The Faiss library
This addresses the problem of scalable vector search for AI developers and researchers, offering a foundational library that is incremental in optimizing existing methods.
The paper tackles the challenge of efficiently managing and searching large collections of embedding vectors in AI applications by introducing the Faiss library, which provides a toolkit of indexing methods and primitives for vector similarity search, with benchmarks demonstrating its performance and broad applicability.
Vector databases typically manage large collections of embedding vectors. Currently, AI applications are growing rapidly, and so is the number of embeddings that need to be stored and indexed. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper describes the trade-off space of vector search and the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected applications to highlight its broad applicability.