def _transform_stream_with_config(
        self,
        input: Iterator[Input],
        transformer: Union[
            Callable[[Iterator[Input]], Iterator[Output]],
            Callable[[Iterator[Input], CallbackManagerForChainRun], Iterator[Output]],
            Callable[
                [
                    Iterator[Input],
                    CallbackManagerForChainRun,
                    RunnableConfig,
                ],
                Iterator[Output],
            ],
        ],
        config: Optional[RunnableConfig],
        run_type: Optional[str] = None,
        **kwargs: Optional[Any],
    ) -> Iterator[Output]:
        """Helper method to transform an Iterator of Input values into an Iterator of
        Output values, with callbacks.
        Use this to implement `stream()` or `transform()` in Runnable subclasses."""
        # tee the input so we can iterate over it twice
        input_for_tracing, input_for_transform = tee(input, 2)
        # Start the input iterator to ensure the input runnable starts before this one
        final_input: Optional[Input] = next(input_for_tracing, None)
        final_input_supported = True
        final_output: Optional[Output] = None
        final_output_supported = True

        config = ensure_config(config)
        callback_manager = get_callback_manager_for_config(config)
        run_manager = callback_manager.on_chain_start(
            dumpd(self),
            {"input": ""},
            run_type=run_type,
            name=config.get("run_name") or self.get_name(),
        )
 

    def _transform_stream_with_config(
        self,
        input: Iterator[Input],
        transformer: Union[
            Callable[[Iterator[Input]], Iterator[Output]],
            Callable[[Iterator[Input], CallbackManagerForChainRun], Iterator[Output]],
            Callable[
                [
                    Iterator[Input],
                    CallbackManagerForChainRun,
                    RunnableConfig,
                ],
                Iterator[Output],
            ],
        ],
        config: Optional[RunnableConfig],
        run_type: Optional[str] = None,
        **kwargs: Optional[Any],
    ) -> Iterator[Output]:
        """Helper method to transform an Iterator of Input values into an Iterator of
        Output values, with callbacks.
        Use this to implement `stream()` or `transform()` in Runnable subclasses."""
        # tee the input so we can iterate over it twice
        input_for_tracing, input_for_transform = tee(input, 2)
        # Start the input iterator to ensure the input runnable starts before this one
        final_input: Optional[Input] = next(input_for_tracing, None)
        final_input_supported = True
        final_output: Optional[Output] = None
        final_output_supported = True

        config = ensure_config(config)
        callback_manager = get_callback_manager_for_config(config)
        run_manager = callback_manager.on_chain_start(
            dumpd(self),
            {"input": ""},
            run_type=run_type,
            name=config.get("run_name") or self.get_name(),
        )
 

def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output:
        from langchain_core.beta.runnables.context import config_with_context

        # setup callbacks and context
        config = config_with_context(ensure_config(config), self.steps)
        callback_manager = get_callback_manager_for_config(config)
        # start the root run
        run_manager = callback_manager.on_chain_start(
            dumpd(self), input, name=config.get("run_name") or self.get_name()
        )

        # invoke all steps in sequence
        try:
            for i, step in enumerate(self.steps):
                input = step.invoke(
                    input,
                    # mark each step as a child run
                    patch_config(
                        config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
                    ),
                )
        # finish the root run
        except BaseException as e:
            run_manager.on_chain_error(e)
            raise
        else:
            run_manager.on_chain_end(input)
            return cast(Output, input)

def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output:
        from langchain_core.beta.runnables.context import config_with_context

        # setup callbacks and context
        config = config_with_context(ensure_config(config), self.steps)
        callback_manager = get_callback_manager_for_config(config)
        # start the root run
        run_manager = callback_manager.on_chain_start(
            dumpd(self), input, name=config.get("run_name") or self.get_name()
        )

        # invoke all steps in sequence
        try:
            for i, step in enumerate(self.steps):
                input = step.invoke(
                    input,
                    # mark each step as a child run
                    patch_config(
                        config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
                    ),
                )
        # finish the root run
        except BaseException as e:
            run_manager.on_chain_error(e)
            raise
        else:
            run_manager.on_chain_end(input)
            return cast(Output, input)

    _inputs = InputsRag(
        {
            "question": GetQuestion(lambda x: x["question"]),
            "chat_history": FormatHistory(
                first=RunnableLambda(lambda x: x["history"]),
                last=RunnableLambda(_format_chat_history),
            ),
            "docs": RetrieveDocs(
                first=(_search_query | retriever),
                last=RunnableLambda(remove_embedding_from_documents),
            ),
            "error": ModerationChain(
                first=(
                    RunnableLambda(lambda x: x["question"])
                    | prompt_moderation_with_config
                ),
                last=RunnableLambda(lambda _: False),
            ),
        }
    )

    _inputs = InputsRag(
        {
            "question": GetQuestion(lambda x: x["question"]),
            "chat_history": FormatHistory(
                first=RunnableLambda(lambda x: x["history"]),
                last=RunnableLambda(_format_chat_history),
            ),
            "docs": RetrieveDocs(
                first=(_search_query | retriever),
                last=RunnableLambda(remove_embedding_from_documents),
            ),
            "error": ModerationChain(
                first=(
                    RunnableLambda(lambda x: x["question"])
                    | prompt_moderation_with_config
                ),
                last=RunnableLambda(lambda _: False),
            ),
        }
    )

    mychain = (
        _inputs
        | _answer
        | (
            response_moderation
            if response_moderation_with_config.moderation_config.filters
            else RunnablePassthrough()
        )
    )

    mychain = (
        _inputs
        | _answer
        | (
            response_moderation
            if response_moderation_with_config.moderation_config.filters
            else RunnablePassthrough()
        )
    )