get_input_schema() — langchain Function Reference

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

Function python LangChainCore Runnables calls 1

Entity Profile

LangChainCore→ Runnables→ get_input_schema() — langchain Function Reference

Dependency Diagram

graph TD
  ffe070be_ec88_fa83_4f8e_d29669becf89["get_input_schema()"]
  8e601be2_5eac_8426_f30d_f1ed37138bd8["RunnableGenerator"]
  ffe070be_ec88_fa83_4f8e_d29669becf89 -->|defined in| 8e601be2_5eac_8426_f30d_f1ed37138bd8
  255c479b_b9fa_44d8_4de5_2562051e06b5["get_name()"]
  ffe070be_ec88_fa83_4f8e_d29669becf89 -->|calls| 255c479b_b9fa_44d8_4de5_2562051e06b5
  style ffe070be_ec88_fa83_4f8e_d29669becf89 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/runnables/base.py lines 4244–4267

    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        # Override the default implementation.
        # For a runnable generator, we need to bring to provide the
        # module of the underlying function when creating the model.
        root_type = self.InputType

        func = getattr(self, "_transform", None) or self._atransform
        module = getattr(func, "__module__", None)

        if (
            inspect.isclass(root_type)
            and not isinstance(root_type, GenericAlias)
            and issubclass(root_type, BaseModel)
        ):
            return root_type

        return create_model_v2(
            self.get_name("Input"),
            root=root_type,
            # To create the schema, we need to provide the module
            # where the underlying function is defined.
            # This allows pydantic to resolve type annotations appropriately.
            module_name=module,
        )