RuVector

Minimal Rust library for fast on-device cosine similarity search with Rayon parallelism and embedded persistence, ideal for low-latency prototyping on edge hardware. Supports quick inserts/queries for real-time AI; lighter than full DBs like Qdrant edge.

Rust core for high-performance on-device HNSW vector search with SIMD and compression, achieving low-latency multi-threaded queries for edge AI RAG. Up to 3,597 QPS; optimized for real-time vs cloud alternatives.

Lightweight Node.js library for low-latency on-device vector similarity search using HNSW and BM25 hybrid, with CRUD, metadata filtering, and persistence for edge RAG pipelines. Enables real-time semantic search without servers; more lightweight than cloud Qdrant.

QEMU microVM launcher for low-latency RVF cognitive containers in RuVector stack, enabling secure on-device vector processing for edge AI environments.

Pure Rust embedding database as lightweight Axum server for low-latency on-device vector search scaling to 100M+ vectors in memory. High accuracy/speed for edge RAG; simpler than Qdrant edge.

Rust library for low-latency on-device vector similarity search using random projection trees and LMDB storage, enabling efficient ANN on edge devices. Supports concurrent multi-process access for real-time AI apps. Ideal for IoT and embedded systems vs cloud alternatives like Qdrant.