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49 lines
1.7 KiB
49 lines
1.7 KiB
"""
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pip install pymilvus gensim
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"""
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from pymilvus import FieldSchema, DataType, utility
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from Config.Config import MS_HOST, MS_PORT, MS_MAX_CONNECTIONS, MS_COLLECTION_NAME, MS_DIMENSION
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from Backup.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
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# 1. 使用连接池管理 Milvus 连接
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milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
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# 2. 从连接池中获取一个连接
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connection = milvus_pool.get_connection()
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# 3. 初始化集合管理器
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collection_name = MS_COLLECTION_NAME
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collection_manager = MilvusCollectionManager(collection_name)
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# 4. 判断集合是否存在，存在则删除
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if utility.has_collection(collection_name):
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print(f"集合 '{collection_name}' 已存在，正在删除...")
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utility.drop_collection(collection_name)
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print(f"集合 '{collection_name}' 已删除。")
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# 5. 定义集合的字段和模式
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fields = [
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FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
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FieldSchema(name="tags", dtype=DataType.JSON), # 改为JSON类型存储多个标签
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FieldSchema(name="user_input", dtype=DataType.VARCHAR, max_length=65535),
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FieldSchema(name="timestamp", dtype=DataType.VARCHAR, max_length=32),
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FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=MS_DIMENSION)
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]
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schema_description = "Chat records collection with tags , user_input, and timestamp"
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# 6. 创建集合
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print(f"正在创建集合 '{collection_name}'...")
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collection_manager.create_collection(fields, schema_description)
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print(f"集合 '{collection_name}' 创建成功。")
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# 7. 释放连接
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milvus_pool.release_connection(connection)
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# 8. 关闭连接池
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milvus_pool.close()
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