AI Research Summarizer - Final Project

📋 Project Overview

An advanced AI-powered research tool that transforms PDF papers into interactive summaries, videos, and intelligent Q&A sessions using Google Gemini, ElevenLabs TTS, and RAG technology.

🎯 Key Features

• PDF Upload & Analysis - Upload research papers with automatic processing
• AI-Generated Summaries - Comprehensive structured summaries
• Interactive Q&A - Chat with documents using RAG technology
• Video Generation - Transform summaries into professional videos with TTS
• User Authentication - Secure multi-user system
• Email Notifications - n8n automation for process alerts

🏗️ Architecture

High-Level Components

• Frontend: React 18 + Vite
• Backend: Flask REST API
• Vector Database: ChromaDB
• AI Services: Google Gemini 2.5 Flash-Lite
• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• TTS: ElevenLabs API
• Automation: n8n workflows

🚀 Quick Start

Backend Setup

cd backend
pip install -r requirements.txt

Environment Configuration

cp .env.example .env
# Add your API keys:
# GOOGLE_API_KEY=your_gemini_key
# ELEVENLABS_API_KEY=your_elevenlabs_key

Start Services

# Backend
cd backend && flask --app app run --port=5001

# Frontend  
cd Frontend && npm install && npm run dev

# n8n (optional)
npx n8n

📊 System Flows

This project includes detailed flow diagrams:

• High-Level Architecture - System component overview
• RAG Flow - Document processing and Q&A pipeline
• Video Generation - TTS and slide creation process
• n8n Automation - Email notification workflow

🔧 Technologies Used

Backend Stack

• Flask - Web framework
• LangChain - Document processing and RAG
• ChromaDB - Vector database
• Sentence Transformers - Local embeddings
• Google Gemini - LLM for analysis and generation
• ElevenLabs - Text-to-speech
• MoviePy - Video composition
• PIL/Pillow - Slide generation

Frontend Stack

• React 18 - UI framework
• Vite - Build tool
• Axios - HTTP client

Automation

• n8n - Workflow automation
• JWT - Authentication

📈 Performance Metrics

• PDF Processing: 2-5 seconds
• Q&A Response: 1-3 seconds
• Video Generation: 2-3 minutes
• Summary Generation: 3-8 seconds

🎓 Academic Use

This project demonstrates:

• Modern full-stack development
• AI/ML integration (RAG, embeddings, LLMs)
• Microservices architecture
• Automated workflows
• Professional UI/UX design

📄 License

MIT License - Academic and research use

🤝 Contributing

This is a final project submission. For issues or questions, please refer to the documentation and code comments.

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Created for Advanced AI Systems Course
Date: December 2025