hwxddlmZddlmZmZddlmZddlmZm Z m Z ddl m Z m Z mZmZdZdZGdd eeZy ) ) annotations)AnyOptional) Embeddings) BaseModel ConfigDictField) IMPORT_ERRORis_ipex_availableis_optimum_intel_availableis_optimum_intel_versionz'sentence-transformers/all-mpnet-base-v2z1.22c eZdZUdZeedZded< dZded< ee Z d ed < ee Z d ed < ee Z d ed < dZ ded< dZded< dfd ZedddZ ddZddZddZxZS)HuggingFaceEmbeddingsaaHuggingFace 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)defaultaliasstr model_nameNz Optional[str] cache_folder)default_factorydict[str, Any] model_kwargs encode_kwargsquery_encode_kwargsFbool multi_process show_progressc t|di| ddl}|jj dddk(ret r ts#dtjd}t|td trd td }t|dd l m }|}n |j}||jfd |j i|j|_y#t$r}d}t||d}~wwxYw)z$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 k/var/www/html/aiagenthome/venv/lib/python3.12/site-packages/langchain_huggingface/embeddings/huggingface.pyr&zHuggingFaceEmbeddings.__init__As "6" , (    G 4 >-/7H7J&|':':?'K&LM!#&&'-AB<+,-'' "#&& =/I.AAI OO *.*;*; ?C?P?P  9 ,N c" +  ,sC C1C,,C1forbidr$T)extraprotected_namespacespopulate_by_namecddl}|Dcgc]}|jdd}}|jrR|jj }|jj ||}|j j|n)|jj|fd|ji|}t|tr d}t||jScc}w)atEmbed a text using the HuggingFace transformer model. Args: texts: The list of texts to embed. encode_kwargs: Keyword arguments to pass when calling the `encode` method for the documents of the SentenceTransformer encode method. Returns: List of embeddings, one for each text. rN  show_progress_barzHExpected embeddings to be a Tensor or a numpy array, got a list instead.)r'replacerr.start_multi_process_poolencode_multi_processr-stop_multi_process_poolencoder isinstancelist TypeErrortolist)r/textsrr'xpool embeddingsr2s r5_embedzHuggingFaceEmbeddings._embedls %/45u!4%u5   <<88:D::5$GJ ! 5 5 M Md S,,,"&"4"4 J j$ '& C.   ""'6sCc:|j||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. )rKr)r/rGs r5embed_documentsz%HuggingFaceEmbeddings.embed_documentss{{5$"4"455ct|jdkDr |jn |j}|j|g|dS)zCompute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. r)lenrrrK)r/text embed_kwargss r5 embed_queryz!HuggingFaceEmbeddings.embed_querysJ4++,q0  $ $##  {{D6<033rN)r0r)rG list[str]rrreturnlist[list[float]])rGrTrUrV)rQrrUz list[float])__name__ __module__ __qualname____doc__r DEFAULT_MODEL_NAMEr__annotations__rdictrrrrrr&r model_configrKrMrS __classcell__)r4s@r5rrs($6gFJF"&L-&K#(#>L.>d%*$$?M>?k+0*EEk M4(M4)# JL $#$#/=$# $#L 64rNrN) __future__rtypingrrlangchain_core.embeddingsrpydanticrrr (langchain_huggingface.utils.import_utilsr r r r r[r+rr$rNr5res<" 011?Y4IzY4rN