智能体Agent-书生浦语大模型实战营学习笔记6&大语言模型10

大语言模型学习：10.智能体Agent

书生浦语大模型实战营学习笔记6

定义

即P（感知）—> P（规划）—>A（行动）。类似人类「做事情」的过程，Agent的核心功能，可以归纳为三个步骤的循环：感知(Perception)、规划(Planning)和行动(Action)。感知(Perception)是指Agent从环境中收集信息并从中提取相关知识的能力，规划(Planning)是指Agent为了某一目标而作出的决策过程，行动(Action)是指基于环境和规划做出的动作。其中，Policy是Agent做出Action的核心决策，而行动又通过观察（Observation）成为进一步Perception的前提和基础，形成自主地闭环学习过程。

组成

智能体范式

Agent的处理更强调workflow，更像一个flow-engineering

ReAct

ReAct: Synergizing Reasoning and Acting in Language Models

ReAct是这几种范式里面最基础的。核心原理是：自己选择需要使用的工具，并使用工具获取输出。

关于ReAct，这里有个Repo实现了一个简易的ReAct Agent，可以去看看具体实现。在这里简单的提一下：

1. 首先定义工具类。这里以谷歌搜索为例：

   class Tools:
       def __init__(self) -> None:
           self.toolConfig = self._tools()
   
       def _tools(self):
           tools = [
               {
                   'name_for_human': '谷歌搜索',
                   'name_for_model': 'google_search',
                   'parameters': [
                       {
                           'name': 'search_query',
                           'description': '搜索关键词或短语',
                           'required': True,
                           'schema': {'type': 'string'},
                       }
                   ],
               }
           ]
           return tools
   
       def google_search(self, search_query: str):
           ...
   

2. 构建系统提示：直接在prompt里告诉模型可以调用的工具（build_system_input），模型就会自己输出自己要调用的工具，之后Agent解析模型自己的输出（parse_latest_plugin_call）并调用工具（call_plugin）。

   TOOL_DESC = """{name_for_model}: Call this tool to interact with the {name_for_human} API. What is the {name_for_human} API useful for? {description_for_model} Parameters: {parameters} Format the arguments as a JSON object."""
   REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:
   
   {tool_descs}
   
   Use the following format:
   
   Question: the input question you must answer
   Thought: you should always think about what to do
   Action: the action to take, should be one of [{tool_names}]
   Action Input: the input to the action
   Observation: the result of the action
   ... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
   Thought: I now know the final answer
   Final Answer: the final answer to the original input question
   
   Begin!
   """
   
   class Agent:
       def __init__(self, path: str = '') -> None:
           self.path = path
           self.tool = Tools()
           self.system_prompt = self.build_system_input()
           self.model = InternLM2Chat(path)
   
       def build_system_input(self):
           tool_descs, tool_names = [], []
           for tool in self.tool.toolConfig:
               tool_descs.append(TOOL_DESC.format(**tool))
               tool_names.append(tool['name_for_model'])
           tool_descs = '\n\n'.join(tool_descs)
           tool_names = ','.join(tool_names)
           sys_prompt = REACT_PROMPT.format(tool_descs=tool_descs, tool_names=tool_names)
           return sys_prompt
   
       def parse_latest_plugin_call(self, text):
           plugin_name, plugin_args = '', ''
           i = text.rfind('\nAction:')
           j = text.rfind('\nAction Input:')
           k = text.rfind('\nObservation:')
           if 0 <= i < j:  # If the text has `Action` and `Action input`,
               if k < j:  # but does not contain `Observation`,
                   text = text.rstrip() + '\nObservation:'  # Add it back.
               k = text.rfind('\nObservation:')
               plugin_name = text[i + len('\nAction:') : j].strip()
               plugin_args = text[j + len('\nAction Input:') : k].strip()
               text = text[:k]
           return plugin_name, plugin_args, text
   
       def call_plugin(self, plugin_name, plugin_args):
           plugin_args = json5.loads(plugin_args)
           if plugin_name == 'google_search':
               return '\nObservation:' + self.tool.google_search(**plugin_args)
   
       def text_completion(self, text, history=[]):
           text = "\nQuestion:" + text
           response, his = self.model.chat(text, history, self.system_prompt)
           print(response)
           plugin_name, plugin_args, response = self.parse_latest_plugin_call(response)
           if plugin_name:
               response += self.call_plugin(plugin_name, plugin_args)
           response, his = self.model.chat(response, history, self.system_prompt)
           return response, his
   

AutoGPT

AutoGPT范式通过将任务发送给任务执行智能体A，将问题与A的结果存储至记忆，再将A的结果发送给任务创建智能体B，将B的结果存储至记忆，再将记忆发送给A，如此迭代直至符合条件。

ReWoo

ReWoo将用户输入进行计划拆分后运行，并将所有的结果整合为最后输出。

Agent与LangChain的关系

Agent属于Langchain的一部分。