Faithfullness

Evaluation modules.

FaithfulnessEvaluator

Bases: BaseEvaluator

Faithfulness evaluator.

Evaluates whether a response is faithful to the contexts (i.e. whether the response is supported by the contexts or hallucinated.)

This evaluator only considers the response string and the list of context strings.

Parameters:

Name	Type	Description	Default
service_context(Optional[ServiceContext])		The service context to use for evaluation.	required
raise_error(bool)		Whether to raise an error when the response is invalid. Defaults to False.	required
eval_template(Optional[Union[str,	BasePromptTemplate]]	The template to use for evaluation.	required
refine_template(Optional[Union[str,	BasePromptTemplate]]	The template to use for refining the evaluation.	required

Source code in llama-index-core/llama_index/core/evaluation/faithfulness.py

class FaithfulnessEvaluator(BaseEvaluator):
    """Faithfulness evaluator.

    Evaluates whether a response is faithful to the contexts
    (i.e. whether the response is supported by the contexts or hallucinated.)

    This evaluator only considers the response string and the list of context strings.

    Args:
        service_context(Optional[ServiceContext]):
            The service context to use for evaluation.
        raise_error(bool): Whether to raise an error when the response is invalid.
            Defaults to False.
        eval_template(Optional[Union[str, BasePromptTemplate]]):
            The template to use for evaluation.
        refine_template(Optional[Union[str, BasePromptTemplate]]):
            The template to use for refining the evaluation.
    """

    def __init__(
        self,
        llm: Optional[LLM] = None,
        raise_error: bool = False,
        eval_template: Optional[Union[str, BasePromptTemplate]] = None,
        refine_template: Optional[Union[str, BasePromptTemplate]] = None,
        # deprecated
        service_context: Optional[ServiceContext] = None,
    ) -> None:
        """Init params."""
        self._llm = llm or llm_from_settings_or_context(Settings, service_context)
        self._raise_error = raise_error

        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._refine_template: BasePromptTemplate
        if isinstance(refine_template, str):
            self._refine_template = PromptTemplate(refine_template)
        else:
            self._refine_template = refine_template or DEFAULT_REFINE_TEMPLATE

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

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

    async def aevaluate(
        self,
        query: str | None = None,
        response: str | None = None,
        contexts: Sequence[str] | None = None,
        sleep_time_in_seconds: int = 0,
        **kwargs: Any,
    ) -> EvaluationResult:
        """Evaluate whether the response is faithful to the contexts."""
        del query  # Unused
        del kwargs  # Unused

        await asyncio.sleep(sleep_time_in_seconds)

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

        docs = [Document(text=context) for context in contexts]
        index = SummaryIndex.from_documents(docs)

        query_engine = index.as_query_engine(
            llm=self._llm,
            text_qa_template=self._eval_template,
            refine_template=self._refine_template,
        )
        response_obj = await query_engine.aquery(response)

        raw_response_txt = str(response_obj)

        if "yes" in raw_response_txt.lower():
            passing = True
        else:
            passing = False
            if self._raise_error:
                raise ValueError("The response is invalid")

        return EvaluationResult(
            response=response,
            contexts=contexts,
            passing=passing,
            score=1.0 if passing else 0.0,
            feedback=raw_response_txt,
        )

aevaluate async

aevaluate(query: str | None = None, response: str | None = None, contexts: Sequence[str] | None = None, sleep_time_in_seconds: int = 0, **kwargs: Any) -> EvaluationResult

Evaluate whether the response is faithful to the contexts.

Source code in llama-index-core/llama_index/core/evaluation/faithfulness.py

async def aevaluate(
    self,
    query: str | None = None,
    response: str | None = None,
    contexts: Sequence[str] | None = None,
    sleep_time_in_seconds: int = 0,
    **kwargs: Any,
) -> EvaluationResult:
    """Evaluate whether the response is faithful to the contexts."""
    del query  # Unused
    del kwargs  # Unused

    await asyncio.sleep(sleep_time_in_seconds)

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

    docs = [Document(text=context) for context in contexts]
    index = SummaryIndex.from_documents(docs)

    query_engine = index.as_query_engine(
        llm=self._llm,
        text_qa_template=self._eval_template,
        refine_template=self._refine_template,
    )
    response_obj = await query_engine.aquery(response)

    raw_response_txt = str(response_obj)

    if "yes" in raw_response_txt.lower():
        passing = True
    else:
        passing = False
        if self._raise_error:
            raise ValueError("The response is invalid")

    return EvaluationResult(
        response=response,
        contexts=contexts,
        passing=passing,
        score=1.0 if passing else 0.0,
        feedback=raw_response_txt,
    )