*NEW: latest commit (2018-02-25) supports on-disk storage of inverted indexes, see demos/demo_ondisk_ivf.py*
*NEW: latest commit (2018-01-09) includes an implementation of the HNSW indexing method, see benchs/bench_hnsw.py*
*NEW: there is now a Facebook public discussion group for Faiss users at https://www.facebook.com/groups/faissusers/*
*UPDATE: As of July 30 2017, the license on Faiss was relaxed to BSD from CC-BY-NC. Read LICENSE for details.*
*NEW: on 2017-07-30, the license on Faiss was relaxed to BSD from CC-BY-NC. Read LICENSE for details.*
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/).
