[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : invoke 메소드를 사용해 채팅하기 2

■ RunnableWithMessageHistory 클래스의 invoke 메소드를 사용해 채팅하는 방법을 보여준다.

※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

▶ main.py


from dotenv                           import load_dotenv
from langchain_openai                 import ChatOpenAI
from langchain_core.tools             import tool
from langchain_core.prompts           import ChatPromptTemplate
from langchain.agents                 import create_tool_calling_agent
from langchain.agents                 import AgentExecutor
from langchain_core.chat_history      import InMemoryChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system"     , "You are a helpful assistant."),
        ("placeholder", "{chat_history}"              ),
        ("human"      , "{input}"                     ),
        ("placeholder", "{agent_scratchpad}"          )
    ]
)

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

inMemoryChatMessageHistory = InMemoryChatMessageHistory(session_id = "test-session")

runnableWithMessageHistory = RunnableWithMessageHistory(
    agentExecutor,
    lambda session_id : inMemoryChatMessageHistory,
    input_messages_key   = "input",
    history_messages_key = "chat_history"
)

configurationDictionary = {"configurable" : {"session_id" : "test-session"}}

responseDictionary1 = runnableWithMessageHistory.invoke({"input" : "Hi, I'm polly! What's the output of magic_function of 3?"}, configurationDictionary)

outputString1 = responseDictionary1["output"]

print(outputString1)
print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke({"input" : "Remember my name?"}, configurationDictionary)

outputString2 = responseDictionary2["output"]

print(outputString2)
print("-" * 100)

responseDictionary3 = runnableWithMessageHistory.invoke({"input": "what was that output again?"}, configurationDictionary)

outputString3 = responseDictionary3["output"]

print(outputString3)

"""
Hello Polly! The output of the magic function for the input 3 is 5.

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langchain_core.prompts import ChatPromptTemplate

from langchain.agents import create_tool_calling_agent

from langchain.agents import AgentExecutor

from langchain_core.chat_history import InMemoryChatMessageHistory

from langchain_core.runnables.history import RunnableWithMessageHistory

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system" , "You are a helpful assistant."),

("placeholder", "{chat_history}" ),

("human" , "{input}" ),

("placeholder", "{agent_scratchpad}" )

]

)

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

inMemoryChatMessageHistory = InMemoryChatMessageHistory(session_id = "test-session")

runnableWithMessageHistory = RunnableWithMessageHistory(

agentExecutor,

lambda session_id : inMemoryChatMessageHistory,

input_messages_key = "input",

history_messages_key = "chat_history"

)

configurationDictionary = {"configurable" : {"session_id" : "test-session"}}

responseDictionary1 = runnableWithMessageHistory.invoke({"input" : "Hi, I'm polly! What's the output of magic_function of 3?"}, configurationDictionary)

outputString1 = responseDictionary1["output"]

print(outputString1)

print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke({"input" : "Remember my name?"}, configurationDictionary)

outputString2 = responseDictionary2["output"]

print(outputString2)

print("-" * 100)

responseDictionary3 = runnableWithMessageHistory.invoke({"input": "what was that output again?"}, configurationDictionary)

outputString3 = responseDictionary3["output"]

print(outputString3)

"""

Hello Polly! The output of the magic function for the input 3 is 5.

Yes, you mentioned your name is Polly!
—————————————————————————————————-
The output of the magic function for the input 3 is 5.
"""
—————————————————————————————————-

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.10
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
distro==1.9.0
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.6
httpx==0.27.2
idna==3.10
jiter==0.6.1
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.3
langchain-core==0.3.10
langchain-openai==0.2.2
langchain-text-splitters==0.3.0
langsmith==0.1.134
multidict==6.1.0
numpy==1.26.4
openai==1.51.2
orjson==3.10.7
packaging==24.1
propcache==0.2.0
pydantic==2.9.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
regex==2024.9.11
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
tiktoken==0.8.0
tqdm==4.66.5
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.14.0

aiohappyeyeballs==2.4.3

aiohttp==3.10.10

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.0

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

distro==1.9.0

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.6

httpx==0.27.2

idna==3.10

jiter==0.6.1

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.3

langchain-core==0.3.10

langchain-openai==0.2.2

langchain-text-splitters==0.3.0

langsmith==0.1.134

multidict==6.1.0

numpy==1.26.4

openai==1.51.2

orjson==3.10.7

packaging==24.1

propcache==0.2.0

pydantic==2.9.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

regex==2024.9.11

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

tiktoken==0.8.0

tqdm==4.66.5

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.14.0

※ pip install python-dotenv langchain langchain-openai 명령을 실행했다.