test: fix test

litellm/llms/openai.py

@@ -770,7 +770,7 @@ class OpenAIChatCompletion(BaseLLM):
         openai_aclient: AsyncOpenAI,
         data: dict,
         timeout: Union[float, httpx.Timeout],
-    ):
+    ) -> Tuple[dict, BaseModel]:
         """
         Helper to:
         - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
@@ -783,7 +783,10 @@ class OpenAIChatCompletion(BaseLLM):
                 )
             )
 
-            headers = dict(raw_response.headers)
+            if hasattr(raw_response, "headers"):
+                headers = dict(raw_response.headers)
+            else:
+                headers = {}
             response = raw_response.parse()
             return headers, response
         except OpenAIError as e:
@@ -800,7 +803,7 @@ class OpenAIChatCompletion(BaseLLM):
         openai_client: OpenAI,
         data: dict,
         timeout: Union[float, httpx.Timeout],
-    ):
+    ) -> Tuple[dict, BaseModel]:
         """
         Helper to:
         - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
@@ -811,7 +814,10 @@ class OpenAIChatCompletion(BaseLLM):
                 **data, timeout=timeout
             )
 
-            headers = dict(raw_response.headers)
+            if hasattr(raw_response, "headers"):
+                headers = dict(raw_response.headers)
+            else:
+                headers = {}
             response = raw_response.parse()
             return headers, response
         except OpenAIError as e:

litellm/tests/test_completion.py

@@ -1637,18 +1637,19 @@ def test_completion_perplexity_api():
         pydantic_obj = ChatCompletion(**response_object)
 
         def _return_pydantic_obj(*args, **kwargs):
-            return pydantic_obj
+            new_response = MagicMock()
+            new_response.headers = {"hello": "world"}
 
-        print(f"pydantic_obj: {pydantic_obj}")
+            new_response.parse.return_value = pydantic_obj
+            return new_response
 
         openai_client = OpenAI()
 
-        openai_client.chat.completions.create = MagicMock()
-
         with patch.object(
-            openai_client.chat.completions, "create", side_effect=_return_pydantic_obj
+            openai_client.chat.completions.with_raw_response,
+            "create",
+            side_effect=_return_pydantic_obj,
         ) as mock_client:
-            pass
             # litellm.set_verbose= True
             messages = [
                 {"role": "system", "content": "You're a good bot"},

litellm/utils.py

@@ -637,7 +637,10 @@ def client(original_function):
                     if is_coroutine is True:
                         pass
                     else:
-                        if isinstance(original_response, ModelResponse):
+                        if (
+                            isinstance(original_response, ModelResponse)
+                            and len(original_response.choices) > 0
+                        ):
                             model_response: Optional[str] = original_response.choices[
                                 0
                             ].message.content  # type: ignore