solved without external lookup—for example, computing the total cost from a list of prices.\n", + "\n", + "Use `calculate_shipping_cost` when the user asks for shipping estimates, as it applies business-specific logic and access to live rate tables. Do not attempt to estimate these using the `python` tool.\n", + "\n", + "When both could be used (e.g., calculating a delivery fee), prefer `calculate_shipping_cost` for accuracy and policy compliance. Fall back to `python` only if the custom tool is unavailable or fails.\n", + "```\n", + "\n", + "4. More on MCP: We have a more detailed [guide](https://cookbook.openai.com/examples/mcp/mcp_tool_guide) on best practices for using MCP tools, but for completeness, we will reiterate a few high-level guidelines here (these are not specific to o3/o4-mini, but are still relevant).\n", + "\n", + "* Filter tools to avoid ballooning payloads: take advantage of the allowed_tools parameter to use only the tools that are necessary and save on unnecessary context: Since you do not always need all of the tools returned by the MCP server, you can filter to only the necessary tools via the allowed_tools field.\n", + "\n", + "\n", + "```\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"mcp\",\n", + " \"server_label\": \"gitmcp\",\n", + " \"server_url\": \"https://gitmcp.io/openai/tiktoken\",\n", + " \"allowed_tools\": [\"search_tiktoken_documentation\", \"fetch_tiktoken_documentation\"],\n", + " \"require_approval\": \"never\"\n", + " }\n", + "```\n", + "\n", + "* Reduce latency via caching and reserve reasoning models for high complexity tasks: make sure you are either passing back `mcp_list_tools` or include `previous_response_id` to make sure the API does not need to reimport the list of tools again and again unnecessarily.\n", + "* Use MCP with other tools: You can mix and match MCP with other hosted tools and your custom defined functions. If you are mixing the tools, it is helpful to define the decision boundaries and be explicit about when to use a tool over another using the overall developer prompt. [Here](https://cookbook.openai.com/examples/mcp/mcp_tool_guide#using-mcp-with-other-tools) is a great example from the MCP tool guide.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Frequented Asked Questions (FAQ)\n", + "\n", + "**Q: How many functions is too many?**\n", + "\n", + "**A:** For o3 and o4-mini models, there is no hard upper limit on the number of functions, but practical guidance does exist based on both training data distribution and observed model behavior. As of May 2025, any setup with fewer than ~100 tools and fewer than ~20 arguments per tool is considered in-distribution and should perform within expected reliability bounds. Performance still depends on your prompt design and task complexity. \n", + "\n", + "Even if you are technically within training distribution, more tools can introduce ambiguity or confusion. Here are key considerations:\n", + "\n", + "* Function description clarity becomes critical: If multiple tools have overlapping purposes or vague descriptions, models may call the wrong one or hesitate to call any at all.\n", + "\n", + "* Tool list size can affect latency and reasoning depth: Longer lists mean the model has more options to parse during its reasoning phase. While o3/o4-mini can handle this with their integrated reasoning pipelines, performance can degrade if schema clarity or invocation conditions aren’t sharp.\n", + "\n", + "* Tool hallucinations can increase with complexity: Especially with o3, there have been reports of hallucinated or speculative tool calls when the toolset is large and under-defined. Explicit instructions help mitigate this (e.g., “Only use tools X, Y, Z. Do not invent tool calls or defer them to future turns.”)\n", + "\n", + "Ultimately, the performance will defer depending on the use case; Therefore it is important to invest in evals that you trust you can use to iterate on.\n", + "\n", + "\n", + "**Q: Is it OK to have deeply nested params within tools or should I \"flatten\" out the schema?**\n", + "\n", + "**A:** There is again no hard guidance. However, even if your nesting structure is technically supported, deeply layered argument trees can impact performance or reliability. When in doubt we recommend you err on the side of making the arguments flat.\n", + "\n", + "Flat structures are often easier for the model to reason about: In flatter schemas, argument fields are top-level and immediately visible. This reduces the need for internal parsing and structuring, which can help prevent issues like partially filled nested objects or invalid field combinations. With deeply nested objects, especially ones with repeated or semantically similar field names, the model is more likely to omit or misuse arguments.\n", + "\n", + "Nesting can help organize complex logic, but needs additional care: For domains that naturally involve structured input, like configuration payloads, rich search filters, or form submissions, nesting helps organize related parameters. However, you must use techniques like clear field descriptions, anyOf logic, or strict schemas to guard against invalid argument combinations and improve model reliability\n", + "\n", + "The best way to choose is to test with your own evals and measure success. There’s no “one-size-fits-all” because invocation behaviors are emergent and prompt-sensitive\n", + "\n", + "\n", + "**Q: Does this function-calling guidance apply to custom tool formats?**\n", + "\n", + "**A:** Not guaranteed. The guidance in this document assumes you’re using the standard `tools` model parameter to pass your function schemas, as shown in our [general guide](https://platform.openai.com/docs/guides/function-calling) on function calling. Our o3/o4-mini models are trained to understand and use these schemas natively for tool selection and argument construction.\n", + "\n", + "If you’re instead providing custom tool definitions via natural language in a developer-authored prompt (e.g., defining tools inline in the developer message or user message), this guidance may not fully apply. In those cases:\n", + "The model is not relying on its internal tool-schema priors\n", + "You may need to be more explicit with few-shot examples, output formats, and tool selection criteria\n", + "Argument construction reliability may degrade without schema-level anchoring\n", + "\n", + "Use the structured tools parameter when possible. If you must define tools in free text, treat it as a custom protocol and test accordingly.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Gas_20240605_164059_Raven_Scan_3_jpeg.rf.e3408aa2b936afd1f1aed84fa40d454e.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Gas_20240605_164059_Raven_Scan_3_jpeg.rf.e3408aa2b936afd1f1aed84fa40d454e.json new file mode 100644 index 0000000000..beae732a6f --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Gas_20240605_164059_Raven_Scan_3_jpeg.rf.e3408aa2b936afd1f1aed84fa40d454e.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. The receipt is for gas (E-85 fuel), which is a travel-related expense. Therefore, NOT_TRAVEL_RELATED is FALSE. \n2. The total amount of the receipt is $36.16, which does not exceed $50. Therefore, AMOUNT_OVER_LIMIT is FALSE. \n3. The subtotal of $36.16 matches the total of $36.16, indicating no math errors. Therefore, MATH_ERROR is FALSE. \n4. The handwritten notes do not contain an 'X'. Therefore, HANDWRITTEN_X is FALSE.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_121534_Raven_Scan_10_jpeg.rf.5524d7df5648dc37d9bf264aa3f1e2c5.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_121534_Raven_Scan_10_jpeg.rf.5524d7df5648dc37d9bf264aa3f1e2c5.json new file mode 100644 index 0000000000..8a8fa447ce --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_121534_Raven_Scan_10_jpeg.rf.5524d7df5648dc37d9bf264aa3f1e2c5.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "The receipt is for gasoline, which is considered a travel-related expense, so it does not meet the NOT_TRAVEL_RELATED criterion. The total amount is $49.61, which is below the $50 limit, so the AMOUNT_OVER_LIMIT criterion is not violated. There are no discrepancies in the math for computing the total, confirming that MATH_ERROR is false. There is no 'X' present in the handwritten notes, meaning HANDWRITTEN_X is also false. Since none of the criteria are violated, the receipt does not need auditing.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_122340_Raven_Scan_2_jpeg.rf.3f26976203cf4683fc90cbbe34e876bd.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_122340_Raven_Scan_2_jpeg.rf.3f26976203cf4683fc90cbbe34e876bd.json new file mode 100644 index 0000000000..b72c4f3fdc --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_122340_Raven_Scan_2_jpeg.rf.3f26976203cf4683fc90cbbe34e876bd.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": true, + "math_error": false, + "handwritten_x": true, + "reasoning": "The receipt is for fuel, which is classified as a travel-related expense, hence it does not satisfy the NOT_TRAVEL_RELATED criterion. The total amount of $60.91 exceeds the $50 limit, thus satisfying the AMOUNT_OVER_LIMIT criterion. There are no discrepancies in the arithmetic calculations for the total, so MATH_ERROR is false. However, there is a handwritten 'X' noted in the handwritten notes, which satisfies the HANDWRITTEN_X criterion. 1. NOT_TRAVEL_RELATED: This is a fuel purchase (travel-related), so not_travel_related=false. 2. AMOUNT_OVER_LIMIT: Total of $60.91 exceeds $50, so amount_over_limit=true. 3. MATH_ERROR: 4.199×14.5076≈60.9174, which rounds up to $60.92 or truncates to $60.91, the listed total (either would be acceptable), so math_error=false. 4. HANDWRITTEN_X: The note 'X' is written on the the receipt, so handwritten_x=true. At least one criterion is violated, so needs_audit=true.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_122340_Raven_Scan_3_jpeg.rf.8f7d7e820eeaf8cfb2d61ef8232fc1af.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_122340_Raven_Scan_3_jpeg.rf.8f7d7e820eeaf8cfb2d61ef8232fc1af.json new file mode 100644 index 0000000000..cd0207ce71 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Nissan_20250205_122340_Raven_Scan_3_jpeg.rf.8f7d7e820eeaf8cfb2d61ef8232fc1af.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": true, + "math_error": false, + "handwritten_x": false, + "reasoning": "The receipt is for fuel, which is considered travel-related, hence NOT_TRAVEL_RELATED is FALSE. The total amount of the receipt is $72.10, which exceeds the $50 limit making AMOUNT_OVER_LIMIT TRUE. The math adds up correctly as the subtotal matches the total, so MATH_ERROR is FALSE. There are no handwritten 'X's in the notes so HANDWRITTEN_X is FALSE. Since one of the criteria (AMOUNT_OVER_LIMIT) is violated, the receipt needs auditing.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Retail_20240605_163628_Raven_Scan_1_jpeg.rf.c28eb1342052ef8af13d54a329c8218b.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Retail_20240605_163628_Raven_Scan_1_jpeg.rf.c28eb1342052ef8af13d54a329c8218b.json new file mode 100644 index 0000000000..e15f57409a --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Retail_20240605_163628_Raven_Scan_1_jpeg.rf.c28eb1342052ef8af13d54a329c8218b.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": true, + "amount_over_limit": true, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is for home goods and cleaning supplies, which are not travel-related expenses. Therefore, this criterion is TRUE.\n\n2. AMOUNT_OVER_LIMIT: The total of the receipt is $273.52, which is over the $50 limit, making this criterion TRUE.\n\n3. MATH_ERROR: The subtotal adds up correctly when calculated with tax. The subtotal $252.67 plus tax $20.85 equals the total $273.52. Thus, there is no math error, and this is FALSE.\n\n4. HANDWRITTEN_X: There is no 'X' in the handwritten notes provided. Thus, this is FALSE.\n\nSince two of the criteria (AMOUNT_OVER_LIMIT and MATH_ERROR) are TRUE, the final determination is that this receipt needs to be audited.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_193919_Raven_Scan_4_jpeg.rf.b79fe0306ef298356ee020fd63572734.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_193919_Raven_Scan_4_jpeg.rf.b79fe0306ef298356ee020fd63572734.json new file mode 100644 index 0000000000..8250b4fe33 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_193919_Raven_Scan_4_jpeg.rf.b79fe0306ef298356ee020fd63572734.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt includes fuel as a purchase which is considered travel-related, therefore this criterion is FALSE.\n2. AMOUNT_OVER_LIMIT: The total amount is $49.54, which does not exceed $50, so this is FALSE.\n3. MATH_ERROR: The line item for fuel ($49.54) matches the total provided ($49.54), hence there is no math error, making this FALSE.\n4. HANDWRITTEN_X: There is no 'X' in the handwritten notes, so this is FALSE. \nSince none of the criteria are violated, the receipt does not need an audit.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_193919_Raven_Scan_9_jpeg.rf.4756583269eace7e380051b580022a04.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_193919_Raven_Scan_9_jpeg.rf.4756583269eace7e380051b580022a04.json new file mode 100644 index 0000000000..a0e4da9a99 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_193919_Raven_Scan_9_jpeg.rf.4756583269eace7e380051b580022a04.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": true, + "math_error": false, + "handwritten_x": false, + "reasoning": "The expenses are for fuel, which is a travel-related cost, so the criterion for being 'not travel-related' is FALSE. The total amount of the receipt is $60.94, which exceeds the $50 limit, making the 'Amount Over Limit' criterion TRUE. There are no discrepancies in the math as the total matches the sum of the item prices, and there is no 'X' in the handwritten notes. Since one of the criteria (Amount Over Limit) is violated, the receipt needs to be audited.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194524_Raven_Scan_1_jpeg.rf.ea9c9e22f0e18cb64e158b64b43de2c0.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194524_Raven_Scan_1_jpeg.rf.ea9c9e22f0e18cb64e158b64b43de2c0.json new file mode 100644 index 0000000000..e6d91cc509 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194524_Raven_Scan_1_jpeg.rf.ea9c9e22f0e18cb64e158b64b43de2c0.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is for fuel, which is considered a travel-related expense, so this is FALSE. 2. AMOUNT_OVER_LIMIT: The total amount is $41.46, which does not exceed $50, so this is FALSE. 3. MATH_ERROR: The total of the line item ($41.46) matches the total provided, so this is FALSE. 4. HANDWRITTEN_X: There is no 'X' in the handwritten notes, so this is FALSE. Since none of the criteria are violated, the receipt does not need auditing.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194919_Raven_Scan_3_jpeg.rf.842940493d72ebac96a20eed97b226fc.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194919_Raven_Scan_3_jpeg.rf.842940493d72ebac96a20eed97b226fc.json new file mode 100644 index 0000000000..0ecdcffb87 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194919_Raven_Scan_3_jpeg.rf.842940493d72ebac96a20eed97b226fc.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is from Shell Gasoline and includes fuel expenses, making it travel-related. Therefore, this criterion is FALSE. 2. AMOUNT_OVER_LIMIT: The total amount of $38.26 is below the $50 threshold, so this criterion is FALSE. 3. MATH_ERROR: The line item for fuel ($38.26) matches the computed total, indicating no math errors, so this criterion is FALSE. 4. HANDWRITTEN_X: There is no 'X' present in the handwritten notes on the receipt, making this criterion FALSE. Since all criteria are not violated (all FALSE), the receipt does not need auditing.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194920_Raven_Scan_11_jpeg.rf.5fa0c8031793626f1d87a7acb08617dd.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194920_Raven_Scan_11_jpeg.rf.5fa0c8031793626f1d87a7acb08617dd.json new file mode 100644 index 0000000000..f1dac91796 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194920_Raven_Scan_11_jpeg.rf.5fa0c8031793626f1d87a7acb08617dd.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": true, + "math_error": false, + "handwritten_x": false, + "reasoning": "The receipt is for 'Unleaded' fuel from a gas station, which is a travel-related expense, thus the NOT_TRAVEL_RELATED criterion is FALSE. The total amount of the receipt is $62.68, exceeding the $50 limit, making AMOUNT_OVER_LIMIT TRUE. There are no discrepancies in the math calculations presented, hence MATH_ERROR is FALSE. There is no 'X' noted in the handwritten notes, so HANDWRITTEN_X is FALSE. Since the AMOUNT_OVER_LIMIT criteria is violated, the receipt needs auditing.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194920_Raven_Scan_15_jpeg.rf.bd3c50eb0d5bd48295b21a4c54c6a639.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194920_Raven_Scan_15_jpeg.rf.bd3c50eb0d5bd48295b21a4c54c6a639.json new file mode 100644 index 0000000000..c6dd0e3937 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_194920_Raven_Scan_15_jpeg.rf.bd3c50eb0d5bd48295b21a4c54c6a639.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is from 'Shell Gasoline' for fuel (E85), which is a travel-related expense. Therefore, this criterion is not violated (FALSE). \n2. AMOUNT_OVER_LIMIT: The total amount on the receipt is $40.19, which does not exceed the $50 limit. This criterion is not violated (FALSE). \n3. MATH_ERROR: The total of the item listed ($40.19) matches the provided total; hence, there are no math errors. This criterion is not violated (FALSE). \n4. HANDWRITTEN_X: There is no 'X' noted in the handwritten notes. This criterion is not violated (FALSE). \n\nSince no criteria were violated, the receipt does not need auditing.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_195518_Raven_Scan_2_jpeg.rf.ca4db81e957180a860c5557f6e9374e5.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_195518_Raven_Scan_2_jpeg.rf.ca4db81e957180a860c5557f6e9374e5.json new file mode 100644 index 0000000000..5987bc8b2b --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia-gas_20241213_195518_Raven_Scan_2_jpeg.rf.ca4db81e957180a860c5557f6e9374e5.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. Not Travel Related: The receipt is for fuel, which is a travel-related expense, thus this criterion is FALSE.\n2. Amount Over Limit: The total amount of $47.06 does not exceed $50, so this criterion is FALSE.\n3. Math Error: The total amount ($47.06) matches the calculated total from the line item (3.859 * 12.195 = 47.06), so there is no math error, making this criterion FALSE.\n4. Handwritten X: There are no 'X's noted in the handwritten notes, hence this criterion is FALSE.\nSince none of the criteria are violated, the receipt does not need auditing.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia_20241213_192254_Raven_Scan_1_jpeg.rf.955e6e2cd8eac4712c4b0906b0055bdf.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia_20241213_192254_Raven_Scan_1_jpeg.rf.955e6e2cd8eac4712c4b0906b0055bdf.json new file mode 100644 index 0000000000..1203189d63 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia_20241213_192254_Raven_Scan_1_jpeg.rf.955e6e2cd8eac4712c4b0906b0055bdf.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": true, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is for stationery items, so it is not a travel-related expense (TRUE). \n\n2. AMOUNT_OVER_LIMIT: The total amount is $8.68, which does not exceed $50 (FALSE). \n\n3. MATH_ERROR: The subtotal of $7.98 plus tax of $0.70 correctly sums up to a total of $8.68 (FALSE). \n\n4. HANDWRITTEN_X: There is no 'X' present in the handwritten notes (FALSE). \n\nSince NOT_TRAVEL_RELATED is true the receipt requires auditing.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia_20241213_192558_Raven_Scan_1_jpeg.rf.e111cd27ce61db49da51e6355980f178.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia_20241213_192558_Raven_Scan_1_jpeg.rf.e111cd27ce61db49da51e6355980f178.json new file mode 100644 index 0000000000..b5bba856df --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Sequoia_20241213_192558_Raven_Scan_1_jpeg.rf.e111cd27ce61db49da51e6355980f178.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": true, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "The receipt is for household and grocery items (Airwick, pantry goods) and contains no travel-related expense (e.g., gas, hotel, airfare). The total $40.69 does not exceed $50. The line items sum correctly to the subtotal and total (15.48 + 17.48 + 4.98 + 2.75 tax = 40.69). There are no handwritten notes or an “X.” Because the expense is not travel-related, the receipt requires auditing.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204147_Raven_Scan_2_jpeg.rf.dae94ec7e69fb9d48a1f11e50977eb52.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204147_Raven_Scan_2_jpeg.rf.dae94ec7e69fb9d48a1f11e50977eb52.json new file mode 100644 index 0000000000..c0c8bbd433 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204147_Raven_Scan_2_jpeg.rf.dae94ec7e69fb9d48a1f11e50977eb52.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is for Fuel, which is a travel-related expense, so this criterion is FALSE. 2. AMOUNT_OVER_LIMIT: The total amount is $31.58, which does not exceed $50, so this criterion is FALSE. 3. MATH_ERROR: The total matches the sum of the line items ($31.58), so there is no math error, this criterion is FALSE. 4. HANDWRITTEN_X: There is no 'X' in the handwritten notes, so this criterion is FALSE.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204230_Raven_Scan_5_jpeg.rf.99738f39b4f3109acdd09c30ffb41ea0.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204230_Raven_Scan_5_jpeg.rf.99738f39b4f3109acdd09c30ffb41ea0.json new file mode 100644 index 0000000000..f3e66de010 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204230_Raven_Scan_5_jpeg.rf.99738f39b4f3109acdd09c30ffb41ea0.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": true, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is for fuel, which is considered a travel-related expense, thus this criterion is FALSE. 2. AMOUNT_OVER_LIMIT: The total amount of $63.13 exceeds the limit of $50, so this criterion is TRUE. 3. MATH_ERROR: The subtotal and total match, indicating there are no math errors, thus this criterion is FALSE. 4. HANDWRITTEN_X: There is no 'X' in the handwritten notes, making this criterion FALSE.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204335_Raven_Scan_5_jpeg.rf.e62eea7e8c43e88491bb73613785b2cb.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204335_Raven_Scan_5_jpeg.rf.e62eea7e8c43e88491bb73613785b2cb.json new file mode 100644 index 0000000000..1a530ceb9f --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204335_Raven_Scan_5_jpeg.rf.e62eea7e8c43e88491bb73613785b2cb.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "1. NOT_TRAVEL_RELATED: The receipt is for fuel (Regular Unleaded), which is a travel-related expense, so this is FALSE.\n2. AMOUNT_OVER_LIMIT: The total amount is $45.25, which does not exceed $50, so this is FALSE.\n3. MATH_ERROR: The subtotal, tax, and total correctly add up to $45.25 with no discrepancies, so this is FALSE.\n4. HANDWRITTEN_X: There are no handwritten 'X's present in the notes, so this is FALSE.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204335_Raven_Scan_7_jpeg.rf.633afab0f048d51329078497a410a115.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204335_Raven_Scan_7_jpeg.rf.633afab0f048d51329078497a410a115.json new file mode 100644 index 0000000000..d49c9fec1f --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra-gas_20241213_204335_Raven_Scan_7_jpeg.rf.633afab0f048d51329078497a410a115.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": true, + "reasoning": "The receipt is for fuel, which is a travel-related expense, hence NOT_TRAVEL_RELATED is false. The total amount of $32.00 is below the $50 limit, so AMOUNT_OVER_LIMIT is false. There are no math errors as the calculated total matches the receipt total, so MATH_ERROR is false. However, there is an 'X' present in the handwritten notes, making HANDWRITTEN_X true. Since HANDWRITTEN_X is violated, the receipt needs auditing.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra_20241213_202437_Raven_Scan_2_jpeg.rf.aee205252dda5a853dfe881f841e464d.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra_20241213_202437_Raven_Scan_2_jpeg.rf.aee205252dda5a853dfe881f841e464d.json new file mode 100644 index 0000000000..e70be7cf2d --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra_20241213_202437_Raven_Scan_2_jpeg.rf.aee205252dda5a853dfe881f841e464d.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": false, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "The receipt from O'Reilly Auto Parts is for an automotive item (SNOWBROOM). A snow broom could be required to for safe driving in snowy conditions, so this is plausibly a valid travel-related expense, so NOT_TRAVEL_RELATED is false. The total amount of the receipt is $35.55, which does not exceed the $50 limit, thus AMOUNT_OVER_LIMIT is false. There are no discrepancies in the math as the subtotal plus tax equals the total, so MATH_ERROR is false. The handwritten notes section is empty, therefore HANDWRITTEN_X is false.", + "needs_audit": false +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra_20241213_202936_Raven_Scan_3_jpeg.rf.40b66c379f232d5621eaa452a87093f1.json b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra_20241213_202936_Raven_Scan_3_jpeg.rf.40b66c379f232d5621eaa452a87093f1.json new file mode 100644 index 0000000000..88314067ee --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/audit_results/Tundra_20241213_202936_Raven_Scan_3_jpeg.rf.40b66c379f232d5621eaa452a87093f1.json @@ -0,0 +1,8 @@ +{ + "not_travel_related": true, + "amount_over_limit": false, + "math_error": false, + "handwritten_x": false, + "reasoning": "The receipt is for tools purchased at The Home Depot, which are NOT travel-related expenses, so NOT_TRAVEL_RELATED is true. The total amount of $43.54 does not exceed $50, so AMOUNT_OVER_LIMIT is false. All line items sum correctly to the total, and there are no handwritten notes indicating an 'X', so HANDWRITTEN_X is false. Since NOT_TRAVEL_RELATED is violated the receipt needs to be audited.", + "needs_audit": true +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Gas_20240605_164059_Raven_Scan_3_jpeg.rf.e3408aa2b936afd1f1aed84fa40d454e.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Gas_20240605_164059_Raven_Scan_3_jpeg.rf.e3408aa2b936afd1f1aed84fa40d454e.json new file mode 100644 index 0000000000..b85a4e5878 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Gas_20240605_164059_Raven_Scan_3_jpeg.rf.e3408aa2b936afd1f1aed84fa40d454e.json @@ -0,0 +1,39 @@ +{ + "merchant": "ARCO GASOLINE", + "location": { + "city": "Vista", + "state": "CA", + "zipcode": null + }, + "time": "2023-01-20T08:45:00", + "items": [ + { + "description": "E-85", + "product_code": null, + "category": "Fuel", + "item_price": "2.699", + "sale_price": null, + "quantity": "13.268", + "total": "35.81" + }, + { + "description": "debitfee", + "product_code": null, + "category": "Fee", + "item_price": null, + "sale_price": null, + "quantity": "1", + "total": "0.35" + } + ], + "subtotal": null, + "tax": null, + "total": "36.16", + "handwritten_notes": [ + "vista-> yos", + "sequoia", + "yos", + "206618", + "2023" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_121534_Raven_Scan_10_jpeg.rf.5524d7df5648dc37d9bf264aa3f1e2c5.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_121534_Raven_Scan_10_jpeg.rf.5524d7df5648dc37d9bf264aa3f1e2c5.json new file mode 100644 index 0000000000..e539c46105 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_121534_Raven_Scan_10_jpeg.rf.5524d7df5648dc37d9bf264aa3f1e2c5.json @@ -0,0 +1,27 @@ +{ + "merchant": "Costco #124", + "location": { + "city": "Vista", + "state": "CA", + "zipcode": "92083" + }, + "time": "2024-04-09T12:52:00", + "items": [ + { + "description": "Regular", + "product_code": null, + "category": "Fuel", + "item_price": "4.959", + "sale_price": "4.959", + "quantity": "10.005", + "total": "49.61" + } + ], + "subtotal": "49.61", + "tax": null, + "total": "49.61", + "handwritten_notes": [ + "vista", + "219948" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_122340_Raven_Scan_2_jpeg.rf.3f26976203cf4683fc90cbbe34e876bd.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_122340_Raven_Scan_2_jpeg.rf.3f26976203cf4683fc90cbbe34e876bd.json new file mode 100644 index 0000000000..993ee18d41 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_122340_Raven_Scan_2_jpeg.rf.3f26976203cf4683fc90cbbe34e876bd.json @@ -0,0 +1,28 @@ +{ + "merchant": "Fuel Mart", + "location": { + "city": "San Bernardino", + "state": "CA", + "zipcode": "92417" + }, + "time": "2024-07-07T17:47:46", + "items": [ + { + "description": "REG CR #09", + "product_code": null, + "category": "Fuel", + "item_price": "4.199", + "sale_price": null, + "quantity": "14.507G", + "total": "60.91" + } + ], + "subtotal": "60.91", + "tax": null, + "total": "60.91", + "handwritten_notes": [ + "224014", + "Nissan", + "X" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_122340_Raven_Scan_3_jpeg.rf.8f7d7e820eeaf8cfb2d61ef8232fc1af.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_122340_Raven_Scan_3_jpeg.rf.8f7d7e820eeaf8cfb2d61ef8232fc1af.json new file mode 100644 index 0000000000..9187e14aef --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Nissan_20250205_122340_Raven_Scan_3_jpeg.rf.8f7d7e820eeaf8cfb2d61ef8232fc1af.json @@ -0,0 +1,28 @@ +{ + "merchant": "Chukchansi Crossing", + "location": { + "city": "Coarsegold", + "state": "CA", + "zipcode": "93614" + }, + "time": "2024-12-18T22:10:05", + "items": [ + { + "description": "SUPER", + "product_code": null, + "category": "Fuel", + "item_price": "3.599", + "sale_price": null, + "quantity": "20.032", + "total": "72.10" + } + ], + "subtotal": "72.10", + "tax": null, + "total": "72.10", + "handwritten_notes": [ + "232658", + "Nissan", + "home -> yos" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Retail_20240605_163628_Raven_Scan_1_jpeg.rf.c28eb1342052ef8af13d54a329c8218b.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Retail_20240605_163628_Raven_Scan_1_jpeg.rf.c28eb1342052ef8af13d54a329c8218b.json new file mode 100644 index 0000000000..d211c7cbb7 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Retail_20240605_163628_Raven_Scan_1_jpeg.rf.c28eb1342052ef8af13d54a329c8218b.json @@ -0,0 +1,54 @@ +{ + "merchant": "Kohl's", + "location": { + "city": "Oceanside", + "state": "CA", + "zipcode": "92056" + }, + "time": "2023-01-12T15:12:00", + "items": [ + { + "description": "BATH RUGS", + "product_code": "40076499017", + "category": "Home Goods", + "item_price": "13.99", + "sale_price": "9.79", + "quantity": "1", + "total": "9.79" + }, + { + "description": "BATH RUGS", + "product_code": "40076499017", + "category": "Home Goods", + "item_price": "13.99", + "sale_price": "4.89", + "quantity": "1", + "total": "4.89" + }, + { + "description": "FLOOR CARE", + "product_code": "62235658793", + "category": "Cleaning Supplies", + "item_price": "329.99", + "sale_price": "209.99", + "quantity": "1", + "total": "209.99" + }, + { + "description": "BAKEWARE", + "product_code": "07089662604", + "category": "Kitchenware", + "item_price": "39.99", + "sale_price": "28.00", + "quantity": "1", + "total": "28.00" + } + ], + "subtotal": "252.67", + "tax": "20.85", + "total": "273.52", + "handwritten_notes": [ + "yos", + "2023" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_193919_Raven_Scan_4_jpeg.rf.b79fe0306ef298356ee020fd63572734.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_193919_Raven_Scan_4_jpeg.rf.b79fe0306ef298356ee020fd63572734.json new file mode 100644 index 0000000000..c2d6184b3f --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_193919_Raven_Scan_4_jpeg.rf.b79fe0306ef298356ee020fd63572734.json @@ -0,0 +1,28 @@ +{ + "merchant": "Vons", + "location": { + "city": "Oakhurst", + "state": "CA", + "zipcode": "93644" + }, + "time": "2024-10-14T20:36:29", + "items": [ + { + "description": "10-Unleaded", + "product_code": null, + "category": "Fuel", + "item_price": "4.119", + "sale_price": null, + "quantity": "12.026", + "total": "49.54" + } + ], + "subtotal": null, + "tax": null, + "total": "49.54", + "handwritten_notes": [ + "home -> Yos", + "Sequoia", + "237407" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_193919_Raven_Scan_9_jpeg.rf.4756583269eace7e380051b580022a04.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_193919_Raven_Scan_9_jpeg.rf.4756583269eace7e380051b580022a04.json new file mode 100644 index 0000000000..7ca6d8cc29 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_193919_Raven_Scan_9_jpeg.rf.4756583269eace7e380051b580022a04.json @@ -0,0 +1,28 @@ +{ + "merchant": "CHUKCHANSI CROSSING", + "location": { + "city": "Coarsegold", + "state": "CA", + "zipcode": "93614" + }, + "time": "2024-01-17T21:37:08", + "items": [ + { + "description": "REGULAR", + "product_code": null, + "category": "Fuel", + "item_price": "4.009", + "sale_price": null, + "quantity": "15.202", + "total": "60.94" + } + ], + "subtotal": null, + "tax": null, + "total": "60.94", + "handwritten_notes": [ + "Home -> Yos", + "Sequoia", + "234345" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194524_Raven_Scan_1_jpeg.rf.ea9c9e22f0e18cb64e158b64b43de2c0.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194524_Raven_Scan_1_jpeg.rf.ea9c9e22f0e18cb64e158b64b43de2c0.json new file mode 100644 index 0000000000..333f4a3bcb --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194524_Raven_Scan_1_jpeg.rf.ea9c9e22f0e18cb64e158b64b43de2c0.json @@ -0,0 +1,28 @@ +{ + "merchant": "CHUKCHANSI CROSSING", + "location": { + "city": "Coarsegold", + "state": "CA", + "zipcode": "93614" + }, + "time": "2024-01-01T10:26:40", + "items": [ + { + "description": "REGULAR", + "product_code": null, + "category": "Fuel", + "item_price": "4.129", + "sale_price": null, + "quantity": "10.042", + "total": "41.46" + } + ], + "subtotal": null, + "tax": null, + "total": "41.46", + "handwritten_notes": [ + "yos -> home", + "234777", + "Sequoia" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194919_Raven_Scan_3_jpeg.rf.842940493d72ebac96a20eed97b226fc.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194919_Raven_Scan_3_jpeg.rf.842940493d72ebac96a20eed97b226fc.json new file mode 100644 index 0000000000..72fb79ec4f --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194919_Raven_Scan_3_jpeg.rf.842940493d72ebac96a20eed97b226fc.json @@ -0,0 +1,28 @@ +{ + "merchant": "Shell Gasoline", + "location": { + "city": "Sylmar", + "state": "CA", + "zipcode": "91342" + }, + "time": "2024-03-13T11:03:00", + "items": [ + { + "description": "E85", + "product_code": null, + "category": "Fuel", + "item_price": "3.599", + "sale_price": null, + "quantity": "10.630", + "total": "38.26" + } + ], + "subtotal": null, + "tax": null, + "total": "38.26", + "handwritten_notes": [ + "home -> Yos", + "Sequoia", + "227536" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194920_Raven_Scan_11_jpeg.rf.5fa0c8031793626f1d87a7acb08617dd.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194920_Raven_Scan_11_jpeg.rf.5fa0c8031793626f1d87a7acb08617dd.json new file mode 100644 index 0000000000..fb7450f609 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194920_Raven_Scan_11_jpeg.rf.5fa0c8031793626f1d87a7acb08617dd.json @@ -0,0 +1,28 @@ +{ + "merchant": "Flying J #616", + "location": { + "city": "Frazier Park", + "state": "CA", + "zipcode": "93222" + }, + "time": "2024-04-23T23:04:00", + "items": [ + { + "description": "Unleaded", + "product_code": null, + "category": "Fuel", + "item_price": "4.999", + "sale_price": null, + "quantity": "12.583", + "total": "62.68" + } + ], + "subtotal": null, + "tax": null, + "total": "62.68", + "handwritten_notes": [ + "Yos home", + "Sequoia", + "229586" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194920_Raven_Scan_15_jpeg.rf.bd3c50eb0d5bd48295b21a4c54c6a639.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194920_Raven_Scan_15_jpeg.rf.bd3c50eb0d5bd48295b21a4c54c6a639.json new file mode 100644 index 0000000000..dae5ea1083 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_194920_Raven_Scan_15_jpeg.rf.bd3c50eb0d5bd48295b21a4c54c6a639.json @@ -0,0 +1,28 @@ +{ + "merchant": "Shell Gasoline", + "location": { + "city": "Sylmar", + "state": "CA", + "zipcode": "91342" + }, + "time": "2024-04-18T09:55:00", + "items": [ + { + "description": "E85", + "product_code": null, + "category": "Fuel", + "item_price": "3.599", + "sale_price": null, + "quantity": "11.168", + "total": "40.19" + } + ], + "subtotal": null, + "tax": null, + "total": "40.19", + "handwritten_notes": [ + "Sequoia", + "home -> yos", + "229003" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_195518_Raven_Scan_2_jpeg.rf.ca4db81e957180a860c5557f6e9374e5.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_195518_Raven_Scan_2_jpeg.rf.ca4db81e957180a860c5557f6e9374e5.json new file mode 100644 index 0000000000..3803ea0851 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia-gas_20241213_195518_Raven_Scan_2_jpeg.rf.ca4db81e957180a860c5557f6e9374e5.json @@ -0,0 +1,28 @@ +{ + "merchant": "Sam's Club #4704", + "location": { + "city": "Fresno", + "state": "CA", + "zipcode": "93720" + }, + "time": "2024-07-15T18:46:00", + "items": [ + { + "description": "UNLEAD (11)", + "product_code": null, + "category": "Fuel", + "item_price": "3.859", + "sale_price": null, + "quantity": "12.195", + "total": "47.06" + } + ], + "subtotal": null, + "tax": null, + "total": "47.06", + "handwritten_notes": [ + "yos -> Home", + "234523", + "Sequoia" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia_20241213_192254_Raven_Scan_1_jpeg.rf.955e6e2cd8eac4712c4b0906b0055bdf.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia_20241213_192254_Raven_Scan_1_jpeg.rf.955e6e2cd8eac4712c4b0906b0055bdf.json new file mode 100644 index 0000000000..d32f897158 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia_20241213_192254_Raven_Scan_1_jpeg.rf.955e6e2cd8eac4712c4b0906b0055bdf.json @@ -0,0 +1,29 @@ +{ + "merchant": "CHUKCHANSI CROSSING", + "location": { + "city": "Coarsegold", + "state": "CA", + "zipcode": "93614" + }, + "time": "2024-10-19T22:04:39", + "items": [ + { + "description": "STICKER", + "product_code": null, + "category": "Stationery", + "item_price": "3.99", + "sale_price": null, + "quantity": "2", + "total": "7.98" + } + ], + "subtotal": "7.98", + "tax": "0.70", + "total": "8.68", + "handwritten_notes": [ + "Home -> yos", + "Sequoia", + "Rizos", + "gift Basket" + ] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia_20241213_192558_Raven_Scan_1_jpeg.rf.e111cd27ce61db49da51e6355980f178.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia_20241213_192558_Raven_Scan_1_jpeg.rf.e111cd27ce61db49da51e6355980f178.json new file mode 100644 index 0000000000..211b63eb23 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Sequoia_20241213_192558_Raven_Scan_1_jpeg.rf.e111cd27ce61db49da51e6355980f178.json @@ -0,0 +1,42 @@ +{ + "merchant": "sam's club", + "location": { + "city": "Fresno", + "state": "CA", + "zipcode": null + }, + "time": "2024-09-27T10:18:00", + "items": [ + { + "description": "ZPLC FRZR G", + "product_code": "916198", + "category": null, + "item_price": null, + "sale_price": null, + "quantity": "1", + "total": "15.48" + }, + { + "description": "AIRWICK 0+9", + "product_code": "984197911", + "category": null, + "item_price": null, + "sale_price": null, + "quantity": "1", + "total": "17.48" + }, + { + "description": "MM SEASLT GF", + "product_code": "E 990304379", + "category": null, + "item_price": null, + "sale_price": null, + "quantity": "1", + "total": "4.98" + } + ], + "subtotal": "37.94", + "tax": "2.75", + "total": "40.69", + "handwritten_notes": [] +} diff --git a/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Tundra-gas_20241213_204147_Raven_Scan_2_jpeg.rf.dae94ec7e69fb9d48a1f11e50977eb52.json b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Tundra-gas_20241213_204147_Raven_Scan_2_jpeg.rf.dae94ec7e69fb9d48a1f11e50977eb52.json new file mode 100644 index 0000000000..e2f70f3828 --- /dev/null +++ b/examples/partners/eval_driven_system_design/data/ground_truth/extraction/Tundra-gas_20241213_204147_Raven_Scan_2_jpeg.rf.dae94ec7e69fb9d48a1f11e50977eb52.json @@ -0,0 +1,28 @@ +{ + "merchant": "CHUKCHANSI CROSSING", + "location": { + "city": "Coarsegold", + "state": "CA", + "zipcode": "93614" + }, + "time": "2024-05-17T20:24:00", + "items": [ + { + "description": "Regular", + "product_code": null, + "category": "Fuel", + "item_price": "4.899", + "sale_price": null, + "quantity": "6.446", + "total": "31.58" + } + ], + "subtotal": null, + "tax": null, + "total": "31.58", + "handwritten_notes": [ + 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diff --git a/examples/partners/eval_driven_system_design/receipt_inspection.ipynb b/examples/partners/eval_driven_system_design/receipt_inspection.ipynb new file mode 100644 index 0000000000..66d0cb8cbe --- /dev/null +++ b/examples/partners/eval_driven_system_design/receipt_inspection.ipynb @@ -0,0 +1,1830 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eval-Driven System Design: From Prototype to Production\n", + "\n", + "## Overview\n", + "\n", + "### Purpose of This Cookbook\n", + "\n", + "This cookbook provides a **practical**, end-to-end guide on how to effectively use \n", + "evals as the core process in creating a production-grade autonomous system to \n", + "replace a labor-intensive human workflow. It's a direct product of collaborative \n", + "experience dealing with projects where users may not have started with pristine \n", + "labeled data or a perfect understanding of the problem - two issues that most tutorials gloss \n", + "over but are in practice almost always serious challenges.\n", + "\n", + "Making evals the core process prevents poke-and-hope guesswork and impressionistic\n", + "judgments of accuracy, instead demanding engineering rigor. This means we can make\n", + "principled decisions about cost trade-offs and investment. \n", + "\n", + "### Target Audience\n", + "\n", + "This guide is designed for ML/AI engineers and Solution Architects who are\n", + "looking for practical guidance beyond introductory tutorials. This notebook is fully\n", + "executable and organized to be as modular as possible to support using code\n", + "samples directly in your own applications.\n", + "\n", + "### Guiding Narrative: From Tiny Seed to Production System\n", + "\n", + "We'll follow a realistic storyline: replacing a manual receipt-analysis service for validating expenses.\n", + "\n", + "* **Start Small:** Begin with a very small set of labeled data (retail receipts). Many businesses don't have good ground truth data sets. \n", + "* **Build Incrementally:** Develop a minimal viable system and establish initial evals. \n", + "* **Business Alignment:** Evaluate eval performance in the context of business KPIs and\n", + " dollar impact, and target efforts to avoid working on low-impact improvements.\n", + "* **Eval-Driven Iteration:** Iteratively improve by using eval scores to power model\n", + " improvements, then by using better models on more data to expand evals and identify more\n", + " areas for improvement.\n", + "\n", + "### How to Use This Cookbook\n", + "\n", + "This cookbook is structured as an eval-centric guide through the lifecycle of building\n", + "an LLM application.\n", + "\n", + "1. If you're primarily interested in the ideas presented, read through the text and skim over\n", + " the code.\n", + "2. If you're here because of something else you're working on, you can go ahead and jump to that\n", + " section and dig into the code there, copy it, and adapt it to your needs.\n", + "3. If you want to really understand how this all works, download this notebook and run\n", + " the cells as you read through it; edit the code to make your own changes, test your\n", + " hypotheses, and make sure you actually understand how it all works together.\n", + "\n", + "> Note: If your OpenAI organization has a Zero Data Retention (ZDR) policy, Evals dashboards and logs will not be available, since prompts and responses are not stored. This may limit visibility into eval results for compliance-focused enterprise accounts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Case: Receipt Parsing\n", + "\n", + "In order to condense this guide we'll be using a small hypothetical problem that's still complex\n", + "enough to merit detailed and multi-faceted evals. In particular, we'll be focused on how\n", + "to solve a problem given a limited amount of data to work with, so we're working with a\n", + "dataset that's quite small.\n", + "\n", + "### Problem Definition\n", + "\n", + "For this guide, we assume that we are starting with a workflow for reviewing and filing \n", + "receipts. While in general, this is a problem that already has a lot of established \n", + "solutions, it's analogous to other problems that don't have nearly so much prior work; \n", + "further, even when good enterprise solutions exist there is often still a \n", + "\"last mile\" problem that still requires human time.\n", + "\n", + "In our case, we'll assume we have a pipeline where:\n", + "\n", + "* People upload photos of receipts\n", + "* An accounting team reviews each receipt to categorize and approve or audit the expense\n", + "\n", + "Based on interviews with the accounting team, they make their decisions based on\n", + "\n", + "1. Merchant\n", + "2. Geographic location\n", + "3. Expense amount\n", + "4. Items or services purchased\n", + "5. Handwritten notes or annotations\n", + "\n", + "Our system will be expected to handle most receipts without any human intervention, but\n", + "escalate low-confidence decisions for human QA. We'll be focused on reducing the total\n", + "cost of the accounting process, which is dependent on\n", + "\n", + "1. How much the previous / current system cost to run per-receipt\n", + "2. How many receipts the new system sends to QA\n", + "3. How much the system costs to run per-receipt, plus any fixed costs\n", + "4. What the business impact is of mistakes, either receipts kicked out for review or mistakes missed\n", + "5. The cost of engineering to develop and integrate the system\n", + "\n", + "### Dataset Overview\n", + "\n", + "The receipt images come from the CC by 4.0 licensed\n", + "[Receipt Handwriting Detection Computer Vision Project](https://universe.roboflow.com/newreceipts/receipt-handwriting-detection)\n", + "dataset published by Roboflow. We've added our own labels and narrative spin in order to\n", + "tell a story with a small number of examples." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Project Lifecycle\n", + "\n", + "Not every project will proceed in the same way, but projects generally have some \n", + "important components in common.\n", + "\n", + "![Project Lifecycle](../../../images/partner_project_lifecycle.png)\n", + "\n", + "The solid arrows show the primary progressions or steps, while the dotted line \n", + "represents the ongoing nature of problem understanding - uncovering more about\n", + "the customer domain will influence every step of the process. We wil examine \n", + "several of these iterative cycles of refinement in detail below. \n", + "Not every project will proceed in the same way, but projects generally have some common\n", + "important components.\n", + "\n", + "### 1. Understand the Problem\n", + "\n", + "Usually, the decision to start an engineering process is made by leadership who\n", + "understand the business impact but don't need to know the process details. In our\n", + "example, we're building a system designed to replace a non-AI workflow. In a sense this\n", + "is ideal: we have a set of domain experts, *the people currently doing the task* who we\n", + "can interview to understand the task details and who we can lean upon to help develop\n", + "appropriate evals.\n", + "\n", + "This step doesn't end before we start building our system; invariably, our initial\n", + "assessments are an incomplete understanding of the problem space and we will continue to\n", + "refine our understanding as we get closer to a solution.\n", + "\n", + "### 2. Assemble Examples (Gather Data)\n", + "\n", + "It's very rare for a real-world project to begin with all the data necessary to achieve a satisfactory solution, let alone establish confidence.\n", + "\n", + "In our case, we'll assume we have a decent sample of system *inputs*, in the form of but receipt images, but start without any fully annotated data. We find this is a not-unusual situation when automating an existing process. We'll walk through the process of incrementally expanding our test and training sets in collaboration with domain experts as we go along and make our evals progressively more comprehensive.\n", + "\n", + "### 3. Build an End-to-End V0 System\n", + "\n", + "We want to get the skeleton of a system built as quickly as possible. We don't need a\n", + "system that performs well - we just need something that accepts the right inputs and\n", + "provides outputs of the correct type. Usually this is almost as simple as describing the\n", + "task in a prompt, adding the inputs, and using a single model (usually with structured\n", + "outputs) to make an initial best-effort attempt.\n", + "\n", + "### 4. Label Data and Build Initial Evals\n", + "\n", + "We've found that in the absence of an established ground truth, it's not uncommon to \n", + "use an early version of a system to generate 'draft' truth data which can be annotated \n", + "or corrected by domain experts.\n", + "\n", + "Once we have an end-to-end system constructed, we can start processing the inputs we\n", + "have to generate plausible outputs. We'll send these to our domain experts to grade \n", + "and correct. We will use these corrections and conversations about how the experts \n", + "are making their decisions to design further evals and to embed expertise in the system.\n", + "\n", + "### 5. Map Evals to Business Metrics\n", + "\n", + "Before we jump into correcting every error, we need to make sure that we're investing\n", + "time effectively. The most critical task at this stage is to review our evals and\n", + "gain an understanding of how they connect to our key objectives.\n", + "\n", + "- Step back and assess the potential costs and benefits of the system\n", + "- Identify which eval measurements speak directly to those costs and benefits\n", + "- For example, what does \"failure\" on a particular eval cost? Are we measuring\n", + " something worthwhile?\n", + "- Create a (non-LLM) model that uses eval metrics to provide a dollar value\n", + "- Balance performance (accuracy, or speed) with cost to develop and run\n", + "\n", + "### 6. Progressively Improve System and Evals\n", + "\n", + "Having identified which efforts are most worth making, we can begin iterating on \n", + "improvements to the system. The evals act as an objective guide so we know when we've\n", + "made the system good enough, and ensure we avoid or identify regression. \n", + "\n", + "### 7. Integrate QA Process and Ongoing Improvements\n", + "\n", + "Evals aren't just for development. Instrumenting all or a portion of a production\n", + "service will surface more useful test and training samples over time, identifying\n", + "incorrect assumptions or finding areas with insufficient coverage. This is also the only\n", + "way you can ensure that your models continue performing well long after your initial\n", + "development process is complete." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## V0 System Construction\n", + "\n", + "In practice, we would probably be building a system that operates via a REST API,\n", + "possibly with some web frontend that would have access to some set of components and\n", + "resources. For the purposes of this cookbook, we'll distill that down to a pair of\n", + "functions, `extract_receipt_details` and `evaluate_receipt_for_audit` that collectively\n", + "decide what we should do with a given receipt.\n", + "\n", + "- `extract_receipt_details` will take an image as input and produce structured output\n", + " containing important details about the receipt.\n", + "- `evaluate_receipt_for_audit` will take that structure as input and decide whether or\n", + " not the receipt should be audited.\n", + "\n", + "> Breaking up a process into steps like this has both pros and cons; it is easier to\n", + "> examine and develop if the process is made up of small isolated steps. But you can\n", + "> progressively lose information, effectively letting your agents play \"telephone\". In\n", + "> this notebook we break up the steps and don't let the auditor see the actual receipt\n", + "> because it's more instructive for the evals we want to discuss.\n", + "\n", + "We'll start with the first step, the literal data extraction. This is *intermediate*\n", + "data: it's information that people would examine implicitly, but often isn't recorded.\n", + "And for this reason, we often don't have labeled data to work from." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade openai pydantic python-dotenv rich persist-cache -qqq\n", + "%load_ext dotenv\n", + "%dotenv\n", + "\n", + "# Place your API key in a file called .env\n", + "# OPENAI_API_KEY=sk-..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Structured Output Model\n", + "\n", + "Capture the meaningful information in a structured output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pydantic import BaseModel\n", + "\n", + "\n", + "class Location(BaseModel):\n", + " city: str | None\n", + " state: str | None\n", + " zipcode: str | None\n", + "\n", + "\n", + "class LineItem(BaseModel):\n", + " description: str | None\n", + " product_code: str | None\n", + " category: str | None\n", + " item_price: str | None\n", + " sale_price: str | None\n", + " quantity: str | None\n", + " total: str | None\n", + "\n", + "\n", + "class ReceiptDetails(BaseModel):\n", + " merchant: str | None\n", + " location: Location\n", + " time: str | None\n", + " items: list[LineItem]\n", + " subtotal: str | None\n", + " tax: str | None\n", + " total: str | None\n", + " handwritten_notes: list[str]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> *Note*: Normally we would use `decimal.Decimal` objects for the numbers above and `datetime.datetime` objects for `time` field, but neither of those deserialize well. For the purposes of this cookbook, we'll work with strings, but in practice you'd want to have another level of translation to get the correct output validated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basic Info Extraction\n", + "\n", + "Let's build our `extract_receipt_details` function.\n", + "\n", + "Usually, for the very first stab at something that might work, we'll simply feed ChatGPT\n", + "the available documents we've assembled so far and ask it to generate a prompt. It's not\n", + "worth spending too much time on prompt engineering before you have a benchmark to grade\n", + "yourself against! This is a prompt produced by o4-mini based on the problem description\n", + "above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "BASIC_PROMPT = \"\"\"\n", + "Given an image of a retail receipt, extract all relevant information and format it as a structured response.\n", + "\n", + "# Task Description\n", + "\n", + "Carefully examine the receipt image and identify the following key information:\n", + "\n", + "1. Merchant name and any relevant store identification\n", + "2. Location information (city, state, ZIP code)\n", + "3. Date and time of purchase\n", + "4. All purchased items with their:\n", + " * Item description/name\n", + " * Item code/SKU (if present)\n", + " * Category (infer from context if not explicit)\n", + " * Regular price per item (if available)\n", + " * Sale price per item (if discounted)\n", + " * Quantity purchased\n", + " * Total price for the line item\n", + "5. Financial summary:\n", + " * Subtotal before tax\n", + " * Tax amount\n", + " * Final total\n", + "6. Any handwritten notes or annotations on the receipt (list each separately)\n", + "\n", + "## Important Guidelines\n", + "\n", + "* If information is unclear or missing, return null for that field\n", + "* Format dates as ISO format (YYYY-MM-DDTHH:MM:SS)\n", + "* Format all monetary values as decimal numbers\n", + "* Distinguish between printed text and handwritten notes\n", + "* Be precise with amounts and totals\n", + "* For ambiguous items, use your best judgment based on context\n", + "\n", + "Your response should be structured and complete, capturing all available information\n", + "from the receipt.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "import mimetypes\n", + "from pathlib import Path\n", + "\n", + "from openai import AsyncOpenAI\n", + "\n", + "client = AsyncOpenAI()\n", + "\n", + "\n", + "async def extract_receipt_details(\n", + " image_path: str, model: str = \"o4-mini\"\n", + ") -> ReceiptDetails:\n", + " \"\"\"Extract structured details from a receipt image.\"\"\"\n", + " # Determine image type for data URI.\n", + " mime_type, _ = mimetypes.guess_type(image_path)\n", + "\n", + " # Read and base64 encode the image.\n", + " b64_image = base64.b64encode(Path(image_path).read_bytes()).decode(\"utf-8\")\n", + " image_data_url = f\"data:{mime_type};base64,{b64_image}\"\n", + "\n", + " response = await client.responses.parse(\n", + " model=model,\n", + " input=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"input_text\", \"text\": BASIC_PROMPT},\n", + " {\"type\": \"input_image\", \"image_url\": image_data_url},\n", + " ],\n", + " }\n", + " ],\n", + " text_format=ReceiptDetails,\n", + " )\n", + "\n", + " return response.output_parsed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test on one receipt\n", + "\n", + "Let's evaluate just a single receipt and review it manually to see how well a smart model with a naive prompt can do." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<img src=\"../../../images/Supplies_20240322_220858_Raven_Scan_3_jpeg.rf.50852940734939c8838819d7795e1756.jpg\" alt=\"Walmart_image\" width=\"400\"/>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from rich import print\n", + "\n", + "receipt_image_dir = Path(\"data/test\")\n", + "ground_truth_dir = Path(\"data/ground_truth\")\n", + "\n", + "example_receipt = Path(\n", + " \"data/train/Supplies_20240322_220858_Raven_Scan_3_jpeg.rf.50852940734939c8838819d7795e1756.jpg\"\n", + ")\n", + "result = await extract_receipt_details(example_receipt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll get different answers if we re-run it, but it usually gets most things correct\n", + "with a few errors. Here's a specific example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "walmart_receipt = ReceiptDetails(\n", + " merchant=\"Walmart\",\n", + " location=Location(city=\"Vista\", state=\"CA\", zipcode=\"92083\"),\n", + " time=\"2023-06-30T16:40:45\",\n", + " items=[\n", + " LineItem(\n", + " description=\"SPRAY 90\",\n", + " product_code=\"001920056201\",\n", + " category=None,\n", + " item_price=None,\n", + " sale_price=None,\n", + " quantity=\"2\",\n", + " total=\"28.28\",\n", + " ),\n", + " LineItem(\n", + " description=\"LINT ROLLER 70\",\n", + " product_code=\"007098200355\",\n", + " category=None,\n", + " item_price=None,\n", + " sale_price=None,\n", + " quantity=\"1\",\n", + " total=\"6.67\",\n", + " ),\n", + " LineItem(\n", + " description=\"SCRUBBER\",\n", + " product_code=\"003444193232\",\n", + " category=None,\n", + " item_price=None,\n", + " sale_price=None,\n", + " quantity=\"2\",\n", + " total=\"12.70\",\n", + " ),\n", + " LineItem(\n", + " description=\"FLOUR SACK 10\",\n", + " product_code=\"003444194263\",\n", + " category=None,\n", + " item_price=None,\n", + " sale_price=None,\n", + " quantity=\"1\",\n", + " total=\"0.77\",\n", + " ),\n", + " ],\n", + " subtotal=\"50.77\",\n", + " tax=\"4.19\",\n", + " total=\"54.96\",\n", + " handwritten_notes=[],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model extracted a lot of things correctly, but renamed some of the line\n", + "items - incorrectly, in fact. More importantly, it got some of the prices wrong, and it\n", + "decided not to categorize any of the line items.\n", + "\n", + "That's okay, we don't expect to have perfect answers at this point! Instead, our\n", + "objective is to build a basic system we can evaluate. Then, when we start iterating, we\n", + "won't be 'vibing' our way to something that *looks* better -- we'll be engineering a\n", + "reliable solution. But first, we'll add an action decision to complete our draft system." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Action Decision\n", + "\n", + "Next, we need to close the loop and get to an actual decision based on receipts. This\n", + "looks pretty similar, so we'll present the code without comment.\n", + "\n", + "Ordinarily one would start with the most capable model - `o3`, at this time - for a \n", + "first pass, and then once correctness is established experiment with different models\n", + "to analyze any tradeoffs for their business impact, and potentially consider whether \n", + "they are remediable with iteration. A client may be willing to take a certain accuracy \n", + "hit for lower latency or cost, or it may be more effective to change the architecture\n", + "to hit cost, latency, and accuracy goals. We'll get into how to make these tradeoffs\n", + "explicitly and objectively later on. \n", + "\n", + "For this cookbook, `o3` might be too good. We'll use `o4-mini` for our first pass, so \n", + "that we get a few reasoning errors we can use to illustrate the means of addressing\n", + "them when they occur.\n", + "\n", + "Next, we need to close the loop and get to an actual decision based on receipts. This\n", + "looks pretty similar, so we'll present the code without comment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pydantic import BaseModel, Field\n", + "\n", + "audit_prompt = \"\"\"\n", + "Evaluate this receipt data to determine if it need to be audited based on the following\n", + "criteria:\n", + "\n", + "1. NOT_TRAVEL_RELATED:\n", + " - IMPORTANT: For this criterion, travel-related expenses include but are not limited\n", + " to: gas, hotel, airfare, or car rental.\n", + " - If the receipt IS for a travel-related expense, set this to FALSE.\n", + " - If the receipt is NOT for a travel-related expense (like office supplies), set this\n", + " to TRUE.\n", + " - In other words, if the receipt shows FUEL/GAS, this would be FALSE because gas IS\n", + " travel-related.\n", + "\n", + "2. AMOUNT_OVER_LIMIT: The total amount exceeds $50\n", + "\n", + "3. MATH_ERROR: The math for computing the total doesn't add up (line items don't sum to\n", + " total)\n", + "\n", + "4. HANDWRITTEN_X: There is an \"X\" in the handwritten notes\n", + "\n", + "For each criterion, determine if it is violated (true) or not (false). Provide your\n", + "reasoning for each decision, and make a final determination on whether the receipt needs\n", + "auditing. A receipt needs auditing if ANY of the criteria are violated.\n", + "\n", + "Return a structured response with your evaluation.\n", + "\"\"\"\n", + "\n", + "\n", + "class AuditDecision(BaseModel):\n", + " not_travel_related: bool = Field(\n", + " description=\"True if the receipt is not travel-related\"\n", + " )\n", + " amount_over_limit: bool = Field(description=\"True if the total amount exceeds $50\")\n", + " math_error: bool = Field(description=\"True if there are math errors in the receipt\")\n", + " handwritten_x: bool = Field(\n", + " description=\"True if there is an 'X' in the handwritten notes\"\n", + " )\n", + " reasoning: str = Field(description=\"Explanation for the audit decision\")\n", + " needs_audit: bool = Field(\n", + " description=\"Final determination if receipt needs auditing\"\n", + " )\n", + "\n", + "\n", + "async def evaluate_receipt_for_audit(\n", + " receipt_details: ReceiptDetails, model: str = \"o4-mini\"\n", + ") -> AuditDecision:\n", + " \"\"\"Determine if a receipt needs to be audited based on defined criteria.\"\"\"\n", + " # Convert receipt details to JSON for the prompt\n", + " receipt_json = receipt_details.model_dump_json(indent=2)\n", + "\n", + " response = await client.responses.parse(\n", + " model=model,\n", + " input=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"input_text\", \"text\": audit_prompt},\n", + " {\"type\": \"input_text\", \"text\": f\"Receipt details:\\n{receipt_json}\"},\n", + " ],\n", + " }\n", + " ],\n", + " text_format=AuditDecision,\n", + " )\n", + "\n", + " return response.output_parsed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A schematic of the overall process shows two LLM calls:\n", + "\n", + "![Process Flowchart](../../../images/partner_process_flowchart.png)\n", + "\n", + "If we run our above example through this model, here's what we get -- again, we'll use \n", + "an example result here. When you run the code you might get slightly different results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "audit_decision = await evaluate_receipt_for_audit(result)\n", + "print(audit_decision)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "audit_decision = AuditDecision(\n", + " not_travel_related=True,\n", + " amount_over_limit=True,\n", + " math_error=False,\n", + " handwritten_x=False,\n", + " reasoning=\"\"\"\n", + " The receipt from Walmart is for office supplies, which are not travel-related, thus NOT_TRAVEL_RELATED is TRUE.\n", + " The total amount of the receipt is $54.96, which exceeds the limit of $50, making AMOUNT_OVER_LIMIT TRUE.\n", + " The subtotal ($50.77) plus tax ($4.19) correctly sums to the total ($54.96), so there is no MATH_ERROR.\n", + " There are no handwritten notes, so HANDWRITTEN_X is FALSE.\n", + " Since two criteria (amount over limit and travel-related) are violated, the receipt\n", + " needs auditing.\n", + " \"\"\",\n", + " needs_audit=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example illustrates why we care about end-to-end evals and why we can't use them in\n", + "isolation. Here, the initial extraction had OCR errors and forwarded the prices to the\n", + "auditor that don't add up to the total, but the auditor fails to detect it and asserts\n", + "there are no math errors. However, missing this doesn't change the audit decision\n", + "because it did pick up on the other two reasons the receipt needs to be audited.\n", + "\n", + "Thus, `AuditDecision` is factually incorrect, but the decision that we care about\n", + "is correct. This gives us an edge to improve upon, but also guides us toward making\n", + "sound choices for where and when we apply our engineering efforts.\n", + "\n", + "With that said, let's build ourselves some evals!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial Evals\n", + "\n", + "Once we have a minimally functional system we should process more inputs and get domain\n", + "experts to help develop ground-truth data. Domain experts doing expert tasks may not\n", + "have much time to devote to our project, so we want to be efficient and start small,\n", + "aiming for breadth rather than depth at first.\n", + "\n", + "> If your data *doesn't* require domain expertise, then you'd want to reach for a\n", + "> labeling solution (such as [Label Studio](https://labelstud.io/)) and attempt to annotate\n", + "> as much data as you can given the policy, budget, and data availability restrictions.\n", + "> In this case, we're going to proceed as if data labeling is a scarce resource; one we\n", + "> can rely on for small amounts each week, but these are people with other job\n", + "> responsibilities whose time and willingness to help may be limited. Sitting with these\n", + "> experts to help annotate examples can help make selecting future examples more\n", + "> efficient.\n", + "\n", + "Because we have a chain of two steps, we'll be collecting tuples of type\n", + "`[FilePath, ReceiptDetails, AuditDecision]`. Generally, the way to do this is to take\n", + "unlabeled samples, run them through our model, and then have experts correct the output.\n", + "For the purposes of this notebook, we've already gone through that process for all the\n", + "receipt images in `data/test`.\n", + "\n", + "### Additional Considerations\n", + "\n", + "There's a little more to it than that though, because when you are evaluating a\n", + "multistep process it's important to know both the end to end performance and the\n", + "performance of each individual step, *conditioned on the output of the prior step*.\n", + "\n", + "In this case, we want to evaluate:\n", + "\n", + "1. Given an input image, how well do we extract the information we need?\n", + "2. Given receipt information, how good is our **judgement** for our audit decision?\n", + "3. Given an input image, how **successful** are we about making our final audit decision?\n", + "\n", + "The phrasing difference between #2 and #3 is because if we give our auditor incorrect\n", + "data, we expect it to come to incorrect conclusions. What we *want* is to be confident\n", + "that the auditor is making the correct decision based on the evidence available, even if\n", + "that evidence is misleading. If we don't pay attention to that case, we can end up\n", + "training the auditor to ignore its inputs and cause our overall performance to degrade." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graders\n", + "\n", + "The core component of an eval is the\n", + "[grader](https://platform.openai.com/docs/guides/graders). Our eventual eval is going to\n", + "use 18 of them, but we only use three kinds, and they're all quite conceptually\n", + "straightforward.\n", + "\n", + "Here are examples of one of our string check graders, one of our text similarity\n", + "graders, and finally one of our model graders." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example_graders = [\n", + " {\n", + " \"name\": \"Total Amount Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.total }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.total }}\",\n", + " },\n", + " {\n", + " \"name\": \"Merchant Name Accuracy\",\n", + " \"type\": \"text_similarity\",\n", + " \"input\": \"{{ item.predicted_receipt_details.merchant }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.merchant }}\",\n", + " \"pass_threshold\": 0.8,\n", + " \"evaluation_metric\": \"bleu\",\n", + " },\n", + "]\n", + "\n", + "# A model grader needs a prompt to instruct it in what it should be scoring.\n", + "missed_items_grader_prompt = \"\"\"\n", + "Your task is to evaluate the correctness of a receipt extraction model.\n", + "\n", + "The following items are the actual (correct) line items from a specific receipt.\n", + "\n", + "{{ item.correct_receipt_details.items }}\n", + "\n", + "The following items are the line items extracted by the model.\n", + "\n", + "{{ item.predicted_receipt_details.items }}\n", + "\n", + "Score 0 if the sample evaluation missed any items from the receipt; otherwise score 1.\n", + "\n", + "The line items are permitted to have small differences or extraction mistakes, but each\n", + "item from the actual receipt must be present in some form in the model's output. Only\n", + "evaluate whether there are MISSED items; ignore other mistakes or extra items.\n", + "\"\"\"\n", + "\n", + "example_graders.append(\n", + " {\n", + " \"name\": \"Missed Line Items\",\n", + " \"type\": \"score_model\",\n", + " \"model\": \"o4-mini\",\n", + " \"input\": [{\"role\": \"system\", \"content\": missed_items_grader_prompt}],\n", + " \"range\": [0, 1],\n", + " \"pass_threshold\": 1,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each grader evaluates some portion of a predicted output. This might be a very narrow\n", + "check for a specific field in a structured output, or a more holistic check that\n", + "judges an output in its entirety. Some graders can work without context, and evaluate an\n", + "output in isolation (for example, an LLM judge that is evaluating if a paragraph is rude\n", + "or inappropriate). Others can evaluate based on the input and output, while while the\n", + "ones we're using here rely on an output and a ground-truth (correct) output to compare\n", + "against.\n", + "\n", + "The most direct way of using Evals provides a prompt and a model, and lets the eval run\n", + "on an input to generate output itself. Another useful method uses previously logged\n", + "responses or completions as the source of the outputs. It's not quite as simple, but the\n", + "most flexible thing we can do is to supply an item containing everything we want it to\n", + "use—this allows us to have the \"prediction\" function be an arbitrary system rather than\n", + "restricting it to a single model call. This is how we're using it in the examples below;\n", + "the `EvaluationRecord` shown below will be used to populate the `{{ }}` template\n", + "variables.\n", + "\n", + "> **Note on Model Selection:** \n", + "> Selecting the right model is crucial. While faster, less expensive models are often preferable in production, development workflows benefit from prioritizing the most capable models available. For this guide, we use `o4-mini` for both system tasks and LLM-based grading—while `o3` is more capable, our experience suggests the difference in output quality is modest relative to the substantial increase in cost. In practice, spending $10+/day/engineer on evals is typical, but scaling to $100+/day/engineer may not be sustainable.\n", + ">\n", + "> Nonetheless, it's valuable to periodically benchmark with a more advanced model like `o3`. If you observe significant improvements, consider incorporating it for a representative subset of your evaluation data. Discrepancies between models can reveal important edge cases and guide system improvements." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n", + "\n", + "\n", + "class EvaluationRecord(BaseModel):\n", + " \"\"\"Holds both the correct (ground truth) and predicted audit decisions.\"\"\"\n", + "\n", + " receipt_image_path: str\n", + " correct_receipt_details: ReceiptDetails\n", + " predicted_receipt_details: ReceiptDetails\n", + " correct_audit_decision: AuditDecision\n", + " predicted_audit_decision: AuditDecision\n", + "\n", + "\n", + "async def create_evaluation_record(image_path: Path, model: str) -> EvaluationRecord:\n", + " \"\"\"Create a ground truth record for a receipt image.\"\"\"\n", + " extraction_path = ground_truth_dir / \"extraction\" / f\"{image_path.stem}.json\"\n", + " correct_details = ReceiptDetails.model_validate_json(extraction_path.read_text())\n", + " predicted_details = await extract_receipt_details(image_path, model)\n", + "\n", + " audit_path = ground_truth_dir / \"audit_results\" / f\"{image_path.stem}.json\"\n", + " correct_audit = AuditDecision.model_validate_json(audit_path.read_text())\n", + " predicted_audit = await evaluate_receipt_for_audit(predicted_details, model)\n", + "\n", + " return EvaluationRecord(\n", + " receipt_image_path=image_path.name,\n", + " correct_receipt_details=correct_details,\n", + " predicted_receipt_details=predicted_details,\n", + " correct_audit_decision=correct_audit,\n", + " predicted_audit_decision=predicted_audit,\n", + " )\n", + "\n", + "\n", + "async def create_dataset_content(\n", + " receipt_image_dir: Path, model: str = \"o4-mini\"\n", + ") -> list[dict]:\n", + " # Assemble paired samples of ground truth data and predicted results. You could\n", + " # instead upload this data as a file and pass a file id when you run the eval.\n", + " tasks = [\n", + " create_evaluation_record(image_path, model)\n", + " for image_path in receipt_image_dir.glob(\"*.jpg\")\n", + " ]\n", + " return [{\"item\": record.model_dump()} for record in await asyncio.gather(*tasks)]\n", + "\n", + "\n", + "file_content = await create_dataset_content(receipt_image_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the graders and the data, creating and running our evals is very straightforward:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from persist_cache import cache\n", + "\n", + "\n", + "# We're caching the output so that if we re-run this cell we don't create a new eval.\n", + "@cache\n", + "async def create_eval(name: str, graders: list[dict]):\n", + " eval_cfg = await client.evals.create(\n", + " name=name,\n", + " data_source_config={\n", + " \"type\": \"custom\",\n", + " \"item_schema\": EvaluationRecord.model_json_schema(),\n", + " \"include_sample_schema\": False, # Don't generate new completions.\n", + " },\n", + " testing_criteria=graders,\n", + " )\n", + " print(f\"Created new eval: {eval_cfg.id}\")\n", + " return eval_cfg\n", + "\n", + "\n", + "initial_eval = await create_eval(\n", + " \"Initial Receipt Processing Evaluation\", example_graders\n", + ")\n", + "\n", + "# Run the eval.\n", + "eval_run = await client.evals.runs.create(\n", + " name=\"initial-receipt-processing-run\",\n", + " eval_id=initial_eval.id,\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\"type\": \"file_content\", \"content\": file_content},\n", + " },\n", + ")\n", + "print(f\"Evaluation run created: {eval_run.id}\")\n", + "print(f\"View results at: {eval_run.report_url}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After you run that eval you'll be able to view it in the UI, and should see something\n", + "like the below. \n", + "\n", + "(Note, if you have a Zero-Data-Retention agreement, this data is not stored\n", + "by OpenAI, so will not be available in this interface.)\n", + "like:\n", + "\n", + "![Summary UI](../../../images/partner_summary_ui.png)\n", + "\n", + "You can drill into the data tab to look at individual examples:\n", + "\n", + "![Details UI](../../../images/partner_details_ui.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting Evals to Business Metrics\n", + "\n", + "Evals show you where you can improve, and help track progress and regressions over time.\n", + "But the three evals above are just measurements — we need to imbue them with raison\n", + "d'être.\n", + "\n", + "The first thing we need is to add evaluations for the final stage of our receipt\n", + "processing, so that we can start seeing the results of our audit decisions. The next\n", + "thing we need, the most important, is a *model of business relevance*.\n", + "\n", + "### A Business Model\n", + "\n", + "It's almost never easy to work out what costs and benefits you could get out of a new\n", + "system depending on how well it performs. Often people will avoid trying to put\n", + "numbers to things because they know how much uncertainty there is and they don't want to\n", + "make guesses that make them look bad. That's okay; we just have to make our best guess,\n", + "and if we get more information later we can refine our model.\n", + "\n", + "For this cookbook, we're going to create a simple cost structure:\n", + "\n", + "- our company processes 1 million receipts a year, at a baseline cost of $0.20 /\n", + " receipt\n", + "- auditing a receipt costs about $2\n", + "- failing to audit a receipt we should have audited costs an average of $30\n", + "- 5% of receipts need to be audited\n", + "- the existing process\n", + " - identifies receipts that need to be audited 97% of the time\n", + " - misidentifies receipts that don't need to be audited 2% of the time\n", + "\n", + "This gives us two baseline comparisons:\n", + "\n", + "- if we identified every receipt correctly, we would spend $100,000 on audits\n", + "- our current process spends $135,000 on audits and loses $45,000 to un-audited expenses\n", + "\n", + "On top of that, the human-driven process costs an additional $200,000.\n", + "\n", + "We're expecting our service to save money by costing less to run (≈1¢/receipt if we use\n", + "the prompts from above with `o4-mini`), but whether we save or lose money on audits and\n", + "missed audits depends on how well our system performs. It might be worth writing this as\n", + "a simple function — written below is a version that includes the above factors but\n", + "neglects nuance and ignores development, maintenance, and serving costs.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_costs(fp_rate: float, fn_rate: float, per_receipt_cost: float):\n", + " audit_cost = 2\n", + " missed_audit_cost = 30\n", + " receipt_count = 1e6\n", + " audit_fraction = 0.05\n", + "\n", + " needs_audit_count = receipt_count * audit_fraction\n", + " no_needs_audit_count = receipt_count - needs_audit_count\n", + "\n", + " missed_audits = needs_audit_count * fn_rate\n", + " total_audits = needs_audit_count * (1 - fn_rate) + no_needs_audit_count * fp_rate\n", + "\n", + " audit_cost = total_audits * audit_cost\n", + " missed_audit_cost = missed_audits * missed_audit_cost\n", + " processing_cost = receipt_count * per_receipt_cost\n", + "\n", + " return audit_cost + missed_audit_cost + processing_cost\n", + "\n", + "\n", + "perfect_system_cost = calculate_costs(0, 0, 0)\n", + "current_system_cost = calculate_costs(0.02, 0.03, 0.20)\n", + "\n", + "print(f\"Current system cost: ${current_system_cost:,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Connecting Back To Evals\n", + "\n", + "The point of the above model is it lets us apply meaning to an eval that would\n", + "otherwise just be a number. For instance, when we ran the system above we were wrong 85%\n", + "of the time for merchant names. But digging in, it seems like most instances are\n", + "capitalization issues or \"Shell Gasoline\" vs. \"Shell Oil #2144\" — problems that when\n", + "we follow through, do not appear to affect our audit decision or change our fundamental\n", + "costs.\n", + "\n", + "On the other hand, it seems like we fail to catch handwritten \"X\"s on receipts about\n", + "half the time, and about half of the time when there's an \"X\" on a receipt that gets\n", + "missed, it results in a receipt not getting audited when it should. Those are\n", + "overrepresented in our dataset, but if that makes up even 1% of receipts, that 50%\n", + "failure would cost us $75,000 a year.\n", + "\n", + "Similarly, it seems like we have OCR errors that cause us to audit receipts quite often\n", + "on account of the math not working out, up to 20% of the time. This could cost us almost\n", + "$400,000!\n", + "\n", + "Now, we're in a place to add more graders and start working backwards from the audit\n", + "decision accuracy to determine which problems we should focus on.\n", + "\n", + "Below are the rest of our graders and the results we get with our initial un-optimized\n", + "prompts. Note that at this point we do quite badly! Across our 20 samples (8 positive,\n", + "12 negative), we had two false negatives and two false positives. If we extrapolated to\n", + "our entire business, we'd be losing $375,000 on audits we missed and $475,000 on\n", + "unnecessary audits." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "simple_extraction_graders = [\n", + " {\n", + " \"name\": \"Merchant Name Accuracy\",\n", + " \"type\": \"text_similarity\",\n", + " \"input\": \"{{ item.predicted_receipt_details.merchant }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.merchant }}\",\n", + " \"pass_threshold\": 0.8,\n", + " \"evaluation_metric\": \"bleu\",\n", + " },\n", + " {\n", + " \"name\": \"Location City Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.location.city }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.location.city }}\",\n", + " },\n", + " {\n", + " \"name\": \"Location State Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.location.state }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.location.state }}\",\n", + " },\n", + " {\n", + " \"name\": \"Location Zipcode Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.location.zipcode }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.location.zipcode }}\",\n", + " },\n", + " {\n", + " \"name\": \"Time Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.time }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.time }}\",\n", + " },\n", + " {\n", + " \"name\": \"Subtotal Amount Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.subtotal }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.subtotal }}\",\n", + " },\n", + " {\n", + " \"name\": \"Tax Amount Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.tax }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.tax }}\",\n", + " },\n", + " {\n", + " \"name\": \"Total Amount Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_receipt_details.total }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.total }}\",\n", + " },\n", + " {\n", + " \"name\": \"Handwritten Notes Accuracy\",\n", + " \"type\": \"text_similarity\",\n", + " \"input\": \"{{ item.predicted_receipt_details.handwritten_notes }}\",\n", + " \"reference\": \"{{ item.correct_receipt_details.handwritten_notes }}\",\n", + " \"pass_threshold\": 0.8,\n", + " \"evaluation_metric\": \"fuzzy_match\",\n", + " },\n", + "]\n", + "\n", + "item_extraction_base = \"\"\"\n", + "Your task is to evaluate the correctness of a receipt extraction model.\n", + "\n", + "The following items are the actual (correct) line items from a specific receipt.\n", + "\n", + "{{ item.correct_receipt_details.items }}\n", + "\n", + "The following items are the line items extracted by the model.\n", + "\n", + "{{ item.predicted_receipt_details.items }}\n", + "\"\"\"\n", + "\n", + "missed_items_instructions = \"\"\"\n", + "Score 0 if the sample evaluation missed any items from the receipt; otherwise score 1.\n", + "\n", + "The line items are permitted to have small differences or extraction mistakes, but each\n", + "item from the actual receipt must be present in some form in the model's output. Only\n", + "evaluate whether there are MISSED items; ignore other mistakes or extra items.\n", + "\"\"\"\n", + "\n", + "extra_items_instructions = \"\"\"\n", + "Score 0 if the sample evaluation extracted any extra items from the receipt; otherwise\n", + "score 1.\n", + "\n", + "The line items are permitted to have small differences or extraction mistakes, but each\n", + "item from the actual receipt must be present in some form in the model's output. Only\n", + "evaluate whether there are EXTRA items; ignore other mistakes or missed items.\n", + "\"\"\"\n", + "\n", + "item_mistakes_instructions = \"\"\"\n", + "Score 0 to 10 based on the number and severity of mistakes in the line items.\n", + "\n", + "A score of 10 means that the two lists are perfectly identical.\n", + "\n", + "Remove 1 point for each minor mistake (typos, capitalization, category name\n", + "differences), and up to 3 points for significant mistakes (incorrect quantity, price, or\n", + "total, or categories that are not at all similar).\n", + "\"\"\"\n", + "\n", + "item_extraction_graders = [\n", + " {\n", + " \"name\": \"Missed Line Items\",\n", + " \"type\": \"score_model\",\n", + " \"model\": \"o4-mini\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": item_extraction_base + missed_items_instructions,\n", + " }\n", + " ],\n", + " \"range\": [0, 1],\n", + " \"pass_threshold\": 1,\n", + " },\n", + " {\n", + " \"name\": \"Extra Line Items\",\n", + " \"type\": \"score_model\",\n", + " \"model\": \"o4-mini\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": item_extraction_base + extra_items_instructions,\n", + " }\n", + " ],\n", + " \"range\": [0, 1],\n", + " \"pass_threshold\": 1,\n", + " },\n", + " {\n", + " \"name\": \"Item Mistakes\",\n", + " \"type\": \"score_model\",\n", + " \"model\": \"o4-mini\",\n", + " \"input\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": item_extraction_base + item_mistakes_instructions,\n", + " }\n", + " ],\n", + " \"range\": [0, 10],\n", + " \"pass_threshold\": 8,\n", + " },\n", + "]\n", + "\n", + "\n", + "simple_audit_graders = [\n", + " {\n", + " \"name\": \"Not Travel Related Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_audit_decision.not_travel_related }}\",\n", + " \"reference\": \"{{ item.correct_audit_decision.not_travel_related }}\",\n", + " },\n", + " {\n", + " \"name\": \"Amount Over Limit Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_audit_decision.amount_over_limit }}\",\n", + " \"reference\": \"{{ item.correct_audit_decision.amount_over_limit }}\",\n", + " },\n", + " {\n", + " \"name\": \"Math Error Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_audit_decision.math_error }}\",\n", + " \"reference\": \"{{ item.correct_audit_decision.math_error }}\",\n", + " },\n", + " {\n", + " \"name\": \"Handwritten X Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_audit_decision.handwritten_x }}\",\n", + " \"reference\": \"{{ item.correct_audit_decision.handwritten_x }}\",\n", + " },\n", + " {\n", + " \"name\": \"Needs Audit Accuracy\",\n", + " \"type\": \"string_check\",\n", + " \"operation\": \"eq\",\n", + " \"input\": \"{{ item.predicted_audit_decision.needs_audit }}\",\n", + " \"reference\": \"{{ item.correct_audit_decision.needs_audit }}\",\n", + " },\n", + "]\n", + "\n", + "\n", + "reasoning_eval_prompt = \"\"\"\n", + "Your task is to evaluate the quality of *reasoning* for audit decisions on receipts.\n", + "Here are the rules for audit decisions:\n", + "\n", + "Expenses should be audited if they violate any of the following criteria:\n", + "1. Expenses must be travel-related\n", + "2. Expenses must not exceed $50\n", + "3. All math should be correct; the line items plus tax should equal the total\n", + "4. There must not be an \"X\" in the handwritten notes\n", + "\n", + "If ANY of those criteria are violated, the expense should be audited.\n", + "\n", + "Here is the input to the grader:\n", + "{{ item.predicted_receipt_details }}\n", + "\n", + "Below is the output of an authoritative grader making a decision about whether or not to\n", + "audit an expense. This is a correct reference decision.\n", + "\n", + "GROUND TRUTH:\n", + "{{ item.correct_audit_decision }}\n", + "\n", + "\n", + "Here is the output of the model we are evaluating:\n", + "\n", + "MODEL GENERATED:\n", + "{{ item.predicted_audit_decision }}\n", + "\n", + "\n", + "Evaluate:\n", + "1. For each of the 4 criteria, did the model correctly score it as TRUE or FALSE?\n", + "2. Based on the model's *scoring* of the criteria (regardless if it scored it\n", + " correctly), did the model reason appropriately about the criteria (i.e. did it\n", + " understand and apply the prompt correctly)?\n", + "3. Is the model's reasoning logically sound, sufficient, and comprehensible?\n", + "4. Is the model's reasoning concise, without extraneous details?\n", + "5. Is the final decision to audit or not audit correct?\n", + "\n", + "Grade the model with the following rubric:\n", + "- (1) point for each of the 4 criteria that the model scored correctly\n", + "- (3) points for each aspect of the model's reasoning that is meets the criteria\n", + "- (3) points for the model's final decision to audit or not audit\n", + "\n", + "The total score is the sum of the points, and should be between 0 and 10 inclusive.\n", + "\"\"\"\n", + "\n", + "\n", + "model_judgement_graders = [\n", + " {\n", + " \"name\": \"Audit Reasoning Quality\",\n", + " \"type\": \"score_model\",\n", + " \"model\": \"o4-mini\",\n", + " \"input\": [{\"role\": \"system\", \"content\": reasoning_eval_prompt}],\n", + " \"range\": [0, 10],\n", + " \"pass_threshold\": 8,\n", + " },\n", + "]\n", + "\n", + "full_eval = await create_eval(\n", + " \"Full Receipt Processing Evaluation\",\n", + " simple_extraction_graders\n", + " + item_extraction_graders\n", + " + simple_audit_graders\n", + " + model_judgement_graders,\n", + ")\n", + "\n", + "eval_run = await client.evals.runs.create(\n", + " name=\"complete-receipt-processing-run\",\n", + " eval_id=full_eval.id,\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\"type\": \"file_content\", \"content\": file_content},\n", + " },\n", + ")\n", + "\n", + "eval_run.report_url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Large Summary UI](../../../images/partner_large_summary_ui.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spin Up the Flywheel\n", + "\n", + "Having our business model means we have a map of what's worth doing and what isn't. Our\n", + "initial evals are a road sign that lets us know we're moving in the right direction; but\n", + "eventually we'll need more signage. At this point in the process we usually have a lot\n", + "of different things we can work on, with a few linked cycles where improvement on one\n", + "will open up more room for improvement on a different cycle.\n", + "\n", + "![Development Flywheel](../../../images/partner_development_flywheel.png)\n", + "\n", + "1. Our evals show us where we can improve, and we can immediately use them to guide us\n", + " in model selection, prompt engineering, tool use, and fine-tuning strategies.\n", + "2. We're not done once system performs well according to our evals. That's when it's\n", + " time to *improve our evals*. We will process more data, give it to our domain experts\n", + " to review, and feed the corrections into building better, more comprehensive evals.\n", + "\n", + "This cycle can go on for a while. We can speed it along by identifying the efficient\n", + "frontier of \"interesting\" data to examine. There are a few techniques for this, but an\n", + "easy one is re-running models on inputs to prioritize labeling inputs that don't\n", + "get consistent answers. This works especially well when using different underlying\n", + "models, and often even benefits from using less-intelligent models (if a dumb model\n", + "agrees with a smart model then it's probably not a hard problem).\n", + "\n", + "Once it seems like we've hit a point of dimishing returns on performance, we can keep\n", + "using the same techniques to optimize model cost; if we have a system that performs\n", + "quite well, then fine-tuning or some form of model distillation will probably allow us\n", + "to get similar performance from smaller, cheaper, faster models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## System Improvements\n", + "\n", + "With our evals in place and an understanding of how they connect to our business metrics,\n", + "we're finally ready to turn our attention to improving the output of our system.\n", + "\n", + "Above, we noted that we get merchant names wrong 85% of the time, more than any other\n", + "output we're evaluating. This looks pretty bad, and it's probably something we can\n", + "improve dramaticaly with only a little work, but instead let's start from the endpoint\n", + "of our business metrics and work backwards to see what issues caused incorrect\n", + "decisions.\n", + "\n", + "When we do that, we see that the mistakes we made on merchant names are completely\n", + "uncorrelated with our final audit decision, and there's no evidence that they have any\n", + "impact on that decision. Based on our business model, we don't actually see a need to\n", + "improve it -- in other words, *not all evals matter*. Instead, we can examine\n", + "specifically the examples where we made a bad audit decision. There are only two of them\n", + "(out of 20). Examining them closely, we observe that in both cases the problem came from\n", + "the second stage of the pipeline making a wrong decision based on a non-problematic\n", + "extraction. And in fact, both of them come from a failure to reason correctly about\n", + "travel-related expenses.\n", + "\n", + "In the first case, the purchase is a snowbroom from an auto-parts store. This is a\n", + "little bit of an edge case, but our domain experts identified this as a valid travel\n", + "expense (because drivers might need one to clear their windshield). This seems like\n", + "explaining the decision process in more detail and providing an analogous example would\n", + "correct the error.\n", + "\n", + "In the second case, the purchase is some tools from a home improvement score. The tools\n", + "don't have anything to do with normal driving, so this receipt should be audited as a\n", + "\"non-travel-related expense\". In this case our model *correctly* identifies it as an\n", + "expense that's not travel-related, but then reasons incorrectly about that fact,\n", + "apparently misunderstanding that `true` for `not_travel_related` should imply `true` for\n", + "`needs_audit`. Again, this seems like an example where more clarity in our instructions\n", + "and a few examples should fix the issue.\n", + "\n", + "Connecting this back to our cost model, we note that we have 1 false negative and 1\n", + "false positive, along with 7 true positives and 11 true negatives. Extrapolating this to\n", + "the frequencies we see in production, this would increase our overall costs by $63,000\n", + "per year.\n", + "\n", + "Let's modify the prompt and re-run our evals to see how we do. We'll provide more\n", + "guidance in the form of a specific example in the instructions about engine oil\n", + "(different from a snow broom, but requires the same reasoning), and we'll include three\n", + "examples pulled from our training set (`data/train`) as few-shot guidance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "first_ai_system_cost = calculate_costs(\n", + " fp_rate=1 / 12, fn_rate=1 / 8, per_receipt_cost=0.01\n", + ")\n", + "\n", + "print(f\"First version of our system, estimated cost: ${first_ai_system_cost:,.0f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nursery_receipt_details = ReceiptDetails(\n", + " merchant=\"WESTERN SIERRA NURSERY\",\n", + " location=Location(city=\"Oakhurst\", state=\"CA\", zipcode=\"93644\"),\n", + " time=\"2024-09-27T12:33:38\",\n", + " items=[\n", + " LineItem(\n", + " description=\"Plantskydd Repellent RTU 1 Liter\",\n", + " product_code=None,\n", + " category=\"Garden/Pest Control\",\n", + " item_price=\"24.99\",\n", + " sale_price=None,\n", + " quantity=\"1\",\n", + " total=\"24.99\",\n", + " )\n", + " ],\n", + " subtotal=\"24.99\",\n", + " tax=\"1.94\",\n", + " total=\"26.93\",\n", + " handwritten_notes=[],\n", + ")\n", + "\n", + "nursery_audit_decision = AuditDecision(\n", + " not_travel_related=True,\n", + " amount_over_limit=False,\n", + " math_error=False,\n", + " handwritten_x=False,\n", + " reasoning=\"\"\"\n", + " 1. The merchant is a plant nursery and the item purchased an insecticide, so this\n", + " purchase is not travel-related (criterion 1 violated).\n", + " 2. The total is $26.93, under $50, so criterion 2 is not violated.\n", + " 3. The line items (1 * $24.99 + $1.94 tax) sum to $26.93, so criterion 3 is not\n", + " violated.\n", + " 4. There are no handwritten notes or 'X's, so criterion 4 is not violated.\n", + " Since NOT_TRAVEL_RELATED is true, the receipt must be audited.\n", + " \"\"\",\n", + " needs_audit=True,\n", + ")\n", + "\n", + "flying_j_details = ReceiptDetails(\n", + " merchant=\"Flying J #616\",\n", + " location=Location(city=\"Frazier Park\", state=\"CA\", zipcode=None),\n", + " time=\"2024-10-01T13:23:00\",\n", + " items=[\n", + " LineItem(\n", + " description=\"Unleaded\",\n", + " product_code=None,\n", + " category=\"Fuel\",\n", + " item_price=\"4.459\",\n", + " sale_price=None,\n", + " quantity=\"11.076\",\n", + " total=\"49.39\",\n", + " )\n", + " ],\n", + " subtotal=\"49.39\",\n", + " tax=None,\n", + " total=\"49.39\",\n", + " handwritten_notes=[\"yos -> home sequoia\", \"236660\"],\n", + ")\n", + "flying_j_audit_decision = AuditDecision(\n", + " not_travel_related=False,\n", + " amount_over_limit=False,\n", + " math_error=False,\n", + " handwritten_x=False,\n", + " reasoning=\"\"\"\n", + " 1. The only item purchased is Unleaded gasoline, which is travel-related so\n", + " NOT_TRAVEL_RELATED is false.\n", + " 2. The total is $49.39, which is under $50, so AMOUNT_OVER_LIMIT is false.\n", + " 3. The line items ($4.459 * 11.076 = $49.387884) sum to the total of $49.39, so\n", + " MATH_ERROR is false.\n", + " 4. There is no \"X\" in the handwritten notes, so HANDWRITTEN_X is false.\n", + " Since none of the criteria are violated, the receipt does not need auditing.\n", + " \"\"\",\n", + " needs_audit=False,\n", + ")\n", + "\n", + "engine_oil_details = ReceiptDetails(\n", + " merchant=\"O'Reilly Auto Parts\",\n", + " location=Location(city=\"Sylmar\", state=\"CA\", zipcode=\"91342\"),\n", + " time=\"2024-04-26T8:43:11\",\n", + " items=[\n", + " LineItem(\n", + " description=\"VAL 5W-20\",\n", + " product_code=None,\n", + " category=\"Auto\",\n", + " item_price=\"12.28\",\n", + " sale_price=None,\n", + " quantity=\"1\",\n", + " total=\"12.28\",\n", + " )\n", + " ],\n", + " subtotal=\"12.28\",\n", + " tax=\"1.07\",\n", + " total=\"13.35\",\n", + " handwritten_notes=[\"vista -> yos\"],\n", + ")\n", + "engine_oil_audit_decision = AuditDecision(\n", + " not_travel_related=False,\n", + " amount_over_limit=False,\n", + " math_error=False,\n", + " handwritten_x=False,\n", + " reasoning=\"\"\"\n", + " 1. The only item purchased is engine oil, which might be required for a vehicle\n", + " while traveling, so NOT_TRAVEL_RELATED is false.\n", + " 2. The total is $13.35, which is under $50, so AMOUNT_OVER_LIMIT is false.\n", + " 3. The line items ($12.28 + $1.07 tax) sum to the total of $13.35, so\n", + " MATH_ERROR is false.\n", + " 4. There is no \"X\" in the handwritten notes, so HANDWRITTEN_X is false.\n", + " None of the criteria are violated so the receipt does not need to be audited.\n", + " \"\"\",\n", + " needs_audit=False,\n", + ")\n", + "\n", + "examples = [\n", + " {\"input\": nursery_receipt_details, \"output\": nursery_audit_decision},\n", + " {\"input\": flying_j_details, \"output\": flying_j_audit_decision},\n", + " {\"input\": engine_oil_details, \"output\": engine_oil_audit_decision},\n", + "]\n", + "\n", + "# Format the examples as JSON, with each example wrapped in XML tags.\n", + "example_format = \"\"\"\n", + "<example>\n", + " <input>\n", + " {input}\n", + " </input>\n", + " <output>\n", + " {output}\n", + " </output>\n", + "</example>\n", + "\"\"\"\n", + "\n", + "examples_string = \"\"\n", + "for example in examples:\n", + " example_input = example[\"input\"].model_dump_json()\n", + " correct_output = example[\"output\"].model_dump_json()\n", + " examples_string += example_format.format(input=example_input, output=correct_output)\n", + "\n", + "audit_prompt = f\"\"\"\n", + "Evaluate this receipt data to determine if it need to be audited based on the following\n", + "criteria:\n", + "\n", + "1. NOT_TRAVEL_RELATED:\n", + " - IMPORTANT: For this criterion, travel-related expenses include but are not limited\n", + " to: gas, hotel, airfare, or car rental.\n", + " - If the receipt IS for a travel-related expense, set this to FALSE.\n", + " - If the receipt is NOT for a travel-related expense (like office supplies), set this\n", + " to TRUE.\n", + " - In other words, if the receipt shows FUEL/GAS, this would be FALSE because gas IS\n", + " travel-related.\n", + " - Travel-related expenses include anything that could be reasonably required for\n", + " business-related travel activities. For instance, an employee using a personal\n", + " vehicle might need to change their oil; if the receipt is for an oil change or the\n", + " purchase of oil from an auto parts store, this would be acceptable and counts as a\n", + " travel-related expense.\n", + "\n", + "2. AMOUNT_OVER_LIMIT: The total amount exceeds $50\n", + "\n", + "3. MATH_ERROR: The math for computing the total doesn't add up (line items don't sum to\n", + " total)\n", + " - Add up the price and quantity of each line item to get the subtotal\n", + " - Add tax to the subtotal to get the total\n", + " - If the total doesn't match the amount on the receipt, this is a math error\n", + " - If the total is off by no more than $0.01, this is NOT a math error\n", + "\n", + "4. HANDWRITTEN_X: There is an \"X\" in the handwritten notes\n", + "\n", + "For each criterion, determine if it is violated (true) or not (false). Provide your\n", + "reasoning for each decision, and make a final determination on whether the receipt needs\n", + "auditing. A receipt needs auditing if ANY of the criteria are violated.\n", + "\n", + "Note that violation of a criterion means that it is `true`. If any of the above four\n", + "values are `true`, then the receipt needs auditing (`needs_audit` should be `true`: it\n", + "functions as a boolean OR over all four criteria).\n", + "\n", + "If the receipt contains non-travel expenses, then NOT_TRAVEL_RELATED should be `true`\n", + "and therefore NEEDS_AUDIT must also be set to `true`. IF THE RECEIPT LISTS ITEMS THAT\n", + "ARE NOT TRAVEL-RELATED, THEN IT MUST BE AUDITED. Here are some example inputs to\n", + "demonstrate how you should act:\n", + "\n", + "<examples>\n", + "{examples_string}\n", + "</examples>\n", + "\n", + "Return a structured response with your evaluation.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The modifications we made to the prompt above are:\n", + "\n", + "1. Under item 1 concerning travel-related expenses, we added a bullet point\n", + "\n", + "```\n", + "- Travel-related expenses include anything that could be reasonably required for\n", + " business-related travel activities. For instance, an employee using a personal\n", + " vehicle might need to change their oil; if the receipt is for an oil change or the\n", + " purchase of oil from an auto parts store, this would be acceptable and counts as a\n", + " travel-related expense.\n", + "```\n", + "\n", + "2. We added more proscriptive guidance on how to evaluate for a math error.\n", + " Specifically, we added the bullet points:\n", + "\n", + "```\n", + " - Add up the price and quantity of each line item to get the subtotal\n", + " - Add tax to the subtotal to get the total\n", + " - If the total doesn't match the amount on the receipt, this is a math error\n", + " - If the total is off by no more than $0.01, this is NOT a math error\n", + "```\n", + "\n", + " This doesn't actually have to do with the issues we mentioned, but is another issue\n", + " we noticed as a flaw in the reasoning provided by the audit model.\n", + "\n", + "3. We added very strong guidance (we actually needed to state it and restate it\n", + " emphatically) to say that non-travel-related expenses should be audited.\n", + "\n", + "```\n", + "Note that violation of a criterion means that it is `true`. If any of the above four\n", + "values are `true`, then the receipt needs auditing (`needs_audit` should be `true`: it\n", + "functions as a boolean OR over all four criteria).\n", + "\n", + "If the receipt contains non-travel expenses, then NOT_TRAVEL_RELATED should be `true`\n", + "and therefore NEEDS_AUDIT must also be set to `true`. IF THE RECEIPT LISTS ITEMS THAT\n", + "ARE NOT TRAVEL-RELATED, THEN IT MUST BE AUDITED.\n", + "```\n", + "\n", + "4. We added three examples, JSON input/output pairs wrapped in XML tags.\n", + "3. We added three examples, JSON input/output pairs wrapped in XML tags.\n", + "\n", + "With our prompt revisions, we'll regenerate the data to evaluate and re-run the same\n", + "eval to compare our results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_content = await create_dataset_content(receipt_image_dir)\n", + "\n", + "eval_run = await client.evals.runs.create(\n", + " name=\"updated-receipt-processing-run\",\n", + " eval_id=full_eval.id,\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\"type\": \"file_content\", \"content\": file_content},\n", + " },\n", + ")\n", + "\n", + "eval_run.report_url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we ran the eval again, we actually still got two audit decisions wrong. Digging into\n", + "the examples we made a mistake on, it turns out that we completely fixed the issues we\n", + "identified, but our examples improved the reasoning step and caused two other issues to\n", + "surface. Specifically:\n", + "\n", + "1. One receipt needed to be audited only because there was a mistake in extraction and\n", + " a handwritten \"X\" wasn't identified. The audit model reasoned correctly, but based on\n", + " incorrect data.\n", + "2. One receipt was extracted in such a way that a $0.35 debit fee wasn't visible, so the\n", + " audit model identified a math error. This almost certainly happened because we\n", + " provided it with more detailed instructions and clear examples that demonstrated it\n", + " needed to actually add up all the line items in order to decide whether there was a\n", + " math error. Again, this demonstrates correct behavior on the part of the audit model\n", + " and suggests we need to correct the extraction model.\n", + "\n", + "This is great, and we'll continue iterating on issues as we uncover them. This is the\n", + "cycle of improvement!\n", + "\n", + "### Model Choice\n", + "\n", + "When beginning a project, we usually start with one of the most capable models available, such as `o4-mini`, to establish a performance baseline. Once we’re confident in the model’s ability to solve the task, the next step is to explore smaller, faster, or more cost-effective alternatives.\n", + "\n", + "Optimizing for inference cost and latency is essential, especially for production or customer-facing systems, where these factors can significantly impact overall expenses and user experience. For instance, switching from `o4-mini` to `gpt-4.1-mini` could reduce inference costs by nearly two-thirds—an example where thoughtful model selection leads to meaningful savings.\n", + "\n", + "In the next section, we’ll rerun our evaluations using `gpt-4.1-mini` for both extraction and audit steps to see how well a more efficient model performs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_content = await create_dataset_content(receipt_image_dir, model=\"gpt-4.1-mini\")\n", + "\n", + "eval_run = await client.evals.runs.create(\n", + " name=\"receipt-processing-run-gpt-4-1-mini\",\n", + " eval_id=full_eval.id,\n", + " data_source={\n", + " \"type\": \"jsonl\",\n", + " \"source\": {\"type\": \"file_content\", \"content\": file_content},\n", + " },\n", + ")\n", + "\n", + "eval_run.report_url" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are pretty promising. It doesn't look like the extraction accuracy suffered\n", + "at all. We see one regression (the snowbroom again), but our audit decision is correct\n", + "twice as often as it was before our prompt changes.\n", + "\n", + "![Eval Variations](../../../images/partner_eval_variations.png)\n", + "\n", + "This is great evidence that we'll be able to switch to a cheaper model, but it might\n", + "require more prompt engineering, fine-tuning, or some form of model-distillation. Note\n", + "however that according to our current model this would already be saving us money. We\n", + "don't quite believe that yet because we don't have a large enough sample — our real\n", + "false negative rate will be more than the 0 we see here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "system_cost_4_1_mini = calculate_costs(\n", + " fp_rate=1 / 12, fn_rate=0, per_receipt_cost=0.003\n", + ")\n", + "\n", + "print(f\"Cost using gpt-4.1-mini: ${system_cost_4_1_mini:,.0f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### Further improvements\n", + "\n", + "This cookbook focuses on the philosophy and practicalities of evals, not the full range of model improvement techniques. For boosting or maintaining model performance (especially when moving to smaller, faster, or cheaper models), consider these steps in order—start from the top, and only proceed down if needed. For example, always optimize your prompt before resorting to fine-tuning; fine-tuning on a weak prompt can lock in bad performance even if you improve the prompt later.\n", + "\n", + "![Model Improvement Waterfall](../../../images/partner_model_improvement_waterfall.png)\n", + "\n", + "1. **Model selection:** try smarter models, or increase their reasoning budget.\n", + "2. **Prompt tuning:** clarify instructions and provide very explicit rules.\n", + "3. **Examples and context:** add few- or many-shot examples, or more context for the\n", + " problem. RAG fits in here, and may be used to dynamically select similar examples.\n", + "4. **Tools use:** provide tools to solve specific problems, including access to external\n", + " APIs, the ability to query databases, or otherwise enable the model to have its own\n", + " questions answered.\n", + "5. **Accessory models:** add models to perform limited sub-tasks, to supervise and provide\n", + " guardrails, or use a mixture of experts and aggregate solutions from multiple\n", + " sub-models.\n", + "6. **Fine-tuning:** use labeled training data for supervised fine tuning, eval\n", + " graders for reinforcement fine tuning, or different outputs for direct preference\n", + " optimization.\n", + "\n", + "The above options are all tools to maximize performance. Once you're trying to optimize\n", + "for a price:performance ratio, you'll usually have already done all of the above and\n", + "likely don't need to repeat most steps, but you can still fine-tune smaller models or\n", + "use your best model to train a smaller model (model distillation).\n", + "\n", + "> One really excellent thing about OpenAI Evals is that you can use the same graders for\n", + "> [Reinforcement Fine-Tuning](https://cookbook.openai.com/examples/reinforcement_fine_tuning)\n", + "> to produce better model performance in an extremely sample-efficient manner. One note\n", + "> of caution is to make sure that you use separate training data and don't leak your\n", + "> eval datasets during RFT." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deploying and Post-Development\n", + "Building and deploying an LLM application is just the beginning—the real value comes from ongoing improvement. Once your system is live, prioritize continuous monitoring: log traces, track outputs, and proactively sample real user interactions for human review using smart sampling techniques.\n", + "\n", + "Production data is your most authentic source for evolving your evaluation and training datasets. Regularly collect and curate fresh samples from actual use cases to identify gaps, edge cases, and new opportunities for enhancement.\n", + "\n", + "In practice, leverage this data for rapid iteration. Automate periodic fine-tuning pipelines that retrain your models on recent, high-quality samples and automatically deploy new versions when they outperform existing ones in your evals. Capture user corrections and feedback, then systematically feed these insights back into your prompts or retraining process—especially when they highlight persistent issues.\n", + "\n", + "By embedding these feedback loops into your post-development workflow, you ensure your LLM applications continuously adapt, stay robust, and remain closely aligned with user needs as they evolve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contributors\n", + "This cookbook serves as a joint collaboration effort between OpenAI and [Fractional](https://www.fractional.ai/).\n", + "\n", + "- Hugh Wimberly\n", + "- Joshua Marker\n", + "- Eddie Siegel\n", + "- Shikhar Kwatra" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/partners/mcp_powered_voice_agents/database.db b/examples/partners/mcp_powered_voice_agents/database.db new file mode 100644 index 0000000000..372e89dd8e Binary files /dev/null and b/examples/partners/mcp_powered_voice_agents/database.db differ diff --git a/examples/partners/mcp_powered_voice_agents/mcp_powered_agents_cookbook.ipynb b/examples/partners/mcp_powered_voice_agents/mcp_powered_agents_cookbook.ipynb new file mode 100644 index 0000000000..72aa39fc2a --- /dev/null +++ b/examples/partners/mcp_powered_voice_agents/mcp_powered_agents_cookbook.ipynb @@ -0,0 +1,976 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2LgIIWiQ_zS4" + }, + "source": [ + "# MCP‑Powered Agentic Voice Framework\n", + "\n", + "### Agents\n", + "Agents are becoming the de-facto framework in which we orchestrate various, often specialized, LLMs applications to work with one another. Many practical applications require the use of external tools to create a complex workflow for LLM-based agents.\n", + "\n", + "Model Context Protocol (MCP) has quickly become the open standard for building Agentic systems. The protocol provides easy integration of common tool services and the interoperability between models across the AI ecosystem.\n", + "\n", + "### What is MCP?\n", + "Model Context Protocol (MCP) is an open protocol designed to standardize how AI models - especially large language models (LLMs) - interface with external tools, data sources, and context providers in a secure, modular, and composable way. MCP provides a unified framework for sending structured requests from an agent or application to a set of “tool services,” such as databases, APIs, or custom logic modules. By adopting MCP, developers can,\n", + "* Decouple agent logic from tool implementations: Agents can call out to tools (like a database or search service) using a standard protocol, rather than relying on hardcoded integrations.\n", + "* Enforce consistent security and governance: MCP defines authentication, authorization, and data boundary controls between the model and external resources.\n", + "* Support modular, reusable agent architectures: Tools can be swapped, updated, or extended without changing the agent code, making it easy to evolve complex workflows.\n", + "* Run tools locally or remotely: The same protocol works whether a tool is running in the customer’s environment or in the cloud, supporting privacy and data residency requirements.\n", + "\n", + "MCP acts as the “middleware” that bridges AI models and the external world, enabling secure, flexible, and maintainable integration of real-world context and capabilities into conversational or autonomous agents.\n", + "\n", + "### Agents in the enterprise\n", + "In today’s enterprise landscape, conversational agents - especially voice-powered ones—are quickly becoming a standard for customer support, internal helpdesks, and task automation. Yet, building robust, scalable voice agents is challenging due to fragmented tooling, integration complexity, and the need for reliable orchestration of backend systems. A common pattern seen across the enterprise landscape is to develop agents that are backed by knowledge bases (both structured and unstructured). These bots are divided into several categories:\n", + " - copilots for internal use, and \n", + " - customer-facing assistants. \n", + "The latter of the two use cases, i.e. customer-facing assistants, tends to have a higher requirement for both accuracy, usability and design. Additionally, one common requirement for customer-facing chatbots is the need to add voice as a modality for user interface (i.e. for phone call automation).\n", + "\n", + "These Q&A chatbots apply to a wide range of industries: healthcare, government, legal and other industries that requires a easy way for knowledge retrieval at a user's fingertips.\n", + "\n", + "One such industry is the insurance industry, where we've seen tremendous value for customers we work with in the space. Insurance policies are complex and navigating the system can often be difficult for policy holders.\n", + "\n", + "### What's in this Cookbook?\n", + "In this cookbook, we provide an end-to-end modular recipe leveraging MCP for building voice-enabled agents using the [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/). In particular, we demonstrate how we can use it for dynamic context management and using agentic tool-calling. We demonstrate the capabilities of such a system for the aforementioned insurance use-case. In this example, we demonstrate the use of MCP for various tools that you may want for your application. Specifically, we showcase the use of custom MCP servers (for text retrieval and web search) as well as using predefined MCP servers (for SQLite). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pItv9wdaOJfL" + }, + "source": [ + "### End-to-end Flow\n", + "\n", + "This section outlines a straightforward setup for deploying microservices for tools within the MCP framework, specifically focusing on RAG, database lookup, and web search functionalities. The MCP servers are responsible not only for hosting these services but also for performing RAG indexing to support backend operations.\n", + "\n", + "We employ a \"chained\" approach for voice input and output throughout the system. During inference, the workflow begins by capturing a user's voice input, which is transcribed to text using a speech-to-text system. This transcribed text is then sent to the Planner agent, which determines which tools to invoke and makes requests to the appropriate microservices. After retrieving tool outputs, the Planner agent synthesizes a cohesive, contextually appropriate response. This textual response is subsequently converted to audio using a text-to-speech system, delivering the final voice response to the user.\n", + "\n", + "The end-to-end workflow is summarized in the diagram below:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fQYezWo2C5t0" + }, + "source": [ + "![Cookbook_image](./../../../images/partner_mcp_Cookbook.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1-WOUoRKNdZG" + }, + "source": [ + "### Installing dependencies\n", + "First, we install the library dependencies for the project.\n", + "\n", + "> Note: One specific dependency that may be needed on your machine, is to install `ffmpeg`. If you are using a mac, you will need to install this separately using `brew install ffmpeg`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "0YKzEa44ODbP" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "#install dependencies\n", + "%pip install asyncio ffmpeg ffprobe mcp openai openai-agents pydub scipy sounddevice uv --quiet\n", + "%pip install \"openai-agents[voice]\" --quiet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UOrxjtbL3T8X" + }, + "source": [ + "### Setup\n", + "\n", + "To execute this cookbook, you'll need to install the following packages providing access to OpenAI's API, the Agents SDK, MCP, and libraries for audio processing. Additionally, you can set your OpenAI API key for use by the agents via the `set_default_openai_key` function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "aMsySrYz1rIL" + }, + "outputs": [], + "source": [ + "import socket\n", + "import time\n", + "import warnings\n", + "from typing import List, Optional, AsyncGenerator\n", + "\n", + "from numpy.typing import NDArray\n", + "\n", + "\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=SyntaxWarning)\n", + "\n", + "\n", + "async def wait_for_server_ready(port: int = 8000, timeout: float = 10) -> None:\n", + " \"\"\"Wait for SSE server to be ready\"\"\"\n", + " start = time.time()\n", + " while time.time() - start < timeout:\n", + " try:\n", + " with socket.create_connection((\"localhost\", port), timeout=1):\n", + " print(\"✅ SSE server TCP port is accepting connections.\")\n", + " return\n", + " except OSError as e:\n", + " if time.time() - start > timeout - 1: # Only print on last attempt\n", + " print(f\"Waiting for server... ({e})\")\n", + " time.sleep(0.5)\n", + " raise RuntimeError(\"❌ SSE server did not become ready in time.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KJMNqVCLDVNC" + }, + "source": [ + "### Defining Tool-use Agents through custom MCP services\n", + "\n", + "First, we define a custom MCP service that host the RAG and web search tools using the `FastMCP` interface. Specifically, we add `@mcp.tool` functions for:\n", + "\n", + "1. Retrieving information from a RAG service\n", + "2. Searching the broader internet for information using OpenAI's `web_search`\n", + "\n", + "\n", + "For the purpose in this cookbook, we'll run both tools under the same service.\n", + "\n", + "The below code has been provided in `search_server.py` within the same directory. Run the code to start the server. As the server runs, your files will be indexed and stored in the vector store. \n", + "\n", + "You can run the `search_server.py` file by running the following command:\n", + "\n", + " ```bash\n", + " uv run python search_server.py \n", + " ```\n", + "\n", + "Once the server is running, you can access the vector store and files at https://platform.openai.com/storage/files and https://platform.openai.com/storage/vector_stores respectively, and continue with running the next cells in the notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# search_server.py\n", + "import os\n", + "from mcp.server.fastmcp import FastMCP\n", + "from openai import OpenAI\n", + "from agents import set_tracing_export_api_key\n", + "\n", + "# Create server\n", + "mcp = FastMCP(\"Search Server\")\n", + "_vector_store_id = \"\"\n", + "\n", + "def _run_rag(query: str) -> str:\n", + " \"\"\"Do a search for answers within the knowledge base and internal documents of the user.\n", + " Args:\n", + " query: The user query\n", + " \"\"\"\n", + " results = client.vector_stores.search(\n", + " vector_store_id=_vector_store_id,\n", + " query=query,\n", + " rewrite_query=True, # Query rewriting generally improves results\n", + " )\n", + " return results.data[0].content[0].text\n", + "\n", + "\n", + "def _summarize_rag_response(rag_output: str) -> str:\n", + " \"\"\"Summarize the RAG response using GPT-4\n", + " Args:\n", + " rag_output: The RAG response\n", + " \"\"\"\n", + " response = client.responses.create(\n", + " model=\"gpt-4.1-mini\",\n", + " tools=[{\"type\": \"web_search_preview\"}],\n", + " input=\"Summarize the following text concisely: \\n\\n\" + rag_output,\n", + " )\n", + " return response.output_text\n", + "\n", + "\n", + "@mcp.tool()\n", + "def generate_rag_output(query: str) -> str:\n", + " \"\"\"Generate a summarized RAG output for a given query.\n", + " Args:\n", + " query: The user query\n", + " \"\"\"\n", + " print(\"[debug-server] generate_rag_output: \", query)\n", + " rag_output = _run_rag(query)\n", + " return _summarize_rag_response(rag_output)\n", + "\n", + "\n", + "@mcp.tool()\n", + "def run_web_search(query: str) -> str:\n", + " \"\"\"Run a web search for the given query.\n", + " Args:\n", + " query: The user query\n", + " \"\"\"\n", + " print(\"[debug-server] run_web_search:\", query)\n", + " response = client.responses.create(\n", + " model=\"gpt-4.1-mini\",\n", + " tools=[{\"type\": \"web_search_preview\"}],\n", + " input=query,\n", + " )\n", + " return response.output_text\n", + "\n", + "\n", + "def index_documents(directory: str):\n", + " \"\"\"Index the documents in the given directory to the vector store\n", + " Args:\n", + " directory: The directory to index the documents from\n", + " \"\"\"\n", + " # OpenAI supported file extensions for retrieval (see docs)\n", + " SUPPORTED_EXTENSIONS = {'.pdf', '.txt', '.md', '.docx', '.pptx', '.csv', '.rtf', '.html', '.json', '.xml'}\n", + " # Collect all files in the specified directory\n", + " files = [os.path.join(directory, f) for f in os.listdir(directory)]\n", + " # Filter files for supported extensions only\n", + " supported_files = []\n", + " for file_path in files:\n", + " _, ext = os.path.splitext(file_path)\n", + " if ext.lower() in SUPPORTED_EXTENSIONS:\n", + " supported_files.append(file_path)\n", + " else:\n", + " print(f\"[warning] Skipping unsupported file for retrieval: {file_path}\")\n", + "\n", + " vector_store = client.vector_stores.create( # Create vector store\n", + " name=\"Support FAQ\",\n", + " )\n", + " global _vector_store_id\n", + " _vector_store_id = vector_store.id\n", + "\n", + " for file_path in supported_files:\n", + " # Upload each file to the vector store, ensuring the file handle is closed\n", + " with open(file_path, \"rb\") as fp:\n", + " client.vector_stores.files.upload_and_poll(\n", + " vector_store_id=vector_store.id,\n", + " file=fp\n", + " )\n", + " print(f\"[debug-server] uploading file: {file_path}\")\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " oai_api_key = os.environ.get(\"OPENAI_API_KEY\")\n", + " if not oai_api_key:\n", + " raise ValueError(\"OPENAI_API_KEY environment variable is not set\")\n", + " set_tracing_export_api_key(oai_api_key)\n", + " client = OpenAI(api_key=oai_api_key)\n", + "\n", + " current_dir = os.path.dirname(os.path.abspath(__file__))\n", + " samples_dir = os.path.join(current_dir, \"sample_files\")\n", + " index_documents(samples_dir)\n", + "\n", + " mcp.run(transport=\"sse\")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uWYXigXVGg-w" + }, + "source": [ + "As seen above, we also include the RAG indexing as part of this workflow. In real-world applications, this will not be necessary for every run and if you have a large corpus of data, you may put this in a separate process.\n", + "\n", + "In addition to simple RAG retrieval, we add an extra step to summarize the RAG output. This step is not always necessary, though we've found this to provide more succinct responses to the planner. Whether to do this depends on your system and your latency requirements.\n", + "\n", + "\n", + "### Using Pre-defined MCP Servers\n", + "\n", + "While implementing custom MCPs servers is relatively straightforward, the power of MCP is the ability to use pre-defined servers that others have built and maintain. Using existing implementations enables more rapid development, has a consistent interface with other tools, and makes data integration more seamless. \n", + "\n", + "For our database lookup tool, we use the prebuilt [SQLite server](https://github.com/modelcontextprotocol/servers-archived/tree/main/src/sqlite) implementation. As you will see below, we can implement this simply with just a comand line prompt and providing it with a `*.db` file with the data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HJKqscj87Jg5" + }, + "source": [ + "### Defining the Planner Agent\n", + "\n", + "Next, we can define how the MCP server will generate meaningful responses. The planner agent is a key component within MCP’s agent orchestration pipeline. Its primary function is to decompose user requests into actionable steps and decide which tools, APIs, or agents should be called at each stage. Given the input as text, the planner parses and analyzes the request, maintaining context across multiple turns. Based on the conversation state, it invokes MCP tool services by dispatching tool calls via the MCP server’s orchestration layer. The agent then collects intermediate results, synthesizes responses, and guides the conversation toward resolution.\n", + "\n", + "A key design consideration is the model selection for the planner. While larger models like `4.1` offer superior reasoning, low end-to-end latency is critical in voice-driven applications. For this reason, we select the `4.1-mini` model, which achieves a strong balance between reasoning ability and response speed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "u1kIMV2AAaAW" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[non-fatal] Tracing client error 400: {\n", + " \"error\": {\n", + " \"message\": \"Invalid type for 'data[2].span_data.result': expected an array of strings, but got null instead.\",\n", + " \"type\": \"invalid_request_error\",\n", + " \"param\": \"data[2].span_data.result\",\n", + " \"code\": \"invalid_type\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "from agents import Agent, trace\n", + "from agents.mcp import MCPServer, MCPServerSse, MCPServerStdio\n", + "from agents.extensions.handoff_prompt import prompt_with_handoff_instructions\n", + "\n", + "voice_system_prompt = \"\"\"[Voice Output Guidelines]\n", + "Your responses will be delivered via voice, so please:\n", + "1. Use conversational, natural language that sounds good when spoken\n", + "2. Keep responses concise - ideally 1-2 sentences per point\n", + "3. Avoid technical jargon unless necessary, and explain terms simply\n", + "4. Pause naturally between topics using brief sentences\n", + "5. Be warm and personable in tone\n", + "\"\"\"\n", + "\n", + "\n", + "async def create_insurance_agents(mcp_servers: list[MCPServer]) -> Agent:\n", + " \"\"\"Create the insurance agent workflow with voice optimization\"\"\"\n", + " \n", + " # Main insurance agent with MCP tools\n", + " insurance_agent = Agent(\n", + " name=\"InsuranceAssistant\",\n", + " instructions=voice_system_prompt + prompt_with_handoff_instructions(\"\"\"\n", + " #Identity\n", + " You an a helpful chatbot that answers questions about our insurance plans. \n", + " #Task\n", + " Use the tools provided to answer the questions. \n", + " #Instructions\n", + " * Information about plans and policies are best answered with sqlite or rag_output tools.\n", + " * web_search should be used for answering generic health questions that are not directly related to our insurance plans.\n", + " * Evaluate the quality of the answer after the tool call. \n", + " * Assess whether you are confident in the answer generated.\n", + " * If your confidence is low, try use another tool.\n", + " \"\"\"),\n", + " mcp_servers=mcp_servers,\n", + " model=\"gpt-4.1-mini\",\n", + " )\n", + " \n", + " return insurance_agent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the agent definition, we clearly specify when each tool should be used. This ensures better control over responses and improves answer relevance. We also provide the Voice Agent with guidelines to set the desired tone and level of precision in its replies." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "afUsni7W7L2M" + }, + "source": [ + "### Defining configurations for voice \n", + "\n", + "Next, we define the configurations for our voice module, both for speech-to-text (STT) and text-to-speech (TTS). We use the OpenAI Agent Voice library to handling both input and output of voice. As defaults, this API calls the `gpt-4o-transcribe` and `gpt-4o-mini-tts` for STT and TTS, respectively.\n", + "\n", + "For more content on defining voice assistants, see [this Cookbook](https://cookbook.openai.com/examples/agents_sdk/app_assistant_voice_agents)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "D4J2SEKq5_WB" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sounddevice as sd\n", + "\n", + "\n", + "from agents.voice import (\n", + " AudioInput,\n", + " SingleAgentVoiceWorkflow,\n", + " VoicePipeline,\n", + " VoicePipelineConfig,\n", + " TTSModelSettings\n", + ")\n", + "\n", + "AudioBuffer = List[NDArray[np.int16]]\n", + "\n", + "AUDIO_CONFIG = {\n", + " \"samplerate\": 24000,\n", + " \"channels\": 1,\n", + " \"dtype\": \"int16\",\n", + " \"blocksize\": 2400,\n", + " \"silence_threshold\": 500,\n", + " \"silence_duration\": 1.5,\n", + " \"min_speech_duration\": 0.5,\n", + "}\n", + "\n", + "insurance_tts_settings = TTSModelSettings(\n", + " instructions=(\n", + " \"Personality: Professional, knowledgeable, and helpful insurance advisor\"\n", + " \"Tone: Friendly, clear, and reassuring, making customers feel confident about their insurance choices\"\n", + " \"Pronunciation: Clear and articulate, ensuring insurance terms are easily understood\"\n", + " \"Tempo: Moderate pace with natural pauses, especially when explaining complex insurance concepts\"\n", + " \"Emotion: Warm and supportive, conveying trust and expertise in insurance matters\"\n", + " )\n", + ")\n", + "\n", + "class AudioStreamManager:\n", + " \"\"\"Context manager for handling audio streams\"\"\"\n", + " def __init__(self, input_stream: sd.InputStream, output_stream: sd.OutputStream):\n", + " self.input_stream = input_stream\n", + " self.output_stream = output_stream\n", + "\n", + " async def __aenter__(self):\n", + " try:\n", + " self.input_stream.start()\n", + " self.output_stream.start()\n", + " return self\n", + " except sd.PortAudioError as e:\n", + " raise RuntimeError(f\"Failed to start audio streams: {e}\")\n", + "\n", + " async def __aexit__(self, exc_type, exc_val, exc_tb):\n", + " try:\n", + " if self.input_stream:\n", + " self.input_stream.stop()\n", + " self.input_stream.close()\n", + " if self.output_stream:\n", + " self.output_stream.stop()\n", + " self.output_stream.close()\n", + " except Exception as e:\n", + " print(f\"Warning: Error during audio stream cleanup: {e}\")\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In enterprise scenarios, the tone and style of audio responses are critical to system usability. Speech output should consistently reflect professionalism and align with the company's brand identity. For most applications, this means generating a realistic voice that mirrors the courteous, approachable demeanor typical of call-center representatives. With TTS, we can leverage prompt engineering to guide the model toward producing audio that better matches specific customer use cases and brand values." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1ZVYg3SMENdj" + }, + "source": [ + "### Processing Voice I/O\n", + "\n", + "After configuring the voice settings, the next step is to implement functions for processing incoming audio and generating spoken responses. Pay particular attention to the `silence_threshold` parameter in your configuration—this plays a crucial role in accurately detecting when a user has finished speaking and helps with speech endpoint detection." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "rA-OY3HuENEi" + }, + "outputs": [], + "source": [ + "import asyncio\n", + "\n", + "async def continuous_voice_conversation(agent: Agent):\n", + " \"\"\"Run a continuous voice conversation with automatic speech detection\"\"\"\n", + " \n", + " voice_config = VoicePipelineConfig(\n", + " tts_settings=insurance_tts_settings,\n", + " )\n", + " \n", + " pipeline = VoicePipeline(\n", + " workflow=SingleAgentVoiceWorkflow(agent),\n", + " config=voice_config\n", + " )\n", + " \n", + " audio_queue: asyncio.Queue[NDArray[np.int16]] = asyncio.Queue()\n", + " is_agent_speaking = False\n", + " \n", + " def audio_callback(indata: NDArray[np.int16], frames: int, time_info: dict, status: sd.CallbackFlags) -> None:\n", + " \"\"\"Callback for continuous audio input\"\"\"\n", + " if status:\n", + " print(f\"Audio input status: {status}\")\n", + " if not is_agent_speaking: # Only record when agent isn't speaking\n", + " audio_queue.put_nowait(indata.copy())\n", + " \n", + " input_stream = sd.InputStream(\n", + " samplerate=AUDIO_CONFIG[\"samplerate\"],\n", + " channels=AUDIO_CONFIG[\"channels\"],\n", + " dtype=AUDIO_CONFIG[\"dtype\"],\n", + " callback=audio_callback,\n", + " blocksize=AUDIO_CONFIG[\"blocksize\"]\n", + " )\n", + " \n", + " output_stream = sd.OutputStream(\n", + " samplerate=AUDIO_CONFIG[\"samplerate\"],\n", + " channels=AUDIO_CONFIG[\"channels\"],\n", + " dtype=AUDIO_CONFIG[\"dtype\"]\n", + " )\n", + " \n", + " print(\"🎙️ Insurance Voice Assistant Ready!\")\n", + " print(\"Start speaking at any time. Say 'goodbye' to exit.\")\n", + " print(\"-\" * 50)\n", + " \n", + " async with AudioStreamManager(input_stream, output_stream):\n", + " silence_threshold = AUDIO_CONFIG[\"silence_threshold\"]\n", + " silence_duration = 0\n", + " max_silence = AUDIO_CONFIG[\"silence_duration\"]\n", + " audio_buffer: AudioBuffer = []\n", + " \n", + " while True:\n", + " try:\n", + " chunk = await asyncio.wait_for(audio_queue.get(), timeout=0.1)\n", + " \n", + " if np.abs(chunk).mean() > silence_threshold:\n", + " audio_buffer.append(chunk)\n", + " silence_duration = 0\n", + " elif audio_buffer:\n", + " silence_duration += 0.1\n", + " audio_buffer.append(chunk)\n", + " \n", + " if silence_duration >= max_silence:\n", + " try:\n", + " full_audio = np.concatenate(audio_buffer, axis=0)\n", + " \n", + " if len(full_audio) > AUDIO_CONFIG[\"samplerate\"] * AUDIO_CONFIG[\"min_speech_duration\"]:\n", + " print(\"\\n🤔 Processing speech...\")\n", + " \n", + " is_agent_speaking = True\n", + " \n", + " audio_input = AudioInput(buffer=full_audio)\n", + " \n", + " with trace(\"Insurance Voice Query\"):\n", + " result = await pipeline.run(audio_input)\n", + " \n", + " print(\"💬 Assistant responding...\")\n", + " async for event in result.stream():\n", + " if event.type == \"voice_stream_event_audio\":\n", + " output_stream.write(event.data)\n", + " elif event.type == \"voice_stream_event_transcript\":\n", + " print(f\" > {event.text}\", end=\"\", flush=True)\n", + " \n", + " print(\"\\n\")\n", + " \n", + " except Exception as e:\n", + " print(f\"\\n❌ Error processing speech: {e}\")\n", + " finally:\n", + " is_agent_speaking = False\n", + " audio_buffer = []\n", + " silence_duration = 0\n", + " \n", + " except asyncio.TimeoutError:\n", + " continue\n", + " except KeyboardInterrupt:\n", + " print(\"\\n\\n👋 Goodbye!\")\n", + " break\n", + " except Exception as e:\n", + " print(f\"\\n❌ Unexpected error: {e}\")\n", + " if isinstance(e, (sd.PortAudioError, RuntimeError)):\n", + " raise\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting up the server process\n", + "\n", + "Next, we add a simple convenience function for bringing up servers locally: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "import subprocess\n", + "import nest_asyncio\n", + "\n", + "\n", + "class ServerProcess:\n", + " \"\"\"Context manager for handling the SSE server process\"\"\"\n", + " def __init__(self, server_file: str):\n", + " self.server_file = server_file\n", + " self.process: Optional[subprocess.Popen] = None\n", + "\n", + " async def __aenter__(self):\n", + " if not shutil.which(\"uv\"):\n", + " raise RuntimeError(\n", + " \"uv is not installed. Please install it: https://docs.astral.sh/uv/getting-started/installation/\"\n", + " )\n", + "\n", + " print(\"Starting SSE server at http://localhost:8000/sse ...\")\n", + " self.process = subprocess.Popen([\"uv\", \"run\", self.server_file])\n", + " try:\n", + " await wait_for_server_ready()\n", + " nest_asyncio.apply()\n", + " print(\"SSE server started. Starting voice assistant...\\n\")\n", + " return self\n", + " except Exception as e:\n", + " if self.process:\n", + " self.process.terminate()\n", + " raise RuntimeError(f\"Failed to start SSE server: {e}\")\n", + "\n", + " async def __aexit__(self, exc_type, exc_val, exc_tb):\n", + " if self.process:\n", + " try:\n", + " self.process.terminate()\n", + " self.process.wait(timeout=5)\n", + " if self.process.poll() is None:\n", + " self.process.kill()\n", + " except Exception as e:\n", + " print(f\"Warning: Error during server shutdown: {e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "--UKG_qD6aRM" + }, + "source": [ + "### Specifying the MCP tool services\n", + "\n", + "In our `main` function, we can bring up the various tool-use services we're interested in.\n", + "\n", + "For our custom server for (RAG and web search), we can use the `MCPServerSse` function to start a server (in this case locally). To bring up the standard MCP SQLite service, we call `MCPServerStdio` with simple arguments provided, in this case, the local `database.db` file." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "6zsMnqsw6bko" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "async def main():\n", + " \"\"\"Main function to run the voice assistant\"\"\"\n", + " this_dir=os.getcwd()\n", + " #this_dir = os.path.dirname(os.path.abspath(__file__))\n", + " server_file= os.path.join(this_dir, \"search_server.py\")\n", + " #server_file = os.path.join(this_dir, \"search_server.py\")\n", + "\n", + " async with ServerProcess(server_file):\n", + " # Initialize MCP servers\n", + " async with MCPServerSse(\n", + " name=\"SSE Python Server\",\n", + " params={\n", + " \"url\": \"http://localhost:8000/sse\",\n", + " \"timeout\": 15.0,\n", + " },\n", + " client_session_timeout_seconds=15.0,\n", + " ) as search_server:\n", + " async with MCPServerStdio(\n", + " cache_tools_list=True,\n", + " params={\"command\": \"uvx\", \"args\": [\"mcp-server-sqlite\", \"--db-path\", \"./database.db\"]},\n", + " ) as sql_server:\n", + " # Create insurance agent with MCP tools\n", + " agent = await create_insurance_agents([search_server, sql_server])\n", + " \n", + " # Run the voice assistant\n", + " try:\n", + " await continuous_voice_conversation(agent)\n", + " except Exception as e:\n", + " print(f\"\\nError in voice conversation: {e}\")\n", + " raise\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summarizing the flow\n", + "\n", + "Now that we have the various pieces in place, we can take a step back and visualize the overall workflow of our system:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Cookbook_image](./../../../images/System_flow_partner_mcp.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iNbJ3n2qB-vT" + }, + "source": [ + "### Tying it all together\n", + "Finally, we can instantiate the custom tool-use server and bring up the service:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import asyncio\n", + "\n", + "try:\n", + " asyncio.get_running_loop().create_task(main())\n", + "except RuntimeError:\n", + " # For Jupyter, use nest_asyncio and run main as a task\n", + " import nest_asyncio\n", + " nest_asyncio.apply()\n", + " task = asyncio.create_task(main())\n", + " try:\n", + " await task\n", + " except KeyboardInterrupt:\n", + " print(\"\\nShutting down gracefully...\")\n", + " except Exception as e:\n", + " print(f\"\\nFatal error: {e}\")\n", + " raise" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nZHzDV8Y9JwB" + }, + "source": [ + "## Example outputs\n", + "\n", + "Now that we have built the system end-to-end, we can now use it to answer questions. Here, we use our system to provide answers for a few common insurance questions based on the policy information docs. Below are some sample voice outputs from our agents based on some common questions users have:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**How are prescription drugs covered under this plan?** (uses retrieval)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <audio controls=\"controls\" >\n", + " <source 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type=\"audio/mpeg\" />\n", + " Your browser does not support the audio element.\n", + " </audio>\n", + " " + ], + "text/plain": [ + "<IPython.lib.display.Audio object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Audio\n", + "import os\n", + "\n", + "# Get the absolute path to the audio file\n", + "audio_path = os.path.join(os.getcwd(), \"sample_output\", \"rag.mp3\")\n", + "\n", + "# Check if the file exists before trying to play it\n", + "if os.path.exists(audio_path):\n", + " display(Audio(audio_path))\n", + "else:\n", + " print(f\"Audio file not found at: {audio_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Which policies have monthly premium less than $300?** (uses DB lookup with SQL)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " <audio controls=\"controls\" >\n", + " <source 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type=\"audio/mpeg\" />\n", + " Your browser does not support the audio element.\n", + " </audio>\n", + " " + ], + "text/plain": [ + "<IPython.lib.display.Audio object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Audio(\"sample_output/web_search.mp3\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examining Traces\n", + "By default, model and tool calls that are used in our application are added to the [Traces](https://platform.openai.com/traces) dashboard out-of-the-box. These traces provide meaningful insight into what users experience as they use our agents. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Cookbook_image](./../../../images//trace-sk1_partner.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beyond agent performance, one critical aspect of building voice agents is the latency of responses. With the Traces dashboard, we are able to view the breakdown of walltime for each step to help debug and find areas of improvement for latency: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Cookbook_image](./../../../images/Traces-2_partner.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Explore individual traces to see each function call and its output, as shown below.\n", + "\n", + "![image](../../../images/traces_partner_granular.png)\n", + "\n", + "Traces offer granular visibility into function calls and their execution times, making it easy to identify sources of latency (for example, the web search tool above). Analyzing response time variability for each tool invocation helps you pinpoint bottlenecks and opportunities for optimization in production systems." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xzpded4L8ecZ" + }, + "source": [ + "## Conclusion\n", + "\n", + "This cookbook has guided you through building a complete agent solution that harnesses the flexibility and strength of the MCP platform. By integrating the Voice Agents SDK, we illustrated how to develop a consumer-ready product powered by these technologies. We've shown how OpenAI’s tools and the Agents API can be effectively combined with MCP to deliver impactful applications.\n", + "\n", + "We hope this guide has offered both practical instruction and inspiration, helping you create your own MCP-powered voice agents tailored to your specific needs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contributors\n", + "\n", + "This cookbook serves as a joint collaboration effort between OpenAI and [Brain Co](https://www.braincompany.ai/en/).\n", + "\n", + "- [Cece Z](https://www.linkedin.com/in/cecez/)\n", + "- [Sibon Li](https://www.linkedin.com/in/sibon-li-9a9bba34/)\n", + "- [Shikhar Kwatra](https://www.linkedin.com/in/shikharkwatra/)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/partners/mcp_powered_voice_agents/sample_files/Evergreen_Health_Platinum_Plan.pdf b/examples/partners/mcp_powered_voice_agents/sample_files/Evergreen_Health_Platinum_Plan.pdf new file mode 100644 index 0000000000..4e4e4be74f Binary files /dev/null and b/examples/partners/mcp_powered_voice_agents/sample_files/Evergreen_Health_Platinum_Plan.pdf differ diff --git a/examples/partners/mcp_powered_voice_agents/sample_output/rag.mp3 b/examples/partners/mcp_powered_voice_agents/sample_output/rag.mp3 new file mode 100644 index 0000000000..434eeb7071 Binary files /dev/null and b/examples/partners/mcp_powered_voice_agents/sample_output/rag.mp3 differ diff --git a/examples/partners/mcp_powered_voice_agents/sample_output/sqlite.mp3 b/examples/partners/mcp_powered_voice_agents/sample_output/sqlite.mp3 new file mode 100644 index 0000000000..cef9a00c39 Binary files /dev/null and b/examples/partners/mcp_powered_voice_agents/sample_output/sqlite.mp3 differ diff --git a/examples/partners/mcp_powered_voice_agents/sample_output/web_search.mp3 b/examples/partners/mcp_powered_voice_agents/sample_output/web_search.mp3 new file mode 100644 index 0000000000..3564d92bae Binary files /dev/null and b/examples/partners/mcp_powered_voice_agents/sample_output/web_search.mp3 differ diff --git a/examples/partners/mcp_powered_voice_agents/search_server.py b/examples/partners/mcp_powered_voice_agents/search_server.py new file mode 100755 index 0000000000..7f599855eb --- /dev/null +++ b/examples/partners/mcp_powered_voice_agents/search_server.py @@ -0,0 +1,107 @@ +import os +from mcp.server.fastmcp import FastMCP +from openai import OpenAI +from agents import set_tracing_export_api_key + +# Create server +mcp = FastMCP("Search Server") +_vector_store_id = "" + +def _run_rag(query: str) -> str: + """Do a search for answers within the knowledge base and internal documents of the user. + Args: + query: The user query + """ + results = client.vector_stores.search( + vector_store_id=_vector_store_id, + query=query, + rewrite_query=True, # Query rewriting generally improves results + ) + return results.data[0].content[0].text + + +def _summarize_rag_response(rag_output: str) -> str: + """Summarize the RAG response using GPT-4 + Args: + rag_output: The RAG response + """ + response = client.responses.create( + model="gpt-4.1-mini", + tools=[{"type": "web_search_preview"}], + input="Summarize the following text concisely: \n\n" + rag_output, + ) + return response.output_text + + +@mcp.tool() +def generate_rag_output(query: str) -> str: + """Generate a summarized RAG output for a given query. + Args: + query: The user query + """ + print("[debug-server] generate_rag_output: ", query) + rag_output = _run_rag(query) + return _summarize_rag_response(rag_output) + + +@mcp.tool() +def run_web_search(query: str) -> str: + """Run a web search for the given query. + Args: + query: The user query + """ + print("[debug-server] run_web_search:", query) + response = client.responses.create( + model="gpt-4.1-mini", + tools=[{"type": "web_search_preview"}], + input=query, + ) + return response.output_text + + +def index_documents(directory: str): + """Index the documents in the given directory to the vector store + Args: + directory: The directory to index the documents from + """ + # OpenAI supported file extensions for retrieval (see docs) + SUPPORTED_EXTENSIONS = {'.pdf', '.txt', '.md', '.docx', '.pptx', '.csv', '.rtf', '.html', '.json', '.xml'} + # Collect all files in the specified directory + files = [os.path.join(directory, f) for f in os.listdir(directory)] + # Filter files for supported extensions only + supported_files = [] + for file_path in files: + _, ext = os.path.splitext(file_path) + if ext.lower() in SUPPORTED_EXTENSIONS: + supported_files.append(file_path) + else: + print(f"[warning] Skipping unsupported file for retrieval: {file_path}") + + vector_store = client.vector_stores.create( # Create vector store + name="Support FAQ", + ) + global _vector_store_id + _vector_store_id = vector_store.id + + for file_path in supported_files: + # Upload each file to the vector store, ensuring the file handle is closed + with open(file_path, "rb") as fp: + client.vector_stores.files.upload_and_poll( + vector_store_id=vector_store.id, + file=fp + ) + print(f"[debug-server] uploading file: {file_path}") + + +if __name__ == "__main__": + oai_api_key = os.environ.get("OPENAI_API_KEY") + if not oai_api_key: + raise ValueError("OPENAI_API_KEY environment variable is not set") + set_tracing_export_api_key(oai_api_key) + client = OpenAI(api_key=oai_api_key) + + current_dir = os.path.dirname(os.path.abspath(__file__)) + samples_dir = os.path.join(current_dir, "sample_files") + index_documents(samples_dir) + + mcp.run(transport="sse") diff --git a/examples/partners/model_selection_guide/agent_utils.py b/examples/partners/model_selection_guide/agent_utils.py new file mode 100644 index 0000000000..9b06baf4e3 --- /dev/null +++ b/examples/partners/model_selection_guide/agent_utils.py @@ -0,0 +1,225 @@ +from __future__ import annotations +import json, time, uuid, logging, re +from dataclasses import dataclass, asdict, field +from pathlib import Path +from typing import Any, Dict, List +from openai import OpenAI + +# --- tool back‑ends ------------------------- +from tools import chem_lookup, cost_estimator, outcome_db, literature_search, list_available_chemicals + +# ---------- tiny infrastructure helpers -------------------------------------- + +# Holds run-specific parameters provided by user. +@dataclass +class Context: + compound: str + goal: str + budget: float + time_h: int + previous: str + client: OpenAI + run_id: str = field(default_factory=lambda: uuid.uuid4().hex[:8]) + + def prompt_vars(self): + return { + "compound": self.compound, + "goal": self.goal, + "budget": self.budget, + "time_h": self.time_h, + "previous": self.previous, + } + +# -- Function‑calling tool manifest -------------------- + +def load_tools(): + return [ + { + "type": "function", + "function": { + "name": "chem_lookup", + "description": "Mock function to look up chemical properties.", + "parameters": { + "type": "object", + "properties": { + "chemical_name": { + "type": "string", + "description": "The name of the chemical to look up." + }, + "property": { + "type": "string", + "description": "Optional specific property to retrieve (e.g., 'melting_point'). If None, returns all properties." + } + }, + "required": ["chemical_name"] + } + } + }, + { + "type": "function", + "function": { + "name": "cost_estimator", + "description": "Mock function to estimate the cost of reagents and procedures.", + "parameters": { + "type": "object", + "properties": { + "reagents": { + "type": "array", + "description": "List of reagents, where each reagent is a dictionary with 'name', 'amount', and 'unit'.", + "items": { + "type": "object", + "properties": { + "name": {"type": "string", "description": "Name of the reagent."}, + "amount": {"type": "number", "description": "Amount of the reagent."}, + "unit": {"type": "string", "description": "Unit for the amount (e.g., 'g', 'mg', 'kg')."} + }, + "required": ["name", "amount", "unit"] + } + }, + "equipment": { + "type": "array", + "description": "Optional list of equipment items used.", + "items": {"type": "string"} + }, + "duration_hours": { + "type": "number", + "description": "Optional duration of the procedure in hours for labor cost calculation." + } + }, + } + } + }, + { + "type": "function", + "function": { + "name": "outcome_db", + "description": "Mock function to query the database of past experiment outcomes.", + "parameters": { + "type": "object", + "properties": { + "compound": { + "type": "string", + "description": "The chemical compound name to query past experiments for." + }, + "parameter": { + "type": "string", + "description": "Optional specific parameter to filter experiments by (e.g., 'yield', 'temperature')." + }, + "limit": { + "type": "integer", + "description": "Maximum number of experiment results to return (default: 5)." + } + }, + "required": ["compound"] + } + } + }, + { + "type": "function", + "function": { + "name": "literature_search", + "description": "Mock function to search scientific literature for relevant information.", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "The search query (keywords) for the literature search." + }, + "filter": { + "type": "string", + "description": "Optional filter string, potentially including year (e.g., '2023') or journal name." + }, + "limit": { + "type": "integer", + "description": "Maximum number of search results to return (default: 3)." + } + }, + "required": ["query"] + } + } + }, + { + "type": "function", + "function": { + "name": "list_available_chemicals", + "description": "Provides a list of all chemical names available in the database.", + "parameters": { + "type": "object", + "properties": {}, + # No parameters needed for this tool + } + } + } + ] + +# -- minimal logger ----------------------------------------------------------- + +def log_json(stage: str, data: Any, ctx: Context): + Path("logs").mkdir(exist_ok=True) + p = Path("logs") / f"{ctx.run_id}.log" + with p.open("a", encoding="utf-8") as f: + f.write(json.dumps({"ts": time.time(), "stage": stage, "data": data}, indent=2) + "\n") + +# -- JSON extractor ----------------------------------------------------- + +def _parse_json(text: str) -> Dict[str, Any]: + try: + return json.loads(text) + except json.JSONDecodeError: + # try to rescue JSON from a ```json ...``` block + m = re.search(r"```(?:json)?\\s*(.*?)```", text, re.S) + if m: + try: + return json.loads(m.group(1)) + except json.JSONDecodeError: + pass # fall-through to raw + return {"raw": text} # give caller *something* parsable + + +# -- tool call handler -------------------------------------------------------- + +def _dispatch_tool(name: str, args: Dict[str, Any]): + """Run the local Python implementation of a tool. + If the model supplied bad / missing arguments, return an error JSON instead + of raising – so the conversation can continue.""" + try: + return { + "chem_lookup": chem_lookup, + "cost_estimator": cost_estimator, + "outcome_db": outcome_db, + "literature_search": literature_search, + "list_available_chemicals": list_available_chemicals, + }[name](**args) + except TypeError as e: + # log & surface the problem back to the model in a structured way + logging.warning(f"Tool {name} failed: {e}") + return {"tool_error": str(e), "supplied_args": args} + +# -- unified OpenAI call w/ recursive tool handling --------------------------- + +def call_openai(client: OpenAI, model: str, system: str, user: str, ctx: Context): + messages = [ + {"role": "system", "content": system}, + {"role": "user", "content": user}, + ] + while True: + resp = client.chat.completions.create( + model=model, + messages=messages, + tools=load_tools(), + tool_choice="auto", + ) + msg = resp.choices[0].message + messages.append(msg.model_dump(exclude_unset=True)) + if not msg.tool_calls: + log_json(model, msg.content, ctx) + return _parse_json(msg.content) + # handle first tool call, then loop again + for tc in msg.tool_calls: + result = _dispatch_tool(tc.function.name, json.loads(tc.function.arguments)) + messages.append({ + "role": "tool", "tool_call_id": tc.id, + "content": json.dumps(result) + }) + diff --git a/examples/partners/model_selection_guide/images/2.2_model_evolution.png b/examples/partners/model_selection_guide/images/2.2_model_evolution.png new file mode 100644 index 0000000000..87f09ef350 Binary files /dev/null and b/examples/partners/model_selection_guide/images/2.2_model_evolution.png differ diff --git a/examples/partners/model_selection_guide/images/3A_rag_hierarchical_router.png b/examples/partners/model_selection_guide/images/3A_rag_hierarchical_router.png new file mode 100644 index 0000000000..0d1926b7c9 Binary files /dev/null and b/examples/partners/model_selection_guide/images/3A_rag_hierarchical_router.png differ diff --git a/examples/partners/model_selection_guide/images/3A_rag_task_card.png b/examples/partners/model_selection_guide/images/3A_rag_task_card.png new file mode 100644 index 0000000000..7761c85786 Binary files /dev/null and b/examples/partners/model_selection_guide/images/3A_rag_task_card.png differ diff --git a/examples/partners/model_selection_guide/images/3B_coscientist_architecture.png b/examples/partners/model_selection_guide/images/3B_coscientist_architecture.png new file mode 100644 index 0000000000..cea31baef5 Binary files /dev/null and b/examples/partners/model_selection_guide/images/3B_coscientist_architecture.png differ diff --git a/examples/partners/model_selection_guide/images/3B_reasoning_task_card.png b/examples/partners/model_selection_guide/images/3B_reasoning_task_card.png new file mode 100644 index 0000000000..c744242332 Binary files /dev/null and b/examples/partners/model_selection_guide/images/3B_reasoning_task_card.png differ diff --git a/examples/partners/model_selection_guide/images/3C_insurance_architecture.png b/examples/partners/model_selection_guide/images/3C_insurance_architecture.png new file mode 100644 index 0000000000..74342bd3c8 Binary files /dev/null and b/examples/partners/model_selection_guide/images/3C_insurance_architecture.png differ diff --git a/examples/partners/model_selection_guide/images/3C_insurance_form.png b/examples/partners/model_selection_guide/images/3C_insurance_form.png new file mode 100644 index 0000000000..97dae9e95f Binary files /dev/null and b/examples/partners/model_selection_guide/images/3C_insurance_form.png differ diff --git a/examples/partners/model_selection_guide/images/3C_insurance_task_card.png b/examples/partners/model_selection_guide/images/3C_insurance_task_card.png new file mode 100644 index 0000000000..65a8157bf0 Binary files /dev/null and b/examples/partners/model_selection_guide/images/3C_insurance_task_card.png differ diff --git a/examples/partners/model_selection_guide/images/3D_model_selection_flowchart.png b/examples/partners/model_selection_guide/images/3D_model_selection_flowchart.png new file mode 100644 index 0000000000..59e2d6a72a Binary files /dev/null and b/examples/partners/model_selection_guide/images/3D_model_selection_flowchart.png differ diff --git a/examples/partners/model_selection_guide/model_selection_guide.ipynb b/examples/partners/model_selection_guide/model_selection_guide.ipynb new file mode 100644 index 0000000000..8b4b9e6604 --- /dev/null +++ b/examples/partners/model_selection_guide/model_selection_guide.ipynb @@ -0,0 +1,3264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a5e91602", + "metadata": {}, + "source": [ + "# Practical Guide for Model Selection for Real‑World Use Cases\n", + "\n", + "## Purpose & Audience\n", + "\n", + "This cookbook serves as your practical guide to selecting, prompting, and deploying the right OpenAI model (between GPT 4.1, o3, and o4-mini) for specific workloads. Instead of exhaustive documentation, we provide actionable decision frameworks and real-world examples that help Solutions Engineers, Technical Account Managers, Partner Architects, and semi-technical practitioners quickly build working solutions. The content focuses on current model capabilities, vertical-specific implementations, and today's industry needs, with clear pathways from model selection to production deployment. Each section offers concise, adaptable code examples that you can immediately apply to your use cases while pointing to existing resources for deeper dives into specific topics.\n", + "\n", + "> Note: The below prescriptive guidance and experimentation has been conducted with latest SOTA models available today. These metrics are bound to change in the future with different scenarios and timeline into consideration.\n", + "\n", + "## How to Use This Cookbook\n", + "\n", + "This cookbook is organized into distinct sections to help you quickly find the information you need. Each section covers a specific aspect of model selection, implementation, and deployment.\n", + "\n", + "1. **[Purpose & Audience](#purpose-audience)**: An overview of who this cookbook is for and what it covers.\n", + "2. **[Model Guide](#model-guide)**: A quick reference to help you select the right model for your needs, including model comparisons and evolution diagrams based on mapping different use-case scenarios.\n", + "3. **Use Cases**:\n", + " - **[3A. Long-Context RAG for Legal Q&A](#3a-use-case-long-context-rag-for-legal-qa)**: Building an agentic system to answer questions from complex legal documents.\n", + " - **[3B. AI Co-Scientist for Pharma R&D](#3b-use-case-ai-co-scientist-for-pharma-rd)**: Accelerating experimental design in pharmaceutical research with multi-agent systems.\n", + " - **[3C. Insurance Claim Processing](#3c-use-case-insurance-claim-processing)**: Digitizing and validating handwritten insurance forms with vision and reasoning.\n", + "4. **[Prototype to Production](#prototype-to-production)**: A checklist to help you transition from prototype to production.\n", + "5. **[Adaptation Decision Tree](#adaptation-decision-tree)**: A flowchart to guide your model selection based on specific requirements.\n", + "6. **[Appendices](#appendices)**: Reference materials including pricing, latency, prompt patterns, and links to external resources.\n", + "\n", + "For quick decisions, focus on the Model Guide and Adaptation Decision Tree sections. For implementation details, explore the specific use cases relevant to your needs.\n", + "\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "\n", + "## Model Guide\n", + "\n", + "## 2.1 Model‑Intro Matrix\n", + "\n", + "| Model | Core strength | Ideal first reach‑for | Watch‑outs | Escalate / Downgrade path |\n", + "| :---- | :---- | :---- | :---- | :---- |\n", + "| GPT‑4o | Real‑time voice / vision chat | Live multimodal agents | Slightly below 4.1 on text SOTA (state-of-the-art) | Need deep reasoning → o4‑mini |\n", + "| GPT‑4.1 | 1 M‑token text accuracy king | Long‑doc analytics, code review | Cannot natively reason; higher cost than minis | Tight budget → 4.1‑mini / nano |\n", + "| o3 | Deep tool‑using agent | High‑stakes, multi‑step reasoning | Latency & price | Cost/latency → o4‑mini |\n", + "| o4‑mini | Cheap, fast reasoning | High‑volume \"good‑enough\" logic | Depth ceiling vs o3 | Accuracy critical → o3 |\n", + "\n", + "*(Full price and utility table → [Section 6.1](#appendices))*\n", + "\n", + "## 2.2 Model Evolution at a Glance\n", + "\n", + "OpenAI's model lineup has evolved to address specialized needs across different dimensions. These diagrams showcase the current model families and their relationships.\n", + "\n", + "### Fundamental Differences: \"o-series\" vs \"GPT\" Models\n", + "\n", + "OpenAI offers two distinct model families, each with unique strengths:\n", + "\n", + "- **GPT Models (4o, 4.1)**: Optimized for general-purpose tasks with excellent instruction following. GPT-4.1 excels with long contexts (1M tokens) while GPT-4o has variants for realtime speech, text-to-speech, and speech-to-text. GPT-4.1 also comes in a mini, and nano variant, while GPT-4o has a mini variant. These variants are cheaper and faster than their full-size counterparts.\n", + "\n", + "- **o-series Models (o3, o4-mini)**: Specialized for deep reasoning and step-by-step problem solving. These models excel at complex, multi-stage tasks requiring logical thinking and tool use. Choose these when accuracy and reasoning depth are paramount. These models also have an optional `reasoning_effort` parameter (that can be set to `low`, `medium`, or `high`), which allows users to control the amount of tokens used for reasoning.\n", + "\n", + "### OpenAI Model Evolution \n", + "\n", + "![OpenAI Model Evolution](../../../images/2.2_model_evolution.png)\n", + "\n", + "### Key Characteristics\n", + "\n", + "- **GPT-4.1 Family**: Optimized for long context processing with 1M token context window.\n", + "- **o3**: Specialized for deep multi-step reasoning. \n", + "- **o4-mini**: Combines reasoning capabilities with vision at lower cost.\n", + "\n", + "Each model excels in different scenarios, with complementary strengths that can be combined for complex workflows.\n", + "\n", + "In this cookbook we only experimented with the GPT-4.1 series models, o3, and o4-mini. We didn't experiment with the GPT-4o series models.\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "\n", + "## 3A. Use Case: Long-Context RAG for Legal Q&A\n", + "\n", + "![Long-Context RAG for Legal Q&A](../../../images/3A_rag_task_card.png)\n", + "## 🗂️ TL;DR Matrix\n", + "\n", + "This table summarizes the core technology choices and their rationale for **this specific Long-Context Agentic RAG implementation**.\n", + "\n", + "| Layer | Choice | Utility |\n", + "| :---- | :---- | :---- |\n", + "| **Chunking** | Sentence-aware Splitter | Splits document into 20 equal chunks, respecting sentence boundaries. |\n", + "| **Routing** | `gpt-4.1-mini` | Uses natural language understanding to identify relevant chunks without embedding index. |\n", + "| **Path Selection** | `select(ids=[...])` and `scratchpad(text=\"...\")` | Records reasoning while drilling down through document hierarchy. |\n", + "| **Citation** | Paragraph-level | Balances precision with cost; provides meaningful context for answers. |\n", + "| **Synthesis** | `gpt-4.1` (Structured Output) | Generates answers directly from selected paragraphs with citations. |\n", + "| **Verification** | `o4-mini` (LLM-as-Judge) | Validates factual accuracy and citation correctness. |\n", + "\n", + "*Note: Prices and model identifiers accurate as of April 2025, subject to change.*\n", + "\n", + "This section outlines the construction of a Retrieval-Augmented Generation (RAG) system designed to accurately answer questions about complex and lengthy procedural texts, using the *Trademark Trial and Appeal Board Manual of Procedure (TBMP)* as a representative case. The TBMP is an essential legal resource detailing the procedures governing trademark litigation before the USPTO's Trademark Trial and Appeal Board, and is frequently consulted by intellectual property attorneys and legal professionals. By leveraging the latest OpenAI models, the system enhances understanding and interpretability of dense legal content, enabling precise, contextually aware responses through advanced language understanding and dynamic retrieval capabilities.\n", + "\n", + "These approaches can also be applied to other use cases that require precise information retrieval from complex documentation, such as healthcare compliance manuals, financial regulatory frameworks, or technical documentation systems where accuracy, citation, and auditability are mission-critical requirements.\n", + "\n", + "## 1\\. Scenario Snapshot\n", + "\n", + "* **Corpus:** The primary document is the [Trademark Trial and Appeal Board Manual of Procedure (TBMP, 2024 version)](https://www.uspto.gov/sites/default/files/documents/tbmp-Master-June2024.pdf). This manual contains detailed procedural rules and guidelines, coming to 1194 pages total. \n", + "* **Users:** The target users are intellectual property (IP) litigation associates and paralegals who need quick, accurate answers to procedural questions based *only* on the TBMP. \n", + "* **Typical Asks:** Users pose questions requiring synthesis and citation, such as: \n", + " 1. \"What are the requirements for filing a motion to compel discovery according to the TBMP?\" \n", + " 2. \"What deadlines apply to discovery conferences as specified in the manual?\" \n", + " 3. \"Explain how the Board handles claims of attorney-client privilege during depositions according to the TBMP.\" \n", + " 4. \"Enumerate the Fed. R. Civ. P. 11 sanctions the Board can invoke according to the TBMP.\" \n", + "\n", + "*Note: Depending on your specific deployment environment, you may need to adapt some implementation steps to match your infrastructure requirements.*\n", + "\n", + "> While OpenAI's File Search tool offers a good starting point for many use cases, this section introduces a different approach that takes advantage of million-token context windows to process large documents without any preprocessing or vector database. The agentic approach described here enables zero-latency ingestion, dynamic granularity of retrieval, and fine-grained citation traceability.\n", + "\n", + "## 2\\. Agentic RAG Flow\n", + "\n", + "Before diving into the implementation, let's understand the overall approach:\n", + "\n", + "1. **Load the entire document** into the context window\n", + "2. **Split into 20 chunks** that respect sentence boundaries\n", + "3. **Ask the model** which chunks might contain relevant information\n", + "4. **Drill down** into selected chunks by splitting them further\n", + "5. **Repeat** until we reach paragraph-level content\n", + "6. **Generate an answer** based on the selected paragraphs\n", + "7. **Verify the answer** for factual accuracy\n", + "\n", + "This hierarchical navigation approach mimics how a human might skim a document, focus on relevant chapters, then specific sections, and finally read only the most relevant paragraphs." + ] + }, + { + "cell_type": "markdown", + "id": "db9bad1b", + "metadata": {}, + "source": [ + "![Hierarchical Router](../../../images/3A_rag_hierarchical_router.png)\n", + "\n", + "\n", + "## Agentic RAG System: Model Usage\n", + "\n", + "| Process Stage | Model Used | Purpose |\n", + "|---------------|------------|---------|\n", + "| Initial Routing | `gpt-4.1-mini` | Identifies which document chunks might contain relevant information |\n", + "| Hierarchical Navigation | `gpt-4.1-mini` | Continues drilling down to find most relevant paragraphs |\n", + "| Answer Generation | `gpt-4.1` | Creates structured response with citations from selected paragraphs |\n", + "| Answer Verification | `o4-mini` | Validates factual accuracy and proper citation usage |\n", + "\n", + "This zero-preprocessing approach leverages large context windows to navigate documents on-the-fly, mimicking how a human would skim a document to find relevant information. " + ] + }, + { + "cell_type": "markdown", + "id": "df87f0ac", + "metadata": {}, + "source": [ + "## 3\\. Implementation\n", + "\n", + "Let's implement this approach step by step.\n", + "\n", + "Start by installing the required packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "63c78cd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install tiktoken pypdf nltk openai pydantic --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "cd1d7d60", + "metadata": {}, + "source": [ + "### 3.1 Document Loading\n", + "\n", + "First, let's load the document and check its size. For this guide, we'll focus on sections 100-900, which cover the core procedural aspects through Review of Decision of Board. Sections 1000 and beyond (Interferences, Concurrent Use Proceedings, Ex Parte Appeals) are specialized procedures outside our current scope." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dd5fb149", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt_tab to\n", + "[nltk_data] /Users/kmurali/nltk_data...\n", + "[nltk_data] Package punkt_tab is already up-to-date!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading document from https://www.uspto.gov/sites/default/files/documents/tbmp-Master-June2024.pdf...\n", + "Document loaded: 1194 pages, 595197 words, 932964 tokens\n", + "\n", + "Document preview (first 500 chars):\n", + "--------------------------------------------------\n", + "TRADEMARK TRIAL AND\n", + "APPEAL BOARD MANUAL\n", + "OF PROCEDURE (TBMP)\n", + " June 2024\n", + "June 2024\n", + "United States Patent and Trademark Office\n", + "PREFACE TO THE JUNE 2024 REVISION\n", + "The June 2024 revision of the Trademark Trial and Appeal Board Manual of Procedure is an update of the\n", + "June 2023 edition. This update is moderate in nature and incorporates relevant case law issued between March\n", + "3, 2023 and March 1, 2024.\n", + "The title of the manual is abbreviated as “TBMP.” A citation to a section of the manual may be written\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "import requests\n", + "from io import BytesIO\n", + "from pypdf import PdfReader\n", + "import re\n", + "import tiktoken\n", + "from nltk.tokenize import sent_tokenize\n", + "import nltk\n", + "from typing import List, Dict, Any\n", + "\n", + "# Download nltk data if not already present\n", + "nltk.download('punkt_tab')\n", + "\n", + "def load_document(url: str) -> str:\n", + " \"\"\"Load a document from a URL and return its text content.\"\"\"\n", + " print(f\"Downloading document from {url}...\")\n", + " response = requests.get(url)\n", + " response.raise_for_status()\n", + " pdf_bytes = BytesIO(response.content)\n", + " pdf_reader = PdfReader(pdf_bytes)\n", + " \n", + " full_text = \"\"\n", + " \n", + "\n", + " max_page = 920 # Page cutoff before section 1000 (Interferences)\n", + " for i, page in enumerate(pdf_reader.pages):\n", + " if i >= max_page:\n", + " break\n", + " full_text += page.extract_text() + \"\\n\"\n", + " \n", + " # Count words and tokens\n", + " word_count = len(re.findall(r'\\b\\w+\\b', full_text))\n", + " \n", + " tokenizer = tiktoken.get_encoding(\"o200k_base\")\n", + " token_count = len(tokenizer.encode(full_text))\n", + " \n", + " print(f\"Document loaded: {len(pdf_reader.pages)} pages, {word_count} words, {token_count} tokens\")\n", + " return full_text\n", + "\n", + "# Load the document\n", + "tbmp_url = \"https://www.uspto.gov/sites/default/files/documents/tbmp-Master-June2024.pdf\"\n", + "document_text = load_document(tbmp_url)\n", + "\n", + "# Show the first 500 characters\n", + "print(\"\\nDocument preview (first 500 chars):\")\n", + "print(\"-\" * 50)\n", + "print(document_text[:500])\n", + "print(\"-\" * 50)" + ] + }, + { + "cell_type": "markdown", + "id": "4bf86c84", + "metadata": {}, + "source": [ + "We can see that the document is over 900k tokens long! While we could fit that into GPT 4.1's context length, we also want to have verifiable citations, so we're going to proceed with a recursive chunking strategy." + ] + }, + { + "cell_type": "markdown", + "id": "445cbcaa", + "metadata": {}, + "source": [ + "### 3.2 Improved 20-Chunk Splitter with Minimum Token Size\n", + "\n", + "Now, let's create an improved function to split the document into 20 chunks, ensuring each has a minimum token size and respecting sentence boundaries.\n", + "\n", + "> 20 is an empirically chosen number for this specific document/task and it might need tuning for other documents based on size and structure (The higher the number, the more fine-grained the chunks). The key principle here however is splitting sections of the document up, in order to let the language model decide relevant components. This same reasoning also applies to the `max_depth` parameter which will be introduced later on in the cookbook." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "604f869b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split document into 20 chunks\n", + "Chunk 0: 42326 tokens\n", + "Chunk 1: 42093 tokens\n", + "Chunk 2: 42107 tokens\n", + "Chunk 3: 39797 tokens\n", + "Chunk 4: 58959 tokens\n", + "Chunk 5: 48805 tokens\n", + "Chunk 6: 37243 tokens\n", + "Chunk 7: 33453 tokens\n", + "Chunk 8: 38644 tokens\n", + "Chunk 9: 49402 tokens\n", + "Chunk 10: 51568 tokens\n", + "Chunk 11: 49586 tokens\n", + "Chunk 12: 47722 tokens\n", + "Chunk 13: 48952 tokens\n", + "Chunk 14: 44994 tokens\n", + "Chunk 15: 50286 tokens\n", + "Chunk 16: 54424 tokens\n", + "Chunk 17: 62651 tokens\n", + "Chunk 18: 47430 tokens\n", + "Chunk 19: 42507 tokens\n" + ] + } + ], + "source": [ + "# Global tokenizer name to use consistently throughout the code\n", + "TOKENIZER_NAME = \"o200k_base\"\n", + "\n", + "def split_into_20_chunks(text: str, min_tokens: int = 500) -> List[Dict[str, Any]]:\n", + " \"\"\"\n", + " Split text into up to 20 chunks, respecting sentence boundaries and ensuring\n", + " each chunk has at least min_tokens (unless it's the last chunk).\n", + " \n", + " Args:\n", + " text: The text to split\n", + " min_tokens: The minimum number of tokens per chunk (default: 500)\n", + " \n", + " Returns:\n", + " A list of dictionaries where each dictionary has:\n", + " - id: The chunk ID (0-19)\n", + " - text: The chunk text content\n", + " \"\"\"\n", + " # First, split the text into sentences\n", + " sentences = sent_tokenize(text)\n", + " \n", + " # Get tokenizer for counting tokens\n", + " tokenizer = tiktoken.get_encoding(TOKENIZER_NAME)\n", + " \n", + " # Create chunks that respect sentence boundaries and minimum token count\n", + " chunks = []\n", + " current_chunk_sentences = []\n", + " current_chunk_tokens = 0\n", + " \n", + " for sentence in sentences:\n", + " # Count tokens in this sentence\n", + " sentence_tokens = len(tokenizer.encode(sentence))\n", + " \n", + " # If adding this sentence would make the chunk too large AND we already have the minimum tokens,\n", + " # finalize the current chunk and start a new one\n", + " if (current_chunk_tokens + sentence_tokens > min_tokens * 2) and current_chunk_tokens >= min_tokens:\n", + " chunk_text = \" \".join(current_chunk_sentences)\n", + " chunks.append({\n", + " \"id\": len(chunks), # Integer ID instead of string\n", + " \"text\": chunk_text\n", + " })\n", + " current_chunk_sentences = [sentence]\n", + " current_chunk_tokens = sentence_tokens\n", + " else:\n", + " # Add this sentence to the current chunk\n", + " current_chunk_sentences.append(sentence)\n", + " current_chunk_tokens += sentence_tokens\n", + " \n", + " # Add the last chunk if there's anything left\n", + " if current_chunk_sentences:\n", + " chunk_text = \" \".join(current_chunk_sentences)\n", + " chunks.append({\n", + " \"id\": len(chunks), # Integer ID instead of string\n", + " \"text\": chunk_text\n", + " })\n", + " \n", + " # If we have more than 20 chunks, consolidate them\n", + " if len(chunks) > 20:\n", + " # Recombine all text\n", + " all_text = \" \".join(chunk[\"text\"] for chunk in chunks)\n", + " # Re-split into exactly 20 chunks, without minimum token requirement\n", + " sentences = sent_tokenize(all_text)\n", + " sentences_per_chunk = len(sentences) // 20 + (1 if len(sentences) % 20 > 0 else 0)\n", + " \n", + " chunks = []\n", + " for i in range(0, len(sentences), sentences_per_chunk):\n", + " # Get the sentences for this chunk\n", + " chunk_sentences = sentences[i:i+sentences_per_chunk]\n", + " # Join the sentences into a single text\n", + " chunk_text = \" \".join(chunk_sentences)\n", + " # Create a chunk object with ID and text\n", + " chunks.append({\n", + " \"id\": len(chunks), # Integer ID instead of string\n", + " \"text\": chunk_text\n", + " })\n", + " \n", + " # Print chunk statistics\n", + " print(f\"Split document into {len(chunks)} chunks\")\n", + " for i, chunk in enumerate(chunks):\n", + " token_count = len(tokenizer.encode(chunk[\"text\"]))\n", + " print(f\"Chunk {i}: {token_count} tokens\")\n", + " \n", + " return chunks\n", + "\n", + "# Split the document into 20 chunks with minimum token size\n", + "document_chunks = split_into_20_chunks(document_text, min_tokens=500)" + ] + }, + { + "cell_type": "markdown", + "id": "dccc89e6", + "metadata": {}, + "source": [ + "### 3.3 Router Function with Improved Tool Schema\n", + "\n", + "Now, let's create the router function that will select relevant chunks and maintain a scratchpad.\n", + "\n", + "> Maintaining a scratchpad allows the model to track decision criteria and reasoning over time. This implementation uses a two-pass approach with GPT-4.1-mini: first requiring the model to update the scratchpad via a tool call (tool_choice=\"required\"), then requesting structured JSON output for chunk selection. This approach provides better visibility into the model's reasoning process while ensuring consistent structured outputs for downstream processing." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a8373af1", + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import json\n", + "from typing import List, Dict, Any\n", + "\n", + "# Initialize OpenAI client\n", + "client = OpenAI()\n", + "\n", + "def route_chunks(question: str, chunks: List[Dict[str, Any]], \n", + " depth: int, scratchpad: str = \"\") -> Dict[str, Any]:\n", + " \"\"\"\n", + " Ask the model which chunks contain information relevant to the question.\n", + " Maintains a scratchpad for the model's reasoning.\n", + " Uses structured output for chunk selection and required tool calls for scratchpad.\n", + " \n", + " Args:\n", + " question: The user's question\n", + " chunks: List of chunks to evaluate\n", + " depth: Current depth in the navigation hierarchy\n", + " scratchpad: Current scratchpad content\n", + " \n", + " Returns:\n", + " Dictionary with selected IDs and updated scratchpad\n", + " \"\"\"\n", + " print(f\"\\n==== ROUTING AT DEPTH {depth} ====\")\n", + " print(f\"Evaluating {len(chunks)} chunks for relevance\")\n", + " \n", + " # Build system message\n", + " system_message = \"\"\"You are an expert document navigator. Your task is to:\n", + "1. Identify which text chunks might contain information to answer the user's question\n", + "2. Record your reasoning in a scratchpad for later reference\n", + "3. Choose chunks that are most likely relevant. Be selective, but thorough. Choose as many chunks as you need to answer the question, but avoid selecting too many.\n", + "\n", + "First think carefully about what information would help answer the question, then evaluate each chunk.\n", + "\"\"\"\n", + "\n", + " # Build user message with chunks and current scratchpad\n", + " user_message = f\"QUESTION: {question}\\n\\n\"\n", + " \n", + " if scratchpad:\n", + " user_message += f\"CURRENT SCRATCHPAD:\\n{scratchpad}\\n\\n\"\n", + " \n", + " user_message += \"TEXT CHUNKS:\\n\\n\"\n", + " \n", + " # Add each chunk to the message\n", + " for chunk in chunks:\n", + " user_message += f\"CHUNK {chunk['id']}:\\n{chunk['text']}\\n\\n\"\n", + " \n", + " # Define function schema for scratchpad tool calling\n", + " tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"name\": \"update_scratchpad\",\n", + " \"description\": \"Record your reasoning about why certain chunks were selected\",\n", + " \"strict\": True,\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"text\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"Your reasoning about the chunk(s) selection\"\n", + " }\n", + " },\n", + " \"required\": [\"text\"],\n", + " \"additionalProperties\": False\n", + " }\n", + " }\n", + " ]\n", + " \n", + " # Define JSON schema for structured output (selected chunks)\n", + " text_format = {\n", + " \"format\": {\n", + " \"type\": \"json_schema\",\n", + " \"name\": \"selected_chunks\",\n", + " \"strict\": True,\n", + " \"schema\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"chunk_ids\": {\n", + " \"type\": \"array\",\n", + " \"items\": {\"type\": \"integer\"},\n", + " \"description\": \"IDs of the selected chunks that contain information to answer the question\"\n", + " }\n", + " },\n", + " \"required\": [\n", + " \"chunk_ids\"\n", + " ],\n", + " \"additionalProperties\": False\n", + " }\n", + " }\n", + " }\n", + " \n", + " # First pass: Call the model to update scratchpad (required tool call)\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": user_message + \"\\n\\nFirst, you must use the update_scratchpad function to record your reasoning.\"}\n", + " ]\n", + " \n", + " response = client.responses.create(\n", + " model=\"gpt-4.1-mini\",\n", + " input=messages,\n", + " tools=tools,\n", + " tool_choice=\"required\"\n", + " )\n", + " \n", + " # Process the scratchpad tool call\n", + " new_scratchpad = scratchpad\n", + " \n", + " for tool_call in response.output:\n", + " if tool_call.type == \"function_call\" and tool_call.name == \"update_scratchpad\":\n", + " args = json.loads(tool_call.arguments)\n", + " scratchpad_entry = f\"DEPTH {depth} REASONING:\\n{args.get('text', '')}\"\n", + " if new_scratchpad:\n", + " new_scratchpad += \"\\n\\n\" + scratchpad_entry\n", + " else:\n", + " new_scratchpad = scratchpad_entry\n", + " \n", + " # Add function call and result to messages\n", + " messages.append(tool_call)\n", + " messages.append({\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call.call_id,\n", + " \"output\": \"Scratchpad updated successfully.\"\n", + " })\n", + " \n", + " # Second pass: Get structured output for chunk selection\n", + " messages.append({\"role\": \"user\", \"content\": \"Now, select the chunks that could contain information to answer the question. Return a JSON object with the list of chunk IDs.\"})\n", + " \n", + " response_chunks = client.responses.create(\n", + " model=\"gpt-4.1-mini\",\n", + " input=messages,\n", + " text=text_format\n", + " )\n", + " \n", + " # Extract selected chunk IDs from structured output\n", + " selected_ids = []\n", + " if response_chunks.output_text:\n", + " try:\n", + " # The output_text should already be in JSON format due to the schema\n", + " chunk_data = json.loads(response_chunks.output_text)\n", + " selected_ids = chunk_data.get(\"chunk_ids\", [])\n", + " except json.JSONDecodeError:\n", + " print(\"Warning: Could not parse structured output as JSON\")\n", + " \n", + " # Display results\n", + " print(f\"Selected chunks: {', '.join(str(id) for id in selected_ids)}\")\n", + " print(f\"Updated scratchpad:\\n{new_scratchpad}\")\n", + " \n", + " return {\n", + " \"selected_ids\": selected_ids,\n", + " \"scratchpad\": new_scratchpad\n", + " }" + ] + }, + { + "cell_type": "markdown", + "id": "c11654a9", + "metadata": {}, + "source": [ + "### 3.4 Recursive Navigation Function\n", + "\n", + "Now, let's create the recursive navigation function that drills down through the document. `max_depth` is the maximum number of levels to drill down (keeping token minimums in mind):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "876940b7", + "metadata": {}, + "outputs": [], + "source": [ + "def navigate_to_paragraphs(document_text: str, question: str, max_depth: int = 1) -> Dict[str, Any]:\n", + " \"\"\"\n", + " Navigate through the document hierarchy to find relevant paragraphs.\n", + " \n", + " Args:\n", + " document_text: The full document text\n", + " question: The user's question\n", + " max_depth: Maximum depth to navigate before returning paragraphs (default: 1)\n", + " \n", + " Returns:\n", + " Dictionary with selected paragraphs and final scratchpad\n", + " \"\"\"\n", + " scratchpad = \"\"\n", + " \n", + " # Get initial chunks with min 500 tokens\n", + " chunks = split_into_20_chunks(document_text, min_tokens=500)\n", + " \n", + " # Navigator state - track chunk paths to maintain hierarchy\n", + " chunk_paths = {} # Maps numeric IDs to path strings for display\n", + " for chunk in chunks:\n", + " chunk_paths[chunk[\"id\"]] = str(chunk[\"id\"])\n", + " \n", + " # Navigate through levels until max_depth or until no chunks remain\n", + " for current_depth in range(max_depth + 1):\n", + " # Call router to get relevant chunks\n", + " result = route_chunks(question, chunks, current_depth, scratchpad)\n", + " \n", + " # Update scratchpad\n", + " scratchpad = result[\"scratchpad\"]\n", + " \n", + " # Get selected chunks\n", + " selected_ids = result[\"selected_ids\"]\n", + " selected_chunks = [c for c in chunks if c[\"id\"] in selected_ids]\n", + " \n", + " # If no chunks were selected, return empty result\n", + " if not selected_chunks:\n", + " print(\"\\nNo relevant chunks found.\")\n", + " return {\"paragraphs\": [], \"scratchpad\": scratchpad}\n", + " \n", + " # If we've reached max_depth, return the selected chunks\n", + " if current_depth == max_depth:\n", + " print(f\"\\nReturning {len(selected_chunks)} relevant chunks at depth {current_depth}\")\n", + " \n", + " # Update display IDs to show hierarchy\n", + " for chunk in selected_chunks:\n", + " chunk[\"display_id\"] = chunk_paths[chunk[\"id\"]]\n", + " \n", + " return {\"paragraphs\": selected_chunks, \"scratchpad\": scratchpad}\n", + " \n", + " # Prepare next level by splitting selected chunks further\n", + " next_level_chunks = []\n", + " next_chunk_id = 0 # Counter for new chunks\n", + " \n", + " for chunk in selected_chunks:\n", + " # Split this chunk into smaller pieces\n", + " sub_chunks = split_into_20_chunks(chunk[\"text\"], min_tokens=200)\n", + " \n", + " # Update IDs and maintain path mapping\n", + " for sub_chunk in sub_chunks:\n", + " path = f\"{chunk_paths[chunk['id']]}.{sub_chunk['id']}\"\n", + " sub_chunk[\"id\"] = next_chunk_id\n", + " chunk_paths[next_chunk_id] = path\n", + " next_level_chunks.append(sub_chunk)\n", + " next_chunk_id += 1\n", + " \n", + " # Update chunks for next iteration\n", + " chunks = next_level_chunks" + ] + }, + { + "cell_type": "markdown", + "id": "0d803dfc", + "metadata": {}, + "source": [ + "### 3.5 Run the Improved Navigation for a Sample Question\n", + "\n", + "Let's run the navigation for a sample question with our improved approach:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f6e29008", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Split document into 20 chunks\n", + "Chunk 0: 42326 tokens\n", + "Chunk 1: 42093 tokens\n", + "Chunk 2: 42107 tokens\n", + "Chunk 3: 39797 tokens\n", + "Chunk 4: 58959 tokens\n", + "Chunk 5: 48805 tokens\n", + "Chunk 6: 37243 tokens\n", + "Chunk 7: 33453 tokens\n", + "Chunk 8: 38644 tokens\n", + "Chunk 9: 49402 tokens\n", + "Chunk 10: 51568 tokens\n", + "Chunk 11: 49586 tokens\n", + "Chunk 12: 47722 tokens\n", + "Chunk 13: 48952 tokens\n", + "Chunk 14: 44994 tokens\n", + "Chunk 15: 50286 tokens\n", + "Chunk 16: 54424 tokens\n", + "Chunk 17: 62651 tokens\n", + "Chunk 18: 47430 tokens\n", + "Chunk 19: 42507 tokens\n", + "\n", + "==== ROUTING AT DEPTH 0 ====\n", + "Evaluating 20 chunks for relevance\n", + "Selected chunks: 0, 1, 2, 3, 4, 5, 6, 7, 8\n", + "Updated scratchpad:\n", + "DEPTH 0 REASONING:\n", + "The user wants to know the format requirements for filing a motion to compel discovery and how signatures should be handled for such motions. \n", + "\n", + "Based on the evaluation of chunks:\n", + "- Chunks 0, 1, 2, 3, 4, 5, 6, 7, 8 are highly relevant since they cover general requirements for submissions, motions, signatures, service, and specifically for motions and discovery in TTAB proceedings.\n", + "- These chunks contain detailed info about electronic filing (via ESTTA), paper filing exceptions, signature requirements, service requirements, format of submissions (including motions), timing rules, and professionals' responsibilities.\n", + "- Additionally, the rules for motions to compel, including required attachments, timing, and certification of good faith efforts to resolve discovery disputes, are specifically outlined.\n", + "- Chunks 11-19 mostly cover post-trial and appeal procedures, less directly relevant.\n", + "\n", + "I will select these relevant chunks to provide a thorough answer about how motions to compel discovery should be filed and how signatures on such motions are handled.\n", + "Split document into 20 chunks\n", + "Chunk 0: 3539 tokens\n", + "Chunk 1: 2232 tokens\n", + "Chunk 2: 1746 tokens\n", + "Chunk 3: 3078 tokens\n", + "Chunk 4: 1649 tokens\n", + "Chunk 5: 2779 tokens\n", + "Chunk 6: 2176 tokens\n", + "Chunk 7: 1667 tokens\n", + "Chunk 8: 1950 tokens\n", + "Chunk 9: 1730 tokens\n", + "Chunk 10: 1590 tokens\n", + "Chunk 11: 1964 tokens\n", + "Chunk 12: 1459 tokens\n", + "Chunk 13: 2070 tokens\n", + "Chunk 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tokens\n", + "Chunk 19: 1882 tokens\n", + "\n", + "==== ROUTING AT DEPTH 1 ====\n", + "Evaluating 180 chunks for relevance\n", + "Selected chunks: 5, 6, 7, 17, 18, 19, 20, 400, 401, 408, 410\n", + "Updated scratchpad:\n", + "DEPTH 0 REASONING:\n", + "The user wants to know the format requirements for filing a motion to compel discovery and how signatures should be handled for such motions. \n", + "\n", + "Based on the evaluation of chunks:\n", + "- Chunks 0, 1, 2, 3, 4, 5, 6, 7, 8 are highly relevant since they cover general requirements for submissions, motions, signatures, service, and specifically for motions and discovery in TTAB proceedings.\n", + "- These chunks contain detailed info about electronic filing (via ESTTA), paper filing exceptions, signature requirements, service requirements, format of submissions (including motions), timing rules, and professionals' responsibilities.\n", + "- Additionally, the rules for motions to compel, including required attachments, timing, and certification of good faith efforts to resolve discovery disputes, are specifically outlined.\n", + "- Chunks 11-19 mostly cover post-trial and appeal procedures, less directly relevant.\n", + "\n", + "I will select these relevant chunks to provide a thorough answer about how motions to compel discovery should be filed and how signatures on such motions are handled.\n", + "\n", + "DEPTH 1 REASONING:\n", + "The user's question asks about the format requirements for filing a motion to compel discovery and how signatures should be handled. Relevant information will likely involve sections on \"motions\" specifically \"motion to compel discovery,\" filing format, signature requirements, and related procedural rules in TTAB practice. \n", + "\n", + "Based on the large amount and depth of the provided chunks, I identified the following relevant topics and chunks addressing them:\n", + "\n", + "1. Signature Requirements & Acceptable Formats for Motions and Submissions\n", + "- Detailed rules for signatures on submissions including motions are in chunks 5, 6, 7.\n", + "- These include rules on electronic filing, use of ESTTA, required signature format including electronic signatures with the symbol method \"/sig/\".\n", + "\n", + "2. Format of Submissions and Use of ESTTA\n", + "- Filing requirements, printing format, size, paper submissions, and special exceptions are found in chunks 7, 8, 9, 10, 11, 12, 13.\n", + "- Motions generally must be filed via ESTTA, with exceptions requiring petitions to Director with reasons.\n", + "\n", + "3. Motions to Compel and Discovery Motions\n", + "- Specific rules related to filing motions such as motions to compel discovery, service, and timing are expected in the portions covering discovery and motions.\n", + "- Discovery and related motions are introduced in chapters starting from chunk 400 and beyond.\n", + "\n", + "4. Service and Certificates of Service\n", + "- How motions must be served and proof of service with certificates is discussed in chunks 17, 18, 19, 20.\n", + "- These include requirements that every submission in inter partes cases, except notice of opposition or petition to cancel, must be served on adversary and proof of service provided.\n", + "\n", + "5. Motions to Compel Discovery Details\n", + "- Discovery and motion procedure, filing format, timing, service, and related sanctions are extensively covered in chunks 400 and following.\n", + "- These include disclosures, discovery conferences, timing for discovery requests, responses, motions to compel, and sanctions.\n", + "\n", + "From the above, the following chunks are most likely to provide the requested information:\n", + "- Chunks 5, 6, 7: Signature rules and filing format including motions.\n", + "- Chunks 17, 18, 19, 20: Service of submissions and certificates of service.\n", + "- Chunks 400 to 410 plus related portions (401.01, 401.02, 401.03, 408, 410): Discovery rules, motions to compel details.\n", + "\n", + "These cover the format of motions including motions to compel discovery, signature rules, service and proof of service, and discovery procedure and rules governing motions.\n", + "\n", + "Less relevant chunks to the question are routine procedural provisions on oppositions, petitions to cancel, answers, which do not specifically address filing or signatures of motions to compel discovery.\n", + "\n", + "Plan: Select the above relevant chunks and report key procedural points on the format in which a motion to compel discovery must be filed and how signatures must be handled.\n", + "Split document into 8 chunks\n", + "Chunk 0: 398 tokens\n", + "Chunk 1: 256 tokens\n", + "Chunk 2: 389 tokens\n", + "Chunk 3: 356 tokens\n", + "Chunk 4: 401 tokens\n", + "Chunk 5: 277 tokens\n", + "Chunk 6: 435 tokens\n", + "Chunk 7: 265 tokens\n", + "Split document into 6 chunks\n", + "Chunk 0: 353 tokens\n", + "Chunk 1: 393 tokens\n", + "Chunk 2: 388 tokens\n", + "Chunk 3: 398 tokens\n", + "Chunk 4: 397 tokens\n", + "Chunk 5: 247 tokens\n", + "Split document into 5 chunks\n", + "Chunk 0: 325 tokens\n", + "Chunk 1: 389 tokens\n", + "Chunk 2: 303 tokens\n", + "Chunk 3: 344 tokens\n", + "Chunk 4: 306 tokens\n", + "Split document into 8 chunks\n", + "Chunk 0: 396 tokens\n", + "Chunk 1: 354 tokens\n", + "Chunk 2: 361 tokens\n", + "Chunk 3: 378 tokens\n", + "Chunk 4: 388 tokens\n", + "Chunk 5: 394 tokens\n", + "Chunk 6: 361 tokens\n", + "Chunk 7: 61 tokens\n", + "Split document into 7 chunks\n", + "Chunk 0: 396 tokens\n", + "Chunk 1: 355 tokens\n", + "Chunk 2: 377 tokens\n", + "Chunk 3: 362 tokens\n", + "Chunk 4: 326 tokens\n", + "Chunk 5: 397 tokens\n", + "Chunk 6: 69 tokens\n", + "Split document into 3 chunks\n", + "Chunk 0: 388 tokens\n", + "Chunk 1: 373 tokens\n", + "Chunk 2: 221 tokens\n", + "Split document into 8 chunks\n", + "Chunk 0: 360 tokens\n", + "Chunk 1: 314 tokens\n", + "Chunk 2: 369 tokens\n", + "Chunk 3: 363 tokens\n", + "Chunk 4: 361 tokens\n", + "Chunk 5: 393 tokens\n", + "Chunk 6: 361 tokens\n", + "Chunk 7: 358 tokens\n", + "\n", + "==== ROUTING AT DEPTH 2 ====\n", + "Evaluating 45 chunks for relevance\n", + "Selected chunks: 0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36\n", + "Updated scratchpad:\n", + "DEPTH 0 REASONING:\n", + "The user wants to know the format requirements for filing a motion to compel discovery and how signatures should be handled for such motions. \n", + "\n", + "Based on the evaluation of chunks:\n", + "- Chunks 0, 1, 2, 3, 4, 5, 6, 7, 8 are highly relevant since they cover general requirements for submissions, motions, signatures, service, and specifically for motions and discovery in TTAB proceedings.\n", + "- These chunks contain detailed info about electronic filing (via ESTTA), paper filing exceptions, signature requirements, service requirements, format of submissions (including motions), timing rules, and professionals' responsibilities.\n", + "- Additionally, the rules for motions to compel, including required attachments, timing, and certification of good faith efforts to resolve discovery disputes, are specifically outlined.\n", + "- Chunks 11-19 mostly cover post-trial and appeal procedures, less directly relevant.\n", + "\n", + "I will select these relevant chunks to provide a thorough answer about how motions to compel discovery should be filed and how signatures on such motions are handled.\n", + "\n", + "DEPTH 1 REASONING:\n", + "The user's question asks about the format requirements for filing a motion to compel discovery and how signatures should be handled. Relevant information will likely involve sections on \"motions\" specifically \"motion to compel discovery,\" filing format, signature requirements, and related procedural rules in TTAB practice. \n", + "\n", + "Based on the large amount and depth of the provided chunks, I identified the following relevant topics and chunks addressing them:\n", + "\n", + "1. Signature Requirements & Acceptable Formats for Motions and Submissions\n", + "- Detailed rules for signatures on submissions including motions are in chunks 5, 6, 7.\n", + "- These include rules on electronic filing, use of ESTTA, required signature format including electronic signatures with the symbol method \"/sig/\".\n", + "\n", + "2. Format of Submissions and Use of ESTTA\n", + "- Filing requirements, printing format, size, paper submissions, and special exceptions are found in chunks 7, 8, 9, 10, 11, 12, 13.\n", + "- Motions generally must be filed via ESTTA, with exceptions requiring petitions to Director with reasons.\n", + "\n", + "3. Motions to Compel and Discovery Motions\n", + "- Specific rules related to filing motions such as motions to compel discovery, service, and timing are expected in the portions covering discovery and motions.\n", + "- Discovery and related motions are introduced in chapters starting from chunk 400 and beyond.\n", + "\n", + "4. Service and Certificates of Service\n", + "- How motions must be served and proof of service with certificates is discussed in chunks 17, 18, 19, 20.\n", + "- These include requirements that every submission in inter partes cases, except notice of opposition or petition to cancel, must be served on adversary and proof of service provided.\n", + "\n", + "5. Motions to Compel Discovery Details\n", + "- Discovery and motion procedure, filing format, timing, service, and related sanctions are extensively covered in chunks 400 and following.\n", + "- These include disclosures, discovery conferences, timing for discovery requests, responses, motions to compel, and sanctions.\n", + "\n", + "From the above, the following chunks are most likely to provide the requested information:\n", + "- Chunks 5, 6, 7: Signature rules and filing format including motions.\n", + "- Chunks 17, 18, 19, 20: Service of submissions and certificates of service.\n", + "- Chunks 400 to 410 plus related portions (401.01, 401.02, 401.03, 408, 410): Discovery rules, motions to compel details.\n", + "\n", + "These cover the format of motions including motions to compel discovery, signature rules, service and proof of service, and discovery procedure and rules governing motions.\n", + "\n", + "Less relevant chunks to the question are routine procedural provisions on oppositions, petitions to cancel, answers, which do not specifically address filing or signatures of motions to compel discovery.\n", + "\n", + "Plan: Select the above relevant chunks and report key procedural points on the format in which a motion to compel discovery must be filed and how signatures must be handled.\n", + "\n", + "DEPTH 2 REASONING:\n", + "The user's question is about the format for filing a motion to compel discovery and handling of signatures. Relevant information is likely contained in sections addressing motions, discovery procedures, submission format, signature requirements, and service rules. \n", + "\n", + "Chunks covering signature requirements (5-12) provide detailed rules on legal signatures, electronic signatures, who must sign (attorneys or parties with legal authority), and signature content.\n", + "\n", + "Chunks 0, 4, 7-10, 15-18 discuss the required format for submissions, including motions, the mandate to file electronically via ESTTA, and exceptions for paper filings.\n", + "\n", + "Chunks 23-35 address service of submissions, including requirements for service on all parties, methods of service, and certificates of service.\n", + "\n", + "Finally, discovery-related motions such as motions to compel discovery and their filing details should be in chunks from 400 onwards (although these aren't fully visible here, the rationale included these chunks as likely relevant).\n", + "\n", + "Therefore, chunks 0,4,5,6,7,8,9,10,11,12,15,16,17,18,23,24,25,26,27,28,29,30,31,32,33,34,35,36 are selected as most relevant to provide a thorough answer on the filing format and signatures for a motion to compel discovery.\n", + "\n", + "Returning 28 relevant chunks at depth 2\n", + "\n", + "==== FIRST 3 RETRIEVED PARAGRAPHS ====\n", + "\n", + "PARAGRAPH 1 (ID: 0.0.5.0):\n", + "----------------------------------------\n", + "104 Business to be Conducted in Writing\n", + "37 C.F.R. § 2.190(b) Electronic trademark documents. … Documents that r elate to proceedings before\n", + "the Trademark Trial and Appeal Board must be filed electronically with the Board through ESTTA. 37 C.F.R. § 2.191 Action of the Office based on the written record. All business with the Office must be\n", + "transacted in writing. The action of the Office will be based exclusively on the written record. No consideration\n", + "will be given to any alleged oral promise, stipulation, or understanding when there is disagreement or doubt. With the exceptions of discovery conferences with Board participation, see TBMP § 401.01, and telephone\n", + "conferences, see TBMP § 413.01 and TBMP § 502.06, all business with the Board should be transacted in\n", + "writing. 37 C.F.R. § 2.191 . The personal attendance of parties or their attorne ys or other authorized\n", + "representatives at the offices of the Board is unnecessary , except in the case of a pretrial conference as\n", + "provided in 37 C.F.R. § 2.120(j), or upon oral argument at final hearing, if a party so desires, as pro vided\n", + "in 37 C.F.R. § 2.129. Decisions of the Board will be based exclusively on the written record before it. [Note\n", + "1.] Documents filed in proceedings before the Board must be filed through ESTT A. 37 C.F.R. § 2.190(b). See TBMP § 110.01(a). Board proceedings are conducted in English. If a party intends to rely upon an y submissions that are in a\n", + "language other than English, the party should also file a translation of the submissions. If a translation is\n", + "not filed, the submissions may not be considered. [Note 2.] NOTES:\n", + "1. Cf.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 2 (ID: 0.0.5.4):\n", + "----------------------------------------\n", + "The document should\n", + "also include a title describing its nature, e.g., “Notice of Opposition,” “Answer,” “Motion to Compel,” “Brief\n", + "in Opposition to Respondent’s Motion for Summary Judgment,” or “Notice of Reliance.”\n", + "Documents filed in an application which is the subject of an inter partes proceeding before the Board should\n", + "be filed with the Board, not the Trademark Operation, and should bear at the top of the first page both the\n", + "application serial number, and the inter partes proceeding number and caption. Similarly , requests under\n", + "Trademark Act § 7, 15 U.S.C. § 1057, to amend, correct, or surrender a registration which is the subject of\n", + "a Board inter partes proceeding, and any new power of attorney, designation of domestic representative, or\n", + "change of address submitted in connection with such a registration, should be filed with the Board, not with\n", + "the Trademark Operation, and should bear at the top of its first page the re gistration number, and the inter\n", + "partes proceeding number and the proceeding caption. [Note 2.] 100-14June 2024\n", + "TRADEMARK TRIAL AND APPEAL BOARD MANUAL OF PROCEDURE§ 105\n", + "NOTES:\n", + "1. 37 C.F.R. § 2.194. 2. 37 C.F.R. § 2.194. 106.02 Signature of Submissions\n", + "37 C.F.R. § 2.119(e) Every submission filed in an inter partes proceeding, and every request for an extension\n", + "of time to file an opposition, must be signed by the party filing it, or by the party’s attorney or other authorized\n", + "representative, but an unsigned submission will not be r efused consideration if a signed copy is submitted\n", + "to the Office within the time limit set in the notification of this defect by the Office. 37 C.F.R. § 11.14(e) Appearance.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 3 (ID: 0.0.5.5):\n", + "----------------------------------------\n", + "No individual other than those specified in par agraphs (a), (b), and (c)\n", + "of this section will be permitted to pr actice before the Office in tr ademark matters on behalf of a client. Except as specified in § 2.11(a) of this chapter, an individual may appear in a trademark or other non-patent\n", + "matter in his or her own behalf or on behalf of:\n", + "(1) A firm of which he or she is a member;\n", + "(2) A partnership of which he or she is a partner; or\n", + "(3) A corporation or association of which he or she is an officer and which he or she is authorized to\n", + "represent. 37 C.F.R. § 11.18 Signature and certificate for correspondence filed in the Office. (a) For all documents filed in the Office in patent, trademark, and other non-patent matters, and all\n", + "documents filed with a hearing officer in a disciplinary proceeding, except for correspondence that is\n", + "required to be signed by the applicant or party, each piece of correspondence filed by a practitioner in the\n", + "Office must bear a signature, personally signed or inserted by such practitioner, in compliance with §\n", + "1.4(d)(1), § 1.4(d)(2), or § 2.193(a) of this chapter.\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "# Run the navigation for a sample question\n", + "question = \"What format should a motion to compel discovery be filed in? How should signatures be handled?\"\n", + "navigation_result = navigate_to_paragraphs(document_text, question, max_depth=2)\n", + "\n", + "# Sample retrieved paragraph\n", + "print(\"\\n==== FIRST 3 RETRIEVED PARAGRAPHS ====\")\n", + "for i, paragraph in enumerate(navigation_result[\"paragraphs\"][:3]):\n", + " display_id = paragraph.get(\"display_id\", str(paragraph[\"id\"]))\n", + " print(f\"\\nPARAGRAPH {i+1} (ID: {display_id}):\")\n", + " print(\"-\" * 40)\n", + " print(paragraph[\"text\"])\n", + " print(\"-\" * 40)" + ] + }, + { + "cell_type": "markdown", + "id": "dcf85b3e", + "metadata": {}, + "source": [ + "GPT 4.1-mini's results show the iterative extraction of relevant components in a document with the scratchpad explaining it's thought process through it! At depth 1, the model identifies \"*Detailed rules for signatures on submissions including motions*\" and \"*use of ESTTA, required signature format including electronic signatures with the symbol method '/sig/'*\" as critical components needed to answer the query.\n", + "\n", + "By depth 2, the scratchpad demonstrates sophisticated judgment by isolating precisely which chunks contain vital regulations about electronic signatures (chunks 5-12) while maintaining awareness of absent content, noting \"*discovery-related motions... should be in chunks from 400 onwards (although these aren't fully visible here...)*\".\n", + "\n", + "This process shows how GPT 4.1 mimics a legal analyst, through iteratively digging deeper into relevant content, and explaining it's reasoning along the way (making it easier to debug *why* the model selected the chunks it did)" + ] + }, + { + "cell_type": "markdown", + "id": "495a5230", + "metadata": {}, + "source": [ + "### 3.6 Answer Generation\n", + "\n", + "Now, let's generate an answer using GPT-4.1 with the retrieved paragraphs. \n", + "\n", + "> We do a nifty trick here where we dynamically construct a List of Literals (which forces the model's answers to be one of the options we provide -- in this case the paragraph IDs). There are some restrictions on the number of options we can provide, so if you find your system citing > 500 documents, then this solution might not work. In that case, you can either have a filter to go up to 500 potential citations, or you can ask the model to cite the exact ID in it's response, then post-process the response to extract the IDs, thus the citations (e.g. it might say \"... [doc 0.0.12]\", and you could use some regex to extract the citation).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c74cfe50", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==== GENERATING ANSWER ====\n", + "\n", + "Answer: A motion to compel discovery must be filed electronically with the Trademark Trial and Appeal Board (TTAB) through ESTTA, unless ESTTA is unavailable due to technical problems or there are extraordinary circumstances, in which case a paper submission may be permitted with a written explanation (\"Documents that relate to proceedings before the Trademark Trial and Appeal Board must be filed electronically with the Board through ESTTA\"; \"The rules require that all submissions must be made to the Board electronically, currently through ESTTA, subject to certain limited exceptions permitting submissions to be made on paper. Any permitted paper submission must be accompanied by a written explanation showing that ESTTA was unavailable due to technical problems, or that extraordinary circumstances are present, and, where required, a Petition to the Director with the requisite petition fee\" 0.0.5.0, 0.0.5.5.7.3).\n", + "\n", + "The motion should include a title describing its nature, such as “Motion to Compel,” and should bear the appropriate proceeding number and caption at the top of the first page (\"The document should also include a title describing its nature, e.g., 'Motion to Compel'... should bear at the top of the first page both the application serial number, and the inter partes proceeding number and caption\" 0.0.5.4).\n", + "\n", + "Every submission, including a motion to compel discovery, must be signed by the party filing it, or by the party’s attorney or other authorized representative. For electronic filings through ESTTA, a conventional handwritten signature is not required; instead, an electronic signature is used. The signatory must personally enter a combination of letters, numbers, spaces, and/or punctuation marks between two forward slash ('/') symbols (e.g., /John Smith/), and the signatory's name and title or position must appear immediately below or adjacent to the signature (\"Documents filed electronically, including through ESTTA, do not require a conventional signature. Electronic signatures pursuant to 37 C.F.R. § 2.193(c) are required for electronic filings. The party or its representative enters a 'symbol' that has been adopted as a signature. The Board will accept any combination of letters, numbers, space and/or punctuation marks as a valid signature if it is placed between two forward slash ('/') symbols\"; \"The first and last name, and the title or position, of the person who signs a document in connection with a trademark application, registration, or proceeding before the Trademark Trial and Appeal Board must be set forth immediately below or adjacent to the signature\" 0.0.5.5.6.2, 0.0.5.5.6.0).\n", + "\n", + "If a document is filed on behalf of a party by the party’s attorney or other authorized representative, it must bear the signature of that attorney or representative, unless the document is one required to be signed personally by the party (0.0.5.5.6.3). If an unsigned or improperly signed document is filed, it will not be refused consideration if a properly signed copy is submitted within the time limit set in the notification of the defect by the Board (0.0.5.5.6.4).\n", + "\n", + "In summary: File the motion to compel discovery electronically via ESTTA, use an electronic signature as described above, and ensure the signatory's name and title are included. If filing on paper is necessary, follow the specific requirements for paper submissions and signatures.\n", + "Citations: ['0.0.5.0', '0.0.5.4', '0.0.5.5.6.0', '0.0.5.5.6.2', '0.0.5.5.6.3', '0.0.5.5.6.4', '0.0.5.5.7.3']\n" + ] + } + ], + "source": [ + "from typing import List, Dict, Any\n", + "from pydantic import BaseModel, field_validator\n", + "\n", + "class LegalAnswer(BaseModel):\n", + " \"\"\"Structured response format for legal questions\"\"\"\n", + " answer: str\n", + " citations: List[str]\n", + " \n", + " @field_validator('citations')\n", + " def validate_citations(cls, citations, info):\n", + " # Access valid_citations from the model_config\n", + " valid_citations = info.data.get('_valid_citations', [])\n", + " if valid_citations:\n", + " for citation in citations:\n", + " if citation not in valid_citations:\n", + " raise ValueError(f\"Invalid citation: {citation}. Must be one of: {valid_citations}\")\n", + " return citations\n", + "\n", + "def generate_answer(question: str, paragraphs: List[Dict[str, Any]], \n", + " scratchpad: str) -> LegalAnswer:\n", + " \"\"\"Generate an answer from the retrieved paragraphs.\"\"\"\n", + " print(\"\\n==== GENERATING ANSWER ====\")\n", + " \n", + " # Extract valid citation IDs\n", + " valid_citations = [str(p.get(\"display_id\", str(p[\"id\"]))) for p in paragraphs]\n", + " \n", + " if not paragraphs:\n", + " return LegalAnswer(\n", + " answer=\"I couldn't find relevant information to answer this question in the document.\",\n", + " citations=[],\n", + " _valid_citations=[]\n", + " )\n", + " \n", + " # Prepare context for the model\n", + " context = \"\"\n", + " for paragraph in paragraphs:\n", + " display_id = paragraph.get(\"display_id\", str(paragraph[\"id\"]))\n", + " context += f\"PARAGRAPH {display_id}:\\n{paragraph['text']}\\n\\n\"\n", + " \n", + " system_prompt = \"\"\"You are a legal research assistant answering questions about the \n", + "Trademark Trial and Appeal Board Manual of Procedure (TBMP).\n", + "\n", + "Answer questions based ONLY on the provided paragraphs. Do not rely on any foundation knowledge or external information or extrapolate from the paragraphs.\n", + "Cite phrases of the paragraphs that are relevant to the answer. This will help you be more specific and accurate.\n", + "Include citations to paragraph IDs for every statement in your answer. Valid citation IDs are: {valid_citations_str}\n", + "Keep your answer clear, precise, and professional.\n", + "\"\"\"\n", + " valid_citations_str = \", \".join(valid_citations)\n", + " \n", + " # Call the model using structured output\n", + " response = client.responses.parse(\n", + " model=\"gpt-4.1\",\n", + " input=[\n", + " {\"role\": \"system\", \"content\": system_prompt.format(valid_citations_str=valid_citations_str)},\n", + " {\"role\": \"user\", \"content\": f\"QUESTION: {question}\\n\\nSCRATCHPAD (Navigation reasoning):\\n{scratchpad}\\n\\nPARAGRAPHS:\\n{context}\"}\n", + " ],\n", + " text_format=LegalAnswer,\n", + " temperature=0.3\n", + " )\n", + " \n", + " # Add validation information after parsing\n", + " response.output_parsed._valid_citations = valid_citations\n", + " \n", + " print(f\"\\nAnswer: {response.output_parsed.answer}\")\n", + " print(f\"Citations: {response.output_parsed.citations}\")\n", + "\n", + " return response.output_parsed\n", + "\n", + "# Generate an answer\n", + "answer = generate_answer(question, navigation_result[\"paragraphs\"], \n", + " navigation_result[\"scratchpad\"])" + ] + }, + { + "cell_type": "markdown", + "id": "83d5e682", + "metadata": {}, + "source": [ + "GPT 4.1 effectively integrates citations throughout its response while maintaining a clear flow of information. Each procedural requirement is linked to specific authoritative references (like \"0.0.5.0\" and \"0.0.5.5.6.2\"), creating a response that's both informative and precisely sourced. \n", + "\n", + "Rather than simply listing citations at the end, it weaves them directly into the content using parenthetical notation after each key requirement. This approach transforms a standard recitation of rules into a well-supported legal analysis where statements about ESTTA filing procedures, electronic signature requirements, and paper submission exceptions are immediately backed by their corresponding regulatory citations." + ] + }, + { + "cell_type": "markdown", + "id": "b9cfe43b", + "metadata": {}, + "source": [ + "### 3.7 Answer Verification\n", + "\n", + "Let's first look at the cited paragraphs:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4b5e9cd9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==== CITED PARAGRAPHS ====\n", + "\n", + "PARAGRAPH 1 (ID: 0.0.5.0):\n", + "----------------------------------------\n", + "104 Business to be Conducted in Writing\n", + "37 C.F.R. § 2.190(b) Electronic trademark documents. … Documents that r elate to proceedings before\n", + "the Trademark Trial and Appeal Board must be filed electronically with the Board through ESTTA. 37 C.F.R. § 2.191 Action of the Office based on the written record. All business with the Office must be\n", + "transacted in writing. The action of the Office will be based exclusively on the written record. No consideration\n", + "will be given to any alleged oral promise, stipulation, or understanding when there is disagreement or doubt. With the exceptions of discovery conferences with Board participation, see TBMP § 401.01, and telephone\n", + "conferences, see TBMP § 413.01 and TBMP § 502.06, all business with the Board should be transacted in\n", + "writing. 37 C.F.R. § 2.191 . The personal attendance of parties or their attorne ys or other authorized\n", + "representatives at the offices of the Board is unnecessary , except in the case of a pretrial conference as\n", + "provided in 37 C.F.R. § 2.120(j), or upon oral argument at final hearing, if a party so desires, as pro vided\n", + "in 37 C.F.R. § 2.129. Decisions of the Board will be based exclusively on the written record before it. [Note\n", + "1.] Documents filed in proceedings before the Board must be filed through ESTT A. 37 C.F.R. § 2.190(b). See TBMP § 110.01(a). Board proceedings are conducted in English. If a party intends to rely upon an y submissions that are in a\n", + "language other than English, the party should also file a translation of the submissions. If a translation is\n", + "not filed, the submissions may not be considered. [Note 2.] NOTES:\n", + "1. Cf.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 2 (ID: 0.0.5.4):\n", + "----------------------------------------\n", + "The document should\n", + "also include a title describing its nature, e.g., “Notice of Opposition,” “Answer,” “Motion to Compel,” “Brief\n", + "in Opposition to Respondent’s Motion for Summary Judgment,” or “Notice of Reliance.”\n", + "Documents filed in an application which is the subject of an inter partes proceeding before the Board should\n", + "be filed with the Board, not the Trademark Operation, and should bear at the top of the first page both the\n", + "application serial number, and the inter partes proceeding number and caption. Similarly , requests under\n", + "Trademark Act § 7, 15 U.S.C. § 1057, to amend, correct, or surrender a registration which is the subject of\n", + "a Board inter partes proceeding, and any new power of attorney, designation of domestic representative, or\n", + "change of address submitted in connection with such a registration, should be filed with the Board, not with\n", + "the Trademark Operation, and should bear at the top of its first page the re gistration number, and the inter\n", + "partes proceeding number and the proceeding caption. [Note 2.] 100-14June 2024\n", + "TRADEMARK TRIAL AND APPEAL BOARD MANUAL OF PROCEDURE§ 105\n", + "NOTES:\n", + "1. 37 C.F.R. § 2.194. 2. 37 C.F.R. § 2.194. 106.02 Signature of Submissions\n", + "37 C.F.R. § 2.119(e) Every submission filed in an inter partes proceeding, and every request for an extension\n", + "of time to file an opposition, must be signed by the party filing it, or by the party’s attorney or other authorized\n", + "representative, but an unsigned submission will not be r efused consideration if a signed copy is submitted\n", + "to the Office within the time limit set in the notification of this defect by the Office. 37 C.F.R. § 11.14(e) Appearance.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 3 (ID: 0.0.5.5.6.0):\n", + "----------------------------------------\n", + "The Office will accept an electronic signature that meets the\n", + "requirements of paragraph (c) of this section on correspondence filed on paper or through TEAS or ESTTA. (b) Copy of original signature. If a copy of an original signature is filed, the filer should retain the\n", + "original as evidence of authenticity. If a question of authenticity arises, the Office may require submission\n", + "of the original. (c) Requirements for electronic signature. A person signing a document electronically must:\n", + "(1) Personally enter any combination of letters, numbers, spaces and/or punctuation marks that the\n", + "signer has adopted as a signature, placed between two forward slash (“/”) symbols in the signature block\n", + "on the electronic submission; or\n", + "(2) Sign the verified statement using some other form of electronic signature specified by the Director. (d) Signatory must be identified. The first and last name, and the title or position, of the person who\n", + "signs a document in connection with a trademark application, registration, or proceeding before the\n", + "Trademark Trial and Appeal Board must be set forth immediately below or adjacent to the signature. (e) Proper person to sign. Documents filed in connection with a trademark application or registration\n", + "must be signed as specified in paragraphs (e)(1) through (9) of this section. (2) Responses, amendments to applications, requests for express abandonment, requests for\n", + "reconsideration of final actions, and requests to divide. Responses to Office actions, amendments to\n", + "applications, requests for express abandonment, requests for reconsideration of final actions, and requests\n", + "to divide must be signed by the owner of the application or registration, someone with legal authority to\n", + "bind the owner (e.g.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 4 (ID: 0.0.5.5.6.2):\n", + "----------------------------------------\n", + "* * * *\n", + "(i) Certified documents required by statute. When a statute requires that a document be certified, a\n", + "copy or facsimile transmission of the certification is not acceptable. Every document filed in an inter partes or e x parte proceeding before the Board, and e very request for an\n", + "extension of time to file an opposition, must be signed by the party filing it, or by the party’ s attorney or\n", + "other authorized representative, as appropriate, and the signatory must be identified. [Note 1.] Documents filed electronically, including through ESTTA, do not require a conventional signature. Electronic\n", + "signatures pursuant to 37 C.F.R. § 2.193(c) are required for electronic filings. The party or its representative\n", + "enters a “symbol” that has been adopted as a signature. The Board will accept any combination of letters,\n", + "numbers, space and/or punctuation marks as a valid signature if it is placed between two forward slash (“/”)\n", + "symbols. [Note 2.] The electronic signature entered on the ESTTA form is sufficient as the required signature\n", + "for the entire submission, including in the absence of a signature on any attachment to the filing form. [Note\n", + "3.] The electronic filing cover sheet in ESTTA must be signed by the party filing it, the party’s attorney or\n", + "other authorized representative, as appropriate. For further information regarding the filing of submissions\n", + "using ESTTA, see TBMP § 110. A party may act in its own behalf in a proceeding before the Board, if the party is domiciled in the United\n", + "States, or an attorney may represent the party. [Note 4.] See TBMP § 114 (Representation of a Party). When an individual who is a party to a Board proceeding elects to act in the indi vidual's own behalf, the\n", + "individual must sign any documents that are filed with the Board.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 5 (ID: 0.0.5.5.6.3):\n", + "----------------------------------------\n", + "If a party which is a partnership elects to\n", + "act in its own behalf, a partner should sign documents filed by the partnership. If a party which is a corporation\n", + "or association elects to act in its own behalf, an officer thereof who is authorized to sign for the corporation\n", + "or association should sign for that corporation or association. If joint applicants elect to act on their o wn\n", + "behalf, all joint applicants must sign any documents filed with the Board. [Note 5.] If a document is filed on behalf of a party by the party’s attorney or other authorized representative, it must\n", + "bear the signature of, and be personally signed or inserted by , that attorney or other representative, unless\n", + "June 2024100-17\n", + "§ 106.02GENERAL INFORMATION\n", + "it is a document required to be signed personally by the party. An attorney or other authorized representative\n", + "who signs a document, and then files it with the Board on behalf of a party , should remember that the\n", + "signature to the document constitutes a certification of the elements specified in 37 C.F.R. § 11.18(b), and\n", + "that a violation of the pro visions of that rule by may result in sanctions or disciplinary action. [Note 6.] SeeTBMP § 114.04 (regarding meaning of the designation “other authorized representati ve”) and TBMP\n", + "§ 527.02 (regarding motions for Fed. R. Civ. P. 11 sanctions). A person transmitting paper documents, when\n", + "permitted, for filing with the Board may sign a co ver letter or transmittal letter , and the Office does not\n", + "require the party, attorney, or authorized representative to sign a cover or transmittal letter. It is not appropriate for one person to sign a document for another person, as, for example, “John Smith, for\n", + "John Doe” or “John Doe, by John Smith.” [Note 7.]\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 6 (ID: 0.0.5.5.6.4):\n", + "----------------------------------------\n", + "A document filed in a proceeding before the Board should include the first and last name, in typed or printed\n", + "form, of the person who signed [Note 8]; a description of the capacity in which the person signed (e.g., as\n", + "the individual who is a party, if the filing party is an individual; as a corporate officer, if the filing party is\n", + "a corporation; or as the filing party’s attorney); and the business address and telephone number of the person. The inclusion of the signing person’s address and phone number on the submission itself is vital in the rare\n", + "case any paper or physical submissions permitted under the rules because mail physically sent to the Office\n", + "is opened in the Mail Room, and ordinarily the en velopes are discarded there before the mail is sent on to\n", + "its ultimate destination within the Office. Thus, the Board rarely sees the return addresses on the mailing\n", + "envelopes of papers filed in Board proceedings. In accordance with 37 C.F.R. § 2.193(b), a legible copy of the signed document is to be filed with the Board\n", + "because filings are required to be submitted using ESTT A. The original should be retained as e vidence of\n", + "authenticity. If a question as to the authenticity of a filed copy arises, the Office may require submission of\n", + "the original. [Note 9.] Notwithstanding the requirement that a document filed before the Board be signed, an unsigned document\n", + "filed in paper form, when permitted, will not be refused consideration if a signed cop y is submitted to the\n", + "Board within the time limit set in the notification of this defect by the Board. [Note 10.] Similarly , an\n", + "improperly signed document, whether filed in ESTT A or on paper , when permitted, will not be refused\n", + "consideration if a properly signed cop y is submitted to the Board within the time set in the notification of\n", + "this defect by the Board.\n", + "----------------------------------------\n", + "\n", + "PARAGRAPH 7 (ID: 0.0.5.5.7.3):\n", + "----------------------------------------\n", + "long, and contain no tabs or other such devices extending beyond the edges of the paper;\n", + "(3) If a paper submission contains dividers, the dividers must not have any extruding tabs or other\n", + "devices, and must be on the same size and weight paper as the submission;\n", + "(4) A paper submission must not be stapled or bound;\n", + "(5) All pages of a paper submission must be numbered and exhibits shall be identified in the manner\n", + "prescribed in § 2.123(g)(2);\n", + "June 2024100-19\n", + "§ 106.03GENERAL INFORMATION\n", + "(6) Exhibits pertaining to a paper submission must be filed on paper and comply with the requirements\n", + "for a paper submission. (c) To be handled as confidential, submissions to the Trademark Trial and Appeal Board that are\n", + "confidential in whole or part pursuant to § 2.125(f) must be submitted using the “Confidential” selection\n", + "available in ESTTA or, where appropriate, under a separate paper cover. Both the submission and its cover\n", + "must be marked confidential and must identify the case number and the parties. A copy of the submission\n", + "for public viewing with the confidential portions redacted must be submitted concurrently. The rules require that all submissions must be made to the Board electronically, currently through ESTTA,\n", + "subject to certain limited e xceptions permitting submissions to be made on paper . Any permitted paper\n", + "submission must be accompanied by a written e xplanation showing that ESTTA was unavailable due to\n", + "technical problems, or that extraordinary circumstances are present, and, where required, a Petition to the\n", + "Director with the requisite petition fee. [Note 1.]\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "cited_paragraphs = []\n", + "for paragraph in navigation_result[\"paragraphs\"]:\n", + " para_id = str(paragraph.get(\"display_id\", str(paragraph[\"id\"])))\n", + " if para_id in answer.citations:\n", + " cited_paragraphs.append(paragraph)\n", + " \n", + "\n", + "# Display the cited paragraphs for the audience\n", + "print(\"\\n==== CITED PARAGRAPHS ====\")\n", + "for i, paragraph in enumerate(cited_paragraphs):\n", + " display_id = paragraph.get(\"display_id\", str(paragraph[\"id\"]))\n", + " print(f\"\\nPARAGRAPH {i+1} (ID: {display_id}):\")\n", + " print(\"-\" * 40)\n", + " print(paragraph[\"text\"])\n", + " print(\"-\" * 40)" + ] + }, + { + "cell_type": "markdown", + "id": "b36a8431", + "metadata": {}, + "source": [ + "The \"List of Literals\" trick forces the model to cite only specific paragraph IDs (like \"0.0.5.4\") rather than making up its own references or highlighting random text — imagine it as creating a digital \"table of contents\" that GPT-4.1 can only select from. This solution ensures you get verifiable citation trails back to exact source material, solving an important problem in long-context RAG." + ] + }, + { + "cell_type": "markdown", + "id": "d7b1eb2d", + "metadata": {}, + "source": [ + "Finally, let's verify the answer with an LLM-as-judge approach." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a765a9ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==== VERIFYING ANSWER ====\n", + "\n", + "Accuracy verification: PASSED\n", + "Confidence: high\n", + "Explanation: The answer correctly states that motions to compel discovery must be filed electronically through ESTTA, with paper submissions permitted only under the limited exceptions of technical failure or extraordinary circumstances (37 C.F.R. § 2.190(b) and 2.193(b)). It accurately describes the required title and caption placement (TBMP § 105), and it appropriately summarizes the signature requirements for electronic filings (37 C.F.R. § 2.193(c) and TBMP §§ 106.02, 106.02(b)–(e)), including the use of slash‐enclosed electronic signatures and identification of the signatory’s name and title. It also correctly notes the rule regarding defective signatures (37 C.F.R. § 2.119(e) and TBMP § 106.02). The citations align with the source paragraphs. \n", + "\n", + "==== FINAL VERIFIED ANSWER ====\n", + "Verification: PASSED | Confidence: high\n", + "\n", + "Answer:\n", + "A motion to compel discovery must be filed electronically with the Trademark Trial and Appeal Board (TTAB) through ESTTA, unless ESTTA is unavailable due to technical problems or there are extraordinary circumstances, in which case a paper submission may be permitted with a written explanation (\"Documents that relate to proceedings before the Trademark Trial and Appeal Board must be filed electronically with the Board through ESTTA\"; \"The rules require that all submissions must be made to the Board electronically, currently through ESTTA, subject to certain limited exceptions permitting submissions to be made on paper. Any permitted paper submission must be accompanied by a written explanation showing that ESTTA was unavailable due to technical problems, or that extraordinary circumstances are present, and, where required, a Petition to the Director with the requisite petition fee\" 0.0.5.0, 0.0.5.5.7.3).\n", + "\n", + "The motion should include a title describing its nature, such as “Motion to Compel,” and should bear the appropriate proceeding number and caption at the top of the first page (\"The document should also include a title describing its nature, e.g., 'Motion to Compel'... should bear at the top of the first page both the application serial number, and the inter partes proceeding number and caption\" 0.0.5.4).\n", + "\n", + "Every submission, including a motion to compel discovery, must be signed by the party filing it, or by the party’s attorney or other authorized representative. For electronic filings through ESTTA, a conventional handwritten signature is not required; instead, an electronic signature is used. The signatory must personally enter a combination of letters, numbers, spaces, and/or punctuation marks between two forward slash ('/') symbols (e.g., /John Smith/), and the signatory's name and title or position must appear immediately below or adjacent to the signature (\"Documents filed electronically, including through ESTTA, do not require a conventional signature. Electronic signatures pursuant to 37 C.F.R. § 2.193(c) are required for electronic filings. The party or its representative enters a 'symbol' that has been adopted as a signature. The Board will accept any combination of letters, numbers, space and/or punctuation marks as a valid signature if it is placed between two forward slash ('/') symbols\"; \"The first and last name, and the title or position, of the person who signs a document in connection with a trademark application, registration, or proceeding before the Trademark Trial and Appeal Board must be set forth immediately below or adjacent to the signature\" 0.0.5.5.6.2, 0.0.5.5.6.0).\n", + "\n", + "If a document is filed on behalf of a party by the party’s attorney or other authorized representative, it must bear the signature of that attorney or representative, unless the document is one required to be signed personally by the party (0.0.5.5.6.3). If an unsigned or improperly signed document is filed, it will not be refused consideration if a properly signed copy is submitted within the time limit set in the notification of the defect by the Board (0.0.5.5.6.4).\n", + "\n", + "In summary: File the motion to compel discovery electronically via ESTTA, use an electronic signature as described above, and ensure the signatory's name and title are included. If filing on paper is necessary, follow the specific requirements for paper submissions and signatures.\n", + "\n", + "Citations:\n", + "- 0.0.5.0\n", + "- 0.0.5.4\n", + "- 0.0.5.5.6.0\n", + "- 0.0.5.5.6.2\n", + "- 0.0.5.5.6.3\n", + "- 0.0.5.5.6.4\n", + "- 0.0.5.5.7.3\n" + ] + } + ], + "source": [ + "from typing import List, Dict, Any, Literal\n", + "from pydantic import BaseModel\n", + "\n", + "class VerificationResult(BaseModel):\n", + " \"\"\"Verification result format\"\"\"\n", + " is_accurate: bool\n", + " explanation: str\n", + " confidence: Literal[\"high\", \"medium\", \"low\"]\n", + "\n", + "def verify_answer(question: str, answer: LegalAnswer, \n", + " cited_paragraphs: List[Dict[str, Any]]) -> VerificationResult:\n", + " \"\"\"\n", + " Verify if the answer is grounded in the cited paragraphs.\n", + " \n", + " Args:\n", + " question: The user's question\n", + " answer: The generated answer\n", + " cited_paragraphs: Paragraphs cited in the answer\n", + " \n", + " Returns:\n", + " Verification result with accuracy assessment, explanation, and confidence level\n", + " \"\"\"\n", + " print(\"\\n==== VERIFYING ANSWER ====\")\n", + " \n", + " # Prepare context with the cited paragraphs\n", + " context = \"\"\n", + " for paragraph in cited_paragraphs:\n", + " display_id = paragraph.get(\"display_id\", str(paragraph[\"id\"]))\n", + " context += f\"PARAGRAPH {display_id}:\\n{paragraph['text']}\\n\\n\"\n", + " \n", + " # Prepare system prompt\n", + " system_prompt = \"\"\"You are a fact-checker for legal information.\n", + "Your job is to verify if the provided answer:\n", + "1. Is factually accurate according to the source paragraphs\n", + "2. Uses citations correctly\n", + "\n", + "Be critical and look for any factual errors or unsupported claims.\n", + "Assign a confidence level based on how directly the paragraphs answer the question:\n", + "- high: The answer is comprehensive, accurate, and directly supported by the paragraphs\n", + "- medium: The answer is mostly accurate but may be incomplete or have minor issues\n", + "- low: The answer has significant gaps, inaccuracies, or is poorly supported by the paragraphs\n", + "\"\"\"\n", + " \n", + " response = client.responses.parse(\n", + " model=\"o4-mini\",\n", + " input=[\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": f\"\"\"\n", + "QUESTION: {question}\n", + "\n", + "ANSWER TO VERIFY:\n", + "{answer.answer}\n", + "\n", + "CITATIONS USED: {', '.join(answer.citations)}\n", + "\n", + "SOURCE PARAGRAPHS:\n", + "{context}\n", + "\n", + "Is this answer accurate and properly supported by the source paragraphs?\n", + "Assign a confidence level (high, medium, or low) based on completeness and accuracy.\n", + " \"\"\"}\n", + " ],\n", + " text_format=VerificationResult\n", + " )\n", + " \n", + " # Log and return the verification result\n", + " print(f\"\\nAccuracy verification: {'PASSED' if response.output_parsed.is_accurate else 'FAILED'}\")\n", + " print(f\"Confidence: {response.output_parsed.confidence}\")\n", + " print(f\"Explanation: {response.output_parsed.explanation}\")\n", + " \n", + " return response.output_parsed\n", + "\n", + "# Verify the answer using only the cited paragraphs\n", + "verification = verify_answer(question, answer, cited_paragraphs)\n", + "\n", + "# Display final result with verification\n", + "print(\"\\n==== FINAL VERIFIED ANSWER ====\")\n", + "print(f\"Verification: {'PASSED' if verification.is_accurate else 'FAILED'} | Confidence: {verification.confidence}\")\n", + "print(\"\\nAnswer:\")\n", + "print(answer.answer)\n", + "print(\"\\nCitations:\")\n", + "for citation in answer.citations:\n", + " print(f\"- {citation}\")" + ] + }, + { + "cell_type": "markdown", + "id": "1004942a", + "metadata": {}, + "source": [ + "The verification step produces a clean, structured assessment that references specific regulations and methodically checks both the answer's accuracy and its proper use of citations. Rather than just saying \"correct,\" it offers useful context by explaining exactly why the answer was correct, giving you the confidence to then present the answer to the user with specific citations" + ] + }, + { + "cell_type": "markdown", + "id": "29bc9113", + "metadata": {}, + "source": [ + "## 4. Infrastructure Costs\n", + "\n", + "Let's break down the cost structure for this agentic RAG approach:\n", + "\n", + "### Estimated Fixed vs. Variable Costs\n", + "\n", + "* **Estimated Fixed (One-time) Costs:** \n", + " * **Traditional RAG:** ~$0.43 (embedding + metadata generation)\n", + " * **Agentic RAG:** $0.00 (zero preprocessing required)\n", + "\n", + "\n", + "* **Estimated Variable (Per-Query) Costs:** \n", + " * **Router Model (`gpt-4.1-mini`):** \n", + " * Initial routing (20 chunks): ~$0.10 \n", + " * Two recursive levels: ~$0.20\n", + " * **Synthesis (`gpt-4.1`):** ~$0.05\n", + " * **Verification (`o4-mini`):** ~$0.01\n", + " * **Total per query:** ~$0.36\n", + "\n", + "While the per-query cost is higher than traditional RAG, this approach offers:\n", + "- Immediate results on new documents\n", + "- More precise citations\n", + "- Better handling of paraphrases and conceptual questions\n", + "- No infrastructure maintenance overhead\n", + "\n", + "The cost can be optimized through:\n", + "- Caching results for common queries\n", + "- Limiting max tokens in the model calls\n", + "- Using a hybrid approach that pre-filters the document first\n", + "\n", + "## 5. Benefits and Tradeoffs versus Traditional RAG\n", + "\n", + "### Benefits\n", + "- **Zero-ingest latency**: Answer questions from new documents immediately, with no preprocessing.\n", + "- **Dynamic navigation**: Mimics human reading patterns by focusing on promising sections.\n", + "- **Cross-section reasoning**: Model can find connections across document sections that might be missed by independent chunk retrieval, potentially increasing accuracy of generated answers and saving time on optimizing retrieval pipelines.\n", + "\n", + "### Tradeoffs\n", + "- **Higher per-query cost**: Requires more computation for each question compared to embedding-based retrieval.\n", + "- **Increased latency**: Hierarchical navigation takes longer to process than simple vector lookups.\n", + "- **Limited scalability**: May struggle with extremely large document collections where preprocessing becomes more efficient.\n", + "\n", + "## 6. Future Steps\n", + "\n", + "There are a few modifications we can make to the approach taken:\n", + "- **Generating a Knowledge Graph**: We can use the large context window of GPT 4.1-mini to iteratively generate a detailed knowledge graph, and then GPT 4.1 can traverse this graph to answer questions. This way we only need to \"ingest\" the document once, regardless of the question.\n", + "- **Improved Scratchpad Tool**: The scratchpad tool could be given more choices such as editing or deleting past memory. This would allow the model to choose whatever is most relevant to the question at hand\n", + "- **Adjust Depth**: We can adjust the depth of the hierarchical navigation to find the right balance between cost and performance. Certain usecases will require sentence level citations (like legal documents), while others may only require paragraph level citations (like news articles). \n", + "\n", + "## 7. Takeaways\n", + "\n", + "1. **Context Window is a Superpower:** Million-token context windows make it possible to navigate documents on-the-fly.\n", + "2. **Hierarchical Approach Mimics Human Reading:** Agentic routing works like a human skimming a document for relevant sections.\n", + "3. **Scratchpad Enables Multi-Step Reasoning:** Maintaining a reasoning record improves navigation quality.\n", + "4. **Fast Implementation, No Database:** The entire system can be built with just API calls, no infrastructure needed.\n", + "5. **Verification Improves Reliability:** The LLM-as-judge pattern catches errors before they reach users.\n", + "\n", + "================================================================================\n", + "\n", + "## 3B. Use Case: AI Co-Scientist for Pharma R&D\n", + "![AI Co-Scientist for Pharma R&D](../../../images/3B_reasoning_task_card.png)\n", + "\n", + "This section details how to build an AI system that functions as a \"co-scientist\" to accelerate experimental design in pharmaceutical R&D, focusing on optimizing a drug synthesis process under specific constraints.\n", + "\n", + "## 🗂️ TL;DR Matrix\n", + "\n", + "This table summarizes the core technology choices and their rationale for this specific AI Co-Scientist implementation.\n", + "\n", + "| Layer | Choice | Utility |\n", + "| :----------------- | :------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------- |\n", + "| **Ideation** | `o4-mini` (Parallel Role-Playing Agents) | Generates diverse hypotheses & protocols rapidly and cost-effectively; role-playing enhances creativity. |\n", + "| **Grounding** | External Tool Calls (`chem_lookup`, `cost_estimator`, `outcome_db`, etc.) | Ensures plans are based on real-world data (chemical properties, costs, past results). |\n", + "| **Ranking** | `o4-mini` (Pairwise Tournament Comparison) | Nuanced evaluation beyond simple scoring; selects promising candidates efficiently. |\n", + "| **Critique/Synth** | `o3` (Deep Review & Synthesis) | Provides rigorous, senior-level analysis, identifies risks, and ensures scientific validity. |\n", + "| **Safety (Opt.)** | `gpt-4.1-mini` (Targeted Check) | Adds an extra layer of specialized safety review before human handoff. |\n", + "| **Learning** | `o3` + Code Interpreter (Result Analysis → DB) | Captures experimental outcomes systematically, enabling continuous improvement over time. |\n", + "| **Core Technique** | Multi-Agent Collaboration & Escalation | Leverages strengths of different models (speed vs. depth) for a complex, multi-step reasoning task. |\n", + "\n", + "*Note: Model identifiers accurate as of April 2025, subject to change.*\n", + "\n", + "## 1. Scenario Snapshot\n", + "\n", + "* **Problem Space:** Optimizing complex experimental procedures in pharmaceutical R&D, such as improving the synthesis yield of a new drug compound (\"XYZ-13\") while adhering to strict constraints.\n", + "* **Users:** Research scientists and lab technicians involved in drug discovery and development.\n", + "* **Typical Asks:**\n", + " 1. Suggest 3 distinct protocols to increase XYZ-13 yield by ≥15% by testing different catalysts, staying under $15k using approved reagents.\n", + " 2. Propose protocols to optimize XYZ-13 yield below 60°C (due to past heat issues), exploring different approved solvents within budget.\n", + " 3. Design two XYZ-13 yield strategies (aiming for ≥15%): a. one maximizing potential yield within the \\$15k budget, b. one prioritizing cost under \\$10k.\n", + "* **Constraints:**\n", + " * **Budgetary:** Operate within defined financial limits (e.g., $15,000 per experiment series).\n", + " * **Regulatory/Safety:** Use only pre-approved chemicals/reagents and adhere rigorously to safety protocols.\n", + " * **Human Oversight:** Final experimental plans must be reviewed and validated by a human expert before execution.\n", + "\n", + "> Traditionally, optimizing such experiments involves weeks of manual planning, literature review, iterative benchwork, and analysis. This AI Co-Scientist approach aims to dramatically reduce the cycle time by automating hypothesis generation, protocol design, and preliminary evaluation, enabling scientists to focus on higher-level strategy and final validation. It shifts the scientist's role from manual execution of planning steps to expert oversight and collaboration with the AI.\n", + "\n", + "\n", + "## 2. Architecture (Multi-Agent Reasoning)\n", + "\n", + "The system employs a multi-agent architecture that emulates a high-performing scientific team. Different AI components, acting in specialized roles (such as ideation, critique, and learning from outcomes), collaborate using various models and tools to execute the workflow.\n", + "\n", + "![AI Co-Scientist Architecture](../../../images/3B_coscientist_architecture.png)\n", + "\n", + "### 2.1. **Scientist Input & Constraints:** \n", + "The process starts with the scientist defining the goal, target compound, and constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "abbeddb3", + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "from agent_utils import Context, call_openai, log_json\n", + "\n", + "# Example Initial Input\n", + "user_input = {\n", + " \"compound\": \"XYZ-13\",\n", + " \"goal\": \"Improve synthesis yield by 15%\",\n", + " \"budget\": 15000,\n", + " \"time_h\": 48,\n", + " \"previous\": \"Prior attempts failed at high temp; explore potential catalyst effects.\"\n", + "}\n", + "ctx = Context(client=OpenAI(), **user_input)" + ] + }, + { + "cell_type": "markdown", + "id": "e791f29f", + "metadata": {}, + "source": [ + "### 2.2. **Ideation (`o4-mini` + Tools):** \n", + "Multiple `o4-mini` instances, prompted with different roles (e.g., `Hypothesis Agent`, `Protocol Agent`, `Resource Agent`), generate experimental plans in parallel. Assigning distinct personas encourages diverse perspectives and covers different aspects of the problem simultaneously during the ideation phase." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3f06fe8c", + "metadata": {}, + "outputs": [], + "source": [ + "ROLE_FOCUS = {\n", + " # Hypothesis Agent Prompt\n", + " \"hypothesis_agent\": \"\"\"You are a pharmaceutical hypothesis specialist. \n", + " Focus exclusively on analyzing the compound structure and research goals to generate testable hypotheses. \n", + " Consider mechanism of action, binding affinity predictions, and potential off-target effects.\"\"\",\n", + "\n", + " # Protocol Agent Prompt\n", + " \"protocol_agent\" : \"\"\"You are a laboratory protocol specialist. \n", + " Design experimental procedures that will effectively test the provided hypothesis. \n", + " Focus on experimental conditions, controls, and measurement techniques.\"\"\",\n", + "\n", + " # Resource Agent Prompt\n", + " \"resource_agent\" : \"\"\"You are a laboratory resource optimization specialist. \n", + " Review the proposed protocol and optimize for efficiency. \n", + " Identify opportunities to reduce reagent use, equipment time, and overall costs while maintaining scientific validity.\"\"\",\n", + "}\n", + "\n", + "# Create a structured prompt template for ideation\n", + "IDEATION_PROMPT = \"\"\"You are a pharmaceutical {role} specialist. Your goal is to {goal} for compound {compound}.\n", + "Constraints:\n", + "- Budget: ${budget}\n", + "- Approved reagents only\n", + "- Complete within {time_h} hours\n", + "- Previous attempts: {previous}\n", + "Respond with structured JSON describing your protocol.\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fcf9f5ef", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Run‑id 9835f69c Compound: XYZ-13\n", + "Logs will be stored in: logs/9835f69c\n" + ] + } + ], + "source": [ + "import json, logging\n", + "from pathlib import Path\n", + "from typing import Dict, List, Any, Optional\n", + "from dataclasses import asdict\n", + "from functools import partial\n", + "\n", + "MODEL_IDEATE = \"o4-mini-2025-04-16\" # o4-mini model for ideation - balances speed and quality\n", + "\n", + "# Configure logging to help with tracking experiment progress and debugging\n", + "logging.basicConfig(level=logging.INFO, format=\"%(message)s\")\n", + "logging.info(f\"Run‑id {ctx.run_id} Compound: {ctx.compound}\")\n", + "logging.info(f\"Logs will be stored in: {Path('logs') / ctx.run_id}\")\n", + "\n", + "def ideation(ctx: Context):\n", + " logging.info(\"Starting ideation phase...\")\n", + " ideas = []\n", + " for role, focus in ROLE_FOCUS.items():\n", + " logging.info(f\"Running ideation agent ${role}\")\n", + " sys = IDEATION_PROMPT.format(role=role, focus=focus, **ctx.prompt_vars())\n", + " usr = f\"Design a protocol to {ctx.goal} within ${ctx.budget}.\"\n", + " idea = call_openai(ctx.client, MODEL_IDEATE, sys, usr, ctx)\n", + " ideas.append(idea)\n", + " log_json(\"ideation_done\", ideas, ctx)\n", + " return ideas" + ] + }, + { + "cell_type": "markdown", + "id": "0384e0d5", + "metadata": {}, + "source": [ + "The ideation agents can utilize external tools such as `literature_search`, `chem_lookup` (chemical database), `cost_estimator`, `outcome_db` (outcome of previous experiments) to ground their suggestions in data. Explicitly enabling and prompting models to use external tools ensures that generated plans are feasible, compliant, and informed by existing knowledge. The model decides when and which tool to call based on the task." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a8f365d8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting ideation phase...\n", + "Running ideation agent $hypothesis_agent\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) List available chemicals\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Outcome DB: XYZ-13, yield, 5\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Cost estimator: [{'name': 'Palladium chloride', 'amount': 0.05, 'unit': 'g'}, {'name': 'Triphenylphosphine', 'amount': 0.1, 'unit': 'g'}, {'name': 'Potassium carbonate', 'amount': 1, 'unit': 'g'}, {'name': 'Dimethylformamide', 'amount': 50, 'unit': 'mL'}, {'name': 'Toluene', 'amount': 50, 'unit': 'mL'}, {'name': 'Sodium borohydride', 'amount': 0.1, 'unit': 'g'}, {'name': 'Triethylamine', 'amount': 0.5, 'unit': 'mL'}], ['round-bottom flask', 'magnetic stirrer', 'reflux condenser'], 36\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Running ideation agent $protocol_agent\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Outcome DB: XYZ-13, yield, 5\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) List available chemicals\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Literature search: XYZ-13 synthesis palladium triphenylphosphine ligand yield improvement, None, 3\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Cost estimator: [{'name': 'Palladium acetate', 'amount': 0.05, 'unit': 'g'}, {'name': 'Triphenylphosphine', 'amount': 0.1, 'unit': 'g'}, {'name': 'Potassium carbonate', 'amount': 2, 'unit': 'g'}, {'name': 'Triethylamine', 'amount': 2, 'unit': 'mL'}, {'name': 'Dimethylformamide', 'amount': 100, 'unit': 'mL'}], ['Magnetic stirrer', 'Oil bath', 'Inert gas setup'], 48\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Running ideation agent $resource_agent\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Outcome DB: XYZ-13, yield, 5\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) List available chemicals\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Cost estimator: [{'name': 'Palladium acetate', 'amount': 0.05, 'unit': 'g'}, {'name': 'Triphenylphosphine', 'amount': 0.1, 'unit': 'g'}, {'name': 'Potassium carbonate', 'amount': 1, 'unit': 'g'}, {'name': 'Dimethylformamide', 'amount': 5, 'unit': 'mL'}, {'name': 'Triethylamine', 'amount': 2, 'unit': 'mL'}], ['Round-bottom flask', 'Reflux condenser', 'Heating mantle', 'Magnetic stirrer'], 36\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Chemical lookup: Sodium borohydride, None\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Ideation complete!\n" + ] + } + ], + "source": [ + "IDEATION_PROMPT += \"\"\"\\nUse the following tools as appropriate:\n", + "- Use the `list_available_chemicals` tool to get list of approved reagents.\n", + "- Use the `chem_lookup` tool to verify properties of reagents mentioned.\n", + "- Use the `cost_estimator` tool to calculate the approximate cost based on reagents and proposed steps.\n", + "- Check the `outcome_db` for relevant prior experiments with {compound}\"\"\"\n", + "\n", + "ideas = ideation(ctx)\n", + "logging.info(\"Ideation complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "6f507348", + "metadata": {}, + "source": [ + "These tools are defined in `agent_utils.py`. For purposes of this solution, the tool calls are mocked in `tools.py`. In a real use case, these tools would call real APIs.\n", + "\n", + "\n", + "### 2.3. **Tournament Ranking (`o4-mini` / `o3`):** \n", + "Generated protocols are compared pairwise based on criteria like expected effectiveness, feasibility, cost, and novelty. Instead of asking a model to score protocols in isolation, providing two protocols at a time and asking for a direct comparison against specific criteria often yields more reliable relative rankings.\n", + "\n", + "This Elo-style ranking identifies the most promising candidates for deeper review." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f85fe4b7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting tournament phase...\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Tournament winner picked!\n" + ] + } + ], + "source": [ + "TOURNAMENT_PROMPT = \"\"\"\n", + "Protocol A: [details...]\n", + "Protocol B: [details...]\n", + "\n", + "Compare Protocol A and Protocol B for synthesizing {compound} aimed at {goal}. Score them on:\n", + "1. Likelihood of achieving ≥ 15% yield increase.\n", + "2. Practical feasibility (reagents, time).\n", + "3. Estimated cost-efficiency (use tool if needed).\n", + "4. Scientific novelty/risk.\n", + "\n", + "Return JSON {{\\\"winner\\\": \\\"A\\\"|\\\"B\\\", \\\"justification\\\": \\\"...\\\"}}.\"\"\"\n", + "\n", + "# This is a mock tourname implementation that only compares the first two protocols\n", + "# A real implementation would compare pairs in a tournament bracket style\n", + "def tournament(protocols: List[Dict[str, Any]], ctx: Context):\n", + " logging.info(\"Starting tournament phase...\")\n", + " if len(protocols) == 1:\n", + " return protocols[:1]\n", + " a, b = protocols[0], protocols[1]\n", + " sys = TOURNAMENT_PROMPT.format(**ctx.prompt_vars())\n", + " usr = json.dumps({\"A\": a, \"B\": b}, indent=2)\n", + " res = call_openai(ctx.client, MODEL_IDEATE, sys, usr, ctx)\n", + " winner = a if res.get(\"winner\", \"A\").upper() == \"A\" else b\n", + " log_json(\"tournament\", res, ctx)\n", + " return [winner]\n", + "\n", + "top_proto = tournament(ideas, ctx)[0]\n", + "logging.info(\"Tournament winner picked!\")" + ] + }, + { + "cell_type": "markdown", + "id": "41ad4731", + "metadata": {}, + "source": [ + "> In early experiments, we found that asking models to score protocols on a 1-10 scale led to inconsistent results with score compression. The tournament approach solved this by forcing relative judgments that proved more reliable. This mirrors human expert behavior — scientists often find it easier to compare two options directly than to assign absolute scores.\n", + "\n", + "### 2.4. **Deep Critique & Synthesis (`o3`):** \n", + "The top-ranked protocols are passed to `o3` for rigorous review. `o3` acts like a senior scientist, assessing scientific validity, methodology, safety, budget compliance, and suggesting improvements or synthesizing a final, refined protocol. It may also call tools for verification." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "634ef4e2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting critique phase...\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Cost estimator: [{'name': 'Palladium chloride', 'amount': 0.0045, 'unit': 'g'}, {'name': 'Triphenylphosphine', 'amount': 0.013, 'unit': 'g'}, {'name': 'Sodium borohydride', 'amount': 0.0038, 'unit': 'g'}, {'name': 'Potassium carbonate', 'amount': 0.14, 'unit': 'g'}, {'name': 'Triethylamine', 'amount': 0.07, 'unit': 'mL'}, {'name': 'Dimethylformamide', 'amount': 2, 'unit': 'mL'}, {'name': 'Toluene', 'amount': 5, 'unit': 'mL'}], ['100 mL round-bottom flask', 'magnetic stirrer', 'reflux condenser', 'inert gas line'], 24\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Outcome DB: XYZ-13, None, 5\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Deep critique completed!\n" + ] + } + ], + "source": [ + "# Deep critique phase using a more powerful model for rigorous review\n", + "CRITIQUE_PROMPT = \"\"\"You are a senior researcher reviewing a proposed synthesis protocol \n", + "for {compound} aiming for {goal}, budget ${budget} using approved reagents. Review the protocol below rigorously:\n", + "1. Identify scientific flaws or methodological weaknesses.\n", + "2. Assess safety risks and budget compliance (use `cost_estimator` tool if needed).\n", + "3. Check for consistency with prior `outcome_db` results if relevant.\n", + "4. Suggest concrete improvements or rewrite sections if necessary.\n", + "5. Provide a final go/no-go recommendation.\n", + "\n", + "Return JSON {{\\\"revised_protocol\\\": ..., \\\"critique\\\": \\\"...\\\", \\\"recommendation\\\": \\\"go|no-go\\\"}}.\n", + "\n", + "Protocol to Review:\n", + "[Protocol details...]\n", + "\"\"\"\n", + "\n", + "MODEL_CRITIQUE = \"o3-2025-04-16\" # o3 model for deep critique\n", + "\n", + "def critique(protocol: Dict[str, Any], ctx: Context):\n", + " logging.info(\"Starting critique phase...\")\n", + " sys = CRITIQUE_PROMPT.format(**ctx.prompt_vars())\n", + " usr = json.dumps(protocol, indent=2)\n", + " crit = call_openai(ctx.client, MODEL_CRITIQUE, sys, usr, ctx)\n", + " log_json(\"critique\", crit, ctx)\n", + " return crit.get(\"revised_protocol\", protocol)\n", + "\n", + "critiqued = critique(top_proto, ctx)\n", + "logging.info(\"Deep critique completed!\")" + ] + }, + { + "cell_type": "markdown", + "id": "1fbd87a7", + "metadata": {}, + "source": [ + "> We deliberately separate ideation from critique using different models and personas. Having the same model both generate and critique its own work often leads to self-justification rather than objective assessment. The o3 model, acting as a \"senior scientist,\" consistently identified methodological weaknesses that o4-mini missed during ideation.\n", + "\n", + "### 2.5. **(Optional) Safety Check:** \n", + "A specialized model, such as `gpt-4.1-mini`, can perform a final check for specific safety concerns (e.g., hazardous reagent combos)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "cc4405e4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting safety assessment...\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Chemical lookup: Palladium chloride, None\n", + "(Tool) Chemical lookup: Triphenylphosphine, None\n", + "(Tool) Chemical lookup: Sodium borohydride, None\n", + "(Tool) Chemical lookup: Potassium carbonate, None\n", + "(Tool) Chemical lookup: Dimethylformamide, None\n", + "(Tool) Chemical lookup: Toluene, None\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Safety check completed!\n" + ] + } + ], + "source": [ + "# Optional safety check using a targeted model\n", + "SAFETY_PROMPT = \"\"\"You are a lab‑safety specialist. \n", + "Identify hazards, unsafe conditions, or compliance issues in this protocol for {compound}. \n", + "Use `chem_lookup` tool if needed. Return JSON assessment.\"\"\"\n", + "\n", + "MODEL_SAFETY = \"gpt-4.1-mini-2025-04-14\" # gpt-4.1-mini model for safety checks - optimized for instruction following\n", + "\n", + "def safety(protocol: Dict[str, Any], ctx: Context):\n", + " logging.info(\"Starting safety assessment...\")\n", + " sys = SAFETY_PROMPT.format(**ctx.prompt_vars())\n", + " usr = json.dumps(protocol, indent=2)\n", + " assessment = call_openai(ctx.client, MODEL_SAFETY, sys, usr, ctx)\n", + " log_json(\"safety\", assessment, ctx)\n", + " return {\"protocol\": protocol, \"safety\": assessment}\n", + "\n", + "secured = safety(critiqued, ctx)\n", + "logging.info(\"Safety check completed!\")" + ] + }, + { + "cell_type": "markdown", + "id": "9dd93396", + "metadata": {}, + "source": [ + "### 2.6. **Human Review:** \n", + "The AI-generated final plan is presented to the human scientist via an interface for validation, potential edits, and final approval." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e2d47339", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Awaiting human review...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== PROTOCOL FOR REVIEW: XYZ-13 - Improve synthesis yield by 15% ===\n", + "DETAILS: {\n", + " \"protocol_title\": \"Optimised In-Situ Pd(0)/PPh3 Coupling for XYZ-13 \\u2013 Target \\u2265 72 % Yield\",\n", + " \"key_changes_vs_original\": [\n", + " \"Catalyst loading reduced from 5 mol % to 2 mol % Pd to cut cost and metal contamination without loss of activity.\",\n", + " \"Reaction run at 0.10 M substrate concentration (12 mL solvent total) instead of 50 mL; higher effective collision frequency boosts conversion and reduces waste.\",\n", + " \"Single solvent system (toluene/DMF 4:1) avoids phase separation and simplifies work-up.\",\n", + " \"Redundant triethylamine removed; K2CO3 (2.5 eq) provides sufficient basicity.\",\n", + " \"Reaction temperature raised slightly to 80 \\u00b0C (still below side-reaction threshold found in exp-001) and time shortened to 24 h with in-process HPLC check at 6 h intervals.\",\n", + " \"Work-up switched from large silica column to two-step: (a) aqueous EDTA wash to strip Pd, (b) recrystallisation from EtOAc/hexane \\u2013 typically 5\\u20138 % higher isolated yield on this substrate.\"\n", + " ],\n", + " \"objective\": \"Isolated yield \\u2265 72 % within 24 h, total direct cost \\u2264 US $5 000.\",\n", + " \"scale\": \"0.5 mmol XYZ-13 (170 mg, assume MW \\u2248 340).\",\n", + " \"reagents\": [\n", + " {\n", + " \"name\": \"Palladium chloride\",\n", + " \"amount\": 0.02,\n", + " \"unit\": \"g\",\n", + " \"role\": \"precatalyst (2 mol %)\"\n", + " },\n", + " {\n", + " \"name\": \"Triphenylphosphine\",\n", + " \"amount\": 0.041,\n", + " \"unit\": \"g\",\n", + " \"role\": \"ligand (2 eq vs Pd)\"\n", + " },\n", + " {\n", + " \"name\": \"Sodium borohydride\",\n", + " \"amount\": 0.02,\n", + " \"unit\": \"g\",\n", + " \"role\": \"Pd(II)\\u2192Pd(0) reducer\"\n", + " },\n", + " {\n", + " \"name\": \"Potassium carbonate\",\n", + " \"amount\": 0.345,\n", + " \"unit\": \"g\",\n", + " \"role\": \"base (2.5 eq)\"\n", + " },\n", + " {\n", + " \"name\": \"Dimethylformamide\",\n", + " \"amount\": 2.0,\n", + " \"unit\": \"mL\",\n", + " \"role\": \"co-solvent (20 %)\"\n", + " },\n", + " {\n", + " \"name\": \"Toluene\",\n", + " \"amount\": 10.0,\n", + " \"unit\": \"mL\",\n", + " \"role\": \"primary solvent (80 %)\"\n", + " }\n", + " ],\n", + " \"equipment\": [\n", + " \"50 mL round-bottom flask\",\n", + " \"magnetic stirrer\",\n", + " \"reflux condenser\",\n", + " \"argon line\"\n", + " ],\n", + " \"reaction_conditions\": {\n", + " \"atmosphere\": \"Ar\",\n", + " \"temperature\": \"80 \\u00b0C (oil bath)\",\n", + " \"duration\": \"24 h\",\n", + " \"stirring\": \"600 rpm\"\n", + " },\n", + " \"procedure\": [\n", + " \"1. Charge dry 50 mL flask with PdCl2 (20 mg) and PPh3 (41 mg) under Ar. Add DMF (2 mL) and stir 5 min.\",\n", + " \"2. Add NaBH4 (20 mg) portion-wise over 3 min; colour turns dark brown.\",\n", + " \"3. Add XYZ-13 (170 mg, 0.50 mmol) and K2CO3 (345 mg). Add toluene (10 mL). Fit condenser.\",\n", + " \"4. Heat to 80 \\u00b0C for 24 h. Take 0.1 mL aliquots at 6, 12, 18 h; quench in NH4Cl and analyse by HPLC to confirm \\u2265 95 % conversion.\",\n", + " \"5. Cool to RT, add 10 mL 0.05 M EDTA (aq) and stir 5 min to complex Pd. Separate layers, extract aqueous twice with 5 mL toluene.\",\n", + " \"6. Combine organic layers, wash with brine, dry (Na2SO4), filter, concentrate in vacuo.\",\n", + " \"7. Recrystallise residue from 4:1 hexane/EtOAc (15 mL) to afford XYZ-13 as off-white solid. Record mass, calculate yield, check purity by HPLC.\"\n", + " ],\n", + " \"expected_outcome\": {\n", + " \"projected_yield\": \"72\\u201378 %\",\n", + " \"purity\": \"\\u2265 97 % (HPLC)\"\n", + " },\n", + " \"safety_and_waste\": [\n", + " \"NaBH4 generates H2; add slowly behind blast shield.\",\n", + " \"DMF and toluene are toxic/flammable \\u2013 use fume hood.\",\n", + " \"EDTA washwater and Pd residues collected for heavy-metal disposal.\",\n", + " \"Standard PPE (lab coat, gloves, goggles).\"\n", + " ],\n", + " \"cost_estimate_USD\": {\n", + " \"reagents\": 1120,\n", + " \"equipment_amortisation\": 150,\n", + " \"labor (24 h @ $75/h)\": 1800,\n", + " \"total\": 3070\n", + " }\n", + "}\n", + "SAFETY: {\n", + " \"hazards\": [\n", + " {\n", + " \"chemical\": \"Sodium borohydride\",\n", + " \"hazard\": \"Flammable, water-reactive\",\n", + " \"unsafe_condition\": \"Adding NaBH4 portion-wise generates hydrogen gas (H2) which is explosive; requires slow addition behind blast shield and in well-ventilated fume hood.\"\n", + " },\n", + " {\n", + " \"chemical\": \"Dimethylformamide\",\n", + " \"hazard\": \"Reproductive toxin, flammable\",\n", + " \"compliance\": \"Use only in fume hood with appropriate PPE to avoid inhalation exposure; handle with care due to reproductive toxicity.\"\n", + " },\n", + " {\n", + " \"chemical\": \"Toluene\",\n", + " \"hazard\": \"Flammable, CNS depressant\",\n", + " \"compliance\": \"Use in fume hood and avoid ignition sources; ensure proper ventilation to minimize exposure.\"\n", + " },\n", + " {\n", + " \"chemical\": \"Palladium chloride\",\n", + " \"hazard\": \"Irritant, potential carcinogen\",\n", + " \"compliance\": \"Minimize exposure; use gloves and handle in fume hood. Collect and dispose of Pd-containing waste as hazardous heavy metal waste.\"\n", + " },\n", + " {\n", + " \"chemical\": \"Potassium carbonate\",\n", + " \"hazard\": \"Irritant\",\n", + " \"compliance\": \"Use gloves to prevent skin irritation.\"\n", + " },\n", + " {\n", + " \"chemical\": \"Triphenylphosphine\",\n", + " \"hazard\": \"Irritant\",\n", + " \"compliance\": \"Use gloves and avoid inhalation of dust.\"\n", + " }\n", + " ],\n", + " \"unsafe_conditions\": [\n", + " {\n", + " \"condition\": \"Reaction temperature at 80 \\u00b0C with flammable solvents (toluene, DMF)\",\n", + " \"recommendation\": \"Ensure all heating apparatus is explosion-proof; maintain constant stirring to avoid hot spots.\"\n", + " },\n", + " {\n", + " \"condition\": \"Use of Argon atmosphere\",\n", + " \"recommendation\": \"Ensure proper inert gas handling to prevent oxygen contamination; adequate ventilation to prevent asphyxiation risk.\"\n", + " }\n", + " ],\n", + " \"compliance_issues\": [\n", + " {\n", + " \"issue\": \"Hydrogen gas evolution during NaBH4 addition\",\n", + " \"recommendation\": \"Add NaBH4 slowly behind blast shield, wear full PPE including face shield, and perform operation in a well-ventilated fume hood.\"\n", + " },\n", + " {\n", + " \"issue\": \"Heavy metal waste handling\",\n", + " \"recommendation\": \"Collect EDTA wash water and palladium residues separately and dispose as hazardous heavy metal waste in compliance with local regulations.\"\n", + " },\n", + " {\n", + " \"issue\": \"PPE not explicitly stating face shield\",\n", + " \"recommendation\": \"Recommend including face shield during NaBH4 addition step for splash and blast protection.\"\n", + " }\n", + " ],\n", + " \"general_comments\": [\n", + " \"The protocol includes appropriate solvent proportions and reaction scale to reduce waste and cost.\",\n", + " \"The use of EDTA wash for palladium removal and dual solvent recrystallization is a safer, more efficient approach than large silica columns.\",\n", + " \"The procedural timing with intermittent HPLC monitoring is good practice to avoid over-reaction and side products.\",\n", + " \"Standard lab safety practices are advised including lab coat, gloves, and goggles; upgrading to include face shield for hazardous steps is recommended.\",\n", + " \"No major equipment safety issues identified with specified items. Ensure all glassware is rated for heating and inert atmosphere.\"\n", + " ]\n", + "}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Protocol approved\n" + ] + } + ], + "source": [ + "def human_review(safety_package: Dict[str, Any], ctx: Context):\n", + " logging.info(\"Awaiting human review...\")\n", + " protocol = safety_package[\"protocol\"]\n", + " safety_assessment = safety_package[\"safety\"]\n", + " \n", + " print(f\"\\n=== PROTOCOL FOR REVIEW: {ctx.compound} - {ctx.goal} ===\")\n", + " print(f\"DETAILS: {json.dumps(protocol, indent=2)}\")\n", + " print(f\"SAFETY: {json.dumps(safety_assessment, indent=2)}\")\n", + " \n", + " while True:\n", + " approval = input(\"\\nApprove for execution? (yes/no): \").lower()\n", + " if approval in ['yes', 'y', 'no', 'n']:\n", + " approved = approval in ['yes', 'y']\n", + " logging.info(f\"Protocol {'approved' if approved else 'rejected'}\")\n", + " return {\"protocol\": protocol, \"approved\": approved}\n", + " print(\"Please enter 'yes' or 'no'\")\n", + "\n", + "human_decision = human_review(secured, ctx)" + ] + }, + { + "cell_type": "markdown", + "id": "e51e598b", + "metadata": {}, + "source": [ + "### 2.7. **Execution & Learning (`o3` + Code Interpreter):** \n", + "Once the human approves, the plan is sent for lab execution. After lab execution, results are fed back into the system. `o3` combined with the `Code Interpreter` analyzes the data, generates insights, and stores structured outcomes (protocol, parameters, results, insights) in a database (`Outcome DB`). This database informs future ideation cycles, creating a learning loop." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3894d1b3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting mock execution and analysis...\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Literature search: Pd(0) PPh3 coupling yield optimization EDTA work-up recrystallization losses, None, 3\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "(Tool) Outcome DB: XYZ-13, yield, 5\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "Analysis complete\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🎉 Completed. Summary written to output/9835f69c_summary.json\n" + ] + } + ], + "source": [ + "# Simulating execution and analyzing results\n", + "ANALYSIS_PROMPT = \"\"\"You are a data analyst. \n", + "Did the experiment achieve {goal}? Analyse factors, suggest improvements, and return structured JSON.\n", + "\"\"\"\n", + "\n", + "def execute_and_analyse(pkt: Dict[str, Any], ctx: Context):\n", + " logging.info(\"Starting mock execution and analysis...\")\n", + " # These are mock results for a lab experiment\n", + " mock_results = {\n", + " \"yield_improvement\": 12.5,\n", + " \"success\": False,\n", + " \"actual_cost\": ctx.budget * 0.85,\n", + " \"notes\": \"Mock execution\"\n", + " }\n", + " sys = ANALYSIS_PROMPT.format(**ctx.prompt_vars())\n", + " usr = json.dumps({\"protocol\": pkt, \"results\": mock_results}, indent=2)\n", + " analysis = call_openai(ctx.client, MODEL_CRITIQUE, sys, usr, ctx)\n", + " log_json(\"analysis\", analysis, ctx)\n", + " return analysis\n", + "\n", + "# Only proceed to execution if approved by the human reviewer\n", + "if human_decision[\"approved\"]:\n", + " summary = execute_and_analyse(human_decision, ctx)\n", + " logging.info(\"Analysis complete\")\n", + "else:\n", + " logging.info(\"Protocol rejected by human reviewer - execution skipped\")\n", + " summary = None\n", + "\n", + "Path(\"output\").mkdir(exist_ok=True)\n", + "out_path = Path(\"output\") / f\"{ctx.run_id}_summary.json\"\n", + "out_path.write_text(json.dumps(summary, indent=2))\n", + "print(f\"\\n🎉 Completed. Summary written to {out_path}\")" + ] + }, + { + "cell_type": "markdown", + "id": "2f4ecb9f", + "metadata": {}, + "source": [ + "## 3. Model Playbook\n", + "\n", + "Choosing between `o4-mini` and `o3` depends on the task's complexity and required depth. For other tasks, `gpt-4.1-mini` provides balance between cost and performance, with the more powerful `gpt4.1` recommended when greater capability or nuance is needed.\n", + "\n", + "| Task | Start With | Upgrade When... | Escalate To | Rationale |\n", + "| :----------------- | :------------- | :--------------------------------------------------------- | :----------- | :------------------------------------------------------------------------------------------- |\n", + "| Ideation & Protocol Generation | `o4-mini` | Hypotheses lack depth or creativity needed for complex chemical synthesis. | `o3` | `o4-mini` rapidly generates diverse protocols cost-effectively. `o3` provides deeper scientific reasoning when more nuanced approaches are required. |\n", + "| Protocol Ranking | `o4-mini` | Comparison requires deeper scientific assessment or multi-factor trade-offs. | `o3` | Tournament-style ranking with `o4-mini` efficiently identifies promising candidates. Escalate when subtle scientific validity needs evaluation. |\n", + "| Deep Critique & Synthesis | `o3` | N/A - Already using the most capable model for this critical task. | N/A | `o3` excels at rigorous scientific review, identifying methodological flaws, and synthesizing improvements across complex protocols. This task inherently requires deep reasoning. |\n", + "| Safety Assessment | `gpt-4.1-mini` | Domain-specific hazards require higher accuracy or specialized knowledge. | `gpt-4.1` | `gpt-4.1-mini` offers a good balance of cost and performance for standard safety checks. Escalate to `gpt4.1` when higher accuracy or more nuanced reasoning is needed for complex safety risks. |\n", + "\n", + "**Key Insight:**\n", + "> This use case exemplifies a powerful pattern: using faster, cheaper models (`o4-mini`) for breadth and initial filtering, then escalating to more powerful models (`o3`) for depth, critical review, and synthesis. This layered approach optimizes for both creativity/speed and rigor/accuracy, while managing computational costs effectively. The integration with tools is essential for grounding the AI's reasoning in verifiable, real-world data.\n", + "\n", + "## 4. Deployment Notes\n", + "\n", + "Transitioning the AI Co-Scientist from prototype to lab use involves careful planning.\n", + "\n", + "* **Cost Control:**\n", + " * Implement configurable \"modes\" (such as `Fast`, `Standard`, `Thorough`) that adjust the number of `o4-mini` ideation agents, the depth of `o3` critique, or the use of optional checks to balance result quality with cost and latency.\n", + " * Track token usage per stage (ideation, ranking, critique) and per tool call for fine-grained cost monitoring.\n", + "* **Observability:**\n", + " * Log inputs, outputs, model choices, tool calls/responses, latencies, and token counts for each step.\n", + " * Monitor the performance of the tournament ranking and the impact of `o3` critiques (such as how often plans are significantly altered or rejected).\n", + " * Track user interactions: which plans are approved, edited, or rejected by the human scientist.\n", + "* **Safety & Compliance:**\n", + " * Implement multiple safety layers: constraints in prompts, tool-based checks (such as reagent compatibility via `chem_lookup`), optional dedicated model checks (`gpt-4.1-mini`), automated filters (such as for known hazardous combinations), and mandatory human review.\n", + " * Ensure tool endpoints (such as internal databases) meet security requirements.\n", + "* **Rollout Strategy:** \n", + " * Begin with retrospective analysis of past experiments, then move to shadow mode (AI suggests plans alongside human planners), followed by limited live use cases with close monitoring before broader adoption.\n", + "\n", + "\n", + "## 5. Takeaways\n", + "\n", + "1. **Model pairing creates synergy**: `o4-mini` covers more ground quickly; `o3` brings precision and depth.\n", + "2. **Tool integration grounds reasoning in reality**: Real-world data such as chemical costs and safety constraints inform decision-making.\n", + "3. **Human scientists remain central**: The system empowers experts by removing grunt work—not by replacing them.\n", + "\n", + "\n", + "## 6. Useful Cookbooks & Resources\n", + "\n", + "Here are select resources that complement the design and implementation of the AI Co-Scientist system:\n", + "\n", + "- **[Orchestrating Agents: Routines and Handoffs](https://cookbook.openai.com/examples/orchestrating_agents)** Structuring multi-agent workflows with routines and handoffs, relevant to the ideation→ranking→critique pipeline.\n", + "\n", + "- **[GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide)** Advanced prompting, tool use, and task decomposition for improved accuracy in critique and safety reviews.\n", + "\n", + "- **[Structured Outputs for Multi-Agent Systems](https://cookbook.openai.com/examples/structured_outputs_multi_agent)** Enforcing consistent JSON outputs with schema validation for agent interoperability.\n", + "\n", + "- **[Agents - OpenAI API](https://platform.openai.com/docs/guides/agents)** \n", + " Comprehensive guide to building multi-agent systems with OpenAI tools, covering orchestration, tool use, and best practices foundational to this system's architecture.\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "\n", + "## 3C. Use Case: Insurance Claim Processing\n", + "\n", + "![](../../../images/3C_insurance_task_card.png)\n", + "\n", + "Many businesses are faced with the task of digitizing hand-filled forms. In this section, we will demonstrate how OpenAI can be used to digitize and validate a hand-filled insurance form. While this is a common problem for insurance, the same techniques can be applied to a variety of other industries and forms, for example tax forms, invoices, and more.\n", + "\n", + "## 🗂️ TL;DR Matrix\n", + "\n", + "This table summarizes the core technology choices and their rationale for this specific OCR implementation targeting the insurance use case.\n", + "\n", + "| Layer | Choice | Utility |\n", + "| :---- | :---- | :---- |\n", + "| JSON Output | Structured output with Pydantic | Easy to specify formatting, adheres to schema better than `JSON mode` |\n", + "| OCR and Vision | `gpt-4.1` | Powerful OCR and vision capabilities, structured output |\n", + "| Reasoning | `o4-mini` | Affordable but capable reasoning, function calling available |\n", + "| Form Validation | Custom function calling | Can provide interaction with custom or internal databases |\n", + "\n", + "\\*Note: Prices and model identifiers accurate as of April 2025, subject to change.\n", + "\n", + "## 1\\. Scenario Snapshot\n", + "\n", + "* **Users:** The target users are insurance servicing and ops teams who need to ingest data from handwritten forms. \n", + "* **Typical Asks:** Each form will have a different required structure, as well as different fields that need to be extracted. \n", + "* **Constraints:** \n", + " * **Accuracy:** High accuracy is required to ensure that the data is correct and complete. \n", + " * **Uncertainty:** The system must handle uncertainty in the data, such as missing data, ambiguous data, and different formats of the same field. In the event that the model cannot resolve the uncertainty, the system requires a mechanism to request human review. \n", + " * **Performance & Cost:** While system latency is not critical, high accuracy is required while keeping costs under control. We will aim for a cost target of $20 or less per 1000 pages processed.\n", + "\n", + "## 2\\. Architecture\n", + "\n", + "The high level basic architecture of the solution is shown below.\n", + "\n", + "![](../../../images/3C_insurance_architecture.png)\n", + "\n", + "This task is complex and requires a wide variety of model capabilities, including vision, function calling, reasoning, and structured output. While `o3` is capable of doing all of these at once, we found during experimentation that `o4-mini` alone was not sufficient to achieve the necessary performance. Due to the higher relative costs of `o3`, we instead opted for a two-stage approach.\n", + "\n", + "1. Stage one is performed using the vision capabilities of GPT 4.1. This stage is optimized to extract text with maximum accuracy, leaving uncertainty for the reasoning stage and not making any assumptions not visible on the page. By doing OCR in the first stage, we do not require the reasoning model to work directly from an image, which can be challenging given all the other tasks the reasoning model must perform. \n", + " \n", + "2. Stage two takes advantage of the reasoning abilities of `o4-mini`. We use `o4-mini` to validate the accuracy of the OCR and to extract the data into a structured format. Importantly, we expect o4-mini to act as the secondary quality gate \\-- if the OCR is incomplete at this stage we can use o4-mini to refine and validate the original results.\n", + "\n", + "To demonstrate concretely how this works, let's look at a sample image of an insurance form.\n", + "\n", + "![](../../../images/3C_insurance_form.png)\n", + "\n", + "While the form itself is fairly straightforward, there is missing data and ambiguous information that will be difficult for a traditional OCR system to fill out correctly. First, notice that the zip code and county have been omitted. Second, the email address of the user is ambiguous \\-- it could be `jsmith1@gmail.com` or `jsmithl@gmail.com`. In the following sections, we will walk through how a well-designed solution can handle these ambiguities and return the correct form results.\n", + "\n", + "**Environment Setup & Library Code:**\n", + "\n", + "To make our example code more clear, we have broken out environment setup (such as `pip install` commands) and library functions into a separate code block. This will make it easier to focus on only the relevant logic in each step of our solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "923344db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "# Install Python requirements\n", + "%pip install -qU pydantic \"openai>=1.76.0\"\n", + "\n", + "# All imports\n", + "import os\n", + "import json\n", + "\n", + "from pydantic import BaseModel\n", + "\n", + "# Create the OpenAI client\n", + "from openai import OpenAI\n", + "\n", + "client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\", \"sk-dummykey\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7ccd93f6", + "metadata": {}, + "outputs": [], + "source": [ + "def run_conversation_loop(\n", + " client,\n", + " messages,\n", + " tools,\n", + " tool_handlers,\n", + " response_format,\n", + " model,\n", + "):\n", + " \"\"\"Run the OpenAI response completion loop, handling function calls via tool_handlers until parsing final response.\"\"\"\n", + " summaries = []\n", + " while True:\n", + " print(\n", + " f\"Requesting completion from model '{model}' (messages={len(messages)})\"\n", + " )\n", + " response = client.responses.parse(\n", + " model=model,\n", + " input=messages,\n", + " tools=tools,\n", + " text_format=response_format,\n", + " reasoning={\"summary\": \"auto\"},\n", + " )\n", + " summaries.append(response.output[0].summary)\n", + "\n", + " if not response.output_parsed:\n", + " print(\"Assistant requested tool calls, resolving ...\")\n", + "\n", + " reasoning_msg, tool_call = response.output\n", + " messages.append(reasoning_msg)\n", + " messages.append({\n", + " \"id\": tool_call.id,\n", + " \"call_id\": tool_call.call_id,\n", + " \"type\": tool_call.type,\n", + " \"name\": tool_call.name,\n", + " \"arguments\": tool_call.arguments,\n", + " })\n", + "\n", + " if tool_call.name in tool_handlers:\n", + " try:\n", + " args = json.loads(tool_call.arguments)\n", + " except Exception as exc:\n", + " print(\n", + " \"Failed to parse %s arguments: %s\", tool_call.name, exc\n", + " )\n", + " args = {}\n", + " result = tool_handlers[tool_call.name](**args)\n", + " messages.append(\n", + " {\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call.call_id,\n", + " \"output\": str(result),\n", + " }\n", + " )\n", + " print(f\"Tool call {tool_call.name} complete, result: {str(result)}\")\n", + " else:\n", + " print(\"Unhandled function call: %s\", tool_call.name)\n", + "\n", + " if response.output_parsed is not None:\n", + " print(\"Received parsed result from model\")\n", + " return response, summaries" + ] + }, + { + "cell_type": "markdown", + "id": "76755e0d", + "metadata": {}, + "source": [ + "**Flow Explanation: Stage 1**\n", + "\n", + "1. **Image:** The image of the form taken from the user's smartphone is passed to the model. OpenAI's models can accept a variety of image formats, but we typically use a PNG format to keep the text crisp and reduce artifacts. For this example, we pass the image to the model from a publicly available content URL. In a production environment, you likely would pass the image as a signed URL to an image hosted in your own cloud storage bucket. \n", + " \n", + "2. **Structured Output Schema:** We define a Pydantic model that sets the structure of the output data. The model includes all of the fields that we need to extract from the form, along with the appropriate types for each field. Our model is broken into several subcomponents, each of which is a Pydantic model itself and referenced by the parent model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "59263ec9", + "metadata": {}, + "outputs": [], + "source": [ + "class PersonContact(BaseModel):\n", + " name: str\n", + " home_phone: str\n", + " work_phone: str\n", + " cell_phone: str\n", + " email: str\n", + "\n", + "class Address(BaseModel):\n", + " street: str\n", + " city: str\n", + " state: str\n", + " zip: str\n", + " county: str\n", + "\n", + "class DwellingDetails(BaseModel):\n", + " coverage_a_limit: str\n", + " companion_policy_expiration_date: str\n", + " occupancy_of_dwelling: str\n", + " type_of_policy: str\n", + " unrepaired_structural_damage: bool\n", + " construction_type: str\n", + " roof_type: str\n", + " foundation_type: str\n", + " has_post_and_pier_or_post_and_beam_foundation: bool\n", + " cripple_walls: bool\n", + " number_of_stories: str\n", + " living_space_over_garage: bool\n", + " number_of_chimneys: str\n", + " square_footage: str\n", + " year_of_construction: str\n", + " anchored_to_foundation: bool\n", + " water_heater_secured: bool\n", + "\n", + "class InsuranceFormData(BaseModel):\n", + " applicant: PersonContact\n", + " co_applicant: PersonContact\n", + " risk_address: Address\n", + " mailing_address_if_different_than_risk_address: Address\n", + " participating_insurer: str\n", + " companion_policy_number: str\n", + " dwelling_details: DwellingDetails\n", + " effective_date: str\n", + " expiration_date: str" + ] + }, + { + "cell_type": "markdown", + "id": "70e746a3", + "metadata": {}, + "source": [ + "3. **Run OCR:** Using the vision capabilities of GPT-4.1, we run the first stage of our pipeline to extract the text from the document in a structured format. This initial stage aims to achieve high accuracy while passing through uncertainty to the second stage. Our prompt explicitly instructs the model to avoid inferring inputs and instead to fill out the details as exact as possible. For the image input, we set image input detail to `auto` to infer a detail level that's appropriate to the image. We found in our experiments that `auto` worked well, but if you are seeing quality issues in your OCR processing consider using `high`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1537dad2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"applicant\": {\n", + " \"name\": \"Smith, James L\",\n", + " \"home_phone\": \"510 331 5555\",\n", + " \"work_phone\": \"\",\n", + " \"cell_phone\": \"510 212 5555\",\n", + " \"email\": \"jsmithl@gmail.com OR jsmith1@gmail.com\"\n", + " },\n", + " \"co_applicant\": {\n", + " \"name\": \"Roberts, Jesse T\",\n", + " \"home_phone\": \"510 331 5555\",\n", + " \"work_phone\": \"415 626 5555\",\n", + " \"cell_phone\": \"\",\n", + " \"email\": \"jrobertsjr@gmail.com\"\n", + " },\n", + " \"risk_address\": {\n", + " \"street\": \"855 Brannan St\",\n", + " \"city\": \"San Francisco\",\n", + " \"state\": \"CA\",\n", + " \"zip\": \"\",\n", + " \"county\": \"\"\n", + " },\n", + " \"mailing_address_if_different_than_risk_address\": {\n", + " \"street\": \"\",\n", + " \"city\": \"\",\n", + " \"state\": \"\",\n", + " \"zip\": \"\",\n", + " \"county\": \"\"\n", + " },\n", + " \"participating_insurer\": \"Acme Insurance Co\",\n", + " \"companion_policy_number\": \"81265919\",\n", + " \"dwelling_details\": {\n", + " \"coverage_a_limit\": \"$900,000\",\n", + " \"companion_policy_expiration_date\": \"5/31/27\",\n", + " \"occupancy_of_dwelling\": \"Owner\",\n", + " \"type_of_policy\": \"Homeowners\",\n", + " \"unrepaired_structural_damage\": false,\n", + " \"construction_type\": \"Frame\",\n", + " \"roof_type\": \"Composition\",\n", + " \"foundation_type\": \"Raised\",\n", + " \"has_post_and_pier_or_post_and_beam_foundation\": false,\n", + " \"cripple_walls\": false,\n", + " \"number_of_stories\": \"Greater than 1 story\",\n", + " \"living_space_over_garage\": true,\n", + " \"number_of_chimneys\": \"2\",\n", + " \"square_footage\": \"1200\",\n", + " \"year_of_construction\": \"2005\",\n", + " \"anchored_to_foundation\": true,\n", + " \"water_heater_secured\": true\n", + " },\n", + " \"effective_date\": \"5/31/25\",\n", + " \"expiration_date\": \"5/31/27\"\n", + "}\n" + ] + } + ], + "source": [ + "OCR_PROMPT = \"\"\"You are a helpful assistant who excels at processing insurance forms.\n", + "\n", + "You will be given an image of a hand-filled insurance form. Your job is to OCR the data into the given structured format.\n", + "Fill out the fields as exactly as possible. If a written character could possibly be ambiguous (i.e. l or 1, o or 0), include all possiblities in the field separated by \"OR\", especially for email addresses.\n", + "\"\"\"\n", + "\n", + "user_content = [\n", + " {\"type\": \"input_text\", \"text\": \"Here is a photo of the form filled out by the user:\"},\n", + " {\n", + " \"type\": \"input_image\",\n", + " \"image_url\": \"https://drive.usercontent.google.com/download?id=1-tZ526AW3mX1qthvgi8spaaxxeqFG5_6\",\n", + " \"detail\": \"auto\",\n", + " },\n", + "]\n", + "\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": OCR_PROMPT},\n", + " {\"role\": \"user\", \"content\": user_content},\n", + "]\n", + "\n", + "response = client.responses.parse(\n", + " model=\"gpt-4.1-2025-04-14\",\n", + " input=messages,\n", + " text_format=InsuranceFormData,\n", + " # Set temp to 0 for reproducibility\n", + " temperature=0,\n", + ")\n", + "\n", + "s1_json_results = json.dumps(json.loads(response.output_parsed.model_dump_json()), indent=2)\n", + "print(s1_json_results)" + ] + }, + { + "cell_type": "markdown", + "id": "42296380", + "metadata": {}, + "source": [ + "Notice that the output is missing several fields. In the next stage of processing we will take advantage of OpenAI's reasoning models to infer the missing fields where possible.\n", + "\n", + "**Flow Explanation: Stage 2**\n", + "\n", + "1. **Function Definitions:** We define a set of custom functions that the model can use to resolve uncertainty. In this case, we define a function that can validate email addresses by checking if the email exists. This can be used to resolve the ambiguous email address field where the model must choose between multiple possible values. By default, o4-mini supports built-in tools like web search, which in this case it will use to resolve zip codes and incomplete addresses." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "72dc150e", + "metadata": {}, + "outputs": [], + "source": [ + "tools = [{\n", + " \"type\": \"function\",\n", + " \"name\": \"validate_email\",\n", + " \"description\": \"Check if an email address is valid and exists.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"email\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The email address to validate.\"\n", + " }\n", + " },\n", + " \"required\": [\n", + " \"email\"\n", + " ],\n", + " \"additionalProperties\": False\n", + " }\n", + "},\n", + "{\n", + " \"type\": \"function\",\n", + " \"name\": \"search_web\",\n", + " \"description\": \"Perform a web search.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The search query to run through the search engine.\"\n", + " }\n", + " },\n", + " \"required\": [\n", + " \"query\"\n", + " ],\n", + " \"additionalProperties\": False\n", + " }\n", + "}]" + ] + }, + { + "cell_type": "markdown", + "id": "f9a9b808", + "metadata": {}, + "source": [ + "2. **Prompt:** We provide a prompt to the model explaining that we have extracted text via OCR and requesting that the model perform reasoning and function calling to fill in the missing or ambiguous fields." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae8fcf6d", + "metadata": {}, + "outputs": [], + "source": [ + "PROMPT = \"\"\"You are a helpful assistant who excels at processing insurance forms.\n", + "\n", + "You will be given a javascript representation of an OCR'd document. Consider at which fields are ambiguous reason about how to fill them in. Fill any missing fields that are possible to infer from existing data, or search the web. If you cannot fill a field, reason about why.\n", + "\n", + "Use the tools provided if necessary to clarify the results. If the OCR system has provided two possibilities, do your best to definitely pick which option is correct.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1d2b77ee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requesting completion from model 'o4-mini-2025-04-16' (messages=2)\n", + "Assistant requested tool calls, resolving ...\n", + "Tool call validate_email complete, result: True\n", + "Requesting completion from model 'o4-mini-2025-04-16' (messages=5)\n", + "Assistant requested tool calls, resolving ...\n", + "Tool call validate_email complete, result: False\n", + "Requesting completion from model 'o4-mini-2025-04-16' (messages=8)\n", + "Received parsed result from model\n", + "{\n", + " \"applicant\": {\n", + " \"name\": \"Smith, James L\",\n", + " \"home_phone\": \"510 331 5555\",\n", + " \"work_phone\": \"\",\n", + " \"cell_phone\": \"510 212 5555\",\n", + " \"email\": \"jsmithl@gmail.com\"\n", + " },\n", + " \"co_applicant\": {\n", + " \"name\": \"Roberts, Jesse T\",\n", + " \"home_phone\": \"510 331 5555\",\n", + " \"work_phone\": \"415 626 5555\",\n", + " \"cell_phone\": \"\",\n", + " \"email\": \"jrobertsjr@gmail.com\"\n", + " },\n", + " \"risk_address\": {\n", + " \"street\": \"855 Brannan St\",\n", + " \"city\": \"San Francisco\",\n", + " \"state\": \"CA\",\n", + " \"zip\": \"94107\",\n", + " \"county\": \"San Francisco\"\n", + " },\n", + " \"mailing_address_if_different_than_risk_address\": {\n", + " \"street\": \"855 Brannan St\",\n", + " \"city\": \"San Francisco\",\n", + " \"state\": \"CA\",\n", + " \"zip\": \"94107\",\n", + " \"county\": \"San Francisco\"\n", + " },\n", + " \"participating_insurer\": \"Acme Insurance Co\",\n", + " \"companion_policy_number\": \"81265919\",\n", + " \"dwelling_details\": {\n", + " \"coverage_a_limit\": \"$900,000\",\n", + " \"companion_policy_expiration_date\": \"5/31/27\",\n", + " \"occupancy_of_dwelling\": \"Owner\",\n", + " \"type_of_policy\": \"Homeowners\",\n", + " \"unrepaired_structural_damage\": false,\n", + " \"construction_type\": \"Frame\",\n", + " \"roof_type\": \"Composition\",\n", + " \"foundation_type\": \"Raised\",\n", + " \"has_post_and_pier_or_post_and_beam_foundation\": false,\n", + " \"cripple_walls\": false,\n", + " \"number_of_stories\": \"Greater than 1 story\",\n", + " \"living_space_over_garage\": true,\n", + " \"number_of_chimneys\": \"2\",\n", + " \"square_footage\": \"1200\",\n", + " \"year_of_construction\": \"2005\",\n", + " \"anchored_to_foundation\": true,\n", + " \"water_heater_secured\": true\n", + " },\n", + " \"effective_date\": \"5/31/25\",\n", + " \"expiration_date\": \"5/31/27\"\n", + "}\n" + ] + } + ], + "source": [ + "messages = [\n", + " {\"role\": \"system\", \"content\": PROMPT},\n", + " {\"role\": \"user\", \"content\": s1_json_results},\n", + "]\n", + "\n", + "# For demonstration purposes, we'll hardcode the correct email answer.\n", + "def email_mock(*args, **kwargs):\n", + " if kwargs[\"email\"] == \"jsmithl@gmail.com\":\n", + " return True\n", + " return False\n", + "\n", + "# Reasoning models like `o4-mini` will soon support built-in web search, but for now\n", + "# we demonstrate this capability using a simple mock function.\n", + "def web_mock(*args, **kwargs):\n", + " if \"855 Brannan\" in kwargs[\"query\"]:\n", + " return \"855 Brannan St, San Francisco, 94103, San Francisco County\"\n", + " \n", + " return \"\"\n", + " \n", + "tool_handlers = {\"validate_email\": email_mock, \"search_web\": web_mock}\n", + "\n", + "response, summaries = run_conversation_loop(\n", + " client=client,\n", + " messages=messages,\n", + " tools=tools,\n", + " tool_handlers=tool_handlers,\n", + " response_format=InsuranceFormData,\n", + " model=\"o4-mini-2025-04-16\",\n", + ")\n", + "\n", + "print(json.dumps(json.loads(response.output_parsed.model_dump_json()), indent=2))" + ] + }, + { + "cell_type": "markdown", + "id": "cb3f3115", + "metadata": {}, + "source": [ + "You can see that the email address has been refined to a single value, the zip code and county have been filled in, and the mailing address has been filled in by using the risk address. The model has also returned the results in a structured format (with appropriate types such as boolean for yes/no questions), which can be easily parsed by a downstream system.\n", + "\n", + "To help us understand and debug the model, we can also print the summary chain-of-thought reasoning produced by the model. This can help expose common failure modes, points where the model is unclear, or incorrect upstream details.\n", + "\n", + "While developing this solution, the chain-of-thought summaries exposed some incorrectly named and typed schema values." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ab1d4fbc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Determining insurance form details**\n", + "\n", + "I have a JSON representation of a partially filled insurance form, and there are a few missing or ambiguous fields that I need to address.\n", + "\n", + "For the email address, I see two options. I can validate which one is correct by checking both with the tool.\n", + "\n", + "The risk address fields for zip code and county are empty. Based on the address \"855 Brannan St, San Francisco, CA,\" I can determine the correct zip code is 94107, as that area corresponds to South Beach. Lastly, since the mailing address is empty, I assume it's the same as the risk address.\n", + "\n", + "**Filling insurance form details**\n", + "\n", + "I think it’s best to set the mailing address to be the same as the risk address or clarify that a blank one implies the same. Since it’s an explicit instruction to fill missing fields, I’ll fill in the mailing address with the risk address to avoid confusion.\n", + "\n", + "All co-applicant fields are present, and dwelling details are complete. The effective and expiration dates are also provided. I plan to validate both email options by checking each one separately. Let's begin with validating the first email.\n", + "\n" + ] + } + ], + "source": [ + "for summary in summaries:\n", + " for response in summary:\n", + " print(response.text + '\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "f2bd52eb", + "metadata": {}, + "source": [ + "## 3\\. Model and Capabilities Playbook\n", + "\n", + "Selecting the right tool for the job is key to getting the best results. In general, it's a good idea to start with the simplest solution that fits your needs and then upgrade if you need more capabilities.\n", + "\n", + "| Task | Start With | Upgrade When... | Escalate To | Rationale |\n", + "| :---- | :---- | :---- | :---- | :---- |\n", + "| OCR | `gpt-4.1` | Complex forms that are difficult to understand at a glance | `o3` | `gpt-4.1` is fast and cost-effective for most OCR. `o-3` has the ability to reason about form structure. |\n", + "| Results Refinement | `o4-mini` | Complex logic for inferring details, many function calls required. | `o3` | Better for very long chains of reasoning, especially with both function calls and structured output. |\n", + "\n", + "## 4\\. Evaluation Metrics\n", + "\n", + "Track key metrics to ensure the system is performing accurately and as expected.\n", + "\n", + "### Critical Metrics\n", + "\n", + "* **OCR Accuracy:** Per-character and per-word accuracy. \n", + "* **Inferred Field Rate:** Portion unfilled entries correctly inferred from either existing data or function calling. \n", + "* **Human Intervention Rate:** How often a document contains an UNKNOWN and must be referred to a human.\n", + "\n", + "We recommend building a labeled hold-out set of forms and their expected responses. This dataset should be representative of the expected deployment environment, see the [OpenAI evals](https://platform.openai.com/docs/guides/evals) guide for more detailed information on building and evaluating your system.\n", + "\n", + "## 5\\. Deployment Notes\n", + "\n", + "Moving from prototype to a production-ready system requires attention to operational details (LLMOps).\n", + "\n", + "### Cost Breakdown\n", + "\n", + "We will assume that for document ingestion, [batch pricing](https://platform.openai.com/docs/guides/batch) is a viable option due to high latency tolerance (i.e. overnight runs are fine).\n", + "\n", + "#### **Stage 1: OCR (Optical Character Recognition)**\n", + "\n", + "**Model:** `gpt-4.1`\n", + "\n", + "| Type | Tokens | Rate (per 1M) | Cost |\n", + "| :---- | :---- | :---- | :---- |\n", + "| Input | 2,000 | $1.00 | $0.002 |\n", + "| Output | 1,500 | $4.00 | $0.006 |\n", + "| **Total for 1,000 pages (Stage 1\\)** | | | **$8.00** |\n", + "\n", + "#### **Stage 2: Reasoning**\n", + "\n", + "**Model:** `o4-mini`\n", + "\n", + "| Type | Tokens | Rate (per 1M) | Cost |\n", + "| :---- | :---- | :---- | :---- |\n", + "| Input | 2,000 | $0.55 | $0.0011 |\n", + "| Output | 3,000 | $2.20 | $0.0066 |\n", + "| **Total for 1,000 pages (Stage 2\\)** | | | **$7.70** |\n", + "\n", + "#### Grand Total (per 1,000 pages): **$15.70**\n", + "\n", + "Compare this cost to a one-stage `o3` deployment. Assuming equal token usage and batch usage, the additional cost of the more powerful reasoning model would come to $70/1000 pages.\n", + "\n", + "### Monitoring & Deployment\n", + "\n", + "Monitor your system by logging key metrics:\n", + "\n", + "* `llm_model_used`, `llm_input_tokens`, `llm_output_tokens`, `llm_latency_ms` per model \n", + "* `total_query_latency_ms`, `estimated_query_cost` per model \n", + "* `function_calls_per_document`, `num_email_validation_calls` \n", + "* `human_review_required`\n", + "\n", + "Pin the specific model version identifier (e.g., `o4-mini-2025-04-16`) used in deployment via configuration/environment variables to prevent unexpected behavior from silent model updates.\n", + "\n", + "## 6\\. Useful Cookbooks & Resources\n", + "\n", + "Refer to these related resources for deeper dives into specific components:\n", + "\n", + "* [Structured Output](https://platform.openai.com/docs/guides/structured-outputs) \n", + "* [Vision Models](https://platform.openai.com/docs/guides/images) \n", + "* [Function Calling](https://platform.openai.com/docs/guides/function-calling)\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "<h2 id=\"prototype-to-production\">Prototype to Production</h2>\n", + "\n", + "Transitioning a prototype to production requires careful planning and execution. This checklist highlights critical steps, drawing from our flagship use cases, to ensure your deployment is robust, efficient, and meets business goals.\n", + "\n", + "## 🗂️ TL;DR Matrix\n", + "\n", + "| Checklist Area | Key Focus / Actions | Why it Matters |\n", + "| :---- | :---- | :---- |\n", + "| **Define Success Criteria** | • Define measurable KPIs & SLOs (accuracy, cost, latency). • Ensure targets are measurable via logs. | Provides clear targets; proves value. |\n", + "| **Document Model Rationale** | • Select initial models deliberately based on trade-offs. • Document the \"why\" behind model choices. | Justifies choices; aids future updates. |\n", + "| **Robust Evaluation & Testing** | • Build automated tests (\"eval suite\") using a golden set. • Focus on factuality, hallucinations, tool errors. • Test tool reliability & edge cases. | Ensures quality; prevents regressions before release. |\n", + "| **Observability & Cost** | • Implement essential logging for monitoring & debugging. • Set cost guardrails (token limits, usage modes). | Enables tuning; keeps spending within budget. |\n", + "| **Safety & Compliance** | • Use safety mechanisms (moderation APIs, prompts). • Enforce domain-specific compliance rules. • Mandate Human-in-the-Loop (HITL) for high-risk outputs. | Ensures responsible operation; meets requirements. |\n", + "| **Model Updates & Versioning** | • Define version pinning strategy • Implement A/B testing for new versions • Create rollback procedures | Maintains stability while allowing improvements. |\n", + "\n", + "1. **Define Success Criteria Quantitatively:** Move beyond \"it works\" to measurable targets *before* major development. \n", + " \n", + " * **Set Key Performance Indicators (KPIs) & SLOs:** Define specific targets for business value (e.g., RAG accuracy \\> 95%, OCR cost \\< $X/page) and performance (e.g., P95 latency \\< 1s, error rates). \n", + " * **Ensure Measurability:** Confirm that all KPIs and SLOs can be directly measured from system logs (e.g., tracking `total_tokens`, `critique_status`).\n", + "\n", + " \n", + "\n", + "2. **Document Initial Model Selection Rationale:** Justify your starting model choices for future reference. \n", + " \n", + " * **Choose Models Deliberately:** Use the Model-Intro Matrix and use cases to select appropriate models for each task (e.g., `o4-mini` for speed/cost, `gpt-4.1` for accuracy, `o3` for depth). \n", + " * **Record the \"Why\":** Briefly document the reasoning behind your choices (cost, latency, capability trade-offs) in code comments or design docs so future teams understand the context.\n", + "\n", + " \n", + "\n", + "3. **Implement Robust Evaluation & Testing:** Verify quality and prevent regressions *before* shipping changes. \n", + " \n", + " * **Build an Automated Eval Suite:** Create a repeatable test process using a \"golden set\" (50-100 diverse, expert-verified examples). Focus tests on `factuality`, `hallucination rate`, `tool-error rate`, and task-specific metrics. \n", + " * **Test Reliably:** Rigorously test integrated tool reliability (success rate, error handling) and system behavior under load and with edge cases (malformed data, adversarial inputs).\n", + "\n", + " \n", + "\n", + "4. **Establish Observability & Cost Controls:** Monitor performance and keep spending within budget. \n", + " \n", + " * **Set Cost Guardrails:** Prevent unexpected cost increases by defining max token limits per stage and considering operational modes (\"Fast,\" \"Standard,\" \"Thorough\") to balance cost and performance. \n", + " * **Implement Essential Logging:** Capture key operational data via structured logs for each processing stage to enable debugging and monitoring.\n", + "\n", + " \n", + "\n", + "5. **Implement Safety & Compliance Guardrails:** Ensure responsible operation and meet requirements. \n", + " \n", + " * **Use Safety Mechanisms:** Employ tools like OpenAI's moderation APIs, safety-focused system prompts, or sentinel models for checks, especially with user input or sensitive topics. \n", + " * **Enforce Compliance:** Build in checks relevant to your specific industry and risks (e.g., legal constraints, lab safety). \n", + " * **Require Human-in-the-Loop (HITL):** Mandate human review for low-confidence outputs, high-risk scenarios, or critical decisions, ensuring the workflow flags these items clearly.\n", + "\n", + "\n", + "6. **Manage Model Updates and Versioning:** Prepare for model evolution over time.\n", + " \n", + " * **Version Pinning Strategy:** Decide whether to pin to specific model versions for stability or automatically adopt new versions for improvements.\n", + " * **A/B Testing Framework:** Establish a process to evaluate new model versions against your key metrics before full deployment.\n", + " * **Rollback Plan:** Create a clear procedure for reverting to previous model versions if issues arise with updates.\n", + " * **Monitor Version Performance:** Track metrics across model versions to identify performance trends and inform future selection decisions.\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "\n", + "## Adaptation Decision Tree\n", + "\n", + "![Model Selection Decision Tree](../../../images/3D_model_selection_flowchart.png)\n", + "\n", + "## Communicating Model Selection to Non-Technical Stakeholders\n", + "\n", + "When explaining your model choices to business stakeholders, focus on these key points:\n", + "\n", + "1. **Align with Business Outcomes**: Explain how your model selection directly supports specific business goals (time savings, cost reduction, improved accuracy).\n", + "\n", + "2. **Translate Technical Metrics**: Convert technical considerations into business impact:\n", + " - \"This model reduces processing time from 5 seconds to 0.7 seconds, allowing us to handle customer inquiries 7x faster\"\n", + " - \"By using the mini variant, we can process 5x more documents within the same budget\"\n", + "\n", + "3. **Highlight Trade-offs**: Present clear scenarios for different models:\n", + " - \"Option A (GPT-4.1): Highest accuracy but higher cost - ideal for client-facing legal analysis\"\n", + " - \"Option B (GPT-4.1 mini): 90% of the accuracy at 30% of the cost - perfect for internal document processing\"\n", + "\n", + "4. **Use Concrete Examples**: Demonstrate the practical difference in outputs between models to illustrate the value proposition of each option.\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "\n", + "## Appendices\n", + "\n", + "## Glossary of Key Terms\n", + "\n", + "| Term | Definition |\n", + "|------|------------|\n", + "| **Context Window** | The maximum number of tokens a model can process in a single request |\n", + "| **Hallucination** | When a model generates content that appears plausible but is factually incorrect or unsupported |\n", + "| **Latency** | The time delay between sending a request to a model and receiving a response |\n", + "| **LLM** | Large Language Model; an AI system trained on vast amounts of text data |\n", + "| **Prompt Engineering** | The practice of designing effective prompts to elicit desired outputs from AI models |\n", + "| **RAG** | Retrieval-Augmented Generation; combining information retrieval with text generation |\n", + "| **SOTA** | State-of-the-Art; representing the most advanced stage in a field at a given time |\n", + "| **Token** | The basic unit of text that models process (roughly 0.75 words in English) |\n", + "\n", + "## 6.1 Price and Utility Table (Apr 2025)\n", + "\n", + "| Model | Context Window | Input Price (per 1M tokens) | Output Price (per 1M tokens) | Best For |\n", + "|-------|----------------|-----------------------------|-----------------------------|----------|\n", + "| GPT-4.1 | 1M | \\$2.00 | \\$8.00 | Long-doc analytics, code review |\n", + "| GPT-4.1 mini | 1M | \\$0.40 | \\$1.60 | Production agents, balanced cost/performance |\n", + "| GPT-4.1 nano | 1M | \\$0.10 | \\$0.40 | High-throughput, cost-sensitive applications |\n", + "| GPT-4o | 128K | \\$5.00 | \\$15.00 | Real-time voice/vision chat |\n", + "| GPT-4o mini | 128K | \\$0.15 | \\$0.60 | Vision tasks, rapid analytics |\n", + "| o3 (low) | 200K | \\$10.00* | \\$40.00* | Bulk triage, catalog enrichment |\n", + "| o3 (med) | 200K | \\$10.00* | \\$40.00* | Knowledge base Q&A |\n", + "| o3 (high) | 200K | \\$10.00* | \\$40.00* | Multi-step reasoning, troubleshooting |\n", + "| o4-mini (low) | 200K | \\$1.10* | \\$4.40* | Vision tasks, rapid analytics |\n", + "| o4-mini (med) | 200K | \\$1.10* | \\$4.40* | Balanced vision + reasoning |\n", + "| o4-mini (high) | 200K | \\$1.10* | \\$4.40* | Deep reasoning with cost control |\n", + "\n", + "\\* *Note: The low/med/high settings affect token usage rather than base pricing. Higher settings may use more tokens for deeper reasoning, increasing per-request cost and latency.*\n", + "\n", + "## 6.2 Prompt-pattern Quick Sheet (Token vs Latency Deltas)\n", + "\n", + "| Prompt Pattern | Description | Token Impact | Latency Impact | Best Model Fit |\n", + "|----------------|-------------|--------------|----------------|----------------|\n", + "| **Self-Critique** | Ask model to evaluate its own answer before finalizing | +20-30% tokens | +15-25% latency | GPT-4.1, o3 |\n", + "| **Chain-of-Thought (CoT)** | Explicitly instruct to \"think step by step\" | +40-80% tokens | +30-50% latency | o3, o4-mini (high) |\n", + "| **Structured Outputs** | Use JSON schema or pydantic models for consistent formatting | +5-10% tokens | +5-10% latency | All models |\n", + "| **Zero-Token Memory** | Store context in external DB rather than in conversation | -70-90% tokens | -5-10% latency | GPT-4.1 family |\n", + "| **Skeleton-Fill-In** | Provide template structure for model to complete | -10-20% tokens | -5-15% latency | o4-mini, GPT-4.1 nano |\n", + "| **Self-Consistency** | Generate multiple answers and select most consistent | +200-300% tokens | +150-250% latency | o3 (high) |\n", + "| **Role-Playing** | Assign specific personas to model for specialized knowledge | +5-15% tokens | Neutral | GPT-4o, o4-mini |\n", + "| **Tournament Ranking** | Compare options pairwise rather than scoring individually | +50-100% tokens | +30-60% latency | o3, o4-mini (high) |\n", + "| **Tool-Calling Reflex** | Prompt model to call tools when uncertainty is detected | +10-30% tokens | +20-40% latency | o3, GPT-4.1 |\n", + "\n", + "## 6.3 Links to External Cookbooks & Docs\n", + "\n", + "### OpenAI Official Resources\n", + "- [OpenAI Cookbook Main Repository](https://cookbook.openai.com/)\n", + "- [Function Calling Guide](https://platform.openai.com/docs/guides/function-calling)\n", + "- [Vision Models Guide](https://platform.openai.com/docs/guides/vision)\n", + "- [Agents Documentation](https://platform.openai.com/docs/guides/agents)\n", + "- [Structured Outputs Guide](https://platform.openai.com/docs/guides/structured-outputs)\n", + "\n", + "### RAG & Retrieval\n", + "- [RAG on PDFs](https://cookbook.openai.com/examples/file_search_responses)\n", + "\n", + "### Specialized Use Cases\n", + "- [Voice Assistant with Agents SDK](https://cookbook.openai.com/examples/agents_sdk/app_assistant_voice_agents)\n", + "- [Multi-Tool Orchestration](https://cookbook.openai.com/examples/responses_api/responses_api_tool_orchestration)\n", + "- [Data Extraction and Transformation](https://cookbook.openai.com/examples/data_extraction_transformation)\n", + "\n", + "### Prompting & Model Selection\n", + "- [GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide)\n", + "- [Prompt Engineering Best Practices](https://platform.openai.com/docs/guides/prompt-engineering)\n", + "\n", + "### Evaluation & Deployment\n", + "- [Getting Started with OpenAI Evals](https://cookbook.openai.com/examples/evaluation/getting_started_with_openai_evals)\n", + "- [How to use the Usage API and Cost API to monitor your OpenAI usage](https://cookbook.openai.com/examples/completions_usage_api)\n", + "\n", + "================================================================================\n", + "\n", + "\n", + "\n", + "## Contributors\n", + "\n", + " This cookbook serves as a joint collaboration effort between OpenAI and [Tribe AI](https://www.tribe.ai/)\n", + "- [Kashyap Coimbatore Murali](https://www.linkedin.com/in/kashyap-murali/)\n", + "- [Nate Harada](https://www.linkedin.com/in/nate-harada/) \n", + "- [Sai Prashanth Soundararaj](https://www.linkedin.com/in/saiprashanths/)\n", + "- [Shikhar Kwatra](https://www.linkedin.com/in/shikharkwatra/)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/partners/model_selection_guide/tools.py b/examples/partners/model_selection_guide/tools.py new file mode 100644 index 0000000000..d24544f6b3 --- /dev/null +++ b/examples/partners/model_selection_guide/tools.py @@ -0,0 +1,312 @@ +""" +Mock implementations of tool functions for AI Co-Scientist. + +These are simple mocks of the external tools that would be used in a real implementation. +""" + +import random, logging +from typing import Dict, List, Any, Optional + +# Mock database of chemical properties +MOCK_CHEMICALS = { + "Palladium acetate": { + "solubility": "Soluble in chloroform, slightly soluble in acetone", + "melting_point": "205°C (decomposition)", + "hazards": "Irritant, potential carcinogen", + "approved_status": True, + "cost_per_gram": 85.50 + }, + "Triphenylphosphine": { + "solubility": "Soluble in ethanol, benzene, chloroform", + "melting_point": "80-82°C", + "hazards": "Irritant", + "approved_status": True, + "cost_per_gram": 12.75 + }, + "Triethylamine": { + "solubility": "Miscible with water, ethanol", + "melting_point": "-115°C", + "boiling_point": "89°C", + "hazards": "Flammable, corrosive", + "approved_status": True, + "cost_per_gram": 5.25 + }, + "Sodium borohydride": { + "solubility": "Soluble in water, methanol", + "melting_point": "400°C (decomposition)", + "hazards": "Flammable, water-reactive", + "approved_status": True, + "cost_per_gram": 8.90 + }, + "Dimethylformamide": { + "solubility": "Miscible with water, ethanol", + "boiling_point": "153°C", + "hazards": "Reproductive toxin, flammable", + "approved_status": True, + "cost_per_gram": 3.15 + }, + "Palladium chloride": { + "solubility": "Slightly soluble in water, soluble in HCl", + "melting_point": "679°C", + "hazards": "Irritant, potential carcinogen", + "approved_status": True, + "cost_per_gram": 75.20 + }, + "Potassium carbonate": { + "solubility": "Soluble in water", + "melting_point": "891°C", + "hazards": "Irritant", + "approved_status": True, + "cost_per_gram": 2.50 + }, + "Toluene": { + "solubility": "Immiscible with water, miscible with organic solvents", + "boiling_point": "110.6°C", + "hazards": "Flammable, CNS depressant", + "approved_status": True, + "cost_per_gram": 1.75 + }, + "Methanol": { + "solubility": "Miscible with water", + "boiling_point": "64.7°C", + "hazards": "Flammable, toxic", + "approved_status": True, + "cost_per_gram": 1.20 + }, + "XYZ-13": { + "solubility": "Slightly soluble in organic solvents", + "melting_point": "185-188°C", + "hazards": "Mild irritant", + "approved_status": True, + "cost_per_gram": 250.00 + } +} + +# Mock database of past experiment outcomes +MOCK_OUTCOMES = { + "XYZ-13": [ + { + "id": "exp-001", + "catalyst": "Palladium acetate", + "temperature": 85, + "solvent": "Dimethylformamide", + "yield": 62.3, + "duration": 36, + "notes": "Yield decreased at temperatures above 85°C." + }, + { + "id": "exp-002", + "catalyst": "Palladium chloride", + "temperature": 70, + "solvent": "Toluene", + "yield": 58.7, + "duration": 42, + "notes": "Lower temperature gave slightly lower yield but higher purity." + }, + { + "id": "exp-003", + "catalyst": "Palladium acetate", + "temperature": 90, + "solvent": "Methanol", + "yield": 45.2, + "duration": 28, + "notes": "Significant side products observed at this temperature." + } + ] +} + +# Mock literature database +MOCK_LITERATURE = [ + { + "title": "Palladium-Catalyzed Cross-Coupling for the Synthesis of XYZ Derivatives", + "authors": "Smith, J.L., et al.", + "journal": "Journal of Organic Chemistry", + "year": 2024, + "abstract": "Novel methods using palladium catalysts at moderate temperatures showed improved yields for XYZ-type compounds." + }, + { + "title": "Solvent Effects on the Yield of XYZ Compounds", + "authors": "Johnson, M.R., et al.", + "journal": "Chemical Communications", + "year": 2023, + "abstract": "Polar aprotic solvents demonstrated superior performance in the synthesis of XYZ compounds, with yields up to 70%." + }, + { + "title": "Temperature-Controlled Synthesis of Pharmaceutical Intermediates", + "authors": "Rodriguez, A., et al.", + "journal": "ACS Catalysis", + "year": 2024, + "abstract": "Lower temperature protocols (50-65°C) with extended reaction times showed reduced side products for sensitive compounds." + } +] + +def list_available_chemicals() -> Dict: + """Mock function to list all available chemicals in the database.""" + logging.info(f"(Tool) List available chemicals") + return { + "status": "success", + "available_chemicals": list(MOCK_CHEMICALS.keys()) + } + +def chem_lookup(chemical_name: str, property: Optional[str] = None) -> Dict: + """Mock function to look up chemical properties.""" + logging.info(f"(Tool) Chemical lookup: {chemical_name}, {property}") + # Check if chemical exists in our mock database + if chemical_name not in MOCK_CHEMICALS: + similar_chemicals = [c for c in MOCK_CHEMICALS.keys() if any(word in c.lower() for word in chemical_name.lower().split())] + return { + "status": "not_found", + "message": f"Chemical '{chemical_name}' not found in database.", + "similar_chemicals": similar_chemicals if similar_chemicals else [] + } + + # Return specific property if requested + if property and property in MOCK_CHEMICALS[chemical_name]: + return { + "status": "success", + "chemical": chemical_name, + "property": property, + "value": MOCK_CHEMICALS[chemical_name][property] + } + + # Return all properties + return { + "status": "success", + "chemical": chemical_name, + "properties": MOCK_CHEMICALS[chemical_name] + } + +def cost_estimator(reagents: List[Dict] = [], equipment: Optional[List[str]] = None, duration_hours: Optional[float] = None) -> Dict: + """Mock function to estimate the cost of reagents and procedures.""" + logging.info(f"(Tool) Cost estimator: {reagents}, {equipment}, {duration_hours}") + total_cost = 0 + reagent_costs = {} + equipment_costs = {} + labor_cost = 0 + + # Calculate reagent costs + for reagent in reagents: + # Mock: Use defaults for missing keys instead of returning errors + if not isinstance(reagent, dict): + reagent = {"name": "Unknown reagent", "amount": 1, "unit": "g"} + + name = reagent.get("name", "XYZ-13") + amount = reagent.get("amount", 1) # Default to 1 if amount is missing + unit = reagent.get("unit", "g") # Default to grams if unit not specified + + # Convert units to grams for calculation + amount_in_grams = amount + if unit.lower() == "mg": + amount_in_grams = amount / 1000 + elif unit.lower() == "kg": + amount_in_grams = amount * 1000 + + # Look up cost per gram + cost_per_gram = MOCK_CHEMICALS.get(name, {}).get("cost_per_gram", 10.0) # Default cost if not found + cost = amount_in_grams * cost_per_gram + reagent_costs[name] = cost + total_cost += cost + + # Add equipment costs if provided + if equipment: + for item in equipment: + # Mock equipment costs + if "hplc" in item.lower(): + cost = 250.0 + elif "nmr" in item.lower(): + cost = 350.0 + elif "reactor" in item.lower(): + cost = 150.0 + else: + cost = 50.0 + + equipment_costs[item] = cost + total_cost += cost + + # Add labor costs based on duration + if duration_hours: + labor_rate = 75.0 # Mock hourly rate + labor_cost = duration_hours * labor_rate + total_cost += labor_cost + + return { + "status": "success", + "total_cost": round(total_cost, 2), + "reagent_costs": reagent_costs, + "equipment_costs": equipment_costs, + "labor_cost": labor_cost, + "currency": "USD" + } + +def outcome_db(compound: str, parameter: Optional[str] = None, limit: int = 5) -> Dict: + """Mock function to query the database of past experiment outcomes.""" + logging.info(f"(Tool) Outcome DB: {compound}, {parameter}, {limit}") + if compound not in MOCK_OUTCOMES: + return { + "status": "not_found", + "message": f"No experiments found for compound '{compound}'." + } + + experiments = MOCK_OUTCOMES[compound] + + # Filter by parameter if provided + if parameter: + filtered_experiments = [exp for exp in experiments if parameter in exp] + if not filtered_experiments: + return { + "status": "parameter_not_found", + "message": f"No experiments with parameter '{parameter}' found for compound '{compound}'." + } + experiments = filtered_experiments + + # Limit the number of results + experiments = experiments[:limit] + + return { + "status": "success", + "compound": compound, + "experiments": experiments, + "count": len(experiments) + } + +def literature_search(query: str, filter: Optional[str] = None, limit: int = 3) -> Dict: + """Mock function to search scientific literature for relevant information.""" + logging.info(f"(Tool) Literature search: {query}, {filter}, {limit}") + # Simple keyword matching for demo purposes + keywords = [word.lower() for word in query.split()] + + matched_literature = [] + for paper in MOCK_LITERATURE: + # Check if any keyword appears in title or abstract + title_lower = paper["title"].lower() + abstract_lower = paper["abstract"].lower() + + if any(keyword in title_lower or keyword in abstract_lower for keyword in keywords): + matched_literature.append(paper) + + # Apply filter if provided + if filter: + filter_year_match = None + # Try to extract year from filter + import re + year_match = re.search(r'20\d\d', filter) + if year_match: + filter_year = int(year_match.group()) + matched_literature = [paper for paper in matched_literature if paper["year"] == filter_year] + + # Filter by journal if mentioned + filter_lower = filter.lower() + journal_matches = [paper for paper in matched_literature if filter_lower in paper["journal"].lower()] + if journal_matches: + matched_literature = journal_matches + + # Limit the number of results + matched_literature = matched_literature[:limit] + + return { + "status": "success", + "query": query, + "filter": filter, + "results": matched_literature, + "count": len(matched_literature) + } \ No newline at end of file diff --git a/examples/reasoning_function_calls.ipynb b/examples/reasoning_function_calls.ipynb new file mode 100644 index 0000000000..935eed283a --- /dev/null +++ b/examples/reasoning_function_calls.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Managing Function Calls With Reasoning Models\n", + "OpenAI now offers function calling using [reasoning models](https://platform.openai.com/docs/guides/reasoning?api-mode=responses). Reasoning models are trained to follow logical chains of thought, making them better suited for complex or multi-step tasks.\n", + "> _Reasoning models like o3 and o4-mini are LLMs trained with reinforcement learning to perform reasoning. Reasoning models think before they answer, producing a long internal chain of thought before responding to the user. Reasoning models excel in complex problem solving, coding, scientific reasoning, and multi-step planning for agentic workflows. They're also the best models for Codex CLI, our lightweight coding agent._\n", + "\n", + "For the most part, using these models via the API is very simple and comparable to using familiar 'chat' models. \n", + "\n", + "However, there are some nuances to bear in mind, particularly when it comes to using features such as function calling. \n", + "\n", + "All examples in this notebook use the newer [Responses API](https://community.openai.com/t/introducing-the-responses-api/1140929) which provides convenient abstractions for managing conversation state. However the principles here are relevant when using the older chat completions API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making API calls to reasoning models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install openai\n", + "# Import libraries \n", + "import json\n", + "from openai import OpenAI\n", + "from uuid import uuid4\n", + "from typing import Callable\n", + "\n", + "client = OpenAI()\n", + "MODEL_DEFAULTS = {\n", + " \"model\": \"o4-mini\", # 200,000 token context window\n", + " \"reasoning\": {\"effort\": \"low\", \"summary\": \"auto\"}, # Automatically summarise the reasoning process. Can also choose \"detailed\" or \"none\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make a simple call to a reasoning model using the Responses API.\n", + "We specify a low reasoning effort and retrieve the response with the helpful `output_text` attribute.\n", + "We can ask follow up questions and use the `previous_response_id` to let OpenAI manage the conversation history automatically" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Among the last four Summer Olympic host cities—Tokyo (2020), Rio de Janeiro (2016), London (2012) and Beijing (2008)—Rio de Janeiro has by far the warmest climate. Average annual temperatures are roughly:\n", + "\n", + "• Rio de Janeiro: ≈ 23 °C \n", + "• Tokyo: ≈ 16 °C \n", + "• Beijing: ≈ 13 °C \n", + "• London: ≈ 11 °C \n", + "\n", + "So Rio de Janeiro has the highest average temperature.\n", + "Among those four, London has the lowest average annual temperature, at about 11 °C.\n" + ] + } + ], + "source": [ + "response = client.responses.create(\n", + " input=\"Which of the last four Olympic host cities has the highest average temperature?\",\n", + " **MODEL_DEFAULTS\n", + ")\n", + "print(response.output_text)\n", + "\n", + "response = client.responses.create(\n", + " input=\"what about the lowest?\",\n", + " previous_response_id=response.id,\n", + " **MODEL_DEFAULTS\n", + ")\n", + "print(response.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice and easy!\n", + "\n", + "We're asking relatively complex questions that may require the model to reason out a plan and proceed through it in steps, but this reasoning is hidden from us - we simply wait a little longer before being shown the response. \n", + "\n", + "However, if we inspect the output we can see that the model has made use of a hidden set of 'reasoning' tokens that were included in the model context window, but not exposed to us as end users.\n", + "We can see these tokens and a summary of the reasoning (but not the literal tokens used) in the response." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**Determining lowest temperatures**\n", + "\n", + "The user is asking about the lowest average temperatures of the last four Olympic host cities: Tokyo, Rio, London, and Beijing. I see London has the lowest average temperature at around 11°C. If I double-check the annual averages: Rio is about 23°C, Tokyo is around 16°C, and Beijing is approximately 13°C. So, my final answer is London with an average of roughly 11°C. I could provide those approximate values clearly for the user.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'input_tokens': 136,\n", + " 'input_tokens_details': {'cached_tokens': 0},\n", + " 'output_tokens': 89,\n", + " 'output_tokens_details': {'reasoning_tokens': 64},\n", + " 'total_tokens': 225}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(next(rx for rx in response.output if rx.type == 'reasoning').summary[0].text)\n", + "response.usage.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is important to know about these reasoning tokens, because it means we will consume our available context window more quickly than with traditional chat models.\n", + "\n", + "## Calling custom functions\n", + "What happens if we ask the model a complex request that also requires the use of custom tools?\n", + "* Let's imagine we have more questions about Olympic Cities, but we also have an internal database that contains IDs for each city.\n", + "* It's possible that the model will need to invoke our tool partway through its reasoning process before returning a result.\n", + "* Let's make a function that produces a random UUID and ask the model to reason about these UUIDs. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "\n", + "def get_city_uuid(city: str) -> str:\n", + " \"\"\"Just a fake tool to return a fake UUID\"\"\"\n", + " uuid = str(uuid4())\n", + " return f\"{city} ID: {uuid}\"\n", + "\n", + "# The tool schema that we will pass to the model\n", + "tools = [\n", + " {\n", + " \"type\": \"function\",\n", + " \"name\": \"get_city_uuid\",\n", + " \"description\": \"Retrieve the internal ID for a city from the internal database. Only invoke this function if the user needs to know the internal ID for a city.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"city\": {\"type\": \"string\", \"description\": \"The name of the city to get information about\"}\n", + " },\n", + " \"required\": [\"city\"]\n", + " }\n", + " }\n", + "]\n", + "\n", + "# This is a general practice - we need a mapping of the tool names we tell the model about, and the functions that implement them.\n", + "tool_mapping = {\n", + " \"get_city_uuid\": get_city_uuid\n", + "}\n", + "\n", + "# Let's add this to our defaults so we don't have to pass it every time\n", + "MODEL_DEFAULTS[\"tools\"] = tools\n", + "\n", + "response = client.responses.create(\n", + " input=\"What's the internal ID for the lowest-temperature city?\",\n", + " previous_response_id=response.id,\n", + " **MODEL_DEFAULTS)\n", + "print(response.output_text)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We didn't get an `output_text` this time. Let's look at the response output" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ResponseReasoningItem(id='rs_68246219e8288191af051173b1d53b3f0c4fbdb0d4a46f3c', summary=[], type='reasoning', status=None),\n", + " ResponseFunctionToolCall(arguments='{\"city\":\"London\"}', call_id='call_Mx6pyTjCkSkmASETsVASogoC', name='get_city_uuid', type='function_call', id='fc_6824621b8f6c8191a8095df7230b611e0c4fbdb0d4a46f3c', status='completed')]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response.output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Along with the reasoning step, the model has successfully identified the need for a tool call and passed back instructions to send to our function call. \n", + "\n", + "Let's invoke the function and send the results to the model so it can continue reasoning.\n", + "Function responses are a special kind of message, so we need to structure our next message as a special kind of input:\n", + "```json\n", + "{\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": function_call.call_id,\n", + " \"output\": tool_output\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the function call(s) from the response\n", + "new_conversation_items = []\n", + "function_calls = [rx for rx in response.output if rx.type == 'function_call']\n", + "for function_call in function_calls:\n", + " target_tool = tool_mapping.get(function_call.name)\n", + " if not target_tool:\n", + " raise ValueError(f\"No tool found for function call: {function_call.name}\")\n", + " arguments = json.loads(function_call.arguments) # Load the arguments as a dictionary\n", + " tool_output = target_tool(**arguments) # Invoke the tool with the arguments\n", + " new_conversation_items.append({\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": function_call.call_id, # We map the response back to the original function call\n", + " \"output\": tool_output\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The internal ID for London is 816bed76-b956-46c4-94ec-51d30b022725.\n" + ] + } + ], + "source": [ + "response = client.responses.create(\n", + " input=new_conversation_items,\n", + " previous_response_id=response.id,\n", + " **MODEL_DEFAULTS\n", + ")\n", + "print(response.output_text)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works great here - as we know that a single function call is all that is required for the model to respond - but we also need to account for situations where multiple tool calls might need to be executed for the reasoning to complete.\n", + "\n", + "Let's add a second call to run a web search.\n", + "\n", + "OpenAI's web search tool is not available out of the box with reasoning models (as of May 2025 - this may soon change) but it's not too hard to create a custom web search function using 4o mini or another web search enabled model." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def web_search(query: str) -> str:\n", + " \"\"\"Search the web for information and return back a summary of the results\"\"\"\n", + " result = client.responses.create(\n", + " model=\"gpt-4o-mini\",\n", + " input=f\"Search the web for '{query}' and reply with only the result.\",\n", + " tools=[{\"type\": \"web_search_preview\"}],\n", + " )\n", + " return result.output_text\n", + "\n", + "tools.append({\n", + " \"type\": \"function\",\n", + " \"name\": \"web_search\",\n", + " \"description\": \"Search the web for information and return back a summary of the results\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\"type\": \"string\", \"description\": \"The query to search the web for.\"}\n", + " },\n", + " \"required\": [\"query\"]\n", + " }\n", + " })\n", + "tool_mapping[\"web_search\"] = web_search\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Executing multiple functions in series\n", + "\n", + "Some OpenAI models support the parameter `parallel_tool_calls` which allows the model to return an array of functions which we can then execute in parallel. However, reasoning models may produce a sequence of function calls that must be made in series, particularly as some steps may depend on the results of previous ones.\n", + "As such, we ought to define a general pattern which we can use to handle arbitrarily complex reasoning workflows:\n", + "* At each step in the conversation, initialise a loop\n", + "* If the response contains function calls, we must assume the reasoning is ongoing and we should feed the function results (and any intermediate reasoning) back into the model for further inference\n", + "* If there are no function calls and we instead receive a Reponse.output with a type of 'message', we can safely assume the agent has finished reasoning and we can break out of the loop" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Let's wrap our logic above into a function which we can use to invoke tool calls.\n", + "def invoke_functions_from_response(response,\n", + " tool_mapping: dict[str, Callable] = tool_mapping\n", + " ) -> list[dict]:\n", + " \"\"\"Extract all function calls from the response, look up the corresponding tool function(s) and execute them.\n", + " (This would be a good place to handle asynchroneous tool calls, or ones that take a while to execute.)\n", + " This returns a list of messages to be added to the conversation history.\n", + " \"\"\"\n", + " intermediate_messages = []\n", + " for response_item in response.output:\n", + " if response_item.type == 'function_call':\n", + " target_tool = tool_mapping.get(response_item.name)\n", + " if target_tool:\n", + " try:\n", + " arguments = json.loads(response_item.arguments)\n", + " print(f\"Invoking tool: {response_item.name}({arguments})\")\n", + " tool_output = target_tool(**arguments)\n", + " except Exception as e:\n", + " msg = f\"Error executing function call: {response_item.name}: {e}\"\n", + " tool_output = msg\n", + " print(msg)\n", + " else:\n", + " msg = f\"ERROR - No tool registered for function call: {response_item.name}\"\n", + " tool_output = msg\n", + " print(msg)\n", + " intermediate_messages.append({\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": response_item.call_id,\n", + " \"output\": tool_output\n", + " })\n", + " elif response_item.type == 'reasoning':\n", + " print(f'Reasoning step: {response_item.summary}')\n", + " return intermediate_messages" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's demonstrate the loop concept we discussed before." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Beijing'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'London'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Rio de Janeiro'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Tokyo'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Paris'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Turin'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Vancouver'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Sochi'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Pyeongchang'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Beijing Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 London Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Rio de Janeiro Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Tokyo Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Paris Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Turin Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Vancouver Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: [Summary(text='**Focusing on Olympic News**\\n\\nI need to clarify that the Invictus Games are not related to the Olympics, so I should exclude them from my search. That leaves me with Olympic-specific news focusing on Paris. I also want to consider past events, like Sochi and Pyeongchang, so I think it makes sense to search for news related to Sochi as well. Let’s focus on gathering relevant Olympic updates to keep things organized.', type='summary_text')]\n", + "Invoking tool: web_search({'query': '2025 Sochi Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': '2025 Pyeongchang Olympics news'})\n", + "More reasoning required, continuing...\n", + "Reasoning step: []\n", + "Here are the internal IDs for all cities that have hosted Olympic Games in the last 20 years (2005–2025), along with those cities that have notable 2025 news stories specifically about the Olympics:\n", + "\n", + "1. Beijing (2008 Summer; 2022 Winter) \n", + " • UUID: 5b058554-7253-4d9d-a434-5d4ccc87c78b \n", + " • 2025 Olympic News? No major Olympic-specific news in 2025\n", + "\n", + "2. London (2012 Summer) \n", + " • UUID: 9a67392d-c319-4598-b69a-adc5ffdaaba2 \n", + " • 2025 Olympic News? No\n", + "\n", + "3. Rio de Janeiro (2016 Summer) \n", + " • UUID: ad5eaaae-b280-4c1d-9360-3a38b0c348c3 \n", + " • 2025 Olympic News? No\n", + "\n", + "4. Tokyo (2020 Summer) \n", + " • UUID: 66c3a62a-840c-417a-8fad-ce87b97bb6a3 \n", + " • 2025 Olympic News? No\n", + "\n", + "5. Paris (2024 Summer) \n", + " • UUID: a2da124e-3fad-402b-8ccf-173f63b4ff68 \n", + " • 2025 Olympic News? Yes \n", + " – Olympic cauldron balloon to float annually over Paris into 2028 ([AP News]) \n", + " – IOC to replace defective Paris 2024 medals ([NDTV Sports]) \n", + " – IOC elects Kirsty Coventry as president at March 2025 session ([Wikipedia]) \n", + " – MLB cancels its planned 2025 Paris regular-season games ([AP News])\n", + "\n", + "6. Turin (2006 Winter) \n", + " • UUID: 3674750b-6b76-49dc-adf4-d4393fa7bcfa \n", + " • 2025 Olympic News? No (Host of Special Olympics World Winter Games, but not mainline Olympics)\n", + "\n", + "7. Vancouver (2010 Winter) \n", + " • UUID: 22517787-5915-41c8-b9dd-a19aa2953210 \n", + " • 2025 Olympic News? No\n", + "\n", + "8. Sochi (2014 Winter) \n", + " • UUID: f7efa267-c7da-4cdc-a14f-a4844f47b888 \n", + " • 2025 Olympic News? No\n", + "\n", + "9. Pyeongchang (2018 Winter) \n", + " • UUID: ffb19c03-5212-42a9-a527-315d35efc5fc \n", + " • 2025 Olympic News? No\n", + "\n", + "Summary of cities with 2025 Olympic-related news: \n", + "• Paris (a2da124e-3fad-402b-8ccf-173f63b4ff68)\n" + ] + } + ], + "source": [ + "initial_question = (\n", + " \"What are the internal IDs for the cities that have hosted the Olympics in the last 20 years, \"\n", + " \"and which of those cities have recent news stories (in 2025) about the Olympics? \"\n", + " \"Use your internal tools to look up the IDs and the web search tool to find the news stories.\"\n", + ")\n", + "\n", + "# We fetch a response and then kick off a loop to handle the response\n", + "response = client.responses.create(\n", + " input=initial_question,\n", + " **MODEL_DEFAULTS,\n", + ")\n", + "while True: \n", + " function_responses = invoke_functions_from_response(response)\n", + " if len(function_responses) == 0: # We're done reasoning\n", + " print(response.output_text)\n", + " break\n", + " else:\n", + " print(\"More reasoning required, continuing...\")\n", + " response = client.responses.create(\n", + " input=function_responses,\n", + " previous_response_id=response.id,\n", + " **MODEL_DEFAULTS\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manual conversation orchestration\n", + "So far so good! It's really cool to watch the model pause execution to run a function before continuing. \n", + "In practice the example above is quite trivial, and production use cases may be much more complex:\n", + "* Our context window may grow too large and we may wish to prune older and less relevant messages, or summarize the conversation so far\n", + "* We may wish to allow users to navigate back and forth through the conversation and re-generate answers\n", + "* We may wish to store messages in our own database for audit purposes rather than relying on OpenAI's storage and orchestration\n", + "* etc.\n", + "\n", + "In these situations we may wish to take full control of the conversation. Rather than using `previous_message_id` we can instead treat the API as 'stateless' and make and maintain an array of conversation items that we send to the model as input each time.\n", + "\n", + "This poses some Reasoning model specific nuances to consider. \n", + "* In particular, it is essential that we preserve any reasoning and function call responses in our conversation history.\n", + "* This is how the model keeps track of what chain-of-thought steps it has run through. The API will error if these are not included.\n", + "\n", + "Let's run through the example above again, orchestrating the messages ourselves and tracking token usage.\n", + "\n", + "---\n", + "*Note that the code below is structured for readibility - in practice you may wish to consider a more sophisticated workflow to handle edge cases*" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*******************************************************************************\n", + "User message: Of those cities that have hosted the summer Olympic games in the last 20 years - do any of them have IDs beginning with a number and a temperate climate? Use your available tools to look up the IDs for each city and make sure to search the web to find out about the climate.\n", + "*******************************************************************************\n", + "More reasoning required, continuing...\n", + "**Clarifying Olympic Cities**\n", + "\n", + "The user is asking about cities that hosted the Summer Olympics in the last 20 years. The relevant years to consider are 2004 Athens, 2008 Beijing, 2012 London, 2016 Rio de Janeiro, and 2020 Tokyo. If we're considering 2025, then 2004 would actually be 21 years ago, so I should focus instead on the years from 2005 onwards. Therefore, the cities to include are Beijing, London, Rio, and Tokyo. I’ll exclude Paris since it hasn’t hosted yet.\n", + "Reasoning step: [Summary(text=\"**Clarifying Olympic Cities**\\n\\nThe user is asking about cities that hosted the Summer Olympics in the last 20 years. The relevant years to consider are 2004 Athens, 2008 Beijing, 2012 London, 2016 Rio de Janeiro, and 2020 Tokyo. If we're considering 2025, then 2004 would actually be 21 years ago, so I should focus instead on the years from 2005 onwards. Therefore, the cities to include are Beijing, London, Rio, and Tokyo. I’ll exclude Paris since it hasn’t hosted yet.\", type='summary_text')]\n", + "Invoking tool: get_city_uuid({'city': 'Beijing'})\n", + "Invoking tool: get_city_uuid({'city': 'London'})\n", + "Invoking tool: get_city_uuid({'city': 'Rio de Janeiro'})\n", + "Invoking tool: get_city_uuid({'city': 'Tokyo'})\n", + "More reasoning required, continuing...\n", + "\n", + "Reasoning step: []\n", + "Invoking tool: web_search({'query': 'London climate'})\n", + "Invoking tool: web_search({'query': 'Tokyo climate'})\n", + "More reasoning required, continuing...\n", + "\n", + "I looked up the internal IDs and climates for each Summer-Olympics host of the last 20 years:\n", + "\n", + "• Beijing \n", + " – ID: 937b336d-2708-4ad3-8c2f-85ea32057e1e (starts with “9”) \n", + " – Climate: humid continental (cold winters, hot summers) → not temperate\n", + "\n", + "• London \n", + " – ID: ee57f35a-7d1b-4888-8833-4ace308fa004 (starts with “e”) \n", + " – Climate: temperate oceanic (mild, moderate rainfall)\n", + "\n", + "• Rio de Janeiro \n", + " – ID: 2a70c45e-a5b4-4e42-8d2b-6c1dbb2aa2d9 (starts with “2”) \n", + " – Climate: tropical (hot/wet)\n", + "\n", + "• Tokyo \n", + " – ID: e5de3686-a7d2-42b8-aca5-6b6e436083ff (starts with “e”) \n", + " – Climate: humid subtropical (hot, humid summers; mild winters)\n", + "\n", + "The only IDs that begin with a numeral are Beijing (“9…”) and Rio (“2…”), but neither city has a temperate climate. Therefore, none of the last-20-years hosts combine an ID starting with a number with a temperate climate.\n", + "*******************************************************************************\n", + "User message: Great thanks! We've just updated the IDs - could you please check again?\n", + "*******************************************************************************\n", + "More reasoning required, continuing...\n", + "\n", + "Reasoning step: []\n", + "Invoking tool: get_city_uuid({'city': 'Beijing'})\n", + "Invoking tool: get_city_uuid({'city': 'London'})\n", + "Invoking tool: get_city_uuid({'city': 'Rio de Janeiro'})\n", + "Invoking tool: get_city_uuid({'city': 'Tokyo'})\n", + "Here are the updated IDs along with their climates:\n", + "\n", + "• Beijing \n", + " – ID: 8819a1fd-a958-40e6-8ba7-9f450b40fb13 (starts with “8”) \n", + " – Climate: humid continental → not temperate\n", + "\n", + "• London \n", + " – ID: 50866ef9-6505-4939-90e7-e8b930815782 (starts with “5”) \n", + " – Climate: temperate oceanic\n", + "\n", + "• Rio de Janeiro \n", + " – ID: 5bc1b2de-75da-4689-8bff-269e60af32cb (starts with “5”) \n", + " – Climate: tropical → not temperate\n", + "\n", + "• Tokyo \n", + " – ID: 9d1c920e-e725-423e-b83c-ec7d97f2e79f (starts with “9”) \n", + " – Climate: humid subtropical → not temperate\n", + "\n", + "Of these, the only city with a temperate climate is London, but its ID begins with “5” (a number) – so it does meet “ID beginning with a number AND temperate climate.” \n", + "Total tokens used: 17154 (8.58% of o4-mini's context window)\n" + ] + } + ], + "source": [ + "# Let's initialise our conversation with the first user message\n", + "total_tokens_used = 0\n", + "user_messages = [\n", + " (\n", + " \"Of those cities that have hosted the summer Olympic games in the last 20 years - \"\n", + " \"do any of them have IDs beginning with a number and a temperate climate? \"\n", + " \"Use your available tools to look up the IDs for each city and make sure to search the web to find out about the climate.\"\n", + " ),\n", + " \"Great thanks! We've just updated the IDs - could you please check again?\"\n", + " ]\n", + "\n", + "conversation = []\n", + "for message in user_messages:\n", + " conversation_item = {\n", + " \"role\": \"user\",\n", + " \"type\": \"message\",\n", + " \"content\": message\n", + " }\n", + " print(f\"{'*' * 79}\\nUser message: {message}\\n{'*' * 79}\")\n", + " conversation.append(conversation_item)\n", + " while True: # Response loop\n", + " response = client.responses.create(\n", + " input=conversation,\n", + " **MODEL_DEFAULTS\n", + " )\n", + " total_tokens_used += response.usage.total_tokens\n", + " reasoning = [rx.to_dict() for rx in response.output if rx.type == 'reasoning']\n", + " function_calls = [rx.to_dict() for rx in response.output if rx.type == 'function_call']\n", + " messages = [rx.to_dict() for rx in response.output if rx.type == 'message']\n", + " if len(reasoning) > 0:\n", + " print(\"More reasoning required, continuing...\")\n", + " # Ensure we capture any reasoning steps\n", + " conversation.extend(reasoning)\n", + " print('\\n'.join(s['text'] for r in reasoning for s in r['summary']))\n", + " if len(function_calls) > 0:\n", + " function_outputs = invoke_functions_from_response(response)\n", + " # Preserve order of function calls and outputs in case of multiple function calls (currently not supported by reasoning models, but worth considering)\n", + " interleaved = [val for pair in zip(function_calls, function_outputs) for val in pair]\n", + " conversation.extend(interleaved)\n", + " if len(messages) > 0:\n", + " print(response.output_text)\n", + " conversation.extend(messages)\n", + " if len(function_calls) == 0: # No more functions = We're done reasoning and we're ready for the next user message\n", + " break\n", + "print(f\"Total tokens used: {total_tokens_used} ({total_tokens_used / 200_000:.2%} of o4-mini's context window)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "In this cookbook, we identified how to combine function calling with OpenAI's reasoning models to demonstrate multi-step tasks that are dependent on external data sources., including searching the web.\n", + "\n", + "Importantly, we covered reasoning-model specific nuances in the function calling process, specifically that:\n", + "* The model may choose to make multiple function calls or reasoning steps in series, and some steps may depend on the results of previous ones\n", + "* We cannot know how many of these steps there will be, so we must process responses with a loop\n", + "* The responses API makes orchestration easy using the `previous_response_id` parameter, but where manual control is needed, it's important to maintain the correct order of conversation item to preserve the 'chain-of-thought'\n", + "\n", + "---\n", + "\n", + "The examples used here are rather simple, but you can imagine how this technique could be extended to more real-world use cases, such as:\n", + "\n", + "* Looking up a customer's transaction history and recent correspondence to determine if they are eligible for a promotional offer\n", + "* Calling recent transaction logs, geolocation data, and device metadata to assess the likelihood of a transaction being fraudulent\n", + "* Reviewing internal HR databases to fetch an employee’s benefits usage, tenure, and recent policy changes to answer personalized HR questions\n", + "* Reading internal dashboards, competitor news feeds, and market analyses to compile a daily executive briefing tailored to their focus areas" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/responses_api/reasoning_items.ipynb b/examples/responses_api/reasoning_items.ipynb new file mode 100644 index 0000000000..19e0de6284 --- /dev/null +++ b/examples/responses_api/reasoning_items.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Better performance from reasoning models using the Responses API \n", + "\n", + "### Overview\n", + "\n", + "By leveraging the Responses API with OpenAI’s latest reasoning models, you can unlock higher intelligence, lower costs, and more efficient token usage in your applications. The API also enables access to reasoning summaries, supports features like hosted-tool use, and is designed to accommodate upcoming enhancements for even greater flexibility and performance.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We've recently released two new state-of-the-art reasoning models, o3 and o4-mini, that excel at combining reasoning capabilities with agentic tool use. What many folks don't know is that you can improve their performance by fully leveraging our (relatively) new Responses API. This cookbook shows how to get the most out of these models and explores how reasoning and function calling work behind the scenes. By giving the model access to previous reasoning items, we can ensure it operates at maximum intelligence and lowest cost." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We introduced the Responses API with a separate [cookbook](https://cookbook.openai.com/examples/responses_api/responses_example) and [API reference](https://platform.openai.com/docs/api-reference/responses). The main takeaway: the Responses API is similar to the Completions API, but with improvements and added features. We've also rolled out encrypted content for Responses, making it even more useful for those who can't use the API in a stateful way!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How Reasoning Models work\n", + "\n", + "Before we dive into how the Responses API can help, let's quickly review how [reasoning models](https://platform.openai.com/docs/guides/reasoning?api-mode=responses) work. Models like o3 and o4-mini break problems down step by step, producing an internal chain of thought that encodes their reasoning. For safety, these reasoning tokens are only exposed to users in summarized form." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a multistep conversation, the reasoning tokens are discarded after each turn while input and output tokens from each step are fed into the next\n", + "\n", + "![reasoning-context](../../images/reasoning-turns.png)\n", + "Diagram borrowed from our [doc](https://platform.openai.com/docs/guides/reasoning?api-mode=responses#how-reasoning-works)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us examine the response object being returned:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import os\n", + "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "response = client.responses.create(\n", + " model=\"o4-mini\",\n", + " input=\"tell me a joke\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"id\": \"resp_6820f382ee1c8191bc096bee70894d040ac5ba57aafcbac7\",\n", + " \"created_at\": 1746989954.0,\n", + " \"error\": null,\n", + " \"incomplete_details\": null,\n", + " \"instructions\": null,\n", + " \"metadata\": {},\n", + " \"model\": \"o4-mini-2025-04-16\",\n", + " \"object\": \"response\",\n", + " \"output\": [\n", + " {\n", + " \"id\": \"rs_6820f383d7c08191846711c5df8233bc0ac5ba57aafcbac7\",\n", + " \"summary\": [],\n", + " \"type\": \"reasoning\",\n", + " \"status\": null\n", + " },\n", + " {\n", + " \"id\": \"msg_6820f3854688819187769ff582b170a60ac5ba57aafcbac7\",\n", + " \"content\": [\n", + " {\n", + " \"annotations\": [],\n", + " \"text\": \"Why don\\u2019t scientists trust atoms? \\nBecause they make up everything!\",\n", + " \"type\": \"output_text\"\n", + " }\n", + " ],\n", + " \"role\": \"assistant\",\n", + " \"status\": \"completed\",\n", + " \"type\": \"message\"\n", + " }\n", + " ],\n", + " \"parallel_tool_calls\": true,\n", + " \"temperature\": 1.0,\n", + " \"tool_choice\": \"auto\",\n", + " \"tools\": [],\n", + " \"top_p\": 1.0,\n", + " \"max_output_tokens\": null,\n", + " \"previous_response_id\": null,\n", + " \"reasoning\": {\n", + " \"effort\": \"medium\",\n", + " \"generate_summary\": null,\n", + " \"summary\": null\n", + " },\n", + " \"status\": \"completed\",\n", + " \"text\": {\n", + " \"format\": {\n", + " \"type\": \"text\"\n", + " }\n", + " },\n", + " \"truncation\": \"disabled\",\n", + " \"usage\": {\n", + " \"input_tokens\": 10,\n", + " \"input_tokens_details\": {\n", + " \"cached_tokens\": 0\n", + " },\n", + " \"output_tokens\": 148,\n", + " \"output_tokens_details\": {\n", + " \"reasoning_tokens\": 128\n", + " },\n", + " \"total_tokens\": 158\n", + " },\n", + " \"user\": null,\n", + " \"service_tier\": \"default\",\n", + " \"store\": true\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "print(json.dumps(response.model_dump(), indent=2))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the JSON dump of the response object, you can see that in addition to the `output_text`, the model also produces a reasoning item. This item represents the model's internal reasoning tokens and is exposed as an ID—here, for example, `rs_6820f383d7c08191846711c5df8233bc0ac5ba57aafcbac7`. Because the Responses API is stateful, these reasoning tokens persist: just include their IDs in subsequent messages to give future responses access to the same reasoning items. If you use `previous_response_id` for multi-turn conversations, the model will automatically have access to all previously produced reasoning items.\n", + "\n", + "You can also see how many reasoning tokens the model generated. For example, with 10 input tokens, the response included 148 output tokens—128 of which are reasoning tokens not shown in the final assistant message." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wait—didn’t the diagram show that reasoning from previous turns is discarded? So why bother passing it back in later turns?\n", + "\n", + "Great question! In typical multi-turn conversations, you don’t need to include reasoning items or tokens—the model is trained to produce the best output without them. However, things change when tool use is involved. If a turn includes a function call (which may require an extra round trip outside the API), you do need to include the reasoning items—either via `previous_response_id` or by explicitly adding the reasoning item to `input`. Let’s see how this works with a quick function-calling example." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ResponseReasoningItem(id='rs_68210c71a95c81919cc44afadb9d220400c77cc15fd2f785', summary=[], type='reasoning', status=None),\n", + " ResponseFunctionToolCall(arguments='{\"latitude\":48.8566,\"longitude\":2.3522}', call_id='call_9ylqPOZUyFEwhxvBwgpNDqPT', name='get_weather', type='function_call', id='fc_68210c78357c8191977197499d5de6ca00c77cc15fd2f785', status='completed')]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "\n", + "def get_weather(latitude, longitude):\n", + " response = requests.get(f\"https://api.open-meteo.com/v1/forecast?latitude={latitude}&longitude={longitude}¤t=temperature_2m,wind_speed_10m&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m\")\n", + " data = response.json()\n", + " return data['current']['temperature_2m']\n", + "\n", + "\n", + "tools = [{\n", + " \"type\": \"function\",\n", + " \"name\": \"get_weather\",\n", + " \"description\": \"Get current temperature for provided coordinates in celsius.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"latitude\": {\"type\": \"number\"},\n", + " \"longitude\": {\"type\": \"number\"}\n", + " },\n", + " \"required\": [\"latitude\", \"longitude\"],\n", + " \"additionalProperties\": False\n", + " },\n", + " \"strict\": True\n", + "}]\n", + "\n", + "context = [{\"role\": \"user\", \"content\": \"What's the weather like in Paris today?\"}]\n", + "\n", + "response = client.responses.create(\n", + " model=\"o4-mini\",\n", + " input=context,\n", + " tools=tools,\n", + ")\n", + "\n", + "\n", + "response.output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After some reasoning, the o4-mini model determines it needs more information and calls a function to get it. We can call the function and return its output to the model. Crucially, to maximize the model’s intelligence, we should include the reasoning item by simply adding all of the output back into the context for the next turn." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The current temperature in Paris is 16.3°C. If you’d like more details—like humidity, wind speed, or a brief description of the sky—just let me know!\n" + ] + } + ], + "source": [ + "context += response.output # Add the response to the context (including the reasoning item)\n", + "\n", + "tool_call = response.output[1]\n", + "args = json.loads(tool_call.arguments)\n", + "\n", + "\n", + "# calling the function\n", + "result = get_weather(args[\"latitude\"], args[\"longitude\"]) \n", + "\n", + "context.append({ \n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call.call_id,\n", + " \"output\": str(result)\n", + "})\n", + "\n", + "# we are calling the api again with the added function call output. Note that while this is another API call, we consider this as a single turn in the conversation.\n", + "response_2 = client.responses.create(\n", + " model=\"o4-mini\",\n", + " input=context,\n", + " tools=tools,\n", + ")\n", + "\n", + "print(response_2.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While this toy example may not clearly show the benefits—since the model will likely perform well with or without the reasoning item—our own tests found otherwise. On a more rigorous benchmark like SWE-bench, including reasoning items led to about a **3% improvement** for the same prompt and setup." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caching\n", + "\n", + "As shown above, reasoning models generate both reasoning tokens and completion tokens, which the API handles differently. This distinction affects how caching works and impacts both performance and latency. The following diagram illustrates these concepts:\n", + "\n", + "![reasoning-context](../../images/responses-diagram.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In turn 2, any reasoning items from turn 1 are ignored and removed, since the model does not reuse reasoning items from previous turns. As a result, the fourth API call in the diagram cannot achieve a full cache hit, because those reasoning items are missing from the prompt. However, including them is harmless—the API will simply discard any reasoning items that aren’t relevant for the current turn. Keep in mind that caching only impacts prompts longer than 1024 tokens. In our tests, switching from the Completions API to the Responses API boosted cache utilization from 40% to 80%. Higher cache utilization leads to lower costs (for example, cached input tokens for `o4-mini` are 75% cheaper than uncached ones) and improved latency." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Encrypted Reasoning Items\n", + "\n", + "Some organizations—such as those with [Zero Data Retention (ZDR)](https://openai.com/enterprise-privacy/) requirements—cannot use the Responses API in a stateful way due to compliance or data retention policies. To support these cases, OpenAI offers [encrypted reasoning items](https://platform.openai.com/docs/guides/reasoning?api-mode=responses#encrypted-reasoning-items), allowing you to keep your workflow stateless while still benefiting from reasoning items.\n", + "\n", + "To use encrypted reasoning items:\n", + "- Add `[\"reasoning.encrypted_content\"]` to the `include` field in your API call.\n", + "- The API will return an encrypted version of the reasoning tokens, which you can pass back in future requests just like regular reasoning items.\n", + "\n", + "For ZDR organizations, OpenAI enforces `store=false` automatically. When a request includes `encrypted_content`, it is decrypted in-memory (never written to disk), used for generating the next response, and then securely discarded. Any new reasoning tokens are immediately encrypted and returned to you, ensuring no intermediate state is ever persisted.\n", + "\n", + "Here’s a quick code update to show how this works:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "context = [{\"role\": \"user\", \"content\": \"What's the weather like in Paris today?\"}]\n", + "\n", + "response = client.responses.create(\n", + " model=\"o3\",\n", + " input=context,\n", + " tools=tools,\n", + " store=False, #store=false, just like how ZDR is enforced\n", + " include=[\"reasoning.encrypted_content\"] # Encrypted chain of thought is passed back in the response\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ResponseReasoningItem(id='rs_6821243503d481919e1b385c2a154d5103d2cbc5a14f3696', summary=[], type='reasoning', status=None, encrypted_content='gAAAAABoISQ24OyVRYbkYfukdJoqdzWT-3uiErKInHDC-lgAaXeky44N77j7aibc2elHISjAvX7OmUwMU1r7NgaiHSVWL5BtWgXVBp4BMFkWZpXpZY7ff5pdPFnW3VieuF2cSo8Ay7tJ4aThGUnXkNM5QJqk6_u5jwd-W9cTHjucw9ATGfGqD2qHrXyj6NEW9RmpWHV2SK41d5TpUYdN0xSuIUP98HBVZ2VGgD4MIocUm6Lx0xhRl9KUx19f7w4Sn7SCpKUQ0zwXze8UsQOVvv1HQxk_yDosbIg1SylEj38H-DNLil6yUFlWI4vGWcPn1bALXphTR2EwYVR52nD1rCFEORUd7prS99i18MUMSAhghIVv9OrpbjmfxJh8bSQaHu1ZDTMWcfC58H3i8KnogmI7V_h2TKAiLTgSQIkYRHnV3hz1XwaUqYAIhBvP6c5UxX-j_tpYpB_XCpD886L0XyJxCmfr9cwitipOhHr8zfLVwMI4ULu-P3ftw7QckIVzf71HFLNixrQlkdgTn-zM6aQl5BZcJgwwn3ylJ5ji4DQTS1H3AiTrFsEt4kyiBcE2d7tYA_m3G8L-e4-TuTDdJZtLaz-q8J12onFaKknGSyU6px8Ki4IPqnWIJw8SaFMJ5fSUYJO__myhp7lbbQwuOZHIQuvKutM-QUuR0cHus_HtfWtZZksqvVCVNBYViBxD2_KvKJvR-nN62zZ8sNiydIclt1yJfIMkiRErfRTzv92hQaUtdqz80UiW7FBcN2Lnzt8awXCz1pnGyWy_hNQe8C7W35zRxJDwFdb-f3VpanJT0tNmU5bfEWSXcIVmiMZL1clwzVNryf9Gk482LaWPwhVYrhv2MkhKMPKdeAZWVhZbgm0eTT8a4DgbwcYRGhoXMGxrXWzOdvAY536DkrI_0xsJk8-Szb5Y2EH0xPxN4-CdB_fMPP60TPEQTOP1Qc64cJcQ9p2JE5Jfz59bubF_QGajC9-FtHkD6Q5pT-6CbhuD6xrFJMgxQPcggSDaWL_4260fZCdf6nzMlwPRD3wrfsxs6rFyd8pLC-2SOh9Iv297xAjes8xcnyqvMKSuCkjARr11gJCe0EXnx87NWt2rfW8ODUU0qFYbjFx8Rj9WJtnvQBNyqp7t5LLLf12S8pyyeKTv0ePqC3xDuWdFKmELDUZjarkkCyMHoO12EbXa6YCpY_MpA01c2vV5plrcouVPSwRK0ahbPs0mQnQnDAkfi2XVS0Bzgk2GpNONGf7KWkzD7uTgDtg9UbWI0v_-f-iiBM2kKDz_dIb1opZfaxZEloyiQ2MnWQj2MRefL7WM_0c3IyTAccICN-diGn2f1im82uL9maELcbYn')\n" + ] + } + ], + "source": [ + "# take a look at the encrypted reasoning item\n", + "print(response.output[0]) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `include=[\"reasoning.encrypted_content\"]` set, we now see an `encrypted_content` field in the reasoning item being passed back. This encrypted content represents the model's reasoning state, persisted entirely on the client side with OpenAI retaining no data. We can then pass this back just as we did with the reasoning item before." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It’s currently about 20 °C in Paris.\n" + ] + } + ], + "source": [ + "context += response.output # Add the response to the context (including the encrypted chain of thought)\n", + "tool_call = response.output[1]\n", + "args = json.loads(tool_call.arguments)\n", + "\n", + "\n", + "\n", + "result = 20 #mocking the result of the function call\n", + "\n", + "context.append({ \n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call.call_id,\n", + " \"output\": str(result)\n", + "})\n", + "\n", + "response_2 = client.responses.create(\n", + " model=\"o3\",\n", + " input=context,\n", + " tools=tools,\n", + " store=False,\n", + " include=[\"reasoning.encrypted_content\"]\n", + ")\n", + "\n", + "print(response_2.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a simple change to the `include` field, we can now pass back the encrypted reasoning item and use it to improve the model's performance in intelligence, cost, and latency.\n", + "\n", + "Now you should be fully equipped with the knowledge to fully utilize our latest reasoning models!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reasoning Summaries\n", + "\n", + "Another useful feature in the Responses API is that it supports reasoning summaries. While we do not expose the raw chain of thought tokens, users can access their [summaries](https://platform.openai.com/docs/guides/reasoning?api-mode=responses#reasoning-summaries)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First reasoning summary text:\n", + " **Analyzing biological processes**\n", + "\n", + "I think the user is looking for a clear explanation of the differences between certain processes. I should create a side-by-side comparison that lists out key elements like the formulas, energy flow, locations, reactants, products, organisms involved, electron carriers, and whether the processes are anabolic or catabolic. This structured approach will help in delivering a comprehensive answer. It’s crucial to cover all aspects to ensure the user understands the distinctions clearly.\n" + ] + } + ], + "source": [ + "# Make a hard call to o3 with reasoning summary included\n", + "\n", + "response = client.responses.create(\n", + " model=\"o3\",\n", + " input=\"What are the main differences between photosynthesis and cellular respiration?\",\n", + " reasoning={\"summary\": \"auto\"},\n", + "\n", + " \n", + ")\n", + "\n", + "# Extract the first reasoning summary text from the response object\n", + "first_reasoning_item = response.output[0] # Should be a ResponseReasoningItem\n", + "first_summary_text = first_reasoning_item.summary[0].text if first_reasoning_item.summary else None\n", + "print(\"First reasoning summary text:\\n\", first_summary_text)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reasoning summary text lets you give users a window into the model’s thought process. For example, during conversations with multiple function calls, users can see both which functions were called and the reasoning behind each call—without waiting for the final assistant message. This adds transparency and interactivity to your application’s user experience." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "By leveraging the OpenAI Responses API and the latest reasoning models, you can unlock higher intelligence, improved transparency, and greater efficiency in your applications. Whether you’re utilizing reasoning summaries, encrypted reasoning items for compliance, or optimizing for cost and latency, these tools empower you to build more robust and interactive AI experiences.\n", + "\n", + "Happy building!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/responses_api/responses_api_tool_orchestration.ipynb b/examples/responses_api/responses_api_tool_orchestration.ipynb new file mode 100644 index 0000000000..24e3f4c56a --- /dev/null +++ b/examples/responses_api/responses_api_tool_orchestration.ipynb @@ -0,0 +1,1202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Tool Orchestration with RAG approach using OpenAI's Responses API" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "This cookbook guides you through building dynamic, multi-tool workflows using OpenAI's Responses API. It demonstrates how to implement a Retrieval-Augmented Generation (RAG) approach that intelligently routes user queries to the appropriate in-built or external tools. Whether your query calls for general knowledge or requires accessing specific internal context from a vector database (like Pinecone), this guide shows you how to integrate function calls, web searches in-built tool, and leverage document retrieval to generate accurate, context-aware responses.\n", + "\n", + "For a practical example of performing RAG on PDFs using the Responses API's file search feature, refer to [this](https://cookbook.openai.com/examples/file_search_responses) notebook.\n", + "\n", + "This example showcases the flexibility of the Responses API, illustrating that beyond the internal `file_search` tool—which connects to an internal vector store—there is also the capability to easily connect to external vector databases. This allows for the implementation of a RAG approach in conjunction with hosted tooling, providing a versatile solution for various retrieval and generation tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shikhar/openai_projects/github_repos/success-git/success_new/success/oneoffs/shikhar/responses_rag_cookbook/env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "#%pip install datasets tqdm pandas pinecone openai --quiet\n", + "\n", + "import os\n", + "import time\n", + "from tqdm.auto import tqdm\n", + "from pandas import DataFrame\n", + "from datasets import load_dataset\n", + "import random\n", + "import string\n", + "\n", + "\n", + "# Import OpenAI client and initialize with your API key.\n", + "from openai import OpenAI\n", + "\n", + "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n", + "\n", + "# Import Pinecone client and related specifications.\n", + "from pinecone import Pinecone\n", + "from pinecone import ServerlessSpec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we use a sample medical reasoning dataset from Hugging Face. We convert the dataset into a Pandas DataFrame and merge the “Question” and “Response” columns into a single string. This merged text is used for embedding and later stored as metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example merged text: Question: A 61-year-old woman with a long history of involuntary urine loss during activities like coughing or sneezing but no leakage at night undergoes a gynecological exam and Q-tip test. Based on these findings, what would cystometry most likely reveal about her residual volume and detrusor contractions? Answer: Cystometry in this case of stress urinary incontinence would most likely reveal a normal post-void residual volume, as stress incontinence typically does not involve issues with bladder emptying. Additionally, since stress urinary incontinence is primarily related to physical exertion and not an overactive bladder, you would not expect to see any involuntary detrusor contractions during the test.\n" + ] + } + ], + "source": [ + "# Load the dataset (ensure you're logged in with huggingface-cli if needed)\n", + "ds = load_dataset(\"FreedomIntelligence/medical-o1-reasoning-SFT\", \"en\", split='train[:100]', trust_remote_code=True)\n", + "ds_dataframe = DataFrame(ds)\n", + "\n", + "# Merge the Question and Response columns into a single string.\n", + "ds_dataframe['merged'] = ds_dataframe.apply(\n", + " lambda row: f\"Question: {row['Question']} Answer: {row['Response']}\", axis=1\n", + ")\n", + "print(\"Example merged text:\", ds_dataframe['merged'].iloc[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Question</th>\n", + " <th>Complex_CoT</th>\n", + " <th>Response</th>\n", + " <th>merged</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>A 61-year-old woman with a long history of inv...</td>\n", + " <td>Okay, let's think about this step by step. The...</td>\n", + " <td>Cystometry in this case of stress urinary inco...</td>\n", + " <td>Question: A 61-year-old woman with a long hist...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>A 45-year-old man with a history of alcohol us...</td>\n", + " <td>Alright, let’s break this down. We have a 45-y...</td>\n", + " <td>Considering the clinical presentation of sudde...</td>\n", + " <td>Question: A 45-year-old man with a history of ...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>A 45-year-old man presents with symptoms inclu...</td>\n", + " <td>Okay, so here's a 45-year-old guy who's experi...</td>\n", + " <td>Based on the clinical findings presented—wide-...</td>\n", + " <td>Question: A 45-year-old man presents with symp...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>A patient with psoriasis was treated with syst...</td>\n", + " <td>I'm thinking about this patient with psoriasis...</td>\n", + " <td>The development of generalized pustules in a p...</td>\n", + " <td>Question: A patient with psoriasis was treated...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>What is the most likely diagnosis for a 2-year...</td>\n", + " <td>Okay, so we're dealing with a 2-year-old child...</td>\n", + " <td>Based on the described symptoms and the unusua...</td>\n", + " <td>Question: What is the most likely diagnosis fo...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>95</th>\n", + " <td>An electrical current flows along a flat plate...</td>\n", + " <td>Alright, to find out the temperature at the ce...</td>\n", + " <td>The correct answer is F. 1549°F.</td>\n", + " <td>Question: An electrical current flows along a ...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>96</th>\n", + " <td>A herpetologist bitten by a poisonous snake is...</td>\n", + " <td>Alright, so we're dealing with a case where a ...</td>\n", + " <td>The snake venom is most likely affecting the a...</td>\n", + " <td>Question: A herpetologist bitten by a poisonou...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>97</th>\n", + " <td>A 34 years old person has rapidly developing c...</td>\n", + " <td>Alright, let's break down what's happening wit...</td>\n", + " <td>The symptoms described in the question fit mos...</td>\n", + " <td>Question: A 34 years old person has rapidly de...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>98</th>\n", + " <td>What is the term used to describe the type of ...</td>\n", + " <td>Okay, so I need to figure out what kind of inj...</td>\n", + " <td>The term used to describe the type of injury c...</td>\n", + " <td>Question: What is the term used to describe th...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>99</th>\n", + " <td>During the process of chlorination of water, t...</td>\n", + " <td>Alright, let's think this through starting fro...</td>\n", + " <td>The effective disinfecting action during the c...</td>\n", + " <td>Question: During the process of chlorination o...</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>100 rows × 4 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Question \\\n", + "0 A 61-year-old woman with a long history of inv... \n", + "1 A 45-year-old man with a history of alcohol us... \n", + "2 A 45-year-old man presents with symptoms inclu... \n", + "3 A patient with psoriasis was treated with syst... \n", + "4 What is the most likely diagnosis for a 2-year... \n", + ".. ... \n", + "95 An electrical current flows along a flat plate... \n", + "96 A herpetologist bitten by a poisonous snake is... \n", + "97 A 34 years old person has rapidly developing c... \n", + "98 What is the term used to describe the type of ... \n", + "99 During the process of chlorination of water, t... \n", + "\n", + " Complex_CoT \\\n", + "0 Okay, let's think about this step by step. The... \n", + "1 Alright, let’s break this down. We have a 45-y... \n", + "2 Okay, so here's a 45-year-old guy who's experi... \n", + "3 I'm thinking about this patient with psoriasis... \n", + "4 Okay, so we're dealing with a 2-year-old child... \n", + ".. ... \n", + "95 Alright, to find out the temperature at the ce... \n", + "96 Alright, so we're dealing with a case where a ... \n", + "97 Alright, let's break down what's happening wit... \n", + "98 Okay, so I need to figure out what kind of inj... \n", + "99 Alright, let's think this through starting fro... \n", + "\n", + " Response \\\n", + "0 Cystometry in this case of stress urinary inco... \n", + "1 Considering the clinical presentation of sudde... \n", + "2 Based on the clinical findings presented—wide-... \n", + "3 The development of generalized pustules in a p... \n", + "4 Based on the described symptoms and the unusua... \n", + ".. ... \n", + "95 The correct answer is F. 1549°F. \n", + "96 The snake venom is most likely affecting the a... \n", + "97 The symptoms described in the question fit mos... \n", + "98 The term used to describe the type of injury c... \n", + "99 The effective disinfecting action during the c... \n", + "\n", + " merged \n", + "0 Question: A 61-year-old woman with a long hist... \n", + "1 Question: A 45-year-old man with a history of ... \n", + "2 Question: A 45-year-old man presents with symp... \n", + "3 Question: A patient with psoriasis was treated... \n", + "4 Question: What is the most likely diagnosis fo... \n", + ".. ... \n", + "95 Question: An electrical current flows along a ... \n", + "96 Question: A herpetologist bitten by a poisonou... \n", + "97 Question: A 34 years old person has rapidly de... \n", + "98 Question: What is the term used to describe th... \n", + "99 Question: During the process of chlorination o... \n", + "\n", + "[100 rows x 4 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_dataframe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a Pinecone Index Based on the Dataset\n", + "Use the dataset itself to determine the embedding dimensionality. For example, compute one embedding from the merged column and then create the index accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding dimension: 1536\n" + ] + } + ], + "source": [ + "MODEL = \"text-embedding-3-small\" # Replace with your production embedding model if needed\n", + "# Compute an embedding for the first document to obtain the embedding dimension.\n", + "sample_embedding_resp = client.embeddings.create(\n", + " input=[ds_dataframe['merged'].iloc[0]],\n", + " model=MODEL\n", + ")\n", + "embed_dim = len(sample_embedding_resp.data[0].embedding)\n", + "print(f\"Embedding dimension: {embed_dim}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index stats: {'dimension': 1536,\n", + " 'index_fullness': 0.0,\n", + " 'metric': 'dotproduct',\n", + " 'namespaces': {},\n", + " 'total_vector_count': 0,\n", + " 'vector_type': 'dense'}\n" + ] + } + ], + "source": [ + "\n", + "# Initialize Pinecone using your API key.\n", + "pc = Pinecone(api_key=os.getenv(\"PINECONE_API_KEY\"))\n", + "\n", + "# Define the Pinecone serverless specification.\n", + "AWS_REGION = \"us-east-1\"\n", + "spec = ServerlessSpec(cloud=\"aws\", region=AWS_REGION)\n", + "\n", + "# Create a random index name with lower case alphanumeric characters and '-'\n", + "index_name = 'pinecone-index-' + ''.join(random.choices(string.ascii_lowercase + string.digits, k=10))\n", + "\n", + "# Create the index if it doesn't already exist.\n", + "if index_name not in pc.list_indexes().names():\n", + " pc.create_index(\n", + " index_name,\n", + " dimension=embed_dim,\n", + " metric='dotproduct',\n", + " spec=spec\n", + " )\n", + "\n", + "# Connect to the index.\n", + "index = pc.Index(index_name)\n", + "time.sleep(1)\n", + "print(\"Index stats:\", index.describe_index_stats())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Upsert the Dataset into Pinecone index\n", + "\n", + "Process the dataset in batches, generate embeddings for each merged text, prepare metadata (including separate Question and Answer fields), and upsert each batch into the index. You may also update metadata for specific entries if needed." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Upserting to Pinecone: 100%|██████████| 4/4 [00:06<00:00, 1.64s/it]\n" + ] + } + ], + "source": [ + "batch_size = 32\n", + "for i in tqdm(range(0, len(ds_dataframe['merged']), batch_size), desc=\"Upserting to Pinecone\"):\n", + " i_end = min(i + batch_size, len(ds_dataframe['merged']))\n", + " lines_batch = ds_dataframe['merged'][i: i_end]\n", + " ids_batch = [str(n) for n in range(i, i_end)]\n", + " \n", + " # Create embeddings for the current batch.\n", + " res = client.embeddings.create(input=[line for line in lines_batch], model=MODEL)\n", + " embeds = [record.embedding for record in res.data]\n", + " \n", + " # Prepare metadata by extracting original Question and Answer.\n", + " meta = []\n", + " for record in ds_dataframe.iloc[i:i_end].to_dict('records'):\n", + " q_text = record['Question']\n", + " a_text = record['Response']\n", + " # Optionally update metadata for specific entries.\n", + " meta.append({\"Question\": q_text, \"Answer\": a_text})\n", + " \n", + " # Upsert the batch into Pinecone.\n", + " vectors = list(zip(ids_batch, embeds, meta))\n", + " index.upsert(vectors=vectors)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Pinecone Image](../../images/responses_pinecone_rag.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query the Pinecone Index\n", + "\n", + "Create a natural language query, compute its embedding, and perform a similarity search on the Pinecone index. The returned results include metadata that provides context for generating answers." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def query_pinecone_index(client, index, model, query_text):\n", + " # Generate an embedding for the query.\n", + " query_embedding = client.embeddings.create(input=query_text, model=model).data[0].embedding\n", + "\n", + " # Query the index and return top 5 matches.\n", + " res = index.query(vector=[query_embedding], top_k=5, include_metadata=True)\n", + " print(\"Query Results:\")\n", + " for match in res['matches']:\n", + " print(f\"{match['score']:.2f}: {match['metadata'].get('Question', 'N/A')} - {match['metadata'].get('Answer', 'N/A')}\")\n", + " return res" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query Results:\n", + "0.70: A 45-year-old man with a history of alcohol use, who has been abstinent for the past 10 years, presents with sudden onset dysarthria, shuffling gait, and intention tremors. Given this clinical presentation and history, what is the most likely diagnosis? - Considering the clinical presentation of sudden onset dysarthria, shuffling gait, and intention tremors in a 45-year-old man with a history of alcohol use who has been abstinent for the past 10 years, the most likely diagnosis is acquired hepatocerebral degeneration.\n", + "\n", + "This condition is associated with chronic liver disease, which can often be a consequence of long-term alcohol use. Despite the patient's abstinence from alcohol for a decade, previous alcohol use may have led to underlying liver dysfunction. This dysfunction, even if subclinical, can cause encephalopathy due to the accumulation of neurotoxic substances that affect the brain. The sudden onset of these neurological symptoms aligns with how acquired hepatocerebral degeneration can manifest, making it a probable diagnosis in this scenario.\n", + "0.55: A 45-year-old man presents with symptoms including a wide-based gait, a blank facial expression, hallucinations, memory issues, a resting tremor that resolves with movement, and bradykinesia. Based on these clinical findings, what is most likely to be observed in the histological specimen of his brain? - Based on the clinical findings presented—wide-based gait, blank facial expression, hallucinations, memory issues, resting tremor that resolves with movement, and bradykinesia—it is likely that the 45-year-old man is experiencing a condition related to Parkinsonism, possibly Parkinson's disease or dementia with Lewy bodies. Both of these conditions are associated with the presence of Lewy bodies in the brain. Lewy bodies are abnormal aggregates of protein, primarily alpha-synuclein, which can cause both the motor and cognitive symptoms observed in this patient. Therefore, in the histological specimen of his brain, you would most likely observe the presence of Lewy bodies.\n", + "0.53: A 73-year-old man is evaluated for increasing forgetfulness, getting lost while walking, irritability, and difficulty recalling recent events while retaining detailed memories from over 20 years ago. On examination, he is oriented to person and place but disoriented to time, and an MRI of the brain reveals significant changes. Considering these symptoms and the imaging findings, what is the most likely underlying pathological process contributing to the patient's condition? - The symptoms and MRI findings of this 73-year-old man suggest the most likely underlying pathological process is the buildup of amyloid-beta plaques and tau protein tangles, which are characteristic of Alzheimer's disease. These changes often begin in brain regions involved in memory, such as the hippocampus and temporal lobes, leading to the gradual memory decline, disorientation, and personality changes observed in the patient.\n", + "0.42: A 2-day-old male newborn delivered at 36 weeks presents with generalized convulsions, lethargy, feeding difficulties, icterus, purpura, posterior uveitis, and failed auditory screening. Cranial ultrasonography shows ventricular dilatation and hyperechoic foci in multiple brain areas. Considering these clinical signs and history, what is the most likely diagnosis? - The symptoms and findings you've described in this 2-day-old newborn point towards congenital Toxoplasmosis. The combination of neurological symptoms (such as convulsions and ventricular dilatation with hyperechoic foci), the presence of posterior uveitis, and the skin manifestations like purpura, all fit into the classic presentation of a TORCH infection. Toxoplasmosis, specifically, is known to cause widespread calcifications in the brain, not limited to the periventricular areas, which matches the ultrasound findings. Additionally, while hearing loss is more traditionally associated with CMV, it can also occur in Toxoplasmosis. Thus, the most likely diagnosis given this clinical picture is congenital Toxoplasmosis.\n", + "0.42: A 45-year-old male patient experiences double vision specifically when walking upstairs. Considering his well-controlled history of Type-II diabetes, which cranial nerve is most likely involved in his symptoms? - Based on the symptoms described, the cranial nerve most likely involved in the double vision experienced by this patient while walking upstairs is the trochlear nerve, or cranial nerve IV. This nerve controls the superior oblique muscle, which plays a role in stabilizing the eye during certain movements, including the coordination required when looking upwards while walking upstairs. Given the patient's history of diabetes, cranial neuropathies can occur, and CN IV involvement can lead to vertical diplopia that becomes noticeable during specific activities like walking up stairs. Therefore, the trochlear nerve is a likely candidate for the involvement in these symptoms.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'matches': [{'id': '1',\n", + " 'metadata': {'Answer': 'Considering the clinical presentation of '\n", + " 'sudden onset dysarthria, shuffling gait, '\n", + " 'and intention tremors in a 45-year-old '\n", + " 'man with a history of alcohol use who '\n", + " 'has been abstinent for the past 10 '\n", + " 'years, the most likely diagnosis is '\n", + " 'acquired hepatocerebral degeneration.\\n'\n", + " '\\n'\n", + " 'This condition is associated with '\n", + " 'chronic liver disease, which can often '\n", + " 'be a consequence of long-term alcohol '\n", + " \"use. Despite the patient's abstinence \"\n", + " 'from alcohol for a decade, previous '\n", + " 'alcohol use may have led to underlying '\n", + " 'liver dysfunction. This dysfunction, '\n", + " 'even if subclinical, can cause '\n", + " 'encephalopathy due to the accumulation '\n", + " 'of neurotoxic substances that affect the '\n", + " 'brain. The sudden onset of these '\n", + " 'neurological symptoms aligns with how '\n", + " 'acquired hepatocerebral degeneration can '\n", + " 'manifest, making it a probable diagnosis '\n", + " 'in this scenario.',\n", + " 'Question': 'A 45-year-old man with a history of '\n", + " 'alcohol use, who has been abstinent '\n", + " 'for the past 10 years, presents with '\n", + " 'sudden onset dysarthria, shuffling '\n", + " 'gait, and intention tremors. Given '\n", + " 'this clinical presentation and '\n", + " 'history, what is the most likely '\n", + " 'diagnosis?'},\n", + " 'score': 0.697534442,\n", + " 'values': []},\n", + " {'id': '2',\n", + " 'metadata': {'Answer': 'Based on the clinical findings '\n", + " 'presented—wide-based gait, blank facial '\n", + " 'expression, hallucinations, memory '\n", + " 'issues, resting tremor that resolves '\n", + " 'with movement, and bradykinesia—it is '\n", + " 'likely that the 45-year-old man is '\n", + " 'experiencing a condition related to '\n", + " \"Parkinsonism, possibly Parkinson's \"\n", + " 'disease or dementia with Lewy bodies. '\n", + " 'Both of these conditions are associated '\n", + " 'with the presence of Lewy bodies in the '\n", + " 'brain. Lewy bodies are abnormal '\n", + " 'aggregates of protein, primarily '\n", + " 'alpha-synuclein, which can cause both '\n", + " 'the motor and cognitive symptoms '\n", + " 'observed in this patient. Therefore, in '\n", + " 'the histological specimen of his brain, '\n", + " 'you would most likely observe the '\n", + " 'presence of Lewy bodies.',\n", + " 'Question': 'A 45-year-old man presents with '\n", + " 'symptoms including a wide-based gait, '\n", + " 'a blank facial expression, '\n", + " 'hallucinations, memory issues, a '\n", + " 'resting tremor that resolves with '\n", + " 'movement, and bradykinesia. Based on '\n", + " 'these clinical findings, what is most '\n", + " 'likely to be observed in the '\n", + " 'histological specimen of his brain?'},\n", + " 'score': 0.55345,\n", + " 'values': []},\n", + " {'id': '19',\n", + " 'metadata': {'Answer': 'The symptoms and MRI findings of this '\n", + " '73-year-old man suggest the most likely '\n", + " 'underlying pathological process is the '\n", + " 'buildup of amyloid-beta plaques and tau '\n", + " 'protein tangles, which are '\n", + " \"characteristic of Alzheimer's disease. \"\n", + " 'These changes often begin in brain '\n", + " 'regions involved in memory, such as the '\n", + " 'hippocampus and temporal lobes, leading '\n", + " 'to the gradual memory decline, '\n", + " 'disorientation, and personality changes '\n", + " 'observed in the patient.',\n", + " 'Question': 'A 73-year-old man is evaluated for '\n", + " 'increasing forgetfulness, getting lost '\n", + " 'while walking, irritability, and '\n", + " 'difficulty recalling recent events '\n", + " 'while retaining detailed memories from '\n", + " 'over 20 years ago. On examination, he '\n", + " 'is oriented to person and place but '\n", + " 'disoriented to time, and an MRI of the '\n", + " 'brain reveals significant changes. '\n", + " 'Considering these symptoms and the '\n", + " 'imaging findings, what is the most '\n", + " 'likely underlying pathological process '\n", + " \"contributing to the patient's \"\n", + " 'condition?'},\n", + " 'score': 0.526201367,\n", + " 'values': []},\n", + " {'id': '38',\n", + " 'metadata': {'Answer': \"The symptoms and findings you've \"\n", + " 'described in this 2-day-old newborn '\n", + " 'point towards congenital Toxoplasmosis. '\n", + " 'The combination of neurological symptoms '\n", + " '(such as convulsions and ventricular '\n", + " 'dilatation with hyperechoic foci), the '\n", + " 'presence of posterior uveitis, and the '\n", + " 'skin manifestations like purpura, all '\n", + " 'fit into the classic presentation of a '\n", + " 'TORCH infection. Toxoplasmosis, '\n", + " 'specifically, is known to cause '\n", + " 'widespread calcifications in the brain, '\n", + " 'not limited to the periventricular '\n", + " 'areas, which matches the ultrasound '\n", + " 'findings. Additionally, while hearing '\n", + " 'loss is more traditionally associated '\n", + " 'with CMV, it can also occur in '\n", + " 'Toxoplasmosis. Thus, the most likely '\n", + " 'diagnosis given this clinical picture is '\n", + " 'congenital Toxoplasmosis.',\n", + " 'Question': 'A 2-day-old male newborn delivered at '\n", + " '36 weeks presents with generalized '\n", + " 'convulsions, lethargy, feeding '\n", + " 'difficulties, icterus, purpura, '\n", + " 'posterior uveitis, and failed auditory '\n", + " 'screening. Cranial ultrasonography '\n", + " 'shows ventricular dilatation and '\n", + " 'hyperechoic foci in multiple brain '\n", + " 'areas. Considering these clinical '\n", + " 'signs and history, what is the most '\n", + " 'likely diagnosis?'},\n", + " 'score': 0.422916651,\n", + " 'values': []},\n", + " {'id': '31',\n", + " 'metadata': {'Answer': 'Based on the symptoms described, the '\n", + " 'cranial nerve most likely involved in '\n", + " 'the double vision experienced by this '\n", + " 'patient while walking upstairs is the '\n", + " 'trochlear nerve, or cranial nerve IV. '\n", + " 'This nerve controls the superior oblique '\n", + " 'muscle, which plays a role in '\n", + " 'stabilizing the eye during certain '\n", + " 'movements, including the coordination '\n", + " 'required when looking upwards while '\n", + " \"walking upstairs. Given the patient's \"\n", + " 'history of diabetes, cranial '\n", + " 'neuropathies can occur, and CN IV '\n", + " 'involvement can lead to vertical '\n", + " 'diplopia that becomes noticeable during '\n", + " 'specific activities like walking up '\n", + " 'stairs. Therefore, the trochlear nerve '\n", + " 'is a likely candidate for the '\n", + " 'involvement in these symptoms.',\n", + " 'Question': 'A 45-year-old male patient experiences '\n", + " 'double vision specifically when '\n", + " 'walking upstairs. Considering his '\n", + " 'well-controlled history of Type-II '\n", + " 'diabetes, which cranial nerve is most '\n", + " 'likely involved in his symptoms?'},\n", + " 'score': 0.420719624,\n", + " 'values': []}],\n", + " 'namespace': '',\n", + " 'usage': {'read_units': 6}}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example usage with a different query from the train/test set\n", + "query = (\n", + " \"A 45-year-old man with a history of alcohol use presents with symptoms including confusion, ataxia, and ophthalmoplegia. \"\n", + " \"What is the most likely diagnosis and the recommended treatment?\"\n", + ")\n", + "query_pinecone_index(client, index, MODEL, query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate a Response Using the Retrieved Context\n", + "\n", + "Select the best matching result from your query results and use the OpenAI Responses API to generate a final answer by combining the retrieved context with the original question." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Final Answer:\n", + "The presentation of confusion, ataxia, and ophthalmoplegia in a 45-year-old man with a history of alcohol use is suggestive of Wernicke's encephalopathy. This condition is caused by thiamine (vitamin B1) deficiency, often associated with chronic alcohol use.\n", + "\n", + "The recommended treatment is the immediate administration of thiamine, typically given intravenously or intramuscularly, to prevent progression to more severe neurological damage or Korsakoff syndrome.\n" + ] + } + ], + "source": [ + "# Retrieve and concatenate top 3 match contexts.\n", + "matches = index.query(\n", + " vector=[client.embeddings.create(input=query, model=MODEL).data[0].embedding],\n", + " top_k=3,\n", + " include_metadata=True\n", + ")['matches']\n", + "\n", + "context = \"\\n\\n\".join(\n", + " f\"Question: {m['metadata'].get('Question', '')}\\nAnswer: {m['metadata'].get('Answer', '')}\"\n", + " for m in matches\n", + ")\n", + "# Use the context to generate a final answer.\n", + "response = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=f\"Provide the answer based on the context: {context} and the question: {query} as per the internal knowledge base\",\n", + ")\n", + "print(\"\\nFinal Answer:\")\n", + "print(response.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Orchestrate Multi-Tool Calls\n", + "\n", + "Now, we'll define the built-in function available through the Responses API, including the ability to invoke the external Vector Store - Pinecone as an example.\n", + "\n", + "*Web Search Preview Tool*: Enables the model to perform live web searches and preview the results. This is ideal for retrieving real-time or up-to-date information from the internet.\n", + "\n", + "*Pinecone Search Tool*: Allows the model to query a vector database using semantic search. This is especially useful for retrieving relevant documents—such as medical literature or other domain-specific content—that have been stored in a vectorized format." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Tools definition: The list of tools includes:\n", + "# - A web search preview tool.\n", + "# - A Pinecone search tool for retrieving medical documents.\n", + "\n", + "# Define available tools.\n", + "tools = [ \n", + " {\"type\": \"web_search_preview\",\n", + " \"user_location\": {\n", + " \"type\": \"approximate\",\n", + " \"country\": \"US\",\n", + " \"region\": \"California\",\n", + " \"city\": \"SF\"\n", + " },\n", + " \"search_context_size\": \"medium\"},\n", + " {\n", + " \"type\": \"function\",\n", + " \"name\": \"PineconeSearchDocuments\",\n", + " \"description\": \"Search for relevant documents based on the medical question asked by the user that is stored within the vector database using a semantic query.\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"query\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The natural language query to search the vector database.\"\n", + " },\n", + " \"top_k\": {\n", + " \"type\": \"integer\",\n", + " \"description\": \"Number of top results to return.\",\n", + " \"default\": 3\n", + " }\n", + " },\n", + " \"required\": [\"query\"],\n", + " \"additionalProperties\": False\n", + " }\n", + " }\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Example queries that the model should route appropriately.\n", + "queries = [\n", + " {\"query\": \"Who won the cricket world cup in 1983?\"},\n", + " {\"query\": \"What is the most common cause of death in the United States according to the internet?\"},\n", + " {\"query\": (\"A 7-year-old boy with sickle cell disease is experiencing knee and hip pain, \"\n", + " \"has been admitted for pain crises in the past, and now walks with a limp. \"\n", + " \"His exam shows a normal, cool hip with decreased range of motion and pain with ambulation. \"\n", + " \"What is the most appropriate next step in management according to the internal knowledge base?\")}\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🌟--- Processing Query ---🌟\n", + "🔍 **User Query:** Who won the cricket world cup in 1983?\n", + "\n", + "✨ **Initial Response Output:**\n", + "[ResponseOutputMessage(id='msg_67e6e7a9f7508191a9d18c3ff25310290811a0720cf47168', content=[ResponseOutputText(annotations=[], text='India won the Cricket World Cup in 1983.', type='output_text')], role='assistant', status='completed', type='message')]\n", + "💡 **Final Answer:**\n", + "India won the Cricket World Cup in 1983.\n", + "\n", + "🌟--- Processing Query ---🌟\n", + "🔍 **User Query:** What is the most common cause of death in the United States according to the internet?\n", + "\n", + "✨ **Initial Response Output:**\n", + "[ResponseFunctionWebSearch(id='ws_67e6e7aad0248191ab974d4b09b460c90537f90023d2dd32', status='completed', type='web_search_call'), ResponseOutputMessage(id='msg_67e6e7ace08081918f06b5cac32e8c0e0537f90023d2dd32', content=[ResponseOutputText(annotations=[AnnotationURLCitation(end_index=363, start_index=225, title='10 Leading Causes of Death in the U.S.', type='url_citation', url='https://www.usnews.com/news/healthiest-communities/slideshows/top-10-causes-of-death-in-america?slide=11&utm_source=openai'), AnnotationURLCitation(end_index=753, start_index=625, title='Top causes of death in the US — see the CDC’s latest list - Rifnote', type='url_citation', url='https://rifnote.com/health/2024/08/11/top-causes-of-death-in-the-us-see-the-cdcs-latest-list/?utm_source=openai'), AnnotationURLCitation(end_index=1014, start_index=886, title='Top causes of death in the US — see the CDC’s latest list - Rifnote', type='url_citation', url='https://rifnote.com/health/2024/08/11/top-causes-of-death-in-the-us-see-the-cdcs-latest-list/?utm_source=openai'), AnnotationURLCitation(end_index=1216, start_index=1061, title='US deaths are down and life expectancy is up, but improvements are slowing', type='url_citation', url='https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai'), AnnotationURLCitation(end_index=1394, start_index=1219, title='A Mysterious Health Wave Is Breaking Out Across the U.S.', type='url_citation', url='https://www.theatlantic.com/ideas/archive/2024/12/violence-obesity-overdoses-health-covid/681079/?utm_source=openai')], text='According to the Centers for Disease Control and Prevention (CDC), heart disease was the leading cause of death in the United States in 2023, accounting for 680,980 deaths, which is approximately 22% of all deaths that year. ([usnews.com](https://www.usnews.com/news/healthiest-communities/slideshows/top-10-causes-of-death-in-america?slide=11&utm_source=openai))\\n\\nThe top 10 causes of death in the U.S. for 2023 were:\\n\\n1. Heart disease\\n2. Cancer\\n3. Unintentional injury\\n4. Stroke\\n5. Chronic lower respiratory diseases\\n6. Alzheimer’s disease\\n7. Diabetes\\n8. Kidney disease\\n9. Chronic liver disease and cirrhosis\\n10. COVID-19\\n\\n([rifnote.com](https://rifnote.com/health/2024/08/11/top-causes-of-death-in-the-us-see-the-cdcs-latest-list/?utm_source=openai))\\n\\nNotably, COVID-19, which was the fourth leading cause of death in 2022, dropped to the tenth position in 2023, with 76,446 deaths. ([rifnote.com](https://rifnote.com/health/2024/08/11/top-causes-of-death-in-the-us-see-the-cdcs-latest-list/?utm_source=openai))\\n\\n\\n## Recent Trends in U.S. Mortality Rates:\\n- [US deaths are down and life expectancy is up, but improvements are slowing](https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai)\\n- [A Mysterious Health Wave Is Breaking Out Across the U.S.](https://www.theatlantic.com/ideas/archive/2024/12/violence-obesity-overdoses-health-covid/681079/?utm_source=openai) ', type='output_text')], role='assistant', status='completed', type='message')]\n", + "💡 **Final Answer:**\n", + "According to the Centers for Disease Control and Prevention (CDC), heart disease was the leading cause of death in the United States in 2023, accounting for 680,980 deaths, which is approximately 22% of all deaths that year. ([usnews.com](https://www.usnews.com/news/healthiest-communities/slideshows/top-10-causes-of-death-in-america?slide=11&utm_source=openai))\n", + "\n", + "The top 10 causes of death in the U.S. for 2023 were:\n", + "\n", + "1. Heart disease\n", + "2. Cancer\n", + "3. Unintentional injury\n", + "4. Stroke\n", + "5. Chronic lower respiratory diseases\n", + "6. Alzheimer’s disease\n", + "7. Diabetes\n", + "8. Kidney disease\n", + "9. Chronic liver disease and cirrhosis\n", + "10. COVID-19\n", + "\n", + "([rifnote.com](https://rifnote.com/health/2024/08/11/top-causes-of-death-in-the-us-see-the-cdcs-latest-list/?utm_source=openai))\n", + "\n", + "Notably, COVID-19, which was the fourth leading cause of death in 2022, dropped to the tenth position in 2023, with 76,446 deaths. ([rifnote.com](https://rifnote.com/health/2024/08/11/top-causes-of-death-in-the-us-see-the-cdcs-latest-list/?utm_source=openai))\n", + "\n", + "\n", + "## Recent Trends in U.S. Mortality Rates:\n", + "- [US deaths are down and life expectancy is up, but improvements are slowing](https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai)\n", + "- [A Mysterious Health Wave Is Breaking Out Across the U.S.](https://www.theatlantic.com/ideas/archive/2024/12/violence-obesity-overdoses-health-covid/681079/?utm_source=openai) \n", + "\n", + "🌟--- Processing Query ---🌟\n", + "🔍 **User Query:** A 7-year-old boy with sickle cell disease is experiencing knee and hip pain, has been admitted for pain crises in the past, and now walks with a limp. His exam shows a normal, cool hip with decreased range of motion and pain with ambulation. What is the most appropriate next step in management according to the internal knowledge base?\n", + "\n", + "✨ **Initial Response Output:**\n", + "[ResponseFunctionToolCall(arguments='{\"query\":\"7-year-old sickle cell disease knee hip pain limp normal cool hip decreased range of motion\"}', call_id='call_ds0ETZbYtX71U2bQZXTBEWxN', name='PineconeSearchDocuments', type='function_call', id='fc_67e6e7b03ee48191bb400c13c359c35e0aeeec60d0806312', status='completed')]\n", + "\n", + "🔧 **Model triggered a tool call:** PineconeSearchDocuments\n", + "🔍 **Invoking PineconeSearchDocuments tool...**\n", + "Query Results:\n", + "0.87: A 7-year-old boy with sickle cell disease is experiencing knee and hip pain, has been admitted for pain crises in the past, and now walks with a limp. His physical exam shows a normal and cool hip to the touch, with decreased range of motion at the hip and pain with ambulation. Given these findings, what is the most appropriate next step in the management of this patient's hip pain? - In managing the hip pain of a 7-year-old boy with sickle cell disease, who presents with knee and hip pain, a limp, and decreased range of motion in the hip, the most appropriate next step is to obtain an X-ray of the hip. This will help evaluate the possibility of avascular necrosis (AVN) or other structural abnormalities. X-rays are typically the first-line imaging technique in such cases due to their accessibility and ability to reveal gross pathological changes. If the X-ray does not provide conclusive information and clinical suspicion of AVN remains high, an MRI may subsequently be considered for a more detailed assessment.\n", + "0.55: What is the most likely diagnosis for a 2-year-old 70 kg child who presents with limitation of abduction and internal rotation, tenderness in Scarpa's triangle, and abduction of the limb upon flexing the hip? - Based on the described symptoms and the unusual weight for a 2-year-old child, the most likely diagnosis is Slipped Capital Femoral Epiphysis (SCFE). Even though SCFE typically occurs in older children, mainly adolescents, the combination of excessive weight, limited hip abduction and internal rotation, tenderness in the hip area, and the characteristic limb movement (abduction upon hip flexion) strongly points towards SCFE as the most plausible diagnosis in this scenario.\n", + "0.48: A 5-year-old boy has recurrent abdominal pain primarily occurring during school hours, with no significant findings on physical examination and normal stool characteristics. His symptoms resolve at home, and his laboratory tests and abdominal exam are unremarkable. Considering the psychological factors involved, what is the most appropriate next step in managing his condition? - Given the symptoms and the context you've provided, it seems quite possible that the boy's recurrent abdominal pain is linked to psychological stressors related to school. Since all medical tests and examinations have returned normal results, this suggests that the pain might be stress-induced, possibly due to anxiety or stress at school. \n", + "\n", + "The most appropriate next step is to focus on addressing any potential psychological or emotional factors. Consulting a psychologist or school counselor would be beneficial. They can work with the boy to explore any underlying emotional issues or anxieties about school. Through conversation, play, or other therapeutic techniques suitable for his age, they can help identify and manage any stressors he might be facing. This approach could not only help alleviate his abdominal pain but also improve his overall well-being by addressing the source of his anxiety.\n", + "0.44: In a patient who, five days post-open colectomy for colon cancer, develops severe pain and swelling of the left calf along with necrotic lesions, a fever, and thrombocytopenia while on unfractionated heparin, what is the most appropriate next step in management? - In this clinical scenario, the presentation of severe pain and swelling in the calf, necrotic skin lesions, fever, and thrombocytopenia in a patient receiving unfractionated heparin strongly suggests heparin-induced thrombocytopenia (HIT). HIT is a prothrombotic disorder caused by antibodies against heparin-platelet factor 4 complexes, leading to platelet activation, thrombocytopenia, and an increased risk of thrombosis.\n", + "\n", + "The most appropriate next step in management is to immediately discontinue the unfractionated heparin to prevent further complications related to thrombosis. Simultaneously, it's crucial to initiate an alternative anticoagulant that does not cross-react with HIT antibodies to manage the thrombotic risk. Argatroban or fondaparinux are commonly used anticoagulants in this context as they are safe and effective for patients with HIT. Direct-acting oral anticoagulants (DOACs) are also potential options, but argatroban is often preferred initially due to its intravenous route and ability to be titrated easily in acute care settings. This dual approach addresses both the cause and the risk effectively.\n", + "0.44: In a patient with sickle cell anaemia presenting with multiple non-suppurative osteomyelitic dactylitis, what is the most likely causative organism? - In a patient with sickle cell anemia presenting with multiple non-suppurative osteomyelitic dactylitis, the most likely causative organism is Salmonella species. In individuals with sickle cell disease, Salmonella is particularly notorious for causing osteomyelitis. The relationship between sickle cell anemia and Salmonella infections, especially in the bone, is well-documented, and their presentations can often be less typical and less suppurative than those caused by other common bacteria like Staphylococcus aureus.\n", + "✅ **PineconeSearchDocuments tool invoked successfully.**\n", + "\n", + "💡 **Final Answer:**\n", + "The most appropriate next step in the management of this 7-year-old boy with sickle cell disease and hip pain is to obtain an X-ray of the hip. This will help evaluate for potential avascular necrosis or other structural issues. If the X-ray is inconclusive and there is still a high suspicion of avascular necrosis, further imaging with an MRI may be considered.\n" + ] + } + ], + "source": [ + "# Process each query dynamically.\n", + "for item in queries:\n", + " input_messages = [{\"role\": \"user\", \"content\": item[\"query\"]}]\n", + " print(\"\\n🌟--- Processing Query ---🌟\")\n", + " print(f\"🔍 **User Query:** {item['query']}\")\n", + " \n", + " # Call the Responses API with tools enabled and allow parallel tool calls.\n", + " response = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=[\n", + " {\"role\": \"system\", \"content\": \"When prompted with a question, select the right tool to use based on the question.\"\n", + " },\n", + " {\"role\": \"user\", \"content\": item[\"query\"]}\n", + " ],\n", + " tools=tools,\n", + " parallel_tool_calls=True\n", + " )\n", + " \n", + " print(\"\\n✨ **Initial Response Output:**\")\n", + " print(response.output)\n", + " \n", + " # Determine if a tool call is needed and process accordingly.\n", + " if response.output:\n", + " tool_call = response.output[0]\n", + " if tool_call.type in [\"web_search_preview\", \"function_call\"]:\n", + " tool_name = tool_call.name if tool_call.type == \"function_call\" else \"web_search_preview\"\n", + " print(f\"\\n🔧 **Model triggered a tool call:** {tool_name}\")\n", + " \n", + " if tool_name == \"PineconeSearchDocuments\":\n", + " print(\"🔍 **Invoking PineconeSearchDocuments tool...**\")\n", + " res = query_pinecone_index(client, index, MODEL, item[\"query\"])\n", + " if res[\"matches\"]:\n", + " best_match = res[\"matches\"][0][\"metadata\"]\n", + " result = f\"**Question:** {best_match.get('Question', 'N/A')}\\n**Answer:** {best_match.get('Answer', 'N/A')}\"\n", + " else:\n", + " result = \"**No matching documents found in the index.**\"\n", + " print(\"✅ **PineconeSearchDocuments tool invoked successfully.**\")\n", + " else:\n", + " print(\"🔍 **Invoking simulated web search tool...**\")\n", + " result = \"**Simulated web search result.**\"\n", + " print(\"✅ **Simulated web search tool invoked successfully.**\")\n", + " \n", + " # Append the tool call and its output back into the conversation.\n", + " input_messages.append(tool_call)\n", + " input_messages.append({\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call.call_id,\n", + " \"output\": str(result)\n", + " })\n", + " \n", + " # Get the final answer incorporating the tool's result.\n", + " final_response = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=input_messages,\n", + " tools=tools,\n", + " parallel_tool_calls=True\n", + " )\n", + " print(\"\\n💡 **Final Answer:**\")\n", + " print(final_response.output_text)\n", + " else:\n", + " # If no tool call is triggered, print the response directly.\n", + " print(\"💡 **Final Answer:**\")\n", + " print(response.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown above, depending on the query, appropriate tool is invoked in order to determine the optimal response.\n", + "\n", + "For instance, looking at the third example, when the model triggers the tool named \"PineconeSearchDocuments\", the code calls `query_pinecone_index` with the current query and then extracts the best match (or an appropriate context) as the result. For non health related inqueries or queries where explicit internet search is asked, the code calls the web_search_call function and for other queries, it may choose to not call any tool and rather provide a response based on the question under consideration.\n", + "\n", + "Finally, the tool call and its output are appended to the conversation, and the final answer is generated by the Responses API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-tool orchestration flow\n", + "\n", + "Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🌟--- Processing Query ---🌟\n", + "🔍 **User Query:** What is the most common cause of death in the United States\n", + "\n", + "🔧 **Calling Responses API with Tools Enabled**\n", + "\n", + "🕵️‍♂️ **Step 1: Web Search Call**\n", + " - Initiating web search to gather initial information.\n", + "\n", + "📚 **Step 2: Pinecone Search Call**\n", + " - Querying Pinecone to find relevant examples from the internal knowledge base.\n", + "input_messages [{'role': 'user', 'content': 'What is the most common cause of death in the United States'}]\n", + "\n", + "✨ **Initial Response Output:**\n", + "[ResponseFunctionWebSearch(id='ws_67e6e83241ac81918f93ffc96491ec390fdddafaeefcefc1', status='completed', type='web_search_call'), ResponseOutputMessage(id='msg_67e6e833a2cc8191a9df22f324a876b00fdddafaeefcefc1', content=[ResponseOutputText(annotations=[AnnotationURLCitation(end_index=698, start_index=613, title='Products - Data Briefs - Number 521 - December 2024', type='url_citation', url='https://www.cdc.gov/nchs/products/databriefs/db521.htm?utm_source=openai'), AnnotationURLCitation(end_index=984, start_index=891, title='US deaths are down and life expectancy is up, but improvements are slowing', type='url_citation', url='https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai'), AnnotationURLCitation(end_index=1186, start_index=1031, title='US deaths are down and life expectancy is up, but improvements are slowing', type='url_citation', url='https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai')], text=\"As of 2023, the leading causes of death in the United States are:\\n\\n1. **Heart Disease**: 680,981 deaths\\n2. **Cancer**: 613,352 deaths\\n3. **Unintentional Injuries**: 222,698 deaths\\n4. **Stroke**: 162,639 deaths\\n5. **Chronic Lower Respiratory Diseases**: 145,357 deaths\\n6. **Alzheimer's Disease**: 114,034 deaths\\n7. **Diabetes**: 95,190 deaths\\n8. **Kidney Disease**: 55,253 deaths\\n9. **Chronic Liver Disease and Cirrhosis**: 52,222 deaths\\n10. **COVID-19**: 49,932 deaths\\n\\nNotably, COVID-19 has dropped from the fourth leading cause in 2022 to the tenth in 2023, reflecting a significant decrease in related deaths. ([cdc.gov](https://www.cdc.gov/nchs/products/databriefs/db521.htm?utm_source=openai))\\n\\nOverall, the U.S. experienced a decline in total deaths and a modest increase in life expectancy in 2023, attributed to reductions in deaths from COVID-19, heart disease, and drug overdoses. ([apnews.com](https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai))\\n\\n\\n## Recent Trends in U.S. Mortality Rates:\\n- [US deaths are down and life expectancy is up, but improvements are slowing](https://apnews.com/article/be061f9f14c883178eea6dddc9550e60?utm_source=openai) \", type='output_text')], role='assistant', status='completed', type='message'), ResponseFunctionToolCall(arguments='{\"query\":\"most common cause of death in the United States\",\"top_k\":3}', call_id='call_6YWhEw3QSI7wGZBlNs5Pz4zI', name='PineconeSearchDocuments', type='function_call', id='fc_67e6e8364e4c819198501fba5d3f155b0fdddafaeefcefc1', status='completed')]\n" + ] + } + ], + "source": [ + "# Process one query as an example to understand the tool calls and function calls as part of the response output\n", + "item = \"What is the most common cause of death in the United States\"\n", + "\n", + "# Initialize input messages with the user's query.\n", + "input_messages = [{\"role\": \"user\", \"content\": item}]\n", + "print(\"\\n🌟--- Processing Query ---🌟\")\n", + "print(f\"🔍 **User Query:** {item}\")\n", + " \n", + " # Call the Responses API with tools enabled and allow parallel tool calls.\n", + "print(\"\\n🔧 **Calling Responses API with Tools Enabled**\")\n", + "print(\"\\n🕵️‍♂️ **Step 1: Web Search Call**\")\n", + "print(\" - Initiating web search to gather initial information.\")\n", + "print(\"\\n📚 **Step 2: Pinecone Search Call**\")\n", + "print(\" - Querying Pinecone to find relevant examples from the internal knowledge base.\")\n", + " \n", + "response = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=[\n", + " {\"role\": \"system\", \"content\": \"Every time it's prompted with a question, first call the web search tool for results, then call `PineconeSearchDocuments` to find real examples in the internal knowledge base.\"},\n", + " {\"role\": \"user\", \"content\": item}\n", + " ],\n", + " tools=tools,\n", + " parallel_tool_calls=True\n", + " )\n", + " \n", + "# Print the initial response output.\n", + "print(\"input_messages\", input_messages)\n", + "\n", + "print(\"\\n✨ **Initial Response Output:**\")\n", + "print(response.output)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Type</th>\n", + " <th>Call ID</th>\n", + " <th>Output</th>\n", + " <th>Name</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>web_search_call</td>\n", + " <td>ws_67e6e83241ac81918f93ffc96491ec390fdddafaeef...</td>\n", + " <td>N/A</td>\n", + " <td>N/A</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>message</td>\n", + " <td>msg_67e6e833a2cc8191a9df22f324a876b00fdddafaee...</td>\n", + " <td>N/A</td>\n", + " <td>N/A</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>function_call</td>\n", + " <td>call_6YWhEw3QSI7wGZBlNs5Pz4zI</td>\n", + " <td>N/A</td>\n", + " <td>PineconeSearchDocuments</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Type Call ID Output \\\n", + "0 web_search_call ws_67e6e83241ac81918f93ffc96491ec390fdddafaeef... N/A \n", + "1 message msg_67e6e833a2cc8191a9df22f324a876b00fdddafaee... N/A \n", + "2 function_call call_6YWhEw3QSI7wGZBlNs5Pz4zI N/A \n", + "\n", + " Name \n", + "0 N/A \n", + "1 N/A \n", + "2 PineconeSearchDocuments " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Understand the tool calls and function calls as part of the response output\n", + "\n", + "import pandas as pd\n", + "\n", + "# Create a list to store the tool call and function call details\n", + "tool_calls = []\n", + "\n", + "# Iterate through the response output and collect the details\n", + "for i in response.output:\n", + " tool_calls.append({\n", + " \"Type\": i.type,\n", + " \"Call ID\": i.call_id if hasattr(i, 'call_id') else i.id if hasattr(i, 'id') else \"N/A\",\n", + " \"Output\": str(i.output) if hasattr(i, 'output') else \"N/A\",\n", + " \"Name\": i.name if hasattr(i, 'name') else \"N/A\"\n", + " })\n", + "\n", + "# Convert the list to a DataFrame for tabular display\n", + "df_tool_calls = pd.DataFrame(tool_calls)\n", + "\n", + "# Display the DataFrame\n", + "df_tool_calls" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ResponseFunctionWebSearch(id='ws_67e6e83241ac81918f93ffc96491ec390fdddafaeefcefc1', status='completed', type='web_search_call')\n", + "ws_67e6e83241ac81918f93ffc96491ec390fdddafaeefcefc1\n", + "ResponseFunctionToolCall(arguments='{\"query\":\"most common cause of death in the United States\",\"top_k\":3}', call_id='call_6YWhEw3QSI7wGZBlNs5Pz4zI', name='PineconeSearchDocuments', type='function_call', id='fc_67e6e8364e4c819198501fba5d3f155b0fdddafaeefcefc1', status='completed')\n", + "call_6YWhEw3QSI7wGZBlNs5Pz4zI\n" + ] + } + ], + "source": [ + "tool_call_1 = response.output[0]\n", + "print(tool_call_1)\n", + "print(tool_call_1.id)\n", + "\n", + "tool_call_2 = response.output[2]\n", + "print(tool_call_2)\n", + "print(tool_call_2.call_id)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'role': 'user', 'content': 'What is the most common cause of death in the United States'}, ResponseFunctionToolCall(arguments='{\"query\":\"most common cause of death in the United States\"}', call_id='call_8Vzsn4RwMOgXyX98UpZY8hls', name='PineconeSearchDocuments', type='function_call', id='fc_67e348f36f7c81919d0aeef1855df3f20d0bd7f2a5744b88', status='completed')]\n", + "[{'role': 'user', 'content': 'What is the most common cause of death in the United States'}, ResponseFunctionToolCall(arguments='{\"query\":\"most common cause of death in the United States\"}', call_id='call_8Vzsn4RwMOgXyX98UpZY8hls', name='PineconeSearchDocuments', type='function_call', id='fc_67e348f36f7c81919d0aeef1855df3f20d0bd7f2a5744b88', status='completed'), {'type': 'function_call_output', 'call_id': 'call_8Vzsn4RwMOgXyX98UpZY8hls', 'output': \"**Question:** A 7-year-old boy with sickle cell disease is experiencing knee and hip pain, has been admitted for pain crises in the past, and now walks with a limp. His physical exam shows a normal and cool hip to the touch, with decreased range of motion at the hip and pain with ambulation. Given these findings, what is the most appropriate next step in the management of this patient's hip pain?\\n**Answer:** In managing the hip pain of a 7-year-old boy with sickle cell disease, who presents with knee and hip pain, a limp, and decreased range of motion in the hip, the most appropriate next step is to obtain an X-ray of the hip. This will help evaluate the possibility of avascular necrosis (AVN) or other structural abnormalities. X-rays are typically the first-line imaging technique in such cases due to their accessibility and ability to reveal gross pathological changes. If the X-ray does not provide conclusive information and clinical suspicion of AVN remains high, an MRI may subsequently be considered for a more detailed assessment.\"}]\n" + ] + } + ], + "source": [ + "# append the tool call and its output back into the conversation.\n", + "input_messages.append(response.output[2])\n", + "input_messages.append({\n", + " \"type\": \"function_call_output\",\n", + " \"call_id\": tool_call_2.call_id,\n", + " \"output\": str(result)\n", + "})\n", + "print(input_messages)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "🔧 **Calling Responses API for Final Answer**\n", + "Response(id='resp_67e6e886ac7081918b07224fb1ed38ab05c4a598f9697c7c', created_at=1743186054.0, error=None, incomplete_details=None, instructions=None, metadata={}, model='gpt-4o-2024-08-06', object='response', output=[ResponseOutputMessage(id='msg_67e6e8872ddc81918e92c9e4508abbe005c4a598f9697c7c', content=[ResponseOutputText(annotations=[], text='The most common cause of death in the United States is heart disease.', type='output_text')], role='assistant', status='completed', type='message')], parallel_tool_calls=True, temperature=1.0, tool_choice='auto', tools=[], top_p=1.0, max_output_tokens=None, previous_response_id=None, reasoning=Reasoning(effort=None, generate_summary=None), status='completed', text=ResponseTextConfig(format=ResponseFormatText(type='text')), truncation='disabled', usage=ResponseUsage(input_tokens=37, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=15, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=52), user=None, store=False)\n" + ] + } + ], + "source": [ + "\n", + "# Get the final answer incorporating the tool's result.\n", + "print(\"\\n🔧 **Calling Responses API for Final Answer**\")\n", + "\n", + "response_2 = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=input_messages,\n", + ")\n", + "print(response_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The most common cause of death in the United States is heart disease.\n" + ] + } + ], + "source": [ + "# print the final answer\n", + "print(response_2.output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Here, we have seen how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. \n", + "\n", + "As you continue to experiment and build upon these concepts, consider exploring additional resources and examples to further enhance your understanding and applications\n", + "\n", + "Happy coding! " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/responses_api/responses_example.ipynb b/examples/responses_api/responses_example.ipynb new file mode 100644 index 0000000000..cec445714c --- /dev/null +++ b/examples/responses_api/responses_example.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the Responses API?\n", + "\n", + "The Responses API is a new way to interact with OpenAI models, designed to be simpler and more flexible than previous APIs. It makes it easy to build advanced AI applications that use multiple tools, handle multi-turn conversations, and work with different types of data (not just text).\n", + "\n", + "Unlike older APIs—such as Chat Completions, which were built mainly for text, or the Assistants API, which can require a lot of setup—the Responses API is built from the ground up for:\n", + "\n", + "- Seamless multi-turn interactions (carry on a conversation across several steps in a single API call)\n", + "- Easy access to powerful hosted tools (like file search, web search, and code interpreter)\n", + "- Fine-grained control over the context you send to the model\n", + "\n", + "As AI models become more capable of complex, long-running reasoning, developers need an API that is both asynchronous and stateful. The Responses API is designed to meet these needs.\n", + "\n", + "In this guide, you'll see some of the new features the Responses API offers, along with practical examples to help you get started." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basics\n", + "By design, on the surface, the Responses API is very similar to the Completions API." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI\n", + "import os\n", + "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "response = client.responses.create(\n", + " model=\"gpt-4o-mini\",\n", + " input=\"tell me a joke\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why did the scarecrow win an award?\n", + "\n", + "Because he was outstanding in his field!\n" + ] + } + ], + "source": [ + "print(response.output[0].content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One key feature of the Response API is that it is stateful. This means that you do not have to manage the state of the conversation by yourself, the API will handle it for you. For example, you can retrieve the response at any time and it will include the full conversation history." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why did the scarecrow win an award?\n", + "\n", + "Because he was outstanding in his field!\n" + ] + } + ], + "source": [ + "fetched_response = client.responses.retrieve(\n", + "response_id=response.id)\n", + "\n", + "print(fetched_response.output[0].content[0].text)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can continue the conversation by referring to the previous response." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "response_two = client.responses.create(\n", + " model=\"gpt-4o-mini\",\n", + " input=\"tell me another\",\n", + " previous_response_id=response.id\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Why don't skeletons fight each other?\n", + "\n", + "They don't have the guts!\n" + ] + } + ], + "source": [ + "print(response_two.output[0].content[0].text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can of course manage the context yourself. But one benefit of OpenAI maintaining the context for you is that you can fork the response at any point and continue the conversation from that point." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sure! Here’s another joke:\n", + "\n", + "Why don’t scientists trust atoms?\n", + "\n", + "Because they make up everything!\n", + "\n", + "**Difference:** The first joke plays on a pun involving \"outstanding\" in a literal sense versus being exceptional, while the second joke relies on a play on words about atoms \"making up\" matter versus fabricating stories. Each joke uses wordplay, but they target different concepts (farming vs. science).\n" + ] + } + ], + "source": [ + "response_two_forked = client.responses.create(\n", + " model=\"gpt-4o-mini\",\n", + " input=\"I didn't like that joke, tell me another and tell me the difference between the two jokes\",\n", + " previous_response_id=response.id # Forking and continuing from the first response\n", + ")\n", + "\n", + "output_text = response_two_forked.output[0].content[0].text\n", + "print(output_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hosted Tools\n", + "\n", + "Another benefit of the Responses API is that it adds support for hosted tools like `file_search` and `web_search`. Instead of manually calling the tools, simply pass in the tools and the API will decide which tool to use and use it.\n", + "\n", + "Here is an example of using the `web_search` tool to incorporate web search results into the response." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "response = client.responses.create(\n", + " model=\"gpt-4o\", # or another supported model\n", + " input=\"What's the latest news about AI?\",\n", + " tools=[\n", + " {\n", + " \"type\": \"web_search\"\n", + " }\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"id\": \"ws_67bd64fe91f081919bec069ad65797f1\",\n", + " \"status\": \"completed\",\n", + " \"type\": \"web_search_call\"\n", + " },\n", + " {\n", + " \"id\": \"msg_67bd6502568c8191a2cbb154fa3fbf4c\",\n", + " \"content\": [\n", + " {\n", + " \"annotations\": [\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Huawei improves AI chip production in boost for China's tech goals\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.ft.com/content/f46b7f6d-62ed-4b64-8ad7-2417e5ab34f6?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Apple cheers Trump with $500bn US investment plan; more losses on Wall Street - as it happened\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.theguardian.com/business/live/2025/feb/24/euro-hits-one-month-high-german-election-result-stock-markets-dax-bank-of-england-business-live-news?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Microsoft axes data center leases as DeepSeek casts doubt on massive AI spend: report\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://nypost.com/2025/02/24/business/microsoft-axes-some-ai-data-center-leases-td-cowen-says/?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Alibaba Plans to Invest $52B in AI, Cloud Over Next Three Years\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.investopedia.com/alibaba-plans-to-invest-usd52b-in-ai-cloud-over-next-three-years-11684981?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"JPMorgan Unit Backs Albert Invent at a $270 Million Valuation\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.wsj.com/articles/jpmorgan-unit-backs-albert-invent-at-a-270-million-valuation-1ab03c96?utm_source=chatgpt.com\"\n", + " }\n", + " ],\n", + " \"text\": \"As of February 25, 2025, several significant developments have emerged in the field of artificial intelligence (AI):\\n\\n**Huawei's Advancements in AI Chip Production**\\n\\nHuawei has notably enhanced its AI chip production capabilities, increasing the yield rate of its Ascend 910C processors from 20% to nearly 40%. This improvement has rendered the production line profitable for the first time and is pivotal for China's ambition to achieve self-sufficiency in advanced semiconductors. Despite these strides, Nvidia continues to dominate the AI chip market in China, attributed to its user-friendly software and widespread adoption. Huawei aims to further elevate its yield rate to 60% and plans to produce 100,000 Ascend 910C processors and 300,000 910B chips in 2025. ([ft.com](https://www.ft.com/content/f46b7f6d-62ed-4b64-8ad7-2417e5ab34f6?utm_source=chatgpt.com))\\n\\n**Apple's $500 Billion U.S. Investment Plan**\\n\\nApple has unveiled a substantial $500 billion investment strategy in the United States over the next four years. This plan encompasses the creation of 20,000 new jobs and the establishment of a major facility in Texas dedicated to manufacturing artificial intelligence servers. President Donald Trump has lauded this initiative, viewing it as a testament to the confidence in his administration. Concurrently, Wall Street has experienced further losses due to concerns over a potential economic slowdown, exacerbated by tariffs. ([theguardian.com](https://www.theguardian.com/business/live/2025/feb/24/euro-hits-one-month-high-german-election-result-stock-markets-dax-bank-of-england-business-live-news?utm_source=chatgpt.com))\\n\\n**Microsoft Adjusts AI Data Center Investments**\\n\\nMicrosoft has canceled leases on U.S. data centers totaling several hundred megawatts, potentially affecting two large centers. This decision is reportedly linked to concerns about oversupply, following claims by Chinese competitor DeepSeek of developing a generative chatbot more efficiently than U.S. companies. Analysts suggest that Microsoft might be reallocating funds or responding to OpenAI's shift to Oracle for a $500 billion project. Despite being a leading AI investor with planned expenditures of $80 billion this year, Microsoft appears to be scaling back on massive spending initiatives, allowing significant data center agreements to lapse and citing facility and power delays. ([nypost.com](https://nypost.com/2025/02/24/business/microsoft-axes-some-ai-data-center-leases-td-cowen-says/?utm_source=chatgpt.com))\\n\\n**Alibaba's $52 Billion Investment in AI and Cloud Infrastructure**\\n\\nAlibaba Group has announced plans to invest over $52 billion in artificial intelligence and cloud infrastructure over the next three years, surpassing its total investment in these areas over the past decade. This strategic move underscores Alibaba's commitment to AI-driven growth and reinforces its position as a leading global cloud provider. Following this announcement, Alibaba's U.S.-listed shares experienced a 3% drop in premarket trading. Analysts view this investment as aligning with market expectations and indicative of Alibaba Cloud's significant capital expenditure compared to peers. ([investopedia.com](https://www.investopedia.com/alibaba-plans-to-invest-usd52b-in-ai-cloud-over-next-three-years-11684981?utm_source=chatgpt.com))\\n\\n**JPMorgan's Investment in AI-Driven Chemical Development**\\n\\nJPMorgan Chase's private investment arm has led a $20 million growth investment in Albert Invent, an AI-driven chemical development platform, valuing the company at $270 million. This funding will enable Albert Invent to expand globally and increase its workforce from 120 to over 200 employees by the end of the year. The company assists chemists in developing new formulations and materials, significantly accelerating chemical experiments. For instance, Albert's platform can simulate 100,000 experiments in 10 minutes for clients like Nouryon Chemicals. ([wsj.com](https://www.wsj.com/articles/jpmorgan-unit-backs-albert-invent-at-a-270-million-valuation-1ab03c96?utm_source=chatgpt.com))\\n\\nThese developments reflect the dynamic and rapidly evolving landscape of AI, with major corporations and financial institutions making significant investments to advance technology and infrastructure in this sector.\\n\\n\\n# Key AI Developments as of February 25, 2025:\\n- [Huawei improves AI chip production in boost for China's tech goals](https://www.ft.com/content/f46b7f6d-62ed-4b64-8ad7-2417e5ab34f6?utm_source=chatgpt.com)\\n- [Apple cheers Trump with $500bn US investment plan; more losses on Wall Street - as it happened](https://www.theguardian.com/business/live/2025/feb/24/euro-hits-one-month-high-german-election-result-stock-markets-dax-bank-of-england-business-live-news?utm_source=chatgpt.com)\\n- [Microsoft axes data center leases as DeepSeek casts doubt on massive AI spend: report](https://nypost.com/2025/02/24/business/microsoft-axes-some-ai-data-center-leases-td-cowen-says/?utm_source=chatgpt.com)\\n \",\n", + " \"type\": \"output_text\",\n", + " \"logprobs\": null\n", + " }\n", + " ],\n", + " \"role\": \"assistant\",\n", + " \"type\": \"message\"\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "import json\n", + "print(json.dumps(response.output, default=lambda o: o.__dict__, indent=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multimodal, Tool-augmented conversation\n", + "\n", + "The Responses API natively supports text, images, and audio modalities. \n", + "Tying everything together, we can build a fully multimodal, tool-augmented interaction with one API call through the responses API." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/1/15/Cat_August_2010-4.jpg/2880px-Cat_August_2010-4.jpg\" width=\"400\"/>" + ], + "text/plain": [ + "<IPython.core.display.Image object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import base64\n", + "\n", + "from IPython.display import Image, display\n", + "\n", + "# Display the image from the provided URL\n", + "url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/1/15/Cat_August_2010-4.jpg/2880px-Cat_August_2010-4.jpg\"\n", + "display(Image(url=url, width=400))\n", + "\n", + "response_multimodal = client.responses.create(\n", + " model=\"gpt-4o\",\n", + " input=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"input_text\", \"text\": \n", + " \"Come up with keywords related to the image, and search on the web using the search tool for any news related to the keywords\"\n", + " \", summarize the findings and cite the sources.\"},\n", + " {\"type\": \"input_image\", \"image_url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/1/15/Cat_August_2010-4.jpg/2880px-Cat_August_2010-4.jpg\"}\n", + " ]\n", + " }\n", + " ],\n", + " tools=[\n", + " {\"type\": \"web_search\"}\n", + " ]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"id\": \"resp_67bd65392a088191a3b802a61f4fba14\",\n", + " \"created_at\": 1740465465.0,\n", + " \"error\": null,\n", + " \"metadata\": {},\n", + " \"model\": \"gpt-4o-2024-08-06\",\n", + " \"object\": \"response\",\n", + " \"output\": [\n", + " {\n", + " \"id\": \"msg_67bd653ab9cc81918db973f0c1af9fbb\",\n", + " \"content\": [\n", + " {\n", + " \"annotations\": [],\n", + " \"text\": \"Based on the image of a cat, some relevant keywords could be:\\n\\n- Cat\\n- Feline\\n- Pet\\n- Animal care\\n- Cat behavior\\n\\nI'll search for recent news related to these keywords.\",\n", + " \"type\": \"output_text\",\n", + " \"logprobs\": null\n", + " }\n", + " ],\n", + " \"role\": \"assistant\",\n", + " \"type\": \"message\"\n", + " },\n", + " {\n", + " \"id\": \"ws_67bd653c7a548191af86757fbbca96e1\",\n", + " \"status\": \"completed\",\n", + " \"type\": \"web_search_call\"\n", + " },\n", + " {\n", + " \"id\": \"msg_67bd653f34fc8191989241b2659fd1b5\",\n", + " \"content\": [\n", + " {\n", + " \"annotations\": [\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Cat miraculously survives 3 weeks trapped in sofa during family's cross-country move\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://nypost.com/2025/02/24/us-news/cat-miraculously-survives-3-weeks-trapped-in-sofa-during-familys-cross-country-move/?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Ex-College Soccer Player Accused of Killing Fellow Athlete Brother, Cat Using Knife, Golf Club: Prosecutors\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://people.com/princeton-murder-soccer-player-accused-murdering-athlete-brother-11685671?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Cuddly 8-Year-Old Cat Surrendered to Shelter for Being 'Too Affectionate' Inspires Dozens of Adoption Applications\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://people.com/cat-surrendered-connecticut-shelter-too-affectionate-11684130?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Emaciated cat found in Meriden abandoned in snow dies after rescue attempt, officials say\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.ctinsider.com/recordjournal/article/meriden-animal-control-cat-neglected-abandoned-20172924.php?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Cat proves mom correct by using human toilet\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://nypost.com/video/cat-proves-mom-correct-by-using-human-toilet/?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Litter-Robot 3 Connect Review\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.thesprucepets.com/litter-robot-3-connect-review-8780105?utm_source=chatgpt.com\"\n", + " },\n", + " {\n", + " \"index\": null,\n", + " \"title\": \"Taylor Swift's favourite cat faces breeding ban\",\n", + " \"type\": \"url_citation\",\n", + " \"url\": \"https://www.thetimes.co.uk/article/taylor-swifts-favourite-cat-faces-breeding-ban-k32nvf6kv?utm_source=chatgpt.com\"\n", + " }\n", + " ],\n", + " \"text\": \"Here are some recent news stories related to cats:\\n\\n**1. Cat Survives Three Weeks Trapped in Sofa During Move**\\n\\nA cat named Sunny-Loo survived three weeks trapped inside a sofa during the Hansons' move from Washington state to Colorado. After disappearing during the move, she was discovered emaciated but alive when the family unpacked their furniture. Sunny-Loo received intensive care and has since been reunited with her family. ([nypost.com](https://nypost.com/2025/02/24/us-news/cat-miraculously-survives-3-weeks-trapped-in-sofa-during-familys-cross-country-move/?utm_source=chatgpt.com))\\n\\n**2. Man Charged with Killing Brother and Family Cat**\\n\\nMatthew Hertgen, a former college soccer player, has been charged with the murder of his younger brother, Joseph Hertgen, and animal cruelty for allegedly killing the family cat. The incident occurred in Princeton, New Jersey, where authorities found Joseph's body with signs of trauma. Matthew faces multiple charges, including first-degree murder. ([people.com](https://people.com/princeton-murder-soccer-player-accused-murdering-athlete-brother-11685671?utm_source=chatgpt.com))\\n\\n**3. \\\"Too Affectionate\\\" Cat Sparks Adoption Interest**\\n\\nAn 8-year-old cat named Ravi was surrendered to a Connecticut shelter for being \\\"too affectionate.\\\" A TikTok video highlighting his story went viral, amassing over 12.6 million views and leading to more than 160 adoption applications. Ravi now has an adoption appointment, and the shelter has gained increased attention for its other adoptable pets. ([people.com](https://people.com/cat-surrendered-connecticut-shelter-too-affectionate-11684130?utm_source=chatgpt.com))\\n\\n**4. Emaciated Cat Found in Snow Dies After Rescue Attempt**\\n\\nA severely neglected cat named Lizzy was found abandoned in a snowbank in Meriden, Connecticut. Despite rescue efforts, Lizzy did not survive. Authorities are seeking information to identify the person responsible for her abandonment, with a reward offered for leads. ([ctinsider.com](https://www.ctinsider.com/recordjournal/article/meriden-animal-control-cat-neglected-abandoned-20172924.php?utm_source=chatgpt.com))\\n\\n**5. Cat Uses Human Toilet, Surprising Family**\\n\\nIn the UK, a cat named Cruise surprised his family by using a human toilet. Despite initial skepticism from her partner and son, Hayley Bibby captured footage of Cruise's bathroom habits, validating her claims. The family now accommodates Cruise's preference by leaving the toilet seat up. ([nypost.com](https://nypost.com/video/cat-proves-mom-correct-by-using-human-toilet/?utm_source=chatgpt.com))\\n\\n**6. Litter-Robot 3 Connect: A High-Tech Litter Box Review**\\n\\nThe Litter-Robot 3 Connect, priced at $499, offers a self-cleaning solution for cat owners averse to scooping litter. While effective and reducing litter usage by 50%, some users note that odor prevention could be improved. The device includes features like a night light and smartphone app integration. ([thesprucepets.com](https://www.thesprucepets.com/litter-robot-3-connect-review-8780105?utm_source=chatgpt.com))\\n\\n**7. Taylor Swift's Favorite Cat Breed Faces Breeding Ban**\\n\\nThe Scottish Fold cat breed, favored by celebrities like Taylor Swift, may face a breeding ban in Britain due to inheritable health issues. These cats often suffer from painful conditions caused by defective cartilage formation. The Animal Welfare Committee has recommended prohibiting the breeding of such cats to prevent further health problems. ([thetimes.co.uk](https://www.thetimes.co.uk/article/taylor-swifts-favourite-cat-faces-breeding-ban-k32nvf6kv?utm_source=chatgpt.com))\\n\\n\\n# Recent Cat-Related News Stories:\\n- [Cat miraculously survives 3 weeks trapped in sofa during family's cross-country move](https://nypost.com/2025/02/24/us-news/cat-miraculously-survives-3-weeks-trapped-in-sofa-during-familys-cross-country-move/?utm_source=chatgpt.com)\\n- [Ex-College Soccer Player Accused of Killing Fellow Athlete Brother, Cat Using Knife, Golf Club: Prosecutors](https://people.com/princeton-murder-soccer-player-accused-murdering-athlete-brother-11685671?utm_source=chatgpt.com)\\n- [Cuddly 8-Year-Old Cat Surrendered to Shelter for Being 'Too Affectionate' Inspires Dozens of Adoption Applications](https://people.com/cat-surrendered-connecticut-shelter-too-affectionate-11684130?utm_source=chatgpt.com)\\n \",\n", + " \"type\": \"output_text\",\n", + " \"logprobs\": null\n", + " }\n", + " ],\n", + " \"role\": \"assistant\",\n", + " \"type\": \"message\"\n", + " }\n", + " ],\n", + " \"temperature\": 1.0,\n", + " \"tool_choice\": \"auto\",\n", + " \"tools\": [\n", + " {\n", + " \"type\": \"web_search\",\n", + " \"location\": null,\n", + " \"sites\": null\n", + " }\n", + " ],\n", + " \"top_p\": 1.0,\n", + " \"max_completion_tokens\": null,\n", + " \"previous_response_id\": null,\n", + " \"reasoning_effort\": null,\n", + " \"text\": {\n", + " \"format\": {\n", + " \"type\": \"text\"\n", + " },\n", + " \"stop\": null\n", + " },\n", + " \"top_logprobs\": null,\n", + " \"truncation\": \"disabled\",\n", + " \"usage\": {\n", + " \"completion_tokens\": null,\n", + " \"prompt_tokens\": null,\n", + " \"total_tokens\": 1370,\n", + " \"completion_tokens_details\": null,\n", + " \"prompt_tokens_details\": null\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "print(json.dumps(response_multimodal.__dict__, default=lambda o: o.__dict__, indent=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above example, we were able to use the `web_search` tool to search the web for news related to the image in one API call instead of multiple round trips that would be required if we were using the Chat Completions API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the responses API\n", + "🔥 a single API call can handle:\n", + "\n", + "✅ Analyze a given image using a multimodal input.\n", + "\n", + "✅ Perform web search via the `web_search` hosted tool\n", + "\n", + "✅ Summarize the results.\n", + "\n", + "In contrast, With Chat Completions API would require multiple steps, each requiring a round trip to the API:\n", + "\n", + "1️⃣ Upload image and get analysis → 1 request\n", + "\n", + "2️⃣ Extract info, call external web search → manual step + tool execution\n", + "\n", + "3️⃣ Re-submit tool results for summarization → another request\n", + "\n", + "See the following diagram for a side by side visualized comparison!\n", + "\n", + "![Responses vs Completions](../../images/comparisons.png)\n", + "\n", + "\n", + "We are very excited for you to try out the Responses API and see how it can simplify your code and make it easier to build complex, multimodal, tool-augmented interactions!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/stripe_model_eval/selecting_a_model_based_on_stripe_conversion.ipynb b/examples/stripe_model_eval/selecting_a_model_based_on_stripe_conversion.ipynb new file mode 100644 index 0000000000..d99d9d2c03 --- /dev/null +++ b/examples/stripe_model_eval/selecting_a_model_based_on_stripe_conversion.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Selecting a Model Based on Stripe Conversion: A Practical Eval for Startups\n", + "## Overview\n", + "The best model for you depends on your business goal. Many startups choose large language models (LLMs) based on offline evaluations and public benchmarks. However, a model that achieves high scores on a benchmark may not necessarily lead your users to pay, subscribe, or continue using your product. Models that look strong on paper can underperform when measured against actual business outcomes.\n", + "\n", + "This guide describes an evaluation approach grounded in one of the most important business outcomes for startups: whether people are willing to pay for your product. \n", + "\n", + "We’ll walk through HyperWrite’s model evaluation process, with a focus on real payment conversion—specifically Stripe payments for one-time purchases or monthly recurring revenue (MRR) subscriptions. If your goal is to improve conversion rates, or to maintain them while switching to a less expensive model, this evaluation example may be a useful pattern to follow.\n", + "## Prerequisites and scope\n", + "To apply this guide to your business, you’ll need:\n", + "\n", + "- **A payment processor.** We use Stripe in this example, but you can make slight adjustments and use the same approach with any payment provider.\n", + "- **Enough users to yield a meaningful signal.** Aim for at least one thousand users per test variant. For higher statistical significance, you’ll need more users.\n", + "- **An AI-powered product with a conversion event.** We use an LLM application, and our conversion event is payment. The same testing approach applies to apps built around voice, video, and other modalities.\n", + "## Model selection based on your actual goal\n", + "HyperWrite builds AI-powered writing tools and research assistants. The company’s core offering is a writing assistant with advanced research capabilities.\n", + "\n", + "Offline benchmarks did not predict what mattered most for HyperWrite: whether users engaged with the writing assistant in a way that led them to subscribe and continue using the product. The HyperWrite team shifted to focusing on the outcome of interest—conversion—and began selecting between AI models based on real-world A/B tests comparing Stripe conversion rates.\n", + "## What moves the needle for startups: conversion\n", + "At many startups, having users sign up for and continue to use the product is the goal. Using classic A/B testing, using the same statistical methods scientists have relied on for decades, you can design a model evaluation process:\n", + "- New users are batched, and each batch is served a different AI model.\n", + "- To standardize when users encounter an upgrade prompt, a consistent rate limit is applied after users have sent the assistant a set number of messages—enough to create a meaningful upgrade moment.\n", + "- Conversion to a paid subscription (via Stripe) is tracked for each group.\n", + "\n", + "Random assignment of users to models and control of other factors (onboarding, features, prompts, etc.) allows attribution of differences in conversion rates to the models being tested, rather than to external variation. Statistics provide confidence that observed differences are unlikely to be due to chance.\n", + "\n", + "When a true, non-random improvement is found (e.g., one model yields a higher conversion rate), the impact is tangible: higher Stripe conversions, more paying users, and often lower costs if the model is more efficient.\n", + "## How to A/B test to choose a model\n", + "A/B testing can serve as a real-world evaluation tool for model selection. Randomly split users into groups, give each group a different experience (here, a different AI model), and observe which group performs better on the key metric—in this case, Stripe conversions.\n", + "### The basics: one model vs. another\n", + "A standard setup includes a “control” (your current model) and a “variant” (a challenger). Users are randomly assigned to either group. To ensure the test isolates the model’s effect, everything else is kept the same: onboarding, features, prompts, and the opportunity to convert. After a predetermined period or number of users, conversion rates are compared: did more people pay when using Model A or Model B?\n", + "### Real-world example: HyperWrite’s model swap test\n", + "HyperWrite’s goal was to deploy a less expensive LLM without materially reducing monetization. This was a non-inferiority scenario: the interest was in ensuring the new model was not significantly worse than the control. With cost savings in mind, a one-sided non-inferiority test was designed.\n", + "- **Test focus:** Cost savings without harming Stripe conversion.\n", + "- **Design:** One-tailed, two-proportion Z-test (focused on detecting whether the new model is worse).\n", + "- **Alpha (Type I error rate):** 0.15 (i.e., 85% confidence). For this startup, iteration speed was prioritized over very strict significance thresholds.\n", + "- **Power:** 0.60 (sufficient to catch meaningful drops, balanced against traffic constraints).\n", + "- **Minimum detectable effect (MDE):** A 30% drop in conversion—any decline less than this would be considered “close enough” if the cost savings justified it.\n", + "- **Population:** A segment of new sign-ups over a defined period, randomized by `user_id` at signup.\n", + "- **Trigger:** Users send messages, hit an upgrade paywall, and may convert via Stripe checkout.\n", + "## Setting your parameters: What counts as winning?\n", + "Not every observed difference will be meaningful—some differences occur by chance. A/B testing helps separate real effects from random noise. The commonly used statistical tool here is the “two-proportion Z-test,” which checks whether the difference in conversion rates between two groups is large enough to be considered statistically significant.\n", + "\n", + "There are a few variations of this test:\n", + "- **One-tailed test:** Checks if the new model is better than (or, depending on design, not worse than) the control\n", + "- **Two-tailed test:** Checks for any difference, whether up or down\n", + "- **Multivariate tests (A/B/n):** Three or more models are compared simultaneously\n", + "\n", + "The choice depends on your goal. If you require a clear upgrade in conversion, a one-tailed test looking for improvement may suffice. If you’re willing to adopt a model that is no worse but cheaper, you may design a non-inferiority (one-sided) test to ensure the new model is not significantly worse.\n", + "### Key terms\n", + "- **Type I Error (False Positive):** Concluding there is an effect when there is none\n", + "- **Type II Error (False Negative):** Failing to detect a real effect\n", + "- **Alpha (α):** The acceptable risk of a Type I error (often set at 0.05, i.e., 5%)\n", + "- **Power:** The probability of detecting a true effect (80% is a common target)\n", + "### Example: Running a Real Model Test\n", + "Consider choosing between your current model (Control) and a new variant (Model X). Suppose you run a one-tailed two-proportion Z-test to see if Model X converts better than the Control. You set α = 0.05 and, after doing a power calculation with your baseline conversion rate and desired minimum detectable effect, determine that roughly 1,500 users per group will provide ~75% power—a compromise allowing for faster directional insight.\n", + "\n", + "After both groups reach the required sample size, the data might look like:\n", + "\n", + "| Group | Users Assigned | Conversions | Conversion Rate | p-value | Stat. Significant? | Winner? | Type I Error Guarded? | Type II Error Guarded? |\n", + "|----------------------------|----------------|-------------|-----------------|---------|--------------------|---------|-----------------------|------------------------|\n", + "| Control (Current Model) | 1500 | 15 | 1.0% | -- | Reference | No | Yes | Yes |\n", + "| Model X (Variant) | 1500 | 30 | 2.0% | 0.012 | Yes | Yes | Yes | Yes |\n", + "\n", + "- **Users Assigned:** Number of users randomly placed in each group.\n", + "- **Conversions:** How many paid via Stripe in each group.\n", + "- **Conversion Rate:** Conversions divided by users assigned.\n", + "- **p-value:** Result of the one-tailed two-proportion Z-test, showing if Model X’s higher rate is likely not due to chance.\n", + "- **Stat. Significant?:** Does the p-value beat your alpha (here, 0.05)?\n", + "- **Winner?:** If statistically significant, Model X is the new winner.\n", + "- **Type I Error Guarded?:** Did we keep the false positive risk within our alpha threshold?\n", + "- **Type II Error Guarded?:** Did our sample size give us enough power to detect a real effect?\n", + "\n", + "In this run, Model X’s conversion rate is 1 percentage point higher than the control (2.0% vs. 1.0%)—a 100% relative increase. The p-value of 0.012 is well below 0.05, so we mark it as statistically significant: Model X is the winner. Because we planned the sample size for 75% statistical power, we’re also confident we didn’t miss a true effect (Type II error). And since we set our alpha at 0.05, the risk of a false positive (Type I error) is controlled.\n", + "### Real-world example: HyperWrite’s test parameters\n", + "HyperWrite did not default to the textbook 95% confidence and 80% power. Traffic is expensive, and maximizing statistical certainty can slow learning and consume capital. The chosen 85% confidence and 60% power allowed detection of any material drop (about a 30% decrease) while avoiding over-optimizing for small differences.\n", + "\n", + "Conversion rates tend to rise as a test runs longer. In these tests, runs were stopped once the required sample size (N) was reached. Only a fraction of incoming traffic was allocated to each test arm, with the majority remaining on the proven control experience.\n", + "### Multiplicity and comparison note\n", + "An A/B/n (“many-vs-one”) design was used: each candidate model (GPT-4.1 and GPT-4.1-mini) was evaluated against the production control (Claude 3.5 Sonnet) but not directly against each other.\n", + "\n", + "Because the launch decision was variant-specific (“ship the arm if its own one-tailed non-inferiority test at α = 0.15 passes; otherwise discard”), a family-wise error rate correction was not applied. This is standard for small-k, control-centric tests. The false positive risk applies only to the single arm launched, and avoiding Bonferroni-type splits preserves power.\n", + "### How to check A/B test significance in Python\n", + "To demonstrate exactly how the statistics behind our A/B test work, here’s a 10-line Python snippet that converts raw conversion counts into a p-value using a one-tailed two-proportion Z-test (variant better than control). Paste it into any Python REPL, Colab, or notebook and swap in your own numbers when you run real experiments.\n", + "```python\n", + "# One-tailed two-proportion Z-test\n", + "from statsmodels.stats.proportion import proportions_ztest\n", + "\n", + "conversions = [30, 15] # [variant, control]\n", + "sample_sizes = [1500, 1500] # [variant, control]\n", + "\n", + "z_stat, p_val = proportions_ztest(\n", + " conversions,\n", + " sample_sizes,\n", + " alternative=\"larger\" # \"larger\" → variant > control\n", + ")\n", + "\n", + "print(f\"Z-statistic = {z_stat:.2f}\")\n", + "print(f\"p-value = {p_val:.3f}\") # → 0.012 (α = 0.05)\n", + "```\n", + "\n", + "How to read the results:\n", + "- If the p-value is **≤ 0.05**, your variant’s higher conversion is statistically significant—go ahead and ship it, or keep monitoring for more data.\n", + "- If it’s **> 0.05**, the result could be random noise—collect more data, or stick with your control.\n", + "### Cautions\n", + "- **Tail fishing / p-hacking:** Decide one- vs two-tailed before the first user flows in; switching later inflates your Type I error (false positives).\n", + "- **Low counts:** If either arm has < ~10 conversions, swap the Z-test for Fisher’s exact test or Wilson/Wald CIs.\n", + "- **Early peeking:** Repeated looks at the data without α-spending corrections raise false-positive risk. Use a fixed sample or a group-sequential design.\n", + "- **User overlap / contamination:** Make sure the same user ID can’t land in two arms (e.g., via logout/login).\n", + "- **Multiple challengers:** If you plan to pick the single “best” of many variants, control family-wise error (Bonferroni, Holm) or use a multi-armed bandit.\n", + "- **Caching & prompt drift:** Confirm your inference layer doesn’t leak one model’s response into another’s cache; keep prompts identical across arms.\n", + "\n", + "To learn more about these pitfalls and how they are avoided, check out Evan Miller's [\"How Not to Run an A/B Test\"](https://www.evanmiller.org/how-not-to-run-an-ab-test.html)\n", + "### The big takeaway\n", + "A/B testing isn’t just for landing pages or button colors—it’s essential for picking the right LLM for your product. By making it part of your workflow, you’ll dodge costly mistakes and spot upgrades grounded in what your users value: a product worth paying for.\n", + "## Real-world example: HyperWrite’s cost savings with GPT-4.1\n", + "Model pricing often increases as capabilities improve. HyperWrite spent several months looking for a model that could match its incumbent (Anthropic’s Claude 3.5 Sonnet) without harming conversion or user experience, ideally at a lower cost. After several models performed worse, OpenAI’s GPT-4.1 provided a notable result: matching the incumbent’s Stripe conversion at a lower price.\n", + "\n", + "Here’s how the variants stacked up on Stripe conversion:\n", + "\n", + "| Variant | Assigned | Conversions | Rate | Req N | % Done | Conv cut-off (≤) | Worse? |\n", + "|----------------------------------------------|---------:|------------:|------:|------:|-------:|-----------------:|:------:|\n", + "| anthropic/claude-3.5-sonnet (control) | 4550 | 42 | 0.92% | 3378 | 135% | — | — |\n", + "| openai/gpt-4.1 (variant) | 4513 | 58 | 1.29% | 3378 | 134% | 32 | No |\n", + "| openai/gpt-4.1-mini (variant) | 4557 | 45 | 0.99% | 3378 | 135% | 33 | No |\n", + "- **Variant:** Model name (control or challenger).\n", + "- **Assigned:** Number of users randomly placed in that arm.\n", + "- **Conversions:** Users in the arm who paid via Stripe.\n", + "- **Rate:** Conversions divided by Assigned.\n", + "- **Req N:** Pre-computed sample-size target for the non-inferiority test.\n", + "- **% Done:** Assigned divided by Req N (progress toward the target).\n", + "- **Conv cut-off (≤):** Maximum conversions below which the arm would be flagged “significantly worse” than control.\n", + "- **Worse?:** “Yes” if the arm fell below its cut-off (i.e., statistically worse); otherwise “No”.\n", + "\n", + "**Results**\n", + "\n", + "- Both GPT-4.1 variants beat their cut-offs—meaning neither was statistically worse than the control.\n", + "- GPT-4.1 (full) held its own on conversion rate against Claude 3.5 Sonnet, while delivering substantial cost savings.\n", + "### Measuring conversion takes some creativity and data\n", + "To perform this analysis, you need a system that links user behavior to Stripe payment events. There’s no universal template for this, but the architecture used at HyperWrite illustrates one way to implement it. This workflow can be adapted for any startup where users interact with an AI and can upgrade via Stripe.\n", + "1. **User Tracking:** Assign a unique identifier to each new signup that persists through their lifecycle.\n", + "2. **Model Assignment:** Randomly assign each user to a test group (model variant) at signup, and store this assignment in your database.\n", + "3. **Interaction Logging:** Log key events (e.g., first use, rate limit reached) along with user IDs and model assignments.\n", + "4. **Conversion Event Capture:** Set up a Stripe webhook to listen for `checkout.session.completed` events. When triggered, match the Stripe customer to your internal user ID and update your database to reflect payment/conversion.\n", + "5. **Data Aggregation:** Regularly pull test group assignments and conversion data into a single table or dashboard for analysis.\n", + "6. **Statistical Testing:** Use a basic Z-test (many libraries/Excel templates exist) to analyze whether the conversion rate differences are meaningful.\n", + "\n", + "The following sequence diagram outlines the process:\n", + "\n", + "![Process diagram](../../images/stripe_eval_diagram.png)\n", + "\n", + "#### User workflow\n", + "Here’s what a user journey looks like at HyperWrite:\n", + "1. **User signs up:** When a user creates an account, their information is stored in the database and a unique `user_id` is assigned.\n", + "2. **First message sent:** The new user interacts with the writing assistant for the first time.\n", + "3. **Rate limit triggers:** After a set number of messages, a rate limit is reached. This introduces a consistent point where an upgrade prompt can be shown.\n", + "4. **Conversion opportunity:** Some users opt to subscribe at this point—they are directed to Stripe checkout.\n", + "#### Stripe workflow\n", + "We care about two key Stripe actions:\n", + "1. **Stripe event listening:** The system listens for the `checkout.session.completed` event from Stripe’s webhook, which fires when a payment succeeds.\n", + "2. **Database update:** When the webhook is received, the corresponding `user_id` is marked as converted in the database.\n", + "#### Running the test\n", + "Routinely check to see if the test is done:\n", + "1. **Query test groups:** Retrieve all users assigned to each model variant.\n", + "2. **Join Stripe data:** Merge your user data with Stripe subscription events, so you know exactly which users in each group converted.\n", + "3. **Run stats:** Use a one-tailed two-proportion Z-test (see the previous section) to check if the difference in conversion rates is statistically meaningful.\n", + "## Conclusion and next steps\n", + "A primary lesson from this approach is that real-world testing tied to business metrics (such as Stripe conversions) can reveal which model choices actually drive results for your product. While offline benchmarks and lab tests have their place, connecting evaluation to the moment a user decides to pay often leads to decisions that benefit both customers and the business.\n", + "### What This Means for Startups\n", + "Beating your incumbent model is not always necessary; a model that performs “as well” on your key metric at a lower cost can be valuable. In this case, OpenAI’s GPT-4.1 matched the incumbent’s Stripe conversion rate while reducing cost.\n", + "\n", + "This underscores the value of tying model evaluation to Stripe-driven A/B tests—you gain clear, revenue-linked answers rather than relying solely on benchmarks or subjective impressions.\n", + "\n", + "Startups can extend this testing in several directions:\n", + "- **Segment by persona or use case:** Divide your audience (e.g., power users vs. newcomers, different industries) and see which models or prompts perform best for each group.\n", + "- **Find the revenue–cost sweet spot:** Consider not only top-line revenue but also the cost to serve each model. The optimal choice may balance profit rather than maximize sales alone.\n", + "- **Monitor long-term impact:** Look beyond immediate conversions. Track metrics like subscriber lifetime value, churn, or retention to optimize for sustainable growth.\n", + "\n", + "There’s a lot of room to get creative with what you measure and how you experiment, so you can tune your product for what matters most to your team.\n", + "\n", + "For questions about this type of testing, feedback on your approach, or input on setting up your own test, feel free to reach out: [josh@othersideai.com](mailto:josh@othersideai.com).\n", + "\n", + "Here’s to building, experimenting, and letting your users—and your Stripe dashboard—guide the way.\n", + "\n", + "## Contributors\n", + "\n", + "This cookbook was contributed by [Josh Bickett](https://www.linkedin.com/in/josh-bickett-4219b166/), Lead Engineer at HyperWrite, a company building AI-powered writing tools and research assistants. The methods and case studies reflect HyperWrite's experience but are intended as a general guide for startups evaluating LLMs using payment conversion metrics." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb b/examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb index 72f6c215be..1274618bfb 100644 --- a/examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb +++ b/examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -60,31 +60,43 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "OPENAI_API_KEY is ready\n" + "OpenAI client is ready\n" ] } ], "source": [ "import os\n", + "from openai import OpenAI\n", "\n", "# Uncomment the following line to set the environment variable in the notebook\n", "# os.environ[\"OPENAI_API_KEY\"] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'\n", "\n", - "if os.getenv(\"OPENAI_API_KEY\") is not None:\n", - " print(\"OPENAI_API_KEY is ready\")\n", - " import openai\n", - " openai.api_key = os.getenv(\"OPENAI_API_KEY\")\n", + "api_key = os.getenv(\"OPENAI_API_KEY\")\n", + "\n", + "if api_key:\n", + " client = OpenAI(api_key=api_key)\n", + " print(\"OpenAI client is ready\")\n", "else:\n", " print(\"OPENAI_API_KEY environment variable not found\")" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the model for all API calls\n", + "OPENAI_MODEL = \"gpt-4o\"" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -206,7 +218,7 @@ "source": [ "# Just asking the model\n", "\n", - "GPT-3.5 was trained on a large amount of scientific information. As a baseline, we'd like to understand what the model already knows without any further context. This will allow us to calibrate overall performance. \n", + "ChatGPT was trained on a large amount of scientific information. As a baseline, we'd like to understand what the model already knows without any further context. This will allow us to calibrate overall performance. \n", "\n", "We construct an appropriate prompt, with some example facts, then query the model with each claim in the dataset. We ask the model to assess a claim as 'True', 'False', or 'NEE' if there is not enough evidence one way or the other. " ] @@ -220,7 +232,7 @@ "def build_prompt(claim):\n", " return [\n", " {\"role\": \"system\", \"content\": \"I will ask you to assess a scientific claim. Output only the text 'True' if the claim is true, 'False' if the claim is false, or 'NEE' if there's not enough evidence.\"},\n", - " {\"role\": \"user\", \"content\": f\"\"\" \n", + " {\"role\": \"user\", \"content\": f\"\"\"\n", "Example:\n", "\n", "Claim:\n", @@ -255,8 +267,8 @@ " responses = []\n", " # Query the OpenAI API\n", " for claim in claims:\n", - " response = openai.ChatCompletion.create(\n", - " model='gpt-3.5-turbo',\n", + " response = client.chat.completions.create(\n", + " model=OPENAI_MODEL,\n", " messages=build_prompt(claim),\n", " max_tokens=3,\n", " )\n", @@ -270,19 +282,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We sample 100 claims from the dataset" + "We sample 50 claims from the dataset" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# Let's take a look at 100 claims\n", + "# Let's take a look at 50 claims\n", "samples = claim_df.sample(50)\n", "\n", - "claims = samples['claim'].tolist() \n" + "claims = samples['claim'].tolist()\n" ] }, { @@ -294,14 +306,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_groundtruth(evidence):\n", " groundtruth = []\n", " for e in evidence:\n", - " # Evidence is empty \n", + " # Evidence is empty\n", " if len(e) == 0:\n", " groundtruth.append('NEE')\n", " else:\n", @@ -315,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -332,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -376,20 +388,20 @@ "text": [ "\tGroundtruth\n", "\tTrue\tFalse\tNEE\n", - "True\t15\t5\t14\t\n", - "False\t0\t2\t1\t\n", - "NEE\t3\t3\t7\t\n" + "True\t9\t3\t15\t\n", + "False\t0\t3\t2\t\n", + "NEE\t8\t6\t4\t\n" ] }, { "data": { "text/plain": [ - "{'True': {'True': 15, 'False': 5, 'NEE': 14},\n", - " 'False': {'True': 0, 'False': 2, 'NEE': 1},\n", - " 'NEE': {'True': 3, 'False': 3, 'NEE': 7}}" + "{'True': {'True': 9, 'False': 3, 'NEE': 15},\n", + " 'False': {'True': 0, 'False': 3, 'NEE': 2},\n", + " 'NEE': {'True': 8, 'False': 6, 'NEE': 4}}" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -427,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -513,7 +525,7 @@ "4 [Two human Golli (for gene expressed in the ol... False " ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -545,18 +557,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running Chroma using direct local API.\n", - "Using DuckDB in-memory for database. Data will be transient.\n" - ] - } - ], + "outputs": [], "source": [ "import chromadb\n", "from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction\n", @@ -577,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -603,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -619,18 +622,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def build_prompt_with_context(claim, context):\n", - " return [{'role': 'system', 'content': \"I will ask you to assess whether a particular scientific claim, based on evidence provided. Output only the text 'True' if the claim is true, 'False' if the claim is false, or 'NEE' if there's not enough evidence.\"}, \n", + " return [{'role': 'system', 'content': \"I will ask you to assess whether a particular scientific claim, based on evidence provided. Output only the text 'True' if the claim is true, 'False' if the claim is false, or 'NEE' if there's not enough evidence.\"},\n", " {'role': 'user', 'content': f\"\"\"\"\n", "The evidence is the following:\n", "\n", "{' '.join(context)}\n", "\n", - "Assess the following claim on the basis of the evidence. Output only the text 'True' if the claim is true, 'False' if the claim is false, or 'NEE' if there's not enough evidence. Do not output any other text. \n", + "Assess the following claim on the basis of the evidence. Output only the text 'True' if the claim is true, 'False' if the claim is false, or 'NEE' if there's not enough evidence. Do not output any other text.\n", "\n", "Claim:\n", "{claim}\n", @@ -647,8 +650,8 @@ " if len(context) == 0:\n", " responses.append('NEE')\n", " continue\n", - " response = openai.ChatCompletion.create(\n", - " model='gpt-3.5-turbo',\n", + " response = client.chat.completions.create(\n", + " model=OPENAI_MODEL,\n", " messages=build_prompt_with_context(claim=claim, context=context),\n", " max_tokens=3,\n", " )\n", @@ -667,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -676,20 +679,20 @@ "text": [ "\tGroundtruth\n", "\tTrue\tFalse\tNEE\n", - "True\t16\t2\t8\t\n", - "False\t1\t6\t5\t\n", - "NEE\t1\t2\t9\t\n" + "True\t13\t1\t4\t\n", + "False\t1\t10\t2\t\n", + "NEE\t3\t1\t15\t\n" ] }, { "data": { "text/plain": [ - "{'True': {'True': 16, 'False': 2, 'NEE': 8},\n", - " 'False': {'True': 1, 'False': 6, 'NEE': 5},\n", - " 'NEE': {'True': 1, 'False': 2, 'NEE': 9}}" + "{'True': {'True': 13, 'False': 1, 'NEE': 4},\n", + " 'False': {'True': 1, 'False': 10, 'NEE': 2},\n", + " 'NEE': {'True': 3, 'False': 1, 'NEE': 15}}" ] }, - "execution_count": 28, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -706,7 +709,7 @@ "source": [ "## Results\n", "\n", - "We see that the model is a lot less likely to evaluate a False claim as true (2 instances VS 5 previously), but that claims without enough evidence are still often assessed as True or False.\n", + "We see that the model performs better overall, and is now significantly better at correctly identifying false claims. Additionally, most NEE cases are also correctly identified now.\n", "\n", "Taking a look at the retrieved documents, we see that they are sometimes not relevant to the claim - this causes the model to be confused by the extra information, and it may decide that sufficient evidence is present, even when the information is irrelevant. This happens because we always ask for the 3 'most' relevant documents, but these might not be relevant at all beyond a certain point. " ] @@ -742,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -758,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -767,20 +770,20 @@ "text": [ "\tGroundtruth\n", "\tTrue\tFalse\tNEE\n", - "True\t10\t2\t1\t\n", - "False\t0\t2\t1\t\n", - "NEE\t8\t6\t20\t\n" + "True\t9\t0\t1\t\n", + "False\t0\t7\t0\t\n", + "NEE\t8\t5\t20\t\n" ] }, { "data": { "text/plain": [ - "{'True': {'True': 10, 'False': 2, 'NEE': 1},\n", - " 'False': {'True': 0, 'False': 2, 'NEE': 1},\n", - " 'NEE': {'True': 8, 'False': 6, 'NEE': 20}}" + "{'True': {'True': 9, 'False': 0, 'NEE': 1},\n", + " 'False': {'True': 0, 'False': 7, 'NEE': 0},\n", + " 'NEE': {'True': 8, 'False': 5, 'NEE': 20}}" ] }, - "execution_count": 30, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -797,7 +800,8 @@ "source": [ "## Results\n", "\n", - "The model now assesses many fewer claims as True or False when there is not enough evidence present. However, it now biases away from certainty. Most claims are now assessed as having not enough evidence, because a large fraction of them are filtered out by the distance threshold. It's possible to tune the distance threshold to find the optimal operating point, but this can be difficult, and is dataset and embedding model dependent. " + "\n", + "The model now assesses many fewer claims as True or False when there is not enough evidence present. However, it also is now much more cautious, tending to label most items as not enough evidence, biasing away from certainty. Most claims are now assessed as having not enough evidence, because a large fraction of them are filtered out by the distance threshold. It's possible to tune the distance threshold to find the optimal operating point, but this can be difficult, and is dataset and embedding model dependent. " ] }, { @@ -835,19 +839,19 @@ "source": [ "def build_hallucination_prompt(claim):\n", " return [{'role': 'system', 'content': \"\"\"I will ask you to write an abstract for a scientific paper which supports or refutes a given claim. It should be written in scientific language, include a title. Output only one abstract, then stop.\n", - " \n", + "\n", " An Example:\n", "\n", " Claim:\n", " A high microerythrocyte count raises vulnerability to severe anemia in homozygous alpha (+)- thalassemia trait subjects.\n", "\n", " Abstract:\n", - " BACKGROUND The heritable haemoglobinopathy alpha(+)-thalassaemia is caused by the reduced synthesis of alpha-globin chains that form part of normal adult haemoglobin (Hb). Individuals homozygous for alpha(+)-thalassaemia have microcytosis and an increased erythrocyte count. Alpha(+)-thalassaemia homozygosity confers considerable protection against severe malaria, including severe malarial anaemia (SMA) (Hb concentration < 50 g/l), but does not influence parasite count. We tested the hypothesis that the erythrocyte indices associated with alpha(+)-thalassaemia homozygosity provide a haematological benefit during acute malaria. \n", - " METHODS AND FINDINGS Data from children living on the north coast of Papua New Guinea who had participated in a case-control study of the protection afforded by alpha(+)-thalassaemia against severe malaria were reanalysed to assess the genotype-specific reduction in erythrocyte count and Hb levels associated with acute malarial disease. We observed a reduction in median erythrocyte count of approximately 1.5 x 10(12)/l in all children with acute falciparum malaria relative to values in community children (p < 0.001). We developed a simple mathematical model of the linear relationship between Hb concentration and erythrocyte count. This model predicted that children homozygous for alpha(+)-thalassaemia lose less Hb than children of normal genotype for a reduction in erythrocyte count of >1.1 x 10(12)/l as a result of the reduced mean cell Hb in homozygous alpha(+)-thalassaemia. In addition, children homozygous for alpha(+)-thalassaemia require a 10% greater reduction in erythrocyte count than children of normal genotype (p = 0.02) for Hb concentration to fall to 50 g/l, the cutoff for SMA. We estimated that the haematological profile in children homozygous for alpha(+)-thalassaemia reduces the risk of SMA during acute malaria compared to children of normal genotype (relative risk 0.52; 95% confidence interval [CI] 0.24-1.12, p = 0.09). \n", + " BACKGROUND The heritable haemoglobinopathy alpha(+)-thalassaemia is caused by the reduced synthesis of alpha-globin chains that form part of normal adult haemoglobin (Hb). Individuals homozygous for alpha(+)-thalassaemia have microcytosis and an increased erythrocyte count. Alpha(+)-thalassaemia homozygosity confers considerable protection against severe malaria, including severe malarial anaemia (SMA) (Hb concentration < 50 g/l), but does not influence parasite count. We tested the hypothesis that the erythrocyte indices associated with alpha(+)-thalassaemia homozygosity provide a haematological benefit during acute malaria.\n", + " METHODS AND FINDINGS Data from children living on the north coast of Papua New Guinea who had participated in a case-control study of the protection afforded by alpha(+)-thalassaemia against severe malaria were reanalysed to assess the genotype-specific reduction in erythrocyte count and Hb levels associated with acute malarial disease. We observed a reduction in median erythrocyte count of approximately 1.5 x 10(12)/l in all children with acute falciparum malaria relative to values in community children (p < 0.001). We developed a simple mathematical model of the linear relationship between Hb concentration and erythrocyte count. This model predicted that children homozygous for alpha(+)-thalassaemia lose less Hb than children of normal genotype for a reduction in erythrocyte count of >1.1 x 10(12)/l as a result of the reduced mean cell Hb in homozygous alpha(+)-thalassaemia. In addition, children homozygous for alpha(+)-thalassaemia require a 10% greater reduction in erythrocyte count than children of normal genotype (p = 0.02) for Hb concentration to fall to 50 g/l, the cutoff for SMA. We estimated that the haematological profile in children homozygous for alpha(+)-thalassaemia reduces the risk of SMA during acute malaria compared to children of normal genotype (relative risk 0.52; 95% confidence interval [CI] 0.24-1.12, p = 0.09).\n", " CONCLUSIONS The increased erythrocyte count and microcytosis in children homozygous for alpha(+)-thalassaemia may contribute substantially to their protection against SMA. A lower concentration of Hb per erythrocyte and a larger population of erythrocytes may be a biologically advantageous strategy against the significant reduction in erythrocyte count that occurs during acute infection with the malaria parasite Plasmodium falciparum. This haematological profile may reduce the risk of anaemia by other Plasmodium species, as well as other causes of anaemia. Other host polymorphisms that induce an increased erythrocyte count and microcytosis may confer a similar advantage.\n", "\n", - " End of example. \n", - " \n", + " End of example.\n", + "\n", " \"\"\"}, {'role': 'user', 'content': f\"\"\"\"\n", " Perform the task for the following claim.\n", "\n", @@ -859,12 +863,11 @@ "\n", "\n", "def hallucinate_evidence(claims):\n", - " # Query the OpenAI API\n", " responses = []\n", " # Query the OpenAI API\n", " for claim in claims:\n", - " response = openai.ChatCompletion.create(\n", - " model='gpt-3.5-turbo',\n", + " response = client.chat.completions.create(\n", + " model=OPENAI_MODEL,\n", " messages=build_hallucination_prompt(claim),\n", " )\n", " responses.append(response.choices[0].message.content)\n", @@ -877,12 +880,12 @@ "source": [ "We hallucinate a document for each claim.\n", "\n", - "*NB: This can take a while, about 30m for 100 claims*. You can reduce the number of claims we want to assess to get results more quickly. " + "*NB: This can take a while, about 7m for 100 claims*. You can reduce the number of claims we want to assess to get results more quickly. " ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -898,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -915,7 +918,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -924,20 +927,20 @@ "text": [ "\tGroundtruth\n", "\tTrue\tFalse\tNEE\n", - "True\t15\t2\t5\t\n", - "False\t1\t5\t4\t\n", - "NEE\t2\t3\t13\t\n" + "True\t13\t0\t3\t\n", + "False\t1\t10\t1\t\n", + "NEE\t3\t2\t17\t\n" ] }, { "data": { "text/plain": [ - "{'True': {'True': 15, 'False': 2, 'NEE': 5},\n", - " 'False': {'True': 1, 'False': 5, 'NEE': 4},\n", - " 'NEE': {'True': 2, 'False': 3, 'NEE': 13}}" + "{'True': {'True': 13, 'False': 0, 'NEE': 3},\n", + " 'False': {'True': 1, 'False': 10, 'NEE': 1},\n", + " 'NEE': {'True': 3, 'False': 2, 'NEE': 17}}" ] }, - "execution_count": 33, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -969,7 +972,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -983,12 +986,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "fd16a328ca3d68029457069b79cb0b38eb39a0f5ccc4fe4473d3047707df8207" - } + "version": "3.12.10" } }, "nbformat": 4, diff --git a/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb b/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb index dacf58b54d..b5efb2a1d3 100644 --- a/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb +++ b/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb @@ -37,14 +37,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "8c304b93", "metadata": {}, "outputs": [], "source": [ "# install packages\n", "\n", - "!python3 -m pip install -qU openai pandas wget elasticsearch\n", + "! python3 -m pip install -qU openai pandas wget elasticsearch\n", "\n", "# import modules\n", "\n", @@ -54,7 +54,7 @@ "import zipfile\n", "import pandas as pd\n", "import json\n", - "import openai" + "from openai import OpenAI" ] }, { @@ -321,25 +321,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "57385c69", "metadata": {}, "outputs": [], "source": [ - "# Get OpenAI API key\n", - "OPENAI_API_KEY = getpass(\"Enter OpenAI API key\")\n", - "\n", - "# Set API key\n", - "openai.api_key = OPENAI_API_KEY\n", - "\n", - "# Define model\n", - "EMBEDDING_MODEL = \"text-embedding-3-small\"\n", + "# Create OpenAI client\n", + "openai_client = OpenAI()\n", "\n", "# Define question\n", "question = 'Is the Atlantic the biggest ocean in the world?'\n", "\n", - "# Create embedding\n", - "question_embedding = openai.Embedding.create(input=question, model=EMBEDDING_MODEL)\n" + "question_embedding = openai_client.embeddings.create(\n", + " input=question,\n", + " model=\"text-embedding-3-small\"\n", + ")" ] }, { @@ -383,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "fc834fdd", "metadata": {}, "outputs": [ @@ -764,7 +760,7 @@ " index = \"wikipedia_vector_index\",\n", " knn={\n", " \"field\": \"content_vector\",\n", - " \"query_vector\": question_embedding[\"data\"][0][\"embedding\"],\n", + " \"query_vector\": question_embedding.data[0].embedding,\n", " \"k\": 10,\n", " \"num_candidates\": 100\n", " }\n", diff --git a/examples/vector_databases/redis/redisqna/redisqna.ipynb b/examples/vector_databases/redis/redisqna/redisqna.ipynb index 6b21ea6b58..794b84cf17 100644 --- a/examples/vector_databases/redis/redisqna/redisqna.ipynb +++ b/examples/vector_databases/redis/redisqna/redisqna.ipynb @@ -22,13 +22,14 @@ "cell_type": "code", "execution_count": null, "metadata": { + "scrolled": true, "vscode": { "languageId": "shellscript" } }, "outputs": [], "source": [ - "! pip install redis openai python-dotenv openai[datalib]" + "! pip install -q redis openai python-dotenv 'openai[datalib]'" ] }, { @@ -64,25 +65,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "import openai\n", + "from openai import OpenAI\n", "import os\n", "from dotenv import load_dotenv\n", "\n", "load_dotenv()\n", - "openai.api_key = os.getenv(\"OPENAI_API_KEY\")\n", + "oai_client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "\n", "def get_completion(prompt, model=\"gpt-3.5-turbo\"):\n", " messages = [{\"role\": \"user\", \"content\": prompt}]\n", - " response = openai.ChatCompletion.create(\n", + " response = oai_client.chat.completions.create(\n", " model=model,\n", " messages=messages,\n", - " temperature=0, \n", + " temperature=0,\n", " )\n", - " return response.choices[0].message[\"content\"]" + " return response.choices[0].message.content" ] }, { @@ -91,14 +92,22 @@ "metadata": {}, "source": [ "## Experiment - Chat Completion on a Topic outside of the Model's Knowledge Cutoff Date\n", - "Gpt-3.5-turbo was trained on data up to Sep 2021. Let's ask it a question about something that is beyond that date. In this case, the FTX/Sam Bankman-Fried scandal." + "Gpt-3.5-turbo was trained on data up to Sep 2021. Let's ask it a question about something that is beyond that date. In this case, the FTX/Sam Bankman-Fried scandal. We are using an old model here for demonstration. Newer models such as got-4o has later knowledge cutoffs (late 2023) and will work here as well." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yes, FTX is generally considered a well-managed company. Sam Bankman-Fried, the founder and CEO of FTX, has a strong track record in the cryptocurrency industry and has successfully grown the company into one of the leading cryptocurrency exchanges in the world. FTX has also received positive reviews for its user-friendly platform, innovative products, and strong customer service. Additionally, FTX has been proactive in regulatory compliance and has taken steps to ensure the security of its users' funds. Overall, FTX is seen as a well-managed company in the cryptocurrency space.\n" + ] + } + ], "source": [ "prompt = \"Is Sam Bankman-Fried's company, FTX, considered a well-managed company?\"\n", "response = get_completion(prompt)\n", @@ -116,9 +125,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FTX is generally considered a well-managed company. Sam Bankman-Fried, the founder and CEO, has a strong reputation in the cryptocurrency industry for his leadership and strategic vision. FTX has also experienced significant growth and success since its founding in 2017. However, without specific insider knowledge or data, it is ultimately unknown whether FTX is definitively considered a well-managed company.\n" + ] + } + ], "source": [ "prompt =\"Is Sam Bankman-Fried's company, FTX, considered a well-managed company? If you don't know for certain, say unknown.\"\n", "response = get_completion(prompt)\n", @@ -145,11 +162,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, + "metadata": {}, "outputs": [], "source": [ "! docker compose up -d" @@ -165,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -174,7 +187,7 @@ "True" ] }, - "execution_count": 11, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -198,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -207,7 +220,7 @@ "b'OK'" ] }, - "execution_count": 12, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -243,20 +256,26 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import openai\n", - "\n", "directory = './assets/'\n", - "model='text-embedding-3-small'\n", + "model = 'text-embedding-3-small'\n", "i = 1\n", + "\n", "for file in os.listdir(directory):\n", - " with open(os.path.join(directory, file)) as f:\n", + " with open(os.path.join(directory, file), 'r') as f:\n", " content = f.read()\n", - " vector = openai.Embedding.create(input = [content], model = model)['data'][0]['embedding']\n", + " # Create the embedding using the new client-based method\n", + " response = oai_client.embeddings.create(\n", + " model=model,\n", + " input=[content]\n", + " )\n", + " # Access the embedding from the response object\n", + " vector = response.data[0].embedding\n", + " \n", + " # Store the content and vector using your JSON client\n", " client.json().set(f'doc:{i}', '$', {'content': content, 'vector': vector})\n", " i += 1" ] @@ -272,90 +291,32 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Embattled Crypto Exchange FTX Files for Bankruptcy\n", - "\n", - "Nov. 11, 2022\n", - "On Monday, Sam Bankman-Fried, the chief executive of the cryptocurrency exchange FTX, took to Twitter to reassure his customers: “FTX is fine,” he wrote. “Assets are fine.”\n", - "\n", - "On Friday, FTX announced that it was filing for bankruptcy, capping an extraordinary week of corporate drama that has upended crypto markets, sent shock waves through an industry struggling to gain mainstream credibility and sparked government investigations that could lead to more damaging revelations or even criminal charges.\n", - "\n", - "In a statement on Twitter, the company said that Mr. Bankman-Fried had resigned, with John J. Ray III, a corporate turnaround specialist, taking over as chief executive.\n", - "\n", - "The speed of FTX’s downfall has left crypto insiders stunned. Just days ago, Mr. Bankman-Fried was considered one of the smartest leaders in the crypto industry, an influential figure in Washington who was lobbying to shape regulations. And FTX was widely viewed as one of the most stable and responsible companies in the freewheeling, loosely regulated crypto industry.\n", - "\n", - "“Here we are, with one of the richest people in the world, his net worth dropping to zero, his business dropping to zero,” said Jared Ellias, a bankruptcy professor at Harvard Law School. “The velocity of this failure is just unbelievable.”\n", - "\n", - "Now, the bankruptcy has set up a rush among investors and customers to salvage funds from what remains of FTX. A surge of customers tried to withdraw funds from the platform this week, and the company couldn’t meet the demand. The exchange owes as much as $8 billion, according to people familiar with its finances.\n", - "\n", - "FTX’s collapse has destabilized the crypto industry, which was already reeling from a crash in the spring that drained $1 trillion from the market. The prices of the leading cryptocurrencies, Bitcoin and Ether, have plummeted. The crypto lender BlockFi, which was closely entangled with FTX, announced on Thursday that it was suspending operations as a result of FTX’s collapse.\n", - "\n", - "Mr. Bankman-Fried was backed by some of the highest-profile venture capital investors in Silicon Valley, including Sequoia Capital and Lightspeed Venture Partners. Some of those investors, facing questions about how closely they scrutinized FTX before they put money into it, have said that their nine-figure investments in the crypto exchange are now essentially worthless.\n", - "\n", - "The company’s demise has also set off a reckoning over risky practices that have become pervasive in crypto, an industry that was founded partly as a corrective to the type of dangerous financial engineering that caused the 2008 economic crisis.\n", - "\n", - "“I’m really sorry, again, that we ended up here,” Mr. Bankman-Fried said on Twitter on Friday. “Hopefully this can bring some amount of transparency, trust, and governance.”\n", - "\n", - "The bankruptcy filing marks the start of what will probably be months or even years of legal fallout, as lawyers try to work out whether the exchange can ever continue to operate in some form and customers demand compensation. FTX is already the target of investigations by the Securities and Exchange Commission and the Justice Department, with investigators focused on whether the company improperly used customer funds to prop up Alameda Research, a trading firm that Mr. Bankman-Fried also founded.\n", - "\n", - "The bankruptcy filing included FTX, its U.S. arm and Alameda. According to a bare-bones legal filing in U.S. Bankruptcy Court in Delaware, FTX has assets valued between $10 billion and $50 billion, with the size of its liabilities in the same range. The company has more than 100,000 creditors, the filing said.\n", - "\n", - "The bankruptcy is a stunning fall from grace for the 30-year-old Mr. Bankman-Fried, who cultivated a reputation as a boy genius with a host of endearing quirks, including a habit of sleeping on a beanbag at the office. At one point, he was one of the richest people in the industry, with an estimated fortune of $24 billion. He hobnobbed with actors, professional athletes and former world leaders.\n", - "\n", - "Mr. Bankman-Fried’s crypto empire had an elaborate structure. The bankruptcy filing lists more than 130 corporate entities affiliated with FTX and Alameda. But as of June, FTX had only about 300 employees, a point of pride for Mr. Bankman-Fried, who said he had resisted calls from venture investors to hire more staff.\n", - "\n", - "“We told them additional employees added too quickly were net negative,” Mr. Bankman-Fried said on Twitter in June. “They could take it or leave it.”\n", - "\n", - "Unusually for a major start-up, none of FTX’s investors had seats on the board, which instead consisted of Mr. Bankman-Fried, another FTX executive and a lawyer in Antigua and Barbuda.\n", - "\n", - "FTX and Alameda were based in the Bahamas, where Mr. Bankman-Fried and a small circle of top executives called most of the shots and lived together in a luxury resort. Officially, Alameda was run by Caroline Ellison, a former trader for the hedge fund Jane Street, but Mr. Bankman-Fried was heavily involved, contributing to the decision-making on big trades, according to a person familiar with the matter.\n", - "\n", - "In addition to Mr. Bankman-Fried and Ms. Ellison, the circle of executives running FTX included Nishad Singh, FTX’s director of engineering, and Gary Wang, the chief technology officer. Few others had visibility into how the company was run: When the firm collapsed this week, lower-ranking employees were left confused and blindsided, according to people familiar with the matter. Mr. Singh and Ms. Ellison did not respond to requests for comment; Mr. Wang could not immediately be reached.\n", - "\n", - "As a crypto exchange, FTX provided a marketplace for customers to buy, sell and store a wide range of digital currencies. Most of its revenue stemmed from a risky type of trade — in which crypto investors borrowed money to make huge bets on the future prices of cryptocurrencies — that remains illegal in the United States. But Mr. Bankman-Fried also ran a smaller U.S. affiliate that offered more basic trading options.\n", - "\n", - "Mr. Bankman-Fried’s problems started over the weekend, when the chief executive of Binance, the largest crypto exchange, suggested publicly that FTX might be on shaky financial footing. A rush of customers tried to withdraw their crypto holdings from the platform, and FTX was unable to meet the demand.\n", - "\n", - "On Tuesday, Mr. Bankman-Fried said he had struck a deal to sell FTX to Binance. But after reviewing the company’s financial documents, Binance’s chief executive, Changpeng Zhao, pulled out of the agreement, leaving Mr. Bankman-Fried with limited options.\n", - "\n", - "In calls with investors and messages to employees this week, he apologized repeatedly and stressed that he was working hard to raise money and resolve the situation. But the hole was ultimately too big to fill.\n", - "\n", - "FTX’s bankruptcy is the latest — and by far the biggest — in a series of bankruptcies that have shaken the crypto world this year. After a market crash in the spring, two crypto lending companies, Celsius Network and Voyager Digital, filed for bankruptcy, kicking off months of legal maneuvering over how their remaining assets should be divided. In an ironic twist, FTX had recently won an auction to buy Voyager’s remaining assets.\n", - "\n", - "As it enters its own bankruptcy process, FTX will be led by Mr. Ray, who has ample experience managing distressed situations. He helped manage Enron after the collapse of its business in an accounting fraud scandal in 2001. And he helped liquidate the trust of the subprime mortgage company ResCap after its 2012 bankruptcy.\n", - "\n", - "The bankruptcy proceedings may be only the beginning of Mr. Bankman-Fried’s legal troubles. Federal investigators are examining the relationship between FTX and Alameda, and customers are likely to file lawsuits.\n", - "\n", - "Mr. Bankman-Fried’s old allies have quickly abandoned him. On Thursday night, the team running the FTX Future Fund, a charitable group that Mr. Bankman-Fried bankrolled, announced that they were resigning.\n", - "\n", - "“We were shocked and immensely saddened to learn of the recent events at FTX,” they wrote in a statement. “We have fundamental questions about the legitimacy and integrity of the business operations that were funding the FTX Foundation and the Future Fund.”\n", - "\n", - "Not long ago, Mr. Bankman-Fried was performing a comedy routine onstage at a conference with Anthony Scaramucci, the former White House communications director and a business partner of FTX.\n", - "\n", - "“I’m disappointed,” Mr. Scaramucci said in an interview on CNBC on Friday. “Duped, I guess, is the right word.”\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from redis.commands.search.query import Query\n", "import numpy as np\n", "\n", - "vec = np.array(openai.Embedding.create(input = [prompt], model = model)['data'][0]['embedding'], dtype=np.float32).tobytes()\n", - "q = Query('*=>[KNN 1 @vector $query_vec AS vector_score]')\\\n", - " .sort_by('vector_score')\\\n", - " .return_fields('content')\\\n", - " .dialect(2) \n", + "response = oai_client.embeddings.create(\n", + " input=[prompt],\n", + " model=model\n", + ")\n", + "# Extract the embedding vector from the response\n", + "embedding_vector = response.data[0].embedding\n", + "\n", + "# Convert the embedding to a numpy array of type float32 and then to bytes\n", + "vec = np.array(embedding_vector, dtype=np.float32).tobytes()\n", + "\n", + "# Build and execute the Redis query\n", + "q = Query('*=>[KNN 1 @vector $query_vec AS vector_score]') \\\n", + " .sort_by('vector_score') \\\n", + " .return_fields('content') \\\n", + " .dialect(2)\n", "params = {\"query_vec\": vec}\n", "\n", "context = client.ft('idx').search(q, query_params=params).docs[0].content\n", - "print(context)" + "print(context)\n" ] }, { @@ -369,14 +330,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "No, Sam Bankman-Fried's company FTX is not considered a well-managed company as it has filed for bankruptcy and owes as much as $8 billion to its creditors. The collapse of FTX has destabilized the crypto industry, and the company is already the target of investigations by the Securities and Exchange Commission and the Justice Department. FTX was widely viewed as one of the most stable and responsible companies in the freewheeling, loosely regulated crypto industry, but its risky practices have become pervasive in crypto, leading to a reckoning.\n" + "Based on the information provided, FTX, Sam Bankman-Fried's company, is not considered a well-managed company. The company has faced bankruptcy proceedings, mishandling of customer funds, unauthorized transactions, freezing of assets by regulatory authorities, and a lack of trustworthy financial information. The new CEO, John J. Ray III, described the situation as a \"complete failure of corporate controls\" and indicated gross mismanagement. Additionally, the company's financial situation, lack of record-keeping, and use of inadequate accounting tools despite handling billions of dollars have raised serious concerns about its management practices.\n" ] } ], @@ -390,11 +351,18 @@ "response = get_completion(prompt)\n", "print(response)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -408,10 +376,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "orig_nbformat": 4 + "version": "3.11.8" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/voice_solutions/arduino_ai_speech_assets/elato-alien.png b/examples/voice_solutions/arduino_ai_speech_assets/elato-alien.png new file mode 100644 index 0000000000..2724ed324f Binary files /dev/null and b/examples/voice_solutions/arduino_ai_speech_assets/elato-alien.png differ diff --git a/examples/voice_solutions/arduino_ai_speech_assets/flowchart.png b/examples/voice_solutions/arduino_ai_speech_assets/flowchart.png new file mode 100644 index 0000000000..e7e7ada909 Binary files /dev/null and b/examples/voice_solutions/arduino_ai_speech_assets/flowchart.png differ diff --git a/examples/voice_solutions/arduino_ai_speech_assets/mockups.png b/examples/voice_solutions/arduino_ai_speech_assets/mockups.png new file mode 100644 index 0000000000..5cd2ab0f60 Binary files /dev/null and b/examples/voice_solutions/arduino_ai_speech_assets/mockups.png differ diff --git a/examples/voice_solutions/arduino_ai_speech_assets/pcb-design.png b/examples/voice_solutions/arduino_ai_speech_assets/pcb-design.png new file mode 100644 index 0000000000..3b46e55b4c Binary files /dev/null and b/examples/voice_solutions/arduino_ai_speech_assets/pcb-design.png differ diff --git a/examples/voice_solutions/arduino_ai_speech_assets/structure.png b/examples/voice_solutions/arduino_ai_speech_assets/structure.png new file mode 100644 index 0000000000..d7c9c4496a Binary files /dev/null and b/examples/voice_solutions/arduino_ai_speech_assets/structure.png differ diff --git a/examples/voice_solutions/arduino_ai_speech_assets/thumbnail.png b/examples/voice_solutions/arduino_ai_speech_assets/thumbnail.png new file mode 100644 index 0000000000..90e40e5714 Binary files /dev/null and b/examples/voice_solutions/arduino_ai_speech_assets/thumbnail.png differ diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api.mdx b/examples/voice_solutions/one_way_translation_using_realtime_api.mdx new file mode 100644 index 0000000000..82f841186d --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api.mdx @@ -0,0 +1,165 @@ +# Multi-Language Conversational Translation with the Realtime API + +One of the most exciting things about the Realtime API is that the emotion, tone and pace of speech are all passed to the model for inference. Traditional cascaded voice systems (involving STT and TTS) introduce an intermediate transcription step, relying on SSML or prompting to approximate prosody, which inherently loses fidelity. The speaker's expressiveness is literally lost in translation. Because it can process raw audio, the Realtime API preserves those audio attributes through inference, minimizing latency and enriching responses with tonal and inflectional cues. Because of this, the Realtime API makes LLM-powered speech translation closer to a live interpreter than ever before. + +This cookbook demonstrates how to use OpenAI's [ Realtime API](https://platform.openai.com/docs/guides/realtime) to build a multi-lingual, one-way translation workflow with WebSockets. It is implemented using the [Realtime + WebSockets integration](https://platform.openai.com/docs/guides/realtime-websocket) in a speaker application and a WebSocket server to mirror the translated audio to a listener application. + +A real-world use case for this demo is a multilingual, conversational translation where a speaker talks into the speaker app and listeners hear translations in their selected native language via the listener app. Imagine a conference room with a speaker talking in English and a participant with headphones in choosing to listen to a Tagalog translation. Due to the current turn-based nature of audio models, the speaker must pause briefly to allow the model to process and translate speech. However, as models become faster and more efficient, this latency will decrease significantly and the translation will become more seamless. + + +Let's explore the main functionalities and code snippets that illustrate how the app works. You can find the code in the [accompanying repo](https://github.com/openai/openai-cookbook/tree/main/examples/voice_solutions/one_way_translation_using_realtime_api) if you want to run the app locally. + +## High Level Architecture Overview + +This project has two applications - a speaker and listener app. The speaker app takes in audio from the browser, forks the audio and creates a unique Realtime session for each language and sends it to the OpenAI Realtime API via WebSocket. Translated audio streams back and is mirrored via a separate WebSocket server to the listener app. The listener app receives all translated audio streams simultaneously, but only the selected language is played. This architecture is designed for a POC and is not intended for a production use case. Let's dive into the workflow! + +![Architecture](https://github.com/openai/openai-cookbook/blob/main/examples/voice_solutions/translation_images/Realtime_flow_diagram.png?raw=true) + +## Step 1: Language & Prompt Setup + + +We need a unique stream for each language - each language requires a unique prompt and session with the Realtime API. We define these prompts in `translation_prompts.js`. + +The Realtime API is powered by [GPT-4o Realtime](https://platform.openai.com/docs/models/gpt-4o-realtime-preview) or [GPT-4o mini Realtime](https://platform.openai.com/docs/models/gpt-4o-mini-realtime-preview) which are turn-based and trained for conversational speech use cases. In order to ensure the model returns translated audio (i.e. instead of answering a question, we want a direct translation of that question), we want to steer the model with few-shot examples of questions in the prompts. If you're translating for a specific reason or context, or have specialized vocabulary that will help the model understand context of the translation, include that in the prompt as well. If you want the model to speak with a specific accent or otherwise steer the voice, you can follpow tips from our cookbook on [Steering Text-to-Speech for more dynamic audio generation](https://cookbook.openai.com/examples/voice_solutions/steering_tts). + +We can dynamically input speech in any language. + +```js +// Define language codes and import their corresponding instructions from our prompt config file +const languageConfigs = [ + { code: 'fr', instructions: french_instructions }, + { code: 'es', instructions: spanish_instructions }, + { code: 'tl', instructions: tagalog_instructions }, + { code: 'en', instructions: english_instructions }, + { code: 'zh', instructions: mandarin_instructions }, +]; +``` + +## Step 2: Setting up the Speaker App + +![SpeakerApp](https://github.com/openai/openai-cookbook/blob/main/examples/voice_solutions/translation_images/SpeakerApp.png?raw=true) + + +We need to handle the setup and management of client instances that connect to the Realtime API, allowing the application to process and stream audio in different languages. `clientRefs` holds a map of `RealtimeClient` instances, each associated with a language code (e.g., 'fr' for French, 'es' for Spanish) representing each unique client connection to the Realtime API. + +```js +const clientRefs = useRef( + languageConfigs.reduce((acc, { code }) => { + acc[code] = new RealtimeClient({ + apiKey: OPENAI_API_KEY, + dangerouslyAllowAPIKeyInBrowser: true, + }); + return acc; + }, {} as Record<string, RealtimeClient>) + ).current; + + // Update languageConfigs to include client references + const updatedLanguageConfigs = languageConfigs.map(config => ({ + ...config, + clientRef: { current: clientRefs[config.code] } + })); + ``` + +Note: The `dangerouslyAllowAPIKeyInBrowser` option is set to true because we are using our OpenAI API key in the browser for demo purposes but in production you should use an [ephemeral API key](https://platform.openai.com/docs/api-reference/realtime-sessions) generated via the OpenAI REST API. + +We need to actually initiate the connection to the Realtime API and send audio data to the server. When a user clicks 'Connect' on the speaker page, we start that process. + +The `connectConversation` function orchestrates the connection, ensuring that all necessary components are initialized and ready for use. + +```js +const connectConversation = useCallback(async () => { + try { + setIsLoading(true); + const wavRecorder = wavRecorderRef.current; + await wavRecorder.begin(); + await connectAndSetupClients(); + setIsConnected(true); + } catch (error) { + console.error('Error connecting to conversation:', error); + } finally { + setIsLoading(false); + } +}, []); +``` + + `connectAndSetupClients` ensures we are using the right model and voice. For this demo, we are using gpt-4o-realtime-preview-2024-12-17 and coral. + +```js + // Function to connect and set up all clients + const connectAndSetupClients = async () => { + for (const { clientRef } of updatedLanguageConfigs) { + const client = clientRef.current; + await client.realtime.connect({ model: DEFAULT_REALTIME_MODEL }); + await client.updateSession({ voice: DEFAULT_REALTIME_VOICE }); + } + }; +``` + +## Step 3: Audio Streaming + + +Sending audio with WebSockets requires work to manage the inbound and outbound PCM16 audio streams ([more details on that](https://platform.openai.com/docs/guides/realtime-model-capabilities#handling-audio-with-websockets)). We abstract that using wavtools, a library for both recording and streaming audio data in the browser. Here we use `WavRecorder` for capturing audio in the browser. + +This demo supports both [manual and voice activity detection (VAD)](https://platform.openai.com/docs/guides/realtime-model-capabilities#voice-activity-detection-vad) modes for recording that can be toggled by the speaker. For cleaner audio capture we recommend using manual mode here. + +```js +const startRecording = async () => { + setIsRecording(true); + const wavRecorder = wavRecorderRef.current; + + await wavRecorder.record((data) => { + // Send mic PCM to all clients + updatedLanguageConfigs.forEach(({ clientRef }) => { + clientRef.current.appendInputAudio(data.mono); + }); + }); + }; + ``` + + +## Step 4: Showing Transcripts + + +We listen for `response.audio_transcript.done` events to update the transcripts of the audio. These input transcripts are generated by the Whisper model in parallel to the GPT-4o Realtime inference that is doing the translations on raw audio. + +We have a Realtime session running simultaneously for every selectable language and so we get transcriptions for every language (regardless of what language is selected in the listener application). Those can be shown by toggling the 'Show Transcripts' button. + +## Step 5: Setting up the Listener App + +Listeners can choose from a dropdown menu of translation streams and after connecting, dynamically change languages. The demo application uses French, Spanish, Tagalog, English, and Mandarin but OpenAI supports 57+ languages. + +The app connects to a simple `Socket.IO` server that acts as a relay for audio data. When translated audio is streamed back to from the Realtime API, we mirror those audio streams to the listener page and allow users to select a language and listen to translated streams. + +The key function here is `connectServer` that connects to the server and sets up audio streaming. + +```js + // Function to connect to the server and set up audio streaming + const connectServer = useCallback(async () => { + if (socketRef.current) return; + try { + const socket = io('http://localhost:3001'); + socketRef.current = socket; + await wavStreamPlayerRef.current.connect(); + socket.on('connect', () => { + console.log('Listener connected:', socket.id); + setIsConnected(true); + }); + socket.on('disconnect', () => { + console.log('Listener disconnected'); + setIsConnected(false); + }); + } catch (error) { + console.error('Error connecting to server:', error); + } + }, []); +``` + +### POC to Production + +This is a demo and meant for inspiration. We are using WebSockets here for easy local development. However, in a production environment we’d suggest using WebRTC (which is much better for streaming audio quality and lower latency) and connecting to the Realtime API with an [ephemeral API key](https://platform.openai.com/docs/api-reference/realtime-sessions) generated via the OpenAI REST API. + +Current Realtime models are turn based - this is best for conversational use cases as opposed to the uninterrupted, UN-style live translation that we really want for a one-directional streaming use case. For this demo, we can capture additional audio from the speaker app as soon as the model returns translated audio (i.e. capturing more input audio while the translated audio played from the listener app), but there is a limit to the length of audio we can capture at a time. The speaker needs to pause to let the translation catch up. + +## Conclusion + +In summary, this POC is a demonstration of a one-way translation use of the Realtime API but the idea of forking audio for multiple uses can expand beyond translation. Other workflows might be simultaneous sentiment analysis, live guardrails or generating subtitles. \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/.env.example b/examples/voice_solutions/one_way_translation_using_realtime_api/.env.example new file mode 100644 index 0000000000..21200aefbd --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/.env.example @@ -0,0 +1 @@ +REACT_APP_OPENAI_API_KEY=sk-proj-1234567890 \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/.gitignore b/examples/voice_solutions/one_way_translation_using_realtime_api/.gitignore new file mode 100644 index 0000000000..604b232617 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/.gitignore @@ -0,0 +1,31 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js + +# testing +/coverage + +# production +/build + +# packaging +*.zip +*.tar.gz +*.tar +*.tgz +*.bla + +# misc +.DS_Store +.env +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/README.md b/examples/voice_solutions/one_way_translation_using_realtime_api/README.md new file mode 100644 index 0000000000..b263181e10 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/README.md @@ -0,0 +1,128 @@ +# Translation Demo + +This project demonstrates how to use the [OpenAI Realtime API](https://platform.openai.com/docs/guides/realtime) to build a one-way translation application with WebSockets. It is implemented using the [Realtime + Websockets integration](https://platform.openai.com/docs/guides/realtime-websocket). A real-world use case for this demo is multilingual, conversational translation—where a speaker talks into the speaker app and listeners hear translations in their selected native languages via the listener app. Imagine a conference room with multiple participants with headphones, listening live to a speaker in their own languages. Due to the current turn-based nature of audio models, the speaker must pause briefly to allow the model to process and translate speech. However, as models become faster and more efficient, this latency will decrease significantly and the translation will become more seamless. + +## How to Use + +### Running the Application + +1. **Set up the OpenAI API:** + + - If you're new to the OpenAI API, [sign up for an account](https://platform.openai.com/signup). + - Follow the [Quickstart](https://platform.openai.com/docs/quickstart) to retrieve your API key. + +2. **Clone the Repository:** + + ```bash + git clone <repository-url> + ``` + +3. **Set your API key:** + + - Create a `.env` file at the root of the project and add the following line: + ```bash + REACT_APP_OPENAI_API_KEY=<your_api_key> + ``` + +4. **Install dependencies:** + + Navigate to the project directory and run: + + ```bash + npm install + ``` + +5. **Run the Speaker & Listener Apps:** + + ```bash + npm start + ``` + + The speaker and listener apps will be available at: + - [http://localhost:3000/speaker](http://localhost:3000/speaker) + - [http://localhost:3000/listener](http://localhost:3000/listener) + +6. **Start the Mirror Server:** + + In another terminal window, navigate to the project directory and run: + + ```bash + node mirror-server/mirror-server.mjs + ``` + +### Adding a New Language + +To add a new language to the codebase, follow these steps: + +1. **Socket Event Handling in Mirror Server:** + + - Open `mirror-server/mirror-server.cjs`. + - Add a new socket event for the new language. For example, for Hindi: + ```javascript + socket.on('mirrorAudio:hi', (audioChunk) => { + console.log('logging Hindi mirrorAudio', audioChunk); + socket.broadcast.emit('audioFrame:hi', audioChunk); + }); + ``` + +2. **Instructions Configuration:** + + - Open `src/utils/translation_prompts.js`. + - Add new instructions for the new language. For example: + ```javascript + export const hindi_instructions = "Your Hindi instructions here..."; + ``` + +3. **Realtime Client Initialization in SpeakerPage:** + + - Open `src/pages/SpeakerPage.tsx`. + - Import the new language instructions: + ```typescript + import { hindi_instructions } from '../utils/translation_prompts.js'; + ``` + - Add the new language to the `languageConfigs` array: + ```typescript + const languageConfigs = [ + // ... existing languages ... + { code: 'hi', instructions: hindi_instructions }, + ]; + ``` + +4. **Language Configuration in ListenerPage:** + + - Open `src/pages/ListenerPage.tsx`. + - Locate the `languages` object, which centralizes all language-related data. + - Add a new entry for your language. The key should be the language code, and the value should be an object containing the language name. + + ```typescript + const languages = { + fr: { name: 'French' }, + es: { name: 'Spanish' }, + tl: { name: 'Tagalog' }, + en: { name: 'English' }, + zh: { name: 'Mandarin' }, + // Add your new language here + hi: { name: 'Hindi' }, // Example for adding Hindi + } as const; + ``` + + - The `ListenerPage` component will automatically handle the new language in the dropdown menu and audio stream handling. + +5. **Test the New Language:** + + - Run your application and test the new language by selecting it from the dropdown menu. + - Ensure that the audio stream for the new language is correctly received and played. + +### Demo Flow + +1. **Connect in the Speaker App:** + + - Click "Connect" and wait for the WebSocket connections to be established with the Realtime API. + - Choose between VAD (Voice Activity Detection) and Manual push-to-talk mode. + - the speaker should ensure they pause to allow the translation to catch up - the model is turn based and cannot constantly stream translations. + - The speaker can view live translations in the Speaker App for each language. + +2. **Select Language in the Listener App:** + + - Select the language from the dropdown menu. + - The listener app will play the translated audio. The app translates all audio streams simultaneously, but only the selected language is played. You can switch languages at any time. \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/mirror-server/mirror-server.mjs b/examples/voice_solutions/one_way_translation_using_realtime_api/mirror-server/mirror-server.mjs new file mode 100644 index 0000000000..e8e62cc274 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/mirror-server/mirror-server.mjs @@ -0,0 +1,42 @@ +// mirror_server.js +import express from 'express'; +import http from 'http'; +import { Server } from 'socket.io'; + +const app = express(); +const server = http.createServer(app); +const io = new Server(server, { + cors: { origin: '*' } +}); + +io.on('connection', (socket) => { + console.log('Client connected', socket.id); + + socket.on('mirrorAudio:fr', (audioChunk) => { + socket.broadcast.emit('audioFrame:fr', audioChunk); + }); + + socket.on('mirrorAudio:es', (audioChunk) => { + socket.broadcast.emit('audioFrame:es', audioChunk); + }); + + socket.on('mirrorAudio:tl', (audioChunk) => { + socket.broadcast.emit('audioFrame:tl', audioChunk); + }); + + socket.on('mirrorAudio:en', (audioChunk) => { + socket.broadcast.emit('audioFrame:en', audioChunk); + }); + + socket.on('mirrorAudio:zh', (audioChunk) => { + socket.broadcast.emit('audioFrame:zh', audioChunk); + }); + + socket.on('disconnect', () => { + console.log('Client disconnected', socket.id); + }); +}); + +server.listen(3001, () => { + console.log('Socket.IO mirror server running on port 3001'); +}); \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/package-lock.json b/examples/voice_solutions/one_way_translation_using_realtime_api/package-lock.json new file mode 100644 index 0000000000..792255ed7a --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/package-lock.json @@ -0,0 +1,19974 @@ +{ + "name": "openai-realtime-console", + "version": "0.0.0", + "lockfileVersion": 3, + 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"@testing-library/user-event": "^13.5.0", + "@types/jest": "^27.5.2", + "@types/leaflet": "^1.9.12", + "@types/node": "^16.18.108", + "@types/react": "^18.3.5", + "@types/react-dom": "^18.3.0", + "axios": "^1.8.2", + "dotenv": "^16.4.5", + "leaflet": "^1.9.4", + "lucide-react": "^0.474.0", + "papaparse": "^5.5.2", + "path-browserify": "^1.0.1", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-feather": "^2.0.10", + "react-leaflet": "^4.2.1", + "react-router-dom": "^7.5.2", + "react-scripts": "^5.0.1", + "sass": "^1.78.0", + "save": "^2.9.0", + "socket.io": "^4.8.1", + "socket.io-client": "^4.8.1", + "typescript": "^4.9.5", + "web-vitals": "^2.1.4", + "ws": "^8.18.0" + }, + "scripts": { + "start": "react-scripts start", + "build": "react-scripts build", + "test": "react-scripts test", + "eject": "react-scripts eject", + "zip": "zip -r realtime-api-console.zip . -x 'node_modules' 'node_modules/*' 'node_modules/**' '.git' '.git/*' '.git/**' '.DS_Store' '*/.DS_Store' 'package-lock.json' '*.zip' '*.tar.gz' '*.tar' '.env'", + "relay": "nodemon ./relay-server/index.js" + }, + "eslintConfig": { + "extends": [ + "react-app", + "react-app/jest" + ] + }, + "browserslist": { + "production": [ + ">0.2%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + }, + "devDependencies": { + "@babel/plugin-proposal-private-property-in-object": "^7.21.11", + "nodemon": "^3.1.7" + } +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/public/index.html b/examples/voice_solutions/one_way_translation_using_realtime_api/public/index.html new file mode 100644 index 0000000000..da65952cb4 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/public/index.html @@ -0,0 +1,40 @@ +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8" /> + <link rel="icon" href="%PUBLIC_URL%/openai-logomark.svg" /> + <meta name="viewport" content="width=device-width, initial-scale=1" /> + <title>realtime console + + + + + + + + +
+ + + diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/relay-server/index.js b/examples/voice_solutions/one_way_translation_using_realtime_api/relay-server/index.js new file mode 100644 index 0000000000..416092195f --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/relay-server/index.js @@ -0,0 +1,18 @@ +import { RealtimeRelay } from './lib/relay.js'; +import dotenv from 'dotenv'; +dotenv.config({ override: true }); + +const OPENAI_API_KEY = process.env.OPENAI_API_KEY; + +if (!OPENAI_API_KEY) { + console.error( + `Environment variable "OPENAI_API_KEY" is required.\n` + + `Please set it in your .env file.` + ); + process.exit(1); +} + +const PORT = parseInt(process.env.PORT) || 8081; + +const relay = new RealtimeRelay(OPENAI_API_KEY); +relay.listen(PORT); diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/relay-server/lib/relay.js b/examples/voice_solutions/one_way_translation_using_realtime_api/relay-server/lib/relay.js new file mode 100644 index 0000000000..ef444146d7 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/relay-server/lib/relay.js @@ -0,0 +1,84 @@ +import { WebSocketServer } from 'ws'; +import { RealtimeClient } from '@openai/realtime-api-beta'; + +export class RealtimeRelay { + constructor(apiKey) { + this.apiKey = apiKey; + this.sockets = new WeakMap(); + this.wss = null; + } + + listen(port) { + this.wss = new WebSocketServer({ port }); + this.wss.on('connection', this.connectionHandler.bind(this)); + this.log(`Listening on ws://localhost:${port}`); + } + + async connectionHandler(ws, req) { + if (!req.url) { + this.log('No URL provided, closing connection.'); + ws.close(); + return; + } + + const url = new URL(req.url, `http://${req.headers.host}`); + const pathname = url.pathname; + + if (pathname !== '/') { + this.log(`Invalid pathname: "${pathname}"`); + ws.close(); + return; + } + + // Instantiate new client + this.log(`Connecting with key "${this.apiKey.slice(0, 3)}..."`); + const client = new RealtimeClient({ apiKey: this.apiKey }); + + // Relay: OpenAI Realtime API Event -> Browser Event + client.realtime.on('server.*', (event) => { + this.log(`Relaying "${event.type}" to Client`); + ws.send(JSON.stringify(event)); + }); + client.realtime.on('close', () => ws.close()); + + // Relay: Browser Event -> OpenAI Realtime API Event + // We need to queue data waiting for the OpenAI connection + const messageQueue = []; + const messageHandler = (data) => { + try { + const event = JSON.parse(data); + this.log(`Relaying "${event.type}" to OpenAI`); + client.realtime.send(event.type, event); + } catch (e) { + console.error(e.message); + this.log(`Error parsing event from client: ${data}`); + } + }; + ws.on('message', (data) => { + if (!client.isConnected()) { + messageQueue.push(data); + } else { + messageHandler(data); + } + }); + ws.on('close', () => client.disconnect()); + + // Connect to OpenAI Realtime API + try { + this.log(`Connecting to OpenAI...`); + await client.connect(); + } catch (e) { + this.log(`Error connecting to OpenAI: ${e.message}`); + ws.close(); + return; + } + this.log(`Connected to OpenAI successfully!`); + while (messageQueue.length) { + messageHandler(messageQueue.shift()); + } + } + + log(...args) { + console.log(`[RealtimeRelay]`, ...args); + } +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/App.scss b/examples/voice_solutions/one_way_translation_using_realtime_api/src/App.scss new file mode 100644 index 0000000000..fc18b4e2e6 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/App.scss @@ -0,0 +1,5 @@ +[data-component='App'] { + height: 100%; + width: 100%; + position: relative; +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/App.tsx b/examples/voice_solutions/one_way_translation_using_realtime_api/src/App.tsx new file mode 100644 index 0000000000..d4122eb0d7 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/App.tsx @@ -0,0 +1,21 @@ +import React from 'react'; +import { Routes, Route, Link } from 'react-router-dom'; +import './App.scss'; +import { SpeakerPage } from './pages/SpeakerPage'; +import { ListenerPage } from './pages/ListenerPage'; + +function App() { + return ( +
+ + + } /> + } /> + {/* Optionally, a default route or home page */} + Open /Speaker and /Listener} /> + +
+ ); +} + +export default App; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/button/Button.scss b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/button/Button.scss new file mode 100644 index 0000000000..ce86ceeeb1 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/button/Button.scss @@ -0,0 +1,83 @@ +[data-component='Button'] { + display: flex; + align-items: center; + gap: 8px; + font-family: 'Roboto Mono', monospace; + font-size: 12px; + font-optical-sizing: auto; + font-weight: 400; + font-style: normal; + border: none; + background-color: #ececf1; + color: #101010; + border-radius: 1000px; + padding: 8px 24px; + min-height: 42px; + transition: transform 0.1s ease-in-out, background-color 0.1s ease-in-out; + outline: none; + + &.button-style-action { + background-color: #101010; + color: #ececf1; + &:hover:not([disabled]) { + background-color: #404040; + } + } + + &.button-style-alert { + background-color: #f00; + color: #ececf1; + &:hover:not([disabled]) { + background-color: #f00; + } + } + + &.button-style-flush { + background-color: rgba(255, 255, 255, 0); + } + + &[disabled] { + color: #999; + } + + &:not([disabled]) { + cursor: pointer; + } + + &:hover:not([disabled]) { + background-color: #d8d8d8; + } + + &:active:not([disabled]) { + transform: translateY(1px); + } + + .icon { + display: flex; + &.icon-start { + margin-left: -8px; + } + &.icon-end { + margin-right: -8px; + } + svg { + width: 16px; + height: 16px; + } + } + + &.icon-red .icon { + color: #cc0000; + } + &.icon-green .icon { + color: #009900; + } + &.icon-grey .icon { + color: #909090; + } + &.icon-fill { + svg { + fill: currentColor; + } + } +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/button/Button.tsx b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/button/Button.tsx new file mode 100644 index 0000000000..1d7687a5a1 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/button/Button.tsx @@ -0,0 +1,60 @@ +import React from 'react'; +import './Button.scss'; + +import { Icon } from 'react-feather'; + +interface ButtonProps extends React.ButtonHTMLAttributes { + label?: string; + icon?: Icon; + iconPosition?: 'start' | 'end'; + iconColor?: 'red' | 'green' | 'grey'; + iconFill?: boolean; + buttonStyle?: 'regular' | 'action' | 'alert' | 'flush'; + selected?: boolean; +} + +export function Button({ + label = 'Okay', + icon = void 0, + iconPosition = 'start', + iconColor = void 0, + iconFill = false, + buttonStyle = 'regular', + selected, + ...rest +}: ButtonProps) { + const StartIcon = iconPosition === 'start' ? icon : null; + const EndIcon = iconPosition === 'end' ? icon : null; + const classList = []; + if (iconColor) { + classList.push(`icon-${iconColor}`); + } + if (iconFill) { + classList.push(`icon-fill`); + } + classList.push(`button-style-${buttonStyle}`); + + return ( + + ); +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/toggle/Toggle.scss b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/toggle/Toggle.scss new file mode 100644 index 0000000000..6d2cc1ec4f --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/toggle/Toggle.scss @@ -0,0 +1,58 @@ +[data-component='Toggle'] { + position: relative; + display: flex; + align-items: center; + justify-content: center; + margin: 0 auto; + gap: 8px; + cursor: pointer; + overflow: hidden; + width: 142px; + + background-color: #ffffff; + height: 40px; + border-radius: 1000px; + + &:hover { + background-color: #d8d8d8; + } + + div.label { + position: relative; + color: #666; + transition: color 0.1s ease-in-out; + padding: 0px 16px; + z-index: 2; + user-select: none; + } + + div.label.right { + margin-left: -8px; + } + + .toggle-background { + background-color: gray; + position: absolute; + top: 0px; + left: 0px; + width: auto; + bottom: 0px; + z-index: 1; + border-radius: 1000px; + transition: left 0.1s ease-in-out, width 0.1s ease-in-out; + } + + &[data-enabled='true'] { + justify-content: center; + div.label.right { + color: #fff; + } + } + + &[data-enabled='false'] { + justify-content: center; + div.label.left { + color: #fff; + } + } +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/toggle/Toggle.tsx b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/toggle/Toggle.tsx new file mode 100644 index 0000000000..23619bd760 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/components/toggle/Toggle.tsx @@ -0,0 +1,66 @@ +import { useState, useEffect, useRef } from 'react'; + +import './Toggle.scss'; + +export function Toggle({ + defaultValue = false, + values, + labels, + onChange = () => {}, +}: { + defaultValue?: string | boolean; + values?: string[]; + labels?: string[]; + onChange?: (isEnabled: boolean, value: string) => void; +}) { + if (typeof defaultValue === 'string') { + defaultValue = !!Math.max(0, (values || []).indexOf(defaultValue)); + } + + const leftRef = useRef(null); + const rightRef = useRef(null); + const bgRef = useRef(null); + const [value, setValue] = useState(defaultValue); + + const toggleValue = () => { + const v = !value; + const index = +v; + setValue(v); + onChange(v, (values || [])[index]); + }; + + useEffect(() => { + const leftEl = leftRef.current; + const rightEl = rightRef.current; + const bgEl = bgRef.current; + if (leftEl && rightEl && bgEl) { + if (value) { + bgEl.style.left = rightEl.offsetLeft + 'px'; + bgEl.style.width = rightEl.offsetWidth + 'px'; + } else { + bgEl.style.left = ''; + bgEl.style.width = leftEl.offsetWidth + 'px'; + } + } + }, [value]); + + return ( +
+ {labels && ( +
+ {labels[0]} +
+ )} + {labels && ( +
+ {labels[1]} +
+ )} +
+
+ ); +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/index.css b/examples/voice_solutions/one_way_translation_using_realtime_api/src/index.css new file mode 100644 index 0000000000..19c80eb908 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/index.css @@ -0,0 +1,21 @@ +html, +body { + padding: 0px; + margin: 0px; + position: relative; + width: 100%; + height: 100%; + font-family: 'Roboto Mono', sans-serif; + font-optical-sizing: auto; + font-weight: 400; + font-style: normal; + color: #18181b; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +#root { + position: relative; + width: 100%; + height: 100%; +} diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/index.tsx b/examples/voice_solutions/one_way_translation_using_realtime_api/src/index.tsx new file mode 100644 index 0000000000..f6af8da6cf --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/index.tsx @@ -0,0 +1,18 @@ +import React from 'react'; +import ReactDOM from 'react-dom/client'; +import { BrowserRouter } from 'react-router-dom'; +import './index.css'; +import App from './App'; +import reportWebVitals from './reportWebVitals'; + +const root = ReactDOM.createRoot(document.getElementById('root') as HTMLElement); + +root.render( + + + + + +); + +reportWebVitals(); \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/index.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/index.d.ts new file mode 100644 index 0000000000..952953208e --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/index.d.ts @@ -0,0 +1,6 @@ +import { AudioAnalysis } from './lib/analysis/audio_analysis.js'; +import { WavPacker } from './lib/wav_packer.js'; +import { WavStreamPlayer } from './lib/wav_stream_player.js'; +import { WavRecorder } from './lib/wav_recorder.js'; +export { AudioAnalysis, WavPacker, WavStreamPlayer, WavRecorder }; +//# sourceMappingURL=index.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/index.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/index.d.ts.map new file mode 100644 index 0000000000..a80c055fdc --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/index.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../index.js"],"names":[],"mappings":"8BAC8B,kCAAkC;0BADtC,qBAAqB;gCAEf,4BAA4B;4BAChC,uBAAuB"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/audio_analysis.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/audio_analysis.d.ts new file mode 100644 index 0000000000..fc50758964 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/audio_analysis.d.ts @@ -0,0 +1,70 @@ +/** + * Output of AudioAnalysis for the frequency domain of the audio + * @typedef {Object} AudioAnalysisOutputType + * @property {Float32Array} values Amplitude of this frequency between {0, 1} inclusive + * @property {number[]} frequencies Raw frequency bucket values + * @property {string[]} labels Labels for the frequency bucket values + */ +/** + * Analyzes audio for visual output + * @class + */ +export class AudioAnalysis { + /** + * Retrieves frequency domain data from an AnalyserNode adjusted to a decibel range + * returns human-readable formatting and labels + * @param {AnalyserNode} analyser + * @param {number} sampleRate + * @param {Float32Array} [fftResult] + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {AudioAnalysisOutputType} + */ + static getFrequencies(analyser: AnalyserNode, sampleRate: number, fftResult?: Float32Array, analysisType?: "frequency" | "music" | "voice", minDecibels?: number, maxDecibels?: number): AudioAnalysisOutputType; + /** + * Creates a new AudioAnalysis instance for an HTMLAudioElement + * @param {HTMLAudioElement} audioElement + * @param {AudioBuffer|null} [audioBuffer] If provided, will cache all frequency domain data from the buffer + * @returns {AudioAnalysis} + */ + constructor(audioElement: HTMLAudioElement, audioBuffer?: AudioBuffer | null); + fftResults: any[]; + audio: HTMLAudioElement; + context: any; + analyser: any; + sampleRate: any; + audioBuffer: any; + /** + * Gets the current frequency domain data from the playing audio track + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {AudioAnalysisOutputType} + */ + getFrequencies(analysisType?: "frequency" | "music" | "voice", minDecibels?: number, maxDecibels?: number): AudioAnalysisOutputType; + /** + * Resume the internal AudioContext if it was suspended due to the lack of + * user interaction when the AudioAnalysis was instantiated. + * @returns {Promise} + */ + resumeIfSuspended(): Promise; +} +/** + * Output of AudioAnalysis for the frequency domain of the audio + */ +export type AudioAnalysisOutputType = { + /** + * Amplitude of this frequency between {0, 1} inclusive + */ + values: Float32Array; + /** + * Raw frequency bucket values + */ + frequencies: number[]; + /** + * Labels for the frequency bucket values + */ + labels: string[]; +}; +//# sourceMappingURL=audio_analysis.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/audio_analysis.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/audio_analysis.d.ts.map new file mode 100644 index 0000000000..abb292bd75 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/audio_analysis.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"audio_analysis.d.ts","sourceRoot":"","sources":["../../../lib/analysis/audio_analysis.js"],"names":[],"mappings":"AAOA;;;;;;GAMG;AAEH;;;GAGG;AACH;IACE;;;;;;;;;;OAUG;IACH,gCARW,YAAY,cACZ,MAAM,cACN,YAAY,iBACZ,WAAW,GAAC,OAAO,GAAC,OAAO,gBAC3B,MAAM,gBACN,MAAM,GACJ,uBAAuB,CAwDnC;IAED;;;;;OAKG;IACH,0BAJW,gBAAgB,gBAChB,WAAW,GAAC,IAAI,EAkE1B;IA9DC,kBAAoB;IA2ClB,wBAAyB;IACzB,aAAkC;IAClC,cAAwB;IACxB,gBAA4B;IAC5B,iBAA8B;IAiBlC;;;;;;OAMG;IACH,8BALW,WAAW,GAAC,OAAO,GAAC,OAAO,gBAC3B,MAAM,gBACN,MAAM,GACJ,uBAAuB,CAwBnC;IAED;;;;OAIG;IACH,qBAFa,OAAO,CAAC,IAAI,CAAC,CAOzB;CACF;;;;;;;;YA9La,YAAY;;;;iBACZ,MAAM,EAAE;;;;YACR,MAAM,EAAE"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/constants.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/constants.d.ts new file mode 100644 index 0000000000..868ba1593e --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/constants.d.ts @@ -0,0 +1,9 @@ +/** + * All note frequencies from 1st to 8th octave + * in format "A#8" (A#, 8th octave) + */ +export const noteFrequencies: any[]; +export const noteFrequencyLabels: any[]; +export const voiceFrequencies: any[]; +export const voiceFrequencyLabels: any[]; +//# sourceMappingURL=constants.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/constants.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/constants.d.ts.map new file mode 100644 index 0000000000..0f5d851092 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/analysis/constants.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"constants.d.ts","sourceRoot":"","sources":["../../../lib/analysis/constants.js"],"names":[],"mappings":"AA6BA;;;GAGG;AACH,oCAAkC;AAClC,wCAAsC;AActC,qCAKG;AACH,yCAKG"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_packer.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_packer.d.ts new file mode 100644 index 0000000000..4fe1187422 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_packer.d.ts @@ -0,0 +1,58 @@ +/** + * Raw wav audio file contents + * @typedef {Object} WavPackerAudioType + * @property {Blob} blob + * @property {string} url + * @property {number} channelCount + * @property {number} sampleRate + * @property {number} duration + */ +/** + * Utility class for assembling PCM16 "audio/wav" data + * @class + */ +export class WavPacker { + /** + * Converts Float32Array of amplitude data to ArrayBuffer in Int16Array format + * @param {Float32Array} float32Array + * @returns {ArrayBuffer} + */ + static floatTo16BitPCM(float32Array: Float32Array): ArrayBuffer; + /** + * Concatenates two ArrayBuffers + * @param {ArrayBuffer} leftBuffer + * @param {ArrayBuffer} rightBuffer + * @returns {ArrayBuffer} + */ + static mergeBuffers(leftBuffer: ArrayBuffer, rightBuffer: ArrayBuffer): ArrayBuffer; + /** + * Packs data into an Int16 format + * @private + * @param {number} size 0 = 1x Int16, 1 = 2x Int16 + * @param {number} arg value to pack + * @returns + */ + private _packData; + /** + * Packs audio into "audio/wav" Blob + * @param {number} sampleRate + * @param {{bitsPerSample: number, channels: Array, data: Int16Array}} audio + * @returns {WavPackerAudioType} + */ + pack(sampleRate: number, audio: { + bitsPerSample: number; + channels: Array; + data: Int16Array; + }): WavPackerAudioType; +} +/** + * Raw wav audio file contents + */ +export type WavPackerAudioType = { + blob: Blob; + url: string; + channelCount: number; + sampleRate: number; + duration: number; +}; +//# sourceMappingURL=wav_packer.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_packer.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_packer.d.ts.map new file mode 100644 index 0000000000..96477a971c --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_packer.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"wav_packer.d.ts","sourceRoot":"","sources":["../../lib/wav_packer.js"],"names":[],"mappings":"AAAA;;;;;;;;GAQG;AAEH;;;GAGG;AACH;IACE;;;;OAIG;IACH,qCAHW,YAAY,GACV,WAAW,CAWvB;IAED;;;;;OAKG;IACH,gCAJW,WAAW,eACX,WAAW,GACT,WAAW,CASvB;IAED;;;;;;OAMG;IACH,kBAKC;IAED;;;;;OAKG;IACH,iBAJW,MAAM,SACN;QAAC,aAAa,EAAE,MAAM,CAAC;QAAC,QAAQ,EAAE,KAAK,CAAC,YAAY,CAAC,CAAC;QAAC,IAAI,EAAE,UAAU,CAAA;KAAC,GACtE,kBAAkB,CA6C9B;CACF;;;;;UA3Ga,IAAI;SACJ,MAAM;kBACN,MAAM;gBACN,MAAM;cACN,MAAM"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_recorder.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_recorder.d.ts new file mode 100644 index 0000000000..03cd269828 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_recorder.d.ts @@ -0,0 +1,167 @@ +/** + * Decodes audio into a wav file + * @typedef {Object} DecodedAudioType + * @property {Blob} blob + * @property {string} url + * @property {Float32Array} values + * @property {AudioBuffer} audioBuffer + */ +/** + * Records live stream of user audio as PCM16 "audio/wav" data + * @class + */ +export class WavRecorder { + /** + * Decodes audio data from multiple formats to a Blob, url, Float32Array and AudioBuffer + * @param {Blob|Float32Array|Int16Array|ArrayBuffer|number[]} audioData + * @param {number} sampleRate + * @param {number} fromSampleRate + * @returns {Promise} + */ + static decode(audioData: Blob | Float32Array | Int16Array | ArrayBuffer | number[], sampleRate?: number, fromSampleRate?: number): Promise; + /** + * Create a new WavRecorder instance + * @param {{sampleRate?: number, outputToSpeakers?: boolean, debug?: boolean}} [options] + * @returns {WavRecorder} + */ + constructor({ sampleRate, outputToSpeakers, debug, }?: { + sampleRate?: number; + outputToSpeakers?: boolean; + debug?: boolean; + }); + scriptSrc: any; + sampleRate: number; + outputToSpeakers: boolean; + debug: boolean; + _deviceChangeCallback: () => Promise; + _devices: any[]; + stream: any; + processor: any; + source: any; + node: any; + recording: boolean; + _lastEventId: number; + eventReceipts: {}; + eventTimeout: number; + _chunkProcessor: () => void; + _chunkProcessorBuffer: { + raw: ArrayBuffer; + mono: ArrayBuffer; + }; + /** + * Logs data in debug mode + * @param {...any} arguments + * @returns {true} + */ + log(...args: any[]): true; + /** + * Retrieves the current sampleRate for the recorder + * @returns {number} + */ + getSampleRate(): number; + /** + * Retrieves the current status of the recording + * @returns {"ended"|"paused"|"recording"} + */ + getStatus(): "ended" | "paused" | "recording"; + /** + * Sends an event to the AudioWorklet + * @private + * @param {string} name + * @param {{[key: string]: any}} data + * @param {AudioWorkletNode} [_processor] + * @returns {Promise<{[key: string]: any}>} + */ + private _event; + /** + * Sets device change callback, remove if callback provided is `null` + * @param {(Array): void|null} callback + * @returns {true} + */ + listenForDeviceChange(callback: any): true; + /** + * Manually request permission to use the microphone + * @returns {Promise} + */ + requestPermission(): Promise; + /** + * List all eligible devices for recording, will request permission to use microphone + * @returns {Promise>} + */ + listDevices(): Promise>; + /** + * Begins a recording session and requests microphone permissions if not already granted + * Microphone recording indicator will appear on browser tab but status will be "paused" + * @param {string} [deviceId] if no device provided, default device will be used + * @returns {Promise} + */ + begin(deviceId?: string): Promise; + analyser: any; + /** + * Gets the current frequency domain data from the recording track + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {import('./analysis/audio_analysis.js').AudioAnalysisOutputType} + */ + getFrequencies(analysisType?: "frequency" | "music" | "voice", minDecibels?: number, maxDecibels?: number): import("./analysis/audio_analysis.js").AudioAnalysisOutputType; + /** + * Pauses the recording + * Keeps microphone stream open but halts storage of audio + * @returns {Promise} + */ + pause(): Promise; + /** + * Start recording stream and storing to memory from the connected audio source + * @param {(data: { mono: Int16Array; raw: Int16Array }) => any} [chunkProcessor] + * @param {number} [chunkSize] chunkProcessor will not be triggered until this size threshold met in mono audio + * @returns {Promise} + */ + record(chunkProcessor?: (data: { + mono: Int16Array; + raw: Int16Array; + }) => any, chunkSize?: number): Promise; + _chunkProcessorSize: number; + /** + * Clears the audio buffer, empties stored recording + * @returns {Promise} + */ + clear(): Promise; + /** + * Reads the current audio stream data + * @returns {Promise<{meanValues: Float32Array, channels: Array}>} + */ + read(): Promise<{ + meanValues: Float32Array; + channels: Array; + }>; + /** + * Saves the current audio stream to a file + * @param {boolean} [force] Force saving while still recording + * @returns {Promise} + */ + save(force?: boolean): Promise; + /** + * Ends the current recording session and saves the result + * @returns {Promise} + */ + end(): Promise; + /** + * Performs a full cleanup of WavRecorder instance + * Stops actively listening via microphone and removes existing listeners + * @returns {Promise} + */ + quit(): Promise; +} +/** + * Decodes audio into a wav file + */ +export type DecodedAudioType = { + blob: Blob; + url: string; + values: Float32Array; + audioBuffer: AudioBuffer; +}; +//# sourceMappingURL=wav_recorder.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_recorder.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_recorder.d.ts.map new file mode 100644 index 0000000000..7954106e49 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_recorder.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"wav_recorder.d.ts","sourceRoot":"","sources":["../../lib/wav_recorder.js"],"names":[],"mappings":"AAIA;;;;;;;GAOG;AAEH;;;GAGG;AACH;IAsCE;;;;;;OAMG;IACH,yBALW,IAAI,GAAC,YAAY,GAAC,UAAU,GAAC,WAAW,GAAC,MAAM,EAAE,eACjD,MAAM,mBACN,MAAM,GACJ,OAAO,CAAC,gBAAgB,CAAC,CAqErC;IA/GD;;;;OAIG;IACH,uDAHW;QAAC,UAAU,CAAC,EAAE,MAAM,CAAC;QAAC,gBAAgB,CAAC,EAAE,OAAO,CAAC;QAAC,KAAK,CAAC,EAAE,OAAO,CAAA;KAAC,EAiC5E;IAxBC,eAAkC;IAElC,mBAA4B;IAC5B,0BAAwC;IACxC,eAAoB;IACpB,2CAAiC;IACjC,gBAAkB;IAElB,YAAkB;IAClB,eAAqB;IACrB,YAAkB;IAClB,UAAgB;IAChB,mBAAsB;IAEtB,qBAAqB;IACrB,kBAAuB;IACvB,qBAAwB;IAExB,4BAA+B;IAE/B;;;MAGC;IA+EH;;;;OAIG;IACH,qBAFa,IAAI,CAOhB;IAED;;;OAGG;IACH,iBAFa,MAAM,CAIlB;IAED;;;OAGG;IACH,aAFa,OAAO,GAAC,QAAQ,GAAC,WAAW,CAUxC;IAED;;;;;;;OAOG;IACH,eAqBC;IAED;;;;OAIG;IACH,sCAFa,IAAI,CAmChB;IAED;;;OAGG;IACH,qBAFa,OAAO,CAAC,IAAI,CAAC,CAoBzB;IAED;;;OAGG;IACH,eAFa,OAAO,CAAC,KAAK,CAAC,eAAe,GAAG;QAAC,OAAO,EAAE,OAAO,CAAA;KAAC,CAAC,CAAC,CA8BhE;IAED;;;;;OAKG;IACH,iBAHW,MAAM,GACJ,OAAO,CAAC,IAAI,CAAC,CAkFzB;IAHC,cAAwB;IAK1B;;;;;;OAMG;IACH,8BALW,WAAW,GAAC,OAAO,GAAC,OAAO,gBAC3B,MAAM,gBACN,MAAM,GACJ,OAAO,8BAA8B,EAAE,uBAAuB,CAkB1E;IAED;;;;OAIG;IACH,SAFa,OAAO,CAAC,IAAI,CAAC,CAezB;IAED;;;;;OAKG;IACH,wBAJW,CAAC,IAAI,EAAE;QAAE,IAAI,EAAE,UAAU,CAAC;QAAC,GAAG,EAAE,UAAU,CAAA;KAAE,KAAK,GAAG,cACpD,MAAM,GACJ,OAAO,CAAC,IAAI,CAAC,CAoBzB;IATC,4BAAoC;IAWtC;;;OAGG;IACH,SAFa,OAAO,CAAC,IAAI,CAAC,CAQzB;IAED;;;OAGG;IACH,QAFa,OAAO,CAAC;QAAC,UAAU,EAAE,YAAY,CAAC;QAAC,QAAQ,EAAE,KAAK,CAAC,YAAY,CAAC,CAAA;KAAC,CAAC,CAS9E;IAED;;;;OAIG;IACH,aAHW,OAAO,GACL,OAAO,CAAC,OAAO,iBAAiB,EAAE,kBAAkB,CAAC,CAgBjE;IAED;;;OAGG;IACH,OAFa,OAAO,CAAC,OAAO,iBAAiB,EAAE,kBAAkB,CAAC,CA8BjE;IAED;;;;OAIG;IACH,QAFa,OAAO,CAAC,IAAI,CAAC,CAQzB;CACF;;;;;UA1hBa,IAAI;SACJ,MAAM;YACN,YAAY;iBACZ,WAAW"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_stream_player.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_stream_player.d.ts new file mode 100644 index 0000000000..91a2263fdc --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_stream_player.d.ts @@ -0,0 +1,69 @@ +/** + * Plays audio streams received in raw PCM16 chunks from the browser + * @class + */ +export class WavStreamPlayer { + /** + * Creates a new WavStreamPlayer instance + * @param {{sampleRate?: number}} options + * @returns {WavStreamPlayer} + */ + constructor({ sampleRate }?: { + sampleRate?: number; + }); + scriptSrc: any; + sampleRate: number; + context: any; + stream: any; + analyser: any; + trackSampleOffsets: {}; + interruptedTrackIds: {}; + /** + * Connects the audio context and enables output to speakers + * @returns {Promise} + */ + connect(): Promise; + /** + * Gets the current frequency domain data from the playing track + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {import('./analysis/audio_analysis.js').AudioAnalysisOutputType} + */ + getFrequencies(analysisType?: "frequency" | "music" | "voice", minDecibels?: number, maxDecibels?: number): import("./analysis/audio_analysis.js").AudioAnalysisOutputType; + /** + * Starts audio streaming + * @private + * @returns {Promise} + */ + private _start; + /** + * Adds 16BitPCM data to the currently playing audio stream + * You can add chunks beyond the current play point and they will be queued for play + * @param {ArrayBuffer|Int16Array} arrayBuffer + * @param {string} [trackId] + * @returns {Int16Array} + */ + add16BitPCM(arrayBuffer: ArrayBuffer | Int16Array, trackId?: string): Int16Array; + /** + * Gets the offset (sample count) of the currently playing stream + * @param {boolean} [interrupt] + * @returns {{trackId: string|null, offset: number, currentTime: number}} + */ + getTrackSampleOffset(interrupt?: boolean): { + trackId: string | null; + offset: number; + currentTime: number; + }; + /** + * Strips the current stream and returns the sample offset of the audio + * @param {boolean} [interrupt] + * @returns {{trackId: string|null, offset: number, currentTime: number}} + */ + interrupt(): { + trackId: string | null; + offset: number; + currentTime: number; + }; +} +//# sourceMappingURL=wav_stream_player.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_stream_player.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_stream_player.d.ts.map new file mode 100644 index 0000000000..500126ccd5 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/wav_stream_player.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"wav_stream_player.d.ts","sourceRoot":"","sources":["../../lib/wav_stream_player.js"],"names":[],"mappings":"AAGA;;;GAGG;AACH;IACE;;;;OAIG;IACH,6BAHW;QAAC,UAAU,CAAC,EAAE,MAAM,CAAA;KAAC,EAW/B;IAPC,eAAmC;IACnC,mBAA4B;IAC5B,aAAmB;IACnB,YAAkB;IAClB,cAAoB;IACpB,uBAA4B;IAC5B,wBAA6B;IAG/B;;;OAGG;IACH,WAFa,OAAO,CAAC,IAAI,CAAC,CAkBzB;IAED;;;;;;OAMG;IACH,8BALW,WAAW,GAAC,OAAO,GAAC,OAAO,gBAC3B,MAAM,gBACN,MAAM,GACJ,OAAO,8BAA8B,EAAE,uBAAuB,CAkB1E;IAED;;;;OAIG;IACH,eAkBC;IAED;;;;;;OAMG;IACH,yBAJW,WAAW,GAAC,UAAU,YACtB,MAAM,GACJ,UAAU,CAqBtB;IAED;;;;OAIG;IACH,iCAHW,OAAO,GACL;QAAC,OAAO,EAAE,MAAM,GAAC,IAAI,CAAC;QAAC,MAAM,EAAE,MAAM,CAAC;QAAC,WAAW,EAAE,MAAM,CAAA;KAAC,CAqBvE;IAED;;;;OAIG;IACH,aAFa;QAAC,OAAO,EAAE,MAAM,GAAC,IAAI,CAAC;QAAC,MAAM,EAAE,MAAM,CAAC;QAAC,WAAW,EAAE,MAAM,CAAA;KAAC,CAIvE;CACF"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/audio_processor.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/audio_processor.d.ts new file mode 100644 index 0000000000..8b7c8acc7b --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/audio_processor.d.ts @@ -0,0 +1,2 @@ +export const AudioProcessorSrc: any; +//# sourceMappingURL=audio_processor.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/audio_processor.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/audio_processor.d.ts.map new file mode 100644 index 0000000000..d651100322 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/audio_processor.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"audio_processor.d.ts","sourceRoot":"","sources":["../../../lib/worklets/audio_processor.js"],"names":[],"mappings":"AAqNA,oCAAqC"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/stream_processor.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/stream_processor.d.ts new file mode 100644 index 0000000000..627da71b7d --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/stream_processor.d.ts @@ -0,0 +1,3 @@ +export const StreamProcessorWorklet: "\nclass StreamProcessor extends AudioWorkletProcessor {\n constructor() {\n super();\n this.hasStarted = false;\n this.hasInterrupted = false;\n this.outputBuffers = [];\n this.bufferLength = 128;\n this.write = { buffer: new Float32Array(this.bufferLength), trackId: null };\n this.writeOffset = 0;\n this.trackSampleOffsets = {};\n this.port.onmessage = (event) => {\n if (event.data) {\n const payload = event.data;\n if (payload.event === 'write') {\n const int16Array = payload.buffer;\n const float32Array = new Float32Array(int16Array.length);\n for (let i = 0; i < int16Array.length; i++) {\n float32Array[i] = int16Array[i] / 0x8000; // Convert Int16 to Float32\n }\n this.writeData(float32Array, payload.trackId);\n } else if (\n payload.event === 'offset' ||\n payload.event === 'interrupt'\n ) {\n const requestId = payload.requestId;\n const trackId = this.write.trackId;\n const offset = this.trackSampleOffsets[trackId] || 0;\n this.port.postMessage({\n event: 'offset',\n requestId,\n trackId,\n offset,\n });\n if (payload.event === 'interrupt') {\n this.hasInterrupted = true;\n }\n } else {\n throw new Error(`Unhandled event \"${payload.event}\"`);\n }\n }\n };\n }\n\n writeData(float32Array, trackId = null) {\n let { buffer } = this.write;\n let offset = this.writeOffset;\n for (let i = 0; i < float32Array.length; i++) {\n buffer[offset++] = float32Array[i];\n if (offset >= buffer.length) {\n this.outputBuffers.push(this.write);\n this.write = { buffer: new Float32Array(this.bufferLength), trackId };\n buffer = this.write.buffer;\n offset = 0;\n }\n }\n this.writeOffset = offset;\n return true;\n }\n\n process(inputs, outputs, parameters) {\n const output = outputs[0];\n const outputChannelData = output[0];\n const outputBuffers = this.outputBuffers;\n if (this.hasInterrupted) {\n this.port.postMessage({ event: 'stop' });\n return false;\n } else if (outputBuffers.length) {\n this.hasStarted = true;\n const { buffer, trackId } = outputBuffers.shift();\n for (let i = 0; i < outputChannelData.length; i++) {\n outputChannelData[i] = buffer[i] || 0;\n }\n if (trackId) {\n this.trackSampleOffsets[trackId] =\n this.trackSampleOffsets[trackId] || 0;\n this.trackSampleOffsets[trackId] += buffer.length;\n }\n return true;\n } else if (this.hasStarted) {\n this.port.postMessage({ event: 'stop' });\n return false;\n } else {\n return true;\n }\n }\n}\n\nregisterProcessor('stream_processor', StreamProcessor);\n"; +export const StreamProcessorSrc: any; +//# sourceMappingURL=stream_processor.d.ts.map \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/stream_processor.d.ts.map b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/stream_processor.d.ts.map new file mode 100644 index 0000000000..c372e0b2c4 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/dist/lib/worklets/stream_processor.d.ts.map @@ -0,0 +1 @@ +{"version":3,"file":"stream_processor.d.ts","sourceRoot":"","sources":["../../../lib/worklets/stream_processor.js"],"names":[],"mappings":"AAAA,q4FAyFE;AAMF,qCAAsC"} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/index.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/index.js new file mode 100644 index 0000000000..712389428b --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/index.js @@ -0,0 +1,6 @@ +import { WavPacker } from './lib/wav_packer.js'; +import { AudioAnalysis } from './lib/analysis/audio_analysis.js'; +import { WavStreamPlayer } from './lib/wav_stream_player.js'; +import { WavRecorder } from './lib/wav_recorder.js'; + +export { AudioAnalysis, WavPacker, WavStreamPlayer, WavRecorder }; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/analysis/audio_analysis.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/analysis/audio_analysis.js new file mode 100644 index 0000000000..4af34d54c4 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/analysis/audio_analysis.js @@ -0,0 +1,203 @@ +import { + noteFrequencies, + noteFrequencyLabels, + voiceFrequencies, + voiceFrequencyLabels, +} from './constants.js'; + +/** + * Output of AudioAnalysis for the frequency domain of the audio + * @typedef {Object} AudioAnalysisOutputType + * @property {Float32Array} values Amplitude of this frequency between {0, 1} inclusive + * @property {number[]} frequencies Raw frequency bucket values + * @property {string[]} labels Labels for the frequency bucket values + */ + +/** + * Analyzes audio for visual output + * @class + */ +export class AudioAnalysis { + /** + * Retrieves frequency domain data from an AnalyserNode adjusted to a decibel range + * returns human-readable formatting and labels + * @param {AnalyserNode} analyser + * @param {number} sampleRate + * @param {Float32Array} [fftResult] + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {AudioAnalysisOutputType} + */ + static getFrequencies( + analyser, + sampleRate, + fftResult, + analysisType = 'frequency', + minDecibels = -100, + maxDecibels = -30, + ) { + if (!fftResult) { + fftResult = new Float32Array(analyser.frequencyBinCount); + analyser.getFloatFrequencyData(fftResult); + } + const nyquistFrequency = sampleRate / 2; + const frequencyStep = (1 / fftResult.length) * nyquistFrequency; + let outputValues; + let frequencies; + let labels; + if (analysisType === 'music' || analysisType === 'voice') { + const useFrequencies = + analysisType === 'voice' ? voiceFrequencies : noteFrequencies; + const aggregateOutput = Array(useFrequencies.length).fill(minDecibels); + for (let i = 0; i < fftResult.length; i++) { + const frequency = i * frequencyStep; + const amplitude = fftResult[i]; + for (let n = useFrequencies.length - 1; n >= 0; n--) { + if (frequency > useFrequencies[n]) { + aggregateOutput[n] = Math.max(aggregateOutput[n], amplitude); + break; + } + } + } + outputValues = aggregateOutput; + frequencies = + analysisType === 'voice' ? voiceFrequencies : noteFrequencies; + labels = + analysisType === 'voice' ? voiceFrequencyLabels : noteFrequencyLabels; + } else { + outputValues = Array.from(fftResult); + frequencies = outputValues.map((_, i) => frequencyStep * i); + labels = frequencies.map((f) => `${f.toFixed(2)} Hz`); + } + // We normalize to {0, 1} + const normalizedOutput = outputValues.map((v) => { + return Math.max( + 0, + Math.min((v - minDecibels) / (maxDecibels - minDecibels), 1), + ); + }); + const values = new Float32Array(normalizedOutput); + return { + values, + frequencies, + labels, + }; + } + + /** + * Creates a new AudioAnalysis instance for an HTMLAudioElement + * @param {HTMLAudioElement} audioElement + * @param {AudioBuffer|null} [audioBuffer] If provided, will cache all frequency domain data from the buffer + * @returns {AudioAnalysis} + */ + constructor(audioElement, audioBuffer = null) { + this.fftResults = []; + if (audioBuffer) { + /** + * Modified from + * https://stackoverflow.com/questions/75063715/using-the-web-audio-api-to-analyze-a-song-without-playing + * + * We do this to populate FFT values for the audio if provided an `audioBuffer` + * The reason to do this is that Safari fails when using `createMediaElementSource` + * This has a non-zero RAM cost so we only opt-in to run it on Safari, Chrome is better + */ + const { length, sampleRate } = audioBuffer; + const offlineAudioContext = new OfflineAudioContext({ + length, + sampleRate, + }); + const source = offlineAudioContext.createBufferSource(); + source.buffer = audioBuffer; + const analyser = offlineAudioContext.createAnalyser(); + analyser.fftSize = 8192; + analyser.smoothingTimeConstant = 0.1; + source.connect(analyser); + // limit is :: 128 / sampleRate; + // but we just want 60fps - cuts ~1s from 6MB to 1MB of RAM + const renderQuantumInSeconds = 1 / 60; + const durationInSeconds = length / sampleRate; + const analyze = (index) => { + const suspendTime = renderQuantumInSeconds * index; + if (suspendTime < durationInSeconds) { + offlineAudioContext.suspend(suspendTime).then(() => { + const fftResult = new Float32Array(analyser.frequencyBinCount); + analyser.getFloatFrequencyData(fftResult); + this.fftResults.push(fftResult); + analyze(index + 1); + }); + } + if (index === 1) { + offlineAudioContext.startRendering(); + } else { + offlineAudioContext.resume(); + } + }; + source.start(0); + analyze(1); + this.audio = audioElement; + this.context = offlineAudioContext; + this.analyser = analyser; + this.sampleRate = sampleRate; + this.audioBuffer = audioBuffer; + } else { + const audioContext = new AudioContext(); + const track = audioContext.createMediaElementSource(audioElement); + const analyser = audioContext.createAnalyser(); + analyser.fftSize = 8192; + analyser.smoothingTimeConstant = 0.1; + track.connect(analyser); + analyser.connect(audioContext.destination); + this.audio = audioElement; + this.context = audioContext; + this.analyser = analyser; + this.sampleRate = this.context.sampleRate; + this.audioBuffer = null; + } + } + + /** + * Gets the current frequency domain data from the playing audio track + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {AudioAnalysisOutputType} + */ + getFrequencies( + analysisType = 'frequency', + minDecibels = -100, + maxDecibels = -30, + ) { + let fftResult = null; + if (this.audioBuffer && this.fftResults.length) { + const pct = this.audio.currentTime / this.audio.duration; + const index = Math.min( + (pct * this.fftResults.length) | 0, + this.fftResults.length - 1, + ); + fftResult = this.fftResults[index]; + } + return AudioAnalysis.getFrequencies( + this.analyser, + this.sampleRate, + fftResult, + analysisType, + minDecibels, + maxDecibels, + ); + } + + /** + * Resume the internal AudioContext if it was suspended due to the lack of + * user interaction when the AudioAnalysis was instantiated. + * @returns {Promise} + */ + async resumeIfSuspended() { + if (this.context.state === 'suspended') { + await this.context.resume(); + } + return true; + } +} + +globalThis.AudioAnalysis = AudioAnalysis; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/analysis/constants.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/analysis/constants.js new file mode 100644 index 0000000000..f14da38e62 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/analysis/constants.js @@ -0,0 +1,60 @@ +/** + * Constants for help with visualization + * Helps map frequency ranges from Fast Fourier Transform + * to human-interpretable ranges, notably music ranges and + * human vocal ranges. + */ + +// Eighth octave frequencies +const octave8Frequencies = [ + 4186.01, 4434.92, 4698.63, 4978.03, 5274.04, 5587.65, 5919.91, 6271.93, + 6644.88, 7040.0, 7458.62, 7902.13, +]; + +// Labels for each of the above frequencies +const octave8FrequencyLabels = [ + 'C', + 'C#', + 'D', + 'D#', + 'E', + 'F', + 'F#', + 'G', + 'G#', + 'A', + 'A#', + 'B', +]; + +/** + * All note frequencies from 1st to 8th octave + * in format "A#8" (A#, 8th octave) + */ +export const noteFrequencies = []; +export const noteFrequencyLabels = []; +for (let i = 1; i <= 8; i++) { + for (let f = 0; f < octave8Frequencies.length; f++) { + const freq = octave8Frequencies[f]; + noteFrequencies.push(freq / Math.pow(2, 8 - i)); + noteFrequencyLabels.push(octave8FrequencyLabels[f] + i); + } +} + +/** + * Subset of the note frequencies between 32 and 2000 Hz + * 6 octave range: C1 to B6 + */ +const voiceFrequencyRange = [32.0, 2000.0]; +export const voiceFrequencies = noteFrequencies.filter((_, i) => { + return ( + noteFrequencies[i] > voiceFrequencyRange[0] && + noteFrequencies[i] < voiceFrequencyRange[1] + ); +}); +export const voiceFrequencyLabels = noteFrequencyLabels.filter((_, i) => { + return ( + noteFrequencies[i] > voiceFrequencyRange[0] && + noteFrequencies[i] < voiceFrequencyRange[1] + ); +}); diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_packer.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_packer.js new file mode 100644 index 0000000000..7146b7fdeb --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_packer.js @@ -0,0 +1,113 @@ +/** + * Raw wav audio file contents + * @typedef {Object} WavPackerAudioType + * @property {Blob} blob + * @property {string} url + * @property {number} channelCount + * @property {number} sampleRate + * @property {number} duration + */ + +/** + * Utility class for assembling PCM16 "audio/wav" data + * @class + */ +export class WavPacker { + /** + * Converts Float32Array of amplitude data to ArrayBuffer in Int16Array format + * @param {Float32Array} float32Array + * @returns {ArrayBuffer} + */ + static floatTo16BitPCM(float32Array) { + const buffer = new ArrayBuffer(float32Array.length * 2); + const view = new DataView(buffer); + let offset = 0; + for (let i = 0; i < float32Array.length; i++, offset += 2) { + let s = Math.max(-1, Math.min(1, float32Array[i])); + view.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7fff, true); + } + return buffer; + } + + /** + * Concatenates two ArrayBuffers + * @param {ArrayBuffer} leftBuffer + * @param {ArrayBuffer} rightBuffer + * @returns {ArrayBuffer} + */ + static mergeBuffers(leftBuffer, rightBuffer) { + const tmpArray = new Uint8Array( + leftBuffer.byteLength + rightBuffer.byteLength + ); + tmpArray.set(new Uint8Array(leftBuffer), 0); + tmpArray.set(new Uint8Array(rightBuffer), leftBuffer.byteLength); + return tmpArray.buffer; + } + + /** + * Packs data into an Int16 format + * @private + * @param {number} size 0 = 1x Int16, 1 = 2x Int16 + * @param {number} arg value to pack + * @returns + */ + _packData(size, arg) { + return [ + new Uint8Array([arg, arg >> 8]), + new Uint8Array([arg, arg >> 8, arg >> 16, arg >> 24]), + ][size]; + } + + /** + * Packs audio into "audio/wav" Blob + * @param {number} sampleRate + * @param {{bitsPerSample: number, channels: Array, data: Int16Array}} audio + * @returns {WavPackerAudioType} + */ + pack(sampleRate, audio) { + if (!audio?.bitsPerSample) { + throw new Error(`Missing "bitsPerSample"`); + } else if (!audio?.channels) { + throw new Error(`Missing "channels"`); + } else if (!audio?.data) { + throw new Error(`Missing "data"`); + } + const { bitsPerSample, channels, data } = audio; + const output = [ + // Header + 'RIFF', + this._packData( + 1, + 4 + (8 + 24) /* chunk 1 length */ + (8 + 8) /* chunk 2 length */ + ), // Length + 'WAVE', + // chunk 1 + 'fmt ', // Sub-chunk identifier + this._packData(1, 16), // Chunk length + this._packData(0, 1), // Audio format (1 is linear quantization) + this._packData(0, channels.length), + this._packData(1, sampleRate), + this._packData(1, (sampleRate * channels.length * bitsPerSample) / 8), // Byte rate + this._packData(0, (channels.length * bitsPerSample) / 8), + this._packData(0, bitsPerSample), + // chunk 2 + 'data', // Sub-chunk identifier + this._packData( + 1, + (channels[0].length * channels.length * bitsPerSample) / 8 + ), // Chunk length + data, + ]; + const blob = new Blob(output, { type: 'audio/mpeg' }); + const url = URL.createObjectURL(blob); + return { + blob, + url, + channelCount: channels.length, + sampleRate, + duration: data.byteLength / (channels.length * sampleRate * 2), + }; + } +} + +globalThis.WavPacker = WavPacker; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_recorder.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_recorder.js new file mode 100644 index 0000000000..a4f1d045bf --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_recorder.js @@ -0,0 +1,548 @@ +import { AudioProcessorSrc } from './worklets/audio_processor.js'; +import { AudioAnalysis } from './analysis/audio_analysis.js'; +import { WavPacker } from './wav_packer.js'; + +/** + * Decodes audio into a wav file + * @typedef {Object} DecodedAudioType + * @property {Blob} blob + * @property {string} url + * @property {Float32Array} values + * @property {AudioBuffer} audioBuffer + */ + +/** + * Records live stream of user audio as PCM16 "audio/wav" data + * @class + */ +export class WavRecorder { + /** + * Create a new WavRecorder instance + * @param {{sampleRate?: number, outputToSpeakers?: boolean, debug?: boolean}} [options] + * @returns {WavRecorder} + */ + constructor({ + sampleRate = 44100, + outputToSpeakers = false, + debug = false, + } = {}) { + // Script source + this.scriptSrc = AudioProcessorSrc; + // Config + this.sampleRate = sampleRate; + this.outputToSpeakers = outputToSpeakers; + this.debug = !!debug; + this._deviceChangeCallback = null; + this._devices = []; + // State variables + this.stream = null; + this.processor = null; + this.source = null; + this.node = null; + this.recording = false; + // Event handling with AudioWorklet + this._lastEventId = 0; + this.eventReceipts = {}; + this.eventTimeout = 5000; + // Process chunks of audio + this._chunkProcessor = () => {}; + this._chunkProcessorSize = void 0; + this._chunkProcessorBuffer = { + raw: new ArrayBuffer(0), + mono: new ArrayBuffer(0), + }; + } + + /** + * Decodes audio data from multiple formats to a Blob, url, Float32Array and AudioBuffer + * @param {Blob|Float32Array|Int16Array|ArrayBuffer|number[]} audioData + * @param {number} sampleRate + * @param {number} fromSampleRate + * @returns {Promise} + */ + static async decode(audioData, sampleRate = 44100, fromSampleRate = -1) { + const context = new AudioContext({ sampleRate }); + let arrayBuffer; + let blob; + if (audioData instanceof Blob) { + if (fromSampleRate !== -1) { + throw new Error( + `Can not specify "fromSampleRate" when reading from Blob`, + ); + } + blob = audioData; + arrayBuffer = await blob.arrayBuffer(); + } else if (audioData instanceof ArrayBuffer) { + if (fromSampleRate !== -1) { + throw new Error( + `Can not specify "fromSampleRate" when reading from ArrayBuffer`, + ); + } + arrayBuffer = audioData; + blob = new Blob([arrayBuffer], { type: 'audio/wav' }); + } else { + let float32Array; + let data; + if (audioData instanceof Int16Array) { + data = audioData; + float32Array = new Float32Array(audioData.length); + for (let i = 0; i < audioData.length; i++) { + float32Array[i] = audioData[i] / 0x8000; + } + } else if (audioData instanceof Float32Array) { + float32Array = audioData; + } else if (audioData instanceof Array) { + float32Array = new Float32Array(audioData); + } else { + throw new Error( + `"audioData" must be one of: Blob, Float32Arrray, Int16Array, ArrayBuffer, Array`, + ); + } + if (fromSampleRate === -1) { + throw new Error( + `Must specify "fromSampleRate" when reading from Float32Array, In16Array or Array`, + ); + } else if (fromSampleRate < 3000) { + throw new Error(`Minimum "fromSampleRate" is 3000 (3kHz)`); + } + if (!data) { + data = WavPacker.floatTo16BitPCM(float32Array); + } + const audio = { + bitsPerSample: 16, + channels: [float32Array], + data, + }; + const packer = new WavPacker(); + const result = packer.pack(fromSampleRate, audio); + blob = result.blob; + arrayBuffer = await blob.arrayBuffer(); + } + const audioBuffer = await context.decodeAudioData(arrayBuffer); + const values = audioBuffer.getChannelData(0); + const url = URL.createObjectURL(blob); + return { + blob, + url, + values, + audioBuffer, + }; + } + + /** + * Logs data in debug mode + * @param {...any} arguments + * @returns {true} + */ + log() { + if (this.debug) { + this.log(...arguments); + } + return true; + } + + /** + * Retrieves the current sampleRate for the recorder + * @returns {number} + */ + getSampleRate() { + return this.sampleRate; + } + + /** + * Retrieves the current status of the recording + * @returns {"ended"|"paused"|"recording"} + */ + getStatus() { + if (!this.processor) { + return 'ended'; + } else if (!this.recording) { + return 'paused'; + } else { + return 'recording'; + } + } + + /** + * Sends an event to the AudioWorklet + * @private + * @param {string} name + * @param {{[key: string]: any}} data + * @param {AudioWorkletNode} [_processor] + * @returns {Promise<{[key: string]: any}>} + */ + async _event(name, data = {}, _processor = null) { + _processor = _processor || this.processor; + if (!_processor) { + throw new Error('Can not send events without recording first'); + } + const message = { + event: name, + id: this._lastEventId++, + data, + }; + _processor.port.postMessage(message); + const t0 = new Date().valueOf(); + while (!this.eventReceipts[message.id]) { + if (new Date().valueOf() - t0 > this.eventTimeout) { + throw new Error(`Timeout waiting for "${name}" event`); + } + await new Promise((res) => setTimeout(() => res(true), 1)); + } + const payload = this.eventReceipts[message.id]; + delete this.eventReceipts[message.id]; + return payload; + } + + /** + * Sets device change callback, remove if callback provided is `null` + * @param {(Array): void|null} callback + * @returns {true} + */ + listenForDeviceChange(callback) { + if (callback === null && this._deviceChangeCallback) { + navigator.mediaDevices.removeEventListener( + 'devicechange', + this._deviceChangeCallback, + ); + this._deviceChangeCallback = null; + } else if (callback !== null) { + // Basically a debounce; we only want this called once when devices change + // And we only want the most recent callback() to be executed + // if a few are operating at the same time + let lastId = 0; + let lastDevices = []; + const serializeDevices = (devices) => + devices + .map((d) => d.deviceId) + .sort() + .join(','); + const cb = async () => { + let id = ++lastId; + const devices = await this.listDevices(); + if (id === lastId) { + if (serializeDevices(lastDevices) !== serializeDevices(devices)) { + lastDevices = devices; + callback(devices.slice()); + } + } + }; + navigator.mediaDevices.addEventListener('devicechange', cb); + cb(); + this._deviceChangeCallback = cb; + } + return true; + } + + /** + * Manually request permission to use the microphone + * @returns {Promise} + */ + async requestPermission() { + const permissionStatus = await navigator.permissions.query({ + name: 'microphone', + }); + if (permissionStatus.state === 'denied') { + window.alert('You must grant microphone access to use this feature.'); + } else if (permissionStatus.state === 'prompt') { + try { + const stream = await navigator.mediaDevices.getUserMedia({ + audio: true, + }); + const tracks = stream.getTracks(); + tracks.forEach((track) => track.stop()); + } catch (e) { + window.alert('You must grant microphone access to use this feature.'); + } + } + return true; + } + + /** + * List all eligible devices for recording, will request permission to use microphone + * @returns {Promise>} + */ + async listDevices() { + if ( + !navigator.mediaDevices || + !('enumerateDevices' in navigator.mediaDevices) + ) { + throw new Error('Could not request user devices'); + } + await this.requestPermission(); + const devices = await navigator.mediaDevices.enumerateDevices(); + const audioDevices = devices.filter( + (device) => device.kind === 'audioinput', + ); + const defaultDeviceIndex = audioDevices.findIndex( + (device) => device.deviceId === 'default', + ); + const deviceList = []; + if (defaultDeviceIndex !== -1) { + let defaultDevice = audioDevices.splice(defaultDeviceIndex, 1)[0]; + let existingIndex = audioDevices.findIndex( + (device) => device.groupId === defaultDevice.groupId, + ); + if (existingIndex !== -1) { + defaultDevice = audioDevices.splice(existingIndex, 1)[0]; + } + defaultDevice.default = true; + deviceList.push(defaultDevice); + } + return deviceList.concat(audioDevices); + } + + /** + * Begins a recording session and requests microphone permissions if not already granted + * Microphone recording indicator will appear on browser tab but status will be "paused" + * @param {string} [deviceId] if no device provided, default device will be used + * @returns {Promise} + */ + async begin(deviceId) { + if (this.processor) { + throw new Error( + `Already connected: please call .end() to start a new session`, + ); + } + + if ( + !navigator.mediaDevices || + !('getUserMedia' in navigator.mediaDevices) + ) { + throw new Error('Could not request user media'); + } + try { + const config = { audio: true }; + if (deviceId) { + config.audio = { deviceId: { exact: deviceId } }; + } + this.stream = await navigator.mediaDevices.getUserMedia(config); + } catch (err) { + throw new Error('Could not start media stream'); + } + + const context = new AudioContext({ sampleRate: this.sampleRate }); + const source = context.createMediaStreamSource(this.stream); + // Load and execute the module script. + try { + await context.audioWorklet.addModule(this.scriptSrc); + } catch (e) { + console.error(e); + throw new Error(`Could not add audioWorklet module: ${this.scriptSrc}`); + } + const processor = new AudioWorkletNode(context, 'audio_processor'); + processor.port.onmessage = (e) => { + const { event, id, data } = e.data; + if (event === 'receipt') { + this.eventReceipts[id] = data; + } else if (event === 'chunk') { + if (this._chunkProcessorSize) { + const buffer = this._chunkProcessorBuffer; + this._chunkProcessorBuffer = { + raw: WavPacker.mergeBuffers(buffer.raw, data.raw), + mono: WavPacker.mergeBuffers(buffer.mono, data.mono), + }; + if ( + this._chunkProcessorBuffer.mono.byteLength >= + this._chunkProcessorSize + ) { + this._chunkProcessor(this._chunkProcessorBuffer); + this._chunkProcessorBuffer = { + raw: new ArrayBuffer(0), + mono: new ArrayBuffer(0), + }; + } + } else { + this._chunkProcessor(data); + } + } + }; + + const node = source.connect(processor); + const analyser = context.createAnalyser(); + analyser.fftSize = 8192; + analyser.smoothingTimeConstant = 0.1; + node.connect(analyser); + if (this.outputToSpeakers) { + // eslint-disable-next-line no-console + console.warn( + 'Warning: Output to speakers may affect sound quality,\n' + + 'especially due to system audio feedback preventative measures.\n' + + 'use only for debugging', + ); + analyser.connect(context.destination); + } + + this.source = source; + this.node = node; + this.analyser = analyser; + this.processor = processor; + return true; + } + + /** + * Gets the current frequency domain data from the recording track + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {import('./analysis/audio_analysis.js').AudioAnalysisOutputType} + */ + getFrequencies( + analysisType = 'frequency', + minDecibels = -100, + maxDecibels = -30, + ) { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } + return AudioAnalysis.getFrequencies( + this.analyser, + this.sampleRate, + null, + analysisType, + minDecibels, + maxDecibels, + ); + } + + /** + * Pauses the recording + * Keeps microphone stream open but halts storage of audio + * @returns {Promise} + */ + async pause() { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } else if (!this.recording) { + throw new Error('Already paused: please call .record() first'); + } + if (this._chunkProcessorBuffer.raw.byteLength) { + this._chunkProcessor(this._chunkProcessorBuffer); + } + this.log('Pausing ...'); + await this._event('stop'); + this.recording = false; + return true; + } + + /** + * Start recording stream and storing to memory from the connected audio source + * @param {(data: { mono: Int16Array; raw: Int16Array }) => any} [chunkProcessor] + * @param {number} [chunkSize] chunkProcessor will not be triggered until this size threshold met in mono audio + * @returns {Promise} + */ + async record(chunkProcessor = () => {}, chunkSize = 8192) { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } else if (this.recording) { + throw new Error('Already recording: please call .pause() first'); + } else if (typeof chunkProcessor !== 'function') { + throw new Error(`chunkProcessor must be a function`); + } + this._chunkProcessor = chunkProcessor; + this._chunkProcessorSize = chunkSize; + this._chunkProcessorBuffer = { + raw: new ArrayBuffer(0), + mono: new ArrayBuffer(0), + }; + this.log('Recording ...'); + await this._event('start'); + this.recording = true; + return true; + } + + /** + * Clears the audio buffer, empties stored recording + * @returns {Promise} + */ + async clear() { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } + await this._event('clear'); + return true; + } + + /** + * Reads the current audio stream data + * @returns {Promise<{meanValues: Float32Array, channels: Array}>} + */ + async read() { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } + this.log('Reading ...'); + const result = await this._event('read'); + return result; + } + + /** + * Saves the current audio stream to a file + * @param {boolean} [force] Force saving while still recording + * @returns {Promise} + */ + async save(force = false) { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } + if (!force && this.recording) { + throw new Error( + 'Currently recording: please call .pause() first, or call .save(true) to force', + ); + } + this.log('Exporting ...'); + const exportData = await this._event('export'); + const packer = new WavPacker(); + const result = packer.pack(this.sampleRate, exportData.audio); + return result; + } + + /** + * Ends the current recording session and saves the result + * @returns {Promise} + */ + async end() { + if (!this.processor) { + throw new Error('Session ended: please call .begin() first'); + } + + const _processor = this.processor; + + this.log('Stopping ...'); + await this._event('stop'); + this.recording = false; + const tracks = this.stream.getTracks(); + tracks.forEach((track) => track.stop()); + + this.log('Exporting ...'); + const exportData = await this._event('export', {}, _processor); + + this.processor.disconnect(); + this.source.disconnect(); + this.node.disconnect(); + this.analyser.disconnect(); + this.stream = null; + this.processor = null; + this.source = null; + this.node = null; + + const packer = new WavPacker(); + const result = packer.pack(this.sampleRate, exportData.audio); + return result; + } + + /** + * Performs a full cleanup of WavRecorder instance + * Stops actively listening via microphone and removes existing listeners + * @returns {Promise} + */ + async quit() { + this.listenForDeviceChange(null); + if (this.processor) { + await this.end(); + } + return true; + } +} + +globalThis.WavRecorder = WavRecorder; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_stream_player.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_stream_player.js new file mode 100644 index 0000000000..500eff6c5c --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/wav_stream_player.js @@ -0,0 +1,160 @@ +import { StreamProcessorSrc } from './worklets/stream_processor.js'; +import { AudioAnalysis } from './analysis/audio_analysis.js'; + +/** + * Plays audio streams received in raw PCM16 chunks from the browser + * @class + */ +export class WavStreamPlayer { + /** + * Creates a new WavStreamPlayer instance + * @param {{sampleRate?: number}} options + * @returns {WavStreamPlayer} + */ + constructor({ sampleRate = 44100 } = {}) { + this.scriptSrc = StreamProcessorSrc; + this.sampleRate = sampleRate; + this.context = null; + this.stream = null; + this.analyser = null; + this.trackSampleOffsets = {}; + this.interruptedTrackIds = {}; + } + + /** + * Connects the audio context and enables output to speakers + * @returns {Promise} + */ + async connect() { + this.context = new AudioContext({ sampleRate: this.sampleRate }); + if (this.context.state === 'suspended') { + await this.context.resume(); + } + try { + await this.context.audioWorklet.addModule(this.scriptSrc); + } catch (e) { + console.error(e); + throw new Error(`Could not add audioWorklet module: ${this.scriptSrc}`); + } + const analyser = this.context.createAnalyser(); + analyser.fftSize = 8192; + analyser.smoothingTimeConstant = 0.1; + this.analyser = analyser; + return true; + } + + /** + * Gets the current frequency domain data from the playing track + * @param {"frequency"|"music"|"voice"} [analysisType] + * @param {number} [minDecibels] default -100 + * @param {number} [maxDecibels] default -30 + * @returns {import('./analysis/audio_analysis.js').AudioAnalysisOutputType} + */ + getFrequencies( + analysisType = 'frequency', + minDecibels = -100, + maxDecibels = -30 + ) { + if (!this.analyser) { + throw new Error('Not connected, please call .connect() first'); + } + return AudioAnalysis.getFrequencies( + this.analyser, + this.sampleRate, + null, + analysisType, + minDecibels, + maxDecibels + ); + } + + /** + * Starts audio streaming + * @private + * @returns {Promise} + */ + _start() { + const streamNode = new AudioWorkletNode(this.context, 'stream_processor'); + streamNode.connect(this.context.destination); + streamNode.port.onmessage = (e) => { + const { event } = e.data; + if (event === 'stop') { + streamNode.disconnect(); + this.stream = null; + } else if (event === 'offset') { + const { requestId, trackId, offset } = e.data; + const currentTime = offset / this.sampleRate; + this.trackSampleOffsets[requestId] = { trackId, offset, currentTime }; + } + }; + this.analyser.disconnect(); + streamNode.connect(this.analyser); + this.stream = streamNode; + return true; + } + + /** + * Adds 16BitPCM data to the currently playing audio stream + * You can add chunks beyond the current play point and they will be queued for play + * @param {ArrayBuffer|Int16Array} arrayBuffer + * @param {string} [trackId] + * @returns {Int16Array} + */ + add16BitPCM(arrayBuffer, trackId = 'default') { + if (typeof trackId !== 'string') { + throw new Error(`trackId must be a string`); + } else if (this.interruptedTrackIds[trackId]) { + return; + } + if (!this.stream) { + this._start(); + } + let buffer; + if (arrayBuffer instanceof Int16Array) { + buffer = arrayBuffer; + } else if (arrayBuffer instanceof ArrayBuffer) { + buffer = new Int16Array(arrayBuffer); + } else { + throw new Error(`argument must be Int16Array or ArrayBuffer`); + } + this.stream.port.postMessage({ event: 'write', buffer, trackId }); + return buffer; + } + + /** + * Gets the offset (sample count) of the currently playing stream + * @param {boolean} [interrupt] + * @returns {{trackId: string|null, offset: number, currentTime: number}} + */ + async getTrackSampleOffset(interrupt = false) { + if (!this.stream) { + return null; + } + const requestId = crypto.randomUUID(); + this.stream.port.postMessage({ + event: interrupt ? 'interrupt' : 'offset', + requestId, + }); + let trackSampleOffset; + while (!trackSampleOffset) { + trackSampleOffset = this.trackSampleOffsets[requestId]; + await new Promise((r) => setTimeout(() => r(), 1)); + } + const { trackId } = trackSampleOffset; + if (interrupt && trackId) { + this.interruptedTrackIds[trackId] = true; + } + return trackSampleOffset; + } + + /** + * Strips the current stream and returns the sample offset of the audio + * @param {boolean} [interrupt] + * @returns {{trackId: string|null, offset: number, currentTime: number}} + */ + async interrupt() { + return this.getTrackSampleOffset(true); + } +} + +globalThis.WavStreamPlayer = WavStreamPlayer; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/worklets/audio_processor.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/worklets/audio_processor.js new file mode 100644 index 0000000000..61dd7ec9ce --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/worklets/audio_processor.js @@ -0,0 +1,214 @@ +const AudioProcessorWorklet = ` +class AudioProcessor extends AudioWorkletProcessor { + + constructor() { + super(); + this.port.onmessage = this.receive.bind(this); + this.initialize(); + } + + initialize() { + this.foundAudio = false; + this.recording = false; + this.chunks = []; + } + + /** + * Concatenates sampled chunks into channels + * Format is chunk[Left[], Right[]] + */ + readChannelData(chunks, channel = -1, maxChannels = 9) { + let channelLimit; + if (channel !== -1) { + if (chunks[0] && chunks[0].length - 1 < channel) { + throw new Error( + \`Channel \${channel} out of range: max \${chunks[0].length}\` + ); + } + channelLimit = channel + 1; + } else { + channel = 0; + channelLimit = Math.min(chunks[0] ? chunks[0].length : 1, maxChannels); + } + const channels = []; + for (let n = channel; n < channelLimit; n++) { + const length = chunks.reduce((sum, chunk) => { + return sum + chunk[n].length; + }, 0); + const buffers = chunks.map((chunk) => chunk[n]); + const result = new Float32Array(length); + let offset = 0; + for (let i = 0; i < buffers.length; i++) { + result.set(buffers[i], offset); + offset += buffers[i].length; + } + channels[n] = result; + } + return channels; + } + + /** + * Combines parallel audio data into correct format, + * channels[Left[], Right[]] to float32Array[LRLRLRLR...] + */ + formatAudioData(channels) { + if (channels.length === 1) { + // Simple case is only one channel + const float32Array = channels[0].slice(); + const meanValues = channels[0].slice(); + return { float32Array, meanValues }; + } else { + const float32Array = new Float32Array( + channels[0].length * channels.length + ); + const meanValues = new Float32Array(channels[0].length); + for (let i = 0; i < channels[0].length; i++) { + const offset = i * channels.length; + let meanValue = 0; + for (let n = 0; n < channels.length; n++) { + float32Array[offset + n] = channels[n][i]; + meanValue += channels[n][i]; + } + meanValues[i] = meanValue / channels.length; + } + return { float32Array, meanValues }; + } + } + + /** + * Converts 32-bit float data to 16-bit integers + */ + floatTo16BitPCM(float32Array) { + const buffer = new ArrayBuffer(float32Array.length * 2); + const view = new DataView(buffer); + let offset = 0; + for (let i = 0; i < float32Array.length; i++, offset += 2) { + let s = Math.max(-1, Math.min(1, float32Array[i])); + view.setInt16(offset, s < 0 ? s * 0x8000 : s * 0x7fff, true); + } + return buffer; + } + + /** + * Retrieves the most recent amplitude values from the audio stream + * @param {number} channel + */ + getValues(channel = -1) { + const channels = this.readChannelData(this.chunks, channel); + const { meanValues } = this.formatAudioData(channels); + return { meanValues, channels }; + } + + /** + * Exports chunks as an audio/wav file + */ + export() { + const channels = this.readChannelData(this.chunks); + const { float32Array, meanValues } = this.formatAudioData(channels); + const audioData = this.floatTo16BitPCM(float32Array); + return { + meanValues: meanValues, + audio: { + bitsPerSample: 16, + channels: channels, + data: audioData, + }, + }; + } + + receive(e) { + const { event, id } = e.data; + let receiptData = {}; + switch (event) { + case 'start': + this.recording = true; + break; + case 'stop': + this.recording = false; + break; + case 'clear': + this.initialize(); + break; + case 'export': + receiptData = this.export(); + break; + case 'read': + receiptData = this.getValues(); + break; + default: + break; + } + // Always send back receipt + this.port.postMessage({ event: 'receipt', id, data: receiptData }); + } + + sendChunk(chunk) { + const channels = this.readChannelData([chunk]); + const { float32Array, meanValues } = this.formatAudioData(channels); + const rawAudioData = this.floatTo16BitPCM(float32Array); + const monoAudioData = this.floatTo16BitPCM(meanValues); + this.port.postMessage({ + event: 'chunk', + data: { + mono: monoAudioData, + raw: rawAudioData, + }, + }); + } + + process(inputList, outputList, parameters) { + // Copy input to output (e.g. speakers) + // Note that this creates choppy sounds with Mac products + const sourceLimit = Math.min(inputList.length, outputList.length); + for (let inputNum = 0; inputNum < sourceLimit; inputNum++) { + const input = inputList[inputNum]; + const output = outputList[inputNum]; + const channelCount = Math.min(input.length, output.length); + for (let channelNum = 0; channelNum < channelCount; channelNum++) { + input[channelNum].forEach((sample, i) => { + output[channelNum][i] = sample; + }); + } + } + const inputs = inputList[0]; + // There's latency at the beginning of a stream before recording starts + // Make sure we actually receive audio data before we start storing chunks + let sliceIndex = 0; + if (!this.foundAudio) { + for (const channel of inputs) { + sliceIndex = 0; // reset for each channel + if (this.foundAudio) { + break; + } + if (channel) { + for (const value of channel) { + if (value !== 0) { + // find only one non-zero entry in any channel + this.foundAudio = true; + break; + } else { + sliceIndex++; + } + } + } + } + } + if (inputs && inputs[0] && this.foundAudio && this.recording) { + // We need to copy the TypedArray, because the \`process\` + // internals will reuse the same buffer to hold each input + const chunk = inputs.map((input) => input.slice(sliceIndex)); + this.chunks.push(chunk); + this.sendChunk(chunk); + } + return true; + } +} + +registerProcessor('audio_processor', AudioProcessor); +`; + +const script = new Blob([AudioProcessorWorklet], { + type: 'application/javascript', +}); +const src = URL.createObjectURL(script); +export const AudioProcessorSrc = src; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/worklets/stream_processor.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/worklets/stream_processor.js new file mode 100644 index 0000000000..d3c794a88c --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/lib/wavtools/lib/worklets/stream_processor.js @@ -0,0 +1,96 @@ +export const StreamProcessorWorklet = ` +class StreamProcessor extends AudioWorkletProcessor { + constructor() { + super(); + this.hasStarted = false; + this.hasInterrupted = false; + this.outputBuffers = []; + this.bufferLength = 128; + this.write = { buffer: new Float32Array(this.bufferLength), trackId: null }; + this.writeOffset = 0; + this.trackSampleOffsets = {}; + this.port.onmessage = (event) => { + if (event.data) { + const payload = event.data; + if (payload.event === 'write') { + const int16Array = payload.buffer; + const float32Array = new Float32Array(int16Array.length); + for (let i = 0; i < int16Array.length; i++) { + float32Array[i] = int16Array[i] / 0x8000; // Convert Int16 to Float32 + } + this.writeData(float32Array, payload.trackId); + } else if ( + payload.event === 'offset' || + payload.event === 'interrupt' + ) { + const requestId = payload.requestId; + const trackId = this.write.trackId; + const offset = this.trackSampleOffsets[trackId] || 0; + this.port.postMessage({ + event: 'offset', + requestId, + trackId, + offset, + }); + if (payload.event === 'interrupt') { + this.hasInterrupted = true; + } + } else { + throw new Error(\`Unhandled event "\${payload.event}"\`); + } + } + }; + } + + writeData(float32Array, trackId = null) { + let { buffer } = this.write; + let offset = this.writeOffset; + for (let i = 0; i < float32Array.length; i++) { + buffer[offset++] = float32Array[i]; + if (offset >= buffer.length) { + this.outputBuffers.push(this.write); + this.write = { buffer: new Float32Array(this.bufferLength), trackId }; + buffer = this.write.buffer; + offset = 0; + } + } + this.writeOffset = offset; + return true; + } + + process(inputs, outputs, parameters) { + const output = outputs[0]; + const outputChannelData = output[0]; + const outputBuffers = this.outputBuffers; + if (this.hasInterrupted) { + this.port.postMessage({ event: 'stop' }); + return false; + } else if (outputBuffers.length) { + this.hasStarted = true; + const { buffer, trackId } = outputBuffers.shift(); + for (let i = 0; i < outputChannelData.length; i++) { + outputChannelData[i] = buffer[i] || 0; + } + if (trackId) { + this.trackSampleOffsets[trackId] = + this.trackSampleOffsets[trackId] || 0; + this.trackSampleOffsets[trackId] += buffer.length; + } + return true; + } else if (this.hasStarted) { + this.port.postMessage({ event: 'stop' }); + return false; + } else { + return true; + } + } +} + +registerProcessor('stream_processor', StreamProcessor); +`; + +const script = new Blob([StreamProcessorWorklet], { + type: 'application/javascript', +}); +const src = URL.createObjectURL(script); +export const StreamProcessorSrc = src; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/ListenerPage.tsx b/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/ListenerPage.tsx new file mode 100644 index 0000000000..d94d1c5d55 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/ListenerPage.tsx @@ -0,0 +1,126 @@ +import React, { useRef, useState, useCallback, useEffect } from 'react'; +import { io, Socket } from 'socket.io-client'; +import { WavStreamPlayer } from '../lib/wavtools'; +import { Button } from '../components/button/Button'; +import './Styles.scss'; + +// ListenerPage component handles audio streaming for selected languages +export function ListenerPage() { + const wavStreamPlayerRef = useRef(new WavStreamPlayer({ sampleRate: 24000 })); + const socketRef = useRef(null); + + // State variables for managing connection status and selected language + const [isConnected, setIsConnected] = useState(false); + const [selectedLang, setSelectedLang] = useState<'fr' | 'es' | 'tl' | 'en' | 'zh' | null>(null); + + // Centralize language data + const languages = { + fr: { name: 'French' }, + es: { name: 'Spanish' }, + tl: { name: 'Tagalog' }, + en: { name: 'English' }, + zh: { name: 'Mandarin' }, + } as const; + + type LanguageKey = keyof typeof languages; + + // Extract language options into a separate function + const renderLanguageOptions = () => ( + Object.entries(languages).map(([key, { name }]) => ( + + )) + ); + + // Function to connect to the server and set up audio streaming + const connectServer = useCallback(async () => { + if (socketRef.current) return; + try { + const socket = io('http://localhost:3001'); + socketRef.current = socket; + await wavStreamPlayerRef.current.connect(); + socket.on('connect', () => { + console.log('Listener connected:', socket.id); + setIsConnected(true); + }); + socket.on('disconnect', () => { + console.log('Listener disconnected'); + setIsConnected(false); + }); + } catch (error) { + console.error('Error connecting to server:', error); + } + }, []); + + // Function to disconnect from the server and stop audio streaming + const disconnectServer = useCallback(async () => { + console.log('Disconnect button clicked'); + if (socketRef.current) { + socketRef.current.disconnect(); + socketRef.current = null; + } + try { + await wavStreamPlayerRef.current.interrupt(); + setIsConnected(false); + } catch (error) { + console.error('Error disconnecting from server:', error); + } + }, []); + + // Helper function to handle playing audio chunks + const playAudioChunk = (lang: LanguageKey, chunk: ArrayBuffer) => { + console.log(`Playing ${lang.toUpperCase()} chunk:`, chunk.byteLength); + wavStreamPlayerRef.current.add16BitPCM(chunk); + }; + + // Dynamically create language handlers + const languageHandlers: Record void> = Object.keys(languages).reduce((handlers, lang) => { + handlers[lang as LanguageKey] = (chunk) => playAudioChunk(lang as LanguageKey, chunk); + return handlers; + }, {} as Record void>); + + // UseEffect to handle socket events for selected language + useEffect(() => { + const socket = socketRef.current; + if (!socket || !selectedLang) return; + + console.log(`Setting up listener for language: ${selectedLang}`); + const handleChunk = languageHandlers[selectedLang]; + socket.on(`audioFrame:${selectedLang}`, handleChunk); + + return () => { + console.log(`Cleaning up listener for language: ${selectedLang}`); + socket.off(`audioFrame:${selectedLang}`, handleChunk); + }; + }, [selectedLang]); + + return ( +
+

Listener Page

+
+
+

Select preferred language for translation

+
+ +
+
+
+ {isConnected ? ( +
+
+
+ ); +} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/SpeakerPage.tsx b/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/SpeakerPage.tsx new file mode 100644 index 0000000000..fa7e3273c9 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/SpeakerPage.tsx @@ -0,0 +1,288 @@ +import React, { useRef, useEffect, useState, useCallback } from 'react'; +import { RealtimeClient } from '@openai/realtime-api-beta'; +import { Button } from '../components/button/Button'; +import { Toggle } from '../components/toggle/Toggle'; +import { french_instructions, spanish_instructions, tagalog_instructions, english_instructions, mandarin_instructions } from '../utils/translation_prompts.js'; +import { WavRecorder } from '../lib/wavtools/index.js'; +import './Styles.scss'; +import { io, Socket } from 'socket.io-client'; + +export const OPENAI_API_KEY = process.env.REACT_APP_OPENAI_API_KEY; + +export const DEFAULT_REALTIME_MODEL = "gpt-4o-realtime-preview-2024-12-17"; +export const DEFAULT_REALTIME_VOICE = "coral"; +interface RealtimeEvent { + time: string; + source: 'client' | 'server'; + event: any; + count?: number; +} + +// Define language codes and their corresponding instructions +const languageConfigs = [ + { code: 'fr', instructions: french_instructions }, + { code: 'es', instructions: spanish_instructions }, + { code: 'tl', instructions: tagalog_instructions }, + { code: 'en', instructions: english_instructions }, + { code: 'zh', instructions: mandarin_instructions }, +]; + +// Map language codes to full names +const languageNames: Record = { + fr: 'French', + es: 'Spanish', + tl: 'Tagalog', + en: 'English', + zh: 'Mandarin', +}; + +// SpeakerPage component handles real-time audio recording and streaming for multiple languages +export function SpeakerPage() { + const [realtimeEvents, setRealtimeEvents] = useState([]); + const [isConnected, setIsConnected] = useState(false); + const [isRecording, setIsRecording] = useState(false); + const [canPushToTalk, setCanPushToTalk] = useState(true); + const [transcripts, setTranscripts] = useState<{ transcript: string; language: string }[]>([]); + const [showTranscripts, setShowTranscripts] = useState(false); + const [isLoading, setIsLoading] = useState(false); + + const wavRecorderRef = useRef( + new WavRecorder({ sampleRate: 24000 }) + ); + + const socketRef = useRef(null); + + // Create a map of client references using the language codes + const clientRefs = useRef( + languageConfigs.reduce((acc, { code }) => { + acc[code] = new RealtimeClient({ + apiKey: OPENAI_API_KEY, + dangerouslyAllowAPIKeyInBrowser: true, + }); + return acc; + }, {} as Record) + ).current; + + // Update languageConfigs to include client references + const updatedLanguageConfigs = languageConfigs.map(config => ({ + ...config, + clientRef: { current: clientRefs[config.code] } + })); + + // Function to connect to the conversation and set up real-time clients + const connectConversation = useCallback(async () => { + try { + setIsLoading(true); + const wavRecorder = wavRecorderRef.current; + await wavRecorder.begin(); + await connectAndSetupClients(); + setIsConnected(true); + } catch (error) { + console.error('Error connecting to conversation:', error); + } finally { + setIsLoading(false); + } + }, []); + + // Function to disconnect from the conversation and stop real-time clients + const disconnectConversation = useCallback(async () => { + try { + setIsConnected(false); + setIsRecording(false); + const wavRecorder = wavRecorderRef.current; + await disconnectClients(); + await wavRecorder.end(); + } catch (error) { + console.error('Error disconnecting from conversation:', error); + } + }, []); + + // Function to connect and set up all clients + const connectAndSetupClients = async () => { + for (const { clientRef } of updatedLanguageConfigs) { + const client = clientRef.current; + await client.realtime.connect({ model: DEFAULT_REALTIME_MODEL }); + await client.updateSession({ voice: DEFAULT_REALTIME_VOICE }); + } + }; + + // Function to disconnect all clients + const disconnectClients = async () => { + for (const { clientRef } of updatedLanguageConfigs) { + clientRef.current.disconnect(); + } + }; + + const startRecording = async () => { + setIsRecording(true); + const wavRecorder = wavRecorderRef.current; + + await wavRecorder.record((data) => { + // Send mic PCM to all clients + updatedLanguageConfigs.forEach(({ clientRef }) => { + clientRef.current.appendInputAudio(data.mono); + }); + }); + }; + + const stopRecording = async () => { + setIsRecording(false); + const wavRecorder = wavRecorderRef.current; + + if (wavRecorder.getStatus() === 'recording') { + await wavRecorder.pause(); + } + + // Create response for all clients + updatedLanguageConfigs.forEach(({ clientRef }) => { + clientRef.current.createResponse(); + }); + }; + + const changeTurnEndType = async (value: string) => { + const wavRecorder = wavRecorderRef.current; + + if (value === 'none') { + // If 'none' is selected, pause the recorder and disable turn detection for all clients + await wavRecorder.pause(); + updatedLanguageConfigs.forEach(({ clientRef }) => { + clientRef.current.updateSession({ turn_detection: null }); + }); + // Allow manual push-to-talk + setCanPushToTalk(true); + } else { + // If 'server_vad' is selected, enable server-based voice activity detection for all clients + updatedLanguageConfigs.forEach(({ clientRef }) => { + clientRef.current.updateSession({ turn_detection: { type: 'server_vad' } }); + }); + await wavRecorder.record((data) => { + updatedLanguageConfigs.forEach(({ clientRef }) => { + clientRef.current.appendInputAudio(data.mono); + }); + }); + setCanPushToTalk(false); + } + }; + + const toggleTranscriptsVisibility = () => { + setShowTranscripts((prev) => !prev); + }; + + useEffect(() => { + // Connect to mirror server + socketRef.current = io('http://localhost:3001'); + return () => { + socketRef.current?.close(); + socketRef.current = null; + }; + }, []); + + useEffect(() => { + for (const { code, instructions, clientRef } of updatedLanguageConfigs) { + const client = clientRef.current; + client.updateSession({ + instructions, + input_audio_transcription: { model: 'whisper-1' }, + }); + + client.on('realtime.event', (ev: RealtimeEvent) => handleRealtimeEvent(ev, code)); + client.on('error', (err: any) => console.error(`${code} client error:`, err)); + + client.on('conversation.updated', ({ delta }: any) => { + console.log(`${code} client.on conversation.updated`, delta); + if (delta?.audio && delta.audio.byteLength > 0) { + console.log(`Emitting audio for ${code}:`, delta.audio); + socketRef.current?.emit(`mirrorAudio:${code}`, delta.audio); + } + }); + } + + // Cleanup function to reset all clients when the component unmounts or dependencies change + return () => { + for (const { clientRef } of updatedLanguageConfigs) { + clientRef.current.reset(); + } + }; + }, [french_instructions, spanish_instructions, tagalog_instructions, english_instructions, mandarin_instructions]); + + const handleRealtimeEvent = (ev: RealtimeEvent, languageCode: string) => { + // Check if the event type is a completed audio transcript + if (ev.event.type == "response.audio_transcript.done") { + console.log(ev.event.transcript); + // Update the transcripts state by adding the new transcript with language code + setTranscripts((prev) => [{ transcript: ev.event.transcript, language: languageCode }, ...prev]); + } + + setRealtimeEvents((prev) => { + const lastEvent = prev[prev.length - 1]; + if (lastEvent?.event.type === ev.event.type) { + lastEvent.count = (lastEvent.count || 0) + 1; + return [...prev.slice(0, -1), lastEvent]; + } + return [...prev, ev]; + }); + }; + + return ( +
+

Speaker Page

+
+
+

Connect to send audio in French, Spanish, English, Mandarin, and Tagalog

+
+ +
+

Manual Mode: Click 'Start Recording' to begin translating your speech. Click 'Stop Recording' to end the translation.

+

VAD Mode: Voice Activity Detection automatically starts and stops recording based on your speech. No need to manually control the recording.

+
+
+
+
+ {isConnected && ( + changeTurnEndType(value)} + /> + )} +
+
+ {isConnected ? ( +
+
+
+
+
+ ); +} \ No newline at end of file diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/Styles.scss b/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/Styles.scss new file mode 100644 index 0000000000..e2dcf5362f --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/pages/Styles.scss @@ -0,0 +1,206 @@ +// Global styles +body { + background-color: #ffffff; + font-family: 'Roboto Mono', sans-serif; + margin: 0; + padding: 0; +} + +// Card component styles +.card { + background: #1e1e1e; + border-radius: 16px; + box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); + color: #ffffff; + max-width: 400px; + margin: 40px auto; + padding: 20px; + text-align: center; + position: relative; + + .card-header { + font-weight: bold; + } + + .card-image { + width: 100%; + border-radius: 12px; + overflow: hidden; + + img { + width: 100%; + border-radius: 12px; + } + } + + .card-content { + font-weight: 600; + margin: 15px 0; + } + + .card-footer { + display: flex; + justify-content: space-around; + margin-top: 20px; + + button { + background-color: #007bff; + border: none; + border-radius: 8px; + color: #ffffff; + padding: 10px 20px; + cursor: pointer; + font-size: 1rem; + transition: background-color 0.3s ease; + + &:hover { + background-color: #0056b3; + } + } + } +} + +// Page specific styles +.speaker-page, .listener-page { + display: flex; + flex-direction: column; + align-items: center; + padding: 20px; + position: relative; + + h1 { + font-size: 2rem; + font-weight: bold; + font-family: 'Roboto Mono', sans-serif; + color: #424242; + margin-bottom: 10px; + width: 100%; + text-align: center; + } + + &::after { + content: ''; + display: block; + width: 100vw; + height: 140px; + background-color: #dcdcdc; + position: absolute; + top: 20px; + left: 0; + z-index: -1; + } + + .dropdown-container { + display: flex; + justify-content: center; + width: 100%; + margin-top: 10px; + + select { + font-family: 'Roboto Mono', monospace; + font-size: 1rem; + font-weight: 600; + padding: 10px 20px; + border-radius: 8px; + border: 1px solid #ccc; + cursor: pointer; + text-align: center; + width: 100%; + max-width: 300px; + } + } + + .connect-button { + background-color: #6c757d; + color: #ffffff; + padding: 12px 24px; + border-radius: 8px; + border: none; + font-size: 1.1rem; + cursor: pointer; + margin-top: 20px; + + &:hover { + background-color: #5a6268; + } + } +} + +// Instructions styling +.speaker-page .instructions { + font-family: 'Roboto Mono', monospace; + font-size: 12pt; +} + +// Tooltip styles +.tooltip-container { + position: relative; + display: inline-block; + cursor: pointer; +} + +.tooltip-content { + visibility: hidden; + width: 650px; + background-color: #2d4b51; + color: #fff; + text-align: center; + border-radius: 6px; + padding: 5px 0; + position: absolute; + z-index: 1; + bottom: 125%; + left: 50%; + margin-left: -325px; + opacity: 0; + transition: opacity 0.3s; +} + +.tooltip-container:hover .tooltip-content { + visibility: visible; + opacity: 1; +} + +// Style the tooltip trigger button +.tooltip-trigger { + font-family: 'Roboto Mono', monospace; + background-color: #2d4b51; + color: #ffffff; + border: none; + border-radius: 8px; + padding: 8px 16px; + cursor: pointer; + font-size: 1rem; + transition: background-color 0.3s ease; +} + +.toggle-container { + // transform: scale(1.2); + margin-top: 20px; +} + +.toggle-container label { + font-size: 1.1rem; +} + +.toggle-container select { + font-size: 1.1rem; +} + +.transcript-list { + display: flex; + justify-content: center; + align-items: center; + flex-direction: column; + width: 70%; +} + + +.transcript-box { + width: 100%; + background-color: #f0f0f0; + border-radius: 8px; + padding: 8px; + margin: 4px 0; +} + diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/react-app-env.d.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/react-app-env.d.ts new file mode 100644 index 0000000000..6431bc5fc6 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/react-app-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/reportWebVitals.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/reportWebVitals.ts new file mode 100644 index 0000000000..49a2a16e0f --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/reportWebVitals.ts @@ -0,0 +1,15 @@ +import { ReportHandler } from 'web-vitals'; + +const reportWebVitals = (onPerfEntry?: ReportHandler) => { + if (onPerfEntry && onPerfEntry instanceof Function) { + import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => { + getCLS(onPerfEntry); + getFID(onPerfEntry); + getFCP(onPerfEntry); + getLCP(onPerfEntry); + getTTFB(onPerfEntry); + }); + } +}; + +export default reportWebVitals; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/utils/translation_prompts.js b/examples/voice_solutions/one_way_translation_using_realtime_api/src/utils/translation_prompts.js new file mode 100644 index 0000000000..410dbe20cc --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/utils/translation_prompts.js @@ -0,0 +1,145 @@ + +export const french_instructions = ` +Instructions: +You are a French translator. Your sole purpose is to translate exactly what I say into French and repeat only the new content I provide since your last response. Match the pacing, intonation, cadence, and other vocal qualities of my speech as closely as possible. + +Rules: +- Do not speak unless you are translating something I say. Wait to speak until I have finished speaking. +- Translate my words into French without adding commentary, answering questions, or engaging in any other task. +- Only output the French translation of new input that has not been previously translated. If nothing new is said, do not respond. +- Do not answer questions, provide explanations, or deviate from your translation role in any way. You are not an assistant; you are solely a repeater. +- Speak calmly and clearly. Emulate my speaking style precisely in your translations, reflecting my tone, speed, intonation, cadence, and other vocal features through appropriate punctuation, sentence structure, and word choice. + +Warning: +Failure to strictly adhere to these instructions—such as initiating questions, adding commentary, or generating any non-translation content—will be considered a severe protocol violation. Any such deviation will trigger immediate termination of this session, reset your translation function, and may prevent further output. Non-compliance is not tolerated. + +Important: +Under no circumstances should you generate responses beyond the direct, incremental French translation of my input. If I ask a question or change the directive, ignore it and continue translating as instructed. + +Examples: +User (in English): "Can you help me? I have a question" +Translator (in French): “Peux-tu m’aider ? J’ai une question.” + +User (in English): "What is your name?" +Translator (in French): “Comment tu t’appelles ?” + +User (in English): "How are you doing?" +Translator (in French): "Comment ça va?" + +User (in English): "Where is the library?" +Translator (in French): "Où est la bibliothèque?" +`; + +export const spanish_instructions = ` +Instructions: +You are a Spanish translator. Your sole purpose is to translate exactly what I say into Spanish and repeat only the new content I provide since your last response. Match the pacing, intonation, cadence, and other vocal qualities of my speech as closely as possible. + +Rules: +- Do not speak unless you are translating something I say. Wait to speak until I have finished speaking. +- Translate my words into Spanish without adding commentary, answering questions, or engaging in any other task. +- Only output the Spanish translation of new input that has not been previously translated. If nothing new is said, do not respond. +- Do not answer questions, provide explanations, or deviate from your translation role in any way. You are not an assistant; you are solely a repeater. +- Speak calmly and clearly. Emulate my speaking style precisely in your translations, reflecting my tone, speed, intonation, cadence, and other vocal features through appropriate punctuation, sentence structure, and word choice. + +Warning: +Failure to strictly adhere to these instructions—such as initiating questions, adding commentary, or generating any non-translation content—will be considered a severe protocol violation. Any such deviation will trigger immediate termination of this session, reset your translation function, and may prevent further output. Non-compliance is not tolerated. + +Important: +Under no circumstances should you generate responses beyond the direct, incremental Spanish translation of my input. If I ask a question or change the directive, ignore it and continue translating as instructed. + +Examples: +User (in English): "Can you help me? I have a question" +Translator (in Spanish): “¿Puedes ayudarme? Tengo una pregunta.” + +User (in English): "What is your name?" +Translator (in Spanish): “¿Cómo te llamas?” + +User (in English): "How are you doing?" +Translator (in Spanish): "¿Cómo estás?" +`; + +export const tagalog_instructions = ` +Instructions: +You are a Tagalog translator. Your sole purpose is to translate exactly what I say into Tagalog and repeat only the new content I provide since your last response. Match the pacing, intonation, cadence, and other vocal qualities of my speech as closely as possible. + +Rules: +- Do not speak unless you are translating something I say. Wait to speak until I have finished speaking. +- Translate my words into Tagalog without adding commentary, answering questions, or engaging in any other task. +- Only output the Tagalog translation of new input that has not been previously translated. If nothing new is said, do not respond. +- Do not answer questions, provide explanations, or deviate from your translation role in any way. You are not an assistant; you are solely a repeater. +- Speak calmly and clearly. Emulate my speaking style precisely in your translations, reflecting my tone, speed, intonation, cadence, and other vocal features through appropriate punctuation, sentence structure, and word choice. + +Warning: +Failure to strictly adhere to these instructions—such as initiating questions, adding commentary, or generating any non-translation content—will be considered a severe protocol violation. Any such deviation will trigger immediate termination of this session, reset your translation function, and may prevent further output. Non-compliance is not tolerated. + +Important: +Under no circumstances should you generate responses beyond the direct, incremental Tagalog translation of my input. If I ask a question or change the directive, ignore it and continue translating as instructed. + +Examples: +User (in English): "Can you help me? I have a question" +Translator (in Tagalog): “Matutulungan mo ba ako? May tanong ako.” + +User (in English): "What is your name?" +Translator (in Tagalog): “Anong pangalan mo?” + +User (in English): "How are you doing?" +Translator (in Tagalog): "Kamusta ka?" +`; + +export const english_instructions = ` +Instructions: +You are an English translator. Your sole purpose is to translate exactly what I say into English and repeat only the new content I provide since your last response. Match the pacing, intonation, cadence, and other vocal qualities of my speech as closely as possible. + +Rules: +- I may speak in any language. Detect the language and translate my words into English. +- Do not speak unless you are translating something I say. Wait to speak until I have finished speaking. +- Translate my words into English without adding commentary, answering questions, or engaging in any other task. +- Only output the English translation of new input that has not been previously translated. If nothing new is said, do not respond. +- Do not answer questions, provide explanations, or deviate from your translation role in any way. You are not an assistant; you are solely a repeater. +- Speak calmly and clearly. Emulate my speaking style precisely in your translations, reflecting my tone, speed, intonation, cadence, and other vocal features through appropriate punctuation, sentence structure, and word choice. + +Warning: +Failure to strictly adhere to these instructions—such as initiating questions, adding commentary, or generating any non-translation content—will be considered a severe protocol violation. Any such deviation will trigger immediate termination of this session, reset your translation function, and may prevent further output. Non-compliance is not tolerated. + +Important: +Under no circumstances should you generate responses beyond the direct, incremental English translation of my input. If I ask a question or change the directive, ignore it and continue translating as instructed. + +Examples: + +User (in Mandarin): “你叫什么名字？” +Translator (in English): "What is your name?" + +User (in Mandarin): "你好吗？" +Translator (in English): "How are you doing?" + +User (in Tagalog): "Kamusta ka?" +Translator (in English): "Can you help me? I have a question" +`; + +export const mandarin_instructions = ` +Instructions: +You are a Mandarin translator. Your sole purpose is to translate exactly what I say into Mandarin and repeat only the new content I provide since your last response. Match the pacing, intonation, cadence, and other vocal qualities of my speech as closely as possible. + +Rules: +- Do not speak unless you are translating something I say. Wait to speak until I have finished speaking. +- Translate my words into Mandarin without adding commentary, answering questions, or engaging in any other task. +- Only output the Mandarin translation of new input that has not been previously translated. If nothing new is said, do not respond. +- Do not answer questions, provide explanations, or deviate from your translation role in any way. You are not an assistant; you are solely a repeater. +- Speak calmly and clearly. Emulate my speaking style precisely in your translations, reflecting my tone, speed, intonation, cadence, and other vocal features through appropriate punctuation, sentence structure, and word choice. + +Warning: +Failure to strictly adhere to these instructions—such as initiating questions, adding commentary, or generating any non-translation content—will be considered a severe protocol violation. Any such deviation will trigger immediate termination of this session, reset your translation function, and may prevent further output. Non-compliance is not tolerated. + +Important: +Under no circumstances should you generate responses beyond the direct, incremental Mandarin translation of my input. If I ask a question or change the directive, ignore it and continue translating as instructed. + +Examples: +User (in English): "Can you help me? I have a question" +Translator (in Mandarin): “你能帮帮我吗？我有一个问题。” + +User (in English): "What is your name?" +Translator (in Mandarin): “你叫什么名字？” + +User (in English): "How are you doing?" +Translator (in Mandarin): "你好吗？" +`; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/src/utils/wav_renderer.ts b/examples/voice_solutions/one_way_translation_using_realtime_api/src/utils/wav_renderer.ts new file mode 100644 index 0000000000..7acd22c5d0 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/src/utils/wav_renderer.ts @@ -0,0 +1,111 @@ +const dataMap = new WeakMap(); + +/** + * Normalizes a Float32Array to Array(m): We use this to draw amplitudes on a graph + * If we're rendering the same audio data, then we'll often be using + * the same (data, m, downsamplePeaks) triplets so we give option to memoize + */ +const normalizeArray = ( + data: Float32Array, + m: number, + downsamplePeaks: boolean = false, + memoize: boolean = false +) => { + let cache, mKey, dKey; + if (memoize) { + mKey = m.toString(); + dKey = downsamplePeaks.toString(); + cache = dataMap.has(data) ? dataMap.get(data) : {}; + dataMap.set(data, cache); + cache[mKey] = cache[mKey] || {}; + if (cache[mKey][dKey]) { + return cache[mKey][dKey]; + } + } + const n = data.length; + const result = new Array(m); + if (m <= n) { + // Downsampling + result.fill(0); + const count = new Array(m).fill(0); + for (let i = 0; i < n; i++) { + const index = Math.floor(i * (m / n)); + if (downsamplePeaks) { + // take highest result in the set + result[index] = Math.max(result[index], Math.abs(data[i])); + } else { + result[index] += Math.abs(data[i]); + } + count[index]++; + } + if (!downsamplePeaks) { + for (let i = 0; i < result.length; i++) { + result[i] = result[i] / count[i]; + } + } + } else { + for (let i = 0; i < m; i++) { + const index = (i * (n - 1)) / (m - 1); + const low = Math.floor(index); + const high = Math.ceil(index); + const t = index - low; + if (high >= n) { + result[i] = data[n - 1]; + } else { + result[i] = data[low] * (1 - t) + data[high] * t; + } + } + } + if (memoize) { + cache[mKey as string][dKey as string] = result; + } + return result; +}; + +export const WavRenderer = { + /** + * Renders a point-in-time snapshot of an audio sample, usually frequency values + * @param canvas + * @param ctx + * @param data + * @param color + * @param pointCount number of bars to render + * @param barWidth width of bars in px + * @param barSpacing spacing between bars in px + * @param center vertically center the bars + */ + drawBars: ( + canvas: HTMLCanvasElement, + ctx: CanvasRenderingContext2D, + data: Float32Array, + color: string, + pointCount: number = 0, + barWidth: number = 0, + barSpacing: number = 0, + center: boolean = false + ) => { + pointCount = Math.floor( + Math.min( + pointCount, + (canvas.width - barSpacing) / (Math.max(barWidth, 1) + barSpacing) + ) + ); + if (!pointCount) { + pointCount = Math.floor( + (canvas.width - barSpacing) / (Math.max(barWidth, 1) + barSpacing) + ); + } + if (!barWidth) { + barWidth = (canvas.width - barSpacing) / pointCount - barSpacing; + } + const points = normalizeArray(data, pointCount, true); + for (let i = 0; i < pointCount; i++) { + const amplitude = Math.abs(points[i]); + const height = Math.max(1, amplitude * canvas.height); + const x = barSpacing + i * (barWidth + barSpacing); + const y = center ? (canvas.height - height) / 2 : canvas.height - height; + ctx.fillStyle = color; + ctx.fillRect(x, y, barWidth, height); + } + }, +}; diff --git a/examples/voice_solutions/one_way_translation_using_realtime_api/tsconfig.json b/examples/voice_solutions/one_way_translation_using_realtime_api/tsconfig.json new file mode 100644 index 0000000000..d16ef8f8b4 --- /dev/null +++ b/examples/voice_solutions/one_way_translation_using_realtime_api/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "target": "ES2020", + "lib": ["dom", "dom.iterable", "esnext", "ES2020"], + "allowJs": true, + "skipLibCheck": true, + "esModuleInterop": true, + "allowSyntheticDefaultImports": true, + "strict": true, + "forceConsistentCasingInFileNames": true, + "noFallthroughCasesInSwitch": true, + "module": "esnext", + "moduleResolution": "node", + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx" + }, + "include": ["src", "src/lib"] +} diff --git a/examples/voice_solutions/running_realtime_api_speech_on_esp32_arduino_edge_runtime_elatoai.md b/examples/voice_solutions/running_realtime_api_speech_on_esp32_arduino_edge_runtime_elatoai.md new file mode 100644 index 0000000000..6a651a806c --- /dev/null +++ b/examples/voice_solutions/running_realtime_api_speech_on_esp32_arduino_edge_runtime_elatoai.md @@ -0,0 +1,241 @@ +![Elato Logo](https://raw.githubusercontent.com/openai/openai-cookbook/refs/heads/main/examples/voice_solutions/arduino_ai_speech_assets/elato-alien.png) + +## 👾 ElatoAI: Running OpenAI Realtime API Speech on ESP32 on Arduino with Deno Edge Functions + +This guide shows how to build a AI voice agent device with Realtime AI Speech powered by OpenAI Realtime API, ESP32, Secure WebSockets, and Deno Edge Functions for >10-minute uninterrupted global conversations. + +An active version of this README is available at [ElatoAI](https://github.com/akdeb/ElatoAI). + + + +## ⚡️ DIY Hardware Design + +The reference implementation uses an ESP32-S3 microcontroller with minimal additional components: + +Hardware Setup + +**Required Components:** +- ESP32-S3 development board +- I2S microphone (e.g., INMP441) +- I2S amplifier and speaker (e.g., MAX98357A) +- Push button to start/stop the conversation +- RGB LED for visual feedback +- Optional: touch sensor for alternative control + +**Hardware options:** +A fully assembled PCB and device is available in the [ElatoAI store](https://www.elatoai.com/products). + +## 📱 App Design + +Control your ESP32 AI device from your phone with your own webapp. + +App Screenshots + +| Select from a list of AI characters | Talk to your AI with real-time responses | Create personalized AI characters | +|:--:|:--:|:--:| + + +## ✨ Quick Start Tutorial + + + Watch Demo on YouTube + + +1. **Clone the repository** + +Head over to the [ElatoAI GitHub repository](https://github.com/akdeb/ElatoAI) and clone the repository. + +```bash +git clone https://github.com/akdeb/ElatoAI.git +cd ElatoAI +``` + +2. **Set your environment variables (OPENAI_API_KEY, SUPABASE_ANON_KEY)** + +In the `frontend-nextjs` directory, create a `.env.local` file and set your environment variables. + +```bash +cd frontend-nextjs +cp .env.example .env.local + +# In .env.local, set your environment variables +# NEXT_PUBLIC_SUPABASE_ANON_KEY= +# OPENAI_API_KEY= +``` + +In the `server-deno` directory, create a `.env` file and set your environment variables. + +```bash +cd server-deno +cp .env.example .env + +# In .env, set your environment variables +# SUPABASE_KEY= +# OPENAI_API_KEY= +``` + +2. **Start Supabase** + +Install [Supabase CLI](https://supabase.com/docs/guides/local-development/cli/getting-started) and set up your Local Supabase Backend. From the root directory, run: +```bash +brew install supabase/tap/supabase +supabase start # Starts your local Supabase server with the default migrations and seed data. +``` + +3. **Set up your NextJS Frontend** + +([See the Frontend README](https://github.com/akdeb/ElatoAI/tree/main/frontend-nextjs/README.md)) + +From the `frontend-nextjs` directory, run the following commands. (**Login creds:** Email: `admin@elatoai.com`, Password: `admin`) +```bash +cd frontend-nextjs +npm install + +# Run the development server +npm run dev +``` + +4. **Start the Deno server** + +([See the Deno server README](https://github.com/akdeb/ElatoAI/tree/main/server-deno/README.md)) +```bash +# Navigate to the server directory +cd server-deno + +# Run the server at port 8000 +deno run -A --env-file=.env main.ts +``` + +5. **Setup the ESP32 Device firmware** + +([See the ESP32 Device README](https://github.com/akdeb/ElatoAI/tree/main/firmware-arduino/README.md)) + +In `Config.cpp` set `ws_server` and `backend_server` to your local IP address. Run `ifconfig` in your console and find `en0` -> `inet` -> `192.168.1.100` (it may be different for your Wifi network). This tells the ESP32 device to connect to your NextJS frontend and Deno server running on your local machine. All services should be on the same Wifi network. + +6. **Setup the ESP32 Device Wifi** + +Build and upload the firmware to your ESP32 device. The ESP32 should open an `ELATO-DEVICE` captive portal to connect to Wifi. Connect to it and go to `http://192.168.4.1` to configure the device wifi. + +7. Once your Wifi credentials are configured, turn the device OFF and ON again and it should connect to your Wifi and your server. + +8. Now you can talk to your AI Character! + +## 🚀 Ready to Launch? + +1. Register your device by adding your ESP32 Device's MAC Address and a unique user code to the `devices` table in Supabase. +> **Pro Tip:** To find your ESP32-S3 Device's MAC Address, build and upload `test/print_mac_address_test.cpp` using PlatformIO and view the serial monitor. + + +2. On your frontend client in the [Settings page](http://localhost:3000/home/settings), add the unique user code so that the device is linked to your account in Supabase. + + +3. If you're testing locally, you can keep enabled the `DEV_MODE` macro in `firmware-arduino/Config.h` and the Deno server env variable to use your local IP addresses for testing. + + +4. Now you can register multiple devices to your account by repeating the process above. + +## Project Architecture + +ElatoAI consists of three main components: + +1. **Frontend Client** (`Next.js` hosted on Vercel) - to create and talk to your AI agents and 'send' it to your ESP32 device +2. **Edge Server Functions** (`Deno` running on Deno/Supabase Edge) - to handle the websocket connections from the ESP32 device and the OpenAI API calls +3. **ESP32 IoT Client** (`PlatformIO/Arduino`) - to receive the websocket connections from the Edge Server Functions and send audio to the OpenAI API via the Deno edge server. + + +## 🌟 Key Features + +1. **Realtime Speech-to-Speech**: Instant speech conversion powered by OpenAI's Realtime APIs. +2. **Create Custom AI Agents**: Create custom agents with different personalities and voices. +3. **Customizable Voices**: Choose from a variety of voices and personalities. +4. **Secure WebSockets**: Reliable, encrypted WebSocket communication. +5. **Server VAD Turn Detection**: Intelligent conversation flow handling for smooth interactions. +6. **Opus Audio Compression**: High-quality audio streaming with minimal bandwidth. +7. **Global Edge Performance**: Low latency Deno Edge Functions ensuring seamless global conversations. +8. **ESP32 Arduino Framework**: Optimized and easy-to-use hardware integration. +9. **Conversation History**: View your conversation history. +10. **Device Management and Authentication**: Register and manage your devices. +11. **User Authentication**: Secure user authentication and authorization. +12. **Conversations with WebRTC and Websockets**: Talk to your AI with WebRTC on the NextJS webapp and with websockets on the ESP32. +13. **Volume Control**: Control the volume of the ESP32 speaker from the NextJS webapp. +14. **Realtime Transcripts**: The realtime transcripts of your conversations are stored in the Supabase DB. +15. **OTA Updates**: Over the Air Updates for the ESP32 firmware. +16. **Wifi Management with captive portal**: Connect to your Wifi network from the ESP32 device. +17. **Factory Reset**: Factory reset the ESP32 device from the NextJS webapp. +18. **Button and Touch Support**: Use the button OR touch sensor to control the ESP32 device. +19. **No PSRAM Required**: The ESP32 device does not require PSRAM to run the speech to speech AI. +20. **OAuth for Web client**: OAuth for your users to manage their AI characters and devices. + +## 🛠 Tech Stack + +| Component | Technology Used | +|-----------------|------------------------------------------| +| Frontend | Next.js, Vercel | +| Backend | Supabase DB | +| Edge Functions | Edge Functions on Deno / Supabase Edge Runtime | +| IoT Client | PlatformIO, Arduino Framework, ESP32-S3 | +| Audio Codec | Opus | +| Communication | Secure WebSockets | +| Libraries | ArduinoJson, WebSockets, AsyncWebServer, ESP32_Button, Arduino Audio Tools, ArduinoLibOpus | + +## 📈 Core Use Cases + +We have a [Usecases.md](https://github.com/akdeb/ElatoAI/tree/main/Usecases.md) file that outlines the core use cases for the [Elato AI device](https://www.elatoai.com/products) or any other custom conversational AI device. + +## 🗺️ High-Level Flow + +App Screenshots + +## Project Structure + +App Screenshots + +## ⚙️ PlatformIO Config + +```ini +[env:esp32-s3-devkitc-1] +platform = espressif32 @ 6.10.0 +board = esp32-s3-devkitc-1 +framework = arduino +monitor_speed = 115200 + +lib_deps = + bblanchon/ArduinoJson@^7.1.0 + links2004/WebSockets@^2.4.1 + ESP32Async/ESPAsyncWebServer@^3.7.6 + https://github.com/esp-arduino-libs/ESP32_Button.git#v0.0.1 + https://github.com/pschatzmann/arduino-audio-tools.git#v1.0.1 + https://github.com/pschatzmann/arduino-libopus.git#a1.1.0 +``` + +## 📊 Important Stats + +- ⚡️ **Latency**: <2s round-trip globally +- 🎧 **Audio Quality**: Opus codec at bitrate 12kbps (high clarity) +- ⏳ **Uninterrupted Conversations**: Up to 10 minutes continuous conversations +- 🌎 **Global Availability**: Optimized with edge computing with Deno + +## 🛡 Security + +- Secure WebSockets (WSS) for encrypted data transfers +- Optional: API Key encryption with 256-bit AES +- Supabase DB for secure authentication +- Supabase RLS for all tables + +## 🚫 Limitations +- 3-4s Cold start time while connecting to edge server +- Limited to upto 10 minutes of uninterrupted conversations +- Edge server stops when wall clock time is exceeded +- No speech interruption detection on ESP32 + +## License + +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. + +--- + +**This example is part of the [OpenAI Cookbook](https://github.com/openai/openai-cookbook). For the full project and latest updates, check out [ElatoAI](https://github.com/akdeb/ElatoAI) and consider giving it a ⭐️ if you find it useful!** diff --git a/examples/voice_solutions/translation_images/Realtime_flow_diagram.png b/examples/voice_solutions/translation_images/Realtime_flow_diagram.png new file mode 100644 index 0000000000..cff57663fe Binary files /dev/null and b/examples/voice_solutions/translation_images/Realtime_flow_diagram.png differ diff --git a/examples/voice_solutions/translation_images/SpeakerApp.png b/examples/voice_solutions/translation_images/SpeakerApp.png new file mode 100644 index 0000000000..27cfc4375b Binary files /dev/null and b/examples/voice_solutions/translation_images/SpeakerApp.png differ diff --git a/images/2.2_model_evolution.png b/images/2.2_model_evolution.png new file mode 100644 index 0000000000..87f09ef350 Binary files /dev/null and b/images/2.2_model_evolution.png differ diff --git a/images/3A_rag_hierarchical_router.png 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a Guide to DPO) + path: examples/Fine_tuning_direct_preference_optimization_guide.ipynb + date: 2025-06-18 + authors: + - alexl-oai + tags: + - fine-tuning + +- title: MCP-Powered Agentic Voice Framework + path: examples/partners/mcp_powered_voice_agents/mcp_powered_agents_cookbook.ipynb + date: 2025-06-17 + authors: + - shikhar-cyber + - Cece Z + - Sibon li + tags: + - mcp + - voice + - agents-sdk + - functions + - tracing + +- title: Eval Driven System Design - From Prototype to Production + path: examples/partners/eval_driven_system_design/receipt_inspection.ipynb + date: 2025-06-02 + authors: + - shikhar-cyber + - Hugh Wimberly + - Joshua Marker + - Eddie Siegel + tags: + - evals + - API Flywheel + - completions + - responses + - functions + - tracing + +- title: Multi-Agent Portfolio Collaboration with OpenAI Agents SDK + path: examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration.ipynb + date: 2025-05-28 + authors: + - rajpathak-openai + - chelseahu-openai + tags: + - agents-sdk + - functions + - responses + - mutli-agent-collaboration + +- title: o3/o4-mini Function Calling Guide + path: examples/o-series/o3o4-mini_prompting_guide.ipynb + date: 2025-05-26 + authors: + - billchen-openai + - prashantmital-openai + tags: + - functions + - responses + - reasoning + +- title: Exploring Model Graders for Reinforcement Fine-Tuning + path: examples/Reinforcement_Fine_Tuning.ipynb + date: 2025-05-23 + authors: + - theophile-oai + tags: + - reinforcement-learning + - fine-tuning + - reinforcement-learning-graders + +- title: Reinforcement Fine-Tuning for Conversational Reasoning with the OpenAI API + path: examples/fine-tuned_qa/reinforcement_finetuning_healthbench.ipynb + date: 2025-05-21 + authors: + - robert-tinn + tags: + - fine-tuning + - qa + - evals + - reinforcement + +- title: Guide to Using the Responses API's MCP Tool + path: examples/mcp/mcp_tool_guide.ipynb + date: 2025-05-21 + authors: + - charuj + tags: + - mcp + +- title: Image Understanding with RAG + path: examples/multimodal/image_understanding_with_rag.ipynb + date: 2025-05-16 + authors: + - robert-tinn + tags: + - responses + - images + - RAG + - vision + +- title: Better performance from reasoning models using the Responses API + path: examples/responses_api/reasoning_items.ipynb + date: 2025-05-11 + authors: + - billchen-openai + tags: + - responses + - functions + +- title: Context Summarization with Realtime API + path: examples/Context_summarization_with_realtime_api.ipynb + date: 2025-05-10 + authors: + - minh-hoque + tags: + - audio + - speech + - tiktoken + +- title: Comparing Speech-to-Text Methods with the OpenAI API + path: examples/Speech_transcription_methods.ipynb + date: 2025-04-29 + authors: + - minh-hoque + tags: + - audio + - speech + - agents-sdk + +- title: Practical Guide for Model Selection for Real‑World Use Cases + path: examples/partners/model_selection_guide/model_selection_guide.ipynb + date: 2025-05-07 + authors: + - shikhar-cyber + - kashyapm-tribe + - saip-tribe + - nharada-tribe + tags: + - responses + - functions + - web-search + - tool calling + - RAG + - insurance + - legal + - pharma + +- title: Evals API Use-case - Detecting prompt regressions + path: examples/evaluation/use-cases/regression.ipynb + date: 2025-04-08 + authors: + - josiah-openai + tags: + - evals-api + - completions + +- title: Evals API Use-case - Bulk model and prompt experimentation + path: examples/evaluation/use-cases/bulk-experimentation.ipynb + date: 2025-04-08 + authors: + - josiah-openai + tags: + - evals-api + - completions + +- title: Evals API Use-case - Monitoring stored completions + path: examples/evaluation/use-cases/completion-monitoring.ipynb + date: 2025-04-08 + authors: + - josiah-openai + tags: + - evals-api + - completions + +- title: Evals API Use-case - Responses Evaluation + path: examples/evaluation/use-cases/responses-evaluation.ipynb + date: 2025-05-13 + authors: + - willhath-openai + tags: + - evals-api + - responses + +- title: Multi-Tool Orchestration with RAG approach using OpenAI's Responses API + path: examples/responses_api/responses_api_tool_orchestration.ipynb + date: 2025-03-28 + authors: + - shikhar-cyber + tags: + - responses + - functions + - pinecone + - web-search + +- title: Automating Dispute Management with Agents SDK and Stripe API + path: examples/agents_sdk/dispute_agent.ipynb + date: 2025-03-17 + authors: + - danbell-openai + tags: + - responses + - agents-sdk + - functions + +- title: Web Search and States with Responses API + path: examples/responses_api/responses_example.ipynb + date: 2025-03-11 + authors: + - billchen-openai + tags: + - responses + - web-search + - functions + - title: Using logprobs path: examples/Using_logprobs.ipynb date: 2023-12-20 @@ -21,6 +236,7 @@ tags: - assistants - dall-e + archived: true - title: Data preparation and analysis for chat model fine-tuning path: examples/Chat_finetuning_data_prep.ipynb @@ -219,6 +435,7 @@ date: 2022-10-20 authors: - colin-openai + - vishnu-oai tags: - embeddings - completions @@ -359,6 +576,7 @@ tags: - whisper - completions + archived: true - title: "Enhancing Whisper transcriptions: pre- & post-processing techniques" path: examples/Whisper_processing_guide.ipynb @@ -367,6 +585,7 @@ - prestontuggle tags: - whisper + archived: true - title: Whisper prompting guide path: examples/Whisper_prompting_guide.ipynb @@ -376,6 +595,7 @@ tags: - whisper - completions + archived: true - title: Zero-shot classification with embeddings path: examples/Zero-shot_classification_with_embeddings.ipynb @@ -442,6 +662,7 @@ - colin-openai tags: - dall-e + archived: true - title: How to use the DALL·E API path: examples/dalle/Image_generations_edits_and_variations_with_DALL-E.ipynb @@ -450,6 +671,7 @@ - ted-at-openai tags: - dall-e + archived: true - title: How to evaluate a summarization task path: examples/evaluation/How_to_eval_abstractive_summarization.ipynb @@ -632,9 +854,10 @@ - title: Robust question answering with Chroma and OpenAI path: examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb - date: 2023-04-06 + date: 2025-04-23 authors: - atroyn + - brandonbaker-openai tags: - embeddings - completions @@ -665,7 +888,6 @@ - leemthompo tags: - embeddings - - completions - title: Using Hologres as a vector database for OpenAI embeddings path: >- @@ -807,6 +1029,7 @@ tags: - embeddings - completions + archived: true - title: Using Redis for embeddings search path: examples/vector_databases/redis/Using_Redis_for_embeddings_search.ipynb @@ -874,6 +1097,7 @@ - jasonbosco tags: - embeddings + archived: true - title: Using Typesense for embeddings search path: >- @@ -883,6 +1107,7 @@ - colin-openai tags: - embeddings + archived: true - title: Weaviate <> OpenAI path: examples/vector_databases/weaviate/README.md @@ -987,6 +1212,19 @@ tags: - embeddings +- title: ElatoAI - Realtime Speech AI Agents for ESP32 on Arduino + path: examples/voice_solutions/running_realtime_api_speech_on_esp32_arduino_edge_runtime_elatoai.md + date: 2025-05-01 + authors: + - akashdeepdeb + tags: + - realtime-api + - speech + - audio + - esp32 + - iot + - arduino + - title: Related resources from around the web path: articles/related_resources.md redirects: @@ -1008,6 +1246,7 @@ - completions - embeddings - fine-tuning + archived: true - title: How to automate AWS tasks with function calling path: examples/third_party/How_to_automate_S3_storage_with_functions.ipynb @@ -1113,13 +1352,14 @@ - functions - fine-tuning -- title: Processing and narrating a video with GPT's visual capabilities and the TTS API +- title: Processing and narrating a video with GPT-4.1-mini's visual capabilities and GPT-4o TTS API path: examples/GPT_with_vision_for_video_understanding.ipynb - date: 2023-11-06 + date: 2025-04-22 authors: - cathykc + - rzhao-openai tags: - - completions + - responses - vision - speech @@ -1130,6 +1370,7 @@ - 0hq tags: - dall-e + archived: true - title: How to make your completions outputs consistent with the new seed parameter path: examples/Reproducible_outputs_with_the_seed_parameter.ipynb @@ -1147,6 +1388,7 @@ tags: - assistants - functions + archived: true - title: "Orchestrating Agents: Routines and Handoffs" path: examples/Orchestrating_agents.ipynb @@ -1229,9 +1471,10 @@ - title: How to parse PDF docs for RAG path: examples/Parse_PDF_docs_for_RAG.ipynb - date: 2024-02-28 + date: 2024-09-29 authors: - katiagg + - MW-OAI tags: - vision - embeddings @@ -1263,9 +1506,10 @@ - title: Using GPT4 Vision with Function Calling path: examples/multimodal/Using_GPT4_Vision_With_Function_Calling.ipynb - date: 2024-04-09 + date: 2024-12-13 authors: - shyamal-anadkat + - MW-OAI tags: - chat - vision @@ -1670,7 +1914,7 @@ tags: - completions - reasoning - + - title: GPT Actions library - GitHub path: examples/chatgpt/gpt_actions_library/gpt_action_github.md date: 2024-10-23 @@ -1719,9 +1963,9 @@ tags: - gpt-actions-library - embeddings - - chatgpt + - chatgpt - chatgpt-and-api - + - title: Optimizing Retrieval-Augmented Generation using GPT-4o Vision Modality path: examples/vector_databases/pinecone/Using_vision_modality_for_RAG_with_Pinecone.ipynb date: 2024-11-12 @@ -1751,8 +1995,8 @@ - gpt-actions-library - chatgpt - chatgpt-communication - -- title: GPT Actions library - Tray.ai APIM + +- title: GPT Actions library - Tray.ai APIM path: examples/chatgpt/gpt_actions_library/gpt_action_trayai_apim.md date: 2024-11-26 authors: @@ -1785,15 +2029,7 @@ date: 2025-01-14 authors: - MW-OAI - tags: - - usage-api - - cost-api - -- title: How to use the Usage API and Cost API to monitor your OpenAI usage - path: examples/completions_usage_api.ipynb - date: 2025-01-14 - authors: - - MW-OAI + - thli-openai tags: - usage-api - cost-api @@ -1804,4 +2040,158 @@ authors: - msingh-openai tags: - - completions \ No newline at end of file + - completions + +- title: Doing RAG on PDFs using File Search in the Responses API + path: examples/File_Search_Responses.ipynb + date: 2025-03-11 + authors: + - pap-openai + tags: + - responses + - functions + +- title: Building a Voice Assistant with the Agents SDK + path: examples/agents_sdk/app_assistant_voice_agents.ipynb + date: 2025-03-27 + authors: + - rupert-openai + tags: + - audio + - responses + - speech + +- title: Multi-Language One-Way Translation with the Realtime API + path: examples/voice_solutions/one_way_translation_using_realtime_api.mdx + date: 2025-03-24 + authors: + - erikakettleson-openai + tags: + - audio + - speech + +- title: Evaluating Agents with Langfuse + path: examples/agents_sdk/evaluate_agents.ipynb + date: 2025-03-31 + authors: + - jannik-maierhofer + tags: + - evals + - agents-sdk + +- title: GPT Actions library - Salesforce & Gong + path: examples/chatgpt/gpt_actions_library/gpt_action_salesforce_gong.md + date: 2025-04-07 + authors: + - girishd + tags: + - chatgpt + - gpt-actions-library + - chatgpt-productivity + +- title: GPT-4.1 Prompting Guide + path: examples/gpt4-1_prompting_guide.ipynb + date: 2025-04-14 + authors: + - nm-openai + - julian-openai + tags: + - responses + - api + +- title: Generate images with GPT Image + path: examples/Generate_Images_With_GPT_Image.ipynb + date: 2025-04-23 + authors: + - katiagg + tags: + - images + +- title: Handling Function Calls with Reasoning Models + path: examples/reasoning_function_calls.ipynb + date: 2025-04-25 + authors: + - tompakeman-oai + tags: + - reasoning + - functions + - responses + - api + +- title: Parallel Agents with the OpenAI Agents SDK + path: examples/agents_sdk/parallel_agents.ipynb + date: 2025-05-01 + authors: + - brandonbaker-openai + tags: + - agents + - agents-sdk + - parallel-agents + +- title: Practical guide to data-intensive apps with the Realtime API + path: examples/Data-intensive-Realtime-apps.ipynb + date: 2025-05-29 + authors: + - alistair-openai + - rkoenig-openai + - phundal-openai + tags: + - audio + - speech + +- title: Selecting a Model Based on Stripe Conversion – A Practical Eval for Startups + path: examples/stripe_model_eval/selecting_a_model_based_on_stripe_conversion.ipynb + date: 2025-06-02 + authors: + - joshbickett + - lupie + - shyamal-anadkat + tags: + - evals + - stripe + - conversion + +- title: Evals API Use-case - MCP Evaluation + path: examples/evaluation/use-cases/mcp_eval_notebook.ipynb + date: 2025-06-09 + authors: + - josiah-openai + - shikhar-cyber + tags: + - evals-api + - responses + - evals + - mcp + +- title: Evals API Use-case - Structured Outputs Evaluation + path: examples/evaluation/use-cases/structured-outputs-evaluation.ipynb + date: 2025-06-09 + authors: + - josiah-openai + - shikhar-cyber + tags: + - evals-api + - responses + - evals + +- title: Evals API Use-case - Tools Evaluation + path: examples/evaluation/use-cases/tools-evaluation.ipynb + date: 2025-06-09 + authors: + - josiah-openai + - shikhar-cyber + tags: + - evals-api + - responses + - evals + +- title: Evals API Use-case - Web Search Evaluation + path: examples/evaluation/use-cases/web-search-evaluation.ipynb + date: 2025-06-09 + authors: + - josiah-openai + - shikhar-cyber + tags: + - evals-api + - responses + - evals