fixes interpolation issues when inputs are type dict,list specificall…

[lorenzejay]

…y when defined on expected_output

fixes:
https://linear.app/crewai/issue/ENG-334/andiron-issue-with-listdictionary-crew-interpolation

[joaomdmoura]

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

Code Review Comment for PR #1992

Overview

This pull request addresses interpolation issues with dictionary and list inputs, specifically in the expected_output field, and modifies two main files: src/crewai/task.py and tests/task_test.py. The changes aim to enhance type support and improve the robustness of the interpolation logic.

src/crewai/task.py Changes

Positive Aspects

• The type support for input interpolation is significantly enhanced, allowing for more flexibility with nested data structures.
• JSON handling for complex data types has been clearly improved, which is essential for handling user-defined inputs correctly.
• The code formatting enhances readability and maintainability.

Issues and Recommendations

1. Debug Print Statement:
   There's a residual debug print statement that needs to be removed:

   print("key", key, value)  # Line 561

   Recommendation: Remove this statement from production code to avoid cluttering output logs.

2. Type Hints Enhancement:
   Consider refining type hints to be more specific:

   def interpolate_inputs_and_add_conversation_history(
       self, inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]]
   ) -> None:

3. JSON Serialization Safety:
   Improve error handling around serialization:

   try:
       inputs[key] = json.dumps(value, ensure_ascii=False)
   except (TypeError, ValueError) as e:
       raise ValueError(f"Failed to serialize value for key '{key}': {str(e)}")

4. Documentation Update:
   Update the docstring to reflect the new types supported:

   def interpolate_only(
       self,
       input_string: Optional[str],
       inputs: Dict[str, Union[str, int, float, dict, list]],
   ) -> str:
       """Interpolate placeholders in a string while preserving JSON structure.
       
       Args:
           input_string: String with placeholders to interpolate.
           inputs: Dictionary of values to interpolate, supporting strings, numbers, dictionaries, and lists.
       
       Returns:
           Interpolated string with preserved JSON structure.
       
       Raises:
           ValueError: If inputs dictionary is empty or contains invalid types.
       """

tests/task_test.py Changes

Positive Aspects

• The test coverage for new functionality is commendable, covering a diverse range of cases.
• Edge cases are well demonstrated and comprehensively tested.

Suggestions for Improvement

1. Test Organization:
   Group related tests into classes for better structure:

   class TestTaskInterpolation:
       def test_interpolate_only_basic(self):
           # Basic interpolation tests
           pass
       
       def test_interpolate_only_with_dict(self):
           # Dictionary interpolation tests
           pass
       
       def test_interpolate_only_edge_cases(self):
           # Edge cases
           pass

2. Test Data Separation:
   Extract test data to constants for clarity:

   TEST_JSON_INPUT = {
       "questions": {
           "main_question": "What is the user's name?",
           "secondary_question": "What is the user's age?"
       }
   }
   
   def test_interpolate_only_with_dict_inside_expected_output():
       task = Task(
           description="Unused in this test",
           expected_output="Unused in this test: {questions}",
       )
       result = task.interpolate_only(
           input_string=json.dumps(TEST_JSON_INPUT),
           inputs=TEST_JSON_INPUT
       )

General Recommendations

1. Implement input validation for nested dictionary depth to prevent potential stack overflow issues.
2. Consider logging instead of debug print statements for production code.
3. Implement performance testing for large nested structures.
4. Introduce a maximum size limit for dictionary/list inputs to enhance robustness.
5. Add type checking for nested dictionary/list values.

Conclusion

The changes made in this PR effectively resolve the issues around input interpolation for complex data types. While the code quality is high, there are opportunities for further improvement, particularly in removing debug statements and enhancing documentation. Once these suggestions are addressed, this PR will be well-prepared for merging.