■ OnnxEmbeddingFunction 클래스의 encode_documents 메소드를 사용해 벡터 리스트를 만드는 방법을 보여준다.
▶ main.py
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from pymilvus import model # https://huggingface.co/에 연결이 실패한 경우 다음 경로의 주석 처리를 제거한다. # import os # os.environ["HF_ENDPOINT":"] = "https://hf-mirror.com" # 작은 임베딩 모델 "paraphrase-albert-small-v2"(~50MB)이 다운로드된다. onnxEmbeddingFunction = model.DefaultEmbeddingFunction() stringList = [ "Artificial intelligence was founded as an academic discipline in 1956.", "Alan Turing was the first person to conduct substantial research in AI.", "Born in Maida Vale, London, Turing was raised in southern England." ] stringVectorList = onnxEmbeddingFunction.encode_documents(stringList) # NDArray list print(len(stringVectorList)) """ 3 """ |
▶ requirements.txt
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certifi==2024.8.30 charset-normalizer==3.3.2 coloredlogs==15.0.1 environs==9.5.0 filelock==3.16.1 flatbuffers==24.3.25 fsspec==2024.9.0 grpcio==1.66.2 huggingface-hub==0.25.1 humanfriendly==10.0 idna==3.10 marshmallow==3.22.0 milvus-lite==2.4.10 milvus-model==0.2.7 mpmath==1.3.0 numpy==2.1.2 onnxruntime==1.19.2 packaging==24.1 pandas==2.2.3 protobuf==5.28.2 pymilvus==2.4.7 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 pytz==2024.2 PyYAML==6.0.2 regex==2024.9.11 requests==2.32.3 safetensors==0.4.5 scipy==1.14.1 six==1.16.0 sympy==1.13.3 tokenizers==0.20.0 tqdm==4.66.5 transformers==4.45.1 typing_extensions==4.12.2 tzdata==2024.2 ujson==5.10.0 urllib3==2.2.3 |
※ pip install pymilvus[model] 명령을 실행했다.