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52 lines
1.8 KiB
52 lines
1.8 KiB
from Backup.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
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from Config.Config import *
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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. 加载集合到内存
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collection_manager.load_collection()
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print(f"集合 '{collection_name}' 已加载到内存。")
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# 5. 直接在代码中指定要查询的标签
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#query_tag = "MATH_DATA_1" # 可以修改为MATH_DATA_2或其他需要的标签
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query_tag = "MATH_DATA_2" # 可以修改为MATH_DATA_2或其他需要的标签
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expr = f"array_contains(tags['tags'], '{query_tag}')"
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print(f"查询表达式: {expr}")
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# 6. 查询数据
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try:
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results = collection_manager.collection.query(
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expr=expr,
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output_fields=["id", "tags", "user_input", "timestamp", "embedding"],
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limit=1000
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)
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print(f"查询标签 '{query_tag}' 结果：")
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if results:
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for result in results:
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try:
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print(f"ID: {result['id']}")
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print(f"标签: {result['tags']}")
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print(f"用户问题: {result['user_input']}")
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print(f"时间: {result['timestamp']}")
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print(f"向量: {result['embedding'][:5]}...")
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print("-" * 40)
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except Exception as e:
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print(f"处理结果失败: {e}")
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else:
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print(f"未找到标签为 '{query_tag}' 的数据。")
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except Exception as e:
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print(f"查询失败: {e}")
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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() |