VSAG

VSAG is a vector indexing library used for similarity search. The indexing algorithm allows users to search through various sizes of vector sets, especially those that cannot fit in memory. The library also provides methods for generating parameters based on vector dimensions and data scale, allowing developers to use it without understanding the algorithm’s principles. VSAG is written in C++ and provides a Python wrapper package called pyvsag.

Performance

The VSAG algorithm achieves a significant boost of efficiency and outperforms the previous state-of-the-art (SOTA) by a clear margin. Specifically, VSAG's QPS exceeds that of the previous SOTA algorithm, Glass, by over 100%, and the baseline algorithm, HNSWLIB, by over 300% according to the ann-benchmark result on the GIST dataset at 90% recall. The test in ann-benchmarks is running on an r6i.16xlarge machine on AWS with --parallelism 31, single-CPU, and hyperthreading disabled. The result is as follows:

gist-960-euclidean

Getting Started

Integrate with CMake

# CMakeLists.txt
cmake_minimum_required(VERSION 3.11)

project (myproject)

set (CMAKE_CXX_STANDARD 11)

# download and compile vsag
include (FetchContent)
FetchContent_Declare (
  vsag
  GIT_REPOSITORY https://github.com/antgroup/vsag
  GIT_TAG main
)
FetchContent_MakeAvailable (vsag)
include_directories (vsag-cmake-example PRIVATE ${vsag_SOURCE_DIR}/include)

# compile executable and link to vsag
add_executable (vsag-cmake-example src/main.cpp)
target_link_libraries (vsag-cmake-example PRIVATE vsag)

# add dependency
add_dependencies (vsag-cmake-example vsag)

Examples

Currently Python and C++ examples are provided, please explore examples directory for details.

We suggest you start with simple_hnsw.cpp and example_hnsw.py.

Building from Source

Please read the DEVELOPMENT guide for instructions on how to build.

Who's Using VSAG

• OceanBase
• TuGraph
• GreptimeDB

If your system uses VSAG, then feel free to make a pull request to add it to the list.

How to Contribute

Although VSAG is initially developed by the Vector Database Team at Ant Group, it's the work of the community, and contributions are always welcome! See CONTRIBUTING for ways to get started.

Community

Thrive together in VSAG community with users and developers from all around the world.

• Discuss at discord.
• Follow us on Weixin Official Accounts（微信公众平台）to get the latest news.

Roadmap

• v0.12 (ETA: Oct. 2024)

  • introduce datacell as the new index framework
  • support pluggable scalar quantization(known as SQ) in datacell
  • implement a new Hierarchical Graph(named HGraph) index based on datacell
  • support INT8 datatype on HNSW Index

• v0.13 (ETA: Nov. 2024)

  • support inverted index(be like IVFFlat) based on datacell
  • introduce pluggable product quantization(known as PQ) in datacell
  • support extrainfo storage within vector

• v0.14 (ETA: Dec. 2024)

  • implement a new MultiIndex that supports efficient pre-filtering on enumerable tags
  • support automated parameter
  • support sparse vector searching

Reference

Reference to cite when you use VSAG in a research paper:

@article{Yang2024EffectiveAG,
  title={Effective and General Distance Computation for Approximate Nearest Neighbor Search},
  author={Mingyu Yang and Wentao Li and Jiabao Jin and Xiaoyao Zhong and Xiangyu Wang and Zhitao Shen and Wei Jia and Wei Wang},
  year={2024},
  url={https://arxiv.org/abs/2404.16322}
}

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License

Apache License 2.0