a bg@sjddlmZmZmZddlmZddlmZddlm Z eeeeee dddZ Gdd d e eZ d S) )AnyCallableList) Embeddings) ConfigDict)SelfHostedPipeline)pipelineargskwargsreturncOs||i|S)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. )rr r r r x/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/self_hosted.py_embed_documents src@sdeZdZUdZeZeed<dZe ed<e ddZ e e e e eddd Ze e ed d d ZdS) 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_kwargsZforbid)extra)textsr cCs6ttdd|}||j|}t|ts2|S|S)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. cSs |ddS)N  )replace)xr r r Rz6SelfHostedEmbeddings.embed_documents..)listmapclient pipeline_ref isinstancetolist)selfr embeddingsr r r embed_documentsIs  z$SelfHostedEmbeddings.embed_documents)textr cCs0|dd}||j|}t|ts,|S|S)zCompute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. rr)rrrrrr)r r#r!r r r embed_queryXs  z SelfHostedEmbeddings.embed_query)__name__ __module__ __qualname____doc__rrr__annotations__rrrZ model_configrstrfloatr"r$r r r r rs -  rN) typingrrrZlangchain_core.embeddingsrZpydanticrZ$langchain_community.llms.self_hostedrr+rrr r r r s