Class GeminiTextGenerator (2.30.0)

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",
            "gemini-2.5-pro",
            "gemini-2.5-flash",
            "gemini-2.5-flash-lite",
        ]
    ] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None,
    max_iterations: int = 300
)

Gemini text generator LLM model.

Methods

GeminiTextGenerator

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",
            "gemini-2.5-pro",
            "gemini-2.5-flash",
            "gemini-2.5-flash-lite",
        ]
    ] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None,
    max_iterations: int = 300
)

Gemini text generator LLM model.

__init__

__init__(
    *, model_name=None, session=None, connection_name=None, max_iterations=300
)

API documentation for __init__ method.

fit

fit(X, y)

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.

Returns
Type Description
GeminiTextGenerator Fitted estimator.

get_params

get_params(deep=True)

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,
    *,
    temperature=0.9,
    max_output_tokens=8192,
    top_k=40,
    top_p=1.0,
    ground_with_google_search=False,
    max_retries=0,
    prompt=None,
    output_schema=None
)

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, y, task_type="text_generation")

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".

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, replace=False)

Save the model to BigQuery.

Returns
Type Description
GeminiTextGenerator Saved model.