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# pip install gensim
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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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from gensim.models import KeyedVectors
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# 加载预训练的 Word2Vec 模型
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model_path = "D:/Tencent_AILab_ChineseEmbedding/Tencent_AILab_ChineseEmbedding.txt" # 替换为你的 Word2Vec 模型路径
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# 参考文档：使用gensim之KeyedVectors操作词向量模型
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# https://www.cnblogs.com/bill-h/p/14655224.html
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# 读取词向量模型（限定前10000个词）
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model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=10000)
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# 将文本转换为嵌入向量
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def text_to_embedding(text):
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words = text.split()
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embeddings = [model[word] for word in words if word in model]
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if embeddings:
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return sum(embeddings) / len(embeddings) # 取词向量的平均值
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else:
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return [0.0] * model.vector_size # 如果文本中没有有效词，返回零向量
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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. 输入一句话
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input_text = input("请输入一句话：") # 例如：“我今天心情不太好”
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# 6. 将文本转换为嵌入向量
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current_embedding = text_to_embedding(input_text)
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# 7. 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=5) # 返回 5 条结果
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end_time = time.time()
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# 8. 输出查询结果
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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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# 9. 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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# 10. 释放连接
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milvus_pool.release_connection(connection)
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# 11. 关闭连接池
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milvus_pool.close() |
