# 加载环境变量
load_dotenv()

# 从环境变量中读取api_key
api_key = os.getenv('ZISHU_API_KEY')
base_url = "https://open.bigmodel.cn/api/paas/v4/"
chat_model = "glm-4-flash"

引入 zigent 相关的方法如Action、Agent、大模型配置等以及 duckduckgo_search ：

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 DDGS

配置 LLM

需要配置大语言模型。这里使用 zigent 封装的 LLM加载和配置 LLM 服务：

llm = LLM(api_key=api_key, base_url=base_url, model_name=chat_model)
response = llm.run("你是谁？")
print(response)

创建智能体

我们实现一个 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：

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,
        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

编排动作

我们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"：

# 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
)

定义 ManagerAgent

接着我们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：

# 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
)

调用 ManagerAgent

最后我们调用这个管理者 Agent，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 :

from zigent.commons import AgentAct, TaskPackage

exp_task = "先有鸡还是先有蛋?"
exp_task_pack = TaskPackage(instruction=exp_task)
manager_agent(exp_task_pack)

我们将获得类似如下的结果：