Parse Complex Technical Documents at Scale with GLM-OCR and Docling

                      
                     
"""
Production implementation for parsing complex technical documents at scale using GLM-OCR and Docling.
Provides secure, scalable operations for document ingestion, processing, and storage.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///documents.db')
    ocr_service_url: str = os.getenv('OCR_SERVICE_URL', 'http://localhost:5000/ocr')

# Setup database connection pooling
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
SessionLocal = sessionmaker(bind=engine)

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
    """
    if 'file_path' not in data:
        raise ValueError('Missing file_path in input data')  # Ensure file path is present
    if not isinstance(data['file_path'], str):
        raise ValueError('file_path must be a string')  # Validate data type
    return True  # Valid input

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

async def fetch_data(file_path: str) -> Optional[str]:
    """Fetch the document data from the specified file path.
    
    Args:
        file_path: Path to the document file
    Returns:
        Document content as string
    Raises:
        IOError: If file cannot be read
    """
    try:
        with open(file_path, 'r') as file:
            return file.read()  # Read file content
    except Exception as e:
        logger.error(f'Error reading file: {file_path} - {str(e)}')
        raise IOError(f'Cannot read file: {file_path}')  # Handle file read error

async def call_ocr_service(document: str) -> Dict[str, Any]:
    """Call the OCR service to process the document.
    
    Args:
        document: Document content to process
    Returns:
        Parsed data from OCR
    Raises:
        RuntimeError: If OCR service fails
    """
    try:
        response = requests.post(Config.ocr_service_url, json={'document': document})
        response.raise_for_status()  # Raise error for bad responses
        return response.json()  # Return parsed data
    except requests.exceptions.RequestException as e:
        logger.error(f'OCR service failed: {str(e)}')
        raise RuntimeError('OCR service request failed')  # Handle service call error

async def save_to_db(parsed_data: Dict[str, Any]) -> None:
    """Save parsed data to the database.
    
    Args:
        parsed_data: Parsed document data to store
    Raises:
        Exception: If database operation fails
    """
    session = SessionLocal()  # Get a new session
    try:
        # Example of inserting parsed data into a table
        session.execute(text('INSERT INTO documents (content) VALUES (:content)'), {'content': parsed_data['content']})
        session.commit()  # Commit transaction
    except Exception as e:
        session.rollback()  # Rollback on error
        logger.error(f'Error saving to database: {str(e)}')
        raise  # Raise exception for handling
    finally:
        session.close()  # Ensure session is closed

async def process_batch(file_paths: List[str]) -> None:
    """Process a batch of documents.
    
    Args:
        file_paths: List of document file paths
    Raises:
        Exception: If any error occurs during processing
    """
    for file_path in file_paths:
        try:
            await validate_input({'file_path': file_path})  # Validate input
            sanitized_input = await sanitize_fields({'file_path': file_path})  # Sanitize input
            document = await fetch_data(sanitized_input['file_path'])  # Fetch document
            parsed_data = await call_ocr_service(document)  # Call OCR service
            await save_to_db(parsed_data)  # Save parsed data
        except Exception as e:
            logger.error(f'Error processing file {file_path}: {str(e)}')  # Log errors

if __name__ == '__main__':
    # Example usage
    import asyncio
    file_paths = ['doc1.txt', 'doc2.txt']  # Example document paths
    asyncio.run(process_batch(file_paths))  # Run the processing batch