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README.md

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Non-Metric Space Library (NMSLIB)

Important Notes

  • NMSLIB is generic but fast, see the results of ANN benchmarks.
  • A standalone implementation of our fastest method HNSW also exists as a header-only library.
  • All the documentation (including using Python bindings and the query server, description of methods and spaces, building the library, etc) can be found on this page.
  • For generic questions/inquiries, please, use the Gitter chat: GitHub issues page is for bugs and feature requests.

Objectives

Non-Metric Space Library (NMSLIB) is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The core-library does not have any third-party dependencies.

The goal of the project is to create an effective and comprehensive toolkit for searching in generic and non-metric spaces. Even though the library contains a variety of metric-space access methods, our main focus is on 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.

NMSLIB is an extendible library, which means that is possible to add new search methods and distance functions. NMSLIB can be used directly in C++ and Python (via Python bindings). In addition, it is also possible to build a query server, which can be used from Java (or other languages supported by Apache Thrift). Java has a native client, i.e., it works on many platforms without requiring a C++ library to be installed.

Authors: Bilegsaikhan Naidan, Leonid Boytsov, Yury Malkov, David Novak. With contributions from Ben Frederickson, Lawrence Cayton, Wei Dong, Avrelin Nikita, Dmitry Yashunin, Bob Poekert, @orgoro, @gregfriedland, Scott Gigante, Maxim Andreev, Daniel Lemire, Nathan Kurz, Alexander Ponomarenko.

Brief History

NMSLIB started as a personal project of Bilegsaikhan Naidan, who created the initial code base, the Python bindings, and participated in earlier evaluations. The most successful class of methods--neighborhood/proximity graphs--is represented by the Hierarchical Navigable Small World Graph (HNSW) due to Malkov and Yashunin (see the publications below). Other most useful methods, include a modification of the VP-tree due to Boytsov and Naidan (2013), a Neighborhood APProximation index (NAPP) proposed by Tellez et al. (2013) and improved by David Novak, as well as a vanilla uncompressed inverted file.

Credits and Citing

If you find this library useful, feel free to cite our SISAP paper [BibTex] as well as other papers listed in the end. One crucial contribution to cite is the fast Hierarchical Navigable World graph (HNSW) method [BibTex]. Please, also check out the stand-alone HNSW implementation by Yury Malkov, which is released as a header-only HNSWLib library.

License

Most of this code is released under the Apache License Version 2.0 http://www.apache.org/licenses/.

  • The LSHKIT, which is embedded in our library, is distributed under the GNU General Public License, see http://www.gnu.org/licenses/.
  • The k-NN graph construction algorithm NN-Descent due to Dong et al. 2011 (see the links below), which is also embedded in our library, seems to be covered by a free-to-use license, similar to Apache 2.
  • FALCONN library's licence is MIT.

Funding

Leonid Boytsov was supported by the Open Advancement of Question Answering Systems (OAQA) group and the following NSF grant #1618159: "Matching and Ranking via Proximity Graphs: Applications to Question Answering and Beyond". Bileg was supported by the iAd Center.

Related Publications

Most important related papers are listed below in the chronological order:

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