AI Doc Read Studio

When teams gather together to review important documents, magic happens. Different perspectives collide, expertise mingles, and insights emerge that no single person could have discovered alone.

This project uses Strands Agents and Amazon Nova models to build AI Doc Read Studio, a web-based collaborative document review application using AI agents to simulate a discussion.

You can use it to prepare for a meeting or just get feedback on what you're working on.

Features

• Document Upload: Support for TXT, Markdown, Word (.docx), and PDF files
• Team Management: Configure AI agents with custom roles and Nova model selection
• Multi-Agent Conversations: Intelligent discussions using Strands Agents framework
• Real-time Updates: WebSocket-based live conversation updates
• Export Options: Download conversations and action plans as Markdown or PDF
• Model Selection: Choose from Amazon Nova Micro, Lite, Pro, or Premier models

Quick Start

Prerequisites

• Python 3.10+
• UV package manager - fastest Python package installer
• AWS credentials configured for Bedrock access

Quick Start

git clone https://github.com/danilop/ai-doc-read-studio
cd ai-doc-read-studio
# Simplified way to install dependencies and start both backend and frontend servers
./start.sh

Open the Web Interface: http://localhost:3000

Installation

1. Clone the repository:

   git clone https://github.com/danilop/ai-doc-read-studio
   cd ai-doc-read-studio

2. Install dependencies with UV:

   uv sync

Running the Application

🚀 Recommended: Unified Startup

# Start both backend and frontend servers
uv run python start_app.py

Alternative: Start Servers Separately

Backend:

uv run uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload

Frontend:

uv run python -m http.server 3000 --directory frontend

Access the Application

• Web Interface: http://localhost:3000
• API Documentation: http://localhost:8000/docs
• Backend API: http://localhost:8000

Usage

Basic Workflow

1. Upload Documents: Drag and drop or select files (TXT, MD, DOCX, PDF)
2. Configure Team: Set up AI agents with different roles and models
3. Start Discussion: Let agents analyze and discuss the document
4. Interactive Chat: Ask follow-up questions and guide the conversation
5. Export Results: Download conversations and action plans

Team Setup

The application supports multiple AI agents with different perspectives:

• Product Manager: Strategy and market analysis
• Tech Lead: Technical architecture and implementation
• UX Designer: User experience and design considerations
• Security Expert: Security and compliance review

Model Options

Choose from Amazon Nova models based on your needs:

• Nova Micro: Fast, basic analysis
• Nova Lite: Balanced performance and cost
• Nova Pro: Advanced reasoning capabilities
• Nova Premier: Highest quality analysis

Development

Project Structure

ai-doc-read-studio/
├── backend/                 # FastAPI backend
│   ├── main.py             # Main application
│   ├── agents.py           # AI agents integration
│   └── document_parser.py  # Document processing
├── frontend/               # Web interface
│   ├── index.html          # Main UI
│   ├── app.js              # Frontend logic
│   └── config.js           # Configuration
├── tests/                  # Test suite
├── pyproject.toml          # UV dependencies
├── config.json             # Application config
└── sample_doc.md          # Sample document

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=backend --cov-report=html

# Run specific test files
uv run pytest tests/test_api.py -v

Code Quality

# Format code
uv run black .

# Lint code  
uv run ruff check .

# Fix linting issues
uv run ruff check . --fix

Development Dependencies

Install development tools:

uv sync --dev

Configuration

Application Settings

Edit config.json to customize:

{
  "backend": {
    "host": "0.0.0.0",
    "port": 8000,
    "log_level": "info"
  },
  "frontend": {
    "host": "localhost",
    "port": 3000
  },
  "api": {
    "base_url": "http://localhost:8000"
  }
}

AWS Configuration

For production use with real AI models:

1. Configure AWS credentials:

   aws configure
   # OR set environment variables
   export AWS_ACCESS_KEY_ID=your_key
   export AWS_SECRET_ACCESS_KEY=your_secret  
   export AWS_DEFAULT_REGION=us-east-1

2. Ensure Bedrock access: Your AWS account needs access to Amazon Bedrock and Nova models

API Endpoints

• POST /upload - Upload documents
• POST /sessions - Create discussion session
• POST /sessions/{id}/prompt - Add conversation prompt
• GET /sessions/{id} - Get session details
• POST /sessions/{id}/export - Export conversation
• POST /export/content - Export arbitrary content
• WebSocket /ws - Real-time updates

Troubleshooting

1. Installation issues: Ensure UV is installed: curl -LsSf https://astral.sh/uv/install.sh | sh
2. Port conflicts: Check if ports 8000/3000 are available
3. AWS credentials: Demo mode works without AWS; production needs Bedrock access
4. Module errors: Always use uv run for Python commands
5. Cache issues: Clear browser cache or use Ctrl+F5

Contributing

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Run quality checks: uv run pytest && uv run ruff check . && uv run black .
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• GitHub Issues: For bugs and feature requests
• Documentation: Check the /docs endpoint when running
• Sample Document: Use sample_doc.md for testing