hnswlib-node

JavaScript/TypeScript SDK/client (Node.js/Deno/Bun) for pgvector PostgreSQL extension, enabling async vector storage and similarity queries. Supports REST-like ops via pg driver, Prisma integration. For JS app integration in RAG/semantic search; official bindings vs. direct SQL or other SDKs.

Rust-based PostgreSQL extension accelerating vector similarity search with diskless HNSW (20x faster than pgvector), DiskANN indexes, and hybrid SQL for ANN on embeddings. Enables FP16/INT8/binary vectors with ACID compliance; perfect for high-throughput RAG in DB, real-time analytics. Superior speed/resource efficiency vs dedicated vector stores like Weaviate, all within Postgres.

Timescale extension for pgvector introducing StreamingDiskANN for disk-optimized, high-recall ANN search (28x lower p95 latency vs Pinecone), with hybrid SQL+vector capabilities. Supports binary quantization, filtered queries; scales RAG/analytics to billions on existing Postgres without sharding.

Hybrid billion-scale vector search method combining HNSW with inverted file indexes, enabling cost-efficient search by keeping centroids in memory while storing vectors on disk.

WASM library for brain-inspired neuromorphic HNSW vector search in 11.8KB. Optimized for edge devices with spiking neurons for energy-efficient similarity search.

Lightweight, in-process Python library for vector similarity search using Milvus engine (HNSW/IVF), zero deps beyond pip, optional disk, no server/K8s. Supports millions of vectors locally; for mobile/edge AI prototyping, LangChain integration; faster startup than Qdrant client, easier than full Milvus vs Chroma.