Python notebooks 📒

This folder contains a range of executable Python notebooks, so you can test everything out for yourself. Use these examples as a blueprint for testing on your own data.

Run notebooks locally using Jupyter, or use the links provided in each notebook to run them in Google Colab. Colab provides an easy-to-use Python virtual environment in the browser.

Notebooks are organized into the following folders:

search: Notebooks that demonstrate the fundamentals of Elasticsearch, like indexing embeddings, running lexical, semantic and hybrid searches, and more.
doc-ingestion-and-chunking: Notebooks that demonstrate how to ingest and chunk documents for indexing in Elasticsearch from PDF, HTML and JSON with ELSER.
generative-ai: Notebooks that demonstrate various use cases for Elasticsearch as the retrieval engine and vector store for LLM-powered applications.
langchain: Notebooks that demonstrate how to integrate Elastic with LangChain, a framework for developing applications powered by language models.
integrations: Notebooks that demonstrate how to integrate popular services and projects with Elasticsearch:
enterprise-search: Notebooks that demonstrate use cases for working with and exporting from Elastic Enterprise Search, App Search, or Workplace Search.