■ pull 함수를 사용해 랭체인 허브에서 RAG 프롬프트 관련 ChatPromptTempalte 객체를 만드는 방법을 보여준다.
▶ main.py
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from langchain import hub chatPromptTemplate = hub.pull("rlm/rag-prompt") print(chatPromptTemplate) """ input_variables = ['context', 'question'] metadata = {'lc_hub_owner' : 'rlm', 'lc_hub_repo' : 'rag-prompt', 'lc_hub_commit_hash' : '50442af133e61576e74536c6556cefe1fac147cad032f4377b60c436e6cdcb6e'} messages = [ HumanMessagePromptTemplate( prompt = PromptTemplate ( input_variables = ['context', 'question'], template = "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question} \nContext: {context} \nAnswer:" ) ) ] """ |
▶ requirements.txt
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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 certifi==2024.8.30 charset-normalizer==3.4.0 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 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.3 langchain-core==0.3.10 langchain-text-splitters==0.3.0 langsmith==0.1.132 multidict==6.1.0 numpy==1.26.4 orjson==3.10.7 packaging==24.1 propcache==0.2.0 pydantic==2.9.2 pydantic_core==2.23.4 PyYAML==6.0.2 requests==2.32.3 requests-toolbelt==1.0.0 sniffio==1.3.1 SQLAlchemy==2.0.35 tenacity==8.5.0 typing_extensions==4.12.2 urllib3==2.2.3 yarl==1.14.0 |
※ pip install langchain 명령을 실행했다.