[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : invoke 메소드를 사용해 채팅하기 1

■ 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_community.tools.tavily_search    import TavilySearchResults
from langchain_community.document_loaders       import WebBaseLoader
from langchain_text_splitters                   import RecursiveCharacterTextSplitter
from langchain_openai                           import OpenAIEmbeddings
from langchain_community.vectorstores           import FAISS
from langchain.tools.retriever                  import create_retriever_tool
from langchain_openai                           import ChatOpenAI
from langchain                                  import hub
from langchain.agents                           import create_tool_calling_agent
from langchain.agents                           import AgentExecutor
from langchain_core.chat_history                import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history           import RunnableWithMessageHistory

load_dotenv()

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

tavilySearchResults = TavilySearchResults(max_results = 2)

webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview")

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

vectorStoreRetrieverTool = create_retriever_tool(
    vectorStoreRetriever,
    "langsmith_search",
    "Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)

toolList = [tavilySearchResults, vectorStoreRetrieverTool]

chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent")

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

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

sessionIDDictionary = {}

def getChatMessageHistory(sessionID : str) -> BaseChatMessageHistory:
    if sessionID not in sessionIDDictionary:
        sessionIDDictionary[sessionID] = ChatMessageHistory()
    return sessionIDDictionary[sessionID]

runnableWithMessageHistory = RunnableWithMessageHistory(
    agentExecutor,
    getChatMessageHistory,
    input_messages_key   = "input",
    history_messages_key = "chat_history",
)

responseDictionary1 = runnableWithMessageHistory.invoke(
    {"input" : "hi! I'm bob"},
    config = {"configurable" : {"session_id" : "<foo>"}}
)

print(responseDictionary1)
print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke(
    {"input" : "what's my name?"},
    config = {"configurable" : {"session_id" : "<foo>"}}
)

print(responseDictionary2)
print("-" * 100)

"""
{'input': "hi! I'm bob", 'chat_history': [], 'output': 'Hello Bob! How can I assist you today?'}

from dotenv import load_dotenv

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_community.document_loaders import WebBaseLoader

from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain.tools.retriever import create_retriever_tool

from langchain_openai import ChatOpenAI

from langchain import hub

from langchain.agents import create_tool_calling_agent

from langchain.agents import AgentExecutor

from langchain_core.chat_history import BaseChatMessageHistory

from langchain_community.chat_message_histories import ChatMessageHistory

from langchain_core.runnables.history import RunnableWithMessageHistory

load_dotenv()

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

tavilySearchResults = TavilySearchResults(max_results = 2)

webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview")

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

vectorStoreRetrieverTool = create_retriever_tool(

vectorStoreRetriever,

"langsmith_search",

"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",

)

toolList = [tavilySearchResults, vectorStoreRetrieverTool]

chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent")

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

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

sessionIDDictionary = {}

def getChatMessageHistory(sessionID : str) -> BaseChatMessageHistory:

if sessionID not in sessionIDDictionary:

sessionIDDictionary[sessionID] = ChatMessageHistory()

return sessionIDDictionary[sessionID]

runnableWithMessageHistory = RunnableWithMessageHistory(

agentExecutor,

getChatMessageHistory,

input_messages_key = "input",

history_messages_key = "chat_history",

)

responseDictionary1 = runnableWithMessageHistory.invoke(

{"input" : "hi! I'm bob"},

config = {"configurable" : {"session_id" : "<foo>"}}

)

print(responseDictionary1)

print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke(

{"input" : "what's my name?"},

config = {"configurable" : {"session_id" : "<foo>"}}

)

print(responseDictionary2)

print("-" * 100)

"""

{'input': "hi! I'm bob", 'chat_history': [], 'output': 'Hello Bob! How can I assist you today?'}

{'input': "what's my name?", 'chat_history': [HumanMessage(content="hi! I'm bob", additional_kwargs={}, response_metadata={}), AIMessage(content='Hello Bob! How can I assist you today?', additional_kwargs={}, response_metadata={})], 'output': 'Your name is Bob! How can I help you today?'}
—————————————————————————————————-
"""
—————————————————————————————————-

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.9
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
beautifulsoup4==4.12.3
bs4==0.0.2
certifi==2024.8.30
charset-normalizer==3.4.0
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.2
faiss-gpu==1.7.2
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-community==0.3.2
langchain-core==0.3.10
langchain-openai==0.2.2
langchain-text-splitters==0.3.0
langsmith==0.1.132
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.51.2
orjson==3.10.7
packaging==24.1
propcache==0.2.0
pydantic==2.9.2
pydantic-settings==2.5.2
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
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
tiktoken==0.8.0
tqdm==4.66.5
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.14.0

aiohappyeyeballs==2.4.3

aiohttp==3.10.9

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.0

async-timeout==4.0.3

attrs==24.2.0

beautifulsoup4==4.12.3

bs4==0.0.2

certifi==2024.8.30

charset-normalizer==3.4.0

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.2

faiss-gpu==1.7.2

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-community==0.3.2

langchain-core==0.3.10

langchain-openai==0.2.2

langchain-text-splitters==0.3.0

langsmith==0.1.132

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.51.2

orjson==3.10.7

packaging==24.1

propcache==0.2.0

pydantic==2.9.2

pydantic-settings==2.5.2

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

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

tiktoken==0.8.0

tqdm==4.66.5

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.14.0

※ pip install python-dotenv langchain-community langchain-openai faiss-gpu bs4 명령을 실행했다.