Process Industrial PDF Archives with Mistral OCR and Haystack

                      
                     
"""
Production implementation for processing industrial PDF archives using Mistral OCR and Haystack.
Provides secure, scalable operations for document ingestion and searching.
"""
from typing import Dict, Any, List
import os
import logging
import time
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import PDFToTextConverter, DensePassageRetriever
from haystack.pipelines import ExtractiveQAPipeline

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

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')
    max_retries: int = int(os.getenv('MAX_RETRIES', 5))

# Initialize the document store and retriever
document_store = InMemoryDocumentStore()
converter = PDFToTextConverter()
retriever = DensePassageRetriever(document_store=document_store)

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data structure.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid, raises ValueError otherwise
    Raises:
        ValueError: If validation fails
    """
    if 'file_path' not in data:
        raise ValueError('Missing file_path in input data')
    if not os.path.exists(data['file_path']):
        raise ValueError(f'File does not exist: {data['file_path']}')
    return True

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

def fetch_data(file_path: str) -> str:
    """Fetch data from the given PDF file.
    
    Args:
        file_path: Path to the PDF file
    Returns:
        str: Raw text from PDF
    Raises:
        FileNotFoundError: If the file does not exist
    """
    if not os.path.isfile(file_path):
        raise FileNotFoundError(f'Cannot find file: {file_path}')
    return converter.convert(file_path)

def save_to_db(text: str) -> None:
    """Save extracted text to the document store.
    
    Args:
        text: Text to save
    """
    document_store.write_documents([{'content': text}])
    logger.info('Document saved to the database.')

def process_batch(file_paths: List[str]) -> None:
    """Process a batch of PDF files.
    
    Args:
        file_paths: List of file paths to process
    """
    for file_path in file_paths:
        try:
            logger.info(f'Processing file: {file_path}')
            data = {'file_path': file_path}
            if validate_input(data):
                text = fetch_data(file_path)
                save_to_db(text)
        except Exception as e:
            logger.error(f'Error processing {file_path}: {e}')

def call_api(query: str) -> List[Dict[str, Any]]:
    """Call the API with a search query.
    
    Args:
        query: Search query string
    Returns:
        List[Dict[str, Any]]: Search results
    """
    pipeline = ExtractiveQAPipeline(retriever)
    return pipeline.run(query=query)

def aggregate_metrics(metrics: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate performance metrics after processing.
    
    Args:
        metrics: List of metrics to aggregate
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    return {'total_documents': len(metrics)}  # Example aggregation

def format_output(results: List[Dict[str, Any]]) -> str:
    """Format the output for display.
    
    Args:
        results: Search results to format
    Returns:
        str: Formatted output
    """
    return '\n'.join([f'{r['content']}' for r in results])

class PDFArchiver:
    """Main class for orchestrating PDF archiving and searching.
    """
    def __init__(self, config: Config):
        self.config = config

    def process_files(self, file_paths: List[str]) -> None:
        logger.info('Starting batch processing...')
        process_batch(file_paths)

    def search(self, query: str) -> str:
        results = call_api(query)
        return format_output(results)

if __name__ == '__main__':
    # Example usage
    config = Config()
    archiver = PDFArchiver(config)
    pdf_files = ['file1.pdf', 'file2.pdf']  # List of PDF files to process
    archiver.process_files(pdf_files)
    search_query = 'What is the production rate?'
    search_results = archiver.search(search_query)
    print(search_results)