示例:购买并连接Milvus实例 本章节以Linux系统为例,介绍从购买到内网连接Milvus实例的操作步骤。 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pythonmilvussdk。 plaintext python3 m pip install pymilvus2.6.10 4. 连接Milvus实例。 plaintext from pymilvus import MilvusClient client MilvusClient( uri" token"${yourUserName}:${yourPassword}" ) 5. 创建集合testtable, 并添加索引。 plaintext schema MilvusClient.createschema( autoidFalse, enabledynamicfieldTrue, ) schema.addfield(fieldname"id", datatypeDataType.INT64, isprimaryTrue) schema.addfield(fieldname"vector", datatypeDataType.FLOATVECTOR, dim5) schema.addfield(fieldname"color", datatypeDataType.VARCHAR, maxlength512) indexparams client.prepareindexparams() indexparams.addindex( fieldname"myid", indextype"AUTOINDEX" ) indexparams.addindex( fieldname"vector", indextype"AUTOINDEX", metrictype"COSINE" ) client.createcollection( collectionname"testtable", schemaschema, indexparamsindexparams ) 6. 向表中插入一条数据。 plaintext data[ {"id": 0, "vector": [0.3580376395471989, 0.6023495712049978, 0.18414012509913835, 0.26286205330961354, 0.9029438446296592], "color": "pink8682"}, {"id": 1, "vector": [0.19886812562848388, 0.06023560599112088, 0.6976963061752597, 0.2614474506242501, 0.838729485096104], "color": "red7025"}, {"id": 2, "vector": [0.43742130801983836, 0.5597502546264526, 0.6457887650909682, 0.7894058910881185, 0.20785793220625592], "color": "orange6781"}, {"id": 3, "vector": [0.3172005263489739, 0.9719044792798428, 0.36981146090600725, 0.4860894583077995, 0.95791889146345], "color": "pink9298"}, {"id": 4, "vector": [0.4452349528804562, 0.8757026943054742, 0.8220779437047674, 0.46406290649483184, 0.30337481143159106], "color": "red4794"}, {"id": 5, "vector": [0.985825131989184, 0.8144651566660419, 0.6299267002202009, 0.1206906911183383, 0.1446277761879955], "color": "yellow4222"}, {"id": 6, "vector": [0.8371977790571115, 0.015764369584852833, 0.31062937026679327, 0.562666951622192, 0.8984947637863987], "color": "red9392"}, {"id": 7, "vector": [0.33445148015177995, 0.2567135004164067, 0.8987539745369246, 0.9402995886420709, 0.5378064918413052], "color": "grey8510"}, {"id": 8, "vector": [0.39524717779832685, 0.4000257286739164, 0.5890507376891594, 0.8650502298996872, 0.6140360785406336], "color": "white9381"}, {"id": 9, "vector": [0.5718280481994695, 0.24070317428066512, 0.3737913482606834, 0.06726932177492717, 0.6980531615588608], "color": "purple4976"} ] res client.insert( collectionname"testdb", datadata ) 7. 加载集合。 plaintext client.loadcollection( collectionname"testdb" ) res client.getloadstate( collectionname"testdb" ) print(res) 8. 相似性检索。 plaintext queryvector [0.3580376395471989, 0.6023495712049978, 0.18414012509913835, 0.26286205330961354, 0.9029438446296592] res client.search( collectionname"testdb", annsfield"vector", data[queryvector], limit3, searchparams{"metrictype": "COSINE"} ) for hits in res: for hit in hits: print(hit) 9. 删除集合。 plaintext client.dropcollection( collectionname"testdb" )