langchain/callbacks/mlflow_callback.py/MlflowCallbackHandler/on_llm_start
class MlflowCallbackHandler:    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        """Run when LLM starts."""
        self.metrics["step"] += 1
        self.metrics["llm_starts"] += 1
        self.metrics["starts"] += 1

        llm_starts = self.metrics["llm_starts"]

        resp: Dict[str, Any] = {}
        resp.update({"action": "on_llm_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.metrics)

        self.mlflg.metrics(self.metrics, step=self.metrics["step"])

        for idx, prompt in enumerate(prompts):
            prompt_resp = deepcopy(resp)
            prompt_resp["prompt"] = prompt
            self.records["on_llm_start_records"].append(prompt_resp)
            self.records["action_records"].append(prompt_resp)
            self.mlflg.jsonf(prompt_resp, f"llm_start_{llm_starts}_prompt_{idx}")