dh1~SSKJr SSKJrJrJr SSKJr SSKJ r J r J r Sr Sr Sr"SS \ \5r"S S \5rg ) )Path)AnyDictList) Embeddings) BaseModel ConfigDictFieldz^[TU]"S0UD6 SSKJn SSKJm SS[S[S [S [4U4S jjjnU"UR5(a/UR"UR4S S 0URD6Ul O,UR"UR40URD6Ul SSKJn URUR5Ulg![an[ S5UeSnAff=f![an[ S5UeSnAff=f![an[ S5UeSnAff=f)$Initialize the sentence_transformer.r)OVModelForFeatureExtractionznCould not import optimum-intel python package. Please install it with: pip install -U 'optimum[openvino,nncf]'N)HfApizjCould not import huggingface_hub python package. Please install it with: `pip install -U huggingface_hub`.model_idrevision subfolderreturncN>[U5nUbX2- nUR5(a7US- R5(+=(d US- R5(+$T "5nURX=(d SS9nUcSO[U5R 5nUR Vs/sH4nUb"UR RU5(dM(UR PM6 nnUcSOUS3n X;=(d U RSS5U;$s snf![a gf=f) Nzopenvino_model.xmlzopenvino_model.binmain)rz/openvino_model.xmlz.xmlz.binT) ris_direxists model_infoas_posixsiblings rfilename startswithreplace Exception) rrr model_dirhf_apir"normalized_subfolderfile model_files ov_model_pathrs _/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/embeddings/openvino.pyrequire_model_export9OpenVINOEmbeddings.__init__..require_model_exportEsHXI$%1 !!"%99AACCG%(<<DDFFWF #..xBTf.U %-D4 ?3K3K3M% !+ 3 3 3+3~~001EF#DNN 3!()011DE "4P$,,VVrBts r/ _text_lengthOpenVINOEmbeddings._text_length|s dD ! !tD/01 1y)) Y!^z$q'377t9 -1A-. .-sB) sentences batch_sizeshow_progress_barconvert_to_numpyconvert_to_tensor mean_poolingnormalize_embeddingsc ^SSKnSSKJn SSKmS[ S[ S [ 4U4S jjn U(aS nS n [ U[5(d[US 5(dU/nS n /n URUVs/sHoRU5*PM sn5nUVs/sHnUUPM nnU "S[U5USU(+S9GH nUUUU-nURRRSR5SnUR (aUR#US S SS9nO!UR#USUR%5S SS9nUR"S0UD6nU(a U "UUS5nO USSS2S4nU(a%TR&R(R+USSS9nU(aUR-5nU R/U5 GM URU5Vs/sHnU UPM n nU(a3[U 5(aTR1U 5n OGTR35n O6U(a/UR5U Vs/sHnUR5PM sn5n U (aU Sn U $![an [S5U eSn A ff=f![an [S5U eSn A ff=f![an [S5U eSn A ff=fs snfs snfs snfs snf)a' Computes sentence embeddings. :param sentences: the sentences to embed. :param batch_size: the batch size used for the computation. :param show_progress_bar: Whether to output a progress bar. :param convert_to_numpy: Whether the output should be a list of numpy vectors. :param convert_to_tensor: Whether the output should be one large tensor. :param mean_pooling: Whether to pool returned vectors. :param normalize_embeddings: Whether to normalize returned vectors. :return: By default, a 2d numpy array with shape [num_inputs, output_dimension]. rNzCUnable to import numpy, please install with `pip install -U numpy`.)trangezAUnable to import tqdm, please install with `pip install -U tqdm`.zCUnable to import torch, please install with `pip install -U torch`. model_outputattention_maskrc>USnURS5RUR55R5nTR X#-S5TR UR S5SS9- $)NrrEg& .>)min) unsqueezeexpandsizefloatrNclamp)r[r\token_embeddingsinput_mask_expandedtorchs r/run_mean_pooling3OpenVINOEmbeddings.encode..run_mean_poolings+  ((,334D4I4I4KLRRT 99-CQG%++#''*KVK FrDTBatches)descdisablerEpt)padding truncationreturn_tensors max_length)rorrrprq)pdimr4)numpyr8tqdmrZrgrrFr:rLargsortrPrHrrequestinputsget_partial_shape is_dynamicr get_lengthnn functional normalizecpuextendstackTensorasarray)r>rRrSrTrUrVrWrXnpr?rZrhinput_was_stringall_embeddingssenlength_sorted_idxidxsentences_sorted start_indexsentences_batchlengthfeatures out_features embeddingsembrgs @r/encodeOpenVINOEmbeddings.encodes.     #    3    $   i % %W y. . # I#  JJ9'U9C):):3)?(?9'UV6GH6GsIcN6GH! s9~z GXCX K/{[:=UVO]]**11!4FFHKF  >>#TdSW* >>#(%002##' * ==484L-lHEU""!&^!<!& ZZ(O(OPN +A.Nq U    S    U  6(VHFX)Ps]I2JJ.4K K$KK2 J < JJ  J+ J&&J+. K 8 KK forbidr4)extraprotected_namespacestextsc[[SU55nUR"U4SUR0URD6nUR 5$)Compute doc embeddings using a HuggingFace transformer model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. c&URSS5$)N  )r')xs r/4OpenVINOEmbeddings.embed_documents.. s199T3#7rjrT)listmaprrrtolist)r>rrs r/embed_documents"OpenVINOEmbeddings.embed_documentssTS7?@[[  %)%7%7 ;?;M;M   ""rjc,URU/5S$)Compute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. r)r)r>rBs r/ embed_queryOpenVINOEmbeddings.embed_querys##TF+A..rj model_pathcURR5 URRU5 URRU5 g)NT)rhalfsave_pretrainedr)r>rs r/ save_modelOpenVINOEmbeddings.save_models;  %%j1 &&z2rj)rr)FTFFT)__name__ __module__ __qualname____firstlineno____doc__rr__annotations__rr:r rGrrrrr;r6rMrPrr model_configrrcrrr__static_attributes__ __classcell__r@s@r/r r sd"Hc Is(#(#>L$sCx.>1$)$$?M4S>?RM4)KPKPZ///*"'!%"'"%)qqq q  q  qq#q qfH2FL#T#Y#4U 3D#" / /U / rjr c^\rSrSr%Sr\r\\S'Sr \\S'S\ 4U4Sjjr S\ \S \ \ \ 4S jrS \S \ \ 4S jrS rU=r$)OpenVINOBgeEmbeddingsi)aOpenVNO BGE embedding models. Bge Example: .. code-block:: python from langchain_community.embeddings import OpenVINOBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'CPU'} encode_kwargs = {'normalize_embeddings': True} ov = OpenVINOBgeEmbeddings( model_name_or_path=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) query_instructionembed_instructionrc ^>[TU]"S0UD6 SUR;a [Ulgg)rz-zhNr4)r5r6r DEFAULT_QUERY_BGE_INSTRUCTION_ZHr)r>rr@s r/r6OpenVINOBgeEmbeddings.__init__@s. "6" D++ +%ED " ,rjrrcUVs/sH!o RURSS5-PM# nnUR"U40URD6nUR 5$s snf)rrr)rr'rrr)r>rrOrs r/r%OpenVINOBgeEmbeddings.embed_documentsGs\INN1''!))D#*>>N[[=$*<*<=   ""Os'ArBcURSS5nUR"URU-40URD6nUR 5$)rrr)r'rrrr)r>rB embeddings r/r!OpenVINOBgeEmbeddings.embed_queryTsG||D#&KK 6 6 =TASAST !!rj)r)rrrrr DEFAULT_QUERY_BGE_INSTRUCTION_ENrr:rrrr6rrcrrrrrs@r/rr)sp">s=1s4FF #T#Y #4U 3D # " "U " "rjrN)pathlibrtypingrrrlangchain_core.embeddingsrpydanticrr r DEFAULT_QUERY_INSTRUCTIONrrr rr4rjr/rsT""011C@!$_ VJVr6".6"rj