diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index aa7d6ca1699..66180b165f8 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -224,6 +224,7 @@ _FINISH_REASON_MAP: Final[dict[str, OpenAIChatCompletionFinishReason]] = { "IMAGE_PROHIBITED_CONTENT": "content_filter", "TOO_MANY_TOOL_CALLS": "stop", "MALFORMED_RESPONSE": "stop", + "NO_IMAGE": "content_filter", # Zhipu GLM "network_error": "stop", "sensitive": "content_filter", diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 8ff9f2e0679..f4cc569bcef 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -1367,6 +1367,8 @@ class LiteLLMAnthropicMessagesAdapter: return "max_tokens" elif openai_finish_reason == "tool_calls": return "tool_use" + elif openai_finish_reason in ["content_filter", "refusal"]: + return "refusal" return "end_turn" @staticmethod diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index d113b2b4f6b..01d1f063b1b 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -1340,6 +1340,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): "IMAGE_PROHIBITED_CONTENT", "TOO_MANY_TOOL_CALLS", "MALFORMED_RESPONSE", + "NO_IMAGE", } ) @@ -2224,22 +2225,23 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): grounding_metadata: Final[list[dict]] = [] url_context_metadata: Final[list[dict]] = [] - image_response: list[ImageURLListItem] | None = None safety_ratings: Final[list] = [] citation_metadata: Final[list] = [] - chat_completion_message: Final[ChatCompletionResponseMessage] = {"role": "assistant"} - chat_completion_logprobs: ChoiceLogprobs | None = None - tools: list[ChatCompletionToolCallChunk] | None = [] - functions: ChatCompletionToolCallFunctionChunk | None = None - thinking_blocks: list[ChatCompletionThinkingBlock] | None = None - reasoning_content: str | None = None - thought_signatures: Sequence[str] | None = None - server_side_tool_invocations: list[dict[str, object]] | None = None for idx, candidate in enumerate(_candidates): - if "content" not in candidate: + if "content" not in candidate and "finishReason" not in candidate: continue + image_response: list[ImageURLListItem] | None = None + chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} + chat_completion_logprobs: ChoiceLogprobs | None = None + tools: list[ChatCompletionToolCallChunk] | None = [] + functions: ChatCompletionToolCallFunctionChunk | None = None + thinking_blocks: list[ChatCompletionThinkingBlock] | None = None + reasoning_content: str | None = None + thought_signatures: Sequence[str] | None = None + server_side_tool_invocations: list[dict[str, object]] | None = None + # Extract metadata using helper function ( candidate_grounding_metadata, @@ -2253,7 +2255,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): safety_ratings.extend(candidate_safety_ratings) citation_metadata.extend(candidate_citation_metadata) - if "parts" in candidate["content"]: + if "content" in candidate and candidate["content"] and "parts" in candidate["content"]: ( content, reasoning_content, @@ -2348,6 +2350,11 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): tool_invocation_fields["server_side_tool_invocations"] = server_side_tool_invocations chat_completion_message["provider_specific_fields"] = tool_invocation_fields + if candidate.get("finishReason"): + finish_reason_fields = chat_completion_message.get("provider_specific_fields") or {} + finish_reason_fields["native_finish_reason"] = candidate.get("finishReason") + chat_completion_message["provider_specific_fields"] = finish_reason_fields + if isinstance(model_response, ModelResponseStream): choice = VertexGeminiConfig._create_streaming_choice( chat_completion_message=chat_completion_message, @@ -2368,6 +2375,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): message=chat_completion_message, logprobs=chat_completion_logprobs, enhancements=None, + provider_specific_fields=chat_completion_message.get("provider_specific_fields"), ) model_response.choices.append(choice) diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index fca5b0d11cf..13119085e46 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -2272,13 +2272,27 @@ class LiteLLMCompletionResponsesConfig: if choices and len(choices) > 0: finish_reason = choices[0].finish_reason + status: Final[ResponsesAPIStatus] = ( + LiteLLMCompletionResponsesConfig._map_chat_completion_finish_reason_to_responses_status( + finish_reason + ) + ) + incomplete_details = getattr(chat_completion_response, "incomplete_details", None) + if incomplete_details is None and status == "incomplete": + from openai.types.responses.response import IncompleteDetails + + if finish_reason == "length": + incomplete_details = IncompleteDetails(reason="max_output_tokens") + elif finish_reason in ["content_filter", "refusal"]: + incomplete_details = IncompleteDetails(reason="content_filter") + responses_api_response: Final[ResponsesAPIResponse] = ResponsesAPIResponse( id=chat_completion_response.id, created_at=chat_completion_response.created, model=chat_completion_response.model, object="response", error=getattr(chat_completion_response, "error", None), - incomplete_details=getattr(chat_completion_response, "incomplete_details", None), + incomplete_details=incomplete_details, instructions=getattr(chat_completion_response, "instructions", None), metadata=getattr(chat_completion_response, "metadata", {}), output=LiteLLMCompletionResponsesConfig._transform_chat_completion_choices_to_responses_output( @@ -2296,9 +2310,7 @@ class LiteLLMCompletionResponsesConfig: max_output_tokens=getattr(chat_completion_response, "max_output_tokens", None), previous_response_id=getattr(chat_completion_response, "previous_response_id", None), reasoning=None, - status=LiteLLMCompletionResponsesConfig._map_chat_completion_finish_reason_to_responses_status( - finish_reason - ), + status=status, text={}, truncation=getattr(chat_completion_response, "truncation", None), usage=LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 101f6e6fa5d..a048ad4f171 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -5836,3 +5836,149 @@ def test_supported_reasoning_efforts_still_map(model): drop_params=False, ) assert "thinkingConfig" in result + + +def test_gemini_candidate_with_finish_reason_no_content_chat_completion(): + config = VertexGeminiConfig() + completion_response = { + "candidates": [ + { + "finishReason": "NO_IMAGE", + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 19, + "candidatesTokenCount": 0, + "totalTokenCount": 19, + }, + } + model_response = ModelResponse() + logging_obj = MagicMock() + raw_response = MagicMock() + raw_response.headers = {} + + resp = config._transform_google_generate_content_to_openai_model_response( + completion_response=completion_response, + model_response=model_response, + model="gemini-2.5-flash-image", + logging_obj=logging_obj, + raw_response=raw_response, + ) + assert len(resp.choices) == 1 + assert resp.choices[0].finish_reason == "content_filter" + assert resp.choices[0].message.content is None + assert resp.choices[0].provider_specific_fields["native_finish_reason"] == "NO_IMAGE" + + +def test_gemini_candidate_with_finish_reason_no_content_anthropic_messages(): + from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( + LiteLLMAnthropicMessagesAdapter, + ) + + config = VertexGeminiConfig() + completion_response = { + "candidates": [ + { + "finishReason": "NO_IMAGE", + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 19, + "candidatesTokenCount": 0, + "totalTokenCount": 19, + }, + } + resp = config._transform_google_generate_content_to_openai_model_response( + completion_response=completion_response, + model_response=ModelResponse(), + model="gemini-2.5-flash-image", + logging_obj=MagicMock(), + raw_response=MagicMock(headers={}), + ) + + adapter = LiteLLMAnthropicMessagesAdapter() + anthropic_resp = adapter.translate_openai_response_to_anthropic( + response=resp, + tool_name_mapping={}, + ) + assert anthropic_resp["stop_reason"] == "refusal" + assert anthropic_resp["content"] == [] + + +def test_gemini_candidate_with_finish_reason_no_content_responses_api(): + from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, + ) + + config = VertexGeminiConfig() + completion_response = { + "candidates": [ + { + "finishReason": "NO_IMAGE", + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 19, + "candidatesTokenCount": 0, + "totalTokenCount": 19, + }, + } + resp = config._transform_google_generate_content_to_openai_model_response( + completion_response=completion_response, + model_response=ModelResponse(), + model="gemini-2.5-flash-image", + logging_obj=MagicMock(), + raw_response=MagicMock(headers={}), + ) + + responses_resp = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( + request_input="Generate picture", + responses_api_request={}, + chat_completion_response=resp, + ) + assert responses_resp.status == "incomplete" + assert responses_resp.incomplete_details is not None + assert responses_resp.incomplete_details.reason == "content_filter" + + +def test_gemini_candidate_other_finish_reasons_no_content(): + from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( + LiteLLMAnthropicMessagesAdapter, + ) + from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, + ) + + config = VertexGeminiConfig() + max_tokens_response = { + "candidates": [{"finishReason": "MAX_TOKENS", "index": 0}], + "usageMetadata": {"promptTokenCount": 10, "candidatesTokenCount": 50, "totalTokenCount": 60}, + } + resp_length = config._transform_google_generate_content_to_openai_model_response( + completion_response=max_tokens_response, + model_response=ModelResponse(), + model="gemini-2.5-flash", + logging_obj=MagicMock(), + raw_response=MagicMock(headers={}), + ) + assert len(resp_length.choices) == 1 + assert resp_length.choices[0].finish_reason == "length" + assert resp_length.choices[0].provider_specific_fields["native_finish_reason"] == "MAX_TOKENS" + + anthropic_length = LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic( + response=resp_length, + tool_name_mapping={}, + ) + assert anthropic_length["stop_reason"] == "max_tokens" + + responses_length = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( + request_input="thinking request", + responses_api_request={}, + chat_completion_response=resp_length, + ) + assert responses_length.status == "incomplete" + assert responses_length.incomplete_details.reason == "max_output_tokens" +