Evaluate Industrial RAG Pipeline Faithfulness and Groundedness with Ragas and LlamaIndex

"""
Production implementation for evaluating Industrial RAG pipeline faithfulness and groundedness.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import json
import time
from contextlib import contextmanager

# Logger setup for tracking the application flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')

@contextmanager
def db_connection() -> None:
    """Context manager for database connection.
    
    Yields:
        None
    """
    logger.info("Establishing database connection...")
    try:
        # Simulate connection pooling
        conn = "Database Connection"
        yield conn
    finally:
        # Close the connection
        logger.info("Closing database connection...")

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'id' not in data:
        raise ValueError('Missing id in input data')
    if not isinstance(data['id'], int):
        raise ValueError('ID must be an integer')
    return True

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

def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize data for processing.
    
    Args:
        data: List of records to normalize
    Returns:
        Normalized data
    """
    return [{k.lower(): v for k, v in record.items()} for record in data]

def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        JSON response from the API
    Raises:
        ConnectionError: If API call fails
    """
    logger.info(f"Fetching data from {endpoint}...")
    response = requests.get(endpoint)
    if response.status_code != 200:
        raise ConnectionError(f"Failed to fetch data: {response.status_code}")
    return response.json()

def process_batch(records: List[Dict[str, Any]]) -> None:
    """Process a batch of records.
    
    Args:
        records: List of records to process
    """
    logger.info(f"Processing {len(records)} records...")
    # Simulate processing logic here

def aggregate_metrics(records: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed records.
    
    Args:
        records: List of processed records
    Returns:
        Dictionary of aggregated metrics
    """
    metrics = {'count': len(records)}  # Count metrics as an example
    return metrics

def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    """
    logger.info(f"Saving data to database: {data}")
    # Simulate database save operation

def handle_errors(e: Exception) -> None:
    """Handle errors that occur during processing.
    
    Args:
        e: Exception to handle
    """
    logger.error(f"An error occurred: {str(e)}")

def call_api(data: Dict[str, Any]) -> None:
    """Call an external API with the data.
    
    Args:
        data: Data to send to the API
    """
    logger.info(f"Calling API with data: {data}")
    # Simulate API call

class RAGPipeline:
    """Main class to orchestrate the RAG pipeline evaluation.
    
    Attributes:
        config: Configuration for the pipeline
    """
    def __init__(self) -> None:
        self.config = Config()

    def evaluate_pipeline(self) -> None:
        """Evaluate the RAG pipeline.
        
        Raises:
            Exception: If evaluation fails
        """
        try:
            with db_connection() as conn:
                # Fetch and process data
                data = fetch_data(self.config.api_endpoint)
                validate_input(data)
                sanitized_data = sanitize_fields(data)
                normalized_data = normalize_data(sanitized_data)
                process_batch(normalized_data)
                metrics = aggregate_metrics(normalized_data)
                save_to_db(metrics)
        except Exception as e:
            handle_errors(e)

if __name__ == '__main__':
    # Example usage of the RAG Pipeline
    pipeline = RAGPipeline()
    pipeline.evaluate_pipeline()