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openai-cookbook/examples/vector_databases/elasticsearch
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Saarika Bhasi 4631e1b74a
[elasticsearch] fix typo in signup url (#726) 
Co-authored-by: Simón Fishman <simonpfish@gmail.com>
2023-09-27 16:02:50 -07:00
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elasticsearch-retrieval-augmented-generation.ipynb [elasticsearch] fix typo in signup url (#726) 2023-09-27 16:02:50 -07:00
elasticsearch-semantic-search.ipynb [elasticsearch] fix typo in signup url (#726) 2023-09-27 16:02:50 -07:00
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)