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32 lines
1.5 KiB
Markdown
32 lines
1.5 KiB
Markdown
1 year ago
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# Elasticsearch
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Elasticsearch is a popular search/analytics engine and [vector database](https://www.elastic.co/elasticsearch/vector-database).
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Elasticsearch offers an efficient way to create, store, and search vector embeddings at scale.
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For technical details, refer to the [Elasticsearch documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html).
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The [`elasticsearch-labs`](https://github.com/elastic/elasticsearch-labs) repo contains executable Python notebooks, sample apps, and resources for testing out the Elastic platform.
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## OpenAI cookbook notebooks 📒
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Check out our notebooks in this repo for working with OpenAI, using Elasticsearch as your vector database.
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### [Semantic search](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb)
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In this notebook you'll learn how to:
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- Index the OpenAI Wikipedia embeddings dataset into Elasticsearch
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- Encode a question with the `openai ada-02` model
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- Perform a semantic search
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<hr>
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### [Retrieval augmented generation](https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/elasticsearch/elasticsearch-retrieval-augmented-generation.ipynb)
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This notebooks builds on the semantic search notebook by:
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- Selecting the top hit from a semantic search
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- 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)
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