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

• Chatbot RAG App
• Internal Knowledge Search
• Relevance Workbench

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.

• question-answering.ipynb
• chatbot.ipynb

Try out Playground in Kibana with the following notebooks:

• OpenAI Example
• Anthropic Claude 3 Example

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

• 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

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

• 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

• upgrading-index-to-use-elser.ipynb

See contributing guidelines.

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

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.

Try posting your question to the Elastic discuss forums and tag it with #esre-elasticsearch-relevance-engine

You can also find us in the #search-esre-relevance-engine channel of the Elastic Community Slack

This software is licensed under the Apache License, version 2 ("ALv2").