■ 예제 프롬프트에 HumanMessage/AIMessage/ToolMessage 객체를 추가해 사용자 정의 함수를 질의 응답하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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from dotenv import load_dotenv from langchain_core.tools import tool from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI from langchain_core.runnables import RunnablePassthrough from langchain_core.messages import HumanMessage from langchain_core.messages import ToolMessage from langchain_core.messages import AIMessage load_dotenv() @tool def add(a : int, b : int) -> int: """Adds a and b.""" return a + b @tool def multiply(a : int, b : int) -> int: """Multiplies a and b.""" return a * b toolList = [add, multiply] exampleMessageList = [ HumanMessage("What's the product of 317253 and 128472 plus four", name = "example_user"), AIMessage("", name = "example_assistant", tool_calls = [{"name" : "Multiply", "args" : {"x" : 100, "y" : 200}, "id" : "1"}]), ToolMessage("20000", tool_call_id = "1"), AIMessage("", name = "example_assistant", tool_calls = [{"name" : "Add", "args" : {"x" : 50, "y": 100}, "id": "2"}]), ToolMessage("150", tool_call_id = "2"), AIMessage("The product of 100 and 200 plus 5000 is 25000", name = "example_assistant") ] systemMessageString = """You are bad at math but are an expert at using a calculator. Use past tool usage as an example of how to correctly use the tools.""" chatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", systemMessageString), *exampleMessageList, ("human", "{query}"), ] ) chatOpenAI = ChatOpenAI(model = "gpt-3.5-turbo-0125") toolRunnableSequence = chatOpenAI.bind_tools(toolList) runnableSequence = {"query" : RunnablePassthrough()} | chatPromptTemplate | toolRunnableSequence inputString = "What is 3 * 12? Also, what is 11 + 49?" messageList = [HumanMessage(inputString)] respnseAIMessage1 = runnableSequence.invoke(messageList) messageList.append(respnseAIMessage1) for toolCall in respnseAIMessage1.tool_calls: tool = {"add" : add, "multiply" : multiply}[toolCall["name"].lower()] toolResult = tool.invoke(toolCall["args"]) messageList.append(ToolMessage(toolResult, tool_call_id = toolCall["id"])) responseAIMessage2 = toolRunnableSequence.invoke(messageList) print(responseAIMessage2.content) """ 3 * 12 is 36 and 11 + 49 is 60. """ |
▶ requirements.txt
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aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.4.0 async-timeout==4.0.3 attrs==23.2.0 certifi==2024.6.2 charset-normalizer==3.3.2 distro==1.9.0 exceptiongroup==1.2.1 frozenlist==1.4.1 greenlet==3.0.3 h11==0.14.0 httpcore==1.0.5 httpx==0.27.0 idna==3.7 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.2.5 langchain-core==0.2.9 langchain-openai==0.1.9 langchain-text-splitters==0.2.1 langsmith==0.1.81 multidict==6.0.5 numpy==1.26.4 openai==1.35.3 orjson==3.10.5 packaging==24.1 pydantic==2.7.4 pydantic_core==2.18.4 python-dotenv==1.0.1 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 SQLAlchemy==2.0.31 tenacity==8.4.2 tiktoken==0.7.0 tqdm==4.66.4 typing_extensions==4.12.2 urllib3==2.2.2 yarl==1.9.4 |
※ psp install python-dotenv langchain langchain-openai 명령을 실행했다.