fix(openai/responses/guardrail_translation): fix basemodel import (#17977)

* fix(openai/responses/guardrail_translation): fix basemodel import

ensures /v1/responses output is correctly processed by guardrail handler

* fix: fix linting error

* fix: fix linting errors

litellm/llms/openai/responses/guardrail_translation/handler.py

@@ -30,7 +30,7 @@ Output: response.output is List[GenericResponseOutputItem] where each has:
 
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
 
-from openai import BaseModel
+from pydantic import BaseModel
 
 from litellm._logging import verbose_proxy_logger
 from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
@@ -424,6 +424,7 @@ class OpenAIResponsesHandler(BaseTranslation):
 
         Override this method to customize text/image/tool extraction logic.
         """
+
         # Check if this is a tool call (OutputFunctionToolCall)
         if isinstance(output_item, OutputFunctionToolCall):
             if tool_calls_to_check is not None:

litellm/utils.py

@@ -810,7 +810,12 @@ def function_setup(  # noqa: PLR0915
             or call_type == CallTypes.responses.value
         ):
             # Handle both 'input' (standard Responses API) and 'messages' (Cursor chat format)
-            messages = args[0] if len(args) > 0 else kwargs.get("input") or kwargs.get("messages", "default-message-value")
+            messages = (
+                args[0]
+                if len(args) > 0
+                else kwargs.get("input")
+                or kwargs.get("messages", "default-message-value")
+            )
         else:
             messages = "default-message-value"
         stream = False
@@ -6900,7 +6905,9 @@ def last_assistant_with_tool_calls_has_no_thinking_blocks(
 
     # Check if it has thinking_blocks
     thinking_blocks = last_assistant_with_tools.get("thinking_blocks")
-    return thinking_blocks is None or len(thinking_blocks) == 0
+    return thinking_blocks is None or (
+        hasattr(thinking_blocks, "__len__") and len(thinking_blocks) == 0
+    )
 
 
 def add_dummy_tool(custom_llm_provider: str) -> List[ChatCompletionToolParam]:
@@ -7032,7 +7039,9 @@ def validate_chat_completion_user_messages(messages: List[AllMessageValues]):
                         for item in user_content:
                             if isinstance(item, dict):
                                 if item.get("type") not in ValidUserMessageContentTypes:
-                                    raise Exception(f"invalid content type={item.get('type')}")
+                                    raise Exception(
+                                        f"invalid content type={item.get('type')}"
+                                    )
         except Exception as e:
             if isinstance(e, KeyError):
                 raise Exception(
@@ -7867,9 +7876,7 @@ class ProviderConfigManager:
 
             return GeminiVideoConfig()
         elif LlmProviders.VERTEX_AI == provider:
-            from litellm.llms.vertex_ai.videos.transformation import (
-                VertexAIVideoConfig,
-            )
+            from litellm.llms.vertex_ai.videos.transformation import VertexAIVideoConfig
 
             return VertexAIVideoConfig()
         elif LlmProviders.RUNWAYML == provider:

tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py

@@ -716,3 +716,108 @@ class TestOpenAIResponsesHandlerToolCallExtraction:
         assert texts_to_check[0] == "I'll check the weather for you"
         assert len(tool_calls_to_check) == 1
         assert tool_calls_to_check[0]["function"]["name"] == "get_current_weather"
+
+    def test_extract_text_from_basemodel_instance(self):
+        """Test extracting text from GenericResponseOutputItem as BaseModel instance
+
+        This test verifies that _extract_output_text_and_images correctly handles
+        GenericResponseOutputItem when passed as a Pydantic BaseModel instance
+        (not as a dict). This addresses the issue where isinstance(output_item, BaseModel)
+        was failing because the handler was importing BaseModel from openai instead of pydantic.
+        """
+        handler = OpenAIResponsesHandler()
+
+        # Create a proper GenericResponseOutputItem instance (Pydantic BaseModel)
+        output_item = GenericResponseOutputItem(
+            type="message",
+            id="msg_123",
+            status="completed",
+            role="assistant",
+            content=[
+                OutputText(
+                    type="output_text",
+                    text="Hi! My name is Ishaan.",
+                    annotations=[],
+                )
+            ],
+        )
+
+        texts_to_check: List[str] = []
+        images_to_check: List[str] = []
+        tool_calls_to_check: List[Any] = []
+        task_mappings: List[Tuple[int, int]] = []
+
+        # Extract text from the BaseModel instance
+        handler._extract_output_text_and_images(
+            output_item=output_item,
+            output_idx=0,
+            texts_to_check=texts_to_check,
+            images_to_check=images_to_check,
+            task_mappings=task_mappings,
+            tool_calls_to_check=tool_calls_to_check,
+        )
+
+        # Verify text was extracted correctly
+        assert len(texts_to_check) == 1
+        assert texts_to_check[0] == "Hi! My name is Ishaan."
+        assert len(task_mappings) == 1
+        assert task_mappings[0] == (0, 0)  # (output_idx, content_idx)
+        assert len(tool_calls_to_check) == 0  # No tool calls in this output
+
+    def test_extract_text_from_basemodel_with_multiple_content_items(self):
+        """Test extracting multiple text items from GenericResponseOutputItem BaseModel
+
+        This test verifies that the handler correctly processes a BaseModel instance
+        with multiple content items in the content array.
+        """
+        handler = OpenAIResponsesHandler()
+
+        # Create GenericResponseOutputItem with multiple content items
+        output_item = GenericResponseOutputItem(
+            type="message",
+            id="msg_456",
+            status="completed",
+            role="assistant",
+            content=[
+                OutputText(
+                    type="output_text",
+                    text="First paragraph.",
+                    annotations=[],
+                ),
+                OutputText(
+                    type="output_text",
+                    text="Second paragraph.",
+                    annotations=[],
+                ),
+                OutputText(
+                    type="output_text",
+                    text="Third paragraph.",
+                    annotations=[],
+                ),
+            ],
+        )
+
+        texts_to_check: List[str] = []
+        images_to_check: List[str] = []
+        tool_calls_to_check: List[Any] = []
+        task_mappings: List[Tuple[int, int]] = []
+
+        # Extract all text items
+        handler._extract_output_text_and_images(
+            output_item=output_item,
+            output_idx=0,
+            texts_to_check=texts_to_check,
+            images_to_check=images_to_check,
+            task_mappings=task_mappings,
+            tool_calls_to_check=tool_calls_to_check,
+        )
+
+        # Verify all text items were extracted
+        assert len(texts_to_check) == 3
+        assert texts_to_check[0] == "First paragraph."
+        assert texts_to_check[1] == "Second paragraph."
+        assert texts_to_check[2] == "Third paragraph."
+        assert len(task_mappings) == 3
+        assert task_mappings[0] == (0, 0)
+        assert task_mappings[1] == (0, 1)
+        assert task_mappings[2] == (0, 2)