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46 lines
1.6 KiB
46 lines
1.6 KiB
import random
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from WxMini.Milvus.Config.MulvusConfig import *
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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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# 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. 插入数据
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data = [
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[random.random() for _ in range(128)], # 第一个 128 维向量
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[random.random() for _ in range(128)], # 第二个 128 维向量
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[random.random() for _ in range(128)] # 第三个 128 维向量
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]
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entities = [data] # 插入的数据
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collection_manager.insert_data(entities)
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print("数据插入成功。")
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# 5. 加载集合到内存
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collection_manager.load_collection()
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# 6. 查询数据，验证插入是否成功
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query_vector = [random.random() for _ in range(128)] # 随机生成一个查询向量
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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": 10} # 设置 IVF_FLAT 的 nprobe 参数
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}
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results = collection_manager.search(query_vector, search_params, limit=2)
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print("查询结果：")
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for hits in results:
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for hit in hits:
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print(f"ID: {hit.id}, Distance: {hit.distance}")
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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() |