Class PaLM2TextEmbeddingGenerator (0.10.0)

PaLM2TextEmbeddingGenerator(
    session: typing.Optional[bigframes.session.Session] = None,
    connection_name: typing.Optional[str] = None,
)

PaLM2 text embedding generator LLM model.

Parameters

Name Description

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>.

__repr__()

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

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(
    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, which needs to contain a column with name "content". Only the column will be used as input. Content can include preamble, questions, suggestions, instructions, or examples.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	Output DataFrame with only 1 column as the output embedding results

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

Parameter
Name	Description
`vertex_ai_model_id`	`Optional[str], default None` optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.