{"repo":"DataArcTech/RAG-ARC","free":true,"listed":false,"github":"https://github.com/DataArcTech/RAG-ARC","clone":"git clone https://github.com/DataArcTech/RAG-ARC.git","description":"A modular, high-performance Retrieval-Augmented Generation framework with multi-path retrieval, graph extraction, and fusion ranking","language":"Python","stars":48,"topics":["agentic","graphrag","knowledge-base","llm","multimodal","rag"],"license":"MIT","category":"ai-agents","readme_excerpt":"🧠 RAG-ARC: Retrieval-Augmented Generation Architecture A modular, high-performance Retrieval-Augmented Generation framework with multi-path retrieval, graph extraction, and fusion ranking 📘 中文文档 • ⭐ Key Features • 🏗️ Architecture • 🚀 Quick Start 🎯 Project Overview RAG-ARC is a modular Retrieval-Augmented Generation (RAG) framework designed to build efficient, scalable architectures that support multi-path retrieval, graph structure extraction, and fusion ranking. The system addresses key challenges in traditional RAG systems when processing unstructured documents (PDF, PPT, Excel, etc.) such as information loss, low retrieval accuracy, and difficulty in recognizing multimodal content. 🎯 Core Use Cases 🧩 Full RAG Pipeline Support : Covers the complete pipeline—from document parsing, text chunking, and embedding generation to multi-path retrieval, graph extraction, reranking, and knowledge graph management. 📚 Knowledge-Intensive Tasks : Ideal for question answering, reasoning, and content generation tasks that rely on large-scale structured and unstructured knowledge, ensuring high recall and semantic consistency. 🌐 Cross-Domain Applications : Supports both Standard RAG and GraphRAG modes, making it adaptable for academic research, personal knowledge bases, and enterprise-level knowledge management systems. 🏗️ Architecture RAG-ARC System Architecture Overview 🔧 Key Features RAG-ARC introduces several key innovations that together build a sophisticated integrated fram","default_branch":null,"files":null,"tree":[],"storefront":"/r/DataArcTech","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/DataArcTech/RAG-ARC/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}