8000 Add HallucinationGuardrail documentation by greysonlalonde · Pull Request #2889 · crewAIInc/crewAI · GitHub
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Merged
merged 3 commits into from
May 22, 2025
Merged

Add HallucinationGuardrail documentation #2889

merged 3 commits into from
May 22, 2025

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greysonlalonde
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Adds enterprise documentation for HallucinationGuardrail feature with usage examples and configuration guide.

@greysonlalonde
docs: enterprise hallucination guardrails
ed25cfd 
Documents the `HallucinationGuardrail` feature for enterprise users, including usage examples, configuration options, and integration patterns.
@greysonlalonde
fix: update import
0c55eab 
in the tin
@joaomdmoura
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Disclaimer: This review was made by a crew of AI Agents.

Code Review Comment: Hallucination Guardrail Documentation PR

Overview

This pull request introduces documentation for the enterprise Hallucination Guardrail feature, alongside a minor import fix. The documentation is segregated into logical sections detailing the configuration, usage, and performance aspects of the guardrail functionality.

Strengths

  1. Well-Structured Content: The documentation is clearly divided into sections, enabling users to easily navigate and find the information they need.
  2. Comprehensive Coverage: The feature functionality is discussed in depth, detailing usage, advanced configurations, and validation processes.
  3. Effective Code Examples: Inline code snippets provide practical guidance on implementation, enhancing user understanding.
  4. Troubleshooting Guides: Inclusion of common issues and their solutions helps users tackle potential problems effectively.
  5. Performance Considerations: Clear explanations of performance implications are crucial for enterprise applications.

Areas for Improvement

  1. Code Example Completeness:

    • The initialization example for HallucinationGuardrail could be expanded to clarify agent setup.
    • Suggested example:
    from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
from crewai import LLM, Task, Agent

# Initialize the agent
my_agent = Agent(
    name="Content Validator",
    llm=LLM(model="gpt-4")
)

# Initialize the guardrail with context
guardrail = HallucinationGuardrail(
    context="AI assists across various tasks including analysis and generation.",
    llm=LLM(model="gpt-4o-mini"),
    threshold=7.0  # Clarifying default threshold
)

  2. Error Handling Documentation:

    • Including a specific error handling example would improve user guidelines significantly.
    try:
    task_output = agent.execute_task(task)
    validation_result = guardrail(task_output)
    if not validation_result.valid:
        logger.warning(f"Validation failed: {validation_result.feedback}")
except ValidationError as e:
    logger.error(f"Validation error: {str(e)}")
except Exception as e:
    logger.error(f"Unexpected error: {str(e)}")

  3. Configuration Interface Documentation:

    • Adding type hints and detailed documentation will enhance clarity regarding the initialization parameters:
    from typing import Optional, Dict

class HallucinationGuardrail:
    def __init__(
        self,
        context: str,
        llm: LLM,
        threshold: Optional[float] = None,
        tool_response: Optional[str] = None,
        config: Optional[Dict] = None
    ):
        """
        Initialization parameters for HallucinationGuardrail.
        Args:
            context: Context for validation.
            llm: Language model instance. 
            threshold: Optional validation threshold.
            tool_response: Optional additional context.
            config: Optional additional configuration options.
        """

  4. Technical Improvements:

    • The minor import fix is correct. Consider grouping related imports for clarity:
    from crewai import (
    LLM,
    Task,
    Agent,
    ValidationError
)
from crewai.tasks.hallucination_guardrail import HallucinationGuardrail

  5. Version Compatibility Note:

    • Adding a note about version requirements can enhance user awareness:
    <Note>
  This feature requires CrewAI Enterprise version 2.0.0 or higher.
  Supported LLM models: gpt-4, gpt-4-turbo, gpt-4o-mini.
</Note>

  6. Performance Metrics Section:

    • Including specific metrics can offer insights into operational efficiency:
    ## Performance Metrics

| Model         | Avg. Validation Time | Cost per 1K validations |
|--------------|---------------------|------------------------|
| gpt-4o-mini  | 1.2s               | $0.50                 |
| gpt-4        | 2.5s               | $2.00                 |
| gpt-4-turbo  | 1.8s               | $1.50                 |

Security Considerations

Adding security notes is crucial for safeguarding sensitive information:

## Security Considerations

- Ensure sensitive information is not included in reference context.
- Implement proper access controls for enterprise features.
- Monitor and audit validation results.
- Consider data retention policies for validation logs.

Conclusion

The documentation is fundamentally strong and valuable for users aiming to implement the Hallucination Guardrail feature. However, addressing the suggested improvements will greatly enhance its effectiveness and user-friendliness.

Follow-up Recommendations

  1. Incorporate unit test examples.
  2. Include benchmark results and details about integration testing.
  3. Expand the troubleshooting section to cover additional scenarios.
  4. Introduce observability guidelines for effective production monitoring.

By implementing these suggestions, the documentation will be significantly improved, providing excellent resources for users and developers alike.

@greysonlalonde greysonlalonde requested a review from a team May 22, 2025 17:14
tonykipkemboi
tonykipkemboi requested changes May 22, 2025
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left you a comment on slack @greysonlalonde

@greysonlalonde
chore: add docs.json route
b2228b6 
Add route for hallucination guardrail mdx
tonykipkemboi
tonykipkemboi approved these changes May 22, 2025
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Looks great!

@tonykipkemboi tonykipkemboi merged commit d131d4e into main May 22, 2025
9 checks passed
didier-durand pushed a commit to didier-durand/crewAI that referenced this pull request Jun 12, 2025
@greysonlalonde @didier-durand
Add HallucinationGuardrail documentation (crewAIInc#2889)
f60127a 
* docs: enterprise hallucination guardrails

Documents the `HallucinationGuardrail` feature for enterprise users, including usage examples, configuration options, and integration patterns.

* fix: update import

in the tin

* chore: add docs.json route

Add route for hallucination guardrail mdx
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@tonykipkemboi tonykipkemboi tonykipkemboi approved these changes

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@greysonlalonde @joaomdmoura @tonykipkemboi
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