engine_info = {
            "index_size": 10000,
            "retrieval_type": "IVFPQ",
            "retrieval_param": {"ncentroids": 2048, "nsubvector": 32},
        }
        fields = [
            vearch.GammaFieldInfo(fi["field"], type_dict[fi["type"]])
            for fi in field_list
        ]
        vector_field = vearch.GammaVectorInfo(
            name="text_embedding",
            type=vearch.dataType.VECTOR,
            is_index=True,
            dimension=dim,
            model_id="",
            store_type="MemoryOnly",
            store_param={"cache_size": 10000},
            has_source=False,
        )
        response_code = self.vearch.create_table(
            engine_info,
            name=self.using_table_name,
            fields=fields,
            vector_field=vector_field,
        )
        return response_code
    def _create_space(
        self,
        dim: int = 1024,
    ) -> int:
        """
        Create VectorStore space
        Args:
            dim:dimension of vector
        Return:
            code,0 failed for ,1 for success
        """
        space_config = {
            "name": self.using_table_name,
            "partition_num": 1,
            "replica_num": 1,
            "engine": {
                "name": "gamma",
                "index_size": 1,
                "retrieval_type": "FLAT",
                "retrieval_param": {
                    "metric_type": "L2",
                },
            },
            "properties": {
                "text": {
                    "type": "string",
                },