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The Faiss Library
Open accessAbstract
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.
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Metadata
- Authors
- Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H.
- Affiliations
- FAIR, Meta, Paris, France
- Zilliz, Redwood City, CA, USA
- DeepSeek (China) (CN)
- Skip Labs, Paris, France
- Venue
- IEEE Transactions on Big Data
- Published
- 2026-04
- Volume
- 12
- Issue
- 2
- Pages
- 346-361
- ISSN
- 2332-7790 (ISSN-L)
- 2372-2096
- OpenAlex
- W4415124086
- Cited by
- 478
- References
- 61
- Citations by year
- 2026: 287
- FWCI
- 167.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Big Data (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Keywords
- vector similarity search, vector databases, vector indexing, vector embeddings, vector clustering, vector compression, benchmarking
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