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A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.
This library assumes that the samples are represented as vectors and that the vectors can be compared by L2 distances or cosine distances. Vectors returned for a query vector are the vectors that have smallest L2 distance or cosine distances with the query vector.
SPTAG provides two methods: kd-tree and relative neighborhood graph (SPTAG-KDT) and balanced k-means tree and relative neighborhood graph (SPTAG-BKT). SPTAG-KDT is advantageous in index building cost, and SPTAG-BKT is advantageous in search accuracy in very high-dimensional data.