MrBoB Agent Tools

A comprehensive suite of tools for insurance agents to manage and analyze policy data, generate reports, and track performance metrics.

Features

• Policy Processing: Efficient processing of policy data with validation and status resolution
• Performance Analytics: Advanced analytics with trend analysis and predictions
• Report Generation: Customizable reports in multiple formats (PDF, Excel)
• Caching Strategy: Optimized performance with intelligent caching
• Event Sourcing: Robust state tracking and audit trails
• Concurrent Processing: Efficient handling of large datasets

Development Setup

1. Clone the repository:

git clone https://github.com/laith-abood/MrBoB-Agent-Tools.git
cd MrBoB-Agent-Tools

2. Set up development environment:

# Make setup script executable
chmod +x scripts/setup_dev.sh

# Run setup script
./scripts/setup_dev.sh

3. Activate virtual environment:

source venv/bin/activate

Project Structure

mrbob/
├── core/               # Core processing functionality
│   ├── policy_processor.py
│   └── status_resolver.py
├── analytics/          # Performance analytics
│   └── performance_analyzer.py
└── reports/           # Report generation
    ├── application/   # Application services
    │   └── report_service.py
    └── domain/        # Domain models
        └── report_aggregate.py

Usage Example

from mrbob import (
    PolicyProcessor,
    ReportService,
    PerformanceAnalyzer
)

# Process policy data
processor = PolicyProcessor()
result = await processor.process_policy_data("data/policies.csv")

# Analyze performance
analyzer = PerformanceAnalyzer()
metrics = analyzer.calculate_agent_metrics(result['data'])

# Generate reports
report_service = ReportService()
report_id = await report_service.generate_report(metrics)

See examples/process_and_report.py for a complete example.

Key Components

Policy Processor

• Validates and processes raw policy data
• Resolves policy statuses with conflict handling
• Supports concurrent processing with retry logic

Performance Analyzer

• Calculates comprehensive performance metrics
• Provides trend analysis and predictions
• Supports various aggregation strategies

Report Service

• Generates customized reports
• Supports multiple output formats
• Implements CQRS pattern for scalability

Contributing

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

License

MIT License - see LICENSE file for details.

Setting Up and Running the Server

1. Ensure you have Python 3.8 or higher installed.
2. Install the required dependencies:

pip install -r requirements.txt

3. Run the FastAPI server:

uvicorn server:app --reload

4. Open your browser and navigate to http://127.0.0.1:8000 to access the application.

Using Different Features of the Repository

Processing Policy Data

To process policy data, use the PolicyProcessor class. Here's an example:

from mrbob import PolicyProcessor

processor = PolicyProcessor()
result = await processor.process_policy_data("data/sample_policy_data.csv")
print(result)

Analyzing Performance

To analyze performance metrics, use the PerformanceAnalyzer class. Here's an example:

from mrbob import PerformanceAnalyzer

analyzer = PerformanceAnalyzer()
metrics = analyzer.calculate_agent_metrics(result['data'])
print(metrics)

Generating Reports

To generate reports, use the ReportService class. Here's an example:

from mrbob import ReportService, GenerateReportCommand, AddSectionCommand, FinalizeReportCommand, ReportType

report_service = ReportService()
command = GenerateReportCommand(
    agent_npn="12345",
    report_type=ReportType.PERFORMANCE_ANALYSIS,
    generated_by="system",
    options={"include_charts": True, "format": "pdf"}
)
report_id = await report_service.handle_command(command)

metrics_section = AddSectionCommand(
    report_id=report_id,
    title="Performance Metrics",
    content=metrics.to_dict()['summary'],
    order=1,
    template_key="metrics"
)
await report_service.handle_command(metrics_section)

policy_section = AddSectionCommand(
    report_id=report_id,
    title="Policy Details",
    content={"policies": result['data']['policies']},
    order=2,
    template_key="policies"
)
await report_service.handle_command(policy_section)

finalize_command = FinalizeReportCommand(
    report_id=report_id,
    output_path="output/reports/agent_12345_report.pdf"
)
await report_service.handle_command(finalize_command)