Class TextEmbeddingModel (1.59.0)

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

count_tokens

count_tokens(
    prompts: typing.List[str],
) -> vertexai.preview.language_models.CountTokensResponse

Counts the tokens and billable characters for a given prompt.

Note: this does not make a prediction request to the model, it only counts the tokens in the request.

Parameter
Name	Description
`prompts`	`List[str]` Required. A list of prompts to ask the model. For example: ["What should I do today?", "How's it going?"]

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,
    output_dimensionality: typing.Optional[int] = None,
    learning_rate_multiplier: typing.Optional[float] = 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.deploy_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