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 jsontools = 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 promptsself.result_language = result_languageself.agent_scratchpad = "" # we initialize our scratchpadself.feedback = "" # we initialize our feedback if we get some error# other settingsself.iterations=10self.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}}```# Observationoutput 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 """)@propertydef tools_description(self)->str:  # we can refer to properties in out prompts tooreturn "\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:```<prompt:system>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:```<prompt:user>{question}```Scratchpad:```<prompt:assistant>{agent_scratchpad}``````<prompt:user>{feedback}```"""returndef 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}"returnelse: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 oneraise eself.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 continueself.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~