265  faiss(1) - Kailash Tool Reference

265.1 faiss

faiss - FAISS — efficient similarity search and clustering of dense vectors.

Layer OPS
Category o-03-vector-stores-retrieval
Safety class safe
Licence MIT
Upstream github.com/facebookresearch/faiss

265.1.1 NAME

faiss - FAISS — efficient similarity search and clustering of dense vectors.

265.1.2 SYNOPSIS & usage

Derived from the upstream README at generation time; verify against --help where the tool is installed.

@article{douze2024faiss,
      title={The Faiss library},
      author={Matthijs Douze and Alexandr Guzhva and Chengqi Deng and Jeff Johnson and Gergely Szilvasy and Pierre-Emmanuel Mazaré and Maria Lomeli and Lucas Hosseini and Hervé Jégou},
      year={2024},
      eprint={2401.08281},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{johnson2019billion,
  title={Billion-scale similarity search with {GPUs}},
  author={Johnson, Jeff and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
  journal={IEEE Transactions on Big Data},
  volume={7},
  number={3},
  pages={535--547},
  year={2019},
  publisher={IEEE}
}

265.1.3 DESCRIPTION (upstream)

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 primarily at Meta’s Fundamental AI Research group. See CHANGELOG.md for detailed information about latest features. Faiss contains several methods for similarity search. It assumes that the insta

265.1.4 SEE ALSO

ref: pin - verified 2026-10-14