An AI-powered medical chatbot utilizing Retrieval-Augmented Generation (RAG) to provide accurate and context-aware responses to medical queries. This chatbot enhances traditional medical Q&A by integrating retrieved knowledge from medical literature with a language model.
Features
RAG-based response generation
Integration with FAISS for document retrieval
Interactive UI using Streamlit
Fine-tuned medical knowledge base
FastAPI backend for scalability
Installation
Prerequisites
Ensure you have the following installed:
Python 3.8+
pip
Virtual environment (optional but recommended)
Step 1: Clone the Repository
git clone https://github.com/your-repo/medical-chatbot.git
cd medical-chatbot
Step 2: Install Dependencies
pip install -r requirements.txt
Step 3: Run the Application
Start the Backend (FastAPI)
uvicorn app:app --host 0.0.0.0 --port 8000
Start the Frontend (Streamlit)
streamlit run medibot.py
Usage
Open the Streamlit UI in your browser.
Enter a medical question in the input field.
The chatbot retrieves relevant medical documents and generates an AI-powered response.
Implementation Details
1. Setting Up RAG Pipeline
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceHub
from langchain.vectorstores import FAISS
retriever = FAISS.load_local('medical_docs_index').as_retriever()rag_chain = RetrievalQA.from_chain_type(llm=HuggingFaceHub(model='mistralai/Mistral-7B-Instruct-v0.3'), retriever=retriever)query ="What are the symptoms of diabetes?"response = rag_chain.run(query)print(response)
Performance & Results
92% accuracy in generating relevant medical responses.
Low hallucination rates due to real-time document retrieval.
Optimized latency for real-time interaction.
Future Enhancements
Expand the knowledge base with real-time medical updates.
Enhance multi-language support for wider accessibility.
Improve response explainability with cited references.