Class TextEmbeddingModel (1.25.0) 

TextEmbeddingModel(model_id: str, endpoint_name: Optional[str] = None)

TextEmbeddingModel converts text into a vector of floating-point numbers.

.. rubric:: Examples

Getting embedding:

model = TextEmbeddingModel.from_pretrained("embedding-gecko@001") embeddings = model.get_embeddings(["What is life?"]) for embedding in embeddings: vector = embedding.values print(len(vector))

Inheritance

builtins.object > vertexai.language_models._language_models._LanguageModel > TextEmbeddingModel

Methods

TextEmbeddingModel

TextEmbeddingModel(model_id: str, endpoint_name: Optional[str] = None)

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

from_pretrained

from_pretrained(model_name: str)

Loads a LanguageModel.

Exceptions
Type Description
ValueError If model_name is unknown.
ValueError If model does not support this class.

get_tuned_model

get_tuned_model(tuned_model_name: str)

Loads the specified tuned language model.

list_tuned_model_names

list_tuned_model_names()

Lists the names of tuned models.

tune_model

tune_model(
    training_data: Union[str, pandas.core.frame.DataFrame],
    *,
    train_steps: int = 1000,
    tuning_job_location: Optional[str] = None,
    tuned_model_location: Optional[str] = None,
    model_display_name: Optional[str] = None
)

Tunes a model based on training data.

This method launches a model tuning job that can take some time.

Exceptions
Type Description
ValueError If the "tuning_job_location" value is not supported
ValueError If the "tuned_model_location" value is not supported
RuntimeError If the model does not support tuning