Build Industrial Parts Visual Similarity Search with OpenCLIP and Qdrant

"""
Production implementation for Building Industrial Parts Visual Similarity Search with OpenCLIP and Qdrant.
Provides secure, scalable operations for searching and retrieving similar industrial parts based on visual features.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from qdrant_client import QdrantClient
from qdrant_client.http.models import PointStruct

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

class Config:
    """
    Configuration class to hold environment variables.
    """  
    qdrant_url: str = os.getenv('QDRANT_URL', 'http://localhost:6333')
    api_key: str = os.getenv('API_KEY')

client = QdrantClient(url=Config.qdrant_url, api_key=Config.api_key)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for similarity search.
    
    Args:
        data: Input to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'image_url' not in data:
        raise ValueError('Missing image_url in input data')
    return True

async 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.
    """
    # Simple sanitization example
    return {k: v.strip() for k, v in data.items()}

async def fetch_image_features(image_url: str) -> List[float]:
    """Fetch image features using OpenCLIP model.
    
    Args:
        image_url: URL of the image to process.
    Returns:
        List of features extracted from the image.
    Raises:
        RuntimeError: If fetching features fails.
    """
    logger.info(f'Fetching image features from {image_url}')
    # Placeholder for actual OpenCLIP call
    response = requests.post('http://openclip-service/get_features', json={'url': image_url})
    if response.status_code != 200:
        raise RuntimeError('Failed to fetch image features')
    return response.json()['features']

async def save_to_db(points: List[PointStruct]) -> None:
    """Save points to Qdrant database.
    
    Args:
        points: List of points to save.
    """
    logger.info(f'Saving {len(points)} points to Qdrant')
    client.upsert(points)

async def process_batch(batch: List[Dict[str, Any]]) -> List[Tuple[str, List[float]]]:
    """Process a batch of images for feature extraction.
    
    Args:
        batch: List of image data to process.
    Returns:
        List of tuples containing image_id and features.
    """
    results = []
    for item in batch:
        try:
            await validate_input(item)
            sanitized_data = await sanitize_fields(item)
            features = await fetch_image_features(sanitized_data['image_url'])
            results.append((sanitized_data['image_url'], features))
        except Exception as e:
            logger.error(f'Error processing {item}: {str(e)}')
    return results

async def aggregate_metrics(results: List[Tuple[str, List[float]]]) -> None:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of processed results.
    """  
    logger.info('Aggregating metrics')
    # Placeholder for actual aggregation logic
    # This could involve saving metrics to a database, logging, etc.

class SimilaritySearch:
    """Main orchestrator for similarity search operations.
    """
    def __init__(self):
        self.config = Config()

    async def run_search(self, batch: List[Dict[str, Any]]) -> None:
        """Run the similarity search for a batch of images.
        
        Args:
            batch: List of image data to search.
        """  
        logger.info('Starting similarity search')
        results = await process_batch(batch)
        points = [PointStruct(id=url, vector=features) for url, features in results]
        await save_to_db(points)
        await aggregate_metrics(results)

if __name__ == '__main__':
    # Example usage: run the similarity search with a sample batch
    import asyncio
    sample_batch = [{'image_url': 'http://example.com/image1.jpg'},
                    {'image_url': 'http://example.com/image2.jpg'}]  
    search = SimilaritySearch()
    asyncio.run(search.run_search(sample_batch))