The Faiss library

TL;DR

The Faiss library provides efficient indexing and search methods for large-scale vector similarity search, addressing the growing need for managing extensive embedding collections in AI applications.

Contribution

It introduces a comprehensive toolkit of indexing methods and design principles tailored for scalable vector similarity search in large datasets.

Findings

Benchmark results demonstrate high performance and scalability.

Faiss supports diverse applications in AI and data analysis.

The library offers flexible trade-offs between speed and accuracy.

Abstract

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.