Run Quantized Industrial LLMs on CPU-Only Edge Hardware with Quanto and Ollama

"""
Production implementation for running quantized industrial LLMs on CPU-only edge hardware with Quanto and Ollama.
This module provides secure and scalable operations suitable for edge environments.
"""

from typing import Dict, List, Any
import os
import logging
import requests
import time

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


class Config:
    """
    Configuration class for environment variables.
    """
    model_url: str = os.getenv('MODEL_URL', 'http://localhost:8000/api/model')
    max_retries: int = int(os.getenv('MAX_RETRIES', 5))
    retry_delay: float = float(os.getenv('RETRY_DELAY', 1.0))


def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for the model.
    
    Args:
        data: Input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not isinstance(data, dict):
        raise ValueError('Input must be a dictionary.')
    if 'input_text' not in data:
        raise ValueError('Missing required field: input_text')
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize.
    Returns:
        Sanitized dictionary.
    """
    return {key: str(value).strip() for key, value in data.items()}


def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data into the required format for processing.
    
    Args:
        data: Raw input data.
    Returns:
        Transformed data ready for the model.
    """
    return {'text': data['input_text']}


def fetch_data(input_data: Dict[str, Any]) -> Dict[str, Any]:
    """Fetch results from the model API.
    
    Args:
        input_data: Input data for the API.
    Returns:
        API response data.
    Raises:
        ConnectionError: If the API call fails.
    """
    url = Config.model_url
    logger.info(f'Fetching data from {url}')
    for attempt in range(Config.max_retries):
        try:
            response = requests.post(url, json=input_data)
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            logger.error(f'Error fetching data: {e}')
            if attempt < Config.max_retries - 1:
                time.sleep(Config.retry_delay)
            else:
                raise ConnectionError('Failed to fetch data after multiple attempts.')


def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data.
    
    Args:
        batch: List of input data dictionaries.
    Returns:
        List of processed results.
    """
    results = []
    for item in batch:
        try:
            validate_input(item)  # Validate input data
            sanitized = sanitize_fields(item)  # Sanitize input fields
            transformed = transform_records(sanitized)  # Transform data
            result = fetch_data(transformed)  # Fetch data from model
            results.append(result)
        except Exception as e:
            logger.error(f'Error processing item {item}: {e}')
    return results


def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save results to the database (mock implementation).
    
    Args:
        results: List of results to save.
    """
    # Mock implementation, replace with actual DB operations
    logger.info('Saving results to the database...')
    for result in results:
        logger.debug(f'Saving result: {result}')  # Log each result
    logger.info('Results saved successfully.')


def format_output(results: List[Dict[str, Any]]) -> str:
    """Format the output results for display or logging.
    
    Args:
        results: List of results to format.
    Returns:
        Formatted string of results.
    """
    return '\n'.join(str(result) for result in results)


class LLMOrchestrator:
    """Main orchestrator class for handling LLM operations.
    """
    def __init__(self):
        self.logger = logger

    def run(self, input_data: List[Dict[str, Any]) -> None:
        """Run the orchestration for the input data.
        
        Args:
            input_data: List of input data dictionaries.
        """ 
        self.logger.info('Starting LLM processing...')
        results = process_batch(input_data)  # Process the input data
        save_to_db(results)  # Save results to the database
        output = format_output(results)  # Format output
        self.logger.info(f'Processing completed. Output: {output}')  # Log output


if __name__ == '__main__':
    # Example usage
    sample_input = [{'input_text': 'Hello, world!'}, {'input_text': 'Another input.'}]
    orchestrator = LLMOrchestrator()  # Initialize orchestrator
    orchestrator.run(sample_input)  # Run processing