if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """Run when LLM ends running."""
        self.step += 1
        self.llm_ends += 1
        self.ends += 1
        resp = self._init_resp()
        resp.update({"action": "on_llm_end"})
        resp.update(flatten_dict(response.llm_output or {}))
        resp.update(self.get_custom_callback_meta())
        for generations in response.generations:
            for generation in generations:
                generation_resp = deepcopy(resp)
                generation_resp.update(flatten_dict(generation.dict()))
                generation_resp.update(self.analyze_text(generation.text))
                self.on_llm_end_records.append(generation_resp)
                self.action_records.append(generation_resp)
                if self.stream_logs:
                    self.logger.report_text(generation_resp)
[docs]    def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
        """Run when LLM errors."""
        self.step += 1
        self.errors += 1
[docs]    def on_chain_start(
        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
    ) -> None:
        """Run when chain starts running."""
        self.step += 1
        self.chain_starts += 1
        self.starts += 1
        resp = self._init_resp()
        resp.update({"action": "on_chain_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.get_custom_callback_meta())
        chain_input = inputs["input"]
        if isinstance(chain_input, str):
            input_resp = deepcopy(resp)