U hQ@sFddlmZmZmZddlmZddlmZmZGdddeeZ dS))AnyDictList) Embeddings) BaseModelroot_validatorc@seZdZUdZeed<dZeed<edde e ddd Z ed d d d Z e ee e e dddZee e dddZd S) 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-v2modelT)pre)valuesreturnc CsLzddlm}Wn,tk r<}ztd|W5d}~XYnX||d<|S)z)Validate that awadb library is installed.r) AwaEmbeddingzJCould not import awadb library. Please install it with `pip install awadb`Nr )Zawadbr ImportError)clsr rexcrF/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/embeddings/awa.pyvalidate_environments z"AwaEmbeddings.validate_environmentN) model_namer cCs||_||j_dS)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)selfrrrr set_model!szAwaEmbeddings.set_model)textsr cCs |j|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 ZEmbeddingBatch)rrrrrembed_documents+s zAwaEmbeddings.embed_documents)textr cCs |j|S)zCompute query embeddings using AwaEmbedding. Args: text: The text to embed. Returns: Embeddings for the text. )r Z Embedding)rrrrr embed_query6s zAwaEmbeddings.embed_query)__name__ __module__ __qualname____doc__r__annotations__r strrrrrrfloatrrrrrrrs     rN) typingrrrZlangchain_core.embeddingsrZlangchain_core.pydantic_v1rrrrrrrs