Fine-Tune Qwen3.5 for Industrial Maintenance Q&A with Axolotl and DSPy

"""
Production implementation for Fine-Tuning Qwen3.5 for Industrial Maintenance Q&A.
Provides secure, scalable operations for industrial maintenance queries.
"""

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

# Configuring logging for tracking operations
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Database engine and session setup using SQLAlchemy
DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
engine = create_engine(DATABASE_URL, connect_args={'check_same_thread': False})
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

class Config:
    """
    Configuration class to hold environment variables.
    """
    max_retries: int = 3
    backoff_factor: float = 0.5

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 'question' not in data:
        raise ValueError('Missing required field: question')
    if not isinstance(data['question'], str):
        raise ValueError('Field question 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:
        Dict[str, Any]: Sanitized data
    """
    return {key: str(value).strip() for key, value in data.items()}

async def fetch_data(query: str) -> List[Dict[str, Any]]:
    """Fetch data from external API or service.
    
    Args:
        query: The query to execute
    Returns:
        List[Dict[str, Any]]: Fetched data
    Raises:
        Exception: If the request fails
    """
    url = f'https://api.example.com/search?q={query}'
    response = requests.get(url)
    response.raise_for_status()  # Raises an error for bad responses
    return response.json()['results']

async def save_to_db(session, data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        session: Database session
        data: Data to save
    """
    session.execute(text("INSERT INTO queries (question, answer) VALUES (:question, :answer)"), data)

async def process_batch(queries: List[Dict[str, Any]]) -> None:
    """Process a batch of queries and save results.
    
    Args:
        queries: List of queries to process
    """
    with SessionLocal() as session:
        for query in queries:
            sanitized_query = await sanitize_fields(query)
            await validate_input(sanitized_query)
            results = await fetch_data(sanitized_query['question'])
            for result in results:
                await save_to_db(session, result)
            session.commit()  # Commit after processing each batch

async def format_output(data: List[Dict[str, Any]]) -> str:
    """Format output for display.
    
    Args:
        data: Data to format
    Returns:
        str: Formatted string
    """
    return '\n'.join([f'Q: {item['question']} A: {item['answer']}' for item in data])

async def handle_errors(func):
    """Decorator for handling errors with retries.
    
    Args:
        func: Function to decorate
    """
    async def wrapper(*args, **kwargs):
        retries = 0
        while retries < Config.max_retries:
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                logger.error(f'Error occurred: {e}')
                retries += 1
                time.sleep(Config.backoff_factor * (2 ** retries))  # Exponential backoff
        logger.critical('Max retries exceeded')
        raise
    return wrapper

@handle_errors
async def main_workflow(question: str) -> str:
    """Main workflow for processing a query.
    
    Args:
        question: User question
    Returns:
        str: Result string
    """
    sanitized_input = await sanitize_fields({'question': question})
    await validate_input(sanitized_input)
    results = await fetch_data(sanitized_input['question'])
    await process_batch(results)
    return await format_output(results)

if __name__ == '__main__':
    # Example usage
    import asyncio
    question = 'What is the best maintenance practice?'
    result = asyncio.run(main_workflow(question))
    print(result)  # Output the result after processing