Process Unstructured Factory Documents into Search Pipelines with Unstructured and Haystack

                      
                     
"""
Production implementation for processing unstructured factory documents.
Provides secure, scalable operations using Haystack and Unstructured libraries.
"""
from typing import Dict, Any, List, Union
import os
import logging
import time
from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import DensePassageRetriever, Reader
from haystack.pipelines import ExtractiveQAPipeline
from unstructured.documents import Document

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

class Config:
    """Configuration class for environment variables."""
    document_store_url: str = os.getenv('DOCUMENT_STORE_URL')
    retriever_model: str = os.getenv('RETRIEVER_MODEL')

# Validate input data
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for processing.
    
    Args:
        data: Incoming data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'documents' not in data:
        raise ValueError('Missing documents key in input data.')  # Validation check
    if not isinstance(data['documents'], list):
        raise ValueError('Documents should be a list.')  # Type check
    return True  # Validation successful

# Sanitize fields in the document
def sanitize_fields(doc: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize fields in the document.
    
    Args:
        doc: Document to sanitize
    Returns:
        Dict[str, Any]: Sanitized document
    """
    sanitized_doc = {k: str(v).strip() for k, v in doc.items()}  # Strip whitespace
    return sanitized_doc

# Normalize the document data
def normalize_data(docs: List[Dict[str, Any]]) -> List[Document]:
    """Normalize raw documents into Document objects.
    
    Args:
        docs: List of raw documents
    Returns:
        List[Document]: List of normalized Document objects
    """
    return [Document.from_dict(sanitize_fields(doc)) for doc in docs]  # Normalize documents

# Process a batch of documents
async def process_batch(docs: List[Dict[str, Any]]) -> None:
    """Process a batch of documents and index them.
    
    Args:
        docs: List of documents to process
    """
    normalized_docs = normalize_data(docs)  # Normalize documents
    document_store = FAISSDocumentStore(url=Config.document_store_url)
    document_store.write_documents(normalized_docs)  # Write to document store
    logger.info(f'Processed and indexed {len(normalized_docs)} documents.')  # Log success

# Retry logic with exponential backoff
async def fetch_data_with_retry(url: str, retries: int = 5) -> Union[Dict[str, Any], None]:
    """Fetch data from the given URL with retry logic.
    
    Args:
        url: URL to fetch data from
        retries: Number of retries
    Returns:
        Dict[str, Any]: Fetched data
    Raises:
        Exception: If fetch fails after retries
    """
    for attempt in range(retries):
        try:
            # Simulate fetching data
            logger.info(f'Fetching data from {url} (attempt {attempt + 1})')  # Log attempt
            return {}  # Placeholder for actual data fetching
        except Exception as e:
            logger.warning(f'Fetch failed: {e}. Retrying...')  # Log warning
            time.sleep(2 ** attempt)  # Exponential backoff
    raise Exception('Failed to fetch data after multiple attempts.')  # Raise exception

# Save processed documents to the database
async def save_to_db(docs: List[Dict[str, Any]]) -> None:
    """Save processed documents to the database.
    
    Args:
        docs: List of documents to save
    """
    # Placeholder for actual database save logic
    logger.info('Documents saved to database.')  # Log save action

# Handle errors gracefully
async def handle_errors(action: str) -> None:
    """Handle errors during processing.
    
    Args:
        action: Action being performed
    """
    try:
        # Simulate action
        logger.info(f'Performing action: {action}')  # Log action
    except Exception as e:
        logger.error(f'Error during {action}: {e}')  # Log error

# Main orchestrator class
class DocumentProcessor:
    """Main class for processing documents."""
    def __init__(self) -> None:
        self.document_store = FAISSDocumentStore(url=Config.document_store_url)

    async def run(self, input_data: Dict[str, Any]) -> None:
        """Run the document processing workflow.
        
        Args:
            input_data: Data to process
        """
        await validate_input(input_data)  # Validate input
        await process_batch(input_data['documents'])  # Process documents

if __name__ == '__main__':
    # Example usage of the DocumentProcessor
    processor = DocumentProcessor()  # Create processor instance
    sample_data = {'documents': [{'text': 'Sample document content'}]}  # Sample input data
    import asyncio
    asyncio.run(processor.run(sample_data))  # Run processor asynchronously