j36dZddlmZddlmZGddeZy)z*Wrapper around model2vec embedding models.)List) EmbeddingscTeZdZdZdefdZdeedeeefdZdedeefdZ y ) Model2vecEmbeddingsa`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." ) modelcx ddlm}|j||_y#t$r}td|d}~wwxYw)zMInitialize embeddings. Args: model: Model name. r) StaticModelzKUnable to import model2vec, please install with `pip install -U model2vec`.N) model2vecr ImportErrorfrom_pretrained_model)selfrr es r/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/model2vec.py__init__zModel2vecEmbeddings.__init__sG   - "11%8  .  s 9 49textsreturncT|jj|jS)zEmbed documents using the model2vec embeddings model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. r encodetolist)rrs rembed_documentsz#Model2vecEmbeddings.embed_documents,s"{{!!%(//11textcT|jj|jS)zEmbed a query using the model2vec embeddings model. Args: text: The text to embed. Returns: Embeddings for the text. r)rrs r embed_queryzModel2vecEmbeddings.embed_query8s"{{!!$'..00rN) __name__ __module__ __qualname____doc__strrrfloatrrrrrrsJ( 9c 9 2T#Y 24U 3D 2 1 1U 1rrN)r typingrlangchain_core.embeddingsrrr#rrr&s00:1*:1r