Class GeminiTextGenerator (2.29.0)

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GeminiTextGenerator(
    *,
    model_name: typing.Optional[
        typing.Literal[
            "gemini-1.5-pro-preview-0514",
            "gemini-1.5-flash-preview-0514",
            "gemini-1.5-pro-001",
            "gemini-1.5-pro-002",
            "gemini-1.5-flash-001",
            "gemini-1.5-flash-002",
            "gemini-2.0-flash-exp",
            "gemini-2.0-flash-001",
            "gemini-2.0-flash-lite-001",
        ]
    ] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None,
    max_iterations: int = 300
)

Gemini text generator LLM model.

Note: gemini-1.5-X are going to be deprecated. Use gemini-2.0-X (https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.GeminiTextGenerator) instead.

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

fit

fit(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
) -> bigframes.ml.llm.GeminiTextGenerator

Fine tune GeminiTextGenerator model. Only support "gemini-1.5-pro-002", "gemini-1.5-flash-002", "gemini-2.0-flash-001", and "gemini-2.0-flash-lite-001"models for now.

Note: This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions (https://cloud.google.com/products#product-launch-stages).

Returns
Type	Description
GeminiTextGenerator	Fitted estimator.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
deep	bool, default True Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
Dictionary	A dictionary of parameter names mapped to their values.

predict

predict(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    *,
    temperature: float = 0.9,
    max_output_tokens: int = 8192,
    top_k: int = 40,
    top_p: float = 1.0,
    ground_with_google_search: bool = False,
    max_retries: int = 0,
    prompt: typing.Optional[
        typing.Iterable[typing.Union[str, bigframes.series.Series]]
    ] = None,
    output_schema: typing.Optional[typing.Mapping[str, str]] = None
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame of shape (n_samples, n_input_columns + n_prediction_columns). Returns predicted values.

score

score(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    task_type: typing.Literal[
        "text_generation", "classification", "summarization", "question_answering"
    ] = "text_generation",
) -> bigframes.dataframe.DataFrame

Calculate evaluation metrics of the model. Only support "gemini-1.5-pro-002", "gemini-1.5-flash-002", "gemini-2.0-flash-lite-001", and "gemini-2.0-flash-001".

Note: This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions (https://cloud.google.com/products#product-launch-stages).

Output matches that of the BigQuery ML.EVALUATE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#remote-model-llm for the outputs relevant to this model type.

Returns
Type	Description
bigframes.dataframe.DataFrame	The DataFrame as evaluation result.

to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.llm.GeminiTextGenerator

Save the model to BigQuery.

Returns
Type	Description
GeminiTextGenerator	Saved model.