Fix import sorting with ruff

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -91,6 +91,7 @@ class CrewAgentExecutorMixin:
                 print(f"Missing attributes for long term memory: {e}")
                 pass
             except Exception as e:
+                # Only log the error; don't let it affect task output
                 print(f"Failed to add to long term memory: {e}")
                 pass
 

src/crewai/utilities/embedding_configurator.py

@@ -104,28 +104,40 @@ class EmbeddingConfigurator:
 
     @staticmethod
     def _configure_vertexai(config, model_name):
-        from chromadb.utils.embedding_functions.google_embedding_function import (
+        try:
-            GoogleVertexEmbeddingFunction,
+            from chromadb.utils.embedding_functions.google_embedding_function import (
-        )
+                GoogleVertexEmbeddingFunction,
-
+            )
-        return GoogleVertexEmbeddingFunction(
+            
-            model_name=model_name,
+            return GoogleVertexEmbeddingFunction(
-            api_key=config.get("api_key"),
+                model_name=model_name,
-            project_id=config.get("project_id"),
+                api_key=config.get("api_key"),
-            region=config.get("region"),
+                project_id=config.get("project_id"),
-        )
+                region=config.get("region"),
+            )
+        except ImportError:
+            raise ImportError(
+                "Google Vertex AI dependencies are not installed. "
+                "Please install them using 'pip install google-cloud-aiplatform'."
+            )
 
     @staticmethod
     def _configure_google(config, model_name):
-        from chromadb.utils.embedding_functions.google_embedding_function import (
+        try:
-            GoogleGenerativeAiEmbeddingFunction,
+            from chromadb.utils.embedding_functions.google_embedding_function import (
-        )
+                GoogleGenerativeAiEmbeddingFunction,
-
+            )
-        return GoogleGenerativeAiEmbeddingFunction(
+            
-            model_name=model_name,
+            return GoogleGenerativeAiEmbeddingFunction(
-            api_key=config.get("api_key"),
+                model_name=model_name,
-            task_type=config.get("task_type"),
+                api_key=config.get("api_key"),
-        )
+                task_type=config.get("task_type"),
+            )
+        except ImportError:
+            raise ImportError(
+                "Google Generative AI dependencies are not installed. "
+                "Please install them using 'pip install google-generativeai'."
+            )
 
     @staticmethod
     def _configure_cohere(config, model_name):

tests/test_output_pydantic.py

@@ -0,0 +1,68 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+from pydantic import BaseModel, Field
+
+from crewai import Agent, Crew, Task
+from crewai.utilities.converter import Converter
+
+
+class ResponseFormat(BaseModel):
+    string: str = Field(description='string needs to be maintained')
+
+def test_pydantic_model_conversion():
+    """Test that pydantic model conversion works without causing import errors."""
+    
+    # Test data
+    test_string = '{"string": "test value"}'
+    
+    # Create a pydantic model directly
+    result = ResponseFormat.model_validate_json(test_string)
+    
+    # Verify the conversion worked
+    assert result is not None
+    assert hasattr(result, "string")
+    assert isinstance(result.string, str)
+    assert result.string == "test value"
+
+@patch('crewai.crew.Crew.kickoff')
+def test_output_pydantic_with_mocked_crew(mock_kickoff):
+    """Test that output_pydantic works properly without causing import errors."""
+    
+    # Mock the crew kickoff to return a valid response
+    mock_result = ResponseFormat(string="mocked result")
+    mock_kickoff.return_value = mock_result
+    
+    # Create a simple agent
+    agent = Agent(
+        role="Test Agent",
+        goal="Test pydantic model output",
+        backstory="Testing pydantic output functionality",
+        verbose=True
+    )
+    
+    # Create a task with output_pydantic
+    task = Task(
+        description="Return a simple string",
+        expected_output="A simple string",
+        agent=agent,
+        output_pydantic=ResponseFormat
+    )
+    
+    # Create a crew with the agent and task
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        verbose=True
+    )
+    
+    # Execute the crew (this will use our mock)
+    result = crew.kickoff()
+    
+    # Verify we got a result
+    assert result is not None
+    
+    # Verify the result has a string attribute (as defined in ResponseFormat)
+    assert hasattr(result, "string")
+    assert isinstance(result.string, str)
+    assert result.string == "mocked result"