Optimise Industrial Agent Prompts for Production Accuracy with AdalFlow and PydanticAI

"""
Production implementation for Optimise Industrial Agent Prompts for Production Accuracy with AdalFlow and PydanticAI.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import time
from pydantic import BaseModel, ValidationError
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker, Session

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

# Database configuration and setup
DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
engine = create_engine(DATABASE_URL)
Base = declarative_base()
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

class PromptData(Base):
    """
    SQLAlchemy model for storing prompt data.
    """
    __tablename__ = 'prompts'
    id = Column(Integer, primary_key=True, index=True)
    content = Column(String, index=True)
    accuracy = Column(Integer)

Base.metadata.create_all(bind=engine)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///./test.db')

class PromptModel(BaseModel):
    """
    Pydantic model for prompt validation.
    """
    content: str
    accuracy: int

def validate_input(data: Dict[str, Any]) -> Dict[str, Any]:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        Validated data
    Raises:
        ValueError: If validation fails
    """
    try:
        prompt = PromptModel(**data)
        return prompt.dict()
    except ValidationError as e:
        logger.error(f'Validation error: {e}')
        raise ValueError('Invalid input data')

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

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize the prompt data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    data['content'] = data['content'].lower()  # Example normalization
    return data

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for further processing.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    return [{'content': r['content'], 'accuracy': r['accuracy']} for r in records]

def fetch_data(session: Session) -> List[PromptData]:
    """Fetch prompt data from the database.
    
    Args:
        session: SQLAlchemy session
    Returns:
        List of PromptData objects
    """
    return session.query(PromptData).all()

def save_to_db(session: Session, data: Dict[str, Any]) -> None:
    """Save validated prompt data to the database.
    
    Args:
        session: SQLAlchemy session
        data: Data to save
    """
    prompt = PromptData(**data)
    session.add(prompt)
    session.commit()
    logger.info('Data saved to database')

def aggregate_metrics(data: List[PromptData]) -> Dict[str, float]:
    """Aggregate accuracy metrics from fetched data.
    
    Args:
        data: List of PromptData objects
    Returns:
        Dictionary of aggregated metrics
    """
    total_accuracy = sum(p.accuracy for p in data)
    average_accuracy = total_accuracy / len(data) if data else 0
    return {'total_accuracy': total_accuracy, 'average_accuracy': average_accuracy}

def handle_errors(func):
    """Decorator for handling errors in functions.
    
    Args:
        func: Function to wrap
    """
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {e}')
            raise
    return wrapper

@handle_errors
def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of prompt data.
    
    Args:
        batch: List of prompt data dictionaries to process
    """
    with SessionLocal() as session:
        for item in batch:
            sanitized_data = sanitize_fields(item)
            validated_data = validate_input(sanitized_data)
            normalized_data = normalize_data(validated_data)
            save_to_db(session, normalized_data)

@handle_errors
def main() -> None:
    """Main function for orchestrating operations.
    """
    with SessionLocal() as session:
        fetched_data = fetch_data(session)
        metrics = aggregate_metrics(fetched_data)
        logger.info(f'Aggregated metrics: {metrics}')

if __name__ == '__main__':
    # Example usage
    sample_data = [
        {'content': 'First prompt', 'accuracy': 85},
        {'content': 'Second prompt', 'accuracy': 90}
    ]
    process_batch(sample_data)  # Process sample data
    main()  # Execute main logic