with_structured_output() — langchain Function Reference

Source Code

libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py lines 1090–1219

    def with_structured_output(
        self,
        schema: dict | type[BaseModel] | None = None,
        *,
        method: Literal[
            "function_calling", "json_mode", "json_schema"
        ] = "function_calling",
        include_raw: bool = False,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, dict | BaseModel]:
        """Model wrapper that returns outputs formatted to match the given schema.

        Args:
            schema: The output schema. Can be passed in as:

                - An OpenAI function/tool schema,
                - A JSON Schema,
                - A `TypedDict` class

                Pydantic class is currently supported.

            method: The method for steering model generation, one of:

                - `'function_calling'`: uses tool-calling features.
                - `'json_schema'`: uses dedicated structured output features.
                - `'json_mode'`: uses JSON mode.

            include_raw:
                If `False` then only the parsed structured output is returned.

                If an error occurs during model output parsing it will be raised.

                If `True` then both the raw model response (a `BaseMessage`) and the
                parsed model response will be returned.

                If an error occurs during output parsing it will be caught and returned
                as well.

                The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
                `'parsing_error'`.

            kwargs:
                Additional parameters to pass to the underlying LLM's
                `langchain_core.language_models.chat.BaseChatModel.bind`
                method, such as `response_format` or `ls_structured_output_format`.

        Returns:
            A `Runnable` that takes same inputs as a
                `langchain_core.language_models.chat.BaseChatModel`. If `include_raw` is
                `False` and `schema` is a Pydantic class, `Runnable` outputs an instance
                of `schema` (i.e., a Pydantic object). Otherwise, if `include_raw` is
                `False` then `Runnable` outputs a `dict`.

                If `include_raw` is `True`, then `Runnable` outputs a `dict` with keys:

                - `'raw'`: `BaseMessage`
                - `'parsed'`: `None` if there was a parsing error, otherwise the type
                    depends on the `schema` as described above.
                - `'parsing_error'`: `BaseException | None`
        """
        _ = kwargs.pop("strict", None)
        if kwargs:
            msg = f"Received unsupported arguments {kwargs}"
            raise ValueError(msg)
        is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema)
        if method == "function_calling":
            if schema is None:
                msg = (
                    "schema must be specified when method is 'function_calling'. "
                    "Received None."
                )
                raise ValueError(msg)
            formatted_tool = convert_to_openai_tool(schema)
            tool_name = formatted_tool["function"]["name"]
            llm = self.bind_tools(
                [schema],
                tool_choice=tool_name,
                ls_structured_output_format={
                    "kwargs": {"method": "function_calling"},
                    "schema": formatted_tool,
                },