return [self.output_key]
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        all_true = False
        count = 0
        output = None
        original_input = inputs[self.input_key]
        chain_input = original_input
        while not all_true and count < self.max_checks:
            output = self.sequential_chain(
                {"summary": chain_input}, callbacks=_run_manager.get_child()
            )
            count += 1
            if output["all_true"].strip() == "True":
                break
            if self.verbose:
                print(output["revised_summary"])
            chain_input = output["revised_summary"]
        if not output:
            raise ValueError("No output from chain")
        return {self.output_key: output["revised_summary"].strip()}
    @property
    def _chain_type(self) -> str:
        return "llm_summarization_checker_chain"
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        create_assertions_prompt: PromptTemplate = CREATE_ASSERTIONS_PROMPT,
        check_assertions_prompt: PromptTemplate = CHECK_ASSERTIONS_PROMPT,
        revised_summary_prompt: PromptTemplate = REVISED_SUMMARY_PROMPT,
        are_all_true_prompt: PromptTemplate = ARE_ALL_TRUE_PROMPT,
        verbose: bool = False,
        **kwargs: Any,
    ) -> LLMSummarizationCheckerChain:
        chain = _load_sequential_chain(
            llm,
            create_assertions_prompt,
            check_assertions_prompt,