使用腾讯 Hy Memory 2.0 与 n1n.ai 构建具备长期记忆的智能代理

pip install hy-memory openai requests

import os
from openai import OpenAI
from hymemory import MemoryManager

# 初始化 n1n.ai 客户端以实现高性价比的 LLM 推理
client = OpenAI(
    api_key=os.environ.get("N1N_API_KEY"),
    base_url="https://api.n1n.ai/v1"
);

# 初始化 Hy Memory 状态管理器
memory_manager = MemoryManager(
    model_path="tencent/Hy-Memory-30B-A3B",
    device="cuda"
);

def process_agent_turn(user_id: str, session_input: str):
    # 从持久化存储中检索相关的历史记忆
    context = memory_manager.retrieve(user_id=user_id, query=session_input);
    
    # 构建包含记忆上下文的提示词
    messages = [
        {"role": "system", "content": f"相关持久记忆：<context>{context}</context>"},
        {"role": "user", "content": session_input}
    ];
    
    # 通过 n1n.ai 的高速路由生成回复
    response = client.chat.completions.create(
        model="deepseek-chat",
        messages=messages,
        temperature=0.2
    );
    
    assistant_output = response.choices[0].message.content;
    
    # 使用新的交互会话更新记忆库
    memory_manager.update(
        user_id=user_id,
        interaction=f"用户: {user_id} - 输入: {session_input}\n助手: {assistant_output}"
    );
    
    return assistant_output

# 运行示例
if __name__ == "__main__":
    os.environ["N1N_API_KEY"] = "your_n1n_ai_api_key_here"
    result = process_agent_turn("user_9921", "请记住，我首选的云部署区域是 us-east-1。");
    print("代理回复：", result);