Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
|---|---|
| 2026-07-24 | 5007 |
| 2026-07-25 | 5007 |
| 2026-07-28 | 5007 |
| 2026-07-30 | 5007 |
| 2026-08-06 | 5007 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# SPTAG: A library for fast approximate nearest neighbor search [](https://github.com/Microsoft/SPTAG/blob/master/LICENSE) [](https://sysdnn.visualstudio.com/SPTAG/_build/latest?definitionId=2) ## **SPTAG** SPTAG (Space Partition Tree And Graph) is a library for large scale vector approximate nearest neighbor search scenario released by [Microsoft Research (MSR)](https://www.msra.cn/) and [Microsoft Bing](http://bing.com). <p align="center"> <img src="docs/img/sptag.png" alt="architecture" width="500"/> </p> ## What's NEW * Result Iterator with Relaxed Monotonicity Signal Support * New Research Paper [SPFresh: Incremental In-Place Update for Billion-Scale Vector Search](https://dl.acm.org/doi/10.1145/3600006.3613166) - _published in SOSP 2023_ * New Research Paper [VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity](https://www.usenix.org/system/files/osdi23-zhang-qianxi_1.pdf) - _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 ```bash cd ThirdParty/spdk ./scripts/pkgdep.sh CC=gcc-9 ./configure CC=gcc-9 make -j ``` > Compile isal-l_crypto ```bash cd ThirdParty/isal-l_crypto ./autogen.sh ./configure make -j ``` > Build RocksDB ```bash 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 ```bash 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: ```bash 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: ```bash docker build -t sptag . ``` Will build a docker container with binaries in `/app/Release/`. ###
Excerpt of 7,655 characters
Read on GitHub
77
Ben Karsin · NVIDIA USA
33
25
Guoxin · MSRA · China
8
5
4
Alexander Sklar · @Microsoft · United States
4
4
Alyssa Ong · @Microsoft
3
Scarlett Li · Microsoft · China
3
3
3
3
Renan S
2
2
2
2
2
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e55b5e5cd70f5cba, topic:vector-search, desc:vector index, desc:vector search