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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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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · cited 2×
“Datasets: Early datasets are based on keypoint features like SIFT [39] used in image matching.”
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Least squares quantization in PCM
Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781
Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Self-citationLocality-sensitive hashing scheme based on p-stable distributions
Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. (2004) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/997817.997857 · Cited by 2,971
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 8×
“Given this search algorithm, relying on a pure k-nearest neighbor graph is not optimal because neighbors are redundant.”
InfluentialSelf-citationDeep Clustering for Unsupervised Learning of Visual Features
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674
Self-citation
Flagged first, then by citations
Research works printed but not linked: 15
- Paterek, Improving regularized singular value decomposition for collaborative filtering, 5
- Radford, Learning transferable visual models from natural language supervision, 8748
- 10.5555/3524938.3525087 · DOI
- Matsui, 2018, A survey of product quantization, ITE Trans. Media Technol. Appl., 16, 2
- Guo, Accelerating large-scale inference with anisotropic vector quantization, 3887
- Subramanya, DiskANN: Fast accurate billion-point nearest neighbor search on a single node, 13766
- Simhadri, 2022, Results of the NeurIPS’21 challenge on billion-scale approximate nearest neighbor search, 176, 177
- Babenko, Efficient indexing of billion-scale datasets of deep descriptors, 2055
- Morozov, Non-metric similarity graphs for maximum inner product search, 4726
- Weber, 1998, A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces, VLDB, 98, 194
- Simhadri, 2022, Results of the NeurIPS’21 challenge on billion-scale approximate nearest neighbor search, 176, 177
- Iris, Residual quantization with implicit neural codebooks, 20682
- Sun, SOAR: Improved quantization for approximate nearest neighbor search, 3189
- Thakur, BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models
- Izacard, 2023, Atlas: Few-shot learning with retrieval augmented language models, J. Mach. Learn. Res., 24, 1
Cited by
Sorted by citations
Items 501–600 of 1,008
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“We compare the asymptotic efficiency of H IPPOCAMPUS ’s DWM-based storage/retrieval against dense-vector search (e.g. FAISS) and knowledge-graph methods.”
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“For RAG on local Wiki corpus, we use Faiss-GPU [17] serving an E5 Base model [54].”
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GPowerT: LLM-Driven Automated Programming for Power-Constrained IoT Applications
Ye, R., & Gao, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-10466-3_24
Hate Speech Detection in Somali-English Code-Switched Texts
Badel, A. M., Zhong, T., Xu, X., Tai, W., & Zhou, F. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_28
Leveraging RAG for Privacy Violation Detection and Explainability
Locci, S., Audrito, D., Livraga, G., Viviani, M., & Di, L. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228403
SECMG: Style Enhancement Commit Message Generation
Liao, Z., Liu, W., & Lan, P. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227578
Enhancing Vector Data Quality through Negative Learning for Retrieval-augmented Large Models
Yao, L., Ma, K., Jin, R., Zheng, H., & Wang, D. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229352
Designing LLM-powered agents to manage clinical text reports in medical institutions
Carlini, G., Durazzi, F., Remondini, D., & Lodi, R. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227666
Book2QA: A Framework for Integrating LLMs to Generate High-quality QA Data from Textbooks
Cui, Z., Ye, W., Huang, X., Wu, W., & Hu, W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227625
CGT-Corrector: Chinese Government Text Correction with Knowledge Bases
Chen, S., Zhao, F. M., Bai, Z., Niu, L., Yuan, C., & Wang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_36
Liu, X., Yun, J., Li, B., Zhang, Y., & Shi, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3001-4_11
Unlocking Clinical Narrative Text for Secondary Clinical Usage
AlMutair, L., Atwell, E., & Ravikumar, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9242-2_28
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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