Build GRPO Post-Training Pipelines for Industrial Quality LLMs with TRL v1.0 and DSPy

                      
                     
"""
Production implementation for building post-training pipelines for industrial quality LLMs.
Provides secure, scalable operations using TRL v1.0 and DSPy.
"""

from typing import Dict, Any, List, Union
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

class Config:
    """Configuration class to manage environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///default.db')
    max_retries: int = 5
    wait_time: int = 1  # seconds

# Database setup
engine = create_engine(Config.database_url)
Session = sessionmaker(bind=engine)

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 'id' not in data:
        raise ValueError('Missing id')  # Ensure 'id' is present
    return True

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

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize the input data.
    
    Args:
        data: Data to normalize
    Returns:
        Normalized data
    """
    # Example normalization logic
    return {key: value.lower() for key, value in data.items()}

def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from a given API endpoint.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        Fetched data
    Raises:
        ConnectionError: If data fetching fails
    """
    try:
        response = requests.get(endpoint)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()  # Return JSON response
    except requests.RequestException as error:
        logger.error(f'Error fetching data: {error}')
        raise ConnectionError('Data fetching failed')

def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save operation fails
    """
    with Session() as session:
        session.execute(text("INSERT INTO records (id, value) VALUES (:id, :value)"), data)
        session.commit()  # Commit transaction

def process_batch(data_batch: List[Dict[str, Any]]) -> None:
    """Process a batch of data records.
    
    Args:
        data_batch: List of data records to process
    """
    for data in data_batch:
        try:
            validate_input(data)  # Validate each record
            sanitized_data = sanitize_fields(data)  # Sanitize
            normalized_data = normalize_data(sanitized_data)  # Normalize
            save_to_db(normalized_data)  # Save to DB
        except Exception as e:
            logger.warning(f'Failed to process record {data}: {e}')  # Log warning

def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, Union[int, float]]:
    """Aggregate metrics from processed data.
    
    Args:
        data: List of processed data
    Returns:
        Aggregated metrics
    """
    total = len(data)
    average = sum(record['value'] for record in data) / total if total > 0 else 0
    return {'total': total, 'average': average}

class Pipeline:
    """Main orchestrator class for managing the pipeline operations."""
    def __init__(self, endpoint: str) -> None:
        self.endpoint = endpoint  # Set API endpoint

    def run(self) -> None:
        """Execute the pipeline workflow."""
        for attempt in range(Config.max_retries):
            try:
                logger.info('Fetching data...')
                data = fetch_data(self.endpoint)  # Fetch data
                logger.info('Processing batch...')
                process_batch(data)  # Process each batch
                break  # Exit on success
            except ConnectionError:
                logger.warning('Retrying...')
                time.sleep(Config.wait_time)  # Wait before retrying
                continue  # Retry logic

if __name__ == '__main__':
    # Example usage
    pipeline = Pipeline(endpoint='https://api.example.com/data')
    pipeline.run()  # Run the pipeline