🏥 Medical RAG Chatbot

Medical RAG Chatbot is a Retrieval-Augmented Generation (RAG) application that answers medical questions using trusted knowledge extracted from PDF documents (e.g., guidelines, manuals, clinical notes). It combines Groq LLM, Hugging Face embeddings, FAISS vector search, and LangChain to provide grounded answers with relevant context.

The app provides a Flask backend API, a lightweight HTML/CSS frontend, containerization with Docker, vulnerability scanning using Trivy, and CI/CD automation via Jenkins for AWS deployment.

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✨ Features

• 📄 Ingest medical PDFs using PyPDF
• 🔍 Semantic retrieval using Hugging Face embeddings + FAISS
• 🤖 Context-grounded answers using Groq LLM
• 🔗 LangChain orchestration for RAG pipeline (retrieval + generation)
• 🌐 Flask API for chat + ingestion endpoints
• 🎨 Simple HTML/CSS web UI
• 🐳 Dockerized application for consistent deployment
• 🔐 Security scanning using Aqua Trivy (Docker image vulnerabilities)
• 🔁 Jenkins CI/CD pipeline for automated build, scan, and deploy
• ☁️ AWS deployment-ready workflow

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🧠 How It Works (RAG Flow)

1. PDFs are loaded and text is extracted using PyPDF
2. Text is chunked and embedded using Hugging Face embeddings
3. Embeddings are indexed in FAISS
4. User query is embedded and matched against FAISS for top relevant chunks
5. Retrieved context is passed to the Groq LLM via LangChain
6. Chatbot returns an answer grounded in retrieved evidence

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🧱 Tech Stack

Category	Tools
LLMs	Groq
Embeddings	Hugging Face
RAG Framework	LangChain
Vector Store	FAISS (local)
PDF Processing	PyPDF
Backend	Flask
Frontend	HTML / CSS
Containerization	Docker
Security Scanning	Aqua Trivy
CI/CD	Jenkins
Cloud	AWS
SCM	GitHub

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⚙️ Setup & Run Locally

1️⃣ Clone

git clone https://github.com/your-username/medical-rag-chatbot.git
cd medical-rag-chatbot

2️⃣ Create virtual environment (recommended)

python -m venv ven
source venv/bin/activate   # Windows: venv\Scripts\activate

3️⃣ Install dependencies

pip install -e .

4️⃣ Run Flask backend

python src/app.py