Fine-Tune Factory LLMs for Continual Learning with Training Hub and PEFT

"""
Production implementation for Fine-Tune Factory LLMs for Continual Learning with Training Hub and PEFT.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import time
from contextlib import contextmanager

# Logger setup for tracking application behavior
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    training_api_url: str = os.getenv('TRAINING_API_URL')
    db_connection_string: str = os.getenv('DB_CONNECTION_STRING')

@contextmanager
def db_connection():
    """
    Context manager for database connection pooling.
    """ 
    conn = create_db_connection(Config.db_connection_string)
    try:
        yield conn
    finally:
        conn.close()  # Ensure connection is closed after use

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'parameters' not in data:
        raise ValueError('Missing parameters')
    return True

async def fetch_data(api_url: str, model_id: str) -> Optional[Dict[str, Any]]:
    """Fetch data from the training API.
    
    Args:
        api_url: URL of the training API
        model_id: ID of the model to fetch
    Returns:
        dict: Retrieved data
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.get(f'{api_url}/{model_id}')
        response.raise_for_status()  # Raise error for bad responses
        return response.json()
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        return None

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input data for processing.
    
    Args:
        data: Raw input data
    Returns:
        dict: Normalized data
    """
    # Example normalization process
    normalized = {k: v.lower() if isinstance(v, str) else v for k, v in data.items()}
    return normalized

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for training.
    
    Args:
        records: List of records to transform
    Returns:
        List of transformed records
    """
    transformed = []
    for record in records:
        # Example transformation logic
        transformed_record = {**record, 'transformed': True}
        transformed.append(transformed_record)
    return transformed

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data for training.
    
    Args:
        data: List of data records to process
    """
    for record in data:
        # Simulate processing time
        time.sleep(1)  # Simulate a delay
        logger.info(f'Processed record: {record}')

async def save_to_db(conn, data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        conn: Database connection
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    try:
        # Example save logic
        with conn.cursor() as cursor:
            for record in data:
                cursor.execute("INSERT INTO training_records (data) VALUES (%s)", (record,))
        conn.commit()  # Commit the transaction
    except Exception as e:
        logger.error(f'Error saving to DB: {e}')

async def aggregate_metrics(metrics: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed data.
    
    Args:
        metrics: List of metrics to aggregate
    Returns:
        dict: Aggregated metrics
    """
    # Placeholder for aggregation logic
    aggregated = {"total": len(metrics)}
    return aggregated

class FineTuneLLM:
    """Main orchestrator class for fine-tuning LLMs.
    """
    def __init__(self, config: Config):
        self.config = config

    async def fine_tune(self, model_id: str) -> None:
        """Main workflow for fine-tuning.
        
        Args:
            model_id: ID of the model to fine-tune
        """
        try:
            # Validate input data
            await validate_input({'model_id': model_id, 'parameters': {}})
            # Fetch data
            raw_data = await fetch_data(self.config.training_api_url, model_id)
            if raw_data is None:
                raise Exception("No data fetched")
            # Normalize data
            normalized_data = await normalize_data(raw_data)
            # Transform records
            transformed_data = await transform_records([normalized_data])
            # Process batch
            await process_batch(transformed_data)
            # Save results to DB
            with db_connection() as conn:
                await save_to_db(conn, transformed_data)
            logger.info('Fine-tuning completed successfully.')
        except Exception as e:
            logger.error(f'Error during fine-tuning: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    fine_tuner = FineTuneLLM(config)
    # Run the fine-tuning process
    import asyncio
    asyncio.run(fine_tuner.fine_tune('model_123'))