jvector

High-performance vector database designed to handle up to 1B vectors on a single node with optimized indexing and execution. Also available as a managed cloud service.

Open-source vector database designed for high performance similarity search and AI/ML workloads.

LangChain4j is a Java library providing vector search and embedding capabilities for LLM applications via integrations with ANN indexes like HNSW and FAISS, without needing full vector databases. Features include support for quantization, tool calling, and seamless embedding in JVM environments like Spring Boot and Quarkus. Suited for prototyping RAG agents and embedded apps; lighter and more JVM-native than Milvus, easier integration vs hnswlib.

Memory-bounded GPU-accelerated framework for graph-based ANN vector search using CUDA and LibTorch, optimized for large-scale workloads beyond GPU memory. Features batch processing for high efficiency; outperforms CPU-only ANN in speed for similarity search in vector databases.

GPU-accelerated vector query processing system using CUDA to handle datasets larger than GPU memory via reordered pipelining and cluster-based retrofitting. Supports batch queries with up to 135x speedup over traditional GPU methods and 23x vs CPU-only for large-scale similarity search and MIPS.

An efficient CPU/GPU cooperative processing architecture for billion-scale approximate nearest neighbor search. FusionANNS achieves up to 13.1× higher QPS compared to SPANN and can handle billion-vector datasets with over 12,000 QPS while maintaining 15ms latency using only one entry-level GPU.