Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Introductionįaiss contains several methods for similarity search. See CHANGELOG.md for detailed information about latest features. Some of the most useful algorithms are implemented on the GPU. Faiss is written in C++ with complete wrappers for Python/numpy. It also contains supporting code for evaluation and parameter tuning. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. Faiss is a library for efficient similarity search and clustering of dense vectors.
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