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    vsag

    vsag is an Alibaba open-source library implementing efficient vector search algorithms, including approximate nearest neighbor search for high-dimensional vectors.

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    About this tool

    vsag

    Brand: Alibaba
    Category: SDKs & Libraries
    Type: Open‑source vector indexing library

    Overview

    vsag is an open-source C++ vector indexing library for high-dimensional similarity search, designed to handle vector datasets that may not fit entirely in memory. It implements efficient vector search algorithms, including approximate nearest neighbor (ANN) search, and provides a Python wrapper package, pyvsag, for easier integration in Python environments.

    Features

    • Vector similarity search

      • Supports high-dimensional similarity search and approximate nearest neighbor (ANN) queries.
      • Designed to work with vector sets of various sizes, including those that do not fit into memory.
    • Indexing algorithms

      • Implements the VSAG algorithm for efficient vector indexing and search.
      • Includes SINDI algorithm optimized for sparse vector search.
      • Provides example implementations such as HNSW-based indexing (e.g., 101_index_hnsw.cpp, example_hnsw.py).
    • Performance characteristics

      • SINDI algorithm for sparse vector search targets state-of-the-art performance, significantly improving QPS (queries per second) compared with prior solutions in internal tests.
      • Demonstrated efficiency on:
        • Sparse-full-inner-product tasks, with substantial QPS gains over previous SOTA and baseline algorithms on benchmark datasets.
        • gist-960-euclidean benchmarks, tested in the ann-benchmarks framework.
      • Benchmarks reported on multi-core CPU environments (e.g., Intel Xeon CPUs, AWS r6i.16xlarge), suitable for large-scale, CPU-based deployments.
    • Automatic parameter generation

      • Provides methods to generate algorithm parameters based on vector dimensions and data scale.
      • Aims to let developers use the library effectively without needing deep knowledge of the underlying algorithms.
    • Language support

      • C++ core implementation.
      • Python wrapper package: pyvsag available on PyPI.
    • Integration & build

      • CMake integration via FetchContent for easy inclusion in C++ projects.
      • Public C++ headers available via include directory.
      • Example CMake configuration for fetching and linking vsag in user projects.
      • Build-from-source instructions provided in a DEVELOPMENT.md guide in the repository.
    • Examples and usage

      • C++ and Python example programs provided in the examples directory.
      • Starter examples include:
        • 101_index_hnsw.cpp (C++)
        • example_hnsw.py (Python)
    • Ecosystem adoption

      • Used by multiple database and data systems, including (as listed in the repo):
        • OceanBase
        • TuGraph
        • GreptimeDB
        • Hologres
        • PolarDB

    Tech Stack

    • Core language: C++ (C++11 standard or later)
    • Build system: CMake (3.11+ recommended)
    • Python wrapper: pyvsag (via PyPI)

    Pricing

    vsag is an open-source library. No pricing plans are listed; usage is free under the terms of its open-source license (see the GitHub repository for license details).

    Links

    • Source code & documentation: https://github.com/alipay/vsag
    • Python package (pyvsag): https://pypi.org/project/pyvsag/
    • Benchmarks information: https://ann-benchmarks.com/ (external benchmark framework referenced by the project)
    Surveys

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    Information

    Websitegithub.com
    PublishedDec 25, 2025

    Categories

    1 Item
    Sdks & Libraries

    Tags

    3 Items
    #Ann#High Dimensional#Vector Search

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