  Vector databases manage large collections of embedding vectors. As AI
applications are growing rapidly, so are 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 first describes the tradeoff space of vector
search, then 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.
