■ ChatPromptTemplate 클래스의 from_messages 정적 메소드를 사용해 FEW SHOT 프롬프트 템플리트 만들고 도구 출력을 모델에 전달하는 방법을 보여준다.
▶ main.py
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
import os from langchain_core.tools import tool from langchain_core.messages import HumanMessage, AIMessage, ToolMessage from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI from langchain_core.runnables import RunnablePassthrough os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>" # add 도구를 정의한다. @tool def add(a : int, b : int) -> int: """Adds a and b.""" return a + b # multiply 도구를 정의한다. @tool def multiply(a : int, b : int) -> int: """Multiplies a and b.""" return a * b # 도구 리스트를 설정한다. toolList = [add, multiply] # FEW SHOW 채팅 프롬프트 템플리트를 설정한다. 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.""" 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" : 10, "y" : 20}, "id" : "1"}] ), ToolMessage("16505054784", tool_call_id = "1"), AIMessage( "", name ="example_assistant", tool_calls = [{"name" : "add", "args" : {"x" : 200, "y" : 4}, "id" : "2"}] ), ToolMessage("16505054788", tool_call_id = "2"), AIMessage("The product of 10 and 20 plus four is 204", name = "example_assistant") ] fewShotChatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", systemMessageString), *exampleMessageList, ("human", "{query}"), ] ) # LLM 모델을 설정한다. chatOpenAI = ChatOpenAI(model="gpt-3.5-turbo-0125") # LLM 모델에 도구를 바인딩한다. toolRunnableSequence = chatOpenAI.bind_tools(toolList) # 실행 체인을 설정한다. executeRunnableSequence = {"query" : RunnablePassthrough()} | fewShotChatPromptTemplate | toolRunnableSequence # 실행 체인을 실행한다. messageList = [HumanMessage("Whats 119 times 8 plus 20?")] aiMessage1 = executeRunnableSequence.invoke(messageList) messageList.append(aiMessage1) for tool_call in aiMessage1.tool_calls: tool = {"add" : add, "multiply" : multiply}[tool_call["name"].lower()] resultInteger = tool.invoke(tool_call["args"]) messageList.append(ToolMessage(resultInteger, tool_call_id = tool_call["id"])) aiMessage2 = executeRunnableSequence.invoke(messageList) print(aiMessage2.content) """ The product of 119 and 8 is 952, and when you add 20 to that, you get a total of 972. """ |
▶ requirements.txt
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
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 dataclasses-json==0.6.7 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-community==0.2.5 langchain-core==0.2.7 langchain-experimental==0.0.61 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.77 marshmallow==3.21.3 multidict==6.0.5 mypy-extensions==1.0.0 numpy==1.26.4 openai==1.34.0 orjson==3.10.5 packaging==24.1 pydantic==2.7.4 pydantic_core==2.18.4 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 SQLAlchemy==2.0.30 tenacity==8.3.0 tiktoken==0.7.0 tqdm==4.66.4 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.1 yarl==1.9.4 |
※ pip install langchain langchain_experimental langchain-openai 명령을 실행했다.