Create a 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],Type[List[long]],Type[List[str]],Type[List[int]], Type[List[float]]]]=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'
)

# Create a collection named quickstart with 4 vector dimensions,
# a vector data type of float (default),
# and a dotproduct (inner product) distance measure.
# Pre-define eight fields: name, weight, age, id, tags, numbers, bank_cards, and grades, with the data types str, float, int, dashvector.long, List[str], List[int], List[dashvector.long], and List[float] respectively.
# Set timeout to -1 to enable asynchronous mode for the create operation.
ret = client.create(
    name='quickstart',
    dimension=4,
    metric='dotproduct',
    dtype=float,
    # For semantic clarity in type annotations, DashVector defines the long type using the typing module. This lets you annotate large integers.
    fields_schema={
        'name': str, 'weight': float, 'age': int, 'id': dashvector.long,
        'tags': List[str],
        'numbers': List[int],
        'bank_cards': List[dashvector.long],
        'grades':  List[float]
        },
    timeout=-1
)


# Check if the collection was created successfully.
if ret:
    print('create collection success!')
# This is equivalent to the following code:
# from dashvector import DashVectorCode
# if ret.code == DashVectorCode.Success:
#     print('create collection success!')