■ VectorStoreIndex 클래스의 insert 메소드를 사용해 문서를 벡터 저장소 인덱스에 추가하는 방법을 보여준다.
▶ main.py
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import os from llama_index.core import SimpleDirectoryReader, GPTVectorStoreIndex os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>" simpleDirectoryReader = SimpleDirectoryReader(input_dir = "/home/king/data") documentList = simpleDirectoryReader.load_data() vectorStoreIndex = GPTVectorStoreIndex.from_documents([], model = "gpt-3.5-instruct") for document in documentList: vectorStoreIndex.insert(document) |
▶ requirements.txt
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aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.4.0 async-timeout==4.0.3 attrs==23.2.0 beautifulsoup4==4.12.3 certifi==2024.6.2 charset-normalizer==3.3.2 click==8.1.7 dataclasses-json==0.6.6 Deprecated==1.2.14 dirtyjson==1.0.8 distro==1.9.0 exceptiongroup==1.2.1 frozenlist==1.4.1 fsspec==2024.6.0 greenlet==3.0.3 h11==0.14.0 httpcore==1.0.5 httpx==0.27.0 idna==3.7 joblib==1.4.2 llama-index==0.10.43 llama-index-agent-openai==0.2.7 llama-index-cli==0.1.12 llama-index-core==0.10.43 llama-index-embeddings-openai==0.1.10 llama-index-indices-managed-llama-cloud==0.1.6 llama-index-legacy==0.9.48 llama-index-llms-openai==0.1.22 llama-index-multi-modal-llms-openai==0.1.6 llama-index-program-openai==0.1.6 llama-index-question-gen-openai==0.1.3 llama-index-readers-file==0.1.23 llama-index-readers-llama-parse==0.1.4 llama-parse==0.4.4 llamaindex-py-client==0.1.19 marshmallow==3.21.3 multidict==6.0.5 mypy-extensions==1.0.0 nest-asyncio==1.6.0 networkx==3.3 nltk==3.8.1 numpy==1.26.4 openai==1.32.0 packaging==24.0 pandas==2.2.2 pillow==10.3.0 pydantic==2.7.3 pydantic_core==2.18.4 pypdf==4.2.0 python-dateutil==2.9.0.post0 pytz==2024.1 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 six==1.16.0 sniffio==1.3.1 soupsieve==2.5 SQLAlchemy==2.0.30 striprtf==0.0.26 tenacity==8.3.0 tiktoken==0.7.0 tqdm==4.66.4 typing-inspect==0.9.0 typing_extensions==4.12.1 tzdata==2024.1 urllib3==2.2.1 wrapt==1.16.0 yarl==1.9.4 |
※ pip install openai llama-index 명령을 실행했다.
