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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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from gensim.models import KeyedVectors
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import jieba
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import time
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# 1. 加载预训练的 Word2Vec 模型
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model_path = MS_MODEL_PATH
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model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
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print(f"模型加载成功，词向量维度: {model.vector_size}")
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# 功能：将文本转换为嵌入向量
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def text_to_embedding(text):
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words = jieba.lcut(text) # 使用 jieba 分词
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print(f"文本: {text}, 分词结果: {words}")
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embeddings = [model[word] for word in words if word in model]
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print(f"有效词向量数量: {len(embeddings)}")
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if embeddings:
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avg_embedding = sum(embeddings) / len(embeddings)
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print(f"生成的平均向量: {avg_embedding[:5]}...") # 打印前 5 维
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return avg_embedding
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else:
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print("未找到有效词，返回零向量")
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return [0.0] * model.vector_size
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# 2. 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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# 3. 从连接池中获取一个连接
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connection = milvus_pool.get_connection()
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# 4. 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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# 5. 输入一个用户问题
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user_input = input("请输入一句话：") # 例如：“我今天心情不太好”
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model_response = "我没听懂，能再说一遍吗？" # 大模型的固定回复
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timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) # 当前时间
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session_id = "session_001" # 会话 ID（可以根据需要动态生成）
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# 6. 将用户问题转换为嵌入向量
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user_embedding = text_to_embedding(user_input)
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# 7. 插入数据，确保字段顺序与集合定义一致
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entities = [
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[session_id], # session_id
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[user_input], # user_input
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[model_response], # model_response
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[timestamp], # timestamp
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[user_embedding] # embedding
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]
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collection_manager.insert_data(entities)
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print("数据插入成功。")
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# 8. 释放连接
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milvus_pool.release_connection(connection)
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# 9. 关闭连接池
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milvus_pool.close()
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