操作步骤一：创建Milvus实例

具体操作，请参见创建实例。

操作步骤二：创建ECS

具体操作，请参见创建弹性云主机。

操作步骤三：连接Milvus实例

1. 本地使用Linux远程连接工具登录ECS。

   其中，Remote host为ECS绑定的弹性公网IP。

2. 输入创建ECS时设置的密码。

3. 安装客户端python-milvus-sdk。

   python3 -m pip install pymilvus==2.6.10

4. 连接Milvus实例。

   from pymilvus import MilvusClient
   
   client = MilvusClient(
       uri="http://${host}:${port}",
       token="${yourUserName}:${yourPassword}"
   )

    

5. 创建集合test_table, 并添加索引。

   schema = MilvusClient.create_schema(
       auto_id=False,
       enable_dynamic_field=True,
   )
   
   schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
   schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=5)
   schema.add_field(field_name="color", datatype=DataType.VARCHAR, max_length=512)
   
   index_params = client.prepare_index_params()
   index_params.add_index(
       field_name="my_id",
       index_type="AUTOINDEX"
   )
   index_params.add_index(
       field_name="vector", 
       index_type="AUTOINDEX",
       metric_type="COSINE"
   )
   client.create_collection(
       collection_name="test_table",
       schema=schema,
       index_params=index_params
   )

6. 向表中插入一条数据。

   data=[
       {"id": 0, "vector": [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592], "color": "pink_8682"},
       {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red_7025"},
       {"id": 2, "vector": [0.43742130801983836, -0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange_6781"},
       {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, -0.36981146090600725, -0.4860894583077995, 0.95791889146345], "color": "pink_9298"},
       {"id": 4, "vector": [0.4452349528804562, -0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red_4794"},
       {"id": 5, "vector": [0.985825131989184, -0.8144651566660419, 0.6299267002202009, 0.1206906911183383, -0.1446277761879955], "color": "yellow_4222"},
       {"id": 6, "vector": [0.8371977790571115, -0.015764369584852833, -0.31062937026679327, -0.562666951622192, -0.8984947637863987], "color": "red_9392"},
       {"id": 7, "vector": [-0.33445148015177995, -0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey_8510"},
       {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, -0.5890507376891594, -0.8650502298996872, -0.6140360785406336], "color": "white_9381"},
       {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, -0.3737913482606834, -0.06726932177492717, -0.6980531615588608], "color": "purple_4976"}
   ]
   
   res = client.insert(
       collection_name="test_db",
       data=data
   )

7. 加载集合。

   client.load_collection(
       collection_name="test_db"
   )
   
   res = client.get_load_state(
       collection_name="test_db"
   )
   
   print(res)

8. 相似性检索。

   query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
   res = client.search(
       collection_name="test_db",
       anns_field="vector",
       data=[query_vector],
       limit=3,
       search_params={"metric_type": "COSINE"}
   )
   
   for hits in res:
       for hit in hits:
           print(hit)

9. 删除集合。

   client.drop_collection(
       collection_name="test_db"
   )