Parse and Chunk Multi-Format Factory Audit Reports for Retrieval with MinerU and Haystack

"""
Production implementation for parsing and chunking multi-format factory audit reports.
Provides secure, scalable operations with MinerU and Haystack.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import json
import csv
import xml.etree.ElementTree as ET
import requests
from contextlib import contextmanager
from sqlalchemy import create_engine, text
from sqlalchemy.exc import SQLAlchemyError

# Logger setup for monitoring application behavior
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:///audit_reports.db')

@contextmanager
def get_db_connection() -> None:
    """
    Provides a database connection using a context manager.
    
    Yields:
        Connection object
    """
    engine = create_engine(Config.database_url)
    connection = engine.connect()
    try:
        yield connection
    except SQLAlchemyError as e:
        logger.error(f'Database connection error: {e}')
        raise
    finally:
        connection.close()  # Ensures the connection is closed

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_type' not in data:
        raise ValueError('Missing file_type')
    if data['file_type'] not in ['json', 'csv', 'xml']:
        raise ValueError('Unsupported file_type')
    return True

async def fetch_data(file_path: str) -> str:
    """Fetch data from the given file path.
    
    Args:
        file_path: Path to the data file
    Returns:
        Raw data as a string
    Raises:
        FileNotFoundError: If the file does not exist
    """
    try:
        with open(file_path, 'r') as file:
            return file.read()
    except FileNotFoundError:
        logger.error(f'File not found: {file_path}')
        raise

def parse_json(data: str) -> List[Dict[str, Any]]:
    """Parse JSON formatted data.
    
    Args:
        data: JSON data as a string
    Returns:
        List of records
    Raises:
        json.JSONDecodeError: If JSON is invalid
    """
    try:
        return json.loads(data)
    except json.JSONDecodeError as e:
        logger.error(f'Invalid JSON data: {e}')
        raise

def parse_csv(data: str) -> List[Dict[str, Any]]:
    """Parse CSV formatted data.
    
    Args:
        data: CSV data as a string
    Returns:
        List of records
    """
    reader = csv.DictReader(data.splitlines())
    return [row for row in reader]  # Returns a list of dictionaries

def parse_xml(data: str) -> List[Dict[str, Any]]:
    """Parse XML formatted data.
    
    Args:
        data: XML data as a string
    Returns:
        List of records
    """
    root = ET.fromstring(data)
    records = []
    for record in root.findall('.//record'):
        records.append({child.tag: child.text for child in record})  # Map each child element
    return records

async def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records and save to the database.
    
    Args:
        records: List of records to process
    """
    with get_db_connection() as conn:
        for record in records:
            try:
                # Example insert operation
                conn.execute(text("INSERT INTO audit_reports (field1, field2) VALUES (:field1, :field2)"),
                             {'field1': record['field1'], 'field2': record['field2']})
                logger.info(f'Successfully inserted record: {record}')
            except SQLAlchemyError as e:
                logger.error(f'Error inserting record {record}: {e}')

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save records to the database.
    
    Args:
        records: List of records to save
    """
    await process_batch(records)  # Call the processing function

async def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from the records.
    
    Args:
        records: List of records to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    return {'count': len(records)}  # Simple count aggregation

async def format_output(metrics: Dict[str, Any]) -> str:
    """Format output metrics as a string.
    
    Args:
        metrics: Metrics to format
    Returns:
        Formatted string
    """
    return json.dumps(metrics, indent=4)

async def run(file_path: str) -> None:
    """Main workflow for parsing and processing audit reports.
    
    Args:
        file_path: Path to the data file
    """
    data = await fetch_data(file_path)  # Fetch data from the file
    file_type = file_path.split('.')[-1]  # Determine file type from extension
    await validate_input({'file_type': file_type})  # Validate input

    if file_type == 'json':
        records = parse_json(data)  # Parse JSON
    elif file_type == 'csv':
        records = parse_csv(data)  # Parse CSV
    elif file_type == 'xml':
        records = parse_xml(data)  # Parse XML
    else:
        logger.error('Unsupported file type')
        return

    await save_to_db(records)  # Save records to the database
    metrics = await aggregate_metrics(records)  # Aggregate metrics
    output = await format_output(metrics)  # Format output
    logger.info(f'Aggregated metrics: {output}')  # Log metrics output

if __name__ == '__main__':
    import asyncio
    file_path = 'path/to/audit_report.json'  # Example file path
    asyncio.run(run(file_path))  # Run the main workflow