Fix issue #2421: Handle missing google.genai dependency gracefully

[devin-ai-integration]

This PR fixes issue #2421 where users encounter an error message 'Failed to add to long term memory: Failed to convert text into a Pydantic model due to error: No module named google.genai' when using pydantic models as output in tasks.

The fix:

1. Makes Google AI-related imports optional in embedding_configurator.py
2. Improves error handling to prevent missing dependencies from affecting task output
3. Adds a test to verify the fix works

Link to Devin run: https://app.devin.ai/sessions/a87c34b30d904c36b8bb9f97bb962d22
User: Joe Moura (joao@crewai.com)

[devin-ai-integration]

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[joaomdmoura]

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

Code Review Comment for PR #2422

Overview

This pull request implements graceful handling of missing Google AI dependencies and enhances test coverage for Pydantic output functionality. The changes are well-structured across three files, significantly improving error handling and testing in the codebase.

Detailed Review

1. base_agent_executor_mixin.py

Review

• Added a comment to clarify the behavior of error handling in _create_long_term_memory, which increases code clarity.

Suggestions

• ✅ The addition is appropriate and adheres to good practices for error handling. It would be beneficial to further enhance comments to note any potential impacts on downstream processes.

2. embedding_configurator.py

Issues Identified

1. Specificity in Error Handling: The error handling could capture specific exceptions rather than general imports.
2. Documentation: New try-catch blocks are missing docstrings, which could improve maintainability.
3. Code Duplication: Similar error handling patterns were observed; refactoring them into a common decorator could streamline the code.

Improvements Suggested

1. Enhanced Exception Handling Example:

   @staticmethod
   def _configure_vertexai(config, model_name):
       try:
           from chromadb.utils.embedding_functions.google_embedding_function import GoogleVertexEmbeddingFunction
           if not all([config.get("api_key"), config.get("project_id"), config.get("region")]):
               raise ValueError("Missing required configuration parameters")
           return GoogleVertexEmbeddingFunction(
               model_name=model_name,
               api_key=config.get("api_key"),
               project_id=config.get("project_id"),
               region=config.get("region"),
           )
       except ImportError as e:
           raise ImportError("Google Vertex AI dependencies are not installed. Install using 'pip install google-cloud-aiplatform'.") from e
       except ValueError as e:
           raise ValueError(f"Configuration error: {str(e)}")

2. Docstring for Clarity:

   @staticmethod
   def _configure_google(config, model_name):
       """
       Configure Google Generative AI embedding function.
       
       Args:
           config (dict): Configuration parameters including api_key and task_type.
           model_name (str): Name of the model to use.
       
       Returns:
           GoogleGenerativeAiEmbeddingFunction: Configured embedding function.
       
       Raises:
           ImportError: If required dependencies are not installed.
           ValueError: If required configuration parameters are missing.
       """

3. Common Decorator for Error Handling:

   def require_dependency(package_name, install_command):
       def decorator(func):
           def wrapper(*args, **kwargs):
               try:
                   return func(*args, **kwargs)
               except ImportError as e:
                   raise ImportError(f"{package_name} dependencies are not installed. Please install them using '{install_command}'.") from e
           return wrapper
       return decorator

3. test_output_pydantic.py

Strengths

• Demonstrates good test coverage for new functionality, employing effective use of mocking and well-defined test cases.

Suggestions for Improvement

1. Edge Case Testing Addition:

   def test_pydantic_model_invalid_input():
       """Test handling of invalid input for Pydantic model conversion."""
       with pytest.raises(ValueError):
           ResponseFormat.model_validate_json('{"invalid": "data"}')

2. Parameterized Tests for Different Input Scenarios:

   @pytest.mark.parametrize("test_input,expected", [
       ('{"string": "test1"}', "test1"),
       ('{"string": ""}', ""),
       ('{"string": "special chars !@#"}', "special chars !@#"),
   ])
   def test_pydantic_model_various_inputs(test_input, expected):
       result = ResponseFormat.model_validate_json(test_input)
       assert result.string == expected

General Recommendations

1. Type Hints: Adding type hints to new methods can enhance clarity and readability.
2. Logging: Utilize logging instead of print statements in error handling to better manage production errors.
3. Integration Tests: Consider writing integration tests for Google AI dependency handling to ensure correct interactions between components.
4. Documentation Updates: Document new error-handling behavior for better maintainability.

Security Considerations

• The design of the error messages provides clear installation instructions without compromising sensitive information.
• Appropriate API key handling through configuration mitigates security risks effectively.

Performance Impact

• The changes seem to have minimal performance repercussions due to the selective import handling and introduction of try-catch blocks.

In conclusion, PR #2422 offers significant improvements in robustness, particularly in error management and testing capabilities. Emphasis on refining error handling and documentation as identified in suggestions can further enhance the quality of this PR and the overall codebase.