{"repo":"MrRezaeiUofT/AMG-RAG","free":true,"listed":false,"github":"https://github.com/MrRezaeiUofT/AMG-RAG","clone":"git clone https://github.com/MrRezaeiUofT/AMG-RAG.git","description":"AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA).","language":"Python","stars":39,"topics":["agents","generative-ai","llms","rag","neo4j","pubmed","vector-database","langchain","langgraph","gemini-api"],"license":null,"category":"ai-agents","readme_excerpt":"AMG-RAG: Agentic Medical Graph-RAG Overview AMG-RAG (Agentic Medical Graph-RAG) is a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graphs (MKGs), integrates reasoning, and retrieves current external evidence for medical Question Answering (QA). Our approach addresses the challenge of rapidly evolving medical knowledge by dynamically linking new findings and complex medical concepts. 🚀 Key Features - 🧠 Enhanced Knowledge Graph Construction : Advanced entity extraction with confidence scoring (1-10 scale) - 🔄 Bidirectional Relationship Analysis : Comprehensive relationship mapping with confidence scoring - 🎯 Context-Aware Entity Processing : LLM-generated descriptions with medical context integration - 📚 Multi-source Evidence Retrieval : Integrates PubMed search, Wikipedia, and vector database retrieval - 🔗 Chain-of-Thought Reasoning : Structured reasoning synthesis with evidence integration - ⚡ Real-time Graph Updates : Dynamically incorporates latest medical literature and research - 📊 Entity Summarization : Enhanced entity understanding with relevance-based confidence scoring 📈 Performance Our evaluations on standard medical QA benchmarks demonstrate superior performance: Dataset Score Metric --------- ------- -------- MEDQA 74.1% F1 Score MEDMCQA 66.34% Accuracy AMG-RAG surpasses both comparable models and those 10 to 100 times larger, while enhancing interpretability for medical queries. 🏗️ Architecture The en","default_branch":null,"files":null,"tree":[],"storefront":"/r/MrRezaeiUofT","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/MrRezaeiUofT/AMG-RAG/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."}