Recursively rebuild models in openai.types to ensure they are available for encoding

Author: tconley1428

temporalio/contrib/openai_agents/__init__.py

@@ -8,6 +8,12 @@
     Use with caution in production environments.
 """
 
+import importlib
+import inspect
+import pkgutil
+
+from pydantic import BaseModel
+
 from temporalio.contrib.openai_agents._invoke_model_activity import ModelActivity
 from temporalio.contrib.openai_agents._model_parameters import ModelActivityParameters
 from temporalio.contrib.openai_agents._trace_interceptor import (
@@ -30,3 +36,23 @@
     "TestModel",
     "TestModelProvider",
 ]
+
+
+def _reload_models(module_name: str) -> None:
+    """Recursively walk through modules and rebuild BaseModel classes."""
+    module = importlib.import_module(module_name)
+
+    # Process classes in the current module
+    for _, obj in inspect.getmembers(module, inspect.isclass):
+        if issubclass(obj, BaseModel) and obj is not BaseModel:
+            obj.model_rebuild()
+
+    # Recursively process submodules
+    if hasattr(module, "__path__"):
+        for _, submodule_name, _ in pkgutil.iter_modules(module.__path__):
+            full_submodule_name = f"{module_name}.{submodule_name}"
+            _reload_models(full_submodule_name)
+
+
+# Recursively call model_rebuild() on all BaseModel classes in openai.types
+_reload_models("openai.types")

tests/contrib/openai_agents/test_openai.py

@@ -45,7 +45,7 @@
 )
 from openai.types.responses.response_function_web_search import ActionSearch
 from openai.types.responses.response_prompt_param import ResponsePromptParam
-from pydantic import ConfigDict, Field
+from pydantic import ConfigDict, Field, TypeAdapter
 
 from temporalio import activity, workflow
 from temporalio.client import Client, WorkflowFailureError, WorkflowHandle
@@ -1736,3 +1736,15 @@ async def test_workflow_method_tools(client: Client):
                 execution_timeout=timedelta(seconds=10),
             )
             await workflow_handle.result()
+
+
+async def test_response_serialization():
+    import json
+
+    from openai.types.responses.response_output_item import ImageGenerationCall
+
+    data = json.loads(
+        b'{"id": "msg_68757ec43348819d86709f0fcb70316301a1194a3e05b38c","type": "image_generation_call","status": "completed"}'
+    )
+    call = TypeAdapter(ImageGenerationCall).validate_python(data)
+    encoded = await pydantic_data_converter.encode([call])