To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.embeddings import HuggingFaceHubEmbeddings
repo_id = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
    repo_id=repo_id,
    task="feature-extraction",
    huggingfacehub_api_token="my-api-key",
)
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field repo_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field task: Optional[str] = 'feature-extraction'#
Task to call the model with.
embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to HuggingFaceHub’s embedding endpoint for embedding search docs.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to HuggingFaceHub’s embedding endpoint for embedding query text.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceInstructEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python packages installed.
Example
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceInstructEmbeddings(
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html