import runhouse as rh
model_name = "hkunlp/instructor-large"
gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
hf = SelfHostedHuggingFaceInstructEmbeddings(
    model_name=model_name, hardware=gpu)
Validators
raise_deprecation » all fields
set_verbose » verbose
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field model_id: str = 'hkunlp/instructor-large'#
Model name to use.
field model_reqs: List[str] = ['./', 'InstructorEmbedding', 'torch']#
Requirements to install on hardware to inference the model.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
langchain.embeddings.SentenceTransformerEmbeddings#
alias of langchain.embeddings.huggingface.HuggingFaceEmbeddings
pydantic model langchain.embeddings.TensorflowHubEmbeddings[source]#
Wrapper around tensorflow_hub embedding models.
To use, you should have the tensorflow_text python package installed.
Example
from langchain.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html