传统硬编码式语音助手（简化示例）
def voice_assistant(user_input):
    if "天气" in user_input:
        return fetch_weather()
    elif "提醒" in user_input:
        return set_reminder(user_input)
    elif "打电话" in user_input:
        return make_call(user_input)
    else:
        return "抱歉，我无法理解您的指令"

用户输入 → 分词(Tokenization) → 向量嵌入(Embedding) → Transformer处理 → 逐词生成 → 输出结果

感知(Perception) → 规划(Planning) → 行动(Action) → 观察(Observation) → 回到规划

 安装必要的依赖包
pip install openai langchain langgraph

 建议使用国内聚合API平台或官方API
export API_KEY="your_api_key"
export BASE_URL="your_api_base_url"

import json
from openai import OpenAI
from typing import List, Dict, Any

class ElderlyAIAgent:
    """长辈AI助手智能体实现"""
    
    def __init__(self, api_key: str, base_url: str = None):
        self.client = OpenAI(
            api_key=api_key,
            base_url=base_url
        )
        self.messages = [
            {
                "role": "system",
                "content": """你是一个面向老年人的智能助手。
                请遵循以下原则：
                1. 回答简洁明了，语速平缓，避免专业术语
                2. 优先考虑长辈的健康和安全
                3. 对不明确的指令主动追问确认
                4. 具备同理心，语气温和耐心"""
            }
        ]
        
         定义工具函数（供LLM调用）
        self.tools = [
            {
                "type": "function",
                "function": {
                    "name": "get_current_weather",
                    "description": "获取指定城市的实时天气信息",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "city": {"type": "string", "description": "城市名称"},
                            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                        },
                        "required": ["city"]
                    }
                }
            },
            {
                "type": "function",
                "function": {
                    "name": "set_medication_reminder",
                    "description": "为老人设置服药提醒",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "medicine_name": {"type": "string", "description": "药品名称"},
                            "time": {"type": "string", "description": "提醒时间，格式HH:MM"},
                            "dosage": {"type": "string", "description": "剂量信息"}
                        },
                        "required": ["medicine_name", "time"]
                    }
                }
            }
        ]
    
    def get_current_weather(self, city: str, unit: str = "celsius") -> str:
        """模拟天气查询函数（实际应调用真实API）"""
        weather_data = {
            "北京": {"temp": 18, "condition": "晴朗"},
            "上海": {"temp": 20, "condition": "多云"},
            "广州": {"temp": 26, "condition": "小雨"}
        }
        info = weather_data.get(city, {"temp": 22, "condition": "晴"})
        return f"{city}天气：{info['condition']}，气温{info['temp']}度{unit}"
    
    def set_medication_reminder(self, medicine_name: str, time: str, dosage: str = "") -> str:
        """设置服药提醒"""
        reminder = {
            "medicine": medicine_name,
            "time": time,
            "dosage": dosage,
            "status": "active"
        }
        print(f"[系统] 已设置提醒：{time}服用{medicine_name} {dosage}")
        return f"好的，已为您设置{time}服用{medicine_name}的提醒"
    
    def execute_tool(self, tool_name: str, arguments: Dict[str, Any]) -> str:
        """工具调用执行器"""
        if tool_name == "get_current_weather":
            return self.get_current_weather(arguments)
        elif tool_name == "set_medication_reminder":
            return self.set_medication_reminder(arguments)
        else:
            return f"未知工具: {tool_name}"
    
    def chat(self, user_input: str) -> str:
        """主对话循环"""
         添加用户消息
        self.messages.append({"role": "user", "content": user_input})
        
         调用LLM
        response = self.client.chat.completions.create(
            model="gpt-4o-mini",   可选：deepseek-chat, claude-3-haiku等
            messages=self.messages,
            tools=self.tools,
            tool_choice="auto"
        )
        
        assistant_message = response.choices[0].message
        
         检查是否需要调用工具
        if assistant_message.tool_calls:
             执行工具调用
            tool_results = []
            for tool_call in assistant_message.tool_calls:
                tool_name = tool_call.function.name
                arguments = json.loads(tool_call.function.arguments)
                result = self.execute_tool(tool_name, arguments)
                tool_results.append({
                    "tool_call_id": tool_call.id,
                    "role": "tool",
                    "content": result
                })
            
             将工具调用结果返回给LLM
            self.messages.append(assistant_message)
            self.messages.extend(tool_results)
            
             再次调用LLM生成最终回复
            final_response = self.client.chat.completions.create(
                model="gpt-4o-mini",
                messages=self.messages
            )
            final_content = final_response.choices[0].message.content
            self.messages.append({"role": "assistant", "content": final_content})
            return final_content
        else:
             无工具调用，直接返回
            content = assistant_message.content
            self.messages.append({"role": "assistant", "content": content})
            return content


 使用示例
if __name__ == "__main__":
    agent = ElderlyAIAgent(api_key="your_api_key")
    
     场景1：查询天气
    response = agent.chat("帮我看看北京今天天气怎么样？")
    print(f"助手：{response}")
    
     场景2：设置服药提醒
    response = agent.chat("提醒我晚上8点吃降压药")
    print(f"助手：{response}")

def route_task(task_type, local_resources):
    if task_type in ['image_processing', '3d_rendering'] and local_resources['gpu'] > 80:
        return 'local_execution'
    elif task_type == 'large_lm_inference':
        return 'hybrid_mode'   本地生成token + 云端补全
    else:
        return 'cloud_execution'