jVLddlmZmZmZddlmZddlmZmZGddeeZ y))AnyDictList) Embeddings) BaseModelmodel_validatorceZdZUdZeed<dZeed<ede de defd Z d edd fd Z d e ede e efdZdede efdZy ) AwaEmbeddingszEmbedding documents and queries with Awa DB. Attributes: client: The AwaEmbedding client. model: The name of the model used for embedding. Default is "all-mpnet-base-v2". clientzall-mpnet-base-v2modelbefore)modevaluesreturncb ddlm}||d<|S#t$r}td|d}~wwxYw)z)Validate that awadb library is installed.r) AwaEmbeddingzJCould not import awadb library. Please install it with `pip install awadb`Nr )awadbr ImportError)clsrrexcs l/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/awa.pyvalidate_environmentz"AwaEmbeddings.validate_environmentsF   * (>x  =  s . ). model_nameNc4||_||j_y)zSet the model used for embedding. The default model used is all-mpnet-base-v2 Args: model_name: A string which represents the name of model. N)r r r)selfrs r set_modelzAwaEmbeddings.set_model"s  !+ textsc8|jj|S)zEmbed a list of documents using AwaEmbedding. Args: texts: The list of texts need to be embedded Returns: List of embeddings, one for each text. )r EmbeddingBatch)rrs rembed_documentszAwaEmbeddings.embed_documents,s{{))%00rtextc8|jj|S)zCompute query embeddings using AwaEmbedding. Args: text: The text to embed. Returns: Embeddings for the text. )r Embedding)rr"s r embed_queryzAwaEmbeddings.embed_query7s{{$$T**r)__name__ __module__ __qualname____doc__r__annotations__r strr classmethodrrrrfloatr!r%rrr r s K$E3$(# $ 3 $ ,C,D, 1T#Y 14U 3D 1 + +U +rr N) typingrrrlangchain_core.embeddingsrpydanticrrr r.rrr2s""0/9+Iz9+r