import numpy as np
import time
from WxMini.Milvus.Utils.MilvusCollectionManager import MilvusCollectionManager
from WxMini.Milvus.Utils.MilvusConnectionPool import *
from WxMini.Milvus.Config.MulvusConfig import *
# 1. 使用连接池管理 Milvus 连接
milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)

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

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

# 4. 加载集合到内存
collection_manager.load_collection()

# 5. 模拟当前对话的嵌入向量
current_embedding = np.random.random(128).tolist()  # 随机生成一个 128 维向量

# 6. 查询与当前对话最相关的历史对话
search_params = {
    "metric_type": "L2",       # 使用 L2 距离度量方式
    "params": {"nprobe": 100}  # 设置 IVF_FLAT 的 nprobe 参数
}
start_time = time.time()
results = collection_manager.search(current_embedding, search_params, limit=2)
end_time = time.time()

# 7. 输出查询结果
print("当前对话的嵌入向量:", current_embedding)
print("最相关的历史对话:")
if results:
    for hits in results:
        for hit in hits:
            try:
                text = collection_manager.query_text_by_id(hit.id)
                print(f"- {text} (距离: {hit.distance})")
            except Exception as e:
                print(f"查询失败: {e}")
else:
    print("未找到相关历史对话，请检查查询参数或数据。")

# 8. 输出查询耗时
print(f"查询耗时: {end_time - start_time:.4f} 秒")

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

# 10. 关闭连接池
milvus_pool.close()