Qdrant

Cloud-hosted Rust-based vector search engine with filtered ANN (HNSW), payload filtering, multi-modal support. Disk-persistent, serverless scaling, high QPS. Use cases: real-time recommendations, semantic search. Lighter than Weaviate with Rust performance; open-source core alternative to Pinecone.

Rust-based edge-deployable vector search engine with filtered ANN (HNSW), payload filtering, multi-modal support. Disk-persistent, offline on-device, high QPS. Use cases: real-time recommendations, semantic search. Lighter than Weaviate with Rust performance; open-source alternative to Pinecone.

Research paper introducing CAPS, a practical partition index designed for filtered similarity search. Published as an arXiv preprint in 2023 by Gaurav Gupta et al., it addresses the challenge of combining attribute filtering with approximate nearest neighbor search efficiently.

Curated datasets for benchmarking filtered approximate nearest neighbor (ANN) search in vector databases. Enriched with payload metadata and pre-generated filtering requests, including synthetic and real-world data for keyword and geo-spatial queries.

Open-source comparative benchmarks evaluating vector search performance of engines like Qdrant, Elasticsearch, Milvus, Redis, and Weaviate. Covers single-node upload/search, filtered search across various datasets and configurations, focusing on RPS, latency, precision, and indexing time using affordable hardware.

Node.js bindings (JavaScript/TypeScript SDK) for HNSWLib C++ library, enabling fast ANN vector search with async operations and V8 optimization. Supports L2/cosine distances, file persistence, filtering, and LangChain integration for seamless app embedding. Ideal for web/serverless JS RAG; lighter JS alternative to Python hnswlib bindings vs. full vector DBs like Chroma.