Changed the import error to show missing module files

[Vidit-Ostwal]

Fixes #2421
Build on top of #2422
Removed unwanted test case

[joaomdmoura]

Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for PR #2423

Overview

This pull request (PR) introduces enhancements for gracefully managing missing Google AI dependencies and updates the tests for pydantic model output handling across three files: base_agent_executor_mixin.py, embedding_configurator.py, and test_output_pydantic.py.

1. base_agent_executor_mixin.py

Code Improvements

• Error Handling: The addition of error handling for long-term memory functionality is commendable. However, consider implementing structured logging for critical failures to improve traceability.

def _create_long_term_memory(self, output) -> None:
    try:
        # Existing logic...
    except AttributeError as e:
        logger.warning(f"Missing attributes for long term memory: {str(e)}")
    except Exception as e:
        logger.error(f"Failed to add to long term memory: {str(e)}", exc_info=True)

Historical Context

The changes here align with previous updates aimed at enhancing agent functionalities. Maintaining this structured error management will significantly improve the debugging process.

2. embedding_configurator.py

Code Improvements

• Helper Function: To avoid duplicated error handling code for Google dependencies, a shared helper function for checking dependencies would be beneficial.

def _check_google_dependencies(package_name: str, install_command: str) -> None:
    try:
        importlib.import_module(package_name)
    except ImportError:
        raise ImportError(f"{package_name} dependencies are not installed. Please install them using '{install_command}'.")

• Type Hints and Validation: Ensure all function parameters have type hints. Additionally, validate required configuration parameters to prevent runtime errors.

@staticmethod
def _configure_google(config: Dict[str, Any], model_name: str) -> GoogleGenerativeAiEmbeddingFunction:
    _check_google_dependencies("google.generativeai", "pip install google-generativeai")
    
    required_params = ["api_key", "task_type"]
    missing_params = [param for param in required_params if not config.get(param)]
    if missing_params:
        raise ValueError(f"Missing required parameters: {', '.join(missing_params)}")
        
    return GoogleGenerativeAiEmbeddingFunction(
        model_name=model_name,
        api_key=config["api_key"],
        task_type=config["task_type"],
    )

Historical Context

Previous PRs have discussed dependency management practices. Unifying the handling approach and ensuring proper backward compatibility will enhance functionality.

3. test_output_pydantic.py

Code Improvements

• Retain and Improve Test File: It is crucial to keep the test file for pydantic output, enhancing its structuring with fixtures and organized test cases.

import pytest
from unittest.mock import patch
from pydantic import BaseModel, Field
from crewai import Agent, Crew, Task

class TestPydanticOutput:
    class ResponseFormat(BaseModel):
        string: str = Field(description='string needs to be maintained')
        
    @pytest.fixture
    def mock_agent(self):
        return Agent(role="Test Agent", goal="Test pydantic model output")

    def test_pydantic_model_conversion(self):
        test_string = '{"string": "test value"}'
        result = self.ResponseFormat.model_validate_json(test_string)
        
        assert result.string == "test value"
    
    @patch('crewai.crew.Crew.kickoff')
    def test_output_pydantic_with_mocked_crew(self, mock_kickoff, mock_agent):
        mock_result = self.ResponseFormat(string="mocked result")
        mock_kickoff.return_value = mock_result
        
        task = Task(
            description="Return a simple string",
            expected_output="A simple string",
            agent=mock_agent,
            output_pydantic=self.ResponseFormat
        )
        
        crew = Crew(agents=[mock_agent], tasks=[task])
        result = crew.kickoff()
        
        assert result.string == "mocked result"

Historical Context

The removal of the test file in patch 4 highlights a potential oversight in test coverage. Retaining this test is essential to assure pydantic functionality continues to work as intended.

General Recommendations

1. Enhance Logging: Implement logging configurations consistently across the codebase to improve error tracking.
2. Consistent Type Hints: Ensure type annotations are universally applied for better code clarity and maintainability.
3. Clarifying Documentation: Add docstrings across all new functions to provide clarity on their purpose and parameters.
4. Integration Testing: Consider adding further integration tests for the Google AI functionality to validate real-world behavior effectively.

By implementing these suggestions, the code quality and maintainability will improve significantly while ensuring that necessary functionalities are rigorously tested. The enhancements proposed in this PR are aligned with the broader goals of robustness and clarity within the codebase.