o j6h6 @snUddlmZddlZddlmZddlZddlmZmZmZm Z m Z ddl m Z ddl mmZddlmZddlmZmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZdZejj Z ee!e"e fZ#ie j$e j$de j$de j$de j$e j%e j%de j%de j%de j%e j&e j&de j&de j&de j&e j'e j'de j'de j'de j'Z(de)d<dLd d!Z*eej+e,e"ej-fej-ffZ.eej/ee.e e"ej-ffZ0dMd%d&Z1ee"ej2ej2ej3ej3fZ4Gd'd(d(e Z5ee4e5ej6fZ7dNd*d+Z8dOd0d1Z9edddddd2dPdAdBZ:eddddddddCdQdHdIZ;eddddddddCdQdJdKZprotos.SemanticRetrieverConfigcCst|tjr|St|}|durd|i}nt|tr#t|d|d<n tdt|jd||ddur;||d<nt|dtrKt |d|d<t|S)Nr)zlInvalid input: Failed to create a 'protos.SemanticRetrieverConfig' from the provided source. Received type: z, Received value: r?) r#r SemanticRetrieverConfigrJdictr0r1r2r$rr8)r)r?rIr&r&r'_make_semantic_retriever_configs&      rO)modelinline_passagessemantic_retriever answer_stylesafety_settings temperaturerPmodel_types.AnyModelNameOptionscontentsrQGroundingPassagesOptions | NonerR%SemanticRetrieverConfigOptions | NonerSAnswerStyle | NonerT(safety_types.SafetySettingOptions | NonerU float | Noneprotos.GenerateAnswerRequestc Cst|}t|}|rt|}|dur$|dur$td|d|d|dur-t|}n|dur9t||d}n t d|d|d|rJt |}t j |||||||dS)a constructs a protos.GenerateAnswerRequest object by organizing the input parameters for the API call to generate a grounded answer from the model. Args: model: Name of the model used to generate the grounded response. contents: Content of the current conversation with the model. For single-turn query, this is a single question to answer. For multi-turn queries, this is a repeated field that contains conversation history and the last `Content` in the list containing the question. inline_passages: Grounding passages (a list of `Content`-like objects or `(id, content)` pairs, or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retriever`, one must be set, but not both. semantic_retriever: A Corpus, Document, or `protos.SemanticRetrieverConfig` to use for grounding. Exclusive with `inline_passages`, one must be set, but not both. answer_style: Style for grounded answers. safety_settings: Safety settings for generated output. temperature: The temperature for randomness in the output. Returns: Call for protos.GenerateAnswerRequest(). NzInvalid configuration: Please set either 'inline_passages' or 'semantic_retriever_config', but not both. Received for inline_passages: z, and for semantic_retriever: .zInvalid configuration: Either 'inline_passages' or 'semantic_retriever_config' must be provided, but currently both are 'None'. Received for inline_passages: rPrWrQrRrTrUrS) r make_model_namer to_contentsrnormalize_safety_settings ValueErrorr;rOr0r(r GenerateAnswerRequest)rPrWrQrRrSrTrUr&r&r'_make_generate_answer_requestsD    rf)rPrQrRrSrTrUclientrequest_optionsrg"glm.GenerativeServiceClient | Nonerh&helper_types.RequestOptionsType | Nonec CsF|duri}|dur t}t|||||||d} |j| fi|} | S)aCalls the GenerateAnswer API and returns a `types.Answer` containing the response. You can pass a literal list of text chunks: >>> from google.generativeai import answer >>> answer.generate_answer( ... content=question, ... inline_passages=splitter.split(document) ... ) Or pass a reference to a retreiver Document or Corpus: >>> from google.generativeai import answer >>> from google.generativeai import retriever >>> my_corpus = retriever.get_corpus('my_corpus') >>> genai.generate_answer( ... content=question, ... semantic_retriever=my_corpus ... ) Args: model: Which model to call, as a string or a `types.Model`. contents: The question to be answered by the model, grounded in the provided source. inline_passages: Grounding passages (a list of `Content`-like objects or (id, content) pairs, or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retriever`, one must be set, but not both. semantic_retriever: A Corpus, Document, or `protos.SemanticRetrieverConfig` to use for grounding. Exclusive with `inline_passages`, one must be set, but not both. answer_style: Style in which the grounded answer should be returned. safety_settings: Safety settings for generated output. Defaults to None. temperature: Controls the randomness of the output. client: If you're not relying on a default client, you pass a `glm.GenerativeServiceClient` instead. request_options: Options for the request. Returns: A `types.Answer` containing the model's text answer response. Nr`)r rfgenerate_answer rPrWrQrRrSrTrUrgrhrequestresponser&r&r'rks3 rkc sN|duri}|durt}t|||||||d} |j| fi|IdH} | S)a^ Calls the API and returns a `types.Answer` containing the answer. Args: model: Which model to call, as a string or a `types.Model`. contents: The question to be answered by the model, grounded in the provided source. inline_passages: Grounding passages (a list of `Content`-like objects or (id, content) pairs, or a `protos.GroundingPassages`) to send inline with the request. Exclusive with `semantic_retriever`, one must be set, but not both. semantic_retriever: A Corpus, Document, or `protos.SemanticRetrieverConfig` to use for grounding. Exclusive with `inline_passages`, one must be set, but not both. answer_style: Style in which the grounded answer should be returned. safety_settings: Safety settings for generated output. Defaults to None. temperature: Controls the randomness of the output. client: If you're not relying on a default client, you pass a `glm.GenerativeServiceClient` instead. Returns: A `types.Answer` containing the model's text answer response. Nr`)r rfrkrlr&r&r'generate_answer_async9s   ro)rrr r!)r)r*r r+)r rF)r)rKr?r>r rL)rPrVrWr>rQrXrRrYrSrZrTr[rUr\r r])rPrVrWr>rQrXrRrYrSrZrTr[rUr\rgrirhrj)= __future__r dataclassescollections.abcr itertoolstypingrrrrtyping_extensionsrgoogle.ai.generativelanguageaigenerativelanguageglmgoogle.generativeair google.generativeai.clientr r google.generativeai.typesr r rrr)google.generativeai.types.retriever_typesrDEFAULT_ANSWER_MODELrer!intr$rANSWER_STYLE_UNSPECIFIED ABSTRACTIVE EXTRACTIVEVERBOSErrEr(r5r7 ContentTypeGroundingPassageOptionsr/r*r;rGrHr=r<rMrKrJrOrfrkror&r&r&r's                 #  DJ