google/generativeai/answer.py [242:295]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    *,
    model: model_types.AnyModelNameOptions = DEFAULT_ANSWER_MODEL,
    contents: content_types.ContentsType,
    inline_passages: GroundingPassagesOptions | None = None,
    semantic_retriever: SemanticRetrieverConfigOptions | None = None,
    answer_style: AnswerStyle | None = None,
    safety_settings: safety_types.SafetySettingOptions | None = None,
    temperature: float | None = None,
    client: glm.GenerativeServiceClient | None = None,
    request_options: helper_types.RequestOptionsType | None = None,
):
    """Calls 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.
    """
    if request_options is None:
        request_options = {}

    if client is None:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



google/generativeai/answer.py [314:348]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    *,
    model: model_types.AnyModelNameOptions = DEFAULT_ANSWER_MODEL,
    contents: content_types.ContentsType,
    inline_passages: GroundingPassagesOptions | None = None,
    semantic_retriever: SemanticRetrieverConfigOptions | None = None,
    answer_style: AnswerStyle | None = None,
    safety_settings: safety_types.SafetySettingOptions | None = None,
    temperature: float | None = None,
    client: glm.GenerativeServiceClient | None = None,
    request_options: helper_types.RequestOptionsType | None = None,
):
    """
    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.
    """
    if request_options is None:
        request_options = {}

    if client is None:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -