AMG-RAG

# AMG-RAG: Agentic Medical Graph-RAG

[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/release/python-380/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2502.13010)

## 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.

![AMG-RAG Demo](demo.gif)

## 🚀 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 enhanced AMG-RAG system consists of six key components:

### 1. En