elastic/elasticsearch-labs resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1123 stars · 280 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of executable Python notebooks and example applications from Elastic demonstrating how to use Elasticsearch as a vector database for search and AI applications. It covers RAG, semantic and hybrid search, question answering, and integrations with OpenAI, LangChain, Hugging Face, and Anthropic.

Use cases

• build a RAG chatbot with elasticsearch
• learn vector search with elasticsearch notebooks
• semantic search examples with python
• hybrid search with reciprocal rank fusion
• use langchain with elasticsearch vector store
• question answering over documents with llms
• try elser sparse embeddings
• example apps for internal knowledge search

When to choose

• you want runnable notebooks to learn Elasticsearch vector search and RAG
• you need reference implementations for chatbot or knowledge search apps on Elastic
• you want to integrate Elasticsearch with LangChain, OpenAI, or Anthropic

When to avoid

• you need a production-ready application rather than examples and tutorials
• you use a search backend other than Elasticsearch
• you want a non-Python or non-notebook workflow

Facets

learning-resource · maturity active

search-engine vector-database rag llm-inference chatbot machine-learning large-language-models artificial-intelligence databases developer-tools python cross-platform elasticsearch jupyter-notebooks example-apps langchain openai semantic-search hybrid-search elser sample-code search retrieval-augmented-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="elastic/elasticsearch-labs")