epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Document]:
        docs_and_scores = self.similarity_search_with_score_by_vector(
            embedding, k, radius, epsilon, timeout, grpc_metadata
        )
        docs = []
        for doc, _ in docs_and_scores:
            docs.append(doc)
        return docs
[docs]    def similarity_search_with_score_by_vector(
        self,
        embedding: List[float],
        k: int = 4,
        radius: float = -1.0,
        epsilon: float = 0.01,
        timeout: int = 3000000000,
        grpc_metadata: Optional[Any] = None,
        **kwargs: Any,
    ) -> List[Tuple[Document, float]]:
        try:
            from vald.v1.payload import payload_pb2
            from vald.v1.vald import search_pb2_grpc
        except ImportError:
            raise ValueError(
                "Could not import vald-client-python python package. "
                "Please install it with `pip install vald-client-python`."
            )
        channel = self._get_channel()
        # Depending on the network quality,
        # it is necessary to wait for ChannelConnectivity.READY.
        # _ = grpc.channel_ready_future(channel).result(timeout=10)
        stub = search_pb2_grpc.SearchStub(channel)
        cfg = payload_pb2.Search.Config(
            num=k, radius=radius, epsilon=epsilon, timeout=timeout
        )
        res = stub.Search(
            payload_pb2.Search.Request(vector=embedding, config=cfg),
            metadata=grpc_metadata,