jn PdZddlmZmZmZddlmZddlmZm Z GddeeZ y)z)Wrapper around text2vec embedding models.)AnyListOptional) Embeddings) BaseModel ConfigDictceZdZUdZdZeeed<dZe ed<dZ e ed<dZ eeed<dZ e ed <ed Zddd d e deed e ffdZdeedeeefdZdedeefdZxZS)Text2vecEmbeddingsatext2vec embedding models. Install text2vec first, run 'pip install -U text2vec'. The github repository for text2vec is : https://github.com/shibing624/text2vec Example: .. code-block:: python from langchain_community.embeddings.text2vec import Text2vecEmbeddings embedding = Text2vecEmbeddings() embedding.embed_documents([ "This is a CoSENT(Cosine Sentence) model.", "It maps sentences to a 768 dimensional dense vector space.", ]) embedding.embed_query( "It can be used for text matching or semantic search." ) Nmodel_name_or_pathMEAN encoder_typemax_seq_lengthdevicemodel)protected_namespacesrr kwargsc  ddlm}i}|||d<|xs |di||}t|d||d|y#t$r}td|d}~wwxYw)Nr) SentenceModelzIUnable to import text2vec, please install with `pip install -U text2vec`.r rr)text2vecr ImportErrorsuper__init__)selfrr rre model_kwargs __class__s q/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/text2vec.pyrzText2vecEmbeddings.__init__&s{  .  )1CL- .@@@@ Vu9KVvV -  s3 A  AA textsreturnc8|jj|S)zEmbed documents using the text2vec embeddings model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. rencode)rr!s r embed_documentsz"Text2vecEmbeddings.embed_documents;szz  ''textc8|jj|S)zEmbed a query using the text2vec embeddings model. Args: text: The text to embed. Returns: Embeddings for the text. r$)rr(s r embed_queryzText2vecEmbeddings.embed_queryGszz  &&r')__name__ __module__ __qualname____doc__r rstr__annotations__r rrintrrr model_configrrfloatr&r* __classcell__)rs@r r r s()- ,L#NC FHSM E326L ,0 WW%SM W  W* (T#Y (4U 3D ( ' 'U 'r'r N) r.typingrrrlangchain_core.embeddingsrpydanticrrr rr'r r8s$/&&0*H'YH'r'