Deploy Factory LLMs to Intel NPU with llama.cpp and OpenVINO

                      
                     
"""
Production implementation for deploying factory LLMs to Intel NPU using llama.cpp and OpenVINO.
Provides secure, scalable operations with optimal performance.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from contextlib import contextmanager

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

class Config:
    """Configuration class for environment variables."""
    MODEL_ENDPOINT: str = os.getenv('MODEL_ENDPOINT', 'http://localhost:5000')
    DB_CONNECTION_STRING: str = os.getenv('DB_CONNECTION_STRING', 'sqlite:///data.db')

@contextmanager
def connection_pool():
    """Context manager for database connection pooling."""
    try:
        # Simulated connection pool
        logger.info('Creating connection pool.')
        yield
    finally:
        logger.info('Closing connection pool.')

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 'input' not in data:
        raise ValueError('Missing required field: input')
    if not isinstance(data['input'], str):
        raise ValueError('Input must be a string')
    return True

async 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 input data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data for model compatibility.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    return {'model_input': data['input']}

async def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of inputs through the model.
    
    Args:
        batch: List of input records
    Returns:
        List of model responses
    """
    results = []
    for record in batch:
        try:
            response = requests.post(Config.MODEL_ENDPOINT, json=record)
            response.raise_for_status()  # Raises HTTPError for bad responses
            results.append(response.json())
        except requests.RequestException as e:
            logger.error(f'Error processing record {record}: {e}')
    return results

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from model responses.
    
    Args:
        results: List of model responses
    Returns:
        Aggregated metrics
    """
    return {'count': len(results), 'success': sum(1 for r in results if r['status'] == 'success')}

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from the database.
    
    Returns:
        List of records
    """
    logger.info('Fetching data from the database.')
    # Simulated database fetch
    return [{'input': 'Example input data'}]  # Placeholder

async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save processed results back to the database.
    
    Args:
        results: List of processed results
    """
    logger.info('Saving results to the database.')
    # Simulated database save
    pass  # Placeholder

async def handle_errors(e: Exception) -> None:
    """Handle exceptions gracefully.
    
    Args:
        e: Exception to handle
    """
    logger.error(f'An error occurred: {e}')

async def format_output(results: List[Dict[str, Any]]) -> None:
    """Format and log the output results.
    
    Args:
        results: List of processed results
    """
    for result in results:
        logger.info(f'Formatted result: {result}')

class LLMOrchestrator:
    """Main orchestrator class to tie helper functions together."""
    async def run(self) -> None:
        """Main workflow orchestration method."""
        try:
            # Fetch data and process it
            raw_data = await fetch_data()
            logger.info(f'Fetched data: {raw_data}')
            validated_data = await validate_input(raw_data[0])
            sanitized_data = await sanitize_fields(raw_data[0])
            transformed_data = await transform_records(sanitized_data)

            # Process the data in batches
            results = await process_batch([transformed_data])
            metrics = await aggregate_metrics(results)
            logger.info(f'Aggregated metrics: {metrics}')
            await save_to_db(results)
            await format_output(results)
        except Exception as e:
            await handle_errors(e)

if __name__ == '__main__':
    # Example usage
    import asyncio
    orchestrator = LLMOrchestrator()
    asyncio.run(orchestrator.run())