# Rust Lang

> Browse 5 items tagged with Rust Lang.

## Items

- [arroy](https://vectordb.ever.works/items/arroy) — 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.
- [rust-vector-db](https://vectordb.ever.works/items/rust-vector-db) — rust-vector-db is a lightweight, educational vector database implemented in Rust, leveraging memory safety, high performance, and SIMD instructions for efficient vector storage and retrieval. It supports HNSW indexing, product quantization, disk persistence, and distance metrics like cosine similarity, Euclidean, and dot product. Perfect for high-perf embedded and edge AI applications or learning purposes; more performant and safer than Python-based libraries like Chroma.
- [nano-vectordb-rs](https://vectordb.ever.works/items/nano-vectordb-rs) — 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.
- [RuVector](https://vectordb.ever.works/items/ruvector) — 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.
- [ruvector-core](https://vectordb.ever.works/items/ruvector-core) — 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.

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_Canonical page: https://vectordb.ever.works/tags/rust-lang_