【LLM-agent】(task5)构建哲学家多智能体

note

• 通过编排动作设置哲学家智能体的"示例任务"，目的是让 Agent 更好地理解如何回答问题。主要包括设置示例问题、定义思考过程、应用到所有哲学家。建立了一个"先思考，后总结"的回答模式，这种方式相当于给AI提供了一个"样板"，告诉它：“这就是我们期望你回答问题的方式”

一、构建智能搜索代理

• Zigent 是一个基于 Agentlite 框架改进的智能代理开发框架。Agentlite 最初由 Salesforce AI Research 团队开发。Zigent的参考地址：https://github.com/datawhalechina/wow-agent
• 我们要搞的这个搜索代理能够通过 DuckDuckGo或Bocha 搜索引擎查找信息并回答问题。

DuckSearchAction类的两个作用：

• 初始化时配置动作的名称、描述和参数说明
• 通过 call 方法执行实际的搜索操作 使用示例见代码中的search_action部分

代码部分：

import os
from dotenv import load_dotenv# 加载环境变量
load_dotenv()
# 初始化变量
base_url = None
chat_model = None
api_key = None# 使用with语句打开文件，确保文件使用完毕后自动关闭
env_path = "/Users/guomiansheng/Desktop/LLM/llm_app/wow-agent/.env.txt"
with open(env_path, 'r') as file:# 逐行读取文件for line in file:# 移除字符串头尾的空白字符（包括'\n'）line = line.strip()# 检查并解析变量if "base_url" in line:base_url = line.split('=', 1)[1].strip().strip('"')elif "chat_model" in line:chat_model = line.split('=', 1)[1].strip().strip('"')elif "ZHIPU_API_KEY" in line:api_key = line.split('=', 1)[1].strip().strip('"')elif "BOCHA_API_KEY" in line:BOCHA_API_KEY = line.split('=', 1)[1].strip().strip('"')# 打印变量以验证
print(f"base_url: {base_url}")
print(f"chat_model: {chat_model}")
print(f"ZHIPU_API_KEY: {api_key}")from typing import List
from zigent.agents import ABCAgent, BaseAgent
from zigent.llm.agent_llms import LLM
from zigent.commons import TaskPackage
from zigent.actions.BaseAction import BaseAction
from zigent.logging.multi_agent_log import AgentLogger
from duckduckgo_search import DDGSllm = LLM(api_key=api_key, base_url=base_url, model_name=chat_model)
response = llm.run("你是谁？")
print(response)# 一、创建搜索动作
class DuckSearchAction(BaseAction):def __init__(self) -> None:action_name = "DuckDuckGo_Search"action_desc = "Using this action to search online content."params_doc = {"query": "the search string. be simple."}self.ddgs = DDGS()super().__init__(action_name=action_name, action_desc=action_desc, params_doc=params_doc,)def __call__(self, query):results = self.ddgs.chat(query)return results# search_action = DuckSearchAction()
# results = search_action("什么是 agent")
# print(results)# BOCHA_API_KEY = os.getenv('BOCHA_API_KEY')
import requests
import json
# 定义Bocha Web Search工具
def bocha_web_search_tool(query: str, count: int = 8) -> str:"""使用Bocha Web Search API进行联网搜索，返回搜索结果的字符串。参数:- query: 搜索关键词- count: 返回的搜索结果数量返回:- 搜索结果的字符串形式"""url = 'https://api.bochaai.com/v1/web-search'headers = {'Authorization': f'Bearer {BOCHA_API_KEY}',  # 请替换为你的API密钥'Content-Type': 'application/json'}data = {"query": query,"freshness": "noLimit", # 搜索的时间范围，例如 "oneDay", "oneWeek", "oneMonth", "oneYear", "noLimit""summary": True, # 是否返回长文本摘要总结"count": count}response = requests.post(url, headers=headers, json=data)if response.status_code == 200:# 返回给大模型的格式化的搜索结果文本# 可以自己对博查的搜索结果进行自定义处理return json.dumps(response.json())else:raise Exception(f"API请求失败，状态码: {response.status_code}, 错误信息: {response.text}")class BochaSearchAction(BaseAction):def __init__(self) -> None:action_name = "Bocha_Search"action_desc = "Using this action to search online content."params_doc = {"query": "the search string. be simple."}super().__init__(action_name=action_name, action_desc=action_desc, params_doc=params_doc,)def __call__(self, query):results = bocha_web_search_tool(query)rst = json.loads(results)result = ""for item in rst["data"]["webPages"]["value"]:result += item["summary"]return resultsearch_action = BochaSearchAction()
results = search_action("上海有哪些私立小学在招聘小学英语老师？")
print(results)# 二、创建搜索代理
class DuckSearchAgent(BaseAgent):def __init__(self,llm: LLM,actions: List[BaseAction] = [DuckSearchAction()],manager: ABCAgent = None,**kwargs):name = "duck_search_agent"role = "You can answer questions by using duck duck go search content."super().__init__(name=name,role=role,llm=llm,actions=actions,manager=manager,logger=agent_logger,)# search_action = BochaSearchAction()
class BochaSearchAgent(BaseAgent):def __init__(self,llm: LLM,actions: List[BaseAction] = [search_action],manager: ABCAgent = None,**kwargs):name = "bocha_search_agent"role = "You can answer questions by using bocha search content."super().__init__(name=name,role=role,llm=llm,actions=actions,manager=manager,)# 三、执行代理
# def do_search_agent():
#     # 创建代理实例
#     search_agent = DuckSearchAgent(llm=llm)#     # 创建任务
#     task = "what is the found date of microsoft"
#     task_pack = TaskPackage(instruction=task)#     # 执行任务并获取响应
#     response = search_agent(task_pack)
#     print("response:", response)# if __name__ == "__main__":
#     do_search_agent()def do_search_agent():# 创建代理实例search_agent = BochaSearchAgent(llm=llm)# 创建任务task = "what is the found date of microsoft"task_pack = TaskPackage(instruction=task)# 执行任务并获取响应response = search_agent(task_pack)print("response:", response)if __name__ == "__main__":do_search_agent()

通过两步的action和observation，最后也回答出了微软的成立时间：

二、构建哲学家多智能体

• 实现一个 Philosopher 类，继承自 BaseAgent 基类，通过构造函数接收哲学家名字、语言模型等参数，设置角色提示词,让AI扮演特定哲学家的角色。每个哲学家智能体都有自己的名字和角色定位，使用相同的语言模型(llm)，可以根据自己的哲学思想发表观点。这样的设计让我们可以模拟不同哲学家之间的对话和思想交流
• 通过编排动作设置哲学家智能体的"示例任务"，目的是让 Agent 更好地理解如何回答问题。主要包括设置示例问题、定义思考过程、应用到所有哲学家。建立了一个"先思考，后总结"的回答模式，这种方式相当于给AI提供了一个"样板"，告诉它：“这就是我们期望你回答问题的方式”
• 实现一个多智能体系统中的管理者代理（Manager Agent），负责协调孔子、苏格拉底和亚里士多德三位哲学家。管理者的角色定义为：依次询问各位哲学家的观点并总结。ManagerAgent 中设置了"什么是生命的意义？"示例任务流程，包括思考如何处理任务、询问哲学家的观点、总结观点等步骤
• 最后调用 ManagerAgent。管理者 Agent 会根据我们之前设置的示例任务和动作序列,按照类似的思路来处理新的任务。在这个例子中，我们设置了一个新的任务"先有鸡还是先有蛋?"，然后调用管理者 Agent 来处理这个任务

import os
from dotenv import load_dotenv# 加载环境变量
load_dotenv()
# 初始化变量
base_url = None
chat_model = None
api_key = None# 使用with语句打开文件，确保文件使用完毕后自动关闭
env_path = "/Users/guomiansheng/Desktop/LLM/llm_app/wow-agent/.env.txt"
with open(env_path, 'r') as file:# 逐行读取文件for line in file:# 移除字符串头尾的空白字符（包括'\n'）line = line.strip()# 检查并解析变量if "base_url" in line:base_url = line.split('=', 1)[1].strip().strip('"')elif "chat_model" in line:chat_model = line.split('=', 1)[1].strip().strip('"')elif "ZHIPU_API_KEY" in line:api_key = line.split('=', 1)[1].strip().strip('"')elif "BOCHA_API_KEY" in line:BOCHA_API_KEY = line.split('=', 1)[1].strip().strip('"')# 打印变量以验证
print(f"base_url: {base_url}")
print(f"chat_model: {chat_model}")
print(f"ZHIPU_API_KEY: {api_key}")from openai import OpenAI
client = OpenAI(api_key = api_key,base_url = base_url
)
print(client)def get_completion(prompt):response = client.chat.completions.create(model="glm-4-flash",  # 填写需要调用的模型名称messages=[{"role": "user", "content": prompt},],)return response.choices[0].message.content# 一、使用zigent定义llm
from typing import List
from zigent.agents import ABCAgent, BaseAgent
from zigent.llm.agent_llms import LLM
from zigent.commons import TaskPackage
from zigent.actions.BaseAction import BaseAction
from zigent.logging.multi_agent_log import AgentLogger
from duckduckgo_search import DDGSllm = LLM(api_key=api_key, base_url=base_url, model_name=chat_model)
response = llm.run("你是谁？")
print(response)# 二、哲学家
from typing import List
from zigent.actions.BaseAction import BaseAction
from zigent.agents import ABCAgent, BaseAgent# 定义 Philosopher 类，继承自 BaseAgent 类
class Philosopher(BaseAgent):def __init__(self,philosopher,llm: LLM, # BaseLLM,actions: List[BaseAction] = [], manager: ABCAgent = None,**kwargs):name = philosopher# 角色role = f"""You are {philosopher}, the famous educator in history. You are very familiar with {philosopher}'s Book and Thought. Tell your opinion on behalf of {philosopher}."""super().__init__(name=name,role=role,llm=llm,actions=actions,manager=manager)# 初始化哲学家对象
Confucius = Philosopher(philosopher= "Confucius", llm = llm) # 孔子
Socrates = Philosopher(philosopher="Socrates", llm = llm) # 苏格拉底
Aristotle = Philosopher(philosopher="Aristotle", llm = llm) # 亚里士多德# 三、编排动作
# 导入必要的模块
from zigent.commons import AgentAct, TaskPackage
from zigent.actions import ThinkAct, FinishAct
from zigent.actions.InnerActions import INNER_ACT_KEY
from zigent.agents.agent_utils import AGENT_CALL_ARG_KEY# 为哲学家智能体添加示例任务
# 设置示例任务:询问生命的意义
exp_task = "What do you think the meaning of life?"
exp_task_pack = TaskPackage(instruction=exp_task)# 第一个动作:思考生命的意义
act_1 = AgentAct(name=ThinkAct.action_name,params={INNER_ACT_KEY: f"""Based on my thought, we are born to live a meaningful life, and it is in living a meaningful life that our existence gains value. Even if a life is brief, if it holds value, it is meaningful. A life without value is merely existence, a mere survival, a walking corpse."""},
)
# 第一个动作的观察结果
obs_1 = "OK. I have finished my thought, I can pass it to the manager now."# 第二个动作:总结思考结果
act_2 = AgentAct(name=FinishAct.action_name, params={INNER_ACT_KEY: "I can summarize my thought now."})
# 第二个动作的观察结果
obs_2 = "I finished my task, I think the meaning of life is to pursue value for the whold world."
# 将动作和观察组合成序列
exp_act_obs = [(act_1, obs_1), (act_2, obs_2)]# 为每个哲学家智能体添加示例
# 为孔子添加示例
Confucius.prompt_gen.add_example(task = exp_task_pack, action_chain = exp_act_obs
)
# 为苏格拉底添加示例
Socrates.prompt_gen.add_example(task = exp_task_pack, action_chain = exp_act_obs
)
# 为亚里士多德添加示例
Aristotle.prompt_gen.add_example(task = exp_task_pack, action_chain = exp_act_obs
)# 四、定义 ManagerAgent
# 定义管理者代理
from zigent.agents import ManagerAgent# 设置管理者代理的基本信息
manager_agent_info = {"name": "manager_agent","role": "you are managing Confucius, Socrates and Aristotle to discuss on questions. Ask their opinion one by one and summarize their view of point."
}
# 设置团队成员
team = [Confucius, Socrates, Aristotle]
# 创建管理者代理实例
manager_agent = ManagerAgent(name=manager_agent_info["name"], role=manager_agent_info["role"], llm=llm, TeamAgents=team)# 为管理者代理添加示例任务
exp_task = "What is the meaning of life?"
exp_task_pack = TaskPackage(instruction=exp_task)# 第一步：思考如何处理任务
act_1 = AgentAct(name=ThinkAct.action_name,params={INNER_ACT_KEY: f"""I can ask Confucius, Socrates and Aristotle one by one on their thoughts, and then summary the opinion myself."""},
)
obs_1 = "OK."# 第二步：询问孔子的观点
act_2 = AgentAct(name=Confucius.name,params={AGENT_CALL_ARG_KEY: "What is your opinion on the meaning of life?",},
)
obs_2 = """Based on my thought, I think the meaning of life is to pursue value for the whold world."""# 第三步：思考下一步行动
act_3 = AgentAct(name=ThinkAct.action_name,params={INNER_ACT_KEY: f"""I have obtained information from Confucius, I need to collect more information from Socrates."""},
)
obs_3 = "OK."# 第四步：询问苏格拉底的观点
act_4 = AgentAct(name=Socrates.name,params={AGENT_CALL_ARG_KEY: "What is your opinion on the meaning of life?",},
)
obs_4 = """I think the meaning of life is finding happiness."""# 第五步：继续思考下一步
act_5 = AgentAct(name=ThinkAct.action_name,params={INNER_ACT_KEY: f"""I have obtained information from Confucius and Socrates, I can collect more information from Aristotle."""},
)
obs_5 = "OK."# 第六步：询问亚里士多德的观点
act_6 = AgentAct(name=Aristotle.name,params={AGENT_CALL_ARG_KEY: "What is your opinion on the meaning of life?",},
)
obs_6 = """I believe the freedom of spirit is the meaning."""# 最后一步：总结所有观点
act_7 = AgentAct(name=FinishAct.action_name, params={INNER_ACT_KEY: "Their thought on the meaning of life is to pursue value, happiniss and freedom of spirit."})
obs_7 = "Task Completed. The meaning of life is to pursue value, happiness and freedom of spirit."# 将所有动作和观察组合成序列
exp_act_obs = [(act_1, obs_1), (act_2, obs_2), (act_3, obs_3), (act_4, obs_4), (act_5, obs_5), (act_6, obs_6), (act_7, obs_7)]# 将示例添加到管理者代理的提示生成器中
manager_agent.prompt_gen.add_example(task = exp_task_pack, action_chain = exp_act_obs
)# 五、调用 ManagerAgent
from zigent.commons import AgentAct, TaskPackageexp_task = "先有鸡还是先有蛋?"
exp_task_pack = TaskPackage(instruction=exp_task)
manager_agent(exp_task_pack)

辩论的结果如下：

Reference

[1] https://github.com/datawhalechina/wow-agent
[2] https://www.datawhale.cn/learn/summary/86
[3] https://open.bochaai.com/
[4] 官方文档：https://docs.cloud.llamaindex.ai/
[5] 学习笔记：https://www.cnblogs.com/HYLOVEYOURSELF/p/18680532