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Bookmark or Share- Faiss | ️ LangChainFacebook AI Similarity Search (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 documentation.
- [2401.08281] The Faiss library - arXiv.orgFaiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper first describes the tradeoff space of vector search, then the design principles of Faiss in terms of structure, approach to optimization and interfacing.
- Home · facebookresearch/faiss Wiki · GitHubFaiss 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 (versions 2 and 3).
- GitHub - facebookresearch/faiss: A library for efficient similarity ...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.
- Getting started · facebookresearch/faiss Wiki · GitHubFaiss is built around the Index object. It encapsulates the set of database vectors, and optionally preprocesses them to make searching efficient. There are many types of indexes, we are going to use the simplest version that just performs brute-force L2 distance search on them: IndexFlatL2.
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