Optimize Structured Output Extraction for Industrial LLMs with DSPy and LangChain

"""
Production implementation for optimizing structured output extraction for industrial LLMs using DSPy and LangChain.
Provides secure, scalable operations and efficient data processing.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import time
import requests
from contextlib import contextmanager

# Set up logging configuration with INFO level
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')

@contextmanager
def db_connection() -> None:
    """
    Context manager for database connection pooling.
    """
    try:
        # Simulate database connection pooling
        logger.info('Establishing database connection...')
        yield
        logger.info('Database connection closed.')
    except Exception as e:
        logger.error(f'Error in database connection: {e}')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data or not isinstance(data['id'], int):
        raise ValueError('Missing or invalid id')
    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:
        Sanitized data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    logger.info('Sanitized input data')
    return sanitized_data

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data to standard format.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    normalized = {key.lower(): value for key, value in data.items()}
    logger.info('Normalized input data')
    return normalized

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records into structured format for output.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    return [{**record, 'processed': True} for record in records]  # Mark records as processed

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from external API.
    
    Args:
        api_url: URL of the API to fetch data from
    Returns:
        Fetched data
    Raises:
        ConnectionError: If the API call fails
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()
        logger.info('Data fetched successfully from API')
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'API call failed: {e}')
        raise ConnectionError('Failed to fetch data from API')

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save to the database
    Raises:
        Exception: If saving fails
    """
    try:
        logger.info(f'Saving {len(data)} records to the database.')
        # Simulate database save operation
        # Actual DB save logic would go here
    except Exception as e:
        logger.error(f'Failed to save data: {e}')
        raise RuntimeError('Error saving data to the database')

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data records.
    
    Args:
        data: Batch of data to process
    """
    try:
        async with db_connection():
            for record in data:
                await validate_input(record)
                sanitized = await sanitize_fields(record)
                normalized = await normalize_data(sanitized)
                await save_to_db([normalized])  # Save each normalized record
                logger.info(f'Processed record: {normalized}')
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')
    except Exception as e:
        logger.error(f'Error processing batch: {e}')

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed data.
    
    Args:
        data: Data records to aggregate
    Returns:
        Aggregated metrics
    """
    metrics = {'count': len(data)}
    logger.info('Aggregated metrics calculated')
    return metrics

class OutputExtractor:
    """Main orchestrator for output extraction workflow.
    """
    def __init__(self, config: Config):
        self.config = config

    async def run(self) -> None:
        """Execute the extraction workflow.
        """
        try:
            logger.info('Starting output extraction workflow...')
            raw_data = await fetch_data(self.config.api_endpoint)
            processed_data = await transform_records(raw_data)
            await process_batch(processed_data)
            metrics = await aggregate_metrics(processed_data)
            logger.info(f'Workflow completed. Metrics: {metrics}')
        except Exception as e:
            logger.error(f'Workflow failed: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    extractor = OutputExtractor(config)
    import asyncio
    asyncio.run(extractor.run())