with_structured_output() — langchain Function Reference

Architecture documentation for the with_structured_output() function in chat_models.py from the langchain codebase.

Function python LangChainCore LanguageModelBase calls 2

Entity Profile

LangChainCore→ LanguageModelBase→ with_structured_output() — langchain Function Reference

Dependency Diagram

graph TD
  539ec970_fba6_1286_7920_4016594274ca["with_structured_output()"]
  1a5cd25a_9420_c6b2_ec8d_2b53c6427514["ChatFireworks"]
  539ec970_fba6_1286_7920_4016594274ca -->|defined in| 1a5cd25a_9420_c6b2_ec8d_2b53c6427514
  ba87ba6a_45d8_03fe_9522_138432f5eb38["bind_tools()"]
  539ec970_fba6_1286_7920_4016594274ca -->|calls| ba87ba6a_45d8_03fe_9522_138432f5eb38
  144b4c88_21d1_eb83_ddf1_b64640d36a54["_is_pydantic_class()"]
  539ec970_fba6_1286_7920_4016594274ca -->|calls| 144b4c88_21d1_eb83_ddf1_b64640d36a54
  style 539ec970_fba6_1286_7920_4016594274ca fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/fireworks/langchain_fireworks/chat_models.py lines 710–1055

    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,
                - 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 Fireworks's [tool-calling features](https://docs.fireworks.ai/guides/function-calling).
                - `'json_schema'`:
                    Uses Fireworks's [structured output feature](https://docs.fireworks.ai/structured-responses/structured-response-formatting).
                - `'json_mode'`:
                    Uses Fireworks's [JSON mode feature](https://docs.fireworks.ai/structured-responses/structured-response-formatting).

                !!! warning "Behavior changed in `langchain-fireworks` 0.2.8"

                    Added support for `'json_schema'`.

            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:
                Any additional parameters to pass to the `langchain.runnable.Runnable`
                constructor.

        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`

        Example: schema=Pydantic class, method="function_calling", include_raw=False:

        ```python
        from typing import Optional

        from langchain_fireworks import ChatFireworks
        from pydantic import BaseModel, Field