def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        user_input = inputs[self.input_keys[0]]
        response = ""
        for i in range(self.max_iter):
            _run_manager.on_text(
                f"Current Response: {response}", color="blue", end="\n"
            )
            _input = {"user_input": user_input, "context": "", "response": response}
            tokens, log_probs = self.response_chain.generate_tokens_and_log_probs(
                _input, run_manager=_run_manager
            )
            low_confidence_spans = _low_confidence_spans(
                tokens,
                log_probs,
                self.min_prob,
                self.min_token_gap,
                self.num_pad_tokens,
            )
            initial_response = response.strip() + " " + "".join(tokens)
            if not low_confidence_spans:
                response = initial_response
                final_response, finished = self.output_parser.parse(response)
                if finished:
                    return {self.output_keys[0]: final_response}
                continue
            marginal, finished = self._do_retrieval(
                low_confidence_spans,
                _run_manager,
                user_input,
                response,
                initial_response,
            )
            response = response.strip() + " " + marginal
            if finished:
                break
        return {self.output_keys[0]: response}
[docs]    @classmethod
    def from_llm(
        cls, llm: BaseLanguageModel, max_generation_len: int = 32, **kwargs: Any