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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 |
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.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. |