IEEE Transactions on Big Data· 2025Q1
The Faiss Library
- 446citations
- Q1SCImago
- 2025year
Short summary
Faiss is a new toolkit for efficient vector similarity search, designed to handle the massive scale of embeddings required by rapidly growing AI applications.
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Key points
- Faiss is a toolkit for vector similarity search, clustering, compression, and transformation.
- It addresses the increasing need to manage large collections of embedding vectors for AI applications.
- The paper discusses design principles, optimization, and benchmarking of key features.
- Selected use cases demonstrate the library's broad applicability.
AI-generated from the title and abstract; the full text is not read.
Abstract
Vector databases typically manage large collections of embedding vectors. As AI applications are growing rapidly, the number of embeddings that need to be stored and indexed is increasing. 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-offs in 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 use cases to highlight its broad applicability.
The authors' abstract, as published at the source. IEEE Transactions on Big Data, 2025 · DOI ↗
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Field: Classics Computer Vision and Pattern Recognition
ClassicsArts and HumanitiesComputer Vision and Pattern Recognition