embedded-vector-db

A tiny embedding database in pure Rust, implemented as a lightweight Axum server for fast vector search on small to medium datasets. It stores all indexes in memory, enabling vertical scaling to over 100 million vectors with comparable speed and slightly better accuracy than advanced vector databases. Open-source under the MIT license, ideal for simple setups like document chat or website search.

A simple, easy-to-hack vector database library implemented in Rust. It supports fast cosine similarity searches with Rayon parallelism, embedded persistence, and a minimal API ideal for prototyping and educational purposes.

Standalone 2MB edge vector database for IoT, mobile, and embedded applications, providing full vector search capabilities without server dependency.

Orama is a lightweight search engine that supports vector and hybrid search functionalities, suitable for browser, server, or edge environments.

Qdrant is an open‑source vector database designed for high‑performance similarity search and AI applications such as RAG, recommendation systems, advanced semantic search, anomaly detection, and AI agents. It provides scalable storage and retrieval of vector embeddings with features like filtering, hybrid search, and production‑grade APIs for integrating with machine learning workloads.

A self-learning, self-optimizing vector database with graph intelligence, local AI runtime, and PostgreSQL integration. It improves search quality over time using GNNs that learn from queries and feedback, supports hybrid search, Graph RAG, DiskANN, and deploys as a single file anywhere including browsers and edge devices. Open-source under MIT license, free forever.