FAISS

News

See CHANGELOG.md for detailed information about latest features.

How Faiss works

Faiss is built around an index type that stores a set of vectors, and provides a function to search in them with L2 and/or dot product vector comparison. Some index types are simple baselines, such as exact search. Most of the available indexing structures correspond to various trade-offs with respect to

search time
search quality
memory used per index vector
training time
adding time
need for external data for unsupervised training

The optional GPU implementation provides what is likely (as of March 2017) the fastest exact and approximate (compressed-domain) nearest neighbor search implementation for high-dimensional vectors, fastest Lloyd’s k-means, and fastest small k-selection algorithm known. The implementation is detailed here.

Full documentation of Faiss

The following are entry points for documentation:

the full documentation can be found on the wiki page, including a tutorial, a FAQ and a troubleshooting section
the doxygen documentation gives per-class information extracted from code comments
to reproduce results from our research papers, Polysemous codes and Billion-scale similarity search with GPUs, refer to the benchmarks README. For Link and code: Fast indexing with graphs and compact regression codes, see the link_and_code README

Authors

The main authors of Faiss are:

Hervé Jégou initiated the Faiss project and wrote its first implementation
Matthijs Douze implemented most of the CPU Faiss
Jeff Johnson implemented all of the GPU Faiss
Lucas Hosseini implemented the binary indexes and the build system
Chengqi Deng implemented NSG, NNdescent and much of the additive quantization code.
Alexandr Guzhva many optimizations: SIMD, memory allocation and layout, fast decoding kernels for vector codecs, etc.
Gergely Szilvasy build system, benchmarking framework.

Reference

References to cite when you use Faiss in a research paper:

@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}
}

For the GPU version of Faiss, please cite:

@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}
}

Join the Faiss community

For public discussion of Faiss or for questions, there is a Facebook group at https://www.facebook.com/groups/faissusers/