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Non-Metric Space Library Manual

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arxiv 1508.05470 v4 pith:SKS27MJG submitted 2015-08-22 cs.MS cs.IR

Bilegsaikhan Naidan , Leonid Boytsov , Yury Malkov , David Novak This is my paper · ORCID
classification cs.MScs.IR
keywords librarymethodsnon-metricsearchnmslibspacespacesaccess
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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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  1. SIEVE: Effective Filtered Vector Search with Collection of Indexes

    cs.DB 2025-07 conditional novelty 6.0 of 10

    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.

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