SIEVE builds a workload-aware collection of small HNSW subindexes and uses a cost model to pick the best one per query, speeding up filtered vector search up to 8.06x versus prior graph-based methods.
Non-Metric Space Library Manual
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This document covers a library for fast similarity (k-NN)search. It describes only search methods and distances (spaces). Details about building, installing, Python bindings can be found online:https://github.com/searchivarius/nmslib/tree/v1.8/. Even though the library contains a variety of exact metric-space access methods, our main focus is on more generic and approximate search methods, in particular, on methods for non-metric spaces. NMSLIB is possibly the first library with a principled support for non-metric space searching.
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SIEVE: Effective Filtered Vector Search with Collection of Indexes
SIEVE builds a workload-aware collection of small HNSW subindexes and uses a cost model to pick the best one per query, speeding up filtered vector search up to 8.06x versus prior graph-based methods.