# Elasticsearch

Elasticsearch is a popular search/analytics engine and [vector database](https://www.elastic.co/elasticsearch/vector-database).
Elasticsearch offers an efficient way to create, store, and search vector embeddings at scale.

For technical details, refer to the [Elasticsearch documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html).

The [`elasticsearch-labs`](https://github.com/elastic/elasticsearch-labs) repo contains executable Python notebooks, sample apps, and resources for testing out the Elastic platform.

## OpenAI cookbook notebooks 📒

Check out our notebooks in this repo for working with OpenAI, using Elasticsearch as your vector database.

### [Semantic search](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb)

In this notebook you'll learn how to:

 - Index the OpenAI Wikipedia embeddings dataset into Elasticsearch
 - Encode a question with the `openai ada-02` model
 - Perform a semantic search

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### [Retrieval augmented generation](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-retrieval-augmented-generation.ipynb)

This notebooks builds on the semantic search notebook by:

- Selecting the top hit from a semantic search
- Sending that result to the OpenAI [Chat Completions](https://platform.openai.com/docs/guides/gpt/chat-completions-api) API endpoint for retrieval augmented generation (RAG)