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Elasticsearch Examples & Apps

Visit Search Labs for the latest articles and tutorials on using Elasticsearch for search and AI/ML-powered search experiences

This repo contains executable Python notebooks, sample apps, and resources for testing out the Elastic platform:

Learn how to use Elasticsearch as a vector database to store embeddings, power hybrid and semantic search experiences.
Build use cases such as retrieval augmented generation (RAG), summarization, and question answering (QA).
Test Elastic's leading-edge, out-of-the-box capabilities like the Elastic Learned Sparse Encoder and reciprocal rank fusion (RRF), which produce best-in-class results without training or tuning.
Integrate with projects like OpenAI, Hugging Face, and LangChain, and use Elasticsearch as the backbone of your LLM-powered applications.

Elastic enables all modern search experiences powered by AI/ML.

Bookmark or subscribe to Elasticsearch Labs on Github
Read our latest articles at elastic.co/search-labs

Apps

Chatbot RAG App
Internal Knowledge Search
Relevance Workbench

Python notebooks ?

The notebooks folder contains a range of executable Python notebooks, so you can test these features out for yourself. Colab provides an easy-to-use Python virtual environment in the browser.

Generative AI

question-answering.ipynb
chatbot.ipynb

Playground RAG Notebooks

Try out Playground in Kibana with the following notebooks:

OpenAI Example
Anthropic Claude 3 Example

LangChain

question-answering.ipynb
langchain-self-query-retriever.ipynb
Question Answering with Self Query Retriever
BM25 and Self-querying retriever with elasticsearch and LangChain
langchain-vector-store.ipynb
langchain-vector-store-using-elser.ipynb
langchain-using-own-model.ipynb

Document Chunking

Document Chunking with Ingest Pipelines
Document Chunking with LangChain Splitters
Calculating tokens for Semantic Search (ELSER and E5)
Fetch surrounding chucks

Search

00-quick-start.ipynb
01-keyword-querying-filtering.ipynb
02-hybrid-search.ipynb
03-ELSER.ipynb
04-multilingual.ipynb
05-query-rules.ipynb
06-synonyms-api.ipynb
07-inference.ipynb
08-learning-to-rank.ipynb
09-semantic-text.ipynb

Semantic reranking

10-semantic-reranking-retriever-cohere.ipynb
11-semantic-reranking-hugging-face.ipynb

Integrations

loading-model-from-hugging-face.ipynb
openai-semantic-search-RAG.ipynb
amazon-bedrock-langchain-qa-example.ipynb
Semantic Search using the Inference API with the Cohere Service

Model Upgrades

upgrading-index-to-use-elser.ipynb

Contributing ?

See contributing guidelines.

Support ?

The Search team at Elastic maintains this repository and is happy to help.

Official Support Services

If you have an Elastic subscription, you are entitled to Support services for your Elasticsearch deployment. See our welcome page for working with our support team. These services do not apply to the sample application code contained in this repository.