Agentic RAG Medical Assistant

Built an advanced medical assistant chatbot using a fine-tuned LLaMA-3.1-8B model and a Retrieval-Augmented Generation (RAG) pipeline with intelligent agent functionality.

• RAG Architecture:

• Agentic RAG Workflow:

Features

• History-Aware Responses: Provides precise medical advice by integrating over 20+ medical resources through a RAG pipeline.
• Intelligent Query Routing: Implemented agents to dynamically route off-topic queries to web search (e.g., Wikipedia) and process on-topic queries through document retrieval pipelines.
• Document Relevance Grading: Graded retrieved documents for relevance, generating responses if relevant or rewriting queries for improved retrieval.
• Fine-Tuned LLaMA Model: Fine-tuned LLaMA 3.1 8B using LoRA, achieving a 0.29 ROUGE1 score for accuracy and reliability.
• Low-Latency Interface: Built an asynchronous chat interface with FastAPI, reducing response latency by 40% for a seamless user experience.

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Tech Stack

1. Language Model

• LLaMA 3.1 (8B) fine-tuned on medical conversational datasets using PEFT (LoRA) for domain-specific expertise.
• Unsloth: Accelerated fine-tuning with 4-bit quantization, reducing resource usage without compromising performance.

  https://GitHub.com/unslothai/unsloth.git

• Ollama: Used for model integration and serving.

2. RAG Pipeline

• LangChain: Enabled context-aware responses and integrated the LLaMA model with document retrieval capabilities.
• ChromaDB: Stored and retrieved embeddings for efficient and accurate responses.

3. Backend

• FastAPI: Provided a robust and asynchronous backend for a seamless chat interface.

4. Other Tools

• Hugging Face: Hosted and served the optimized model in GGUF format for efficient inference.

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Setup Instructions

1. Clone the Repository

   git clone https://github.com/SathvikNayak123/Agentic-RAG.git

2. Install Dependencies

   pip install -r requirements.txt

3. Setup

   • Populate the database with medical documents.
   • Generate and store embeddings using the fine-tuned LLaMA 3.1 model.
   • Install Ollama and pull the model from Hugging Face:

     ollama pull hf.co/sathvik123/llama3-ChatDoc

4. Run the Application

   uvicorn app:app --reload

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Results

• Model Performance: Achieved a 0.29 ROUGE1 score with fine-tuned LLaMA 3.1.

• RAG Responses: Demonstrated accurate and history-aware conversational capabilities.

• Agent Functionality: Effectively routed and processed queries based on topic relevance.