_colored_text = get_colored_text(prompt.to_string(), "green")
            _text = "Prompt after formatting:\n" + _colored_text
            if run_manager:
                await run_manager.on_text(_text, end="\n", verbose=self.verbose)
            if "stop" in inputs and inputs["stop"] != stop:
                raise ValueError(
                    "If `stop` is present in any inputs, should be present in all."
                )
            prompts.append(prompt)
        return prompts, stop
[docs]    def apply(
        self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
    ) -> List[Dict[str, str]]:
        """Utilize the LLM generate method for speed gains."""
        callback_manager = CallbackManager.configure(
            callbacks, self.callbacks, self.verbose
        )
        run_manager = callback_manager.on_chain_start(
            dumpd(self),
            {"input_list": input_list},
        )
        try:
            response = self.generate(input_list, run_manager=run_manager)
        except (KeyboardInterrupt, Exception) as e:
            run_manager.on_chain_error(e)
            raise e
        outputs = self.create_outputs(response)
        run_manager.on_chain_end({"outputs": outputs})
        return outputs
[docs]    async def aapply(
        self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
    ) -> List[Dict[str, str]]:
        """Utilize the LLM generate method for speed gains."""
        callback_manager = AsyncCallbackManager.configure(
            callbacks, self.callbacks, self.verbose
        )
        run_manager = await callback_manager.on_chain_start(
            dumpd(self),
            {"input_list": input_list},
        )
        try:
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/chains/llm.html