with_structured_output() — langchain Function Reference

Source Code

libs/partners/xai/langchain_xai/chat_models.py lines 650–734

    def with_structured_output(
        self,
        schema: _DictOrPydanticClass | None = None,
        *,
        method: Literal[
            "function_calling", "json_mode", "json_schema"
        ] = "function_calling",
        include_raw: bool = False,
        strict: bool | None = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, _DictOrPydantic]:
        """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,
                - Or a Pydantic class.

                If `schema` is a Pydantic class then the model output will be a
                Pydantic instance of that class, and the model-generated fields will be
                validated by the Pydantic class. Otherwise the model output will be a
                dict and will not be validated.

                See `langchain_core.utils.function_calling.convert_to_openai_tool` for
                more on how to properly specify types and descriptions of schema fields
                when specifying a Pydantic or `TypedDict` class.

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

                - `'function_calling'`:
                    Uses xAI's [tool-calling features](https://docs.x.ai/docs/guides/function-calling).
                - `'json_schema'`:
                    Uses xAI's [structured output feature](https://docs.x.ai/docs/guides/structured-outputs).
                - `'json_mode'`:
                    Uses xAI's JSON mode feature.

            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'`.
            strict:
                - `True`:
                    Model output is guaranteed to exactly match the schema.
                    The input schema will also be validated according to the [supported schemas](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas?api-mode=responses#supported-schemas).
                - `False`:
                    Input schema will not be validated and model output will not be
                    validated.
                - `None`:
                    `strict` argument will not be passed to the model.

            kwargs: Additional keyword args aren't supported.

        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`
        """
        # Some applications require that incompatible parameters (e.g., unsupported
        # methods) be handled.
        if method == "function_calling" and strict: