KKi'pSSKJr SSKJr SSKJr SSKJrJrJ r SSK J r J r J r Jr Sr"SS\\5rg ) ) annotations)Any) Embeddings) BaseModel ConfigDictField) IMPORT_ERRORis_ipex_availableis_optimum_intel_availableis_optimum_intel_versionz1.22c^\rSrSr%Sr\"SSS9rS\S'SrS \S '\"\ S 9r S \S '\"\ S 9r S \S'\"\ S 9r S \S'Sr S\S'SrS\S'SU4Sjjr\"SSSS9rSSjrSSjrS SjrSrU=r$)!HuggingFaceEmbeddingsaHuggingFace sentence_transformers embedding models. To use, you should have the `sentence_transformers` python package installed. Example: ```python from langchain_huggingface import HuggingFaceEmbeddings model_name = "sentence-transformers/all-mpnet-base-v2" model_kwargs = {"device": "cpu"} encode_kwargs = {"normalize_embeddings": False} hf = HuggingFaceEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, ) ``` z'sentence-transformers/all-mpnet-base-v2model)defaultaliasstr model_nameNz str | None cache_folder)default_factorydict[str, Any] model_kwargs encode_kwargsquery_encode_kwargsFbool multi_process show_progressc >[TU]"S0UD6 SSKnURR SS5S:Xau[ 5(a[5(d$S[R"S53n[U5e[S [5(aS [S 3n[U5eSS K J n UnO URnU"UR4S UR 0URD6Ulg![anSn[U5UeSnAff=f)z$Initialize the sentence_transformer.rNzrCould not import sentence_transformers python package. Please install it with `pip install sentence-transformers`.backendtorchipexzBackend: ipex z optimum[ipex]=z_. You can install it with pip: `pip install --upgrade --upgrade-strategy eager `optimum[ipex]`.)IPEXSentenceTransformerr)super__init__sentence_transformers ImportErrorrgetr r r formatr _MIN_OPTIMUM_VERSION optimum.intelr#SentenceTransformerrr_client)selfkwargsr'excmsgr# model_cls __class__s n/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/langchain_huggingface/embeddings/huggingface.pyr&HuggingFaceEmbeddings.__init__@s "6" , (    G 4 >-//7H7J7J&|':':?'K&LM!#&&'-ABB<+,-'' "#&& =/I.AAI OO *.*;*; ?C?P?P  9 ,N c" +  ,sC'' D1C??Dforbidr$T)extraprotected_namespacespopulate_by_namecSSKnUVs/sHoDRSS5PM nnUR(aQURR 5nURR X5nUR RU5 O)URR"U4SUR0UD6n[U[5(a Sn[U5eUR5$s snf)a4Embed a text using the HuggingFace transformer model. Args: texts: The list of texts to embed. encode_kwargs: Keyword arguments to pass when calling the `encode` method for the documents of the SentenceTransformer encode method. Returns: List of embeddings, one for each text. rN  show_progress_barzHExpected embeddings to be a Tensor or a numpy array, got a list instead.)r'replacerr.start_multi_process_poolencode_multi_processr-stop_multi_process_poolencoder isinstancelist TypeErrortolist)r/textsrr'xpool embeddingsr2s r5_embedHuggingFaceEmbeddings._embedks %/45u!4%u5   <<88:D::5GJ ! 5 5 M Md S,,"&"4"4 J j$ ' '& C.   ""'6sC#c8URXR5$)zCompute doc embeddings using a HuggingFace transformer model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. )rLr)r/rHs r5embed_documents%HuggingFaceEmbeddings.embed_documentss{{5"4"455c[UR5S:a URO URnURU/U5S$)zCompute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. r)lenrrrL)r/text embed_kwargss r5 embed_query!HuggingFaceEmbeddings.embed_querysJ4++,q0  $ $##  {{D6<033rQ)r.)r0r)rH list[str]rrreturnlist[list[float]])rHrXrYrZ)rTrrYz list[float])__name__ __module__ __qualname____firstlineno____doc__rr__annotations__rdictrrrrrr&r model_configrLrOrV__static_attributes__ __classcell__)r4s@r5rrs&9J#L*#K#(#>L.>d%*$$?M>?k+0*EEk M4(M4)# JL $#$#/=$# $#L 644rQrN) __future__rtypingrlangchain_core.embeddingsrpydanticrrr(langchain_huggingface.utils.import_utilsr r r r r+rr$rQr5rjs6"011Z4IzZ4rQ