used to parse the chain output into a score.
        Returns:
            TrajectoryEvalChain: The TrajectoryEvalChain object.
        """
        if not isinstance(llm, BaseChatModel):
            raise NotImplementedError(
                "Only chat models supported by the current trajectory eval"
            )
        if agent_tools:
            prompt = EVAL_CHAT_PROMPT
        else:
            prompt = TOOL_FREE_EVAL_CHAT_PROMPT
        eval_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(
            agent_tools=agent_tools,
            eval_chain=eval_chain,
            output_parser=output_parser or TrajectoryOutputParser(),
            **kwargs,
        )
    @property
    def input_keys(self) -> List[str]:
        """Get the input keys for the chain.
        Returns:
            List[str]: The input keys.
        """
        return ["question", "agent_trajectory", "answer", "reference"]
    @property
    def output_keys(self) -> List[str]:
        """Get the output keys for the chain.
        Returns:
            List[str]: The output keys.
        """
        return ["score", "reasoning"]
[docs]    def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
        """Validate and prep inputs."""
        if "reference" not in inputs:
            inputs["reference"] = self._format_reference(inputs.get("reference"))
        return super().prep_inputs(inputs)
    def _call(
        self,
        inputs: Dict[str, str],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Run the chain and generate the output.
        Args: