Compose Industrial Diagnostic Agent Networks with Event-Driven Routing Using BeeAI and PydanticAI

"""\nProduction implementation for composing industrial diagnostic agent networks using event-driven routing with BeeAI and PydanticAI.\nProvides secure, scalable operations.\n"""\nfrom typing import Dict, Any, List, Optional\nimport os\nimport logging\nimport httpx\nfrom pydantic import BaseModel, ValidationError\n\n# Configure logging for the application\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nclass Config:\n    """Configuration class for environment variables."""\n    database_url: str = os.getenv('DATABASE_URL')\n    api_url: str = os.getenv('API_URL')\n\nclass InputData(BaseModel):\n    """Data model for input validation using Pydantic."""\n    id: str\n    value: float\n\ndef validate_input(data: Dict[str, Any]) -> bool:\n    """Validate request data.\n    \n    Args:\n        data: Input to validate\n    Returns:\n        True if valid\n    Raises:\n        ValueError: If validation fails\n    """\n    try:\n        InputData(**data)  # Validate and parse data using Pydantic\n    except ValidationError as e:\n        logger.error(f'Validation error: {e}')\n        raise ValueError('Invalid input data')\n    return True\n\ndef sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:\n    """Sanitize input fields.\n    \n    Args:\n        data: Raw input data\n    Returns:\n        Sanitized data\n    """\n    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace\n\ndef normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:\n    """Normalize input data for processing.\n    \n    Args:\n        data: Input data to normalize\n    Returns:\n        Normalized data\n    """\n    data['value'] = round(data['value'], 2)  # Round value to 2 decimal places\n    return data\n\ndef transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n    """Transform list of records for processing.\n    \n    Args:\n        records: List of input records\n    Returns:\n        Transformed records\n    """\n    return [normalize_data(record) for record in records]  # Normalize each record\n\ndef process_batch(batch: List[Dict[str, Any]]) -> None:\n    """Process a batch of data records.\n    \n    Args:\n        batch: List of normalized records\n    """\n    for record in batch:\n        logger.info(f'Processing record: {record}')  # Log each record being processed\n        # Add business logic processing here\n\ndef aggregate_metrics(metrics: List[float]) -> float:\n    """Aggregate metrics from processed data.\n    \n    Args:\n        metrics: List of metrics to aggregate\n    Returns:\n        Aggregated value\n    """\n    return sum(metrics) / len(metrics) if metrics else 0.0  # Average value\n\nasync def fetch_data(url: str) -> Dict[str, Any]:\n    """Fetch data from an external API.\n    \n    Args:\n        url: API endpoint to fetch data from\n    Returns:\n        Data fetched from the API\n    Raises:\n        httpx.HTTPStatusError: If the request fails\n    """\n    async with httpx.AsyncClient() as client:\n        response = await client.get(url)\n        response.raise_for_status()  # Raise error for bad responses\n        return response.json()\n\ndef save_to_db(data: Dict[str, Any]) -> None:\n    """Save processed data to the database.\n    \n    Args:\n        data: Data to save to the database\n    """\n    # Placeholder for actual DB save logic\n    logger.info(f'Saving data to DB: {data}')\n\ndef format_output(data: Any) -> str:\n    """Format output for display.\n    \n    Args:\n        data: Data to format\n    Returns:\n        Formatted string output\n    """\n    return f'Formatted Output: {data}'\n\ndef handle_errors(func):\n    """Decorator for error handling.\n    \n    Args:\n        func: Function to wrap\n    Returns:\n        Wrapped function\n    """\n    async def wrapper(*args, **kwargs):\n        try:\n            return await func(*args, **kwargs)\n        except ValueError as e:\n            logger.error(f'Value error occurred: {e}')\n        except Exception as e:\n            logger.error(f'Unexpected error: {e}')\n    return wrapper\n\nclass DiagnosticAgent:\n    """Main orchestrator class for diagnostic agent operations."""\n    def __init__(self, config: Config):\n        self.config = config\n\n    async def run(self, data: Dict[str, Any]) -> None:\n        """Main workflow to run the diagnostic agent.\n        \n        Args:\n            data: Input data for processing\n        """\n        try:\n            validate_input(data)  # Validate input data\n            sanitized_data = sanitize_fields(data)  # Sanitize input fields\n            transformed_data = transform_records([sanitized_data])  # Transform data\n            await self.process_data(transformed_data)  # Process data\n        except Exception as e:\n            logger.error(f'Error running diagnostic agent: {e}')\n\n    async def process_data(self, data: List[Dict[str, Any]]) -> None:\n        """Process the incoming data.\n        \n        Args:\n            data: Data to process\n        """\n        metrics = []\n        for record in data:\n            await fetch_data(self.config.api_url)  # Fetch additional data\n            process_batch(data)  # Process the batch\n            metrics.append(record['value'])  # Collect metrics\n        aggregated_metric = aggregate_metrics(metrics)  # Aggregate metrics\n        logger.info(f'Aggregated Metric: {aggregated_metric}')  # Log aggregated metrics\n        save_to_db({'metric': aggregated_metric})  # Save aggregated metrics to DB\n\nif __name__ == '__main__':\n    config = Config()  # Load configuration\n    agent = DiagnosticAgent(config)  # Instantiate the agent\n    example_data = {'id': '123', 'value': 12.3456}  # Example input data\n    import asyncio\n    asyncio.run(agent.run(example_data))  # Run the agent with example data\n