函数模式

def generate_func_documentation(
    func: Callable[..., Any], style: DocstringStyle | None = None
) -> FuncDocumentation:
    """
    Extracts metadata from a function docstring, in preparation for sending it to an LLM as a tool.

    Args:
        func: The function to extract documentation from.
        style: The style of the docstring to use for parsing. If not provided, we will attempt to
            auto-detect the style.

    Returns:
        A FuncDocumentation object containing the function's name, description, and parameter
        descriptions.
    """
    name = func.__name__
    doc = inspect.getdoc(func)
    if not doc:
        return FuncDocumentation(name=name, description=None, param_descriptions=None)

    with _suppress_griffe_logging():
        docstring = Docstring(doc, lineno=1, parser=style or _detect_docstring_style(doc))
        parsed = docstring.parse()

    description: str | None = next(
        (section.value for section in parsed if section.kind == DocstringSectionKind.text), None
    )

    param_descriptions: dict[str, str] = {
        param.name: param.description
        for section in parsed
        if section.kind == DocstringSectionKind.parameters
        for param in section.value
    }

    return FuncDocumentation(
        name=func.__name__,
        description=description,
        param_descriptions=param_descriptions or None,
    )

def function_schema(
    func: Callable[..., Any],
    docstring_style: DocstringStyle | None = None,
    name_override: str | None = None,
    description_override: str | None = None,
    use_docstring_info: bool = True,
    strict_json_schema: bool = True,
) -> FuncSchema:
    """
    Given a python function, extracts a `FuncSchema` from it, capturing the name, description,
    parameter descriptions, and other metadata.

    Args:
        func: The function to extract the schema from.
        docstring_style: The style of the docstring to use for parsing. If not provided, we will
            attempt to auto-detect the style.
        name_override: If provided, use this name instead of the function's `__name__`.
        description_override: If provided, use this description instead of the one derived from the
            docstring.
        use_docstring_info: If True, uses the docstring to generate the description and parameter
            descriptions.
        strict_json_schema: Whether the JSON schema is in strict mode. If True, we'll ensure that
            the schema adheres to the "strict" standard the OpenAI API expects. We **strongly**
            recommend setting this to True, as it increases the likelihood of the LLM providing
            correct JSON input.

    Returns:
        A `FuncSchema` object containing the function's name, description, parameter descriptions,
        and other metadata.
    """

    # 1. Grab docstring info
    if use_docstring_info:
        doc_info = generate_func_documentation(func, docstring_style)
        param_descs = doc_info.param_descriptions or {}
    else:
        doc_info = None
        param_descs = {}

    func_name = name_override or doc_info.name if doc_info else func.__name__

    # 2. Inspect function signature and get type hints
    sig = inspect.signature(func)
    type_hints = get_type_hints(func)
    params = list(sig.parameters.items())
    takes_context = False
    filtered_params = []

    if params:
        first_name, first_param = params[0]
        # Prefer the evaluated type hint if available
        ann = type_hints.get(first_name, first_param.annotation)
        if ann != inspect._empty:
            origin = get_origin(ann) or ann
            if origin is RunContextWrapper:
                takes_context = True  # Mark that the function takes context
            else:
                filtered_params.append((first_name, first_param))
        else:
            filtered_params.append((first_name, first_param))

    # For parameters other than the first, raise error if any use RunContextWrapper.
    for name, param in params[1:]:
        ann = type_hints.get(name, param.annotation)
        if ann != inspect._empty:
            origin = get_origin(ann) or ann
            if origin is RunContextWrapper:
                raise UserError(
                    f"RunContextWrapper param found at non-first position in function"
                    f" {func.__name__}"
                )
        filtered_params.append((name, param))

    # We will collect field definitions for create_model as a dict:
    #   field_name -> (type_annotation, default_value_or_Field(...))
    fields: dict[str, Any] = {}

    for name, param in filtered_params:
        ann = type_hints.get(name, param.annotation)
        default = param.default

        # If there's no type hint, assume `Any`
        if ann == inspect._empty:
            ann = Any

        # If a docstring param description exists, use it
        field_description = param_descs.get(name, None)

        # Handle different parameter kinds
        if param.kind == param.VAR_POSITIONAL:
            # e.g. *args: extend positional args
            if get_origin(ann) is tuple:
                # e.g. def foo(*args: tuple[int, ...]) -> treat as List[int]
                args_of_tuple = get_args(ann)
                if len(args_of_tuple) == 2 and args_of_tuple[1] is Ellipsis:
                    ann = list[args_of_tuple[0]]  # type: ignore
                else:
                    ann = list[Any]
            else:
                # If user wrote *args: int, treat as List[int]
                ann = list[ann]  # type: ignore

            # Default factory to empty list
            fields[name] = (
                ann,
                Field(default_factory=list, description=field_description),  # type: ignore
            )

        elif param.kind == param.VAR_KEYWORD:
            # **kwargs handling
            if get_origin(ann) is dict:
                # e.g. def foo(**kwargs: dict[str, int])
                dict_args = get_args(ann)
                if len(dict_args) == 2:
                    ann = dict[dict_args[0], dict_args[1]]  # type: ignore
                else:
                    ann = dict[str, Any]
            else:
                # e.g. def foo(**kwargs: int) -> Dict[str, int]
                ann = dict[str, ann]  # type: ignore

            fields[name] = (
                ann,
                Field(default_factory=dict, description=field_description),  # type: ignore
            )

        else:
            # Normal parameter
            if default == inspect._empty:
                # Required field
                fields[name] = (
                    ann,
                    Field(..., description=field_description),
                )
            else:
                # Parameter with a default value
                fields[name] = (
                    ann,
                    Field(default=default, description=field_description),
                )

    # 3. Dynamically build a Pydantic model
    dynamic_model = create_model(f"{func_name}_args", __base__=BaseModel, **fields)

    # 4. Build JSON schema from that model
    json_schema = dynamic_model.model_json_schema()
    if strict_json_schema:
        json_schema = ensure_strict_json_schema(json_schema)

    # 5. Return as a FuncSchema dataclass
    return FuncSchema(
        name=func_name,
        description=description_override or doc_info.description if doc_info else None,
        params_pydantic_model=dynamic_model,
        params_json_schema=json_schema,
        signature=sig,
        takes_context=takes_context,
        strict_json_schema=strict_json_schema,
    )