DocAI :- Intelligent Document Analysis Platform

Live Demo: https://docai-analysis-platform.vercel.app/

An AI-powered document analysis platform that processes 100-page PDFs in under 30 seconds and answers questions with 90%+ accuracy using advanced RAG (Retrieval-Augmented Generation) technology.

✨ Features

• Multi-Format Support - Process PDFs, DOCX, Excel files and scanned documents with OCR
• AI-Powered Q&A - Ask questions and get instant answers powered by GPT-4, Claude and Gemini
• Lightning Fast - Optimized RAG pipeline processes documents in seconds
• Multi-Model AI - Intelligent model selection with automatic fallback for reliability
• Advanced Analytics - Extract entities, analyze sentiment and visualize insights
• Secure & Private - End-to-end encryption with secure cloud storage

🚀 Tech Stack

• Frontend: Next.js 16, React, TypeScript, Tailwind CSS, shadcn/ui
• Backend: Next.js API Routes, Drizzle ORM
• AI/ML: LangChain, OpenAI, Anthropic Claude, Google Gemini
• Storage: AWS S3, Pinecone Vector Database
• Auth: Better Auth
• Document Processing: PDF.js, Mammoth (DOCX), SheetJS (Excel)

📦 Installation

1. Clone the repository

git clone https://github.com/anjola-adeuyi/docai-analysis-platform.git
cd docai-analysis-platform

1. Install dependencies

npm install

1. Set up environment variables

Copy .env.example to .env.local and fill in your values:

cp .env.example .env.local

Required environment variables:

# Database
DATABASE_URL=postgresql://user:password@host:5432/database

# Auth
BETTER_AUTH_SECRET=your-secret-key-here
BETTER_AUTH_URL=http://localhost:3000

# AI Models
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_AI_API_KEY=...

# Vector Database
PINECONE_API_KEY=...
PINECONE_INDEX=your-index-name

# File Storage
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
AWS_REGION=us-east-1
S3_BUCKET_NAME=your-bucket-name

# Caching (Optional but recommended)
UPSTASH_REDIS_URL=https://...
UPSTASH_REDIS_TOKEN=...

See Environment Variables Guide for detailed information.

1. Run the development server

npm run dev

Open http://localhost:3000 to see the application.

Database Setup

Run database migrations:

npm run db:migrate

Or use Drizzle Kit to push schema:

npx drizzle-kit push

🏗️ Project Structure

├── app/                  # Next.js app directory
│   ├── (auth)/           # Authentication pages
│   ├── (dashboard)/      # Dashboard pages
│   └── api/              # API routes
├── components/           # React components
│   ├── auth/             # Authentication components
│   ├── dashboard/        # Dashboard components
│   ├── document/         # Document viewer & uploader
│   └── ui/               # shadcn/ui components
├── lib/                  # Core libraries
│   ├── ai/               # AI & RAG pipeline
│   ├── db/               # Database schema
│   ├── processors/       # Document processors
│   └── vector/           # Vector database
├── public/               # Static assets
├── tests/                # Test files
│   ├── ai/               # AI pipeline tests
│   ├── integration/      # Integration tests
│   └── e2e/              # E2E tests
└── docs/                 # Documentation

📚 Documentation

• API Documentation - Complete API reference with all endpoints
• Environment Variables - Environment setup guide
• CI/CD Guide - Continuous integration and deployment
• Security Best Practices - Security guidelines

🧪 Testing

Run the test suite:

# Unit and integration tests
npm test

# E2E tests (requires dev server running)
npm run test:e2e

# Test with UI
npm run test:ui

# Coverage report
npm run test:coverage

🚀 Deployment

Deploy to Vercel

1. Connect your GitHub repository to Vercel
2. Configure environment variables in Vercel dashboard:
   • Go to Project Settings → Environment Variables
   • Add all required variables from .env.example
3. Deploy:
   • Vercel will automatically deploy on every push to main branch
   • Or manually trigger deployment from Vercel dashboard

Production Checklist

• All environment variables configured
• Database migrations run
• Pinecone index created
• S3 bucket configured with proper permissions
• Redis cache configured (optional but recommended)
• Domain configured (if using custom domain)
• SSL certificate active

See Deployment Guide for detailed instructions.

🔑 Key Features

RAG Pipeline

Advanced retrieval-augmented generation with:

• Intelligent document chunking
• Vector embeddings with Pinecone
• Multi-model query processing
• Context-aware responses

Document Processing

• PDF: Extract text, images and metadata
• DOCX: Parse Word documents with formatting
• Excel: Process spreadsheets and data tables
• OCR: Handle scanned documents

AI Integration

• OpenAI GPT-4: General-purpose reasoning
• Anthropic Claude: Long-context analysis
• Google Gemini: Multimodal understanding
• Intelligent Fallback: Automatic model switching for reliability

🛠️ Development

Available Scripts

# Development
npm run dev          # Start development server
npm run build        # Build for production
npm run start        # Start production server

# Database
npm run db:migrate   # Run database migrations
npm run db:studio    # Open Drizzle Studio

# Testing
npm test             # Run unit/integration tests
npm run test:e2e     # Run E2E tests
npm run test:ui      # Run tests with UI
npm run test:coverage # Generate coverage report

# Code Quality
npm run lint         # Run ESLint
npm run type-check   # TypeScript type checking

📝 License

MIT License - feel free to use this project for personal or commercial purposes.

👨‍💻 Author

Built by Anjola Adeuyi - Senior Full-Stack + AI Engineer

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