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* Initial commit of vector database cookbooks split out individually * Moved notebooks to each provider's named folder and updated the README * Removed vector DB overall cookbook in favour of individual directories
21 lines
1.4 KiB
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21 lines
1.4 KiB
Markdown
# Vector Databases
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This section of the OpenAI Cookbook showcases many of the vector databases available to support your semantic search use cases.
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Vector databases can be a great accompaniment for knowledge retrieval applications, which reduce hallucinations by providing the LLM with the relevant context to answer questions.
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Each provider has their own named directory, with a standard notebook to introduce you to using our API with their product, and any supplementary notebooks they choose to add to showcase their functionality.
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## Guides & deep dives
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- [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/get-started-with-analyticdb-for-postgresql)
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- [Chroma](https://docs.trychroma.com/getting-started)
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- [Hologres](https://www.alibabacloud.com/help/en/hologres/latest/procedure-to-use-hologres)
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- [Kusto](https://learn.microsoft.com/en-us/azure/data-explorer/web-query-data)
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- [Milvus](https://milvus.io/docs/example_code.md)
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- [MyScale](https://docs.myscale.com/en/quickstart/)
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- [Pinecone](https://docs.pinecone.io/docs/quickstart)
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- [Qdrant](https://qdrant.tech/documentation/quick-start/)
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- [Redis](https://github.com/RedisVentures/simple-vecsim-intro)
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- [SingleStoreDB](https://www.singlestore.com/blog/how-to-get-started-with-singlestore/)
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- [Typesense](https://typesense.org/docs/guide/)
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- [Weaviate](https://weaviate.io/developers/weaviate/quickstart) |