Enforce queries has two dimensions (#167)

apis/python/src/tiledb/vector_search/index.py

@@ -126,8 +126,8 @@ def __init__(
         self.thread_executor = futures.ThreadPoolExecutor()
 
     def query(self, queries: np.ndarray, k, **kwargs):
-        if queries.ndim != 1 and queries.ndim != 2:
-            raise TypeError(f"Expected queries to have either 1 or 2 dimensions (i.e. [...] or [[...], [...]]), but it had {queries.ndim} dimensions")
+        if queries.ndim != 2:
+            raise TypeError(f"Expected queries to have 2 dimensions (i.e. [[...], etc.]), but it had {queries.ndim} dimensions")
 
         query_dimensions = queries.shape[0] if queries.ndim == 1 else queries.shape[1]
         if query_dimensions != self.get_dimensions():

apis/python/test/test_index.py

@@ -102,13 +102,14 @@ def test_index_with_incorrect_dimensions(tmp_path):
         # Wrong number of dimensions will raise a TypeError.
         with pytest.raises(TypeError):
             index.query(np.array(1, dtype=np.float32), k=3)
+        with pytest.raises(TypeError):
+            index.query(np.array([1, 1, 1], dtype=np.float32), k=3)
         with pytest.raises(TypeError):
             index.query(np.array([[[1, 1, 1]]], dtype=np.float32), k=3)
         with pytest.raises(TypeError):
             index.query(np.array([[[[1, 1, 1]]]], dtype=np.float32), k=3)
 
         # Okay otherwise.
-        index.query(np.array([1, 1, 1], dtype=np.float32), k=3)
         index.query(np.array([[1, 1, 1]], dtype=np.float32), k=3)
 
 def test_index_with_incorrect_num_of_query_columns_simple(tmp_path):
@@ -156,37 +157,3 @@ def test_index_with_incorrect_num_of_query_columns_complex(tmp_path):
                 else:
                     with pytest.raises(TypeError):
                         index.query(query, k=1)
-
-                # TODO(paris): This will throw with the following error. Fix and re-enable, then remove 
-                # test_index_with_incorrect_num_of_query_columns_in_single_vector_query:
-                #   def array_to_matrix(array: np.ndarray):
-                #           if array.dtype == np.float32:
-                #   >           return pyarray_copyto_matrix_f32(array)
-                #   E           RuntimeError: Number of dimensions must be two
-                # Here we test with a query which is just a vector, i.e. [1, 2, 3].
-                # query = query[0]
-                # if num_columns_for_query == num_columns:
-                #     index.query(query, k=1)
-                # else:
-                #     with pytest.raises(TypeError):
-                #         index.query(query, k=1)
-
-def test_index_with_incorrect_num_of_query_columns_in_single_vector_query(tmp_path):
-    # Tests that we raise a TypeError if the number of columns in the query is not the same as the 
-    # number of columns in the indexed data, specifically for a single vector query.
-    # i.e. queries = [1, 2, 3]  instead of queries = [[1, 2, 3], [4, 5, 6]].
-    indexes = [flat_index, ivf_flat_index]
-    for index_type in indexes:
-        uri = os.path.join(tmp_path, f"array_{index_type.__name__}")
-        index = index_type.create(uri=uri, dimensions=3, vector_type=np.dtype(np.uint8))
-
-        # Wrong number of columns will raise a TypeError.
-        with pytest.raises(TypeError):
-            index.query(np.array([1], dtype=np.float32), k=3)
-        with pytest.raises(TypeError):
-            index.query(np.array([1, 1], dtype=np.float32), k=3)
-        with pytest.raises(TypeError):
-            index.query(np.array([1, 1, 1, 1], dtype=np.float32), k=3)
-
-        # Okay otherwise.
-        index.query(np.array([1, 1, 1], dtype=np.float32), k=3)

apis/python/test/test_ingestion.py

@@ -197,10 +197,6 @@ def test_ivf_flat_ingestion_fvec(tmp_path):
     _, result = index.query(query_vectors, k=k, nprobe=nprobe)
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY
 
-    # Test single query vector handling
-    _, result1 = index.query(query_vectors[10], k=k, nprobe=nprobe)
-    assert accuracy(result1, np.array([gt_i[10]])) > MINIMUM_ACCURACY
-
     index_ram = IVFFlatIndex(uri=index_uri)
     _, result = index_ram.query(query_vectors, k=k, nprobe=nprobe)
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY
@@ -242,10 +238,6 @@ def test_ivf_flat_ingestion_numpy(tmp_path):
     _, result = index.query(query_vectors, k=k, nprobe=nprobe)
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY
 
-    # Test single query vector handling
-    _, result1 = index.query(query_vectors[10], k=k, nprobe=nprobe)
-    assert accuracy(result1, np.array([gt_i[10]])) > MINIMUM_ACCURACY
-
     index_ram = IVFFlatIndex(uri=index_uri)
     _, result = index_ram.query(query_vectors, k=k, nprobe=nprobe)
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY
@@ -286,10 +278,6 @@ def test_ivf_flat_ingestion_multiple_workers(tmp_path):
     _, result = index.query(query_vectors, k=k, nprobe=nprobe)
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY
 
-    # Test single query vector handling
-    _, result1 = index.query(query_vectors[10], k=k, nprobe=nprobe)
-    assert accuracy(result1, np.array([gt_i[10]])) > MINIMUM_ACCURACY
-
     index_ram = IVFFlatIndex(uri=index_uri)
     _, result = index_ram.query(query_vectors, k=k, nprobe=nprobe)
     assert accuracy(result, gt_i) > MINIMUM_ACCURACY