Billion-scale graph-based ANNS index with direct insertion capabilities. Achieves <1ms search latency with >10x less memory than in-memory indexes through GC-free design and update combining.
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Disk-based approximate nearest neighbor search framework with page-aligned graph structure. Achieves 1.85x-10.83x higher throughput than state-of-the-art methods through optimized SSD utilization.
Low-latency, billion-scale updatable graph-based vector store on SSD. Achieves <1ms search latency with 10x less memory than in-memory indexes through alignment of best-first search with SSD characteristics.
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.
jvector is a high-performance Java-based library and engine for vector search and approximate nearest neighbor indexing.
PilotANN is a memory-bounded GPU-accelerated framework for large-scale vector search, designed to improve performance and efficiency of approximate nearest neighbor (ANN) search workloads, making it relevant as a high-performance engine/component in vector database and vector search systems.
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.
