Daddy Makers: LangGraph 기반 데이터 분석 멀티 에이전트 만들기

2024년 10월 6일 일요일

LangGraph 기반 데이터 분석 멀티 에이전트 만들기

이 글은 LangGraph 기반 데이터 분석 멀티 에이전트 만드는 방법을 간략히 설명한다.

LangChain Tool 기반 수학 에이전트 개발

다음 코드를 입력해 실행한다.

from langchain_openai import OpenAI

from langchain_community.chat_models import ChatOllama

from langchain.chains import LLMMathChain, LLMChain

from langchain.prompts import PromptTemplate

from langchain_community.utilities import WikipediaAPIWrapper

from langchain.agents.agent_types import AgentType

from langchain.agents import Tool, initialize_agent

from dotenv import load_dotenv

import chainlit as cl

load_dotenv()

@cl.on_chat_start

def math_chatbot():

llm = ChatOllama(model='llama3', temperature=0.0)

# prompt for reasoning based tool

word_problem_template = """You are a reasoning agent tasked with solving t he user's logic-based questions. Logically arrive at the solution, and be factual. In your answers, clearly detail the steps involved and give the final answer. Provide the response in bullet points. Question {question} Answer"""

math_assistant_prompt = PromptTemplate(

input_variables=["question"],

template=word_problem_template

)

# chain for reasoning based tool

word_problem_chain = LLMChain(llm=llm,

prompt=math_assistant_prompt)

# reasoning based tool

word_problem_tool = Tool.from_function(name="Reasoning Tool",

func=word_problem_chain.run,

description="Useful for when you need to answer logic-based/reasoning questions."

)

# calculator tool for arithmetics

problem_chain = LLMMathChain.from_llm(llm=llm)

math_tool = Tool.from_function(name="Calculator",

func=problem_chain.run,

description="Useful for when you need to answer numeric questions. This tool is only for math questions and nothing else. Only input math expressions, without text",

)

# Wikipedia Tool

wikipedia = WikipediaAPIWrapper()

wikipedia_tool = Tool(

name="Wikipedia",

func=wikipedia.run,

description="A useful tool for searching the Internet to find information on world events, issues, dates, years, etc. Worth using for general topics. Use precise questions.")

# agent

agent = initialize_agent(

tools=[wikipedia_tool, math_tool, word_problem_tool],

llm=llm,

agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,

verbose=False,

handle_parsing_errors=True

)

cl.user_session.set("agent", agent)

@cl.on_message

async def process_user_query(message: cl.Message):

agent = cl.user_session.get("agent")