指南

class GuidancePydanticProgram(BaseLLMFunctionProgram["GuidanceLLM"]):
    """
    A guidance-based function that returns a pydantic model.

    Note: this interface is not yet stable.
    """

    def __init__(
        self,
        output_cls: Type[BaseModel],
        prompt_template_str: str,
        guidance_llm: Optional["GuidanceLLM"] = None,
        verbose: bool = False,
    ):
        if not guidance_llm:
            llm = guidance_llm
        else:
            llm = OpenAI("gpt-3.5-turbo")

        full_str = prompt_template_str + "\n"
        self._full_str = full_str
        self._guidance_program = partial(self.program, llm=llm, silent=not verbose)
        self._output_cls = output_cls
        self._verbose = verbose

    def program(
        self,
        llm: "GuidanceLLM",
        silent: bool,
        tools_str: str,
        query_str: str,
        **kwargs: dict,
    ) -> "GuidanceLLM":
        """A wrapper to execute the program with new guidance version."""
        given_query = self._full_str.replace("{{tools_str}}", tools_str).replace(
            "{{query_str}}", query_str
        )
        with user():
            llm = llm + given_query

        with assistant():
            llm = llm + gen(stop=".")

        return llm  # noqa: RET504

    @classmethod
    def from_defaults(
        cls,
        output_cls: Type[BaseModel],
        prompt_template_str: Optional[str] = None,
        prompt: Optional[PromptTemplate] = None,
        llm: Optional["GuidanceLLM"] = None,
        **kwargs: Any,
    ) -> "BaseLLMFunctionProgram":
        """From defaults."""
        if prompt is None and prompt_template_str is None:
            raise ValueError("Must provide either prompt or prompt_template_str.")
        if prompt is not None and prompt_template_str is not None:
            raise ValueError("Must provide either prompt or prompt_template_str.")
        if prompt is not None:
            prompt_template_str = prompt.template
        prompt_template_str = cast(str, prompt_template_str)
        return cls(
            output_cls,
            prompt_template_str,
            guidance_llm=llm,
            **kwargs,
        )

    @property
    def output_cls(self) -> Type[BaseModel]:
        return self._output_cls

    def __call__(
        self,
        *args: Any,
        **kwargs: Any,
    ) -> BaseModel:
        executed_program = self._guidance_program(**kwargs)
        response = str(executed_program)

        return parse_pydantic_from_guidance_program(
            response=response, cls=self._output_cls
        )