■ CharacterTextSplitter 클래스의 from_tiktoken_encoder 메소드를 사용해 문서 분할기를 만드는 방법을 보여준다.
▶ main.py
|
1 2 3 4 5 6 7 8 9 10 11 12 |
from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import CharacterTextSplitter # 문서 리스트를 로드한다. webBaseLoader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") documentList = webBaseLoader.load() # 문서 분할기를 설정한다. characterTextSplitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size = 1000, chunk_overlap = 0) |
▶ requirements.txt
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 async-timeout==4.0.3 attrs==23.2.0 beautifulsoup4==4.12.3 bs4==0.0.2 certifi==2024.6.2 charset-normalizer==3.3.2 dataclasses-json==0.6.7 frozenlist==1.4.1 greenlet==3.0.3 idna==3.7 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.2.5 langchain-community==0.2.5 langchain-core==0.2.7 langchain-text-splitters==0.2.1 langsmith==0.1.77 marshmallow==3.21.3 multidict==6.0.5 mypy-extensions==1.0.0 numpy==1.26.4 orjson==3.10.5 packaging==24.1 pydantic==2.7.4 pydantic_core==2.18.4 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 soupsieve==2.5 SQLAlchemy==2.0.30 tenacity==8.3.0 tiktoken==0.7.0 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.1 yarl==1.9.4 |
※ pip install langchain-community bs4 tiktoken 명령을 실행했다.