■ trim_messages 함수의 strategy/max_tokens/token_counter 인자를 사용해 RunnableLambda 객체를 만드는 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.messages import trim_messages from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI from langchain_core.runnables import RunnablePassthrough from operator import itemgetter from langchain_community.chat_message_histories import ChatMessageHistory from langchain_core.runnables.history import RunnableWithMessageHistory load_dotenv() runnableLambda = trim_messages(strategy = "last", max_tokens = 2, token_counter = len) chatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system" , "You are a helpful assistant. Answer all questions to the best of your ability."), ("placeholder", "{chat_history}"), ("human" , "{input}") ] ) chatOpenAI = ChatOpenAI(model = "gpt-4o-mini") runnableSequence = RunnablePassthrough.assign(chat_history = itemgetter("chat_history") | runnableLambda) | chatPromptTemplate | chatOpenAI chatMessageHistory = ChatMessageHistory() chatMessageHistory.add_user_message("Hey there! I'm Nemo.") chatMessageHistory.add_ai_message("Hello!") chatMessageHistory.add_user_message("How are you today?") chatMessageHistory.add_ai_message("Fine thanks!") runnableWithMessageHistory = RunnableWithMessageHistory( runnableSequence, lambda session_id : chatMessageHistory, input_messages_key = "input", history_messages_key = "chat_history", ) responseMessage1 = runnableWithMessageHistory.invoke( {"input" : "What's my name?"}, {"configurable" : {"session_id" : "unused"}} ) print(responseMessage1.content) print("-" * 50) responseMessage2 = runnableWithMessageHistory.invoke( {"input" : "Where does P. Sherman live?"}, {"configurable" : {"session_id" : "unused"}} ) print(responseMessage2.content) print("-" * 50) """ I'm sorry, but I don't know your name. If you'd like to share it, feel free! -------------------------------------------------- P. Sherman lives at 42 Wallaby Way, Sydney. This is a reference from the animated movie "Finding Nemo." -------------------------------------------------- """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.10 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.7.0 attrs==24.2.0 certifi==2024.8.30 charset-normalizer==3.4.0 colorama==0.4.6 dataclasses-json==0.6.7 distro==1.9.0 frozenlist==1.5.0 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.7 httpx==0.28.1 httpx-sse==0.4.0 idna==3.10 jiter==0.8.0 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.10 langchain-community==0.3.10 langchain-core==0.3.22 langchain-openai==0.2.11 langchain-text-splitters==0.3.2 langsmith==0.1.147 marshmallow==3.23.1 multidict==6.1.0 mypy-extensions==1.0.0 numpy==2.2.0 openai==1.57.0 orjson==3.10.12 packaging==24.2 propcache==0.2.1 pydantic==2.10.3 pydantic-settings==2.6.1 pydantic_core==2.27.1 python-dotenv==1.0.1 PyYAML==6.0.2 regex==2024.11.6 requests==2.32.3 requests-toolbelt==1.0.0 sniffio==1.3.1 SQLAlchemy==2.0.36 tenacity==9.0.0 tiktoken==0.8.0 tqdm==4.67.1 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.3 yarl==1.18.3 |
※ pip install python-dotenv langchain-community langchain-openai 명령을 실행했다.