Optimize Industrial Knowledge Base Retrieval with LlamaIndex and DSPy

                      
                     
"""
Production implementation for optimizing industrial knowledge base retrieval using LlamaIndex and DSPy.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import asyncio
import aiohttp
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session
from tenacity import retry, stop_after_attempt, wait_exponential

Base = declarative_base()

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

class Config:
    """
    Configuration settings for the application.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
    max_retries: int = 5

class KnowledgeBase(Base):
    """
    Database model for the knowledge base.
    """
    __tablename__ = 'knowledge_base'
    id = Column(Integer, primary_key=True, index=True)
    title = Column(String)
    content = Column(String)

# Database connection setup
engine = create_engine(Config.database_url)
Base.metadata.create_all(bind=engine)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

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 'title' not in data or 'content' not in data:
        raise ValueError('Missing title or content')
    return True

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

async def fetch_data(session: aiohttp.ClientSession, url: str) -> Optional[Dict[str, Any]]:
    """Fetch data from a given URL.
    
    Args:
        session: Aiohttp session object
        url: URL to fetch data from
    Returns:
        JSON response as a dictionary
    Raises:
        Exception: If fetching fails
    """
    async with session.get(url) as response:
        if response.status == 200:
            return await response.json()
        else:
            raise Exception(f'Error fetching data: {response.status}')  

@retry(stop=stop_after_attempt(Config.max_retries), wait=wait_exponential(multiplier=1, min=2, max=10))
async def save_to_db(session: Session, data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        session: SQLAlchemy session object
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    try:
        kb_entry = KnowledgeBase(**data)
        session.add(kb_entry)
        session.commit()
    except Exception as e:
        logger.error(f'Error saving to DB: {e}')
        session.rollback()
        raise

async def process_batch(data_list: List[Dict[str, Any]]) -> None:
    """Process a batch of data entries.
    
    Args:
        data_list: List of data entries to process
    """
    async with SessionLocal() as session:
        for data in data_list:
            await validate_input(data)  # Validate input
            sanitized_data = await sanitize_fields(data)  # Sanitize fields
            await save_to_db(session, sanitized_data)  # Save to DB

async def format_output(data: List[KnowledgeBase]) -> List[Dict[str, Any]]:
    """Format output from database query.
    
    Args:
        data: List of KnowledgeBase objects
    Returns:
        Formatted output as a list of dictionaries
    """
    return [{'id': entry.id, 'title': entry.title, 'content': entry.content} for entry in data]

async def handle_errors():
    """Handle errors gracefully.
    
    This function can be expanded to include specific error handling logic.
    """
    logger.warning('An error occurred, please check logs for details.')

async def main():
    """Main workflow for the application.
    
    This function orchestrates the data retrieval and processing.
    """
    async with aiohttp.ClientSession() as session:
        url = 'https://api.example.com/data'
        try:
            raw_data = await fetch_data(session, url)  # Fetch data
            await process_batch(raw_data)  # Process data
        except Exception as e:
            logger.error(f'An error occurred: {e}')  # Log error
            await handle_errors()  # Handle errors

if __name__ == '__main__':
    # Entry point for the application
    asyncio.run(main())