diff --git a/libs/vertexai/langchain_google_vertexai/_base.py b/libs/vertexai/langchain_google_vertexai/_base.py
index 32d895a4..69d531c4 100644
--- a/libs/vertexai/langchain_google_vertexai/_base.py
+++ b/libs/vertexai/langchain_google_vertexai/_base.py
@@ -24,8 +24,9 @@
 from langchain_core.outputs import Generation, LLMResult
 from pydantic import BaseModel, ConfigDict, Field, model_validator
 from typing_extensions import Self
-from vertexai.generative_models._generative_models import (  # type: ignore
+from vertexai.generative_models._generative_models import (  # type: ignore  # type: ignore
     SafetySettingsType,
+    _convert_schema_dict_to_gapic,
 )
 from vertexai.language_models import (  # type: ignore[import-untyped]
     TextGenerationModel,
@@ -284,9 +285,33 @@ def _prepare_params(
         stop_sequences = stop or self.stop
         params_mapping = {"n": "candidate_count"}
         params = {params_mapping.get(k, k): v for k, v in kwargs.items()}
+
         params = {**self._default_params, "stop_sequences": stop_sequences, **params}
+
         if stream or self.streaming:
             params.pop("candidate_count")
+        
+        if "response_schema" in params:
+            
+            params["response_schema"] = _convert_schema_dict_to_gapic(
+                params["response_schema"])
+            
+            if "response_mime_type" not in params:
+                error_message = (
+                    "`response_mime_type` must be set when `response_schema`" 
+                    " is specified."
+                )
+                raise ValueError(error_message)
+            
+            if "response_mime_type" in params:
+                allowed_mime_types = ("application/json", "text/x.enum")
+                if params["response_mime_type"] not in allowed_mime_types:
+                    error_message = (
+                        "`response_schema` is only supported when "
+                        f"`response_mime_type` is set to one of {allowed_mime_types}"
+                    )
+                    raise ValueError(error_message)
+
         return params
 
     def get_num_tokens(self, text: str) -> int:
diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
index ebb9464a..b3bf7f24 100644
--- a/libs/vertexai/langchain_google_vertexai/chat_models.py
+++ b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -1148,22 +1148,14 @@ def _is_gemini_advanced(self) -> bool:
 
     @property
     def _default_params(self) -> Dict[str, Any]:
+        
         updated_params = super()._default_params
 
         if self.response_mime_type is not None:
             updated_params["response_mime_type"] = self.response_mime_type
 
         if self.response_schema is not None:
-            allowed_mime_types = ("application/json", "text/x.enum")
-            if self.response_mime_type not in allowed_mime_types:
-                error_message = (
-                    "`response_schema` is only supported when "
-                    f"`response_mime_type` is set to one of {allowed_mime_types}"
-                )
-                raise ValueError(error_message)
-
-            gapic_response_schema = _convert_schema_dict_to_gapic(self.response_schema)
-            updated_params["response_schema"] = gapic_response_schema
+            updated_params["response_schema"] = self.response_schema
 
         return updated_params
 
@@ -1795,13 +1787,14 @@ class AnswerWithJustification(BaseModel):
                 # that takes care of this if necessary.
                 schema_json = schema.model_json_schema()
                 schema_json = replace_defs_in_schema(schema_json)
-                self.response_schema = schema_json
+                response_schema = schema_json
                 parser = PydanticOutputParser(pydantic_object=schema)
             else:
                 parser = JsonOutputParser()
-                self.response_schema = schema
-            self.response_mime_type = "application/json"
-            llm: Runnable = self
+                response_schema = schema
+            response_mime_type = "application/json"
+            llm: Runnable = self.bind(
+                response_schema=response_schema, response_mime_type=response_mime_type)
 
         else:
             tool_name = _get_tool_name(schema)
diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py
index f9c860a7..18df20e2 100644
--- a/libs/vertexai/tests/integration_tests/test_chat_models.py
+++ b/libs/vertexai/tests/integration_tests/test_chat_models.py
@@ -670,6 +670,45 @@ class MyModel(BaseModel):
         "age": 27,
     }
 
+@pytest.mark.release
+def test_with_structured_output_thread_safety() -> None:
+
+    model = ChatVertexAI(
+        model_name="gemini-1.5-pro-001"
+    )
+
+    structured_model = model.with_structured_output(
+        {
+            "title": "ID Extraction",
+            "description": "Extracts IDs from the input text.",
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "description": ("The type"),
+                        "enum": [
+                            "ORDER_ID",
+                            "PO_ID",
+                            "INVOICE_ID",
+                            "TRACKING_ID",
+                            "OTHER",
+                        ],
+                    },
+                    "value": {"type": "string", "description": "The value of the entity."},
+                },
+            },
+        },
+        method="json_mode",
+    )
+    response = structured_model.invoke(input="Order ID: AE22334455", temperature=0)
+    assert isinstance(response, list)
+    
+    response_2 = model.invoke(input="Order ID: AE22334455", temperature=0)
+    assert isinstance(response_2, AIMessage)
+    
+
 
 @pytest.mark.release
 def test_chat_vertexai_gemini_with_structured_output_nested_model() -> None: