Scalable Vector Search

Scalable Vector Search (SVS) is a performance library for vector similarity search. Thanks to the use of Locally-adaptive Vector Quantization [ABHT23] and its highly optimized indexing and search algorithms, SVS provides vector similarity search:

• on billions of high-dimensional vectors,
• at high accuracy
• and state-of-the-art speed,
• while enabling the use of less memory than its alternatives.

This enables application and framework developers using similarity search to unleash its performance on Intel ® Xeon CPUs (2nd generation and newer).

SVS offers a fully-featured and yet simple Python API, compatible with most standard libraries. SVS is written in C++ to facilitate its integration into performance-critical applications.

Performance

SVS provides state-of-the-art performance and accuracy [ABHT23] for billion-scale similarity search on standard benchmarks.

For example, for the standard billion-scale Deep-1B dataset, different configurations of SVS yield significantly increased performance (measured in queries per second, QPS) with a smaller memory footprint (horizontal axis) than the alternatives¹:

SVS is primarily optimized for large-scale similarity search but it still offers state-of-the-art performance at million-scale.

Best performance is obtained with 4th generation (Sapphire Rapids) by making use of AVX-512 instructions, with excellent results also with 2nd and 3rd Intel ® Xeon ® processors (Cascade Lake and Ice Lake).

Performance will be degraded if AVX-512 instructions are not available. A warning message will appear when loading the SVS Python module if the system does not support AVX-512 instructions.

Key Features

SVS supports:

• Similarity functions: Euclidean distance, inner product, cosine similarity.
• Vectors with individual values encoded as: float32, float16, uint8, int8.
• Vector compression (including Locally-adaptive Vector Quantization [ABHT23])
• Optimizations for Intel ® Xeon ® processors:
  • 2nd generation (Cascade Lake)
  • 3rd generation (Ice Lake)
  • 4th generation (Sapphire Rapids)

See Roadmap for upcoming features.

Usage Example

Follow a step by step example at Getting Started, or directly access the entire example code.

Documentation

Our documentation provides a reference of the library API, as well as several guides, tutorials, and benchmark comparisons.

Installation

Prerequisites

• A C++20 capable compiler:

  • GCC >= 11.0
  • Clang >= 13.0

To install the Python module you'll also need:

• Python >= 3.7
• A working internet connection

Python build

To build and install the Python module, pysvs, clone the repo and run the following pip install command.

# Clone the repository
git clone https://github.com/IntelLabs/ScalableVectorSearch.git
cd ScalableVectorSearch

# Install pysvs using pip
CC=gcc-11 CXX=g++-11 pip install bindings/python

To uninstall, simply run

pip uninstall pysvs

If you encounter any issues with the pip install command, we suggest you follow an alternative installation procedure using cibuildwheel. To generate a wheel using your current version of Python you will need to have cibuildwheel installed as well as docker. Once those are installed, follow these steps:

1. Navigate to the root directory of the source and, if the bindings/python/_skbuild folder exists, remove it.

2. From the root directory of the source run

# Build the manylinux-based container needed by SVS
cd ./docker/x86_64/manylinux2014/
./build.sh
# Return the the root directory and run `cibuildwheel`
cd ../../..
cibuildwheel --only $(python tools/pybuild.py) bindings/python

3. Then simply run

    pip install ./wheelhouse/pysvs*.whl

For more advanced building options see Advanced Library Building.

References

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

@article{aguerrebere2023similarity,
        title={Similarity search in the blink of an eye with compressed indices},
        volume = {16},
        number = {11},
        pages = {3433--3446},
        journal = {Proceedings of the VLDB Endowment},
        author={Cecilia Aguerrebere and Ishwar Bhati and Mark Hildebrand and Mariano Tepper and Ted Willke},
        year = {2023}
}

[ABHT23] Aguerrebere, C.; Bhati I.; Hildebrand M.; Tepper M.; Willke T.:Similarity search in the blink of an eye with compressed indices. In: Proceedings of the VLDB Endowment, 16, 11, 3433 - 3446. (2023)

How to Contribute

We welcome your contributions to this project. See How to Contribute for more details.

Legal

Refer to the LICENSE file for details.

Footnotes

1. Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. No product or component can be absolutely secure. Your costs and results may vary. Intel technologies may require enabled hardware, software or service activation. © Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others. ↩