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 …
Welcome to Faiss Documentation — Faiss documentation
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.
Class list — Faiss documentation
Class faiss::gpu::GpuIndexIVFPQ Class faiss::gpu::GpuIndexIVFScalarQuantizer Class faiss::gpu::GpuResources Class faiss::gpu::GpuResourcesProvider Class …
File distances.h — Faiss documentation
Functions float fvec_L2sqr(const float *x, const float *y, size_t d) Squared L2 distance between two vectors. float fvec_inner_product(const float *x, const float *y, size_t d) inner product float …
File MetricType.h — Faiss documentation
The metric space for vector comparison for Faiss indices and algorithms. Most algorithms support both inner product and L2, with the flat (brute-force) indices supporting additional metric types for vector …
Struct faiss::IndexIVFPQ
Struct faiss::IndexIVFPQ struct IndexIVFPQ : public faiss::IndexIVF Inverted file with Product Quantizer encoding. Each residual vector is encoded as a product quantizer code. Subclassed by …
Struct faiss::Clustering — Faiss documentation
Struct faiss::Clustering struct Clustering : public faiss::ClusteringParameters K-means clustering based on assignment - centroid update iterations The clustering is based on an Index object that assigns …
Namespace faiss — Faiss documentation
The metric space for vector comparison for Faiss indices and algorithms. Most algorithms support both inner product and L2, with the flat (brute-force) indices supporting additional metric types for vector …
Namespace faiss::gpu — Faiss documentation
A wrapper for gpu/impl/Distance.cuh to expose direct brute-force k-nearest neighbor searches on an externally-provided region of memory (e.g., from a pytorch tensor). The data (vectors, queries, …
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