Index 2024 Talk: Vector Search and the FAISS Library

Speaker: Matthijs Douze, Research Scientist, FAIR
FAISS is a library for approximate nearest neighbor search (ANN), providing indexing methods that are used to search, cluster, compress and transform vector embeddings at scale. Over the years, it has become one of the most popular vector search libraries, that powers several production database engines and inspired many others. FAISS supports trillion-scale indexing and is used for semantic search, recommendation and knowledge base assistant applications and more. In this talk, Matthijs Douze will discuss the tradeoff space of vector search and how different FAISS index implementations strike different operating points in this space. FAISS is designed to scale from a quick tool called in a Python notebook to the core engine of a production-grade database engine. This scalability is enabled by a clear separation of concerns and an open API design.