■ SemanticChunker 클래스의 create_documents 메소드를 사용해 문자열을 문서 리스트로 분할하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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from dotenv import load_dotenv from langchain_openai.embeddings import OpenAIEmbeddings from langchain_experimental.text_splitter import SemanticChunker load_dotenv() with open("appendix-keywords.txt") as textIOWrapper: fileContent = textIOWrapper.read() openAIEmbeddings = OpenAIEmbeddings() semanticChunker = SemanticChunker(openAIEmbeddings) documentList = semanticChunker.create_documents([fileContent]) print(len(documentList)) """ 6 """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.11 aiosignal==1.3.2 annotated-types==0.7.0 anyio==4.8.0 async-timeout==4.0.3 attrs==24.3.0 certifi==2024.12.14 charset-normalizer==3.4.1 dataclasses-json==0.6.7 distro==1.9.0 exceptiongroup==1.2.2 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==0.8.2 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.14 langchain-community==0.3.14 langchain-core==0.3.29 langchain-experimental==0.3.4 langchain-openai==0.3.0 langchain-text-splitters==0.3.5 langsmith==0.2.10 marshmallow==3.25.1 multidict==6.1.0 mypy-extensions==1.0.0 numpy==1.26.4 openai==1.59.7 orjson==3.10.14 packaging==24.2 propcache==0.2.1 pydantic==2.10.5 pydantic-settings==2.7.1 pydantic_core==2.27.2 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==2.0.37 tenacity==9.0.0 tiktoken==0.8.0 tqdm==4.67.1 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.3.0 yarl==1.18.3 |
※ pip install python-dotenv langchain_experimental langchain_openai 명령을 실행했다.
