■ OPENAI 모델을 사용해 PDF 문서 로드 및 질의 응답 애플리케이션을 만드는 방법을 보여준다.
▶ main.py
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import os from langchain_community.document_loaders import PyPDFLoader from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_openai import OpenAIEmbeddings from langchain_chroma import Chroma from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain.chains.combine_documents import create_stuff_documents_chain from langchain.chains import create_retrieval_chain os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>" # PDF 문서를 로드한다. filePath = "./nke-10k-2023.pdf" pyPDFLoader = PyPDFLoader(filePath) documentList = pyPDFLoader.load() # 문서를 분할한다. recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200) splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList) # 벡터 저장소를 설정한다. chroma = Chroma.from_documents(documents = splitDocumentList, embedding = OpenAIEmbeddings()) # 벡터 저장소 검색기를 설정한다. vectorStoreRetriever = chroma.as_retriever() # LLM 모델을 설정한다. chatOpenAI = ChatOpenAI(model = "gpt-4o") # 채팅 프롬프트 템플리트를 설정한다. systemMessage = ( "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, say that you " "don't know. Use three sentences maximum and keep the " "answer concise." "\n\n" "{context}" ) chatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", systemMessage), ("human" , "{input}"), ] ) # 질의 응답 체인을 설정한다. qnaRunnableSequence = create_stuff_documents_chain(chatOpenAI, chatPromptTemplate) # RAG 체인을 설정한다. ragRunnableSequence = create_retrieval_chain(vectorStoreRetriever, qnaRunnableSequence) # 질의 응답을 실행한다. resultDictionary = ragRunnableSequence.invoke({"input" : "What was Nike's revenue in 2023?"}) print(resultDictionary["context"][0].page_content) """ FISCAL 2023 NIKE BRAND REVENUE HIGHLIGHTS The following tables present NIKE Brand revenues disaggregated by reportable operating segment, distribution channel and major product line: FISCAL 2023 COMPARED TO FISCAL 2022 •NIKE, Inc. Revenues were $51.2 billion in fiscal 2023, which increased 10% and 16% compared to fiscal 2022 on a reported and currency-neutral basis, respectively. The increase was due to higher revenues in North America, Europe, Middle East & Africa ("EMEA"), APLA and Greater China, which contributed approximately 7, 6, 2 and 1 percentage points to NIKE, Inc. Revenues, respectively. •NIKE Brand revenues, which represented over 90% of NIKE, Inc. Revenues, increased 10% and 16% on a reported and currency-neutral basis, respectively. This increase was primarily due to higher revenues in Men's, the Jordan Brand, Women's and Kids' which grew 17%, 35%,11% and 10%, respectively, on a wholesale equivalent basis. """ |
▶ requirements.txt
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aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.4.0 asgiref==3.8.1 async-timeout==4.0.3 attrs==23.2.0 backoff==2.2.1 bcrypt==4.1.3 build==1.2.1 cachetools==5.3.3 certifi==2024.6.2 charset-normalizer==3.3.2 chroma-hnswlib==0.7.3 chromadb==0.5.0 click==8.1.7 coloredlogs==15.0.1 dataclasses-json==0.6.7 Deprecated==1.2.14 distro==1.9.0 dnspython==2.6.1 email_validator==2.1.1 exceptiongroup==1.2.1 fastapi==0.111.0 fastapi-cli==0.0.4 filelock==3.15.1 flatbuffers==24.3.25 frozenlist==1.4.1 fsspec==2024.6.0 google-auth==2.30.0 googleapis-common-protos==1.63.1 greenlet==3.0.3 grpcio==1.64.1 h11==0.14.0 httpcore==1.0.5 httptools==0.6.1 httpx==0.27.0 huggingface-hub==0.23.3 humanfriendly==10.0 idna==3.7 importlib_metadata==7.1.0 importlib_resources==6.4.0 Jinja2==3.1.4 jsonpatch==1.33 jsonpointer==3.0.0 kubernetes==30.1.0 langchain==0.2.4 langchain-chroma==0.1.1 langchain-community==0.2.4 langchain-core==0.2.6 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.77 markdown-it-py==3.0.0 MarkupSafe==2.1.5 marshmallow==3.21.3 mdurl==0.1.2 mmh3==4.1.0 monotonic==1.6 mpmath==1.3.0 multidict==6.0.5 mypy-extensions==1.0.0 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.18.0 openai==1.34.0 opentelemetry-api==1.25.0 opentelemetry-exporter-otlp-proto-common==1.25.0 opentelemetry-exporter-otlp-proto-grpc==1.25.0 opentelemetry-instrumentation==0.46b0 opentelemetry-instrumentation-asgi==0.46b0 opentelemetry-instrumentation-fastapi==0.46b0 opentelemetry-proto==1.25.0 opentelemetry-sdk==1.25.0 opentelemetry-semantic-conventions==0.46b0 opentelemetry-util-http==0.46b0 orjson==3.10.5 overrides==7.7.0 packaging==24.1 posthog==3.5.0 protobuf==4.25.3 pyasn1==0.6.0 pyasn1_modules==0.4.0 pydantic==2.7.4 pydantic_core==2.18.4 Pygments==2.18.0 pypdf==4.2.0 PyPika==0.48.9 pyproject_hooks==1.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 python-multipart==0.0.9 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 requests-oauthlib==2.0.0 rich==13.7.1 rsa==4.9 shellingham==1.5.4 six==1.16.0 sniffio==1.3.1 SQLAlchemy==2.0.30 starlette==0.37.2 sympy==1.12.1 tenacity==8.3.0 tiktoken==0.7.0 tokenizers==0.19.1 tomli==2.0.1 tqdm==4.66.4 typer==0.12.3 typing-inspect==0.9.0 typing_extensions==4.12.2 ujson==5.10.0 urllib3==2.2.1 uvicorn==0.30.1 uvloop==0.19.0 watchfiles==0.22.0 websocket-client==1.8.0 websockets==12.0 wrapt==1.16.0 yarl==1.9.4 zipp==3.19.2 |
※ pip install langchain langchain_chroma langchain_community langchain_openai langchain_openai pypdf 명령을 실행했다.
