Build Real-Time Anomaly Detection Pipelines on Intel Edge with vLLM and OpenVINO

"""
Production implementation for building real-time anomaly detection pipelines.
Utilizes Intel Edge with vLLM and OpenVINO for efficient processing.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
import numpy as np

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

class Config:
    """
    Configuration class for environment variables.
    """
    def __init__(self):
        self.model_endpoint: str = os.getenv('MODEL_ENDPOINT')
        self.database_url: str = os.getenv('DATABASE_URL')

config = Config()  # Load configuration settings

# Helper function to validate input data
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the incoming data for anomalies.
    
    Args:
        data: Input data to validate.
    Returns:
        bool: True if valid, otherwise raises ValueError.
    Raises:
        ValueError: If validation fails.
    """
    if 'sensor_id' not in data:
        raise ValueError('Missing sensor_id in data')  # Ensure sensor_id is present
    if not isinstance(data['readings'], list):
        raise ValueError('Readings must be a list')  # Ensure readings are of the correct type
    return True  # Data is valid

# Helper function to normalize input data
async def normalize_data(data: List[float]) -> List[float]:
    """Normalize sensor readings for processing.
    
    Args:
        data: List of sensor readings.
    Returns:
        List of normalized readings.
    """
    mean = np.mean(data)
    stddev = np.std(data)
    normalized = [(x - mean) / stddev for x in data]  # Standard normalization
    return normalized

# Helper function to transform records for model input
async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform data into model-compatible format.
    
    Args:
        data: Input data to transform.
    Returns:
        Dict: Transformed data for model.
    """
    return {
        'sensor_id': data['sensor_id'],
        'normalized_readings': await normalize_data(data['readings'])
    }

# Helper function to fetch data from an API
async def fetch_data(api_url: str) -> Dict[str, Any]:
    """Fetch sensor data from the specified API.
    
    Args:
        api_url: The API endpoint to fetch data from.
    Returns:
        Dict: The fetched data.
    Raises:
        Exception: If API call fails.
    """
    try:
        response = requests.get(api_url)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f'Failed to fetch data: {e}')
        raise Exception('API fetch failed')

# Helper function to save results to the database
async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed results to the database.
    
    Args:
        data: Data to save in the database.
    Raises:
        Exception: If database operation fails.
    """
    try:
        # Placeholder for database save operation
        logger.info(f'Saving data: {data}')  # Log the data being saved
    except Exception as e:
        logger.error(f'Failed to save to database: {e}')
        raise Exception('Database save failed')

# Main orchestrator class for anomaly detection
class AnomalyDetectionPipeline:
    """Main class to handle the anomaly detection workflow.
    """
    def __init__(self, config: Config):
        self.config = config  # Inject configuration into the pipeline

    async def process_batch(self, batch_data: List[Dict[str, Any]]) -> None:
        """Process a batch of data for anomaly detection.
        
        Args:
            batch_data: List of data records to process.
        """
        for record in batch_data:
            try:
                await validate_input(record)  # Validate each record
                transformed = await transform_records(record)  # Transform for model
                result = await self.call_model(transformed)  # Call model for prediction
                await save_to_db(result)  # Save results to the database
            except Exception as e:
                logger.error(f'Error processing record {record}: {e}')  # Log processing errors

    async def call_model(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Call the anomaly detection model with the transformed data.
        
        Args:
            data: Transformed data for model.
        Returns:
            Dict: Model prediction results.
        Raises:
            Exception: If model call fails.
        """
        try:
            response = requests.post(self.config.model_endpoint, json=data)
            response.raise_for_status()  # Raise error for bad responses
            return response.json()  # Return JSON response
        except requests.RequestException as e:
            logger.error(f'Model call failed: {e}')
            raise Exception('Model call failed')

if __name__ == '__main__':
    # Example usage
    pipeline = AnomalyDetectionPipeline(config)
    sample_data = [{'sensor_id': 'sensor_1', 'readings': [1.0, 2.0, 1.5]}]
    # Process a sample batch
    import asyncio
    asyncio.run(pipeline.process_batch(sample_data))