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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.