U h @s&ddlmZddlZddlmZmZmZmZmZddl m m Z ddl mZddlmZddlmZddd d d Zd dddddddd dddddddddd ddZdddddddddddddddddddd ddZejd d!Gd"d#d#ejZd+dd$d#d%d&d'Zd,ddd$d(d)d*ZdS)-) annotationsN)ListIterableIteratorOptionalUnion)get_default_text_client) text_types) model_typeszUnion[str, dict[str, str]]zglm.TextPrompt)promptreturncCs6t|trtj|dSt|tr*t|StddS)N)textz.Expected string or dictionary for text prompt.) isinstancestrglmZ TextPromptdict TypeErrorr r|D]\}}t|||qd|_|jr:|jdd|_dS)Nroutput)itemssetattrresult candidates)selfkwargskeyvaluerrr__init__s zCompletion.__init__N)__name__ __module__ __qualname__r2rrrrr(sr(zglm.TextServiceClient)r$r#r cCs8|dkrt}||}t||}tfd|i|S)NZ_client)rr&typeto_dictr()r$r#responserrrr%s  r%)rr r#cCs`|dkrd}n t|}|dkr&t}tj||d}||}t||}|dd|d<|S)aCalls the API to create an embedding for the text passed in. Args: model: Which model to call, as a string or a `types.Model`. text: Free-form input text given to the model. Given a string, the model will generate an embedding based on the input text. client: If you're not relying on a default client, you pass a `glm.TextServiceClient` instead. Returns: Dictionary containing the embedding (list of float values) for the input text. Nr)rr Z embeddingr1)r r rrZEmbedTextRequestZ embed_textr6r7)rr r#Zembedding_requestZembedding_responseZembedding_dictrrrgenerate_embeddingss  r9)N)N) __future__rZ dataclassestypingrrrrrZgoogle.ai.generativelanguageZaiZgenerativelanguagerZgoogle.generativeai.clientrZgoogle.generativeai.typesr r rr"r&Z dataclassr(r%r9rrrrs<     " $<