Build Visual RAG Pipelines for Factory Schematics and Blueprints with ColPali and Haystack

"""
Production implementation for building Visual RAG Pipelines for Factory Schematics and Blueprints.
Provides secure, scalable operations using ColPali and Haystack.
"""

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

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

# Database connection pooling setup
DATABASE_URL = os.getenv('DATABASE_URL')
engine = create_engine(DATABASE_URL, pool_size=5, max_overflow=10)
Session = sessionmaker(bind=engine)

class Config:
    """
    Configuration settings for the application.
    """
    database_url: str = os.getenv('DATABASE_URL')

def retry(exceptions: tuple, tries: int = 3, delay: float = 1.0, backoff: float = 2.0):
    """
    Retry decorator for functions that may fail.
    
    Args:
        exceptions (tuple): Exception types to catch.
        tries (int): Number of retries.
        delay (float): Initial delay between retries.
        backoff (float): Backoff multiplier.
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            attempt = 0
            while attempt < tries:
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    logger.warning(f'Attempt {attempt + 1} failed: {e}')
                    time.sleep(delay)
                    attempt += 1
                    delay *= backoff
            raise Exception(f'Func {func.__name__} failed after {attempt} attempts')
        return wrapper
    return decorator

@retry((requests.HTTPError, ValueError), tries=5)
def fetch_data(api_url: str) -> Dict[str, Any]:
    """
    Fetch data from an API.
    
    Args:
        api_url: The URL of the API to fetch data from.
    Returns:
        The JSON response from the API.
    Raises:
        requests.HTTPError: If the request to the API fails.
    """
    response = requests.get(api_url)
    response.raise_for_status()  # Raise an error for bad responses
    return response.json()

def validate_input_data(data: Dict[str, Any]) -> bool:
    """
    Validate incoming data.
    
    Args:
        data: Input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if not data.get('schematic_id'):
        raise ValueError('Missing schematic_id')
    if not isinstance(data.get('parameters'), dict):
        raise ValueError('Parameters must be a dictionary')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields for security.
    
    Args:
        data: Input data with possible unsafe fields.
    Returns:
        Cleaned data.
    """
    return {key: str(value).strip() for key, value in data.items()}

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Normalize data for processing.
    
    Args:
        data: Input data to normalize.
    Returns:
        Normalized data.
    """
    return {key.lower(): value for key, value in data.items()}

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Transform list of records for analysis.
    
    Args:
        records: List of records to transform.
    Returns:
        Transformed list of records.
    """
    return [{'id': rec['schematic_id'], 'data': rec['parameters']} for rec in records]

def process_batch(data: List[Dict[str, Any]]) -> None:
    """
    Process a batch of data.
    
    Args:
        data: List of data to process.
    """
    with Session() as session:
        # Imagine we save processed data to the database
        for record in data:
            logger.info(f'Processing record: {record}')
            # Simulated database save operation
            session.execute(text('INSERT INTO processed_data (id, data) VALUES (:id, :data)'),
                           {'id': record['id'], 'data': record['data']})
        session.commit()  # Commit the transaction

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.
    """
    return {'count': len(records), 'total_data_points': sum(len(rec['data']) for rec in records)}

def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save data to the database.
    
    Args:
        data: Data to save.
    """
    with Session() as session:
        # Simulated save operation
        session.execute(text('INSERT INTO metrics (count, total_data_points) VALUES (:count, :total)'),
                       {'count': data['count'], 'total': data['total_data_points']})
        session.commit()  # Commit to the database

class RAGPipeline:
    """
    Main class to orchestrate the RAG pipeline.
    """
    def __init__(self, api_url: str):
        self.api_url = api_url

    def run(self, input_data: Dict[str, Any]) -> None:
        """
        Run the RAG pipeline.
        
        Args:
            input_data: Data to process.
        """
        try:
            # Step 1: Validate input data
            logger.info('Validating input data')
            validate_input_data(input_data)
            # Step 2: Sanitize fields
            sanitized_data = sanitize_fields(input_data)
            # Step 3: Fetch additional data
            logger.info('Fetching data from API')
            fetched_data = fetch_data(self.api_url)
            # Step 4: Normalize and transform data
            normalized_data = normalize_data(fetched_data)
            transformed_data = transform_records([normalized_data])
            # Step 5: Process the batch
            logger.info('Processing batch of data')
            process_batch(transformed_data)
            # Step 6: Aggregate metrics
            metrics = aggregate_metrics(transformed_data)
            # Step 7: Save metrics to the database
            logger.info('Saving metrics to the database')
            save_to_db(metrics)
        except Exception as e:
            logger.error(f'Error occurred: {e}')

if __name__ == '__main__':
    # Example usage
    api_url = 'https://api.example.com/data'
    input_data = {'schematic_id': '1234', 'parameters': {'param1': 'value1', 'param2': 'value2'}}
    pipeline = RAGPipeline(api_url)
    pipeline.run(input_data)  # Run the pipeline with example input