def relevance(
        self,
        prompt: str,
        response: str,
        criteria: Optional[str] = None,
        examples: Optional[List[str]] = None,
        min_score_val: int = 0,
        max_score_val: int = 3,
        temperature: float = 0.0,
    ) -> float:
        """
        Uses chat completion model. A function that completes a
        template to check the relevance of the response to a prompt.

        Example:
            ```python
            feedback = Feedback(provider.relevance).on_input_output()
            ```

        Usage on RAG Contexts:
            ```python
            feedback = Feedback(provider.relevance).on_input().on(
                TruLlama.select_source_nodes().node.text # See note below
            ).aggregate(np.mean)
            ```

        Args:
            prompt (str): A text prompt to an agent.
            response (str): The agent's response to the prompt.
            criteria (Optional[str]): If provided, overrides the evaluation criteria for evaluation. Defaults to None.
            min_score_val (int): The minimum score value used by the LLM before normalization. Defaults to 0.
            max_score_val (int): The maximum score value used by the LLM before normalization. Defaults to 3.
            temperature (float): The temperature for the LLM response, which might have impact on the confidence level of the evaluation. Defaults to 0.0.

        Returns:
            float: A value between 0 and 1. 0 being "not relevant" and 1 being "relevant".
        """

        output_space = self._determine_output_space(
            min_score_val, max_score_val
        )

        system_prompt = (
            feedback_v2.PromptResponseRelevance.generate_system_prompt(
                min_score=min_score_val,
                max_score=max_score_val,
                criteria=criteria,
                examples=examples,
                output_space=output_space,
            )
        )

        return self.generate_score(
            system_prompt=system_prompt,
            user_prompt=str.format(
                feedback_prompts.ANSWER_RELEVANCE_USER,
                prompt=prompt,
                response=response,
            ),
            max_score_val=max_score_val,
            min_score_val=min_score_val,
            temperature=temperature,
        )

        Uses chat completion model. A function that completes a
        template to check the relevance of the response to a prompt.

        Example:
            ```python
            feedback = Feedback(provider.relevance).on_input_output()
            ```

        Usage on RAG Contexts:
            ```python
            feedback = Feedback(provider.relevance).on_input().on(
                TruLlama.select_source_nodes().node.text # See note below
            ).aggregate(np.mean)
            ```

        Args:
            prompt (str): A text prompt to an agent.
            response (str): The agent's response to the prompt.
            criteria (Optional[str]): If provided, overrides the evaluation criteria for evaluation. Defaults to None.
            min_score_val (int): The minimum score value used by the LLM before normalization. Defaults to 0.
            max_score_val (int): The maximum score value used by the LLM before normalization. Defaults to 3.
            temperature (float): The temperature for the LLM response, which might have impact on the confidence level of the evaluation. Defaults to 0.0.

        Returns:
            float: A value between 0 and 1. 0 being "not relevant" and 1 being "relevant".
        
src/feedback/trulens/feedback/llm_provider.py