🧠 DocAnalyzer AI - Intelligent Document Processing Platform

Transform your documents into intelligent conversations with AI-powered analysis, persistent chat history, and seamless Google Drive integration.

✨ Features

🔐 Secure Authentication

• Google OAuth integration with NextAuth.js
• Protected routes and middleware
• Persistent user sessions

📄 Smart Document Processing

• Multi-format Support: PDF, DOCX, TXT files
• Google Drive Integration: Automatic cloud storage and sync
• Gemini 2.5 Flash: Advanced AI-powered document analysis
• Intelligent Insights: Summary generation, sentiment analysis, entity extraction

💬 RAG-Powered Conversations

• Context-Aware Chat: Ask questions about your documents
• Persistent History: All conversations saved to MySQL database
• Vector Search: Powered by Pinecone and Cohere embeddings
• Source Citations: Answers with references to original document sections

🎨 Modern User Interface

• Dark-First Design: Sleek violet and purple gradient themes
• Responsive Layout: Mobile-first design with Tailwind CSS
• Smooth Animations: Framer Motion powered interactions
• Glass Morphism: Modern frosted glass effects

📊 Analytics & Management

• Upload History: Track all processed documents
• Usage Analytics: Monitor API usage and costs
• Real-time Status: Live processing and connection indicators

🏗️ Architecture

graph TB
    A[Next.js Frontend] --> B[NextAuth.js]
    A --> C[FastAPI Backend]
    C --> D[MySQL Database]
    C --> E[Google Drive API]
    C --> F[Gemini LLM]
    C --> G[Pinecone Vector DB]
    C --> H[Cohere Embeddings]
    
    subgraph "Authentication"
        B --> I[Google OAuth]
    end
    
    subgraph "Document Processing"
        F --> J[Analysis & Insights]
        H --> K[Text Embeddings]
        G --> L[Vector Storage]
    end
    
    subgraph "Data Persistence"
        D --> M[User Data]
        D --> N[Chat History]
        D --> O[Document Metadata]
        E --> P[File Storage]
    end

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🛠️ Tech Stack

Frontend

• Framework: Next.js 14 (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• Animations: Framer Motion
• Authentication: NextAuth.js
• Database ORM: Prisma
• Icons: React Icons

Backend

• Framework: FastAPI
• Language: Python 3.9+
• Database: MySQL 8.0
• Authentication: JWT with NextAuth integration

AI & ML Services

• LLM: Google Gemini 2.5 Flash
• Embeddings: Cohere embed-multilingual-v3.0
• Vector Database: Pinecone
• Cloud Storage: Google Drive API

DevOps & Deployment

• Frontend Hosting: Vercel
• Backend Hosting: Railway/Heroku
• Database: PlanetScale/AWS RDS
• Monitoring: Built-in analytics

🚀 Quick Start

Prerequisites

• Node.js 18+
• Python 3.9+
• MySQL 8.0+
• Google Cloud Platform account
• Pinecone account
• Cohere account

Installation

# Clone the repository
git clone https://github.com/yourusername/document-analyzer.git

cd document-analyzer

# Setup frontend
cd frontend
npm install
cp .env.example .env.local

# Setup backend
cd ../backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env

# Database setup
mysql -u root -p -e "CREATE DATABASE document_analyzer_db;"
mysql -u root -p document_analyzer_db < database_schema.sql

# Run the application
# Terminal 1: Backend
cd backend && uvicorn main:app --reload

# Terminal 2: Frontend
cd frontend && npm run dev

📖 For detailed setup instructions, see Installation Guide

📁 Project Structure

docanalyzer-ai/
├── frontend/                 # Next.js application
│   ├── src/
│   │   ├── app/             # App router pages
│   │   │   ├── page.tsx     # Homepage
│   │   │   ├── dashboard/   # User dashboard
│   │   │   ├── upload/      # Document upload
│   │   │   ├── chat/        # Chat interface
│   │   │   └── api/         # API routes
│   │   ├── components/      # React components
│   │   │   ├── Navbar.tsx
│   │   │   ├── Hero.tsx
│   │   │   ├── Features.tsx
│   │   │   └── Footer.tsx
│   │   ├── middleware.ts    # Route protection
│   │   └── auth.ts         # NextAuth config
│   ├── prisma/             # Database schema
│   └── tailwind.config.js  # Styling config
├── backend/                # FastAPI application
│   ├── main.py            # FastAPI main app
│   ├── requirements.txt   # Python dependencies
│   ├── database_schema.sql # MySQL schema
│   └── google-credentials.json # Google API credentials
├── docs/                  # Documentation
│   ├── installation-guide.md
│   ├── api-documentation.md
│   └── interview-qa.md
└── README.md             # This file

🔧 Configuration

Environment Variables

Frontend (.env.local)

NEXTAUTH_URL="http://localhost:3000"
NEXTAUTH_SECRET="your-secret-key"
GOOGLE_CLIENT_ID="your-google-client-id"
GOOGLE_CLIENT_SECRET="your-google-client-secret"
DATABASE_URL="mysql://user:password@localhost:3306/document_analyzer_db"

Backend (.env)

GEMINI_API_KEY="your-gemini-api-key"
PINECONE_API_KEY="your-pinecone-api-key"
COHERE_API_KEY="your-cohere-api-key"
PINECONE_INDEX_NAME="document-analyzer"
GOOGLE_DRIVE_FOLDER_ID="your-folder-id"
GOOGLE_APPLICATION_CREDENTIALS="./google-credentials.json"
MYSQL_HOST="127.0.0.1"
MYSQL_USER="root"
MYSQL_PASSWORD="your-password"
MYSQL_DATABASE="document_analyzer_db"

📋 API Documentation

Authentication Endpoints

• GET /api/auth/signin - Google OAuth sign-in
• GET /api/auth/signout - Sign out user
• GET /api/auth/session - Get current session

Document Endpoints

• POST /api/upload - Upload document to Google Drive
• GET /api/documents - Get user's document history
• GET /api/documents/{id} - Get specific document details

Chat Endpoints

• POST /api/chat - Send message about document
• GET /api/chat-history/{document_id} - Get chat history for document

Analytics Endpoints

• GET /api/analytics/usage - Get user usage statistics
• GET /api/analytics/costs - Get API usage costs

🎯 Use Cases

📚 Academic Research

• Upload research papers and ask specific questions
• Extract key findings and methodologies
• Generate comprehensive summaries

💼 Business Analysis

• Process financial reports and business documents
• Extract KPIs and important metrics
• Generate executive summaries

⚖️ Legal Document Review

• Analyze contracts and legal documents
• Extract key terms and obligations
• Identify potential risks or issues

🏥 Medical Documentation

• Process medical reports and research
• Extract patient information and recommendations
• Summarize complex medical terminology

🔒 Security & Privacy

• End-to-End Encryption: All data encrypted in transit and at rest
• Private Storage: Documents stored in user's private Google Drive
• Secure Authentication: OAuth 2.0 with Google
• Data Isolation: Each user's data completely isolated
• No Data Sharing: Your documents and conversations remain private

🚀 Performance

• Fast Processing: Average document analysis in 2-3 seconds
• Scalable Architecture: Handles concurrent users efficiently
• Optimized Queries: Database indexes for quick retrieval
• CDN Integration: Fast global content delivery
• Caching Strategy: Redis caching for improved response times

🔮 Roadmap

Version 2.0 (Q2 2025)

• Multi-language document support
• Batch document processing
• Advanced analytics dashboard
• API rate limiting and quotas

Version 2.1 (Q3 2025)

• Team collaboration features
• Document comparison tools
• Integration with Microsoft Office
• Mobile app development

Version 3.0 (Q4 2025)

• Custom AI model training
• Enterprise SSO integration
• Advanced security features
• White-label solutions

🤝 Contributing

We welcome contributions!

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

Code Style

• Frontend: ESLint + Prettier
• Backend: Black + isort
• Commit messages: Conventional Commits

📞 Support

• Email Support: [email protected]
• GitHub Issues: Create an issue

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Google Cloud for Gemini API and Drive integration
• Pinecone for vector database services
• Cohere for multilingual embeddings
• Vercel for seamless deployment
• Next.js team for the amazing framework

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Made with ❤️ by Harsh Srivastava