Class PaLM2TextEmbeddingGenerator (1.26.0)

Version latest

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PaLM2TextEmbeddingGenerator(
    *,
    model_name: typing.Literal[
        "textembedding-gecko", "textembedding-gecko-multilingual"
    ] = "textembedding-gecko",
    version: typing.Optional[str] = None,
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None
)

PaLM2 text embedding generator LLM model.

Note: PaLM2TextEmbeddingGenerator has been deprecated. Use TextEmbeddingGenerator(https://cloud.google.com/python/docs/reference/bigframes/latest/bigframes.ml.llm.TextEmbeddingGenerator) instead.

Parameters
Name	Description
model_name	str, Default to "textembedding-gecko" The model for text embedding. “textembedding-gecko” returns model embeddings for text inputs. "textembedding-gecko-multilingual" returns model embeddings for text inputs which support over 100 languages. Default to "textembedding-gecko".
version	str or None Model version. Accepted values are "001", "002", "003", "latest" etc. Will use the default version if unset. See https://cloud.google.com/vertex-ai/docs/generative-ai/learn/model-versioning for details.
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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ]
) -> bigframes.dataframe.DataFrame

Predict the result from input DataFrame.

Parameter
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.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.PaLM2TextEmbeddingGenerator

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
PaLM2TextEmbeddingGenerator	Saved model.