Dispatch Quality Control Agents with smolagents and OpenAI Agents SDK

                      
                     
"""
Production implementation for Dispatch Quality Control Agents using smolagents and OpenAI Agents SDK.
This script coordinates quality control tasks and integrates with an external AI service.
"""

from typing import Dict, Any, List
import os
import logging
import requests
import time
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Setup logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 1.0))

# Create a SQLAlchemy engine and session factory for database interactions
engine = create_engine(Config.database_url)
session_factory = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate incoming request data.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'agent_id' not in data:
        raise ValueError('Missing agent_id')
    if 'task' not in data:
        raise ValueError('Missing task')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch data from external API.
    
    Args:
        api_url: URL of the API to call
    Returns:
        Dict[str, Any]: JSON response from API
    Raises:
        RuntimeError: If the API call fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'API request failed: {e}')
        raise RuntimeError('Failed to fetch data from API')

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    """
    with session_factory() as session:
        session.execute(text('INSERT INTO quality_control (agent_id, task) VALUES (:agent_id, :task)'), data)
        session.commit()  # Commit the transaction
        logger.info('Data saved to database')

async def call_openai_api(task: str) -> Dict[str, Any]:
    """Call OpenAI API to process the task.
    
    Args:
        task: Task description to process
    Returns:
        Dict[str, Any]: Response from OpenAI API
    Raises:
        RuntimeError: If the API call fails
    """
    api_key = os.getenv('OPENAI_API_KEY')
    headers = {'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json'}
    payload = {'prompt': task, 'max_tokens': 100}
    try:
        response = requests.post('https://api.openai.com/v1/engines/davinci-codex/completions', json=payload, headers=headers)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'OpenAI API request failed: {e}')
        raise RuntimeError('Failed to call OpenAI API')

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of quality control tasks.
    
    Args:
        data: List of tasks to process
    """
    for record in data:
        try:
            await validate_input(record)  # Validate each record
            sanitized_record = await sanitize_fields(record)  # Sanitize input
            openai_response = await call_openai_api(sanitized_record['task'])  # Call OpenAI API
            await save_to_db(sanitized_record)  # Save to database
            logger.info(f'Processed task for agent_id: {sanitized_record['agent_id']}')
        except Exception as e:
            logger.error(f'Error processing record {record}: {e}')  # Log error

async def aggregate_metrics() -> Dict[str, Any]:
    """Aggregate metrics for reporting.
    
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    metrics = {'total_tasks': 0, 'successful_tasks': 0}
    # Sample aggregation logic
    with session_factory() as session:
        result = session.execute(text('SELECT COUNT(*) FROM quality_control'))
        metrics['total_tasks'] = result.scalar()
    return metrics

class QualityControlOrchestrator:
    """Main orchestrator class to manage quality control operations."""
    async def run(self, tasks: List[Dict[str, Any]]) -> None:
        """Execute the quality control workflow.
        
        Args:
            tasks: List of tasks to process
        """
        logger.info('Starting quality control process')
        await process_batch(tasks)  # Process all tasks
        metrics = await aggregate_metrics()  # Aggregate metrics
        logger.info(f'Final metrics: {metrics}')  # Log metrics

if __name__ == '__main__':
    # Example usage
    tasks_to_process = [{'agent_id': '123', 'task': 'Verify quality of product X'}, {'agent_id': '456', 'task': 'Check compliance of product Y'}]
    orchestrator = QualityControlOrchestrator()
    import asyncio
    asyncio.run(orchestrator.run(tasks_to_process))