abatch() — langchain Function Reference

Architecture documentation for the abatch() function in base.py from the langchain codebase.

Function python LangChainCore LanguageModelBase calls 1

Entity Profile

LangChainCore→ LanguageModelBase→ abatch() — langchain Function Reference

Dependency Diagram

graph TD
  1829ff07_b1a8_905c_d8f2_b6e368c51f36["abatch()"]
  74f697e6_2555_becc_640e_6f41ea707eba["_ConfigurableModel"]
  1829ff07_b1a8_905c_d8f2_b6e368c51f36 -->|defined in| 74f697e6_2555_becc_640e_6f41ea707eba
  4d79fd76_22aa_8160_64a3_c62bd0bfe875["_model()"]
  1829ff07_b1a8_905c_d8f2_b6e368c51f36 -->|calls| 4d79fd76_22aa_8160_64a3_c62bd0bfe875
  style 1829ff07_b1a8_905c_d8f2_b6e368c51f36 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain_v1/langchain/chat_models/base.py lines 775–801

    async def abatch(
        self,
        inputs: list[LanguageModelInput],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Any]:
        config = config or None
        # If <= 1 config use the underlying models batch implementation.
        if config is None or isinstance(config, dict) or len(config) <= 1:
            if isinstance(config, list):
                config = config[0]
            return await self._model(config).abatch(
                inputs,
                config=config,
                return_exceptions=return_exceptions,
                **kwargs,
            )
        # If multiple configs default to Runnable.batch which uses executor to invoke
        # in parallel.
        return await super().abatch(
            inputs,
            config=config,
            return_exceptions=return_exceptions,
            **kwargs,
        )