환각 방어책

from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
from crewai import LLM

# Basic usage - will use task's expected_output as context
guardrail = HallucinationGuardrail(
    llm=LLM(model="gpt-4o-mini")
)

# With explicit reference context
context_guardrail = HallucinationGuardrail(
    context="AI helps with various tasks including analysis and generation.",
    llm=LLM(model="gpt-4o-mini")
)

from crewai import Task

# Create your task with the guardrail
task = Task(
    description="Write a summary about AI capabilities",
    expected_output="A factual summary based on the provided context",
    agent=my_agent,
    guardrail=guardrail  # Add the guardrail to validate output
)

# Strict guardrail requiring high faithfulness score
strict_guardrail = HallucinationGuardrail(
    context="Quantum computing uses qubits that exist in superposition states.",
    llm=LLM(model="gpt-4o-mini"),
    threshold=8.0  # Requires score >= 8 to pass validation
)

# Guardrail with tool response context
weather_guardrail = HallucinationGuardrail(
    context="Current weather information for the requested location",
    llm=LLM(model="gpt-4o-mini"),
    tool_response="Weather API returned: Temperature 22°C, Humidity 65%, Clear skies"
)

# Example of guardrail result structure
{
    "valid": False,
    "feedback": "Content appears to be hallucinated (score: 4.2/10, verdict: HALLUCINATED). The output contains information not supported by the provided context."
}

# Task output validation flow
task_output = agent.execute_task(task)
validation_result = guardrail(task_output)

if validation_result.valid:
    # Task completes successfully
    return task_output
else:
    # Task fails with validation feedback
    raise ValidationError(validation_result.feedback)