# elastic/elasticsearch-labs

Notebooks & Example Apps for Search & AI Applications with Elasticsearch

Repository: https://github.com/elastic/elasticsearch-labs
Canonical: https://ross.abutalabs.com/products/elasticsearch-labs
Homepage: https://www.elastic.co/search-labs
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: ai, chatgpt, chatlog, elastic, elasticsearch, genai, genaistack, openai, openai-chatgpt, search, langchain, applications, python, vector, vectordatabase, langchain-python
Last push: 2026-08-26T14:22:31+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 84
- inputs: {"age_days": 1176, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1123, forks 280 (observed 2026-08-28T04:03:40.566953+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: search-engine, vector-database, rag, llm-inference, chatbot, machine-learning
- domain: large-language-models, artificial-intelligence, databases, developer-tools
- platform: python, cross-platform
- tags: elasticsearch, jupyter-notebooks, example-apps, langchain, openai, semantic-search, hybrid-search, elser, sample-code, search, retrieval-augmented-generation

## Member repositories
- elastic/elasticsearch-labs (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:40.566953+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:40:45.143281+00:00, confidence not recorded.
  - readme: https://github.com/elastic/elasticsearch-labs (fetched 2026-08-28T04:03:40.566953+00:00, sha 495181a202cb)
  - homepage: https://www.elastic.co/search-labs (fetched 2026-08-29T12:44:36.869063+00:00, sha 4474e57454b5)
  - site_page: https://www.elastic.co/docs/get-started (fetched 2026-08-29T12:44:36.890831+00:00, sha db2e52d19b84)
  - site_page: https://www.elastic.co/about (fetched 2026-08-29T12:44:36.878094+00:00, sha dcc190ae197d)
  - site_page: https://www.elastic.co/getting-started (fetched 2026-08-29T12:44:36.881820+00:00, sha 7de41ab1d939)
  - site_page: https://www.elastic.co/docs (fetched 2026-08-29T12:44:36.885293+00:00, sha c4139162fd2c)
  - site_page: https://www.elastic.co/partners/ai-ecosystem (fetched 2026-08-29T12:44:36.880063+00:00, sha 87dba7e5abe6)
  - site_page: https://www.elastic.co/integrations/data-integrations (fetched 2026-08-29T12:44:36.883529+00:00, sha 75f0e785f92a)
  - site_page: https://www.elastic.co/pricing (fetched 2026-08-29T12:44:36.887298+00:00, sha 6ab7886d790c)
  - site_page: https://www.elastic.co/search-labs/integrations (fetched 2026-08-29T12:44:36.889108+00:00, sha 0457e27ae2af)
- Data as of 2026-08-30T08:39:29.467469+00:00.