diff --git a/.github/tests/multimodality_tests.py b/.github/tests/multimodality_tests.py
index 669b33bc..c6311d81 100644
--- a/.github/tests/multimodality_tests.py
+++ b/.github/tests/multimodality_tests.py
@@ -138,6 +138,23 @@ def test_topk_operation(setup_models, model):
 
         top_2_actual = set(sorted_df["image"].values)
         assert top_2_expected == top_2_actual
+        
+@pytest.mark.parametrize("model", get_enabled("gpt-4o-mini"))
+def test_topk_with_groupby_operation(setup_models, model):
+    image_url = [
+        "https://img.etsystatic.com/il/4bee20/1469037676/il_340x270.1469037676_iiti.jpg?version=0",
+        "https://i1.wp.com/www.alloverthemap.net/wp-content/uploads/2014/02/2012-09-25-12.46.15.jpg?resize=400%2C284&ssl=1",
+        "https://i.pinimg.com/236x/a4/3a/65/a43a65683a0314f29b66402cebdcf46d.jpg",
+        "https://pravme.ru/wp-content/uploads/2018/01/sobor-Bogord-1.jpg",
+    ]
+    elements = ["doll", "bird"]
+    image_df = pd.DataFrame({"image": ImageArray(image_url)})
+    element_df = pd.DataFrame({"element": elements})
+
+    df = image_df.join(element_df, how="cross")
+    df.sem_topk("the {image} is most likely an {element}", K=1, group_by=["element"])
+    assert(len(set(df["element"])) == 2)
+
 
 
 @pytest.mark.parametrize("model", get_enabled("clip-ViT-B-32"))
diff --git a/lotus/dtype_extensions/image.py b/lotus/dtype_extensions/image.py
index dd2a522c..9fecab50 100644
--- a/lotus/dtype_extensions/image.py
+++ b/lotus/dtype_extensions/image.py
@@ -82,6 +82,23 @@ def copy(self) -> "ImageArray":
         new_array._cached_images = self._cached_images.copy()
         return new_array
 
+    def _concat_same_type(cls, to_concat: Sequence["ImageArray"]) -> "ImageArray":
+        """
+        Concatenate multiple ImageArray instances into a single one.
+        
+        Args:
+            to_concat (Sequence[ImageArray]): A sequence of ImageArray instances to concatenate.
+        
+        Returns:
+            ImageArray: A new ImageArray containing all elements from the input arrays.
+        """
+        # create list of all data
+        combined_data = np.concatenate([arr._data for arr in to_concat])
+        return cls._from_sequence(combined_data)
+
+
+
+
     @classmethod
     def _from_sequence(cls, scalars, dtype=None, copy=False):
         if copy: