Lightweight implementation of ANN (Approximate Nearest‑Neighbor) search
-
Updated
May 3, 2025 - Rust
Lightweight implementation of ANN (Approximate Nearest‑Neighbor) search
Open Source Vector Database implemented in the Rust Programming Language.
ArcMind Vector DB
A tiny Rust proxy that batches high-QPS embedding requests before forwarding them to Hugging Face Text Embeddings Inference (TEI).
A Model Context Protocol (MCP) server that provides agentic search capabilities with support for vector search using Qdrant, full-text search using TiDB, or both combined.
VectorLite is a Rust-native, in-process vector store that brings sub-millisecond search and local embeddings to your AI agents and edge systems.
This is a thin wrapper around LanceDb (VectorDb) meant to provide a means to create/store/query embeddings in a LanceDb without the need to grok the lower level Arrow/ColumnarDb tech.
MXP: The first protocol designed specifically for AI agent communication. Fast enough to never be a bottleneck (37M msg/s), with built-in observability, agent discovery, and lifecycle management. No external dependencies, no instrumentation required
⚡🧠 Vectro+ — High-Performance Embedding Engine in Rust 🦀💾 Compress, quantize, and accelerate vector search 🚀 Boost retrieval speed, cut memory, keep semantic precision 🎯🔥
CLI tool for OpenAI compatible APIs
An overview and analysis of structures optimized for nearest neighbour searches.
VectorXLite — A fast, lightweight vector search with payload support and SQL-based filtering.
The SQLite of Vector Database in Rust.
A vector database for querying meaningfully similar data.
This tool provides a fast and efficient way to convert text into vector embeddings and store them in the Qdrant search engine. Built with Rust, this tool is designed to handle large datasets and deliver lightning-fast search results.
High-performance rust vector search library and service
🐙 Meta-AI Orchestrator unifies multiple LLMs with dynamic routing, RAG search, and DAG pipelines for enterprise AI workloads across providers, with observability and QA.
Discover your next great read with AI-powered RAG semantic search and intelligent recommendations.
Verifiable vector similarity queries with Halo2.
Add a description, image, and links to the vector-database topic page so that developers can more easily learn about it.
To associate your repository with the vector-database topic, visit your repo's landing page and select "manage topics."