import numpy as np
import faiss
import logging

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

def test_faiss():
    try:
        # Create a small test index
        dimension = 64
        nb = 100
        
        # Generate random data
        xb = np.random.random((nb, dimension)).astype('float32')
        
        # Create index
        index = faiss.IndexFlatL2(dimension)
        
        # Add vectors
        index.add(xb)
        
        # Test search
        k = 5
        xq = np.random.random((1, dimension)).astype('float32')
        D, I = index.search(xq, k)
        
        logger.info("FAISS test successful!")
        logger.info(f"Found {k} nearest neighbors")
        
        return True
        
    except Exception as e:
        logger.error(f"FAISS test failed: {str(e)}")
        return False
    
import torch

def test_torch():
    try:
        x = torch.rand(5, 3)
        print("PyTorch is working correctly. Tensor:", x)
    except Exception as e:
        print("Error with PyTorch:", e)


if __name__ == "__main__":
    test_faiss()
    test_torch()