_acall() — langchain Function Reference

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

Function python AgentOrchestration ClassicChains calls 1

Entity Profile

AgentOrchestration→ ClassicChains→ _acall() — langchain Function Reference

Dependency Diagram

graph TD
  22a1a9dc_c6e0_c618_8717_53ecc649af29["_acall()"]
  2f364d76_a69d_403d_0a63_04792fe626bb["BaseCombineDocumentsChain"]
  22a1a9dc_c6e0_c618_8717_53ecc649af29 -->|defined in| 2f364d76_a69d_403d_0a63_04792fe626bb
  ee1c6299_1a3e_95ec_7e69_f8653c5fb3f0["acombine_docs()"]
  22a1a9dc_c6e0_c618_8717_53ecc649af29 -->|calls| ee1c6299_1a3e_95ec_7e69_f8653c5fb3f0
  style 22a1a9dc_c6e0_c618_8717_53ecc649af29 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/chains/combine_documents/base.py lines 149–165

    async def _acall(
        self,
        inputs: dict[str, list[Document]],
        run_manager: AsyncCallbackManagerForChainRun | None = None,
    ) -> dict[str, str]:
        """Prepare inputs, call combine docs, prepare outputs."""
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        docs = inputs[self.input_key]
        # Other keys are assumed to be needed for LLM prediction
        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
        output, extra_return_dict = await self.acombine_docs(
            docs,
            callbacks=_run_manager.get_child(),
            **other_keys,
        )
        extra_return_dict[self.output_key] = output
        return extra_return_dict