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Multi modal

Evaluation modules.

MultiModalRetrieverEvaluator #

Bases: BaseRetrievalEvaluator

Retriever evaluator.

This module will evaluate a retriever using a set of metrics.

Parameters:

Name Type Description Default
metrics List[BaseRetrievalMetric]

Sequence of metrics to evaluate

 required
retriever BaseRetriever

Retriever to evaluate.

 required
node_postprocessors Optional[List[BaseNodePostprocessor]]

Post-processor to apply after retrieval.

 None
Source code in llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py 
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class MultiModalRetrieverEvaluator(BaseRetrievalEvaluator):
    """Retriever evaluator.

    This module will evaluate a retriever using a set of metrics.

    Args:
        metrics (List[BaseRetrievalMetric]): Sequence of metrics to evaluate
        retriever: Retriever to evaluate.
        node_postprocessors (Optional[List[BaseNodePostprocessor]]): Post-processor to apply after retrieval.

    """

    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:
        """Init params."""
        super().__init__(
            metrics=metrics,
            retriever=retriever,
            node_postprocessors=node_postprocessors,
            **kwargs,
        )

    async def _aget_retrieved_ids_texts(
        self, query: str, mode: RetrievalEvalMode = RetrievalEvalMode.TEXT
    ) -> Tuple[List[str], List[str]]:
        """Get retrieved ids."""
        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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