BaseLanguageModel Class — langchain Architecture

Source Code

libs/core/langchain_core/language_models/base.py lines 139–373

class BaseLanguageModel(
    RunnableSerializable[LanguageModelInput, LanguageModelOutputVar], ABC
):
    """Abstract base class for interfacing with language models.

    All language model wrappers inherited from `BaseLanguageModel`.

    """

    cache: BaseCache | bool | None = Field(default=None, exclude=True)
    """Whether to cache the response.

    * If `True`, will use the global cache.
    * If `False`, will not use a cache
    * If `None`, will use the global cache if it's set, otherwise no cache.
    * If instance of `BaseCache`, will use the provided cache.

    Caching is not currently supported for streaming methods of models.
    """

    verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False)
    """Whether to print out response text."""

    callbacks: Callbacks = Field(default=None, exclude=True)
    """Callbacks to add to the run trace."""

    tags: list[str] | None = Field(default=None, exclude=True)
    """Tags to add to the run trace."""

    metadata: dict[str, Any] | None = Field(default=None, exclude=True)
    """Metadata to add to the run trace."""

    custom_get_token_ids: Callable[[str], list[int]] | None = Field(
        default=None, exclude=True
    )
    """Optional encoder to use for counting tokens."""

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    @field_validator("verbose", mode="before")
    def set_verbose(cls, verbose: bool | None) -> bool:  # noqa: FBT001
        """If verbose is `None`, set it.

        This allows users to pass in `None` as verbose to access the global setting.

        Args:
            verbose: The verbosity setting to use.

        Returns:
            The verbosity setting to use.

        """
        if verbose is None:
            return _get_verbosity()
        return verbose

    @property
    @override
    def InputType(self) -> TypeAlias:
        """Get the input type for this `Runnable`."""
        # This is a version of LanguageModelInput which replaces the abstract
        # base class BaseMessage with a union of its subclasses, which makes
        # for a much better schema.
        return str | StringPromptValue | ChatPromptValueConcrete | list[AnyMessage]

    @abstractmethod
    def generate_prompt(
        self,
        prompts: list[PromptValue],
        stop: list[str] | None = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Pass a sequence of prompts to the model and return model generations.

        This method should make use of batched calls for models that expose a batched
        API.

        Use this method when you want to: