DiskANN is a graph-based approximate nearest neighbor search (ANNS) system optimized for fast and accurate billion-point nearest neighbor search on a single node, leveraging SSD storage. It is highly relevant for large-scale vector database applications requiring efficient vector search at scale.
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jvector is a high-performance Java-based library and engine for vector search and approximate nearest neighbor indexing.
NVIDIA CAGRA is a GPU-accelerated graph-based library for approximate nearest neighbor searches, optimized for high-performance vector search leveraging modern GPU parallelism. It is suitable for scenarios requiring rapid, large-scale vector retrieval.
BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.
NGT (Neighborhood Graph and Tree) is an open-source vector search engine designed for fast and scalable approximate nearest neighbor search.
KDB is a high-performance vector database supporting billion-scale vector search, with features aimed at enterprises needing large-scale vector storage and retrieval.
EFANNA is an extremely fast approximate nearest neighbor search algorithm based on kNN graphs and randomized KD-trees. The provided implementation offers a high-performance ANN index suitable as a building block in custom vector search and retrieval infrastructure.