Integrations#
Aside from using cuVS directly, it can be consumed through a number of sdk and vector database integrations.
FAISS#
FAISS v1.8 provides a special conda package that enables a RAFT backend for the Flat, IVF-Flat and IVF-PQ indexes on the GPU. Like the classical FAISS GPU indexes, the RAFT backend also enables interoperability between FAISS CPU indexes, allowing an index to be trained on GPU, searched on CPU, and vice versa.
The RAFT backend can be enabled by building FAISS from source with the FAISS_USE_RAFT cmake flag enabled and setting the use_raft configuration option for the RAFT-enabled GPU indexes.
A pre-compiled conda package can also be installed using the following command:
conda install -c conda-forge -c pytorch -c rapidsai -c nvidia -c "nvidia/label/cuda-11.8.0" faiss-gpu-raft
The next release of FAISS will feature cuVS support as we continue to migrate the vector search algorithms from RAFT to cuVS.
Milvus#
In version 2.3, Milvus released support for IVF-Flat and IVF-PQ indexes on the GPU through RAFT. Version 2.4 adds support for brute-force and the graph-based CAGRA index on the GPU. Please refer to the Milvus documentation to install Milvus with GPU support.
The GPU indexes can be enabled by using the index types prefixed with GPU_, as outlined in the Milvus index build guide.
Milvus will be migrating their GPU support from RAFT to cuVS as we continue to move the vector search algorithms out of RAFT and into cuVS.
Kinetica#
Starting with release 7.2, Kinetica supports the graph-based the CAGRA algorithm from RAFT. Kinetica will continue to improve its support over coming versions, while also migrating to cuVS as we work to move the vector search algorithms out of RAFT and into cuVS.
Kinetica currently offers the ability to create a CAGRA index in a SQL CREATE_TABLE statement, as outlined in their vector search indexing docs. Kinetica is not open source, but the RAFT indexes can be enabled in the developer edition, which can be installed here.