🤖 Multimodal Web Research Intelligence Agent

A cutting-edge AI-powered research agent that combines web browsing, screenshot analysis, and NVIDIA's advanced AI models to provide comprehensive multimodal research insights.

🌟 Features

🧠 Advanced AI Capabilities

• NVIDIA Nemotron Integration: Leverages NVIDIA's latest LLM models for advanced reasoning
• Vision Analysis: Screenshot analysis using NVIDIA Vision models
• Multimodal Understanding: Combines text and visual content for comprehensive insights

🌐 Web Research Automation

• Intelligent Browsing: Automated web navigation with Playwright
• Content Extraction: Smart text extraction and cleaning
• Screenshot Capture: Automatic visual content capture
• Multi-URL Processing: Parallel processing of multiple sources

💾 Session Management

• Memory System: Persistent storage of research sessions
• Search History: Query tracking and suggestions
• User Preferences: Customizable settings and preferences

🎨 Modern Interface

• Beautiful UI: Responsive, modern web interface
• Real-time Updates: Live progress tracking
• Interactive Results: Rich formatting and source attribution

🏗️ Architecture

graph TB
    A[Frontend Interface] --> B[FastAPI Backend]
    B --> C[Agent Core]
    C --> D[Browser Tools]
    C --> E[Vision Analysis]
    C --> F[NVIDIA AI Models]
    D --> G[Playwright Browser]
    E --> H[Vision Models]
    F --> I[Nemotron LLM]
    C --> J[Memory System]
    J --> K[SQLite Database]

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js (for Playwright)
• NVIDIA API Access (for full functionality)

Installation

1. Clone the repository:

git clone <repository-url>
cd multimodal-browser-agent

2. Install Python dependencies:

pip install -r requirements.txt

3. Install Playwright browsers:

playwright install

4. Configure environment variables:

cp .env.example .env
# Edit .env with your NVIDIA API credentials

5. Start the application:

python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

6. Access the interface:

• Web UI: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• Health Check: http://localhost:8000/health

⚙️ Configuration

Environment Variables

Create a .env file with the following configurations:

# NVIDIA API Configuration
NVIDIA_API_KEY=your_nvidia_nim_api_key_here
NVIDIA_VISION_ENDPOINT=https://your-vision-api-endpoint.com/api/v1/vision
NIM_ENDPOINT=https://your-nim-endpoint.com/api/v1/generate

# Database Configuration
DATABASE_URL=sqlite:///./agent_memory.db

# FastAPI Configuration
API_HOST=0.0.0.0
API_PORT=8000
DEBUG=True

# Browser Configuration
HEADLESS_BROWSER=True
BROWSER_TIMEOUT=30000

NVIDIA API Setup

1. Get API Access:

   • Visit NVIDIA Developer
   • Sign up for NIM (NVIDIA Inference Microservices) access
   • Obtain your API key

2. Configure Endpoints:

   • Update NVIDIA_API_KEY with your API key
   • Set NIM_ENDPOINT to your NIM service URL
   • Configure NVIDIA_VISION_ENDPOINT for vision capabilities

🔧 API Reference

Research Endpoint

POST /agent/research

Perform multimodal web research on provided URLs.

{
  "query": "What are the latest AI trends?",
  "urls": [
    "https://example.com/ai-news",
    "https://example.com/tech-trends"
  ],
  "max_tokens": 512,
  "include_screenshots": true
}

Response:

{
  "result": "Comprehensive analysis results...",
  "sources_analyzed": ["https://example.com/ai-news"],
  "vision_insights": ["Screenshot analysis results..."],
  "processing_time": 15.2,
  "timestamp": "2024-01-15T10:30:00Z"
}

Vision Analysis Endpoint

POST /agent/vision

Analyze uploaded images using NVIDIA vision models.

• Upload image file
• Optional query parameter for targeted analysis

Health Check

GET /health

Check system health and service status.

🧪 Usage Examples

Basic Research Query

import requests

response = requests.post("http://localhost:8000/agent/research", json={
    "query": "Compare the latest GPU architectures from NVIDIA",
    "urls": [
        "https://www.nvidia.com/en-us/geforce/graphics-cards/",
        "https://www.nvidia.com/en-us/data-center/a100/"
    ],
    "max_tokens": 1024,
    "include_screenshots": True
})

results = response.json()
print(results["result"])

Image Analysis

import requests

with open("screenshot.png", "rb") as f:
    response = requests.post(
        "http://localhost:8000/agent/vision",
        files={"file": f},
        data={"query": "What UI elements are visible in this screenshot?"}
    )

analysis = response.json()
print(analysis["analysis"])

🏭 Production Deployment

Docker Deployment

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
RUN playwright install --with-deps

COPY . .
EXPOSE 8000

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Cloud Deployment Options

1. NVIDIA DGX Cloud: Optimal for GPU-accelerated inference
2. AWS/GCP/Azure: Standard cloud deployment
3. Kubernetes: Scalable container orchestration
4. Docker Compose: Multi-service local deployment

🔍 Development

Project Structure

multimodal-browser-agent/
├── app/
│   ├── main.py              # FastAPI application
│   ├── agent/
│   │   ├── core.py          # Agent orchestration
│   │   ├── browser_tools.py # Playwright automation
│   │   └── vision.py        # Vision model integration
│   ├── models/
│   │   └── schemas.py       # API schemas
│   └── utils/
│       └── memory.py        # Memory management
├── frontend/
│   └── index.html          # Web interface
├── requirements.txt        # Python dependencies
├── .env.example           # Environment template
└── README.md             # Documentation

Development Setup

1. Create virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

2. Install dependencies:

pip install -r requirements.txt
pip install pytest pytest-asyncio  # For testing

3. Run tests:

pytest

4. Development server:

uvicorn app.main:app --reload --log-level debug

Adding New Features

1. New Agent Tools: Extend app/agent/ modules
2. API Endpoints: Add routes to app/main.py
3. Frontend Features: Modify frontend/index.html
4. Data Models: Update app/models/schemas.py

🧪 Testing

Unit Tests

# Run all tests
pytest

# Run specific test file
pytest tests/test_agent.py

# Run with coverage
pytest --cov=app tests/

Integration Tests

# Test API endpoints
pytest tests/test_api.py

# Test browser automation
pytest tests/test_browser.py

Load Testing

# Using Locust
pip install locust
locust -f tests/load_test.py --host=http://localhost:8000

📊 Monitoring & Analytics

Built-in Monitoring

• Health Check: /health endpoint
• Agent Status: /agent/status endpoint
• Database Stats: Memory usage and session statistics

Logging

The application uses structured logging:

import logging
logger = logging.getLogger(__name__)

# Logs are automatically formatted with timestamps and levels
logger.info("Research completed successfully")
logger.error("NVIDIA API error", extra={"status_code": 500})

Performance Metrics

• Processing time tracking
• Memory usage monitoring
• API response times
• Success/failure rates

🔒 Security Considerations

API Security

• Rate Limiting: Implement request rate limiting
• Authentication: Add API key authentication for production
• Input Validation: Comprehensive input sanitization
• CORS Configuration: Proper cross-origin settings

Data Privacy

• Secure Storage: Encrypted database connections
• Data Retention: Configurable session cleanup
• User Privacy: No sensitive data logging

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Code Style

• Follow PEP 8 for Python code
• Use type hints
• Add docstrings to functions
• Keep functions focused and modular

📝 License

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

🙏 Acknowledgments

• NVIDIA: For providing cutting-edge AI models and infrastructure
• FastAPI: For the excellent web framework
• Playwright: For robust browser automation
• LangChain: For AI application development tools

📞 Support

• Documentation: Check the /docs endpoint
• Issues: Report bugs via GitHub issues
• Discussions: Join community discussions
• Email: Contact the development team

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Built with ❤️ using NVIDIA AI Stack

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