fix(python): accept NumPy scalar values in DenseVector

Author: dhairyathareja1

client/python/tests/test_models.py

@@ -1,15 +1,16 @@
+from dataclasses import FrozenInstanceError
+
 import pytest
 
+from vortexdb.grpc import vector_db_pb2
 from vortexdb.models import (
+    ContentType,
     DenseVector,
     Payload,
     Point,
     Similarity,
-    ContentType,
 )
 
-from vortexdb.grpc import vector_db_pb2
-
 # DenseVector Tests
 
 
@@ -31,6 +32,13 @@ def test_dense_vector_accepts_numpy_array():
     assert v.values == [1.0, 2.5, 3.0]
 
 
+def test_dense_vector_accepts_numpy_scalars_in_list():
+    numpy = pytest.importorskip("numpy")
+    v = DenseVector([numpy.float32(1.5), numpy.float32(2.5)])
+    assert v.values == [1.5, 2.5]
+    assert all(type(value) is float for value in v.values)
+
+
 def test_dense_vector_accepts_pytorch_tensor():
     torch = pytest.importorskip("torch")
     v = DenseVector(torch.tensor([1, 2.5, 3]))
@@ -135,7 +143,7 @@ def test_dense_vector_rejects_multidimensional_tensor_like_value():
 
 def test_dense_vector_is_frozen():
     v = DenseVector([1, 2, 3])
-    with pytest.raises(Exception):
+    with pytest.raises(FrozenInstanceError):
         v.values = [4, 5, 6]
 
 

client/python/vortexdb/models.py

@@ -1,8 +1,9 @@
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, List
-from vortexdb.grpc import vector_db_pb2
+from numbers import Real
+from typing import Any
 
+from vortexdb.grpc import vector_db_pb2
 
 # I found this to be a good idea, because
 # 1. readability
@@ -45,7 +46,7 @@ def from_proto(value: int) -> "ContentType":
 
 @dataclass(frozen=True)
 class DenseVector:
-    values: List[float]
+    values: list[float]
 
     def __post_init__(self):
         if isinstance(self.values, (list, tuple)):
@@ -60,7 +61,7 @@ def __post_init__(self):
             raise ValueError("DenseVector expects a one-dimensional vector")
 
         for v in normalized_values:
-            if not isinstance(v, (int, float)):
+            if not isinstance(v, Real):
                 raise TypeError("DenseVector values must be numeric (int or float)")
 
         object.__setattr__(self, "values", [float(v) for v in normalized_values])