Zilliz Cloud

from xparse_client import MilvusDestination

destination = MilvusDestination(
    db_path='https://xxxxxxx.serverless.xxxxxxx.cloud.zilliz.com.cn',  # Zilliz Cloud 连接地址
    collection_name='my_collection',      # 集合名称
    dimension=1024,                      # 向量维度
    api_key='your-zilliz-api-key'        # Zilliz Cloud API Key
)

from xparse_client import MilvusDestination

destination = MilvusDestination(
    db_path='https://xxxxxxx.serverless.xxxxxxx.cloud.zilliz.com.cn',
    collection_name='my_collection',
    dimension=1024,
    token='your-zilliz-token'            # 使用 token 替代 api_key
)

from xparse_client import MilvusDestination, Pipeline

destination = MilvusDestination(
    db_path='https://xxxxxxx.serverless.xxxxxxx.cloud.zilliz.com.cn',
    collection_name='documents',
    dimension=1024,
    api_key='your-zilliz-api-key'
)

# ... 其他配置

pipeline = Pipeline(
    source=source,
    destination=destination,
    # ...其他配置
)
pipeline.run()

from pymilvus import MilvusClient

client = MilvusClient(
    uri='https://xxxxxxx.serverless.xxxxxxx.cloud.zilliz.com.cn',
    token='your-zilliz-api-key'  # 或 token
)

query_vector = [0.1, 0.2, 0.3, ...]  # 1024 维向量

results = client.search(
    collection_name='documents',
    data=[query_vector],
    limit=5,
    search_params={"metric_type": "COSINE", "params": {"nprobe": 10}},
    output_fields=['text', 'metadata']
)

for result in results[0]:
    print(f"相似度: {1 - result['distance']:.4f}")
    print(f"文本: {result['entity']['text'][:100]}...")