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openai-cookbook/examples/vector_databases/elasticsearch
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Logan Kilpatrick f1e13cfcc7
Misc updates (#1022)
2024-01-25 11:59:05 -06:00
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elasticsearch-retrieval-augmented-generation.ipynb Misc updates (#1022) 2024-01-25 11:59:05 -06:00
elasticsearch-semantic-search.ipynb Misc updates (#1022) 2024-01-25 11:59:05 -06:00
README.md Add elasticsearch examples to vector databases folder (#622) 2023-08-29 10:54:08 -07:00

README.md

Elasticsearch

Elasticsearch is a popular search/analytics engine and vector database. Elasticsearch offers an efficient way to create, store, and search vector embeddings at scale.

For technical details, refer to the Elasticsearch documentation.

The 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

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

Retrieval augmented generation

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 API endpoint for retrieval augmented generation (RAG)