Merge df8e815d8c into b6f91a221b

litellm/litellm_core_utils/health_check_helpers.py

@@ -307,7 +307,9 @@ class HealthCheckHelpers:
             ),
             "responses": lambda: litellm.aresponses(
                 **_filter_model_params(model_params=model_params),
-                input=prompt or "test",
+                input=[
+                    {"role": "user", "content": prompt or "test"},
+                ],
             ),
             "anthropic_messages": lambda: litellm.anthropic_messages(
                 **{

litellm/llms/base_llm/chat/transformation.py

@@ -92,6 +92,9 @@ class BaseConfig(ABC):
     def __init__(self):
         pass
 
+    def get_health_check_mode(self) -> str | None:
+        return None
+
     @classmethod
     def get_config(cls):
         return {

litellm/llms/chatgpt/chat/transformation.py

@@ -26,6 +26,9 @@ class ChatGPTConfig(OpenAIConfig):
     def api_base_without_login(self) -> str:
         return self.authenticator.get_api_base()
 
+    def get_health_check_mode(self) -> str:
+        return "responses"
+
     def _get_openai_compatible_provider_info(
         self,
         model: str,

litellm/main.py

@@ -9100,8 +9100,16 @@ async def ahealth_check(
             api_base=api_base_from_params,
             api_key=api_key_from_params,
         )
-
         model_params["cache"] = {"no-cache": True}  # don't used cached responses for making health check calls
+        if mode is None and custom_llm_provider in {provider.value for provider in LlmProviders}:
+            provider_config: Final = ProviderConfigManager.get_provider_chat_config(
+                model=model,
+                provider=LlmProviders(custom_llm_provider),
+            )
+            if provider_config is not None:
+                mode = (  # rebind-ok: resolve provider-config default
+                    provider_config.get_health_check_mode()
+                )
         mode = mode or default_health_check_mode(
             requested_model=requested_model, model=model, custom_llm_provider=custom_llm_provider
         )

tests/unit/litellm_core_utils/test_health_check_helpers.py

@@ -150,6 +150,68 @@ async def test_ahealth_check_supports_image_edit_mode():
 
     assert "error" not in result
     assert "Mode image_edit not supported" not in str(result)
+@pytest.mark.asyncio
+async def test_ahealth_check_uses_provider_mode_and_responses_message_input():
+    mock_response = MagicMock()
+    mock_response._hidden_params = {}
+    mock_aresponses = AsyncMock(return_value=mock_response)
+    mock_acompletion = AsyncMock()
+
+    with (
+        patch(  # test-quality-ok: isolate provider and handler calls for focused health-check routing
+            "litellm.main.get_llm_provider",
+            return_value=("gpt-5.6-sol", "chatgpt", None, None),
+        ),
+        patch.object(  # test-quality-ok: isolate health-check metadata plumbing for routing assertion
+            HealthCheckHelpers,
+            "_update_model_params_with_health_check_tracking_information",
+            side_effect=lambda model_params: model_params,
+        ),
+        patch("litellm.aresponses", new=mock_aresponses),  # test-quality-ok: verify provider-mode dispatch
+        patch("litellm.acompletion", new=mock_acompletion),  # test-quality-ok: verify chat fallback dispatch
+    ):
+        result = await ahealth_check(
+            model_params={"model": "chatgpt/gpt-5.6-sol"},
+            mode=None,
+            prompt="health check",
+        )
+
+    assert "error" not in result
+    mock_aresponses.assert_awaited_once()
+    assert mock_aresponses.await_args.kwargs["input"] == [
+        {"role": "user", "content": "health check"}
+    ]
+    mock_acompletion.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_ahealth_check_provider_without_default_mode_keeps_chat():
+    mock_response = MagicMock()
+    mock_response._hidden_params = {}
+    mock_aresponses = AsyncMock()
+    mock_acompletion = AsyncMock(return_value=mock_response)
+
+    with (
+        patch(  # test-quality-ok: isolate provider and handler calls for focused health-check routing
+            "litellm.main.get_llm_provider",
+            return_value=("custom-model", "custom", None, None),
+        ),
+        patch.object(  # test-quality-ok: isolate health-check metadata plumbing for routing assertion
+            HealthCheckHelpers,
+            "_update_model_params_with_health_check_tracking_information",
+            side_effect=lambda model_params: model_params,
+        ),
+        patch("litellm.aresponses", new=mock_aresponses),  # test-quality-ok: verify provider-mode dispatch
+        patch("litellm.acompletion", new=mock_acompletion),  # test-quality-ok: verify chat fallback dispatch
+    ):
+        result = await ahealth_check(
+            model_params={"model": "custom/custom-model"},
+            mode=None,
+        )
+
+    assert "error" not in result
+    mock_acompletion.assert_awaited_once()
+    mock_aresponses.assert_not_awaited()
 
 
 @pytest.mark.asyncio
@@ -469,7 +531,7 @@ async def test_realtime_health_check_uses_model_level_vertex_params():
             "websockets.connect",
             lambda url, **kwargs: _FakeWebsocketConnect(connect_calls, url, **kwargs),
         ),
-        patch.object(
+        patch.object(  # test-quality-ok: isolate health-check metadata plumbing for routing assertion
             HealthCheckHelpers,
             "update_model_params_with_health_check_tracking_information",
             staticmethod(lambda model_params: model_params),