rust-vector-db

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.

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.

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.

Self-optimizing on-device vector database with HNSW, graph RAG, and WASM deployment for low-latency edge AI ops across browsers/IoT/mobile. Supports real-time self-learning retrieval; lighter and offline vs cloud Qdrant.

Coherence Gate engine using sheaf Laplacian for mathematical consistency checks in AI responses. Implements compute ladder routing (Reflex to Human), LLM hallucination blocking, GPU/SIMD acceleration, and cryptographic audit trails.

Library providing sublinear-time solvers for large-scale math problems like PageRank, graph Laplacians, and AI attention using 8 algorithms including Neumann Series, Conjugate Gradient, Forward/Backward Push, and more. Optimized for scale with SIMD SpMV, fused kernels, and arena allocators; supports WASM and NAPI bindings.