diff --git a/python/scripts/test_index.py b/python/scripts/test_index.py
index cee1f307..5ac4f8a9 100644
--- a/python/scripts/test_index.py
+++ b/python/scripts/test_index.py
@@ -29,7 +29,7 @@
     ScalarKind.BF16,
     ScalarKind.I8,
 ]
-dtypes = [np.float32, np.float64, np.float16]
+dtypes = [np.float32, np.float64, np.float16, np.int8, np.uint8]
 threads = 2
 
 connectivity_options = [3, 13, 50, DEFAULT_CONNECTIVITY]
@@ -49,8 +49,10 @@ def reset_randomness():
 @pytest.mark.parametrize("metric", [MetricKind.Cos, MetricKind.L2sq])
 @pytest.mark.parametrize("batch_size", [1, 7, 1024])
 @pytest.mark.parametrize("quantization", [ScalarKind.F32, ScalarKind.I8])
-@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
-def test_index_initialization_and_addition(ndim, metric, quantization, dtype, batch_size):
+@pytest.mark.parametrize("dtype", dtypes)
+def test_index_initialization_and_addition(
+    ndim, metric, quantization, dtype, batch_size
+):
     reset_randomness()
 
     index = Index(ndim=ndim, metric=metric, dtype=quantization, multi=False)
@@ -63,7 +65,9 @@ def test_index_initialization_and_addition(ndim, metric, quantization, dtype, ba
 @pytest.mark.parametrize("ndim", [3, 97, 256])
 @pytest.mark.parametrize("metric", [MetricKind.Cos, MetricKind.L2sq])
 @pytest.mark.parametrize("batch_size", [1, 7, 1024])
-@pytest.mark.parametrize("quantization", [ScalarKind.F32, ScalarKind.F16, ScalarKind.I8])
+@pytest.mark.parametrize(
+    "quantization", [ScalarKind.F32, ScalarKind.F16, ScalarKind.I8]
+)
 @pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
 def test_index_retrieval(ndim, metric, quantization, dtype, batch_size):
     reset_randomness()
@@ -103,7 +107,9 @@ def test_index_retrieval(ndim, metric, quantization, dtype, batch_size):
         # Try a transposed version of the same vectors, that is not C-contiguous
         # and should raise an exception!
         index = Index(ndim=ndim, metric=metric, dtype=quantization, multi=False)
-        vectors = random_vectors(count=ndim, ndim=batch_size, dtype=dtype)  #! reversed dims
+        vectors = random_vectors(
+            count=ndim, ndim=batch_size, dtype=dtype
+        )  #! reversed dims
         assert vectors.strides == (batch_size * dtype().itemsize, dtype().itemsize)
         assert vectors.T.strides == (dtype().itemsize, batch_size * dtype().itemsize)
         with pytest.raises(Exception):
@@ -220,7 +226,9 @@ def test_index_save_load_restore_copy(ndim, quantization, batch_size):
     copied_index = index.copy()
     assert len(copied_index) == len(index)
     if batch_size > 0:
-        assert np.allclose(np.vstack(copied_index.get(keys)), np.vstack(index.get(keys)))
+        assert np.allclose(
+            np.vstack(copied_index.get(keys)), np.vstack(index.get(keys))
+        )
 
     index.save("tmp.usearch")
     index.clear()
@@ -244,7 +252,9 @@ def test_index_save_load_restore_copy(ndim, quantization, batch_size):
     copied_index = index.copy()
     assert len(copied_index) == len(index)
     if batch_size > 0:
-        assert np.allclose(np.vstack(copied_index.get(keys)), np.vstack(index.get(keys)))
+        assert np.allclose(
+            np.vstack(copied_index.get(keys)), np.vstack(index.get(keys))
+        )
 
     # Perform the same operations in RAM, without touching the filesystem
     serialized_index = index.save()
@@ -255,7 +265,9 @@ def test_index_save_load_restore_copy(ndim, quantization, batch_size):
     assert len(deserialized_index) == len(index)
     assert set(np.array(deserialized_index.keys)) == set(np.array(index.keys))
     if batch_size > 0:
-        assert np.allclose(np.vstack(deserialized_index.get(keys)), np.vstack(index.get(keys)))
+        assert np.allclose(
+            np.vstack(deserialized_index.get(keys)), np.vstack(index.get(keys))
+        )
 
     deserialized_index.reset()
     index.reset()
@@ -280,7 +292,7 @@ def test_index_contains_remove_rename(batch_size):
     removed_keys = keys[: batch_size // 2]
     remaining_keys = keys[batch_size // 2 :]
     index.remove(removed_keys)
-    del index[removed_keys] # ! This will trigger the `__delitem__` dunder method
+    del index[removed_keys]  # ! This will trigger the `__delitem__` dunder method
     assert len(index) == (len(keys) - len(removed_keys))
     assert np.sum(index.contains(keys)) == len(remaining_keys)
     assert np.sum(index.count(keys)) == len(remaining_keys)
@@ -313,7 +325,9 @@ def test_index_oversubscribed_search(batch_size: int, threads: int):
     assert np.all(index.contains(keys))
     assert np.all(index.count(keys) == np.ones(batch_size))
 
-    batch_matches: BatchMatches = index.search(vectors, batch_size * 10, threads=threads)
+    batch_matches: BatchMatches = index.search(
+        vectors, batch_size * 10, threads=threads
+    )
     for i, match in enumerate(batch_matches):
         assert i == match.keys[0]
         assert len(match.keys) == batch_size