NGT

NGT (Neighborhood Graph and Tree) is an open-source vector search engine designed for fast and scalable approximate nearest neighbor (ANN) search in high-dimensional data. It provides both command-line tools and a library for performing efficient searches on large datasets.

GitHub Repository

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Features

• High-speed Approximate Nearest Neighbor Search: Optimized for high-dimensional vector data (from several tens to several thousands of dimensions).
• Graph and Tree-based Indexing: Utilizes neighborhood graphs and tree structures for efficient indexing and retrieval.
• Scalable to Large Datasets: Supports indexing and searching on datasets with millions of objects.
• Shared Memory Support: Can place the index in shared memory using memory-mapped files, allowing multiple processes to reference the same index and handle indexes too large for memory.
• Large-scale Data Optimization: Build options available for improving search time with very large datasets (over 5 million objects).
• Configurable Build Options:
  • Enable/disable shared memory allocator
  • Optimize for large datasets
  • Enable/disable quantized graph (QG) and quantized blob graph (QBG) methods (which require BLAS and LAPACK)
• Multiple Methods Supported:
  • NGT (graph and tree-based method)
  • QG (quantized graph-based method)
  • QBG (quantized blob graph-based method)
• Cross-platform Support: Installation instructions provided for Linux (Ubuntu, CentOS), and macOS (including Homebrew support).
• Python Support: Includes a Python interface for integration with Python applications.
• Benchmark Results: Publicly available benchmark results (e.g., ann-benchmarks) demonstrating performance on various datasets (GloVe, GIST, Fashion-MNIST, NYTimes, SIFT).

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Installation

NGT can be built from source on Linux and macOS, with options to enable/disable specific features. Pre-built binaries are available via Homebrew for macOS.

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Supported Programming Languages

• C++ (core library)
• Python (bindings available)

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Pricing

NGT is open-source software and is available free of charge under an open-source license.

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Tags

open-source vector-search ann scalable