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https://github.com/BerriAI/litellm.git
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Bug Fix - Responses API raises error with Gemini Tool Calls in input (#13260)
* add _transform_responses_api_function_call_to_chat_completion_message * test_responses_api_with_tool_calls * TestFunctionCallTransformation * fixes for responses API testing google ai studio * TestGoogleAIStudioResponsesAPITest * test_responses_api_with_tool_calls * test_responses_api_with_tool_calls * test_basic_openai_responses_streaming_delete_endpoint
This commit is contained in:
Ishaan Jaff
GitHub
parent
dae72003a7
commit
f3749709b8
4 changed files with 377 additions and 8 deletions
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@ -22,6 +22,7 @@ from litellm.caching import InMemoryCache
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.types.llms.openai import (
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AllMessageValues,
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ChatCompletionImageUrlObject,
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ChatCompletionResponseMessage,
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ChatCompletionSystemMessage,
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ChatCompletionToolCallChunk,
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@ -40,7 +41,6 @@ from litellm.types.llms.openai import (
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ResponsesAPIOptionalRequestParams,
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ResponsesAPIResponse,
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ResponseTextConfig,
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ChatCompletionImageUrlObject,
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)
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from litellm.types.responses.main import (
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GenericResponseOutputItem,
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@ -264,6 +264,10 @@ class LiteLLMCompletionResponsesConfig:
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chat_completion_messages = LiteLLMCompletionResponsesConfig._transform_responses_api_input_item_to_chat_completion_message(
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input_item=_input
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)
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#########################################################
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# If Input Item is a Tool Call Output, add it to the tool_call_output_messages list
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#########################################################
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if LiteLLMCompletionResponsesConfig._is_input_item_tool_call_output(
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input_item=_input
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):
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@ -316,6 +320,11 @@ class LiteLLMCompletionResponsesConfig:
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return LiteLLMCompletionResponsesConfig._transform_responses_api_tool_call_output_to_chat_completion_message(
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tool_call_output=input_item
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)
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elif LiteLLMCompletionResponsesConfig._is_input_item_function_call(input_item):
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# handle function call input items
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return LiteLLMCompletionResponsesConfig._transform_responses_api_function_call_to_chat_completion_message(
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function_call=input_item
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)
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else:
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return [
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GenericChatCompletionMessage(
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@ -337,6 +346,13 @@ class LiteLLMCompletionResponsesConfig:
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"computer_call_output",
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]
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@staticmethod
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def _is_input_item_function_call(input_item: Any) -> bool:
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"""
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Check if the input item is a function call
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"""
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return input_item.get("type") == "function_call"
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@staticmethod
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def _transform_responses_api_tool_call_output_to_chat_completion_message(
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tool_call_output: Dict[str, Any],
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@ -402,6 +418,52 @@ class LiteLLMCompletionResponsesConfig:
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return [tool_output_message]
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@staticmethod
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def _transform_responses_api_function_call_to_chat_completion_message(
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function_call: Dict[str, Any],
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) -> List[
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Union[
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AllMessageValues,
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GenericChatCompletionMessage,
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ChatCompletionResponseMessage,
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]
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]:
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"""
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Transform a Responses API function_call into a Chat Completion message with tool calls
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Handles Input items of this type:
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function_call:
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```json
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{
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"type": "function_call",
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"arguments":"{\"location\": \"São Paulo, Brazil\"}",
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"call_id": "call_v2wlBzrlTIFl9FxPeY774GHZ",
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"name": "get_weather",
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"id": "fc_685c42deefc0819a822b6936faaa30be0c76bc1491ab6619",
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"status": "completed"
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}
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```
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"""
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# Create a tool call for the function call
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tool_call = ChatCompletionToolCallChunk(
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id=function_call.get("call_id") or function_call.get("id") or "",
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type="function",
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function=ChatCompletionToolCallFunctionChunk(
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name=function_call.get("name") or "",
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arguments=function_call.get("arguments") or "",
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),
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index=0,
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)
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# Create an assistant message with the tool call
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chat_completion_response_message = ChatCompletionResponseMessage(
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tool_calls=[tool_call],
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role="assistant",
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content=None, # Function calls don't have content
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)
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return [chat_completion_response_message]
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@staticmethod
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def _transform_input_file_item_to_file_item(item: Dict[str, Any]) -> Dict[str, Any]:
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"""
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@ -25,6 +25,9 @@ from litellm.types.llms.openai import (
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ResponseAPIUsage,
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IncompleteDetails,
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)
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from openai.types.responses.response_create_params import (
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ResponseInputParam,
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)
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
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@ -184,8 +187,8 @@ class BaseResponsesAPITest(ABC):
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# basic test assert the usage seems reasonable
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print("response_completed_event.response.usage=", response_completed_event.response.usage)
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assert response_completed_event.response.usage.input_tokens > 0 and response_completed_event.response.usage.input_tokens < 100
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assert response_completed_event.response.usage.output_tokens > 0 and response_completed_event.response.usage.output_tokens < 1000
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assert response_completed_event.response.usage.total_tokens > 0 and response_completed_event.response.usage.total_tokens < 1000
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assert response_completed_event.response.usage.output_tokens > 0 and response_completed_event.response.usage.output_tokens < 2000
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assert response_completed_event.response.usage.total_tokens > 0 and response_completed_event.response.usage.total_tokens < 2000
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# total tokens should be the sum of input and output tokens
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assert response_completed_event.response.usage.total_tokens == response_completed_event.response.usage.input_tokens + response_completed_event.response.usage.output_tokens
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@ -229,6 +232,7 @@ class BaseResponsesAPITest(ABC):
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.flaky(retries=3, delay=2)
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@pytest.mark.asyncio
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async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode):
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#litellm._turn_on_debug()
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@ -278,6 +282,7 @@ class BaseResponsesAPITest(ABC):
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)
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@pytest.mark.parametrize("sync_mode", [False, True])
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@pytest.mark.flaky(retries=3, delay=2)
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@pytest.mark.asyncio
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async def test_basic_openai_responses_get_endpoint(self, sync_mode):
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litellm._turn_on_debug()
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@ -318,6 +323,7 @@ class BaseResponsesAPITest(ABC):
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raise ValueError("response is not a ResponsesAPIResponse")
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@pytest.mark.asyncio
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@pytest.mark.flaky(retries=3, delay=2)
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async def test_basic_openai_list_input_items_endpoint(self):
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"""Test that calls the OpenAI List Input Items endpoint"""
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litellm._turn_on_debug()
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@ -364,3 +370,73 @@ class BaseResponsesAPITest(ABC):
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# assert the response is not None
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assert response_1 is not None
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assert response_2 is not None
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@pytest.mark.asyncio
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async def test_responses_api_with_tool_calls(self):
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"""Test that calls the Responses API with tool calls including function call and output"""
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litellm._turn_on_debug()
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litellm.set_verbose = True
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base_completion_call_args = self.get_base_completion_call_args()
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# Define the input with message, function call, and function call output
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input_data: ResponseInputParam = [
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{
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"type": "message",
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"role": "user",
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"content": "How is the weather in São Paulo today ?"
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},
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{
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"type": "function_call",
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"arguments": "{\"location\": \"São Paulo, Brazil\"}",
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"call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"name": "get_weather",
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"id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"status": "completed"
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},
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{
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"type": "function_call_output",
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"call_id": "fc_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"output": "Rainy"
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}
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]
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# Define the tools
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tools = [
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{
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"type": "function",
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"name": "get_weather",
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"description": "Get current temperature for a given location.",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "City and country e.g. Bogotá, Colombia"
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}
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},
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"required": ["location"],
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"additionalProperties": False
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}
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}
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]
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try:
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# Make the responses API call
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response = await litellm.aresponses(
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input=input_data,
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store=False,
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tools=tools,
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**base_completion_call_args
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)
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except litellm.InternalServerError:
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pytest.skip("Skipping test due to litellm.InternalServerError")
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print("litellm response=", json.dumps(response, indent=4, default=str))
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# Validate the response structure
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validate_responses_api_response(response, final_chunk=True)
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# Additional assertions specific to tool calls
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assert response is not None
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assert "output" in response
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assert len(response["output"]) > 0
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@ -5,7 +5,7 @@ from unittest.mock import patch, AsyncMock
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sys.path.insert(0, os.path.abspath("../.."))
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import litellm
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import json
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from base_responses_api import BaseResponsesAPITest
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@pytest.mark.asyncio
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async def test_basic_google_ai_studio_responses_api_with_tools():
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litellm._turn_on_debug()
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@ -85,10 +85,22 @@ async def test_mock_basic_google_ai_studio_responses_api_with_tools():
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assert call_kwargs["messages"][0]["content"] == "what is the latest version of supabase python package and when was it released?"
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assert call_kwargs["tools"] == [] # web search tools are converted to web_search_options, not kept as tools
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class TestGoogleAIStudioResponsesAPITest(BaseResponsesAPITest):
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def get_base_completion_call_args(self):
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#litellm._turn_on_debug()
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return {
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"model": "gemini/gemini-2.5-flash-lite"
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}
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async def test_basic_openai_responses_delete_endpoint(self, sync_mode=False):
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pass
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async def test_basic_openai_responses_streaming_delete_endpoint(self, sync_mode=False):
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pass
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async def test_basic_openai_responses_get_endpoint(self, sync_mode=False):
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pass
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@ -8,7 +8,11 @@ sys.path.insert(
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from litellm.responses.litellm_completion_transformation.transformation import (
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LiteLLMCompletionResponsesConfig,
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)
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from litellm.types.utils import ModelResponse, Choices, Message
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from litellm.types.llms.openai import (
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ChatCompletionResponseMessage,
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ChatCompletionToolMessage,
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)
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from litellm.types.utils import Choices, Message, ModelResponse
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class TestLiteLLMCompletionResponsesConfig:
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@ -364,3 +368,218 @@ class TestLiteLLMCompletionResponsesConfig:
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item for item in responses_api_response.output if item.type == "message"
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]
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assert len(message_items) == 2, "Should have two message items"
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class TestFunctionCallTransformation:
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"""Test cases for function_call input transformation"""
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def test_function_call_detection(self):
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"""Test that function_call items are correctly detected"""
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function_call_item = {
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"type": "function_call",
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"name": "get_weather",
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"arguments": '{"location": "test"}',
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"call_id": "test_id"
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}
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function_call_output_item = {
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"type": "function_call_output",
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"call_id": "test_id",
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"output": "result"
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}
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regular_message = {
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"type": "message",
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"role": "user",
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"content": "Hello"
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}
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# Test function_call detection
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assert LiteLLMCompletionResponsesConfig._is_input_item_function_call(function_call_item)
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assert not LiteLLMCompletionResponsesConfig._is_input_item_function_call(function_call_output_item)
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assert not LiteLLMCompletionResponsesConfig._is_input_item_function_call(regular_message)
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# Test function_call_output detection (should still work)
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assert LiteLLMCompletionResponsesConfig._is_input_item_tool_call_output(function_call_output_item)
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assert not LiteLLMCompletionResponsesConfig._is_input_item_tool_call_output(function_call_item)
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assert not LiteLLMCompletionResponsesConfig._is_input_item_tool_call_output(regular_message)
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def test_function_call_transformation(self):
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"""Test that function_call items are correctly transformed to assistant messages with tool calls"""
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function_call_item = {
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"type": "function_call",
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"name": "get_weather",
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"arguments": '{"location": "São Paulo, Brazil"}',
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"call_id": "call_123",
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"id": "call_123",
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"status": "completed"
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}
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result = LiteLLMCompletionResponsesConfig._transform_responses_api_function_call_to_chat_completion_message(
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function_call=function_call_item
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)
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assert len(result) == 1
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message = result[0]
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# Should be an assistant message
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assert message.get("role") == "assistant"
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assert message.get("content") is None # Function calls don't have content
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# Should have tool calls
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tool_calls = message.get("tool_calls", [])
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assert len(tool_calls) == 1
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tool_call = tool_calls[0]
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assert tool_call.get("id") == "call_123"
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assert tool_call.get("type") == "function"
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function = tool_call.get("function", {})
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assert function.get("name") == "get_weather"
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assert function.get("arguments") == '{"location": "São Paulo, Brazil"}'
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def test_complete_input_transformation_with_function_calls(self):
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"""Test the complete transformation with the exact input from the issue"""
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test_input = [
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{
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"type": "message",
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"role": "user",
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"content": "How is the weather in São Paulo today ?"
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},
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{
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"type": "function_call",
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"arguments": '{"location": "São Paulo, Brazil"}',
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"call_id": "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"name": "get_weather",
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"id": "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"status": "completed"
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},
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{
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"type": "function_call_output",
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"call_id": "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"output": "Rainy"
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}
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]
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# This should not raise an error (previously would raise "Invalid content type: <class 'NoneType'>")
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messages = LiteLLMCompletionResponsesConfig._transform_response_input_param_to_chat_completion_message(
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input=test_input
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)
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assert len(messages) == 3
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# First message: user message
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user_msg = messages[0]
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assert user_msg.get("role") == "user"
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assert user_msg.get("content") == "How is the weather in São Paulo today ?"
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# Second message: assistant message with tool call
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assistant_msg = messages[1]
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assert assistant_msg.get("role") == "assistant"
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assert assistant_msg.get("tool_calls") is not None
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assert len(assistant_msg.get("tool_calls", [])) == 1
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tool_call = assistant_msg.get("tool_calls")[0]
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assert tool_call.get("function", {}).get("name") == "get_weather"
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# Third message: tool output
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tool_msg = messages[2]
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assert tool_msg.get("role") == "tool"
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assert tool_msg.get("content") == "Rainy"
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assert tool_msg.get("tool_call_id") == "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5"
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def test_complete_request_transformation_with_function_calls(self):
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"""Test the complete request transformation that would be used by the responses API"""
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test_input = [
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{
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"type": "message",
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"role": "user",
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"content": "How is the weather in São Paulo today ?"
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},
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{
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"type": "function_call",
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"arguments": '{"location": "São Paulo, Brazil"}',
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"call_id": "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"name": "get_weather",
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"id": "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"status": "completed"
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},
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{
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"type": "function_call_output",
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"call_id": "call_1fe70e2a-a596-45ef-b72c-9b8567c460e5",
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"output": "Rainy"
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}
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]
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tools = [
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{
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"type": "function",
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"name": "get_weather",
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"description": "Get current temperature for a given location.",
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"parameters": {
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"type": "object",
|
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"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "City and country e.g. Bogotá, Colombia"
|
||||
}
|
||||
},
|
||||
"required": ["location"],
|
||||
"additionalProperties": False
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
responses_api_request = {
|
||||
"store": False,
|
||||
"tools": tools
|
||||
}
|
||||
|
||||
# This should work without errors for non-OpenAI models
|
||||
result = LiteLLMCompletionResponsesConfig.transform_responses_api_request_to_chat_completion_request(
|
||||
model="gemini/gemini-2.0-flash",
|
||||
input=test_input,
|
||||
responses_api_request=responses_api_request
|
||||
)
|
||||
|
||||
assert "messages" in result
|
||||
assert "model" in result
|
||||
assert "tools" in result
|
||||
|
||||
messages = result["messages"]
|
||||
assert len(messages) == 3
|
||||
assert result["model"] == "gemini/gemini-2.0-flash"
|
||||
|
||||
# Verify the structure is correct for chat completion
|
||||
user_msg = messages[0]
|
||||
assert user_msg["role"] == "user"
|
||||
|
||||
assistant_msg = messages[1]
|
||||
assert assistant_msg["role"] == "assistant"
|
||||
assert "tool_calls" in assistant_msg
|
||||
|
||||
tool_msg = messages[2]
|
||||
assert tool_msg["role"] == "tool"
|
||||
|
||||
def test_function_call_without_call_id_fallback_to_id(self):
|
||||
"""Test that function_call items can use 'id' field when 'call_id' is missing"""
|
||||
function_call_item = {
|
||||
"type": "function_call",
|
||||
"name": "get_weather",
|
||||
"arguments": '{"location": "test"}',
|
||||
"id": "fallback_id" # Only has 'id', not 'call_id'
|
||||
}
|
||||
|
||||
result = LiteLLMCompletionResponsesConfig._transform_responses_api_function_call_to_chat_completion_message(
|
||||
function_call=function_call_item
|
||||
)
|
||||
|
||||
assert len(result) == 1
|
||||
message = result[0]
|
||||
tool_calls = message.get("tool_calls", [])
|
||||
assert len(tool_calls) == 1
|
||||
|
||||
tool_call = tool_calls[0]
|
||||
assert tool_call.get("id") == "fallback_id"
|
||||
Loading…
Add table
Reference in a new issue