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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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ChatVis: Large Language Model Agent for Generating Scientific Visualizations
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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