Bases: BaseRetrievalEvaluator
检索器评估器。
该模块将使用一组指标评估检索器。
Parameters:
Source code in llama_index/core/evaluation/retrieval/evaluator.py
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129 | class MultiModalRetrieverEvaluator(BaseRetrievalEvaluator):
"""检索器评估器。
该模块将使用一组指标评估检索器。
Args:
metrics (List[BaseRetrievalMetric]): 要评估的指标序列
retriever: 要评估的检索器。
node_postprocessors (Optional[List[BaseNodePostprocessor]]): 检索后要应用的后处理器。"""
retriever: BaseRetriever = Field(..., description="Retriever to evaluate")
node_postprocessors: Optional[List[BaseNodePostprocessor]] = Field(
default=None, description="Optional post-processor"
)
def __init__(
self,
metrics: Sequence[BaseRetrievalMetric],
retriever: BaseRetriever,
node_postprocessors: Optional[List[BaseNodePostprocessor]] = None,
**kwargs: Any,
) -> None:
"""初始化参数。"""
super().__init__(
metrics=metrics,
retriever=retriever,
node_postprocessors=node_postprocessors,
**kwargs,
)
async def _aget_retrieved_ids_and_texts(
self, query: str, mode: RetrievalEvalMode = RetrievalEvalMode.TEXT
) -> Tuple[List[str], List[str]]:
"""获取检索到的id。"""
retrieved_nodes = await self.retriever.aretrieve(query)
image_nodes: List[ImageNode] = []
text_nodes: List[TextNode] = []
if self.node_postprocessors:
for node_postprocessor in self.node_postprocessors:
retrieved_nodes = node_postprocessor.postprocess_nodes(
retrieved_nodes, query_str=query
)
for scored_node in retrieved_nodes:
node = scored_node.node
if isinstance(node, ImageNode):
image_nodes.append(node)
if node.text:
text_nodes.append(node)
if mode == "text":
return (
[node.node_id for node in text_nodes],
[node.text for node in text_nodes],
)
elif mode == "image":
return (
[node.node_id for node in image_nodes],
[node.text for node in image_nodes],
)
else:
raise ValueError("Unsupported mode.")
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