Cited by 1,008Advanced Image and Video Retrieval TechniquesTopic ModelingAdvanced Database Systems and Queries

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The Faiss Library

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Douze, Matthijs, Guzhva, Alexandr, Deng, Chengqi, Johnson, Jeff, Szilvasy, Gergely, Mazaré, Pierre-Emmanuel, Lomeli, Maria, Hosseini, Lucas, Jégou, Hervé·2026·IEEE Transactions on Big Data

DOI 10.1109/tbdata.2025.3618474

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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.

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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
ORCID: Douze, Matthijs
Deng, Chengqi
Johnson, Jeff
Hosseini, Lucas
Venue: IEEE Transactions on Big Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2026-04
Volume: 12
Issue: 2
Pages: 346-361
ISSN: 2332-7790 (ISSN-L)
2372-2096
DOI: 10.1109/tbdata.2025.3618474
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/legalcode (vor)
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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