# Vector Databases

This section of the OpenAI Cookbook showcases many of the vector databases available to support your semantic search use cases. 

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.

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.

## Guides & deep dives
- [AnalyticDB](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/get-started-with-analyticdb-for-postgresql)
- [Chroma](https://docs.trychroma.com/getting-started)
- [Hologres](https://www.alibabacloud.com/help/en/hologres/latest/procedure-to-use-hologres)
- [Kusto](https://learn.microsoft.com/en-us/azure/data-explorer/web-query-data)
- [Milvus](https://milvus.io/docs/example_code.md)
- [MyScale](https://docs.myscale.com/en/quickstart/)
- [Pinecone](https://docs.pinecone.io/docs/quickstart)
- [PolarDB](https://www.alibabacloud.com/help/en/polardb/latest/quick-start)
- [Qdrant](https://qdrant.tech/documentation/quick-start/)
- [Redis](https://github.com/RedisVentures/simple-vecsim-intro)
- [SingleStoreDB](https://www.singlestore.com/blog/how-to-get-started-with-singlestore/)
- [Typesense](https://typesense.org/docs/guide/)
- [Weaviate](https://weaviate.io/developers/weaviate/quickstart)
- [Zilliz](https://docs.zilliz.com/docs/quick-start-1)