[PYTHON/LANGCHAIN] HuggingFaceEmbeddings 클래스 : embed_documents 메소드를 이용해 문자열 리스트에서 벡터 리스트의 리스트 구하기

■ HuggingFaceEmbeddings 클래스의 embed_documents 메소드를 이용해 문자열 리스트에서 벡터 리스트의 리스트를 구하는 방법을 보여준다.

▶ main.py


from langchain_huggingface import HuggingFaceEmbeddings

huggingFaceEmbeddings = HuggingFaceEmbeddings(model_name = "sentence-transformers/all-mpnet-base-v2")

sourceList = [
     "Hi there!",
     "Oh, hello!",
     "What's your name?",
     "My friends call me World",
     "Hello World!"
]

embeddingListList = huggingFaceEmbeddings.embed_documents(sourceList)

print("임베딩 리스트의 리스트 :", len(embeddingListList))
print()

for embeddingList in embeddingListList:
    print("임베딩 리스트 :", len(embeddingList))

"""
임베딩 리스트의 리스트 : 5

임베딩 리스트 : 768
임베딩 리스트 : 768
임베딩 리스트 : 768
임베딩 리스트 : 768
임베딩 리스트 : 768
"""

from langchain_huggingface import HuggingFaceEmbeddings

huggingFaceEmbeddings = HuggingFaceEmbeddings(model_name = "sentence-transformers/all-mpnet-base-v2")

sourceList = [

"Hi there!",

"Oh, hello!",

"What's your name?",

"My friends call me World",

"Hello World!"

]

embeddingListList = huggingFaceEmbeddings.embed_documents(sourceList)

print("임베딩 리스트의 리스트 :", len(embeddingListList))

print()

for embeddingList in embeddingListList:

print("임베딩 리스트 :", len(embeddingList))

"""

임베딩 리스트의 리스트 : 5

임베딩 리스트 : 768

"""

▶ requirements.txt


annotated-types==0.7.0
certifi==2024.6.2
charset-normalizer==3.3.2
filelock==3.15.4
fsspec==2024.6.1
huggingface-hub==0.23.4
idna==3.7
Jinja2==3.1.4
joblib==1.4.2
jsonpatch==1.33
jsonpointer==3.0.0
langchain-core==0.2.10
langchain-huggingface==0.0.3
langsmith==0.1.82
MarkupSafe==2.1.5
mpmath==1.3.0
networkx==3.3
numpy==1.26.4
nvidia-cublas-cu12==12.1.3.1
nvidia-cuda-cupti-cu12==12.1.105
nvidia-cuda-nvrtc-cu12==12.1.105
nvidia-cuda-runtime-cu12==12.1.105
nvidia-cudnn-cu12==8.9.2.26
nvidia-cufft-cu12==11.0.2.54
nvidia-curand-cu12==10.3.2.106
nvidia-cusolver-cu12==11.4.5.107
nvidia-cusparse-cu12==12.1.0.106
nvidia-nccl-cu12==2.20.5
nvidia-nvjitlink-cu12==12.5.40
nvidia-nvtx-cu12==12.1.105
orjson==3.10.5
packaging==24.1
pillow==10.3.0
pydantic==2.7.4
pydantic_core==2.18.4
PyYAML==6.0.1
regex==2024.5.15
requests==2.32.3
safetensors==0.4.3
scikit-learn==1.5.0
scipy==1.14.0
sentence-transformers==3.0.1
sympy==1.12.1
tenacity==8.4.2
threadpoolctl==3.5.0
tokenizers==0.19.1
torch==2.3.1
tqdm==4.66.4
transformers==4.42.3
triton==2.3.1
typing_extensions==4.12.2
urllib3==2.2.2

annotated-types==0.7.0

certifi==2024.6.2

charset-normalizer==3.3.2

filelock==3.15.4

fsspec==2024.6.1

huggingface-hub==0.23.4

idna==3.7

Jinja2==3.1.4

joblib==1.4.2

jsonpatch==1.33

jsonpointer==3.0.0

langchain-core==0.2.10

langchain-huggingface==0.0.3

langsmith==0.1.82

MarkupSafe==2.1.5

mpmath==1.3.0

networkx==3.3

numpy==1.26.4

nvidia-cublas-cu12==12.1.3.1

nvidia-cuda-cupti-cu12==12.1.105

nvidia-cuda-nvrtc-cu12==12.1.105

nvidia-cuda-runtime-cu12==12.1.105

nvidia-cudnn-cu12==8.9.2.26

nvidia-cufft-cu12==11.0.2.54

nvidia-curand-cu12==10.3.2.106

nvidia-cusolver-cu12==11.4.5.107

nvidia-cusparse-cu12==12.1.0.106

nvidia-nccl-cu12==2.20.5

nvidia-nvjitlink-cu12==12.5.40

nvidia-nvtx-cu12==12.1.105

orjson==3.10.5

packaging==24.1

pillow==10.3.0

pydantic==2.7.4

pydantic_core==2.18.4

PyYAML==6.0.1

regex==2024.5.15

requests==2.32.3

safetensors==0.4.3

scikit-learn==1.5.0

scipy==1.14.0

sentence-transformers==3.0.1

sympy==1.12.1

tenacity==8.4.2

threadpoolctl==3.5.0

tokenizers==0.19.1

torch==2.3.1

tqdm==4.66.4

transformers==4.42.3

triton==2.3.1

typing_extensions==4.12.2

urllib3==2.2.2

※ pip install langchain-huggingface 명령을 실행했다.