NMSLIB
NMSLIB is an efficient similarity search library and toolkit for high-dimensional vector spaces, supporting a variety of indexing algorithms for vector database use cases.
About this tool
NMSLIB
NMSLIB (Non-Metric Space Library) is an efficient and extensible similarity search library and toolkit for high-dimensional vector spaces, with a particular focus on generic and non-metric spaces. It is widely used for approximate nearest neighbor (ANN) search and evaluation of k-NN methods.
- Website/Source: https://github.com/nmslib/nmslib
- License: Apache-2.0
- Category: SDKs & Libraries
- Tags: open-source, ANN, similarity-search, high-dimensional
Features
- Efficient Similarity Search: Provides fast search methods in high-dimensional and non-metric spaces.
- Extensible Architecture: Supports adding new search methods and distance functions.
- Multiple Algorithms: Implements various algorithms for approximate nearest neighbor search, including:
- Hierarchical Navigable Small World Graphs (HNSW)
- VP-tree (Vantage Point tree)
- Neighborhood Approximation index (NAPP)
- Inverted file based methods
- k-NN graph construction (NN-Descent)
- Metric and Non-Metric Spaces: Principled support for both metric and non-metric space search.
- Cross-Platform: Usable on multiple platforms with no third-party dependencies in the core library.
- Language Support:
- Native C++ API
- Python bindings
- Query server for Java and other languages via Apache Thrift (Java native client available)
- Benchmarking Toolkit: Includes tools to evaluate and benchmark k-NN methods.
- Standalone HNSW: Fastest method (HNSW) also available as a header-only standalone library.
- Open Source: Actively maintained, with contributions from multiple authors and a strong community.
- Used in Production: Integrated into services like Amazon Elasticsearch Service.
Pricing
NMSLIB is open-source and free to use under the Apache-2.0 license.
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