Class TextEmbeddingModel (1.80.0)

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.

Methods

batch_predict

batch_predict(
 *,
 dataset: typing.Union[str, typing.List[str]],
 destination_uri_prefix: str,
 model_parameters: typing.Optional[typing.Dict] = None
) -> google.cloud.aiplatform.jobs.BatchPredictionJob

Starts a batch prediction job with the model.

Exceptions
Type	Description
`ValueError`	When source or destination URI is not supported.

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.

from_pretrained

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

Loads a _ModelGardenModel.

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.

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.

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

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