RAG Analysis Model is a Retrieval-Augmented Generation (RAG) based web application that allows users to upload documents through a simple user interface and prepare them for future analysis using AI-driven retrieval systems. The project currently focuses on the frontend upload interface and Flask backend integration for document handling.

This system is designed as a foundation for a full RAG pipeline where uploaded documents will later be processed for embeddings, retrieval, and intelligent question answering.

• 📂 Document Upload Interface (UI)
• 📡 Flask Backend API for file uploads
• 🗂️ Automatic file storage in the uploads folder
• 🖥️ Simple and clean user interface (HTML, CSS)
• 🔗 REST API integration with frontend
• 🧱 Scalable architecture for future RAG pipeline (Embeddings + Vector Search)

Frontend:

• HTML
• CSS
• JavaScript (Basic)

Backend:

• Python
• Flask
• Flask-CORS

Development Tools:

• VS Code
• Git & GitHub

RAG-Analysis-Model/
│── app.py
│── uploads/
│── templates/
│── static/
│── README.md

• app.py → Flask backend server
• uploads/ → Stores uploaded documents
• templates/ → Frontend HTML files
• static/ → CSS and JS files

git clone https://github.com/TM11005/TM11005.git
cd RAG-Analysis-Model

python -m venv venv
venv\Scripts\activate   # For Windows

pip install flask flask-cors

python app.py

Open your browser and go to:

http://localhost:5000

Endpoint: /api/upload Method: POST Description: Uploads documents and saves them to the uploads folder for future RAG processing.

✔️ UI for document upload completed ✔️ Flask backend for file handling implemented ❌ Query/Chat RAG pipeline not yet implemented ❌ Embeddings and vector database integration pending

• 🔍 Add vector database (FAISS / ChromaDB)
• 💬 Implement chat/query endpoint for document Q&A
• 🤖 Integrate LLM for intelligent responses
• 🔐 Add authentication (Student/Teacher/Admin roles)
• ⚡ Upgrade UI with React or Angular
• 📊 Document analysis dashboard

This project was developed to:

• Understand Retrieval-Augmented Generation (RAG) architecture
• Build full-stack AI applications using Flask
• Design REST APIs for AI workflows
• Create a scalable frontend for AI-based systems

Developed as part of a B.E. Computer Science project focusing on AI, cybersecurity, and intelligent document analysis systems.

This project is for educational and academic purposes.