def test_ndarray_to_proto_numpy():
X = np.array([1, 2, 3], dtype="float32")
proto = serializers.ndarray_to_proto(X)
assert proto.shape == [3]
assert proto.dtype == tensors_pb2.NDArray.FLOAT32
assert proto.numpy_content == X.tobytes()
proto = serializers.ndarray_to_proto(X, dtype="float64")
assert proto.dtype == tensors_pb2.NDArray.FLOAT64
def test_proto_to_ndarray_numpy(X):
proto = serializers.ndarray_to_proto(X)
X_prime = serializers.proto_to_ndarray(proto)
assert np.array_equal(X, X_prime)
def test_ndarray_to_proto_unsupported_input(X, dtype, shape, error):
with pytest.raises(ValueError) as excinfo:
serializers.ndarray_to_proto(X, dtype=dtype, shape=shape)
assert error in str(excinfo.value)
def test_ndarray_to_proto_list():
X = [1, 2, 3]
proto = serializers.ndarray_to_proto(X, dtype="int8", shape=(3, ))
assert proto.shape == [3]
assert proto.dtype == tensors_pb2.NDArray.INT8
assert proto.numpy_content is not None
def retrieve(self, id: str) -> indices_pb2.RetrievalResultItem:
vector, metadata = self.get_vector_and_metadata(id)
vector_proto = None if vector is None else serializers.ndarray_to_proto(
vector)
return indices_pb2.RetrievalResultItem(vector=vector_proto,
metadata=metadata)