插入或更新Doc

import dashvector
from dashvector import Doc
import numpy as np

client = dashvector.Client(
    api_key='YOUR_API_KEY',
    endpoint='YOUR_CLUSTER_ENDPOINT'
)
collection = client.get(name='quickstart')

# 通過Doc對象upsert
ret = collection.upsert(
    Doc(
        id='1',
        vector=[0.1, 0.2, 0.3, 0.4]
    )
)
# 判斷upsert是否成功
assert ret

# 簡化形式：通過Tuple upsert
ret = collection.upsert(
    ('2', [0.1, 0.1, 0.1, 0.1])               # (id, vector)
)

# 通過Doc對象upsert
ret = collection.upsert(
    Doc(vector=[0.1, 0.2, 0.3, 0.4])
)
# 簡化形式：通過Tuple upsert
ret = collection.upsert(
    ([0.1, 0.1, 0.1, 0.1],)          
)

# upsert單條資料，並設定Fields Value
ret = collection.upsert(
    Doc(
        id='3',
        vector=np.random.rand(4),
        fields={
            # 設定建立Collection時預定義的Fileds Value
            'name': 'zhangsan', 'weight':70.0, 'age':30, 
            # 設定Schema-Free的Field & Value
            'anykey1': 'str-value', 'anykey2': 1,
            'anykey3': True, 'anykey4': 3.1415926
        }
    )
)

# upsert單條資料，並設定Fields Value
ret = collection.upsert(
    ('4', np.random.rand(4), {'foo': 'bar'})  # (id, vector, fields)
)

# 通過Doc對象，批量upsert 10條資料
ret = collection.upsert(
    [
        Doc(id=str(i+5), vector=np.random.rand(4)) for i in range(10)
    ]
)

# 簡化形式：通過Tuple，批量upsert 3條資料
ret = collection.upsert(
    [
        ('15', [0.2,0.7,0.8,1.3], {'age': 20}),
        ('16', [0.3,0.6,0.9,1.2], {'age': 30}),
        ('17', [0.4,0.5,1.0,1.1], {'age': 40})
    ]                                         # List[(id, vector, fields)]
)

# 判斷批量upsert是否成功
assert ret

# 非同步批量upsert 10條資料
ret_funture = collection.upsert(
    [
        Doc(id=str(i+18), vector=np.random.rand(4), fields={'name': 'foo' + str(i)}) for i in range(10)
    ],
    async_req=True
)
# 等待並擷取非同步upsert結果
ret = ret_funture.get()

ret = collection.upsert(
    Doc(
        id='28',
        vector=[0.1, 0.2, 0.3, 0.4],
        sparse_vector={1:0.4, 10000:0.6, 222222:0.8}
    )
)