Class TextEmbeddingGenerator (1.22.0)

Version 1.22.0

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TextEmbeddingGenerator(
    *,
    model_name: typing.Literal[
        "text-embedding-004", "text-multilingual-embedding-002"
    ] = "text-embedding-004",
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

Text embedding generator LLM model.

Parameters
Name	Description
model_name	str, Default to "text-embedding-004" The model for text embedding. Possible values are "text-embedding-004" or "text-multilingual-embedding-002". text-embedding models returns model embeddings for text inputs. text-multilingual-embedding models returns model embeddings for text inputs which support over 100 languages. Default to "text-embedding-004".
session	bigframes.Session or None BQ session to create the model. If None, use the global default session.
connection_name	str or None Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.

Methods

__repr__

__repr__()

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

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]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Parameter
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series Input DataFrame or Series, can contain one or more columns. If multiple columns are in the DataFrame, it must contain a "content" column for prediction.

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

to_gbq

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

Save the model to BigQuery.

Parameters
Name	Description
model_name	str The name of the model.
replace	bool, default False Determine whether to replace if the model already exists. Default to False.

Returns
Type	Description
TextEmbeddingGenerator	Saved model.