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

The developer-guide contains resources for developers who want to learn how to use Elasticsearch for vector search and other use cases.

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

• langchain-elasticsearch-embeddings.ipynb
• langchain-self-query-retriever.ipynb
• langchain-vector-store.ipynb
• langchain-vector-store-using-elser.ipynb
• langchain-using-own-model.ipynb

• 00-quick-start.ipynb
• 01-keyword-querying-filtering.ipynb
• 02-hybrid-search.ipynb
• 03-ELSER.ipynb
• 04-multilingual.ipynb

• loading-model-from-hugging-face.ipynb
• openai-semantic-search-RAG.ipynb

The example-apps folder contains example apps that demonstrate Elasticsearch for a number of use cases, using different programming languages and frameworks.

The supporting-blog-content folder has content that is referenced in Elastic blogs.

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").