)
        result = eval_chain.evaluate_agent_trajectory(
            input=question,
            agent_trajectory=response["intermediate_steps"],
            prediction=response["output"],
            reference="Paris",
        )
        print(result["score"])
        # 0
    """  # noqa: E501
    agent_tools: Optional[List[BaseTool]] = None
    """A list of tools available to the agent."""
    eval_chain: LLMChain
    """The language model chain used for evaluation."""
    output_parser: TrajectoryOutputParser = Field(
        default_factory=TrajectoryOutputParser
    )
    """The output parser used to parse the output."""
    return_reasoning: bool = False  # :meta private:
    """DEPRECATED. Reasoning always returned."""
    class Config:
        """Configuration for the QAEvalChain."""
        extra = Extra.ignore
    @property
    def requires_reference(self) -> bool:
        """Whether this evaluator requires a reference label."""
        return False
    @property
    def _tools_description(self) -> str:
        """Get the description of the agent tools.
        Returns:
            str: The description of the agent tools.
        """
        if self.agent_tools is None:
            return ""
        return "\n\n".join(
            [
                f"""Tool {i}: {tool.name}
Description: {tool.description}"""
                for i, tool in enumerate(self.agent_tools, 1)
            ]
        )
[docs]    @staticmethod
    def get_agent_trajectory(
        steps: Union[str, Sequence[Tuple[AgentAction, str]]]
    ) -> str:
        """Get the agent trajectory as a formatted string.
        Args: