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56 lines
2.1 KiB
56 lines
2.1 KiB
from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
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from WxMini.Milvus.Utils.MilvusConnectionPool import *
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from WxMini.Milvus.Config.MulvusConfig 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. 查询所有数据
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try:
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# 使用 Milvus 的 query 方法查询所有数据
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results = collection_manager.collection.query(
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expr="", # 空表达式表示查询所有数据
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output_fields=["id", "person_id", "user_input", "model_response", "timestamp", "embedding"], # 指定返回的字段
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limit=1000 # 设置最大返回记录数
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)
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print("查询结果：")
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if results:
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for result in results:
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try:
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# 获取字段值
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person_id = result["person_id"]
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user_input = result["user_input"]
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model_response = result["model_response"]
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timestamp = result["timestamp"]
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embedding = result["embedding"]
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# 打印结果
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print(f"ID: {result['id']}")
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print(f"会话 ID: {person_id}")
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print(f"用户问题: {user_input}")
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print(f"大模型回复: {model_response}")
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print(f"时间: {timestamp}")
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print(f"向量: {embedding[:5]}...") # 只打印前 5 维向量
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print("-" * 40) # 分隔线
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except Exception as e:
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print(f"查询失败: {e}")
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else:
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print("未找到相关数据，请检查查询参数或数据。")
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except Exception as e:
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print(f"查询失败: {e}")
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# 6. 释放连接
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milvus_pool.release_connection(connection)
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# 7. 关闭连接池
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milvus_pool.close() |