a bŠÝg3ã@s0dZddlmZddlmZGdd„deƒZdS)z*Wrapper around model2vec embedding models.é)ÚList)Ú Embeddingsc@sNeZdZdZedœdd„Zeeeeedœdd„Zeeedœd d „Z d S) ÚModel2vecEmbeddingsa`Model2Vec embedding models. Install model2vec first, run 'pip install -U model2vec'. The github repository for model2vec is : https://github.com/MinishLab/model2vec Example: .. code-block:: python from langchain_community.embeddings import Model2vecEmbeddings embedding = Model2vecEmbeddings("minishlab/potion-base-8M") embedding.embed_documents([ "It's dangerous to go alone!", "It's a secret to everybody.", ]) embedding.embed_query( "Take this with you." ) )Úmodelc CsPzddlm}Wn.ty>}ztdƒ|‚WYd}~n d}~00| |¡|_dS)zMInitialize embeddings. Args: model: Model name. r)Ú StaticModelzKUnable to import model2vec, please install with `pip install -U model2vec`.N)Z model2vecrÚ ImportErrorZfrom_pretrainedÚ_model)ÚselfrrÚe©r úv/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_community/embeddings/model2vec.pyÚ__init__sÿýzModel2vecEmbeddings.__init__)ÚtextsÚreturncCs|j |¡ ¡S)zÁEmbed documents using the model2vec embeddings model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. ©rÚencodeÚtolist)r rr r r Úembed_documents,s z#Model2vecEmbeddings.embed_documents)ÚtextrcCs|j |¡ ¡S)z§Embed a query using the model2vec embeddings model. Args: text: The text to embed. Returns: Embeddings for the text. r)r rr r r Ú embed_query8s zModel2vecEmbeddings.embed_queryN) Ú__name__Ú __module__Ú __qualname__Ú__doc__Ústrr rÚfloatrrr r r r rs rN)rÚtypingrZlangchain_core.embeddingsrrr r r r Ús