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141 | class RelevancyEvaluator(BaseEvaluator):
"""
Relenvancy evaluator.
Evaluates the relevancy of retrieved contexts and response to a query.
This evaluator considers the query string, retrieved contexts, and response string.
Args:
raise_error(Optional[bool]):
Whether to raise an error if 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 refinement.
"""
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,
) -> None:
"""Init params."""
self._llm = llm or Settings.llm
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 contexts and response are relevant to the query."""
del kwargs # Unused
if query is None or contexts is None or response is None:
raise ValueError("query, contexts, and response must be provided")
docs = [Document(text=context) for context in contexts]
index = SummaryIndex.from_documents(docs)
query_response = f"Question: {query}\nResponse: {response}"
await asyncio.sleep(sleep_time_in_seconds)
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(query_response)
raw_response_txt = str(response_obj)
if "yes" in raw_response_txt.lower():
passing = True
else:
if self._raise_error:
raise ValueError("The response is invalid")
passing = False
return EvaluationResult(
query=query,
response=response,
passing=passing,
score=1.0 if passing else 0.0,
feedback=raw_response_txt,
contexts=contexts,
)
|
