cuVS: Vector Search and Clustering on the GPU
=============================================


Welcome to cuVS, the premier library for GPU-accelerated vector search and clustering! cuVS provides several core building blocks for constructing new algorithms, as well as end-to-end vector search and clustering algorithms for use either standalone or through a growing list of :doc:`integrations <integrations>`.

There are several benefits to using cuVS and GPUs for vector search, including

#. Fast index build
#. Latency critical and high throughput search
#. Parameter tuning
#. Cost savings
#. Interoperability (build on GPU, deploy on CPU)
#. Multiple language support
#. Building blocks for composing new or accelerating existing algorithms

Useful Resources
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.. _cuvs_reference: https://docs.rapids.ai/api/cuvs/stable/

- `Example Notebooks <https://github.com/rapidsai/cuvs/tree/HEAD/notebooks>`_: Example notebooks
- `Code Examples <https://github.com/rapidsai/cuvs/tree/HEAD/examples>`_: Self-contained code examples
- `RAPIDS Community <https://rapids.ai/community.html>`_: Get help, contribute, and collaborate.
- `GitHub repository <https://github.com/rapidsai/cuvs>`_: Download the cuVS source code.
- `Issue tracker <https://github.com/rapidsai/cuvs/issues>`_: Report issues or request features.


Contents
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.. toctree::
   :maxdepth: 4

   build.rst
   getting_started.rst
   integrations.rst
   cuvs_bench/index.rst
   api_docs.rst
   contributing.md