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test(search): cover zero cosine vectors and batch boundaries

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Added tests for batch cosine similarity with zero vectors and empty batches.
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yunyv 2026-10-24 10:54:11 -06:00 • committed by GitHub
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tests/unit/test_utils.py
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@ -38,6 +38,42 @@ def test_batch_cosine_similarity_pairwise_matrix():
np.testing.assert_allclose(result[1], [0.0, 2**-0.5])
@pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
def test_batch_cosine_similarity_zero_vectors(dtype):
"""Zero rows and columns have finite zero scores, including stored float16 embeddings."""
left = np.array([[0, 0], [1, 0], [0, 1]], dtype=dtype)
right = np.array([[1, 0], [0, 0], [-1, 0]], dtype=dtype)
with np.errstate(invalid="raise", divide="raise"):
result = batch_cosine_similarity(left, right)
reverse = batch_cosine_similarity(right, left)
zeros = batch_cosine_similarity(np.zeros((2, 2), dtype=dtype), right)
all_zeros = batch_cosine_similarity(np.zeros((2, 2), dtype=dtype), np.zeros((3, 2), dtype=dtype))
assert result.dtype == dtype
np.testing.assert_array_equal(result, [[0, 0, 0], [1, 0, -1], [0, 0, 0]])
np.testing.assert_array_equal(reverse, result.T)
np.testing.assert_array_equal(zeros, np.zeros((2, 3)))
np.testing.assert_array_equal(all_zeros, np.zeros((2, 3)))
@pytest.mark.parametrize("left_rows,right_rows", [(0, 2), (2, 0), (0, 0)])
def test_batch_cosine_similarity_empty_batches(left_rows, right_rows):
"""Empty batches retain the pairwise output shape."""
result = batch_cosine_similarity(
np.zeros((left_rows, 2), dtype=np.float16),
np.zeros((right_rows, 2), dtype=np.float16),
)
assert result.shape == (left_rows, right_rows)
assert result.dtype == np.float16
def test_batch_cosine_similarity_rejects_mismatched_dimensions():
"""Batched embeddings must share their feature dimension."""
with pytest.raises(ValueError, match="Embedding dimensions must match"):
batch_cosine_similarity(np.zeros((2, 2)), np.zeros((2, 3)))
def test_cosine_similarity_rejects_mismatched_lengths():
"""Single-vector cosine validates dimensions before computing."""
with pytest.raises(ValueError, match="Vectors must have same length"):