Build Stateful Logistics Planning Agents with AutoGen and LangGraph

"""
Production implementation for building stateful logistics planning agents with AutoGen and LangGraph.
Provides secure, scalable operations for logistics decision-making.
"""
from typing import Dict, Any, List
import os
import logging
import asyncio
import httpx
from pydantic import BaseModel, ValidationError

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

class Config:
    """Configuration class to load environment variables."""
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

# Data model for input validation using Pydantic
class LogisticsInput(BaseModel):
    id: str
    location: str
    quantity: int
    destination: str

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    try:
        LogisticsInput(**data)  # Validate against the Pydantic model
    except ValidationError as e:
        logger.error(f'Validation error: {e}')
        raise ValueError('Invalid input data')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injections.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized input data
    """  
    # Here we would implement actual sanitization logic
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.info('Sanitized input data')
    return sanitized_data

async def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for processing.
    
    Args:
        data: List of input records
    Returns:
        Transformed records
    """  
    transformed = [{'id': record['id'], 'location': record['location'].upper(), 'quantity': record['quantity']} for record in data]
    logger.info('Transformed records for processing')
    return transformed

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of records.
    
    Args:
        data: List of records to process
    Returns:
        List of processed results
    """  
    processed_results = []
    for record in data:
        # Simulated processing logic
        processed_results.append({'id': record['id'], 'status': 'processed'})
    logger.info('Processed batch of records')
    return processed_results

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        url: URL of the API to fetch data from
    Returns:
        Response data as a dictionary
    Raises:
        Exception: If fetch fails
    """  
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Fetched data from API')
        return response.json()

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: List of data records to save
    Raises:
        Exception: If save fails
    """  
    # Simulated DB save operation
    logger.info('Saving data to the database')
    # Here you would use an ORM or raw SQL commands
    await asyncio.sleep(1)  # Simulating an async DB operation
    logger.info('Data saved to database')

async def call_api(data: Dict[str, Any]) -> None:
    """Call an external API with the transformed data.
    
    Args:
        data: Data to send to the API
    Raises:
        Exception: If API call fails
    """  
    logger.info('Calling external API')
    await fetch_data(Config.api_url)

async def format_output(data: List[Dict[str, Any]]) -> str:
    """Format output for user display.
    
    Args:
        data: Data to format
    Returns:
        Formatted string output
    """  
    return '\n'.join([f"ID: {item['id']}, Status: {item['status']}" for item in data])

async def handle_errors(func):
    """Decorator to handle errors and log them.
    
    Args:
        func: Function to wrap
    """  
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error occurred: {e}')
            raise
    return wrapper

class LogisticsAgent:
    """Main class to manage logistics operations."""

    async def orchestrate(self, input_data: Dict[str, Any]) -> None:
        """Orchestrate the workflow from input to processing.
        
        Args:
            input_data: Input data for logistics
        """  
        logger.info('Starting orchestration process')
        await validate_input(input_data)  # Validate input
        sanitized_data = await sanitize_fields(input_data)  # Sanitize input
        transformed = await transform_records([sanitized_data])  # Transform data
        processed_results = await process_batch(transformed)  # Process data
        await save_to_db(processed_results)  # Save to DB
        output = format_output(processed_results)
        logger.info('Orchestration complete')
        return output

if __name__ == '__main__':
    # Example usage
    try:
        agent = LogisticsAgent()
        example_data = {'id': '123', 'location': 'Warehouse A', 'quantity': 10, 'destination': 'City B'}
        loop = asyncio.get_event_loop()
        result = loop.run_until_complete(agent.orchestrate(example_data))
        print(result)
    except Exception as e:
        logger.error(f'Execution failed: {e}')