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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. It has been gaining popularity recently. In particular, it has become a part of Amazon Elasticsearch Service.

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 (version 0.12). 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

The code is released under the Apache License Version 2.0 http://www.apache.org/licenses/. Older versions of the library include additional components, which have different licenses (but this does not apply to NMLISB 2.x):

Older versions of the library included the following components:

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:

L. Boytsov, D. Novak, Y. Malkov, E. Nyberg (2016). Off the Beaten Path: Let’s Replace Term-Based Retrieval with k-NN Search. In proceedings of CIKM'16. [BibTex] We use a special branch of this library, plus the following Java code.
Malkov, Y.A., Yashunin, D.A.. (2016). Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs. CoRR, abs/1603.09320. [BibTex]
Bilegsaikhan, N., Boytsov, L. 2015 Permutation Search Methods are Efficient, Yet Faster Search is Possible PVLDB, 8(12):1618--1629, 2015 [BibTex]
Ponomarenko, A., Averlin, N., Bilegsaikhan, N., Boytsov, L., 2014. Comparative Analysis of Data Structures for Approximate Nearest Neighbor Search. [BibTex]
Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V., 2014. Approximate nearest neighbor algorithm based on navigable small world graphs. Information Systems, 45, 61-68. [BibTex]
Boytsov, L., Bilegsaikhan, N., 2013. Engineering Efficient and Effective Non-Metric Space Library. In Proceedings of the 6th International Conference on Similarity Search and Applications (SISAP 2013). [BibTex]
Boytsov, L., Bilegsaikhan, N., 2013. Learning to Prune in Metric and Non-Metric Spaces. In Advances in Neural Information Processing Systems 2013. [BibTex]
Tellez, Eric Sadit, Edgar Chávez, and Gonzalo Navarro. Succinct nearest neighbor search. Information Systems 38.7 (2013): 1019-1030. [BibTex]
A. Ponomarenko, Y. Malkov, A. Logvinov, and V. Krylov Approximate nearest neighbor search small world approach. ICTA 2011
Dong, Wei, Charikar Moses, and Kai Li. 2011. Efficient k-nearest neighbor graph construction for generic similarity measures. Proceedings of the 20th international conference on World wide web. ACM, 2011. [BibTex]
L. Cayton, 2008 Fast nearest neighbor retrieval for bregman divergences. Twenty-Fifth International Conference on Machine Learning (ICML). [BibTex]
Amato, Giuseppe, and Pasquale Savino. 2008 Approximate similarity search in metric spaces using inverted files. [BibTex]
Gonzalez, Edgar Chavez, Karina Figueroa, and Gonzalo Navarro. Effective proximity retrieval by ordering permutations. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30.9 (2008): 1647-1658. [BibTex]

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