diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py b/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py
index 783de5ac..96b2e041 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/_searcher.py
@@ -35,7 +35,9 @@ def find_neighbors(
         filter_: Union[List[Namespace], None] = None,
         numeric_filter: Union[List[NumericNamespace], None] = None,
         *,
-        sparse_embeddings: Optional[List[Dict[str, List[int] | List[float]]]] = None,
+        sparse_embeddings: Optional[
+            List[Dict[str, Union[List[int], List[float]]]]
+        ] = None,
         rrf_ranking_alpha: float = 1,
     ) -> List[List[Dict[str, Any]]]:
         """Finds the k closes neighbors of each instance of embeddings.
@@ -70,7 +72,9 @@ def add_to_index(
         metadatas: Union[List[dict], None] = None,
         is_complete_overwrite: bool = False,
         *,
-        sparse_embeddings: Optional[List[Dict[str, List[int] | List[float]]]] = None,
+        sparse_embeddings: Optional[
+            List[Dict[str, Union[List[int], List[float]]]]
+        ] = None,
         **kwargs: Any,
     ) -> None:
         """Adds documents to the index.
@@ -191,7 +195,9 @@ def add_to_index(
         metadatas: Union[List[dict], None] = None,
         is_complete_overwrite: bool = False,
         *,
-        sparse_embeddings: Optional[List[Dict[str, List[int] | List[float]]]] = None,
+        sparse_embeddings: Optional[
+            List[Dict[str, Union[List[int], List[float]]]]
+        ] = None,
         **kwargs: Any,
     ) -> None:
         """Adds documents to the index.
@@ -233,7 +239,9 @@ def find_neighbors(
         filter_: Union[List[Namespace], None] = None,
         numeric_filter: Union[List[NumericNamespace], None] = None,
         *,
-        sparse_embeddings: Optional[List[Dict[str, List[int] | List[float]]]] = None,
+        sparse_embeddings: Optional[
+            List[Dict[str, Union[List[int], List[float]]]]
+        ] = None,
         rrf_ranking_alpha: float = 1,
     ) -> List[List[Dict[str, Any]]]:
         """Finds the k closes neighbors of each instance of embeddings.
diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py b/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py
index 541c931c..dbd76970 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/_utils.py
@@ -65,7 +65,7 @@ def batch_update_index(
 def to_data_points(
     ids: List[str],
     embeddings: List[List[float]],
-    sparse_embeddings: Optional[List[Dict[str, List[int] | List[float]]]] = None,
+    sparse_embeddings: Optional[List[Dict[str, Union[List[int], List[float]]]]] = None,
     metadatas: Union[List[Dict[str, Any]], None] = None,
 ) -> List["meidx_types.IndexDataPoint"]:
     """Converts triplets id, embedding, metadata into IndexDataPoints instances.
diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
index 70b4dc30..9d2b6160 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
@@ -278,7 +278,9 @@ def add_texts_with_embeddings(
         embeddings: List[List[float]],
         metadatas: Union[List[dict], None] = None,
         *,
-        sparse_embeddings: Optional[List[Dict[str, List[int] | List[float]]]] = None,
+        sparse_embeddings: Optional[
+            List[Dict[str, Union[List[int], List[float]]]]
+        ] = None,
         ids: Optional[List[str]] = None,
         is_complete_overwrite: bool = False,
         **kwargs: Any,
diff --git a/libs/vertexai/tests/integration_tests/test_vectorstores.py b/libs/vertexai/tests/integration_tests/test_vectorstores.py
index d2fd5aac..6c3c4bbb 100644
--- a/libs/vertexai/tests/integration_tests/test_vectorstores.py
+++ b/libs/vertexai/tests/integration_tests/test_vectorstores.py
@@ -11,7 +11,7 @@
 """
 
 import os
-from typing import Dict, List
+from typing import Dict, List, Union
 from uuid import uuid4
 
 import pytest
@@ -280,7 +280,7 @@ def test_vector_store_hybrid_search(
 
     query = "What are your favourite animals?"
     embedding = embeddings.embed_query(query)
-    sparse_embedding: Dict[str, List[int] | List[float]] = {
+    sparse_embedding: Dict[str, Union[List[int], List[float]]] = {
         "values": [0.5, 0.7],
         "dimensions": [2, 4],
     }
@@ -317,7 +317,7 @@ def test_add_texts_with_embeddings(
     )
     assert len(ids1) == 2
 
-    sparse_embeddings: List[Dict[str, List[int] | List[float]]] = [
+    sparse_embeddings: List[Dict[str, Union[List[int], List[float]]]] = [
         {"values": [0.5, 0.7], "dimensions": [2, 4]}
     ] * 2
     ids2 = vector_store.add_texts_with_embeddings(
diff --git a/libs/vertexai/tests/unit_tests/test_vectorstores.py b/libs/vertexai/tests/unit_tests/test_vectorstores.py
index add2c6b6..24653013 100644
--- a/libs/vertexai/tests/unit_tests/test_vectorstores.py
+++ b/libs/vertexai/tests/unit_tests/test_vectorstores.py
@@ -1,4 +1,4 @@
-from typing import Dict, List
+from typing import Dict, List, Union
 from unittest.mock import MagicMock
 
 import pytest
@@ -11,7 +11,7 @@
 def test_to_data_points():
     ids = ["Id1"]
     embeddings = [[0.0, 0.0]]
-    sparse_embeddings: List[Dict[str, List[int] | List[float]]] = [
+    sparse_embeddings: List[Dict[str, Union[List[int], List[float]]]] = [
         {"values": [0.9, 0.3], "dimensions": [3, 20]}
     ]
     metadatas = [
@@ -112,7 +112,7 @@ def test_add_texts_with_embeddings():
 
 def test_similarity_search_by_vector_with_score_output_shape():
     embedding = [0.0, 0.5, 0.8]
-    sparse_embedding: Dict[str, List[int] | List[float]] = {
+    sparse_embedding: Dict[str, Union[List[int], List[float]]] = {
         "values": [0.9, 0.3],
         "dimensions": [3, 20],
     }