microsoft/SPTAG

microsoft/SPTAG

Description: A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

Language: C++

License: MIT

Stars: 5003

Forks: 618

Open issues: 143

Created: 2018-09-12T10:42:51Z

Pushed: 2026-06-08T10:30:36Z

Default branch: main

Fork: no

Archived: no

README:

SPTAG: A library for fast approximate nearest neighbor search


SPTAG

SPTAG (Space Partition Tree And Graph) is a library for large scale vector approximate nearest neighbor search scenario released by Microsoft Research (MSR) and Microsoft Bing.

What's NEW

• Result Iterator with Relaxed Monotonicity Signal Support
• New Research Paper SPFresh: Incremental In-Place Update for Billion-Scale Vector Search - _published in SOSP 2023_
• New Research Paper VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity - _published in OSDI 2023_

Introduction

This library assumes that the samples are represented as vectors and that the vectors can be compared by L2 distances or cosine distances. Vectors returned for a query vector are the vectors that have smallest L2 distance or cosine distances with the query vector.

SPTAG provides two methods: kd-tree and relative neighborhood graph (SPTAG-KDT) and balanced k-means tree and relative neighborhood graph (SPTAG-BKT). SPTAG-KDT is advantageous in index building cost, and SPTAG-BKT is advantageous in search accuracy in very high-dimensional data.

How it works

SPTAG is inspired by the NGS approach [[WangL12](#References)]. It contains two basic modules: index builder and searcher. The RNG is built on the k-nearest neighborhood graph [[WangWZTG12](#References), [WangWJLZZH14](#References)] for boosting the connectivity. Balanced k-means trees are used to replace kd-trees to avoid the inaccurate distance bound estimation in kd-trees for very high-dimensional vectors. The search begins with the search in the space partition trees for finding several seeds to start the search in the RNG. The searches in the trees and the graph are iteratively conducted.

Highlights

• Fresh update: Support online vector deletion and insertion
• Distributed serving: Search over multiple machines

Build

Requirements

• swig >= 4.0.2
• cmake >= 3.12.0
• boost >= 1.67.0

Fast clone

set GIT_LFS_SKIP_SMUDGE=1
git clone --recurse-submodules https://github.com/microsoft/SPTAG

OR

git config --global filter.lfs.smudge "git-lfs smudge --skip -- %f"
git config --global filter.lfs.process "git-lfs filter-process --skip"

Install

> For Linux: > Compile SPDK

cd ThirdParty/spdk
./scripts/pkgdep.sh
CC=gcc-9 ./configure
CC=gcc-9 make -j

> Compile isal-l_crypto

cd ThirdParty/isal-l_crypto
./autogen.sh
./configure
make -j

> Build RocksDB

mkdir build && cd build
cmake -DUSE_RTTI=1 -DWITH_JEMALLOC=1 -DWITH_SNAPPY=1 -DCMAKE_C_COMPILER=gcc-7 -DCMAKE_CXX_COMPILER=g++-7 -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-fPIC" ..
make -j
sudo make install

> Build SPTAG

mkdir build
cd build && cmake -DSPDK=OFF -DROCKSDB=OFF .. && make

It will generate a Release folder in the code directory which contains all the build targets.

> For Windows:

mkdir build
cd build && cmake -A x64 -DSPDK=OFF -DROCKSDB=OFF ..

It will generate a SPTAGLib.sln in the build directory. Compiling the ALL_BUILD project in the Visual Studio (at least 2019) will generate a Release directory which contains all the build targets.

For detailed instructions on installing Windows binaries, please see [here](docs/WindowsInstallation.md)

> Using Docker:

docker build -t sptag .

Will build a docker container with binaries in /app/Release/.

Verify

Run the SPTAGTest (or Test.exe) in the Release folder to verify all the tests have passed.

Usage

The detailed usage can be found in [Get started](docs/GettingStart.md). There is also an end-to-end tutorial for building vector search online service using Python Wrapper in [Python Tutorial](docs/Tutorial.ipynb). The detailed parameters tunning can be found in [Parameters](docs/Parameters.md).

References

Please cite SPTAG in your publications if it helps your research:

@inproceedings{xu2023spfresh,
title={SPFresh: Incremental In-Place Update for Billion-Scale Vector Search},
author={Xu, Yuming and Liang, Hengyu and Li, Jin and Xu, Shuotao and Chen, Qi and Zhang, Qianxi and Li, Cheng and Yang, Ziyue and Yang, Fan and Yang, Yuqing and others},
booktitle={Proceedings of the 29th Symposium on Operating Systems Principles},
pages={545--561},
year={2023}
}

@inproceedings{zhang2023vbase,
title={$\{$VBASE$\}$: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity},
author={Zhang, Qianxi and Xu, Shuotao and Chen, Qi and Sui, Guoxin and Xie, Jiadong and Cai, Zhizhen and Chen, Yaoqi and He, Yinxuan and Yang, Yuqing and Yang, Fan and others},
booktitle={17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23)},
year={2023}
}

@inproceedings{ChenW21,
author = {Qi Chen and
Bing Zhao and
Haidong Wang and
Mingqin Li and
Chuanjie Liu and
Zengzhong Li and
Mao Yang and
Jingdong Wang},
title = {SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search},
booktitle = {35th Conference on Neural Information Processing Systems (NeurIPS 2021)},
year = {2021}
}

@manual{ChenW18,
author = {Qi Chen and
Haidong Wang and
Mingqin Li and
Gang Ren and
Scarlett Li and
Jeffery Zhu and
Jason Li and
Chuanjie Liu and
Lintao Zhang and
Jingdong Wang},
title = {SPTAG: A library for fast approximate nearest neighbor search},
url = {https://github.com/Microsoft/SPTAG},
year = {2018}
}

@inproceedings{WangL12,
author = {Jingdong Wang and
Shipeng Li},
title = {Query-driven iterated neighborhood graph search for large scale indexing},
booktitle = {ACM Multimedia 2012},
pages = {179--188},
year = {2012}
}

@inproceedings{WangWZTGL12,
author = {Jing Wang and
Jingdong Wang and
Gang Zeng and
Zhuowen Tu and
Rui Gan…