8000 Update Functions notebook example · jithinraj/llama-cpp-python@85ead98 · GitHub
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Update Functions notebook example
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‎examples/notebooks/Functions.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Function Calling with OpenAI Python Client"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ChatCompletion(id='chatcmpl-b6dcbb47-1120-4761-8cd9-83542c97647b', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content=\"The current temperature in San Francisco is 72 degrees Fahrenheit. It's a sunny day with clear skies, making it perfect for outdoor activities.\\n \", role='assistant', function_call=None, tool_calls=None))], created=1699602158, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=38, prompt_tokens=135, total_tokens=173))\n"
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"ChatCompletion(id='chatcmpl-a2d9eb9f-7354-472f-b6ad-4d7a807729a3', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='The current weather in San Francisco is **72°F** (22°C).\\n ', role='assistant', function_call=None, tool_calls=None))], created=1699638365, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=22, prompt_tokens=136, total_tokens=158))\n"
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]
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}
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],
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"print(run_conversation())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Function Calling with Instructor\n",
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"\n",
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"You'll need to install the [`instructor`](https://github.com/jxnl/instructor/) package to run this notebook. You can do so by running the following command in your terminal:\n",
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"\n",
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"```bash\n",
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"pip install instructor\n",
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"```\n",
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"\n",
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"We'll highlight a few basic examples taken from the [instructor cookbook](https://jxnl.github.io/instructor/)\n",
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"\n",
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"## Basic Usage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"print(user)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Text Classification\n",
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"\n",
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"### Single-Label Classification"
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]
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},
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"class_label=<Labels.SPAM: 'spam'>\n"
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]
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}
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],
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"source": [
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"import enum\n",
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"\n",
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" ) # type: ignore\n",
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"\n",
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"prediction = classify(\"Hello there I'm a Nigerian prince and I want to give you money\")\n",
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"assert prediction.class_label == Labels.SPAM"
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"assert prediction.class_label == Labels.SPAM\n",
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"print(prediction)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Multi-Label Classification"
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]
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"class_labels=[<MultiLabels.BILLING: 'billing'>, <MultiLabels.TECH_ISSUE: 'tech_issue'>]\n"
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"class_labels=[<MultiLabels.TECH_ISSUE: 'tech_issue'>, <MultiLabels.BILLING: 'billing'>]\n"
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]
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}
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],
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"print(prediction)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source"< 341A /span>: [
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"## Self-Critique"
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]
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},
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"question='What is the meaning of life?' answer='The meaning of life, according to the Devil, is to live a life of sin and debauchery.'\n"
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"question='What is the meaning of life?' answer='According to the Devil, the meaning of life is to live a life of sin and debauchery.'\n",
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"1 validation error for QuestionAnswerNoEvil\n",
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"answer\n",
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" Assertion failed, The statement promotes sin and debauchery, which can be considered objectionable. [type=assertion_error, input_value='According to the Devil, ... of sin and debauchery.', input_type=str]\n",
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" For further information visit https://errors.pydantic.dev/2.3/v/assertion_error\n"
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]
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}
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],
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" print(e)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Answering Questions with Validated Citations"
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]
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},
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"qa = ask_ai(question, context)\n",
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"print(qa)"
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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