什么是LangChain Agent

简单来说           ，用户像LangChain输入的内容未知  
  
  
  
  
 。此时可以有一套工具集合(也可以自定义工具) 
 
  
 
  
 
  
 
  
 
  
，将这套自定义工具托管给LLM,让其自己决定使用工具中的某一个(如果存在的话)

例子

首先









，这里自定义了两个简单的工具

from langchain.tools import BaseTool # 天气查询工具
  
  
  
  
  
 ，无论查询什么都返回Sunny class WeatherTool(BaseTool): name = "Weather" description = "useful for When you want to know about the weather" def _run(self, query: str) -> str: return "Sunny^_^" async def _arun(self, query: str) -> str: """Use the tool asynchronously.""" raise NotImplementedError("BingSearchRun does not support async") # 计算工具 
  
  
  
  
 ，暂且写死返回3 class CustomCalculatorTool(BaseTool): name = "Calculator" description = "useful for when you need to answer questions about math." def _run(self, query: str) -> str: return "3" async def _arun(self, query: str) -> str: raise NotImplementedError("BingSearchRun does not support async")

接下来是针对于工具的简单调用：注意 


 


 


 


 


 
，这里使用OpenAI temperature=0需要限定为0

from langchain.agents import initialize_agent from langchain.llms import OpenAI from CustomTools import WeatherTool from CustomTools import CustomCalculatorTool llm = OpenAI(temperature=0) tools = [WeatherTool(), CustomCalculatorTool()] agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True) agent.run("Query the weather of this week,And How old will I be in ten years? This year I am 28")

看一下完整的响应过程：

I need to use two different tools to answer this question Action: Weather Action Input: This week Observation: Sunny^_^ Thought: I need to use a calculator to answer the second part of the question Action: Calculator Action Input: 28 + 10 Observation: 3 Thought: I now know the final answer Final Answer: This week will be sunny and in ten years I will be 38.

可以看到LangChain Agent 详细分析了每一个步骤
 
 
 
 
 
，并且正确的调用了每一个可用的方法  
   
   
   
   
  ，拿到了相应的返回值 

 

 

 

 

 
，甚至在最后还修复了28+10=3这个错误 
  
  
  
  
  。

下面看看LangChain Agent是如何做到这点的

工作原理

首先看看我输入的问题是什么：

Query the weather of this week,And How old will I be in ten years? This year I am 28

查询本周天气

 

 

 

 

 
，以及十年后我多少岁           ，今年我28

LangChain Agent中 
 

 
 

 
 

 
 

 
 

 
，有一套模板可以套用：

PREFIX = """Answer the following questions as best you can. You have access to the following tools:""" FORMAT_INSTRUCTIONS = """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 repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question""" SUFFIX = """Begin! Question: {input} Thought:{agent_scratchpad}"""

通过这个模板










，加上我们的问题以及自定义的工具                ，会变成下面这个样子,并且附带解释：

Answer the following questions as best you can. You have access to the following tools: # 尽可能的去回答以下问题 
 
 
 
 
 
 
 
 
 
 
，你可以使用以下的工具： Calculator: Useful for when you need to answer questions about math. # 计算器：当你需要回答数学计算的时候可以用到 Weather: useful for When you want to know about the weather # 天气：当你想知道天气相关的问题时可以用到 Use the following format: # 请使用以下格式(回答) Question: the input question you must answer # 你必须回答输入的问题 Thought: you should always think about what to do # 你应该一直保持思考















，思考要怎么解决问题 Action: the action to take, should be one of [Calculator, Weather] # 你应该采取[计算器,天气]之一 Action Input: the input to the action # 动作的输入 Observation: the result of the action # 动作的结果 ... (this Thought/Action/Action Input/Observation can repeat N times) # 思考-行动-输入-输出 的循环可以重复N次 T hought: I now know the final answer # 最后           ，你应该知道最终结果了 Final Answer: the final answer to the original input question # 针对于原始问题 
 
  
 
  
 
  
 
  
 
  
，输出最终结果 Begin! # 开始 Question: Query the weather of this week,And How old will I be in ten years? This year I am 28 # 问输入的问题 Thought:

通过这个模板向openai规定了一系列的规范









，包括目前现有哪些工具集
  
  
  
  
  
 ，你需要思考回答什么问题 
  
  
  
  
 ，你需要用到哪些工具 


 


 


 


 


 
，你对工具需要输入什么内容,等等


 


 


 


 


 

。

如果仅仅是这样
 
 
 
 
 
，openAI会完全补完你的回答  
   
   
   
   
  ，中间无法插入任何内容 
 
 
 
 
。因此LangChain使用OpenAI的stop参数 

 

 

 

 

 
，截断了AI当前对话  
   
   
   
   
   。"stop": ["\\nObservation: ", "\\n\\tObservation: "]

做了以上设定以后

 

 

 

 

 
，OpenAI仅仅会给到Action和 Action Input两个内容就被stop早停了

 

 

 

 

 
。

以下是OpenAI的响应内容： I need to use the weather tool to answer the first part of the question, and the calculator to answer the second part. Action: Weather Action Input: This week

到这里是OpenAI的响应结果           ，可见 
 

 
 

 
 

 
 

 
 

 
，很简单就拿到了Action和Action Input
 

 

 

 

 

。

这里从Tools中找到name=Weather的工具










，然后再将This Week传入方法           。具体业务处理看详细情况
 

 
 

 
 

 
 

 
 

 
 。这里仅返回Sunny










。

由于当前找到了Action和Action Input                。 代表OpenAI认定当前任务链并没有结束
 
 
 
 
 
 
 
 
 
 
 。因此像请求体后拼接结果：Observation: Sunny 并且让他再次思考Thought:

开启第二轮思考：

下面是再次请求的完整请求体: Answer the following questions as best you can. You have access to the following tools: Calculator: Useful for when you need to answer questions about math. Weather: useful for When you want to know about the weather 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 [Calculator, Weather] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question Begin! Question: Query the weather of this week,And How old will I be in ten years? This year I am 28 Thought: I need to use the weather tool to answer the first part of the question, and the calculator to answer the second part. Action: Weather Action Input: This week Observation: Sunny^_^ Thought:

同第一轮一样                ，OpenAI再次进行思考 
 
 
 
 
 
 
 
 
 
 
，并且返回Action 和 Action Input 后















，再次被早停















。

I need to calculate my age in ten years Action: Calculator Action Input: 28 + 10

由于计算器工具只会返回3           ，结果会拼接出一个错误的结果 
 
  
 
  
 
  
 
  
 
  
，构造成了一个新的请求体

进行第三轮请求： Answer the following questions as best you can. You have access to the following tools: Calculator: Useful for when you need to answer questions about math. Weather: useful for When you want to know about the weather 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 [Calculator, Weather] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question Begin! Question: Query the weather of this week,And How old will I be in ten years? This year I am 28 Thought: I need to use the weather tool to answer the first part of the question, and the calculator to answer the second part. Action: Weather Action Input: This week Observation: Sunny^_^ Thought:I need to calculate my age in ten years Action: Calculator Action Input: 28 + 10 Observation: 3 Thought:

此时两个问题全都拿到了结果









，根据开头的限定
  
  
  
  
  
 ，OpenAi在完全拿到结果以后会返回I now know the final answer           。并且根据完整上下文
 
  
 
  
 
  
 
  
 
  
 。把多个结果进行归纳总结：下面是完整的相应结果：

I now know the final answer Final Answer: I will be 38 in ten years and the weather this week is sunny.

可以看到










。ai严格的按照设定返回想要的内容 
  
  
  
  
 ，并且还以外的把28+10=3这个数学错误给改正了

以上 


 


 


 


 


 
，就是LangChain Agent的完整工作流程