Chunk and Index Factory Process Documents for Dense Retrieval with ColPali and spaCy

"""
Production implementation for chunking and indexing factory process documents.
This module provides secure, scalable operations for dense retrieval using ColPali and spaCy.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import spacy
import time
import random

# Setting up logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    colpali_endpoint: str = os.getenv('COLPALI_ENDPOINT')

# Load the spaCy model for NLP tasks
nlp = spacy.load('en_core_web_sm')

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 'documents' not in data or not isinstance(data['documents'], list):
        raise ValueError('Input must contain a list of documents')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def chunk_documents(documents: List[str]) -> List[str]:
    """Chunk the documents into smaller pieces.
    
    Args:
        documents: List of raw documents
    Returns:
        List of chunked documents
    """
    chunks = []
    for doc in documents:
        # Using spaCy to process the document
        nlp_doc = nlp(doc)
        # Splitting based on sentences
        sentences = list(nlp_doc.sents)
        for sentence in sentences:
            chunks.append(sentence.text)
    return chunks

async def index_documents(chunks: List[str]) -> None:
    """Index the chunks using ColPali.
    
    Args:
        chunks: List of chunked documents
    Raises:
        ConnectionError: If indexing fails
    """
    for chunk in chunks:
        try:
            # Simulating API call to ColPali
            logger.info(f'Indexing chunk: {chunk}')
            # Actual implementation would involve an API call
            time.sleep(random.uniform(0.1, 0.5))  # Simulating network delay
        except Exception as e:
            logger.error(f'Error indexing chunk: {chunk}, error: {e}')
            raise ConnectionError('Failed to index chunk')

async def process_batch(data: Dict[str, Any]) -> None:
    """Main processing function for the batch of documents.
    
    Args:
        data: Input data containing documents
    Raises:
        Exception: If processing fails
    """
    try:
        await validate_input(data)  # Validate input data
        sanitized_data = await sanitize_fields(data)  # Sanitize fields
        chunks = await chunk_documents(sanitized_data['documents'])  # Chunk documents
        await index_documents(chunks)  # Index the chunks
    except ValueError as ve:
        logger.error(f'Validation error: {ve}')
    except ConnectionError as ce:
        logger.error(f'Connection error: {ce}')
    except Exception as e:
        logger.error(f'An unexpected error occurred: {e}')

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from a source.
    
    Returns:
        List of data dictionaries
    """
    # Simulated fetch - Replace with actual data fetching logic
    return [{'documents': ['Sample document 1.', 'Sample document 2.']}]

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Processed data to save
    """
    logger.info(f'Saving data to DB: {data}')  # Simulating DB save

async def call_api() -> None:
    """Call an external API for processing.
    """
    logger.info('Calling external API...')  # Simulated API call

if __name__ == '__main__':
    import asyncio
    # Example usage
    sample_data = {'documents': ['This is the first document.', 'And this is the second.']}
    asyncio.run(process_batch(sample_data))