# 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.

- **URL:** https://github.com/broisnischal/rust-vector-db
- **Category:** Rust Based Vector DBs
- **Tags:** Rust Lang, Memory Safe, Simd, Embedded Rust, Disk Persistence
- **Updated:** 2026-04-28 11:38
- **Canonical page:** https://vectordb.ever.works/pt/items/rust-vector-db

## Details

## Overview

rust-vector-db is a hands-on project demonstrating the creation of a vector database in Rust, focusing on storing and retrieving high-dimensional vectors for AI applications.

## Key Concepts

- **Vector Embeddings**: Numerical representations capturing semantic meaning of unstructured data like text, images, audio, and video.
- **Vector Indexing**: Organizes data using structures like KD-trees, VP-trees, or graph-based indices for efficient similarity searches.
- **Distance Metrics**: Cosine similarity, Euclidean distance, dot product for measuring vector similarity.

## Workflow

1. Embed raw data into vectors using ML models.
2. Store vectors in the database.
3. For queries: Embed query, retrieve most similar vectors via similarity search.

## Use Cases

- Recommendation systems
- Image and video search
- NLP applications

## Pricing

Free and open-source.

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