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52 lines
1.8 KiB
52 lines
1.8 KiB
import numpy as np
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import time
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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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# 5. 模拟当前对话的嵌入向量
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current_embedding = np.random.random(128).tolist() # 随机生成一个 128 维向量
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# 6. 查询与当前对话最相关的历史对话
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search_params = {
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"metric_type": "L2", # 使用 L2 距离度量方式
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"params": {"nprobe": 100} # 设置 IVF_FLAT 的 nprobe 参数
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}
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start_time = time.time()
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results = collection_manager.search(current_embedding, search_params, limit=2)
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end_time = time.time()
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# 7. 输出查询结果
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print("当前对话的嵌入向量:", current_embedding)
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print("最相关的历史对话:")
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if results:
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for hits in results:
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for hit in hits:
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try:
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text = collection_manager.query_text_by_id(hit.id)
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print(f"- {text} (距离: {hit.distance})")
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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("未找到相关历史对话，请检查查询参数或数据。")
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# 8. 输出查询耗时
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print(f"查询耗时: {end_time - start_time:.4f} 秒")
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# 9. 释放连接
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milvus_pool.release_connection(connection)
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# 10. 关闭连接池
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milvus_pool.close() |