Build Retrieval-Augmented Fine-Tuning Pipelines for Industrial LLMs with Axolotl and LlamaIndex

"""
Production implementation for building retrieval-augmented fine-tuning pipelines for industrial LLMs using Axolotl and LlamaIndex.
Provides secure and scalable operations.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import time
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    axolotl_endpoint: str = os.getenv('AXOLOTL_ENDPOINT')
    llama_index_endpoint: str = os.getenv('LLAMA_INDEX_ENDPOINT')

# Database connection pooling
engine = create_engine(Config.database_url, pool_size=20, max_overflow=0)
Session = sessionmaker(bind=engine)

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

def fetch_data(query: str) -> List[Dict[str, Any]]:
    """Fetch data from Axolotl endpoint.
    
    Args:
        query: Search query
    Returns:
        List of results
    Raises:
        RuntimeError: If request fails
    """
    try:
        response = requests.get(f'{Config.axolotl_endpoint}/search', params={'query': query})
        response.raise_for_status()  # Raise an error for bad responses
    except requests.exceptions.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise RuntimeError('Failed to fetch data')
    return response.json()['results']

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for fine-tuning.
    
    Args:
        records: List of raw records
    Returns:
        List of transformed records
    """
    return [{'input': record['text'], 'output': record['label']} for record in records]

def save_to_db(session, records: List[Dict[str, Any]]) -> None:
    """Save records to the database.
    
    Args:
        session: Database session
        records: List of records to save
    """
    try:
        # Insert records into the database
        for record in records:
            session.execute(text('INSERT INTO fine_tuning (input, output) VALUES (:input, :output)'), 
                             {'input': record['input'], 'output': record['output']})
        session.commit()  # Commit changes to the database
    except Exception as e:
        session.rollback()  # Rollback in case of error
        logger.error(f'Error saving to database: {e}')
        raise RuntimeError('Database save failed')

def call_api(data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    """Call LlamaIndex API for processing.
    
    Args:
        data: Data to send to the API
    Returns:
        Response data from API
    Raises:
        RuntimeError: If API call fails
    """
    try:
        response = requests.post(Config.llama_index_endpoint, json=data)
        response.raise_for_status()  # Raise an error for bad responses
    except requests.exceptions.RequestException as e:
        logger.error(f'Error calling API: {e}')
        raise RuntimeError('Failed to call API')
    return response.json()

def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from results.
    
    Args:
        results: List of results to aggregate
    Returns:
        Dictionary of aggregated metrics
    """
    return {'total': len(results), 'success': sum(1 for r in results if r['status'] == 'success')}

class FineTuningPipeline:
    """Class to orchestrate the fine-tuning pipeline.
    """
    def __init__(self):
        self.session = Session()  # Create a new database session

    def run_pipeline(self, query: str) -> None:
        """Run the fine-tuning pipeline.
        
        Args:
            query: Search query for data
        """
        try:
            validate_input({'text': query})  # Validate input data
            logger.info('Input validated.')  
            records = fetch_data(query)  # Fetch data from Axolotl
            logger.info(f'Retrieved {len(records)} records.')  
            transformed_records = transform_records(records)  # Transform data for fine-tuning
            logger.info('Records transformed.')  
            save_to_db(self.session, transformed_records)  # Save to database
            logger.info('Records saved to database.')  
            results = call_api({'records': transformed_records})  # Call LlamaIndex API
            logger.info('API call successful.')  
            metrics = aggregate_metrics(results)  # Aggregate metrics
            logger.info(f'Metrics aggregated: {metrics}')  
        except Exception as e:
            logger.error(f'Pipeline execution failed: {e}')  
        finally:
            self.session.close()  # Ensure session is closed

if __name__ == '__main__':
    # Example usage
    pipeline = FineTuningPipeline()
    test_query = 'What is retrieval-augmented generation?'
    pipeline.run_pipeline(test_query)  # Execute the pipeline with a test query