facebookresearch/faiss is a C++ library for efficient similarity search and clustering of dense vectors. A dense vector is a vector representation packed with numeric coordinates, and it is used to find similar items within such a set of vectors. The repository was created on February 7, 2017, and the most recent version in the collected release records is v1.15.1. It has 41,178 GitHub stars, and the license is MIT.
Faiss is a search library that handles large-scale vector collections. Approximate nearest neighbor search (approximate nearest neighbor) is a method that finds the closest items by scanning only nearby candidates instead of comparing every vector one by one, and Faiss provides this algorithm together with CPU and GPU indexes. An index is a data structure that organizes in advance the vectors and structures needed for searching. The README explains that it can also search vector collections too large to fit in RAM. There are Python/numpy wrappers and supporting code for evaluation and parameter tuning, and GPU support can be added optionally through CUDA or AMD ROCm.
Still, the records collected this time contain no changelog and no performance comparison figures for v1.15.1. How much the features described in the README pay off at which data scale is hard to confirm from this record alone. In the evaluation record, the momentum score is 3.7 out of 10, and the remaining metrics are not given. The MIT license and 41,178 stars only tell us that it is a widely used public project. It appears worth keeping on the list as a vector search candidate, but judging whether it is better under specific conditions requires separate measurement.