Microsoft recently announced the introduction of vector search functionality in Azure Cosmos DB for MongoDB vCore. This feature enhances the capabilities of Cosmos DB by allowing developers to perform complex similarity searches on high-dimensional data, which is particularly useful in RAG-based applications, recommendation systems, image and document retrieval, and more. I am also participating in the Cosmos DB hackathon to explore more about how we can use this inside retrieval augmented generation.

In this article, we will explore the details of this new functionality, its use cases, and provide a sample implementation using Python.

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