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Correctness

Evaluation modules.

CorrectnessEvaluator #

Bases: BaseEvaluator

Correctness evaluator.

Evaluates the correctness of a question answering system. This evaluator depends on reference answer to be provided, in addition to the query string and response string.

It outputs a score between 1 and 5, where 1 is the worst and 5 is the best, along with a reasoning for the score. Passing is defined as a score greater than or equal to the given threshold.

Parameters:

Name Type Description Default
service_context Optional[ServiceContext]

Service context.

 None
eval_template Optional[Union[BasePromptTemplate, str]]

Template for the evaluation prompt.

 None
score_threshold float

Numerical threshold for passing the evaluation, defaults to 4.0.

 4.0
Source code in llama-index-core/llama_index/core/evaluation/correctness.py 
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class CorrectnessEvaluator(BaseEvaluator):
    """Correctness evaluator.

    Evaluates the correctness of a question answering system.
    This evaluator depends on `reference` answer to be provided, in addition to the
    query string and response string.

    It outputs a score between 1 and 5, where 1 is the worst and 5 is the best,
    along with a reasoning for the score.
    Passing is defined as a score greater than or equal to the given threshold.

    Args:
        service_context (Optional[ServiceContext]): Service context.
        eval_template (Optional[Union[BasePromptTemplate, str]]):
            Template for the evaluation prompt.
        score_threshold (float): Numerical threshold for passing the evaluation,
            defaults to 4.0.
    """

    def __init__(
        self,
        llm: Optional[LLM] = None,
        eval_template: Optional[Union[BasePromptTemplate, str]] = None,
        score_threshold: float = 4.0,
        # deprecated
        service_context: Optional[ServiceContext] = None,
        parser_function: Callable[
            [str], Tuple[Optional[float], Optional[str]]
        ] = default_parser,
    ) -> None:
        self._llm = llm or llm_from_settings_or_context(Settings, service_context)

        self._eval_template: BasePromptTemplate
        if isinstance(eval_template, str):
            self._eval_template = PromptTemplate(eval_template)
        else:
            self._eval_template = eval_template or DEFAULT_EVAL_TEMPLATE

        self._score_threshold = score_threshold
        self.parser_function = parser_function

    def _get_prompts(self) -> PromptDictType:
        """Get prompts."""
        return {
            "eval_template": self._eval_template,
        }

    def _update_prompts(self, prompts: PromptDictType) -> None:
        """Update prompts."""
        if "eval_template" in prompts:
            self._eval_template = prompts["eval_template"]

    async def aevaluate(
        self,
        query: Optional[str] = None,
        response: Optional[str] = None,
        contexts: Optional[Sequence[str]] = None,
        reference: Optional[str] = None,
        sleep_time_in_seconds: int = 0,
        **kwargs: Any,
    ) -> EvaluationResult:
        del kwargs  # Unused
        del contexts  # Unused

        await asyncio.sleep(sleep_time_in_seconds)

        if query is None or response is None:
            raise ValueError("query, and response must be provided")

        eval_response = await self._llm.apredict(
            prompt=self._eval_template,
            query=query,
            generated_answer=response,
            reference_answer=reference or "(NO REFERENCE ANSWER SUPPLIED)",
        )

        # Use the parser function
        score, reasoning = self.parser_function(eval_response)

        return EvaluationResult(
            query=query,
            response=response,
            passing=score >= self._score_threshold if score is not None else None,
            score=score,
            feedback=reasoning,
        )