from WxMini.Milvus.Config.MulvusConfig import *
from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from WxMini.Milvus.Utils.MilvusConnectionPool import *
from gensim.models import KeyedVectors

import jieba
import time
from openai import OpenAI

# 初始化 OpenAI 客户端
client = OpenAI(
    api_key=MODEL_API_KEY,
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

# 1. 加载预训练的 Word2Vec 模型
model_path = MS_MODEL_PATH
model = KeyedVectors.load_word2vec_format(model_path, binary=False, limit=MS_MODEL_LIMIT)
print(f"模型加载成功，词向量维度: {model.vector_size}")

# 功能：将文本转换为嵌入向量
def text_to_embedding(text):
    words = jieba.lcut(text)  # 使用 jieba 分词
    print(f"文本: {text}, 分词结果: {words}")
    embeddings = [model[word] for word in words if word in model]
    print(f"有效词向量数量: {len(embeddings)}")
    if embeddings:
        avg_embedding = sum(embeddings) / len(embeddings)
        print(f"生成的平均向量: {avg_embedding[:5]}...")  # 打印前 5 维
        return avg_embedding
    else:
        print("未找到有效词，返回零向量")
        return [0.0] * model.vector_size

# 2. 使用连接池管理 Milvus 连接
milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)

# 3. 从连接池中获取一个连接
connection = milvus_pool.get_connection()

# 4. 初始化集合管理器
collection_name = MS_COLLECTION_NAME
collection_manager = MilvusCollectionManager(collection_name)

# 5. 输入一个用户问题
user_input = "我给你起的名字叫小云，记住你的名字，你是我的私人助理。"

# 6. 调用大模型生成回复
try:
    response = client.chat.completions.create(
        model=MODEL_NAME,
        messages=[
            {"role": "system", "content": "你是一个非常好的聊天伙伴，可以疏导用户，帮他解压,一句控制在20字以内。"},
            {"role": "user", "content": user_input}
        ],
        max_tokens=500
    )
    if response.choices and response.choices[0].message.content:
        model_response = response.choices[0].message.content.strip()
    else:
        model_response = "大模型未返回有效结果"
except Exception as e:
    model_response = f"调用大模型失败: {str(e)}"

print(f"大模型回复: {model_response}")

# 7. 获取当前时间和会话 ID
timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())  # 当前时间
person_id = "F9D1C319-215D-3B4E-FAA0-0E024AC12D3C"  # 会话 ID（可以根据需要动态生成）

# 8. 将用户问题转换为嵌入向量
user_embedding = text_to_embedding(user_input)

# 9. 插入数据，确保字段顺序与集合定义一致
entities = [
    [person_id],  # person_id
    [user_input],  # user_input
    [model_response],  # model_response
    [timestamp],  # timestamp
    [user_embedding]  # embedding
]
collection_manager.insert_data(entities)
print("数据插入成功。")

# 10. 释放连接
milvus_pool.release_connection(connection)

# 11. 关闭连接池
milvus_pool.close()