8000 Add HallucinationGuardrail no-op implementation with tests by greysonlalonde · Pull Request #2869 · crewAIInc/crewAI · GitHub
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Merged
merged 9 commits into from
May 21, 2025
Merged

Add HallucinationGuardrail no-op implementation with tests #2869

merged 9 commits into from
May 21, 2025

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greysonlalonde
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  • Add HallucinationGuardrail class as enterprise feature placeholder
  • Update LLM guardrail events to support HallucinationGuardrail instances
  • Add comprehensive tests for HallucinationGuardrail initialization and behavior
  • Add integration tests for HallucinationGuardrail with task execution system
  • Ensure no-op behavior always returns True

@greysonlalonde
Add HallucinationGuardrail no-op implementation with tests
d46ee5d 
- Add `HallucinationGuardrail` class as enterprise feature placeholder
- Update LLM guardrail events to support `HallucinationGuardrail` instances
  - Add comprehensive tests for `HallucinationGuardrail` initialization and behavior
- Add integration tests for `HallucinationGuardrail` with task execution system
- Ensure no-op behavior always returns True
@greysonlalonde greysonlalonde requested a review from lorenzejay May 20, 2025 21:52
@joaomdmoura
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Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment for PR #2869 - HallucinationGuardrail Implementation

Code Quality Findings

The implementation of the HallucinationGuardrail provides a solid foundation for future enhancements. The following observations highlight areas that are well-executed and suggest further improvements:

1. Strengths

  • Well-Documented Code: The use of comprehensive docstrings helps clarify class and method purposes, contributing to better maintainability.
  • Type Hints: Appropriate type annotations enhance code readability and enable type-checking tools to catch potential errors.
  • Error Logging: Good error logging practices are in place, which will be beneficial during enterprise feature deployment.

2. Suggestions for Improvement

  • Use of Dataclass: Adding the @dataclass decorator can help manage attributes more effectively, as demonstrated below:

    from dataclasses import dataclass

@dataclass
class HallucinationGuardrail:
    context: str
    llm: LLM
    threshold: Optional[float] = None
    tool_response: str = ""

  • Threshold Validation: Implement input validation for the threshold property to enforce acceptable value ranges:

    @property
def threshold(self) -> Optional[float]:
    return self._threshold

@threshold.setter
def threshold(self, value: Optional[float]) -> None:
    if value is not None and not (0.0 <= value <= 10.0):
        raise ValueError("Threshold must be between 0.0 and 10.0")
    self._threshold = value

  • Immutability: Use frozen=True in the dataclass to prevent accidental modifications:

    @dataclass(frozen=True)
class HallucinationGuardrail:

3. Testing Enhancements

  • Edge Case Tests: Add tests to cover edge cases for initializing the HallucinationGuardrail to ensure robustness:

    def test_hallucination_guardrail_with_invalid_threshold():
    mock_llm = Mock(spec=LLM)
    with pytest.raises(ValueError, match="Threshold must be between 0.0 and 10.0"):
        HallucinationGuardrail(context="Test context", llm=mock_llm, threshold=11.0)

  • Test for Empty Context: Include tests to validate appropriate exceptions when the context is empty:

    def test_hallucination_guardrail_with_empty_context():
    mock_llm = Mock(spec=LLM)
    with pytest.raises(ValueError, match="Context cannot be empty"):
        HallucinationGuardrail(context="", llm=mock_llm)

4. General Recommendations

  • Performance Metrics: Introduce logging for performance metrics in preparation for the enterprise version.
  • Configuration Options: Allow for different levels of hallucination detection strictness to cater to varied use cases.
  • Async Methods: Consider implementing asynchronous versions of guardrail methods for improved performance.

Historical Context from Related PRs

While specific historical references cannot be fetched due to access limitations, reviewing related PRs that add or modify utility guardrails can shed light on best practices regarding integration, testing strategies, and error handling within similar contexts. It’s beneficial to investigate previous implementations in src/crewai/utilities/events/ or tests/ that deal with guardrails to align with established patterns.

Implications for Related Files

The modifications made in this PR may impact files managing task executions and event logging associated with guardrails. Proper integration tests ensure that these modifications do not introduce regressions. It's necessary to maintain synergy between the hallucinatory features of this class and existing systems, particularly focusing on how task outputs are processed.

Final Thoughts

The PR's implementation is a commendable effort towards preparing for an enterprise-level guardrail system. The feedback provided here aims at solidifying code quality, ensuring test coverage, and paving the way for scalable features. Addressing these suggestions will enhance maintainability and performance in the long run.

Let’s ensure we are prepared for a detailed follow-up with proof of concept or further discussions based on insights derived from testing and implementation feedback.

@greysonlalonde greysonlalonde requested a review from a team May 21, 2025 05:11
lorenzejay
lorenzejay requested changes May 21, 2025
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@greysonlalonde greysonlalonde requested a review from lorenzejay May 21, 2025 17:44
lorenzejay
lorenzejay approved these changes May 21, 2025
View reviewed changes
src/crewai/tasks/hallucination_guardrail.py
self._logger = Logger(verbose=True)
self._logger.log(
"warning",
"""Hallucination detection is a no-op in open source, use it for free at https://app.crewai.com\n""",
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perfect!

@greysonlalonde greysonlalonde merged commit 9945da7 into main May 21, 2025
9 checks passed
@greysonlalonde greysonlalonde deleted the gl/feat/hallucination-guardrail-no-op branch May 21, 2025 17:47
didier-durand pushed a commit to didier-durand/crewAI that referenced this pull request Jun 12, 2025
@greysonlalonde @didier-durand
Add HallucinationGuardrail no-op implementation with tests (crewAIInc…
96bf942 
…#2869)

- Add `HallucinationGuardrail` class as enterprise feature placeholder
- Update LLM guardrail events to support `HallucinationGuardrail` instances
- Add comprehensive tests for `HallucinationGuardrail` initialization and behavior
- Add integration tests for `HallucinationGuardrail` with task execution system
- Ensure no-op behavior always returns True
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@lorenzejay lorenzejay lorenzejay approved these changes

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@greysonlalonde @joaomdmoura @lorenzejay
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