+#hGXddlmZmZmZmZddlmZddlmZm Z m Z dZ GddeeZ y))AnyDictListOptional) Embeddings) BaseModel ConfigDictFieldz'sentence-transformers/all-mpnet-base-v2c eZdZUdZeZeed< dZe eed< e e Z e eefed< e e Ze eefed< dZeed < dZeed < d effd Zed dZdeedeeefdZdedeefdZxZS)HuggingFaceEmbeddingsa`HuggingFace sentence_transformers embedding models. To use, you should have the ``sentence_transformers`` python package installed. Example: .. code-block:: python from langchain_huggingface 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 ) model_nameN cache_folder)default_factory model_kwargs encode_kwargsF multi_process show_progresskwargsc t|di| ddl}|j|j fd|j i|j|_y#t$r}td|d}~wwxYw)z$Initialize the sentence_transformer.rNzrCould not import sentence_transformers python package. Please install it with `pip install sentence-transformers`.r) super__init__sentence_transformers ImportErrorSentenceTransformerr rr_client)selfrrexc __class__s i/var/www/html/sandstorm/venv/lib/python3.12/site-packages/langchain_huggingface/embeddings/huggingface.pyrzHuggingFaceEmbeddings.__init__0s "6"  (A,@@ OO *.*;*; ?C?P?P   N  sA A) A$$A)forbidr)extraprotected_namespacestextsreturncddl}ttd|}|jrR|jj }|jj ||}|jj|n3|jj|fd|ji|j}t|tr td|jS)zCompute doc embeddings using a HuggingFace transformer model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. rNc&|jddS)N  )replace)xs r z7HuggingFaceEmbeddings.embed_documents..Os199T3#7show_progress_barzHExpected embeddings to be a Tensor or a numpy array, got a list instead.)rlistmaprrstart_multi_process_poolencode_multi_processrstop_multi_process_poolencoderr isinstance TypeErrortolist)rr$rpool embeddingss r embed_documentsz%HuggingFaceEmbeddings.embed_documentsDs %S7?@   <<88:D::5$GJ ! 5 5 M Md S,,,"&"4"4$$J j$ '&    ""r-textc,|j|gdS)zCompute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. r)r:)rr;s r embed_queryz!HuggingFaceEmbeddings.embed_querycs##TF+A..r-)__name__ __module__ __qualname____doc__DEFAULT_MODEL_NAMEr str__annotations__rrr dictrrrrrboolrrr model_configrfloatr:r= __classcell__)rs@r r r s&)J("&L(3-&K#(#>L$sCx.>d%*$$?M4S>?k M4(M4)    L #T#Y#4U 3D#> / /U /r-r N) typingrrrrlangchain_core.embeddingsrpydanticrr r rBr rr-r rMs*,,011>c/Izc/r-