BaseLLM Class — langchain Architecture

Architecture documentation for the BaseLLM class in llms.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  76008743_94c2_792b_9e4f_e1278ae47565["BaseLLM"]
  7deca977_9668_e807_4e77_0bc4519c0bfb["PromptValue"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|extends| 7deca977_9668_e807_4e77_0bc4519c0bfb
  e444f60a_82db_8ae6_63c7_dc75096294ec["llms.py"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|defined in| e444f60a_82db_8ae6_63c7_dc75096294ec
  9dbfb50c_893c_4563_b4db_6783374675ee["_serialized()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| 9dbfb50c_893c_4563_b4db_6783374675ee
  654d40dd_c333_4a88_bcd6_0d7ac2820770["OutputType()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| 654d40dd_c333_4a88_bcd6_0d7ac2820770
  a7aab318_bfe0_a0e0_edf0_51778f410f99["_convert_input()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| a7aab318_bfe0_a0e0_edf0_51778f410f99
  e4e417de_48b8_652f_a98e_96bf2ac2efb9["_get_ls_params()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| e4e417de_48b8_652f_a98e_96bf2ac2efb9
  7fcd2060_4361_37eb_227f_2c40ad909233["invoke()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| 7fcd2060_4361_37eb_227f_2c40ad909233
  83380da9_8709_2e6f_ecf4_1e88757580fa["ainvoke()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| 83380da9_8709_2e6f_ecf4_1e88757580fa
  ec40beb4_e7fe_ff24_8f09_8fdc5556bed6["batch()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| ec40beb4_e7fe_ff24_8f09_8fdc5556bed6
  e6660fe6_14e6_584e_aad4_3ebb2271d015["abatch()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| e6660fe6_14e6_584e_aad4_3ebb2271d015
  e46085fd_99f0_971d_3d9a_1cd202b72d44["stream()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| e46085fd_99f0_971d_3d9a_1cd202b72d44
  974215fb_0217_49ef_2034_3bc77b1a795b["astream()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| 974215fb_0217_49ef_2034_3bc77b1a795b
  e180eb37_127d_0088_05e7_a91d4dec7eb3["_generate()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| e180eb37_127d_0088_05e7_a91d4dec7eb3
  cee99796_51df_4a14_cd70_308ac8fe0e19["_agenerate()"]
  76008743_94c2_792b_9e4f_e1278ae47565 -->|method| cee99796_51df_4a14_cd70_308ac8fe0e19

Relationship Graph

Source Code

libs/core/langchain_core/language_models/llms.py lines 292–1398

class BaseLLM(BaseLanguageModel[str], ABC):
    """Base LLM abstract interface.

    It should take in a prompt and return a string.
    """

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    @functools.cached_property
    def _serialized(self) -> dict[str, Any]:
        # self is always a Serializable object in this case, thus the result is
        # guaranteed to be a dict since dumps uses the default callback, which uses
        # obj.to_json which always returns TypedDict subclasses
        return cast("dict[str, Any]", dumpd(self))

    # --- Runnable methods ---

    @property
    @override
    def OutputType(self) -> type[str]:
        """Get the input type for this `Runnable`."""
        return str

    def _convert_input(self, model_input: LanguageModelInput) -> PromptValue:
        if isinstance(model_input, PromptValue):
            return model_input
        if isinstance(model_input, str):
            return StringPromptValue(text=model_input)
        if isinstance(model_input, Sequence):
            return ChatPromptValue(messages=convert_to_messages(model_input))
        msg = (
            f"Invalid input type {type(model_input)}. "
            "Must be a PromptValue, str, or list of BaseMessages."
        )
        raise ValueError(msg)

    def _get_ls_params(
        self,
        stop: list[str] | None = None,
        **kwargs: Any,
    ) -> LangSmithParams:
        """Get standard params for tracing."""
        # get default provider from class name
        default_provider = self.__class__.__name__
        default_provider = default_provider.removesuffix("LLM")
        default_provider = default_provider.lower()

        ls_params = LangSmithParams(ls_provider=default_provider, ls_model_type="llm")
        if stop:
            ls_params["ls_stop"] = stop

        # model
        if "model" in kwargs and isinstance(kwargs["model"], str):
            ls_params["ls_model_name"] = kwargs["model"]
        elif hasattr(self, "model") and isinstance(self.model, str):
            ls_params["ls_model_name"] = self.model
        elif hasattr(self, "model_name") and isinstance(self.model_name, str):
            ls_params["ls_model_name"] = self.model_name

        # temperature
        if "temperature" in kwargs and isinstance(kwargs["temperature"], float):
            ls_params["ls_temperature"] = kwargs["temperature"]
        elif hasattr(self, "temperature") and isinstance(self.temperature, float):
            ls_params["ls_temperature"] = self.temperature

        # max_tokens
        if "max_tokens" in kwargs and isinstance(kwargs["max_tokens"], int):
            ls_params["ls_max_tokens"] = kwargs["max_tokens"]
        elif hasattr(self, "max_tokens") and isinstance(self.max_tokens, int):
            ls_params["ls_max_tokens"] = self.max_tokens

        return ls_params

    @override
    def invoke(
        self,
        input: LanguageModelInput,
        config: RunnableConfig | None = None,
        *,