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https://github.com/facebookresearch/faiss

Score: 33.51032846860524

Last synced: 43 minutes ago
JSON representation

Repository metadata:

A library for efficient similarity search and clustering of dense vectors.

Owner metadata:

Committers metadata

Last synced: about 21 hours ago

Total Commits: 2,268
Total Committers: 350
Avg Commits per committer: 6.48
Development Distribution Score (DDS): 0.851

Commits in past year: 703
Committers in past year: 132
Avg Commits per committer in past year: 5.326
Development Distribution Score (DDS) in past year: 0.851

Name Email Commits
Matthijs Douze m****s@f****m 338
Michael Norris m****s@m****m 150
Gergely Szilvasy g****z@m****m 110
Alibek Zhakubayev a****k@m****m 107
Lucas Hosseini l****i@g****m 104
Richard Barnes r****s@m****m 88
Justin Gibbs g****s@m****m 74
Alexandr Guzhva a****a@g****m 63
Lucas Hosseini h****s@f****m 58
Ramil Bakhshyiev r****l@m****m 57
Junjie Qi j****i@m****m 55
Alexandr Guzhva a****a@m****m 49
Mengdi Lin m****n@m****m 40
Jeff Johnson j****j@f****m 38
Tia Lim t****m@m****m 33
Tarang Jain j****5@g****m 28
Check Deng c****3@g****m 27
generatedunixname89002005287564 g****4@m****m 27
Tom Jackson t****n@m****m 22
Kumar Saurabh Arora k****a@m****m 22
generatedunixname949130641157030 n****0@f****m 21
Subhadeep Karan s****n@m****m 17
Mulugeta Mammo m****o@i****m 16
George Wang g****1@m****m 16
H. Vetinari h****i@g****m 15
Jeff Johnson j****j@m****m 15
Maria m****i@f****m 15
Alexander Andreev a****s@g****m 13
Max Buckley m****y@g****m 13
Amir Sadoughi s****i@f****m 12
and 320 more...

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 956
Total pull requests: 1,655
Average time to close issues: 7 months
Average time to close pull requests: 22 days
Total issue authors: 728
Total pull request authors: 197
Average comments per issue: 2.77
Average comments per pull request: 3.27
Merged pull request: 4
Bot issues: 0
Bot pull requests: 1

Past year issues: 30
Past year pull requests: 159
Past year average time to close issues: 2 months
Past year average time to close pull requests: 10 days
Past year issue authors: 17
Past year pull request authors: 62
Past year average comments per issue: 1.57
Past year average comments per pull request: 2.09
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 1

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/facebookresearch/faiss

Top Issue Authors

  • mdouze (23)
  • cjnolet (10)
  • shiwanghua (10)
  • alexanderguzhva (10)
  • mmnhgo (8)
  • asadoughi (8)
  • yurivict (7)
  • chasingegg (6)
  • navneet1v (6)
  • tarang-jain (6)
  • qwevdb (6)
  • mnorris11 (5)
  • leemeii (5)
  • fferroni (4)
  • zhenglaizhang (4)

Top Pull Request Authors

  • ramilbakhshyiev (160)
  • mnorris11 (145)
  • mengdilin (108)
  • mdouze (103)
  • alexanderguzhva (86)
  • junjieqi (77)
  • algoriddle (70)
  • asadoughi (67)
  • gtwang01 (57)
  • kuarora (40)
  • tarang-jain (39)
  • facebook-github-bot (32)
  • r-barnes (29)
  • fxdawnn (28)
  • limqiying (25)

Top Issue Labels

  • install (154)
  • question (128)
  • GPU (126)
  • enhancement (89)
  • autoclose (86)
  • help wanted (72)
  • Implementation (72)
  • unconfirmed-bug (65)
  • stale (60)
  • feature request (51)
  • cant-repro (37)
  • bug (29)
  • Performance (28)
  • backlog (27)
  • Platform (25)
  • Integration (21)
  • documentation (19)
  • out-of-scope (18)
  • langchain (12)
  • ez (10)
  • CLA Signed (8)
  • wontfix (5)
  • fb-exported (5)
  • ARM / PPC / exotic hardware (4)
  • duplicate (4)
  • invalid (4)
  • ARM (1)
  • kind/bug (1)
  • difficulty/tbd (1)
  • module: rocm (1)

Top Pull Request Labels

  • CLA Signed (1,453)
  • fb-exported (781)
  • Merged (581)
  • ignore (121)
  • Implementation (83)
  • meta-exported (63)
  • module: rocm (50)
  • install (44)
  • GPU (39)
  • Platform (27)
  • Performance (19)
  • enhancement (11)
  • to-benchmark (9)
  • Reverted (7)
  • autoclose (6)
  • bug (6)
  • documentation (6)
  • feature request (5)
  • ez (5)
  • backlog (3)
  • Integration (3)
  • ARM / PPC / exotic hardware (2)
  • simple-fix (2)
  • fh:direct-merge-enabled (1)
  • ARM (1)
  • unconfirmed-bug (1)

Package metadata

debian-12: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://packages.debian.org/bookworm/faiss
  • Licenses: mit
  • Latest release: 1.7.3-2 (published 7 months ago)
  • Last Synced: 2026-03-12T19:14:55.439Z (6 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 0.013%
    • Stargazers count: 0.018%
    • Forks count: 0.036%
nixpkgs-24.11: faissWithCuda

Library for efficient similarity search and clustering of dense vectors by Facebook Research

pypi.org: faiss

A library for efficient similarity search and clustering of dense vectors

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://faiss.readthedocs.io/
  • Licenses: BSD
  • Latest release: 1.5.3 (published over 7 years ago)
  • Last Synced: 2026-09-07T14:02:53.587Z (1 day ago)
  • Versions: 1
  • Dependent Packages: 13
  • Dependent Repositories: 1,255
  • Downloads: 443 Last month
  • Docker Downloads: 8,295
  • Rankings:
    • Stargazers count: 0.08%
    • Forks count: 0.175%
    • Dependent repos count: 0.325%
    • Dependent packages count: 0.907%
    • Average: 0.932%
    • Docker downloads count: 1.463%
    • Downloads: 2.64%
  • Maintainers (1)
pypi.org: faiss-cpu

A library for efficient similarity search and clustering of dense vectors.

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://github.com/facebookresearch/faiss/wiki
  • Licenses: MIT
  • Latest release: 1.15.0 (published about 1 month ago)
  • Last Synced: 2026-09-07T14:03:14.332Z (1 day ago)
  • Versions: 29
  • Dependent Packages: 352
  • Dependent Repositories: 5,592
  • Downloads: 15,074,136 Last month
  • Docker Downloads: 490,247
  • Rankings:
    • Dependent repos count: 0.135%
    • Dependent packages count: 0.147%
    • Downloads: 0.248%
    • Docker downloads count: 0.972%
    • Average: 2.252%
    • Stargazers count: 4.506%
    • Forks count: 7.505%
  • Maintainers (3)
pypi.org: faiss-gpu

A library for efficient similarity search and clustering of dense vectors (GPU support).

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://github.com/facebookresearch/faiss/wiki
  • Licenses: MIT
  • Latest release: 1.15.0 (published about 1 month ago)
  • Last Synced: 2026-09-07T14:03:14.408Z (1 day ago)
  • Versions: 15
  • Dependent Packages: 51
  • Dependent Repositories: 367
  • Downloads: 157,071 Last month
  • Docker Downloads: 2,012
  • Rankings:
    • Dependent packages count: 0.381%
    • Downloads: 0.625%
    • Dependent repos count: 0.765%
    • Docker downloads count: 1.861%
    • Average: 2.607%
    • Stargazers count: 4.506%
    • Forks count: 7.505%
  • Maintainers (3)
proxy.golang.org: github.com/facebookresearch/faiss

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/facebookresearch/faiss#section-documentation
  • Licenses: mit
  • Latest release: v1.15.0 (published about 1 month ago)
  • Last Synced: 2026-09-07T14:11:20.366Z (1 day ago)
  • Versions: 31
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Rankings:
    • Forks count: 0.119%
    • Stargazers count: 0.232%
    • Average: 3.654%
    • Dependent repos count: 4.706%
    • Dependent packages count: 9.561%
conda-forge.org: libfaiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by [Facebook AI Research](https://research.fb.com/category/facebook-ai-research-fair/). For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack. You can ensure that this is installed by adding "libblas =*=*mkl" to your dependencies.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.7.2 (published over 4 years ago)
  • Last Synced: 2026-03-14T16:22:04.471Z (6 months ago)
  • Versions: 6
  • Dependent Packages: 4
  • Dependent Repositories: 7
  • Downloads: 2,289,398 Total
  • Rankings:
    • Stargazers count: 1.652%
    • Forks count: 2.041%
    • Average: 7.285%
    • Dependent packages count: 12.501%
    • Dependent repos count: 12.945%
conda-forge.org: faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by [Facebook AI Research](https://research.fb.com/category/facebook-ai-research-fair/). For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack. You can ensure that this is installed by adding "libblas =*=*mkl" to your dependencies.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.7.2 (published over 4 years ago)
  • Last Synced: 2026-03-25T05:10:05.744Z (6 months ago)
  • Versions: 6
  • Dependent Packages: 3
  • Dependent Repositories: 4
  • Downloads: 3,529,205 Total
  • Rankings:
    • Stargazers count: 1.662%
    • Forks count: 2.051%
    • Average: 8.886%
    • Dependent packages count: 15.649%
    • Dependent repos count: 16.183%
conda-forge.org: libfaiss-avx2

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by [Facebook AI Research](https://research.fb.com/category/facebook-ai-research-fair/). For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack. You can ensure that this is installed by adding "libblas =*=*mkl" to your dependencies.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.7.2 (published over 4 years ago)
  • Last Synced: 2026-03-14T16:14:43.180Z (6 months ago)
  • Versions: 3
  • Dependent Packages: 1
  • Dependent Repositories: 1
  • Downloads: 1,565,015 Total
  • Rankings:
    • Stargazers count: 1.652%
    • Forks count: 2.041%
    • Average: 14.273%
    • Dependent repos count: 24.412%
    • Dependent packages count: 28.988%
spack.io: faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. Faiss contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: []
  • Latest release: 1.14.1 (published 4 months ago)
  • Last Synced: 2026-09-07T14:02:52.338Z (1 day ago)
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Stargazers count: 0.445%
    • Forks count: 0.528%
    • Average: 14.562%
    • Dependent packages count: 57.274%
  • Maintainers (3)
pypi.org: faiss-centos

A library for efficient similarity search and clustering of dense vectors

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://faiss-centos.readthedocs.io/
  • Licenses: MIT
  • Latest release: 1.5.2 (published about 7 years ago)
  • Last Synced: 2026-09-07T14:02:53.429Z (1 day ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 19 Last month
  • Rankings:
    • Stargazers count: 0.084%
    • Forks count: 0.177%
    • Dependent packages count: 7.529%
    • Average: 16.023%
    • Dependent repos count: 22.563%
    • Downloads: 49.764%
  • Maintainers (1)
conda-forge.org: faiss-gpu

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by [Facebook AI Research](https://research.fb.com/category/facebook-ai-research-fair/). For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack. You can ensure that this is installed by adding "libblas =*=*mkl" to your dependencies.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.7.2 (published over 4 years ago)
  • Last Synced: 2026-08-26T17:27:57.017Z (13 days ago)
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 13
  • Downloads: 916,965 Total
  • Rankings:
    • Stargazers count: 1.662%
    • Forks count: 2.051%
    • Dependent repos count: 9.826%
    • Average: 16.288%
    • Dependent packages count: 51.614%
conda-forge.org: faiss-cpu

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by [Facebook AI Research](https://research.fb.com/category/facebook-ai-research-fair/). For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack. You can ensure that this is installed by adding "libblas =*=*mkl" to your dependencies.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.7.2 (published over 4 years ago)
  • Last Synced: 2026-03-14T16:23:04.964Z (6 months ago)
  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 11
  • Downloads: 737,105 Total
  • Rankings:
    • Stargazers count: 1.662%
    • Forks count: 2.051%
    • Dependent repos count: 10.694%
    • Average: 16.505%
    • Dependent packages count: 51.614%
conda-forge.org: faiss-proc

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by [Facebook AI Research](https://research.fb.com/category/facebook-ai-research-fair/). For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack. You can ensure that this is installed by adding "libblas =*=*mkl" to your dependencies.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.0.0 (published over 6 years ago)
  • Last Synced: 2026-03-25T05:09:41.292Z (6 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 61,651 Total
  • Rankings:
    • Stargazers count: 1.597%
    • Forks count: 1.922%
    • Average: 22.18%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%
pypi.org: rf-faiss

A library for efficient similarity search and clustering of dense vectors

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://rf-faiss.readthedocs.io/
  • Licenses: MIT
  • Latest release: 1.13.0 (published 10 months ago)
  • Last Synced: 2026-09-07T14:02:52.152Z (1 day ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 17 Last month
  • Rankings:
    • Dependent packages count: 8.314%
    • Average: 27.653%
    • Dependent repos count: 46.992%
  • Maintainers (1)
anaconda.org: libfaiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.14.1 (published 5 months ago)
  • Last Synced: 2026-04-06T19:03:36.185Z (5 months ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 80 Total
  • Rankings:
    • Dependent packages count: 38.901%
    • Average: 40.658%
    • Dependent repos count: 42.416%
anaconda.org: faiss

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.14.1 (published 5 months ago)
  • Last Synced: 2026-05-20T22:03:23.217Z (4 months ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 407 Total
  • Rankings:
    • Dependent packages count: 38.901%
    • Average: 40.658%
    • Dependent repos count: 42.416%
anaconda.org: libfaiss-avx2

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.14.1 (published 5 months ago)
  • Last Synced: 2026-04-06T19:03:37.006Z (5 months ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 53 Total
  • Rankings:
    • Dependent packages count: 38.901%
    • Average: 40.658%
    • Dependent repos count: 42.416%
anaconda.org: faiss-gpu

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.14.1 (published 5 months ago)
  • Last Synced: 2026-04-06T19:03:33.526Z (5 months ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 32 Total
  • Rankings:
    • Dependent packages count: 38.901%
    • Average: 40.658%
    • Dependent repos count: 42.416%
anaconda.org: faiss-proc

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.0.0 (published 8 months ago)
  • Last Synced: 2026-04-06T19:03:34.251Z (5 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 47 Total
  • Rankings:
    • Dependent packages count: 38.901%
    • Average: 40.658%
    • Dependent repos count: 42.416%
anaconda.org: faiss-cpu

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. For best performance, the maintainers of the package [recommend](https://github.com/conda-forge/staged-recipes/pull/11337#issuecomment-623718460) using the MKL implementation of blas/lapack.

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: MIT
  • Latest release: 1.14.1 (published 5 months ago)
  • Last Synced: 2026-04-06T19:03:32.661Z (5 months ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 44 Total
  • Rankings:
    • Dependent packages count: 38.901%
    • Average: 40.658%
    • Dependent repos count: 42.416%
ubuntu-24.10: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: mit
  • Latest release: 1.8.0-6 (published 7 months ago)
  • Last Synced: 2026-03-08T12:40:38.853Z (6 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
ubuntu-23.04: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: mit
  • Latest release: 1.7.3-2build1 (published 7 months ago)
  • Last Synced: 2026-07-25T11:01:50.249Z (about 2 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
registry.bazel.build: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://registry.bazel.build/docs/faiss
  • Licenses: mit
  • Latest release: 1.14.1 (published about 2 months ago)
  • Last Synced: 2026-09-07T14:02:51.587Z (1 day ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
  • Maintainers (1)
nixpkgs-23.11: faissWithCuda

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-unstable: python313Packages.faiss-cpu

Bindings for faiss, the similarity search library

nixpkgs-24.11: python311Packages.faiss

Bindings for faiss, the similarity search library

nixpkgs-unstable: python314Packages.faiss-cpu

Bindings for faiss, the similarity search library

ubuntu-22.04: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: mit
  • Latest release: 1.7.2-5 (published 7 months ago)
  • Last Synced: 2026-07-31T09:54:27.314Z (about 1 month ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-unstable: python313Packages.faiss

Bindings for faiss, the similarity search library

nixpkgs-23.11: faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

guix: faiss-for-go-faiss

Efficient similarity search and clustering of dense vectors

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://git.savannah.gnu.org/cgit/guix.git/tree/gnu/packages/graph.scm#n586
  • Licenses: mit
  • Latest release: 1.11.0-0.608356b (published 6 months ago)
  • Last Synced: 2026-04-27T18:09:56.715Z (4 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
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    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-24.05: faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-unstable: faiss

Library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-23.05: faissWithCuda

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-23.11: python310Packages.faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-unstable: python314Packages.faiss

Bindings for faiss, the similarity search library

debian-13: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://packages.debian.org/trixie/faiss
  • Licenses: mit
  • Latest release: 1.11.0-3 (published 7 months ago)
  • Last Synced: 2026-03-13T17:11:26.933Z (6 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-24.11: python312Packages.faiss

Bindings for faiss, the similarity search library

guix: python-faiss

Efficient similarity search and clustering of dense vectors

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://git.savannah.gnu.org/cgit/guix.git/tree/gnu/packages/graph.scm#n625
  • Licenses: mit
  • Latest release: 1.10.0 (published 6 months ago)
  • Last Synced: 2026-04-27T18:09:59.267Z (4 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
ubuntu-23.10: faiss

  • Homepage: https://github.com/facebookresearch/faiss
  • Licenses: mit
  • Latest release: 1.7.4-3 (published 7 months ago)
  • Last Synced: 2026-03-10T01:03:00.961Z (6 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-23.05: python311Packages.faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-24.05: python311Packages.faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

guix: faiss

Efficient similarity search and clustering of dense vectors

  • Homepage: https://github.com/facebookresearch/faiss
  • Documentation: https://git.savannah.gnu.org/cgit/guix.git/tree/gnu/packages/graph.scm#n542
  • Licenses: bsd-3
  • Latest release: 1.10.0 (published 6 months ago)
  • Last Synced: 2026-08-02T09:22:33.410Z (about 1 month ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Dependent packages count: 0.0%
    • Average: 100%
nixpkgs-24.05: faissWithCuda

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-23.05: faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-unstable: faissWithCuda

Library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-24.05: python312Packages.faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-23.05: python310Packages.faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-24.11: faiss

Library for efficient similarity search and clustering of dense vectors by Facebook Research

nixpkgs-23.11: python311Packages.faiss

A library for efficient similarity search and clustering of dense vectors by Facebook Research

Dependencies

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