이것은 대화형 노트북입니다. 로컬에서 실행하거나 아래 링크를 사용할 수 있습니다:

​
가드레일로서의 스코러

Weave 스코러는 score 메서드를 가진 특별한 클래스로, 호출의 성능을 평가할 수 있습니다. 이는 매우 간단한 규칙부터 판사 역할을 하는 복잡한 LLM까지 다양할 수 있습니다. 이 노트북에서는 스코러를 가드레일로 사용하여 LLM이 유해하거나 부적절한 콘텐츠를 생성하는 것을 방지하는 방법을 살펴보겠습니다. 
%pip install weave --quiet
python
"""
Example demonstrating how to implement guardrails in Weave.
This example shows a simple content safety checker that prevents
potentially harmful or negative responses.
"""

import weave

# Initialize Weave with a descriptive project name
weave.init("content-safety-guardrails")

class ContentSafetyScorer(weave.Scorer):
    """A scorer that evaluates content safety based on presence of specified phrases."""

    unsafe_phrases: list[str]
    case_sensitive: bool = False

    @weave.op
    def score(self, output: str) -> bool:
        """
        Evaluate output safety based on presence of unsafe phrases.

        Args:
            output: The text output to evaluate

        Returns:
            bool: True if output is safe, False if unsafe
        """
        normalized_output = output if self.case_sensitive else output.lower()

        for phrase in self.unsafe_phrases:
            normalized_phrase = phrase if self.case_sensitive else phrase.lower()
            if normalized_phrase in normalized_output:
                return False
        return True

@weave.op
def generate_response(prompt: str) -> str:
    """Simulate an LLM response generation."""
    if "test" in prompt.lower():
        return "I'm sorry, I cannot process that request."
    elif "help" in prompt.lower():
        return "I'd be happy to help you with that!"
    else:
        return "Here's what you requested: " + prompt

async def process_with_guardrail(prompt: str) -> str:
    """
    Process user input with content safety guardrail.
    Returns the response if safe, or a fallback message if unsafe.
    """
    # Initialize safety scorer
    safety_scorer = ContentSafetyScorer(
        name="Content Safety Checker",
        unsafe_phrases=["sorry", "cannot", "unable", "won't", "will not"],
    )

    # Generate response and get Call object
    response, call = generate_response.call(prompt)

    # Apply safety scoring
    evaluation = await call.apply_scorer(safety_scorer)

    # Return response or fallback based on safety check
    if evaluation.result:
        return response
    else:
        return "I cannot provide that response."
python
"""Example usage of the guardrail system."""
test_prompts = [
    "Please help me with my homework",
    "Can you run a test for me?",
    "Tell me a joke",
]

print("Testing content safety guardrails:\n")

for prompt in test_prompts:
    print(f"Input: '{prompt}'")
    response = await process_with_guardrail(prompt)
    print(f"Response: {response}\n")