fix: Handle missing keys gracefully in TaskEvaluator

[devin-ai-integration]

Description

This PR fixes a KeyError that occurs in the TaskEvaluator when accessing training data dictionary keys.

Core Issue

The TaskEvaluator.evaluate_training_data() method was directly accessing dictionary keys ('initial_output', 'human_feedback', 'improved_output') without checking for their existence, which caused KeyError exceptions when these keys were missing.

Fix

Modified the code to use the safer dict.get() method with empty string defaults:

data.get('improved_output', '')  # Instead of data['improved_output']

This change makes the code more resilient by:

1. Gracefully handling missing keys in the training data
2. Providing empty string defaults instead of raising exceptions
3. Maintaining backward compatibility with existing training data formats

Testing

The fix addresses the specific KeyError shown in the error trace:

KeyError: 'improved_output'

This error was occurring during crew.train() execution when evaluating training data.

Closes #1935

[devin-ai-integration]

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[joaomdmoura]

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

Code Review Comment for PR #1940

Overview

This pull request introduces critical improvements to the task_evaluator.py file by enhancing dictionary access safety through the use of the dict.get() method. This update is a significant step toward increasing the robustness of our codebase by minimizing the potential for KeyError exceptions.

Positive Changes

1. Robustness Improvement: Using dict.get() provides a safer way to access dictionary elements, which reduces the likelihood of runtime errors due to missing keys.
2. Error Prevention: The modifications substantially decrease the risk of KeyError exceptions, allowing the function to gracefully handle cases where expected keys might be absent.
3. Consistency in Outputs: By implementing consistent default values (empty strings) for missing data, the function maintains a uniform output format.

Detailed Analysis of Changes

Example Changes:

# Before
f"Initial Output:\n{data['initial_output']}\n\n"
f"Human Feedback:\n{data['human_feedback']}\n\n"
f"Improved Output:\n{data['improved_output']}\n\n"

# After
f"Initial Output:\n{data.get('initial_output', '')}\n\n"
f"Human Feedback:\n{data.get('human_feedback', '')}\n\n"
f"Improved Output:\n{data.get('improved_output', '')}\n\n"

These changes demonstrate a clear transition from direct dictionary access to a more defensive approach using the dict.get() method, ensuring that the code remains functional even with missing keys.

Suggested Further Improvements

While the changes made are positive, here are some suggestions that could further enhance the quality and maintainability of the code:

1. Type Annotations

Incorporating type hints across the codebase will improve readability and help with early error detection.

def evaluate_training_data(
    self,
    output_training_data: Dict[str, Dict[str, str]]
) -> str:

2. Use Constants for Dictionary Keys

Utilizing constants for keys will contribute to better maintainability and reduce the risk of typos.

# At module level
TRAINING_DATA_KEYS = {
    'INITIAL_OUTPUT': 'initial_output',
    'HUMAN_FEEDBACK': 'human_feedback',
    'IMPROVED_OUTPUT': 'improved_output'
}

# Usage
f"Initial Output:\n{data.get(TRAINING_DATA_KEYS['INITIAL_OUTPUT'], '')}\n\n"

3. Validation of Input Data

Adding validation checks can prevent processing of invalid or empty inputs.

def evaluate_training_data(self, output_training_data):
    if not output_training_data:
        raise ValueError("output_training_data cannot be empty")
    
    final_aggregated_data = ""
    for key, data in output_training_data.items():
        if not isinstance(data, dict):
            raise TypeError(f"Training data for key {key} must be a dictionary")
        
        final_aggregated_data += (
            f"Initial Output:\n{data.get('initial_output', '')}\n\n"
            f"Human Feedback:\n{data.get('human_feedback', '')}\n\n"
            f"Improved Output:\n{data.get('improved_output', '')}\n\n"
        )

Security Considerations

The introduction of dict.get() makes the code safer against unexpected behaviors. However, it is essential that we also focus on validating input data to ensure integrity.

Testing Recommendations

To ensure the reliability of the modifications, I recommend the following testing protocols:

1. Unit tests to handle cases of missing dictionary keys.
2. Tests for empty dictionary scenarios.
3. Tests that assess how the function behaves when provided with malformed data structures.

Final Verdict

The changes from this pull request are commendable and enhance the overall quality of the code. Implementing the suggested improvements can further solidify the code's reliability and maintainability. I approve of the changes with a strong recommendation to consider these enhancements. ✅

[bhancockio]

@pythonbyte Please checkout this issue: #1935

In their error message, you can see the root issue is that improved_output wasn't provided.

Is it okay if training doesn't have improved_output and we just set an empty string like we're doing in this PR?

Or, is there a deeper issue?