'commit'

AI/WxMini/Milvus/T1_create_collection.py

@@ -1,12 +1,8 @@
 from pymilvus import FieldSchema, DataType, utility
-from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
-from WxMini.Milvus.Utils.MilvusConnectionPool import *
-from WxMini.Milvus.Config.MulvusConfig import *
 
-from pymilvus import FieldSchema, DataType, utility
+from WxMini.Milvus.Config.MulvusConfig import *
 from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
 from WxMini.Milvus.Utils.MilvusConnectionPool import *
-from WxMini.Milvus.Config.MulvusConfig import *
 
 # 1. 使用连接池管理 Milvus 连接
 milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)

AI/WxMini/Milvus/T3_insert_data.py

@@ -4,12 +4,6 @@ from WxMini.Milvus.Config.MulvusConfig import *
 from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
 from WxMini.Milvus.Utils.MilvusConnectionPool import *
 
-import random
-
-from WxMini.Milvus.Config.MulvusConfig import *
-from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
-from WxMini.Milvus.Utils.MilvusConnectionPool import *
-
 # 1. 使用连接池管理 Milvus 连接
 milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
 

AI/WxMini/Milvus/T5_search_near_word.py

@@ -29,7 +29,7 @@ results = collection_manager.search(current_embedding, search_params, limit=2)
 end_time = time.time()
 
 # 7. 输出查询结果
-print("当前对话的嵌入向量:", current_embedding)
+#print("当前对话的嵌入向量:", current_embedding)
 print("最相关的历史对话:")
 if results:
     for hits in results: