Faiss
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About
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. Faiss builds a data structure (index) in RAM from a set of vectors. When given a new vector, it efficiently finds the nearest neighbors. It can trade precision for speed, perform maximum inner product search, return elements within a given radius, store the index on disk, index binary vectors, and ignore a subset of index vectors according to a predicate on the vector ids.
Platform
Task
Features
• clustering of dense vectors
• efficient similarity search
• k-nearest neighbor search
• range search
• maximum inner product search
• gpu implementation of algorithms
• evaluation and parameter tuning
• algorithms for searching in large vector sets
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