a bgn @sJdZddlmZmZmZddlmZddlmZm Z GdddeeZ dS)z)Wrapper around text2vec embedding models.)AnyListOptional) Embeddings) BaseModel ConfigDictcseZdZUdZdZeeed<dZe ed<dZ e ed<dZ eeed<dZ e ed <ed d Zddd e eee d fddZeeeeedddZeeedddZZS)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_pathZMEAN encoder_typemax_seq_lengthdevicemodel)Zprotected_namespacesrr )rr kwargsc szddlm}Wn.ty>}ztd|WYd}~n d}~00i}|durT||d<|ph|fi||}tjf||d|dS)Nr) SentenceModelzIUnable to import text2vec, please install with `pip install -U text2vec`.r r)Ztext2vecr ImportErrorsuper__init__)selfrr rreZ model_kwargs __class__ru/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/text2vec.pyr&szText2vecEmbeddings.__init__)textsreturncCs |j|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)rrrrrembed_documents;s z"Text2vecEmbeddings.embed_documents)textrcCs |j|S)zEmbed a query using the text2vec embeddings model. Args: text: The text to embed. Returns: Embeddings for the text. r)rr rrr embed_queryGs zText2vecEmbeddings.embed_query)__name__ __module__ __qualname____doc__r rstr__annotations__r rr intr rrZ model_configrrfloatrr! __classcell__rrrrr s      rN) r%typingrrrZlangchain_core.embeddingsrZpydanticrrrrrrrs