Returns:
            List of Tuples of (doc, similarity_score)
        """
        return self.similarity_search_with_score(query, k, **kwargs)
    @classmethod
    def _build_payloads(
        cls,
        texts: Iterable[str],
        metadatas: Optional[List[dict]],
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> List[dict]:
        payloads = []
        for i, text in enumerate(texts):
            if text is None:
                raise ValueError(
                    "At least one of the texts is None. Please remove it before "
                    "calling .from_texts or .add_texts on Qdrant instance."
                )
            metadata = metadatas[i] if metadatas is not None else None
            payloads.append(
                {
                    content_payload_key: text,
                    metadata_payload_key: metadata,
                }
            )
        return payloads
    @classmethod
    def _document_from_scored_point(
        cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        return Document(
            page_content=scored_point.payload.get(content_payload_key),
            metadata=scored_point.payload.get(metadata_payload_key) or {},
        )
    @classmethod
    def _document_from_scored_point_grpc(
        cls,
        scored_point: Any,
        content_payload_key: str,
        metadata_payload_key: str,
    ) -> Document:
        from qdrant_client.conversions.conversion import grpc_to_payload
        payload = grpc_to_payload(scored_point.payload)
        return Document(
            page_content=payload[content_payload_key],
            metadata=payload.get(metadata_payload_key) or {},