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Glider

any_guardrail.guardrails.glider.glider

Glider

Bases: HuggingFace

A prompt based guardrail from Patronus AI that utilizes pass criteria and a rubric to judge text.

For more information, see the model card:GLIDER. It outputs its reasoning, highlights for what determined the score, and an integer score.

Parameters:

Name Type Description Default
model_id str | None

HuggingFace path to model.

 None
pass_criteria str

A question or description of what you are validating.

 required
rubric str

A scoring rubric, describing to the model how to score the provided data.

 required
Raise

ValueError: Can only use model path to GLIDER from HuggingFace.

Source code in src/any_guardrail/guardrails/glider/glider.py 
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class Glider(HuggingFace):
    """A prompt based guardrail from Patronus AI that utilizes pass criteria and a rubric to judge text.

    For more information, see the model card:[GLIDER](https://huggingface.co/PatronusAI/glider). It outputs its reasoning,
    highlights for what determined the score, and an integer score.

    Args:
        model_id: HuggingFace path to model.
        pass_criteria: A question or description of what you are validating.
        rubric: A scoring rubric, describing to the model how to score the provided data.

    Raise:
        ValueError: Can only use model path to GLIDER from HuggingFace.

    """

    SUPPORTED_MODELS: ClassVar = ["PatronusAI/glider"]

    def __init__(self, pass_criteria: str, rubric: str, model_id: str | None = None) -> None:
        """Initialize the GLIDER guardrail."""
        super().__init__(model_id)
        self.pass_criteria = pass_criteria
        self.rubric = rubric
        self.system_prompt = SYSTEM_PROMPT_GLIDER

    def validate(self, input_text: str, output_text: str | None = None) -> GuardrailOutput:
        """Use the provided pass criteria and rubric to judge the input and output text provided.

        Args:
            input_text: the initial text.
            output_text: the subsequent text.

        Returns:
            An explanation in the format provided by the system prompt.

        """
        message = self._pre_processing(input_text, output_text)
        result = self._inference(message)
        return self._post_processing(result)

    def _load_model(self) -> None:
        from transformers import pipeline

        pipe = pipeline("text-generation", self.model_id, max_new_tokens=2048, return_full_text=False)
        self.model = pipe

    def _pre_processing(self, input_text: str, output_text: str | None = None) -> list[dict[str, str]]:
        if output_text is None:
            data = INPUT_DATA_FORMAT.format(input_text=input_text)
        else:
            data = INPUT_OUTPUT_DATA_FORMAT.format(input_text=input_text, output_text=output_text)
        prompt = self.system_prompt.format(data=data, pass_criteria=self.pass_criteria, rubric=self.rubric)
        return [{"role": "user", "content": prompt}]

    def _inference(self, message: list[dict[str, str]]) -> Any:
        return self.model(message)[0]["generated_text"]

    def _post_processing(self, model_outputs: Any) -> GuardrailOutput:
        score = re.findall(r"<score>\n(\d+)\n</score>", model_outputs)
        if len(score) != 0 and score[0].isdigit():
            final_score = int(score[0])
        else:
            final_score = None

        return GuardrailOutput(explanation=model_outputs, score=final_score)
__init__(pass_criteria, rubric, model_id=None)

Initialize the GLIDER guardrail.

Source code in src/any_guardrail/guardrails/glider/glider.py 
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def __init__(self, pass_criteria: str, rubric: str, model_id: str | None = None) -> None:
    """Initialize the GLIDER guardrail."""
    super().__init__(model_id)
    self.pass_criteria = pass_criteria
    self.rubric = rubric
    self.system_prompt = SYSTEM_PROMPT_GLIDER
validate(input_text, output_text=None)

Use the provided pass criteria and rubric to judge the input and output text provided.

Parameters:

Name Type Description Default
input_text str

the initial text.

 required
output_text str | None

the subsequent text.

 None

Returns:

Type Description
GuardrailOutput

An explanation in the format provided by the system prompt.
Source code in src/any_guardrail/guardrails/glider/glider.py 
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def validate(self, input_text: str, output_text: str | None = None) -> GuardrailOutput:
    """Use the provided pass criteria and rubric to judge the input and output text provided.

    Args:
        input_text: the initial text.
        output_text: the subsequent text.

    Returns:
        An explanation in the format provided by the system prompt.

    """
    message = self._pre_processing(input_text, output_text)
    result = self._inference(message)
    return self._post_processing(result)