Extract Layout-Aware Text from Industrial Equipment Manuals with Surya and Docling

"""
Production implementation for extracting layout-aware text from industrial equipment manuals using Surya and Docling.
Provides secure, scalable operations.
"""
from typing import List, Dict, Any
import os
import logging
import requests
from contextlib import contextmanager

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

class Config:
    """
    Configuration class for environment variables.
    """
    SURYA_API_URL: str = os.getenv('SURYA_API_URL')
    DOCLING_API_URL: str = os.getenv('DOCLING_API_URL')
    DATABASE_URL: str = os.getenv('DATABASE_URL')

@contextmanager
def resource_manager():
    """
    Context manager for resource management.
    Ensures resources are cleaned up properly.
    """
    try:
        yield
    finally:
        logger.info('Cleaning up resources...')

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 'file_path' not in data:
        raise ValueError('Missing file_path')
    return True

async def fetch_data(file_path: str) -> Dict[str, Any]:
    """Fetch data from Surya API.
    
    Args:
        file_path: Path to the manual
    Returns:
        Parsed data from the API
    Raises:
        HTTPError: If API call fails
    """
    logger.info(f'Fetching data for: {file_path}')
    response = requests.get(f'{Config.SURYA_API_URL}/extract', params={'file': file_path})
    response.raise_for_status()  # Raises HTTPError for bad responses
    return response.json()

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Raw data to sanitize
    Returns:
        Sanitized data
    """
    # Example sanitization logic
    return {key: value.strip() for key, value in data.items()}

async def transform_records(raw_data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Transform raw data into structured records.
    
    Args:
        raw_data: Data fetched from API
    Returns:
        List of transformed records
    """
    # Transform logic here, e.g., extracting fields
    return [record for record in raw_data.get('records', [])]

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save records to the database.
    
    Args:
        records: List of records to save
    Raises:
        RuntimeError: If saving fails
    """
    logger.info(f'Saving {len(records)} records to the database.')
    # Implement database saving logic here, handle exceptions

async def process_batch(file_paths: List[str]) -> None:
    """Process a batch of files.
    
    Args:
        file_paths: List of manual file paths
    """
    for file_path in file_paths:
        try:
            await validate_input({'file_path': file_path})  # Validate input
            raw_data = await fetch_data(file_path)  # Fetch data
            sanitized_data = await sanitize_fields(raw_data)  # Sanitize data
            records = await transform_records(sanitized_data)  # Transform data
            await save_to_db(records)  # Save to DB
        except Exception as e:
            logger.error(f'Error processing {file_path}: {str(e)}')  # Log any errors

if __name__ == '__main__':
    # Example usage
    file_list = ['manual1.pdf', 'manual2.pdf']  # Sample manual files
    with resource_manager():
        # Process the batch of files
        await process_batch(file_list)  # Ensure this is called within an async context