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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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# 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. 插入数据
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texts = [
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"我今天心情不太好，因为工作压力很大。", # 第一个对话文本
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"我最近在学习 Python，感觉很有趣。", # 第二个对话文本
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"我打算周末去爬山，放松一下。", # 第三个对话文本
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"吉林省广告产业园是东师理想的办公地点。" # 第四个对话文本
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]
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embeddings = [text_to_embedding(text) for text in texts] # 使用文本模型生成向量
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# 6. 打印生成的向量值
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for text, embedding in zip(texts, embeddings):
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print(f"文本: {text}, 向量: {embedding[:5]}...") # 打印前 5 维
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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 = [texts, embeddings] # 第一个列表是 text 字段，第二个列表是 embedding 字段
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collection_manager.insert_data(entities)
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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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