Class TextEmbeddingModel (1.53.0)

Version 1.53.0

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

Creates a LanguageModel.

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

Parameters
Name	Description
model_id	str Identifier of a Vertex LLM. Example: "text-bison@001"
endpoint_name	typing.Optional[str] Vertex Endpoint resource name for the model

Methods

deploy_tuned_model

deploy_tuned_model(
    tuned_model_name: str,
    machine_type: typing.Optional[str] = None,
    accelerator: typing.Optional[str] = None,
    accelerator_count: typing.Optional[int] = None,
) -> vertexai.language_models._language_models._LanguageModel

Loads the specified tuned language model.

Parameters
Name	Description
machine_type	typing.Optional[str] Machine type. E.g., "a2-highgpu-1g". See also: https://cloud.google.com/vertex-ai/docs/training/configure-compute.
accelerator	typing.Optional[str] Kind of accelerator. E.g., "NVIDIA_TESLA_A100". See also: https://cloud.google.com/vertex-ai/docs/training/configure-compute.
accelerator_count	typing.Optional[int] Count of accelerators.

from_pretrained

from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T

Loads a _ModelGardenModel.

Parameter
Name	Description
model_name	str Name of the model.

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

get_embeddings

get_embeddings(
    texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],
    *,
    auto_truncate: bool = True,
    output_dimensionality: typing.Optional[int] = None
) -> typing.List[vertexai.language_models.TextEmbedding]

Calculates embeddings for the given texts.

Parameter
Name	Description
texts	typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]] A list of texts or TextEmbeddingInput objects to embed.

get_embeddings_async

get_embeddings_async(
    texts: typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]],
    *,
    auto_truncate: bool = True,
    output_dimensionality: typing.Optional[int] = None
) -> typing.List[vertexai.language_models.TextEmbedding]

Asynchronously calculates embeddings for the given texts.

Parameter
Name	Description
texts	typing.List[typing.Union[str, vertexai.language_models.TextEmbeddingInput]] A list of texts or TextEmbeddingInput objects to embed.

get_tuned_model

get_tuned_model(*args, **kwargs)

Loads the specified tuned language model.

list_tuned_model_names

list_tuned_model_names() -> typing.Sequence[str]

Lists the names of tuned models.

tune_model

tune_model(
    *,
    training_data: typing.Optional[str] = None,
    corpus_data: typing.Optional[str] = None,
    queries_data: typing.Optional[str] = None,
    test_data: typing.Optional[str] = None,
    validation_data: typing.Optional[str] = None,
    batch_size: typing.Optional[int] = None,
    train_steps: typing.Optional[int] = None,
    tuned_model_location: typing.Optional[str] = None,
    model_display_name: typing.Optional[str] = None,
    task_type: typing.Optional[str] = None,
    machine_type: typing.Optional[str] = None,
    accelerator: typing.Optional[str] = None,
    accelerator_count: typing.Optional[int] = None
) -> vertexai.language_models._language_models._TextEmbeddingModelTuningJob

Tunes a model based on training data.

This method launches and returns an asynchronous model tuning job. Usage:

tuning_job = model.tune_model(...)
... do some other work
tuned_model = tuning_job.get_tuned_model()  # Blocks until tuning is complete

Exceptions
Type	Description
ValueError	If the provided parameter combinations or values are not supported.
RuntimeError	If the model does not support tuning