HuggingFaceEmbeddings(
self,
**kwargs: Any = {},
)Keyword arguments to pass to the Sentence Transformer model, such as device,
prompts, default_prompt_name, revision, trust_remote_code, or token.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer
Keyword arguments to pass when calling the encode method of the Sentence
Transformer model, such as prompt_name, prompt, batch_size, precision,
normalize_embeddings, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode
HuggingFace sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
Example:
.. code-block:: python
from langchain_community.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': False} hf = HuggingFaceEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs )