embeddings

VortexDB - A lightweight, vector database built from scratch in Rust 🦀

Modern ChromaDB web UI for browsing collections, inspecting records, running queries, and editing documents, metadata, and embeddings

Vortex is a self-hosted RAG (Retrieval-Augmented Generation) application that lets you chat with your documents using any LLM provider. Upload PDFs, ingest URLs, and get accurate answers grounded in your own knowledge bases — all with a clean, modern interface.

Vectorstores is a framework for using vector databases in your AI applications

A thread-safe vector database for model inference inside LMDB.

A vector database for querying meaningfully similar data.

Nautobot Model Context Protocol (MCP) Server - Contains STDIO and HTTP Deployments with Embedding Search and RAG.

Production-ready Retrieval Augmented Generation (RAG) system with hybrid retrieval, advanced evaluation metrics, and monitoring. Build enterprise LLM applications with reduced hallucinations, better context management, and comprehensive observability.

For your repository, issuedigger will notify authors of new issues about similar, previously submitted ones, saving manual digging. Implemented as a single, AI-enabled Cloudflare Worker.

Add different kinds of data: Youtube videos, PDFs, Internet links and ask questions based on the content provided using an intuitive UI.

Embeddable vector database for Rust and Python. HNSW indexing, metadata filtering, hybrid search, and snapshots. The SQLite of vector search.

This project implements RAG using OpenAI's embedding models and LangChain's Python library

The in-browser vector store that remembers — a Rust→WASM HNSW engine that persists to OPFS and stays consistent across tabs. Private, offline semantic search in 3 lines.

🔒 Privacy-first MCP server for Claude using PostgreSQL + Ollama. Local alternative to cloud-based code context with full data sovereignty. No API keys, no external calls, 100% local.

A clean, modular implementation of an Agentic RAG (Retrieval-Augmented Generation) system built with a production-ready architecture.

LLM-powered financial analyst using LoRA-tuned Llama-3 and RAG pipeline to answer complex queries over SEC 10-K filings with contextual accuracy.

🧠 Personal knowledge MCP server with vector database for Opencode. Store and retrieve knowledge using semantic search, powered by local embeddings.

Find the images in your dataset most similar to a query image from URL or drag-and-drop, with FiftyOne!

Universal memory layer for AI applications. Self-host in minutes. Open source.

本项目是一个完整的RAG系统教程，涵盖了从基础概念到高级技术的全面内容。通过Jupyter Notebook的形式，逐步讲解如何构建和优化一个高效的检索增强生成系统。本项目使用的所有模型都是本地化部署的，在3090上可以运行。