Example
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
Validators
raise_deprecation » all fields
set_verbose » verbose
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field model_load_fn: Callable = <function load_embedding_model>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'sentence_transformers', 'torch']#
Requirements to install on hardware to inference the model.
pydantic model langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings[source]#
Runs InstructorEmbedding embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example
from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
import runhouse as rh
model_name = "hkunlp/instructor-large"
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html