如何通过 Python SDK 创建一个新的 Collection

Client.create(    name: str,    dimension: int,    dtype: Union[Type[int], Type[float]]=float,    fields_schema: Optional[Dict[str, Union[Type[str], Type[int], Type[float], Type[bool], Type[long]]]]=None,    metric: str='cosine',    extra_params: Dict[str, Any]=None,    timeout: Optional[int]=None,    vectors: Union[None, VectorParam, Dict[str, VectorParam]] = None,    sparse_vectors: Union[None, Dict[str, VectorParam]] = None,) -> DashVectorResponse

import dashvector
client = dashvector.Client(    api_key='YOUR_API_KEY',    endpoint='YOUR_CLUSTER_ENDPOINT')
# 创建一个名称为quickstart、向量维度为4、# 向量数据类型为float（默认）、# 距离度量方式为dotproduct（内积）的Collection# 并预先定义四个Field，名称为name、weight、age、id，数据类型分别为str、float、int、dashvector.long# timeout为-1 ,开启create接口异步模式ret = client.create(    name='quickstart',    dimension=4,    metric='dotproduct',    dtype=float,    # 为保留类型注解的语义明确性，DashVector通过typing模块定义long类型，为需要显式标注大整数场景提供了类型标注支持    fields_schema={'name': str, 'weight': float, 'age': int, 'id': dashvector.long},    timeout=-1)

# 判断collection是否创建成功if ret:    print('create collection success!')# 等同于下列代码# from dashvector import DashVectorCode# if ret.code == DashVectorCode.Success:#     print('create collection success!')

ret = client.create(    'multi_vector_demo',    vectors={        "title": VectorParam(4),        "content": VectorParam(6, metric="euclidean"),    },    sparse_vectors={        "abstruct": VectorParam(metric="dotproduct"),        "keywords": VectorParam(metric="dotproduct"),        # 稀疏向量索引目前仅支持内积度量，dimension/dtype使用默认值无需设置    },    fields_schema={        'author': str,    })assert ret