DashVector:Crie uma coleção

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',
    timeout: Optional[int] = None
) -> DashVectorResponse

import dashvector

client = dashvector.Client(
    api_key='YOUR_API_KEY',
    endpoint='YOUR_CLUSTER_ENDPOINT'
)

# Create a collection and set its name to quickstart, the number of vector dimensions to 4, 
# vector data type to the default value of float, 
# and distance metric to dotproduct.
# Predefine the name, weight, age and id fields, and set their data types to str, float, int and dashvector.long respectively.
# Set timeout to -1 to enable the asynchronous mode for the create method.
ret = client.create(
    name='quickstart', 
    dimension=4, 
    metric='dotproduct', 
    dtype=float,
    # To preserve the semantic clarity of type annotations,
    # DashVector defines the Long type through the typing module,
    # which provides type annotation support for scenarios where large integers need to be explicitly annotated
    fields_schema={'name': str, 'weight': float, 'age': int, 'id': dashvector.long},
    timeout=-1
)

# Check whether the collection is successfully created.
if ret:
    print('create collection success!')
# You can also use the following code to check whether the collection is successfully created.
# from dashvector import DashVectorCode
# if ret.code == DashVectorCode.Success:
#     print('create collection success!')