facebookresearch/faiss 源码下载_部署教程

项目概述

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

项目地址

https://github.com/facebookresearch/faiss

项目页面预览

facebookresearch/faiss preview

关键指标

Stars：38740
主要语言：C++
License：MIT License
最近更新：2026-01-14T02:05:43Z
默认分支：main

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源码压缩包下载：点击下载（本站镜像）
SHA256：83a747de31a554977e5c4f9bb694b26ebc344b06478599f5aeb656febdb89192

安装部署要点（README 精选）

Installing

Faiss comes with precompiled libraries for Anaconda in Python, see faiss-cpu, faiss-gpu and faiss-gpu-cuvs. The library is mostly implemented in C++, the only dependency is a BLAS implementation. Optional GPU support is provided via CUDA or AMD ROCm, and the Python interface is also optional. The backend GPU implementations of NVIDIA cuVS can also be enabled optionally. It compiles with cmake. See INSTALL.md for details.

常用命令（从 README 提取）

@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}
}