LangChain-Decorators 包学习

• libraryIO的链接：https://libraries.io/pypi/langchain-decorators

• 来colab玩玩它的demo
  

• 感觉这确实是个挺好用的库

  • 想到之前纯调prompt来控制输出格式的痛苦，这个可太有效了
    
    

• cool~
  

• 最下面这个react的多智能体例子很好玩，来看看！

  	from typing import List
  from langchain_decorators import llm_prompt
  from langchain.agents import load_tools
  from langchain.tools.base import BaseTool
  from textwrap import dedent
  from langchain_decorators import PromptTypes
  from langchain_decorators.output_parsers import JsonOutputParser
  import json
  
  tools = load_tools([ "llm-math"], llm=GlobalSettings.get_current_settings().default_llm)
  
  # you may, or may not use pydantic as your base class... totally up to you
  class MultilingualAgent:
  
      def __init__(self,  tools:List[BaseTool],result_language:str=None) -> None:
          self.tools = tools
  
          # we can refer to our field in all out prompts
          self.result_language = result_language
          
          self.agent_scratchpad = "" # we initialize our scratchpad
          self.feedback = "" # we initialize our feedback if we get some error
  
          # other settings
          self.iterations=10
          self.agent_format_instructions = dedent("""\
              # Reasoning
              ... write your reasoning here ...
  
              # Tool
              ```json
                  {{
                      "tool": name of the tool to use,
                      "tool_input": the input for the tool
                  }}
              ```
  
              # Observation
              output from the tool
  
              ... repeat this # Reasoning, # Tool, # Observation sequence multiple times until you know the final answer, when you write:
  
              # Final answer
              ... write the final answer 
              """)
  
      @property
      def tools_description(self)->str:  # we can refer to properties in out prompts too
          return "\n".join([f" - {tool.name}: {tool.description}" for tool in self.tools])
  
      # we defined prompt type here, which will make 
      @llm_prompt(prompt_type=PromptTypes.AGENT_REASONING, output_parser="markdown", stop_tokens=["Observation"], verbose=True)
      def reason(self)->dict:
          """
          The system prompt:
          ```
          You are an assistant that uses reasoning and tools to help user. You use tools for the task the tool is designed to. 
          Before answering the question and/or using the tool, you should write down the explanation. 
          
          Here is the list of tools available:
          {tools_description}
          
          Use this format:
          
          {agent_format_instructions}{? in {result_language}?} here ...{?
          Make sure to write the final answer in in {result_language}!?} 
          
          ```
          User question:
          ```
          {question}
          ```
          Scratchpad:
          ```
          {agent_scratchpad}
          ```
          ```
          {feedback}
          ```
          """
          return
      
      
      def act(self, tool_name:str, tool_input:str)->str:
          tool = next((tool for tool in self.tools if tool.name.lower()==tool_name.lower()==tool_name.lower()))
          if tool is None:
              self.feedback = f"Tool {tool_name} is not available. Available tools are: {self.tools_description}"
              return
          else:
              try:
                  result = tool.run(tool_input)
              except Exception as e:
                  if self.feedback is not None:
                      # we've already experienced an error, so we are not going to try forever... let's raise this one
                      raise e
                  self.feedback = f"Tool {tool_name} failed with error: {e}.\nLet's fix it and try again."
              tool_instructions = json.dumps({"tool":tool.name, "tool_input":tool_input})
              self.agent_scratchpad += f"# Tool\n```json\n{tool_instructions}\n```\n# Observation\n\nResult from tool {tool_name}:\n\t{result}\n"
  
  
  
      def run(self, question):
          for i in range(self.iterations):
              reasoning = self.reason(question=question)
              if reasoning.get("Final answer") is not None:
                  return reasoning.get("Final answer")
              else:
                  tool_info = reasoning.get("Tool")
                  tool_name, tool_input = (None, None)
                  if tool_info:
                      tool_info_parsed = JsonOutputParser().parse(tool_info)
                      tool_name = tool_info_parsed.get("tool")
                      tool_input = tool_info_parsed.get("tool_input")
  
                  if tool_name is None or tool_input is None:
                      self.feedback = "Your response was not in the expected format. Please make sure to response in correct format:\n" + self.agent_format_instructions 
                      continue
                  self.act(tool_name, tool_input)
          raise Exception(f"Failed to answer the question after {self.iterations} iterations. Last result: {reasoning}")
  
          
  agent = MultilingualAgent(tools=tools, result_language="German" )
  
  result = agent.run("What is the surface of a sphere with radius with diameter of 100km?")
  
  print("\n\nHere is the agent's answer:", result)
  

• 下面是输出
  

• 这个包确实能带来不少方便~