dhVLSSKJrJrJr SSKJr SSKJrJr "SS\\5r g))AnyDictList) Embeddings) BaseModelmodel_validatorc\rSrSr%Sr\\S'Sr\\S'\ "SS9\ S\ S \4S j55r S \S S 4S jr S\\S \\\4SjrS\S \\4SjrSrg ) 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)modevaluesreturncdSSKJn U"5US'U$![an[S5UeSnAff=f)z)Validate that awadb library is installed.r) AwaEmbeddingzJCould not import awadb library. Please install it with `pip install awadb`Nr )awadbr ImportError)clsrrexcs Z/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/embeddings/awa.pyvalidate_environment"AwaEmbeddings.validate_environmentsF   * (>x  =  s / */ model_nameNc0XlXRlg)zSet the model used for embedding. The default model used is all-mpnet-base-v2 Args: model_name: A string which represents the name of model. N)r r r)selfrs r set_modelAwaEmbeddings.set_model"s !+ textsc8URRU5$)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)rr!s rembed_documentsAwaEmbeddings.embed_documents,s{{))%00r textc8URRU5$)zxCompute query embeddings using AwaEmbedding. Args: text: The text to embed. Returns: Embeddings for the text. )r Embedding)rr&s r embed_queryAwaEmbeddings.embed_query7s{{$$T**r )r )__name__ __module__ __qualname____firstlineno____doc__r__annotations__r strr classmethodrrrrfloatr$r)__static_attributes__r rr r s K$E3$(# $ 3 $ ,C,D, 1T#Y 14U 3D 1 + +U +r r N) typingrrrlangchain_core.embeddingsrpydanticrrr r5r rr9s""0/9+Iz9+r