Fix e2e test failures with datasets>=4.0.0 and missing dependencies

[anistark]

Issue Link / Problem Description

• Fixes Error importing module #2170
• Derived from PR Closes Issue #2170: Address issue 2170 by specifying 'datasets' core dependency version to >=4.0.0 #2201

Changes Made

• Fixed e2e test suite compatibility with datasets>=4.0.0
• Resolved missing dependency issues (unstructured package)
• Handled missing keys in tests.
• formatting and type checks cleared

Testing

How to Test

• Automated tests added/updated
• Manual testing steps:
  1. make run-ci
  2. make test
  3. make test-e2e

[anistark]

/claude-review

[claude]

Claude finished @anistark's task —— View job

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PR Review Analysis

🐛 Change Type Classification: Bug Fix

This PR addresses compatibility issues with datasets>=4.0.0 and missing dependencies in e2e tests. It's a focused bug fix that ensures test stability and compatibility.

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Code Review

✅ Strengths

1. Smart Dataset Loading Strategy (tests/e2e/test_dataset_utils.py:34-56)

   • Excellent fallback mechanism with load_amnesty_dataset_safe()
   • Graceful handling of remote dataset failures with local sample data
   • Proper logging for debugging connectivity issues
   • Well-structured sample data that matches the expected schema

2. Consistent Test Updates

   • All e2e tests now use the safe dataset loading utility
   • Proper imports and type annotations maintained
   • Skip conditions preserved for API key requirements

3. Dependency Management

   • datasets>=4.0.0 requirement properly updated in pyproject.toml:9
   • Addresses compatibility issues proactively

🔍 Areas for Improvement

1. Error Handling Specificity (tests/e2e/test_dataset_utils.py:49)

   except Exception as e:  # Too broad

   Suggestion: Catch specific exceptions (e.g., ConnectionError, HTTPError) to avoid masking unexpected errors.

2. Sample Data Maintenance

   • The hardcoded SAMPLE_AMNESTY_DATA could become outdated
   • Suggestion: Consider adding a comment about when this data was last validated against the real dataset

3. Minor Code Style (src/ragas/metrics/_answer_relevance.py)

   • The changes look good - removing unused imports and cleaning up code
   • Import consolidation is appropriate

🧪 Testing Considerations

• Good: Maintains existing test coverage while improving reliability
• Good: Tests will now pass even with network issues or dataset service downtime
• Consideration: The fallback to sample data means some test scenarios might be missed. Consider adding a warning when using fallback data.

🔒 Security & Performance

• No security concerns identified
• Performance impact is minimal - fallback only triggers on failures
• Proper logging prevents silent failures

📝 Documentation

The new utility function is well-documented with clear docstrings and parameter descriptions.

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Summary

This is a well-executed bug fix that addresses real compatibility and reliability issues. The implementation is thoughtful, with proper error handling and fallback strategies. The changes are minimal, focused, and maintain backward compatibility.

Recommended Action: ✅ Approve - This PR successfully resolves the identified issues and improves test reliability.

Minor Suggestions:

• Consider more specific exception handling
• Add timestamp comment to sample data for maintenance tracking