Vector Databases

Chapter 3. Similarity Search with FAISS

FAISS, or Facebook AI Similarity Search, is an open source library developed at Meta (formerly Facebook) to efficiently perform similarity search and clustering of dense vectors. Its evolution reflects Meta’s growing need for scalable solutions to handle the massive datasets driving its personalized recommendations, content filtering, and image/video search functionalities.

This chapter is for developers comfortable with mathematical foundations and interested in the internals of vector search. You can skim it if your main interest lies in building applications rather than writing your own vector database. If you want to experiment with creating your own vector database, FAISS is a great place to start. It is both a robust similarity search engine out of the box and a powerful, flexible toolkit for building a customized vector database and similarity search engine. You will also find it useful for extending the capabilities of sqlite-vss, the vector search extension for SQLite3, which we will explore in detail in the next chapter. For now, understand that FAISS is a powerful tool to have in your custom search toolset—it is heavy artillery. Reading this chapter will also give you an inside look at what makes vector search engines tick.

FAISS can use a GPU, but in this book we will only use the CPU version to make the code accessible to the widest audience.

Foundations

The core motivation behind FAISS stemmed from limitations in existing ...