Christoph Bussler

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AI and Databases

Vector Databases

"Vector Databases (are All The Rage)" (https://medium.com/google-cloud/vector-databases-are-all-the-rage-872c888fa348) is a blog post that I maintain continously collecting new and existing databases implementing similarity search.

It is noteworthy that in principle there are two classes of vector databases: vector database systems that are special engineered for vector similarity search (like for example Milvus or Zilliz) and existing database systems that add vector similarity search to an existing implementation (like for example PostgreSQL or Oracle).

Retrieval Augmented Generation (RAG)

An AI architecture called RAG (Retrieval Augmented Generation) (https://www.google.com/search?q=retrieval+augmented+generation) uses vector databases as its fundamental storage technology for vectors derived from any type of media by means of an AI model that for a given object generates a vector.

A basic RAG architecture based on AlloyDB AI is discussed by me in the following blog: https://medium.com/google-cloud/core-rag-architecture-with-alloydb-ai-7c7388f33ff1. It shows that not only similarity search, but also embedding generation is possible from within SQL statements.

A distributed RAG architecture on a global level is supported by database partitioning in Cloud Spanner. In this blog I show how geo-local and global similarity queries can be executed: https://medium.com/@chbussler/distributed-ai-global-and-geo-partitioned-rag-architecture-with-google-cloud-spanner-for-aba9a75fc4c3 . This demonstrates a combination of core database technology (partitioning), with a global implementation of Cloud Spanner combined with vector database functionality (similarity search).


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© Christoph Bussler