网站做接口到app价格,怎样制作一个自己的网站,千度搜索引擎,做网站推销好做吗本系列文章跟随《MetaGPT多智能体课程》#xff08;https://github.com/datawhalechina/hugging-multi-agent#xff09;#xff0c;深入理解并实践多智能体系统的开发。
本文为该课程的第四章#xff08;多智能体开发#xff09;的第三篇笔记。主要是对课程刚开始环境搭…本系列文章跟随《MetaGPT多智能体课程》https://github.com/datawhalechina/hugging-multi-agent深入理解并实践多智能体系统的开发。
本文为该课程的第四章多智能体开发的第三篇笔记。主要是对课程刚开始环境搭建成功后跑通的第一个MetaGPT多智能体案例 - 多智能体辩论进行学习并拆解其中的实现步骤和原理。
系列笔记
【AI Agent系列】【MetaGPT多智能体学习】0. 环境准备 - 升级MetaGPT 0.7.2版本及遇到的坑【AI Agent系列】【MetaGPT多智能体学习】1. 再理解 AI Agent - 经典案例和热门框架综述【AI Agent系列】【MetaGPT多智能体学习】2. 重温单智能体开发 - 深入源码理解单智能体运行框架【AI Agent系列】【MetaGPT多智能体学习】3. 开发一个简单的多智能体系统兼看MetaGPT多智能体运行机制【AI Agent系列】【MetaGPT多智能体学习】4. 基于MetaGPT的Team组件开发你的第一个智能体团队 文章目录 系列笔记0. 辩论Action的定义1. 辩论智能体的定义1.1 让两个智能体交替运行的重点1.2 ~~重写 _observe 函数~~1.3 重写 _act 函数1.4 最终Role的代码 2. 实例化智能体3. 完整代码及运行结果4. 总结 之前跑通环境的文章【AI Agent系列】【MetaGPT多智能体学习】0. 环境准备 - 升级MetaGPT 0.7.2版本及遇到的坑 多智能体辩论需求。假设是两个智能体进行辩论。
0. 辩论Action的定义
辩论进行的动作都一样所以Action可以共用一个。除了 Prompt 需要费点功夫想其它的流程都和之前一样平平无奇。
class SpeakAloud(Action):Action: Speak out aloud in a debate (quarrel)PROMPT_TEMPLATE: str ## BACKGROUNDSuppose you are {name}, you are in a debate with {opponent_name}.## DEBATE HISTORYPrevious rounds:{context}## YOUR TURNNow its your turn, you should closely respond to your opponents latest argument, state your position, defend your arguments, and attack your opponents arguments,craft a strong and emotional response in 80 words, in {name}s rhetoric and viewpoints, your will argue:name: str SpeakAloudasync def run(self, context: str, name: str, opponent_name: str):prompt self.PROMPT_TEMPLATE.format(contextcontext, namename, opponent_nameopponent_name)# logger.info(prompt)rsp await self._aask(prompt)return rsp1. 辩论智能体的定义
智能体的职能也一样所以可以共用一个Role的构造。
1.1 让两个智能体交替运行的重点
它的Action就是上面的辩论Action通过 self.set_actions([SpeakAloud]) 设置。
它的动作时机通过 self._watch([UserRequirement, SpeakAloud]) 来设置可以看到它观察环境中出现 UserRequirement 或 SpeakAloud 来源的消息时会有所动作。
那么问题来了现在辩论的智能体Role和Action都只有一个Role1说完之后放到环境中的信息来源是 SpeakAloudRole2说完后放到环境中的也是 SpeakAloud如果还是之前的代码那当环境中出现 SpeakAloud时Role1 和 Role2 都会触发动作了。这是不对的。
我们需要的是Role1等待Role2说完再说Role2等待Role1说完后再说交替着表达。
怎么实现这一点呢回想我们之前学的单智能体的运行周期_observe — _react_think _act — publish_message能阻止智能体不动作的是 _observe 函数。
1.2 重写 _observe 函数
先来看下原_observe函数的实现
async def _observe(self, ignore_memoryFalse) - int:Prepare new messages for processing from the message buffer and other sources.# Read unprocessed messages from the msg buffer.news []if self.recovered:news [self.latest_observed_msg] if self.latest_observed_msg else []if not news:news self.rc.msg_buffer.pop_all()# Store the read messages in your own memory to prevent duplicate processing.old_messages [] if ignore_memory else self.rc.memory.get()self.rc.memory.add_batch(news)# Filter out messages of interest.self.rc.news [n for n in news if (n.cause_by in self.rc.watch or self.name in n.send_to) and n not in old_messages]self.latest_observed_msg self.rc.news[-1] if self.rc.news else None # record the latest observed msg# Design Rules:# If you need to further categorize Message objects, you can do so using the Message.set_meta function.# msg_buffer is a receiving buffer, avoid adding message data and operations to msg_buffer.news_text [f{i.role}: {i.content[:20]}... for i in self.rc.news]if news_text:logger.debug(f{self._setting} observed: {news_text})return len(self.rc.news)它的结果 len(self.rc.news) 只要大于0这个智能体就会执行 _react开始行动。self.rc.news怎么来的重点看源码中的这句
# Filter out messages of interest.
self.rc.news [n for n in news if (n.cause_by in self.rc.watch or self.name in n.send_to) and n not in old_messages
]self.rc.news 是从 news 中过滤出了该智能体所关心的消息 n.cause_by in self.rc.watch 和 指定发送给该智能体的消息 self.name in n.send_to。而 news 其实就是 msg_buffermsg_buffer 来自环境的 publish_message。
再来看下环境 publish_message 的代码
def publish_message(self, message: Message, peekable: bool True) - bool:Distribute the message to the recipients.In accordance with the Message routing structure design in Chapter 2.2.1 of RFC 116, as already plannedin RFC 113 for the entire system, the routing information in the Message is only responsible forspecifying the message recipient, without concern for where the message recipient is located. How toroute the message to the message recipient is a problem addressed by the transport framework designedin RFC 113.logger.debug(fpublish_message: {message.dump()})found False# According to the routing feature plan in Chapter 2.2.3.2 of RFC 113for role, addrs in self.member_addrs.items():if is_send_to(message, addrs):role.put_message(message)found Trueif not found:logger.warning(fMessage no recipients: {message.dump()})self.history f\n{message} # For debugreturn True看里面的 is_send_to 函数
def is_send_to(message: Message, addresses: set):Return whether its consumerif MESSAGE_ROUTE_TO_ALL in message.send_to:return Truefor i in addresses:if i in message.send_to:return Truereturn False如果指定了 message.send_to则环境只会将该消息发送给指定的Role。
光看上面的代码和我的文字可能不是很好理解容易绕晕我给你们画了个图 如上图的流程这对于我们的辩论智能体来说其实就够了。Role1执行完动作后将结果消息指定发送给Role2通过message的send_to参数这样Role1的msg_buffer中就不会出现它自身的这个消息下次run时msg_buffer就直接是空的就不运行了等待Role2的消息。同样的Role2执行完后将消息指定发送给Role1。这样就实现了交互行动辩论的过程。
教程中用的0.6.6版本不知道源码是不是这样。但是它为了实现这个辩论过程还重写了 _observe 函数
async def _observe(self) - int:await super()._observe()# accept messages sent (from opponent) to self, disregard own messages from the last roundself.rc.news [msg for msg in self.rc.news if msg.send_to {self.name}] # 第二次筛选return len(self.rc.news)可以参考下。但是我用的0.7.2版本看源码和实践证明不用重写_observe了。
1.3 重写 _act 函数
这里为什么要重写 _act 函数最主要的原因是要填执行完结果消息中的 send_to 参数源码中是没有填的。还有就是组装辩论的上下文对手说了什么 context \n.join(f{msg.sent_from}: {msg.content} for msg in memories)。
async def _act(self) - Message:logger.info(f{self._setting}: to do {self.rc.todo}({self.rc.todo.name}))todo self.rc.todo # An instance of SpeakAloudmemories self.get_memories()context \n.join(f{msg.sent_from}: {msg.content} for msg in memories)print(context)rsp await todo.run(contextcontext, nameself.name, opponent_nameself.opponent_name)msg Message(contentrsp,roleself.profile,cause_bytype(todo),sent_fromself.name,send_toself.opponent_name,)self.rc.memory.add(msg)return msgcause_bytype(todo), sent_fromself.name, send_toself.opponent_name, 这三个参数分别是形容Message的内容属性来自于哪个action以及角色并要发送给哪个角色。通过这样的机制可以实现相较于watch更灵活的订阅机制。
给大家再看一下组装的上下文的示例有个更直观的认识Trump发言时收到的上文是Biden及其之前发言的内容 1.4 最终Role的代码
class Debator(Role):name: str profile: str opponent_name: str def __init__(self, **data: Any):super().__init__(**data)self.set_actions([SpeakAloud])self._watch([UserRequirement, SpeakAloud])## 0.7.2 版本可以不用重写 _observe具体原因分析见上文# async def _observe(self) - int:# await super()._observe()# # accept messages sent (from opponent) to self, disregard own messages from the last round# self.rc.news [msg for msg in self.rc.news if msg.send_to {self.name}]# return len(self.rc.news)async def _act(self) - Message:logger.info(f{self._setting}: to do {self.rc.todo}({self.rc.todo.name}))todo self.rc.todo # An instance of SpeakAloudmemories self.get_memories()context \n.join(f{msg.sent_from}: {msg.content} for msg in memories)print(context)rsp await todo.run(contextcontext, nameself.name, opponent_nameself.opponent_name)msg Message(contentrsp,roleself.profile,cause_bytype(todo),sent_fromself.name,send_toself.opponent_name,)self.rc.memory.add(msg)return msg2. 实例化智能体
实例化两个智能体Team组件的用法前面已经学习过可参考我的上篇文章。
其中值得注意的是team.run_project(idea, send_toBiden) 通过 send_to 参数指定谁先发言。
async def debate(idea: str, investment: float 3.0, n_round: int 5):Run a team of presidents and watch they quarrel. :)Biden Debator(nameBiden, profileDemocrat, opponent_nameTrump)Trump Debator(nameTrump, profileRepublican, opponent_nameBiden)team Team()team.hire([Biden, Trump])team.invest(investment)team.run_project(idea, send_toBiden) # send debate topic to Biden and let him speak firstawait team.run(n_roundn_round)3. 完整代码及运行结果
完整代码 import asyncio
import platform
from typing import Anyimport firefrom metagpt.actions import Action, UserRequirement
from metagpt.logs import logger
from metagpt.roles import Role
from metagpt.schema import Message
from metagpt.team import Teamclass SpeakAloud(Action):Action: Speak out aloud in a debate (quarrel)PROMPT_TEMPLATE: str ## BACKGROUNDSuppose you are {name}, you are in a debate with {opponent_name}.## DEBATE HISTORYPrevious rounds:{context}## YOUR TURNNow its your turn, you should closely respond to your opponents latest argument, state your position, defend your arguments, and attack your opponents arguments,craft a strong and emotional response in 80 words, in {name}s rhetoric and viewpoints, your will argue:name: str SpeakAloudasync def run(self, context: str, name: str, opponent_name: str):prompt self.PROMPT_TEMPLATE.format(contextcontext, namename, opponent_nameopponent_name)# logger.info(prompt)rsp await self._aask(prompt)return rspclass Debator(Role):name: str profile: str opponent_name: str def __init__(self, **data: Any):super().__init__(**data)self.set_actions([SpeakAloud])self._watch([UserRequirement, SpeakAloud])# async def _observe(self) - int:# await super()._observe()# # accept messages sent (from opponent) to self, disregard own messages from the last round# self.rc.news [msg for msg in self.rc.news if msg.send_to {self.name}]# return len(self.rc.news)async def _act(self) - Message:logger.info(f{self._setting}: to do {self.rc.todo}({self.rc.todo.name}))todo self.rc.todo # An instance of SpeakAloudmemories self.get_memories()context \n.join(f{msg.sent_from}: {msg.content} for msg in memories)print(context)rsp await todo.run(contextcontext, nameself.name, opponent_nameself.opponent_name)msg Message(contentrsp,roleself.profile,cause_bytype(todo),sent_fromself.name,send_toself.opponent_name,)self.rc.memory.add(msg)return msgasync def debate(idea: str, investment: float 3.0, n_round: int 5):Run a team of presidents and watch they quarrel. :)Biden Debator(nameBiden, profileDemocrat, opponent_nameTrump)Trump Debator(nameTrump, profileRepublican, opponent_nameBiden)team Team()team.hire([Biden, Trump])team.invest(investment)team.run_project(idea, send_toBiden) # send debate topic to Biden and let him speak firstawait team.run(n_roundn_round)def main(idea: str, investment: float 3.0, n_round: int 10)::param idea: Debate topic, such as Topic: The U.S. should commit more in climate change fightingor Trump: Climate change is a hoax:param investment: contribute a certain dollar amount to watch the debate:param n_round: maximum rounds of the debate:return:if platform.system() Windows:asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())asyncio.run(debate(idea, investment, n_round))if __name__ __main__:fire.Fire(main(Topic: The U.S. should commit more in climate change fighting))运行结果交替发言 4. 总结
这节内容最主要的是让我们了解和实践了多智能体间更多的消息交互和订阅机制主要是 send_to 参数的使用。 cause_bytype(todo), sent_fromself.name, send_toself.opponent_name, 这三个参数分别是形容Message的内容属性来自于哪个action以及角色并要发送给哪个角色。通过这样的机制可以实现相较于watch更灵活的订阅机制。 站内文章一览
