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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() |