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Welcome to Faiss Documentation — Faiss documentation

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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. Some of the most useful algorithms are implemented on the GPU. It is developed primarily at FAIR, the fundamental AI research team of Meta. Given a set of vectors 𝑥 𝑖 in dimension 𝑑 , Faiss builds a data structure in RAM from it. After the structure is constructed, when given a new vector 𝑥 in dimension 𝑑 it performs efficiently the operation: where ‖ ⋅ ‖ is the Euclidean distance ( 𝐿 2 ). In Faiss terms, the data structure is an index, an object that has an add method to add 𝑥 𝑖 vectors. Note that the 𝑥 𝑖 ’s are assumed to be fixed. Computing the argmin is the search operation on the index. This is all what Faiss is about. It can also: return no

Welcome to Faiss Documentation - Faiss documentation Welcome to Faiss Documentation View page source Welcome to Faiss Documentation  Faiss  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. Some of the most useful algorithms are implemented on the GPU. It is developed primarily at FAIR , the fundamental AI research team of

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