fix(transformation.py): pass back in gemini thinking content to api (#10173)

Ensures thinking content always returned

litellm/llms/vertex_ai/gemini/transformation.py

@@ -216,6 +216,11 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                     msg_dict = messages[msg_i]  # type: ignore
                 assistant_msg = ChatCompletionAssistantMessage(**msg_dict)  # type: ignore
                 _message_content = assistant_msg.get("content", None)
+                reasoning_content = assistant_msg.get("reasoning_content", None)
+                if reasoning_content is not None:
+                    assistant_content.append(
+                        PartType(thought=True, text=reasoning_content)
+                    )
                 if _message_content is not None and isinstance(_message_content, list):
                     _parts = []
                     for element in _message_content:
@@ -223,6 +228,7 @@ def _gemini_convert_messages_with_history(  # noqa: PLR0915
                             if element["type"] == "text":
                                 _part = PartType(text=element["text"])
                                 _parts.append(_part)
+
                     assistant_content.extend(_parts)
                 elif (
                     _message_content is not None

litellm/types/llms/openai.py

@@ -651,6 +651,7 @@ class OpenAIChatCompletionAssistantMessage(TypedDict, total=False):
     name: Optional[str]
     tool_calls: Optional[List[ChatCompletionAssistantToolCall]]
     function_call: Optional[ChatCompletionToolCallFunctionChunk]
+    reasoning_content: Optional[str]
 
 
 class ChatCompletionAssistantMessage(OpenAIChatCompletionAssistantMessage, total=False):
@@ -823,12 +824,12 @@ class OpenAIChatCompletionChunk(ChatCompletionChunk):
 
 class Hyperparameters(BaseModel):
     batch_size: Optional[Union[str, int]] = None  # "Number of examples in each batch."
-    learning_rate_multiplier: Optional[Union[str, float]] = (
-        None  # Scaling factor for the learning rate
-    )
-    n_epochs: Optional[Union[str, int]] = (
-        None  # "The number of epochs to train the model for"
-    )
+    learning_rate_multiplier: Optional[
+        Union[str, float]
+    ] = None  # Scaling factor for the learning rate
+    n_epochs: Optional[
+        Union[str, int]
+    ] = None  # "The number of epochs to train the model for"
 
 
 class FineTuningJobCreate(BaseModel):
@@ -855,18 +856,18 @@ class FineTuningJobCreate(BaseModel):
 
     model: str  # "The name of the model to fine-tune."
     training_file: str  # "The ID of an uploaded file that contains training data."
-    hyperparameters: Optional[Hyperparameters] = (
-        None  # "The hyperparameters used for the fine-tuning job."
-    )
-    suffix: Optional[str] = (
-        None  # "A string of up to 18 characters that will be added to your fine-tuned model name."
-    )
-    validation_file: Optional[str] = (
-        None  # "The ID of an uploaded file that contains validation data."
-    )
-    integrations: Optional[List[str]] = (
-        None  # "A list of integrations to enable for your fine-tuning job."
-    )
+    hyperparameters: Optional[
+        Hyperparameters
+    ] = None  # "The hyperparameters used for the fine-tuning job."
+    suffix: Optional[
+        str
+    ] = None  # "A string of up to 18 characters that will be added to your fine-tuned model name."
+    validation_file: Optional[
+        str
+    ] = None  # "The ID of an uploaded file that contains validation data."
+    integrations: Optional[
+        List[str]
+    ] = None  # "A list of integrations to enable for your fine-tuning job."
     seed: Optional[int] = None  # "The seed controls the reproducibility of the job."
 
 

litellm/types/llms/vertex_ai.py

@@ -39,6 +39,7 @@ class PartType(TypedDict, total=False):
     file_data: FileDataType
     function_call: FunctionCall
     function_response: FunctionResponse
+    thought: bool
 
 
 class HttpxFunctionCall(TypedDict):

tests/llm_translation/test_gemini.py

@@ -89,3 +89,31 @@ def test_gemini_image_generation():
 
 
 
+def test_gemini_thinking():
+    litellm._turn_on_debug()
+    from litellm.types.utils import Message, CallTypes
+    from litellm.utils import return_raw_request
+    import json
+
+    messages = [
+        {"role": "user", "content": "Explain the concept of Occam's Razor and provide a simple, everyday example"}
+    ]
+    reasoning_content = "I'm thinking about Occam's Razor."
+    assistant_message = Message(content='Okay, let\'s break down Occam\'s Razor.', reasoning_content=reasoning_content, role='assistant', tool_calls=None, function_call=None, provider_specific_fields=None)
+
+    messages.append(assistant_message)
+
+    raw_request = return_raw_request(
+        endpoint=CallTypes.completion,
+        kwargs={
+            "model": "gemini/gemini-2.5-flash-preview-04-17",
+            "messages": messages,
+        }
+    )
+    assert reasoning_content in json.dumps(raw_request)
+    response = completion(
+        model="gemini/gemini-2.5-flash-preview-04-17",
+        messages=messages, # make sure call works
+    )
+    print(response.choices[0].message)
+    assert response.choices[0].message.content is not None