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[!IMPORTANT]
📋 Version Updates from v2.0 to v2.1 and further:

1. Document Processing Upgrade: Unstructured.io has been replaced with Docling for document parsing and extraction of text, tables, and images to be embedded.
2. Enhanced RAG References: Links to source documents and reference images present in reranked retrieved chunks stored in local storage are added to the bottom of the RAG responses.

To use Unstructured.io based solution, refer release - v2.0.

📚 Table of Contents

• Overview
• Demo
• Technical Flow Chart
• Key Features
• Tech Stack
• Installation and Setup
  • Using Docker
  • Manual Installation
• Usage
• Contributions
• License
• Citing
• Contact

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📌 Overview

The Multi-Agent Medical Assistant is an AI-powered chatbot designed to assist with medical diagnosis, research, and patient interactions.

🚀 Powered by Multi-Agent Intelligence, this system integrates:

• 🤖 Large Language Models (LLMs)
• 🖼️ Computer Vision Models for medical imaging analysis
• 📚 Retrieval-Augmented Generation (RAG) leveraging vector databases
• 🌐 Real-time Web Search for up-to-date medical insights
• 👨‍⚕️ Human-in-the-Loop Validation to verify AI-based medical image diagnoses

What You’ll Learn from This Project 📖

🔹 👨‍💻 Multi-Agent Orchestration with structured graph workflows
🔹 🔍 Advanced RAG Techniques – hybrid retrieval, semantic chunking, and vector search
🔹 ⚡ Confidence-Based Routing & Agent-to-Agent Handoff
🔹 🔒 Scalable, Production-Ready AI with Modularized Code & Robust Exception Handling

📂 For learners: Check out agents/README.md for a detailed breakdown of the agentic workflow! 🎯

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💫 Demo

https://github.com/user-attachments/assets/d27d4a2e-1c7d-45e2-bbc5-b3d95ccd5b35

If you like what you see and would want to support the project's developer, you can ! :)

📂 For an even more detailed demo video: Check out Multi-Agent-Medical-Assistant-v1.9. 📽️

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🛡️ Technical Flow Chart

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✨ Key Features

• 🤖 Multi-Agent Architecture : Specialized agents working in harmony to handle diagnosis, information retrieval, reasoning, and more

• 🔍 Advanced Agentic RAG Retrieval System :

  • Docling based parsing to extract text, tables, and images from PDFs.
  • Embedding markdown formatted text, tables and LLM based image summaries.
  • LLM based semantic chunking with structural boundary awareness.
  • LLM based query expansion with related medical domain terms.
  • Qdrant hybrid search combining BM25 sparse keyword search along with dense embedding vector search.
  • HuggingFace Cross-Encoder based reranking of retrieved document chunks for accurate LLM reponses.
  • Input-output guardrails to ensure safe and relevant responses.
  • Links to source documents and images present in reference document chunks