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Scalable Vector Search

Scalable Vector Search (SVS) is a performance library for vector similarity search. Thanks to the use of Locally-adaptive Vector Quantization (LVQ) [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.

Please note that this repository only contains the open-source portion of the SVS library, which supports all functionalities and features described in the documentation, except for our proprietary vector compression techniques, specifically LVQ [ABHT23] and Leanvec [TBAH24]. These techniques are closed-source and supported exclusively on Intel hardware. We provide shared library and PyPI package to enable these vector compression techniques in C++ and Python, respectively.

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 Intel(R) AVX-512 instructions, with excellent results also with 2nd and 3rd Intel ® Xeon ® processors (Cascade Lake and Ice Lake).

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

Documentation

SVS documentation includes getting started tutorials with installation instructions for Python and C++ and step-by-step search examples, an API reference, as well as several guides and benchmark comparisons.

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}
}

<a id="1">[ABHT23]</a> 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)

<a id="2">[TBAH24]</a> Tepper M.; Bhati I.; Aguerrebere, C.; Hildebrand M.; Willke T.:LeanVec: Searching vectors faster by making them fit. In: Transactions on Machine Learning Research(TMLR), ISSN, 2835 - 8856. (2024)

Legal

Refer to the LICENSE file for details.