dh zSSKJrJrJr SSKJr SSKJr SSKJ r S\S\S\S\\\ 4S jr "S S \ \5r g ) )AnyCallableList) Embeddings) ConfigDict)SelfHostedPipelinepipelineargskwargsreturncU"U0UD6$)zInference function to send to the remote hardware. Accepts a sentence_transformer model_id and returns a list of embeddings for each document in the batch. )r r r s b/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/embeddings/self_hosted.py_embed_documentsr s T $V $$c\rSrSr%Sr\r\\S'Sr \ \S'\ "SS9r S\ \S \ \ \4S jrS \S \ \4S jrS rg)SelfHostedEmbeddingsaCustom 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 using a model load function: .. code-block:: python from langchain_community.embeddings import SelfHostedEmbeddings from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import runhouse as rh gpu = rh.cluster(name="rh-a10x", instance_type="A100:1") def get_pipeline(): model_id = "facebook/bart-large" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) return pipeline("feature-extraction", model=model, tokenizer=tokenizer) embeddings = SelfHostedEmbeddings( model_load_fn=get_pipeline, hardware=gpu model_reqs=["./", "torch", "transformers"], ) Example passing in a pipeline path: .. code-block:: python from langchain_community.embeddings import SelfHostedHFEmbeddings import runhouse as rh from transformers import pipeline gpu = rh.cluster(name="rh-a10x", instance_type="A100:1") pipeline = pipeline(model="bert-base-uncased", task="feature-extraction") rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(gpu, path="models") embeddings = SelfHostedHFEmbeddings.from_pipeline( pipeline="models/pipeline.pkl", hardware=gpu, model_reqs=["./", "torch", "transformers"], ) inference_fnNinference_kwargsforbid)extratextsr c[[SU55nURURU5n[ U[5(dUR 5$U$)zCompute doc embeddings using a HuggingFace transformer model. Args: texts: The list of texts to embed.s Returns: List of embeddings, one for each text. c&URSS5$)N  )replace)xs r6SelfHostedEmbeddings.embed_documents..Rs199T3#7r)listmapclient pipeline_ref isinstancetolist)selfr embeddingss rembed_documents$SelfHostedEmbeddings.embed_documentsIsNS7?@[[!2!2E: *d++$$& &rtextcURSS5nURURU5n[U[5(dUR 5$U$)zCompute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. rr)rr$r%r&r"r')r(r,r)s r embed_query SelfHostedEmbeddings.embed_queryXsL||D#&[[!2!2D9 *d++$$& &rr)__name__ __module__ __qualname____firstlineno____doc__rrr__annotations__rrr model_configrstrfloatr*r.__static_attributes__rrrrrsi+Z.L(-N c ?L T#Y 4U 3D   U rrN) typingrrrlangchain_core.embeddingsrpydanticr$langchain_community.llms.self_hostedrr8rrrrrr>sO&&0C%s%3%#%$tE{BS%S-zSr