Package Methods (1.74.0)

Summary of entries of Methods for aiplatform.

vertexai.init

init(
    *,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    experiment: typing.Optional[str] = None,
    experiment_description: typing.Optional[str] = None,
    experiment_tensorboard: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard,
            bool,
        ]
    ] = None,
    staging_bucket: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    service_account: typing.Optional[str] = None,
    api_endpoint: typing.Optional[str] = None,
    api_key: typing.Optional[str] = None,
    api_transport: typing.Optional[str] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = None
)

Updates common initialization parameters with provided options.

See more: vertexai.init

vertexai.preview.end_run

end_run(
    state: google.cloud.aiplatform_v1.types.execution.Execution.State = State.COMPLETE,
)

Ends the the current experiment run.

See more: vertexai.preview.end_run

vertexai.preview.get_experiment_df

get_experiment_df(
    experiment: typing.Optional[str] = None, *, include_time_series: bool = True
) -> pd.DataFrame

Returns a Pandas DataFrame of the parameters and metrics associated with one experiment.

See more: vertexai.preview.get_experiment_df

vertexai.preview.log_classification_metrics

log_classification_metrics(
    *,
    labels: typing.Optional[typing.List[str]] = None,
    matrix: typing.Optional[typing.List[typing.List[int]]] = None,
    fpr: typing.Optional[typing.List[float]] = None,
    tpr: typing.Optional[typing.List[float]] = None,
    threshold: typing.Optional[typing.List[float]] = None,
    display_name: typing.Optional[str] = None
) -> (
    google.cloud.aiplatform.metadata.schema.google.artifact_schema.ClassificationMetrics
)

Create an artifact for classification metrics and log to ExperimentRun.

See more: vertexai.preview.log_classification_metrics

vertexai.preview.log_metrics

log_metrics(metrics: typing.Dict[str, typing.Union[float, int, str]])

Log single or multiple Metrics with specified key and value pairs.

See more: vertexai.preview.log_metrics

vertexai.preview.log_params

log_params(params: typing.Dict[str, typing.Union[float, int, str]])

Log single or multiple parameters with specified key and value pairs.

See more: vertexai.preview.log_params

vertexai.preview.log_time_series_metrics

log_time_series_metrics(
    metrics: typing.Dict[str, float],
    step: typing.Optional[int] = None,
    wall_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
)

Logs time series metrics to to this Experiment Run.

See more: vertexai.preview.log_time_series_metrics

vertexai.preview.start_run

start_run(
    run: str,
    *,
    tensorboard: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str
        ]
    ] = None,
    resume=False
) -> google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun

Start a run to current session.

See more: vertexai.preview.start_run

vertexai.preview.tuning.sft.rebase_tuned_model

rebase_tuned_model(
    tuned_model_ref: str,
    *,
    artifact_destination: typing.Optional[str] = None,
    deploy_to_same_endpoint: typing.Optional[bool] = False
)

Re-runs fine tuning on top of a new foundational model.

See more: vertexai.preview.tuning.sft.rebase_tuned_model

vertexai.preview.tuning.sft.train

train(
    *,
    source_model: typing.Union[str, vertexai.generative_models.GenerativeModel],
    train_dataset: str,
    validation_dataset: typing.Optional[str] = None,
    tuned_model_display_name: typing.Optional[str] = None,
    epochs: typing.Optional[int] = None,
    learning_rate_multiplier: typing.Optional[float] = None,
    adapter_size: typing.Optional[typing.Literal[1, 4, 8, 16]] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None
) -> vertexai.tuning._supervised_tuning.SupervisedTuningJob

Tunes a model using supervised training.

See more: vertexai.preview.tuning.sft.train

vertexai.evaluation.CustomMetric

CustomMetric(
    name: str,
    metric_function: typing.Callable[
        [typing.Dict[str, typing.Any]], typing.Dict[str, typing.Any]
    ],
)

Initializes the evaluation metric.

See more: vertexai.evaluation.CustomMetric

vertexai.evaluation.EvalTask

EvalTask(
    *,
    dataset: typing.Union[pd.DataFrame, str, typing.Dict[str, typing.Any]],
    metrics: typing.List[
        typing.Union[
            typing.Literal[
                "exact_match",
                "bleu",
                "rouge_1",
                "rouge_2",
                "rouge_l",
                "rouge_l_sum",
                "tool_call_valid",
                "tool_name_match",
                "tool_parameter_key_match",
                "tool_parameter_kv_match",
            ],
            vertexai.evaluation.CustomMetric,
            vertexai.evaluation.metrics._base._AutomaticMetric,
            vertexai.evaluation.metrics._base._TranslationMetric,
            vertexai.evaluation.metrics.pointwise_metric.PointwiseMetric,
            vertexai.evaluation.metrics.pairwise_metric.PairwiseMetric,
        ]
    ],
    experiment: typing.Optional[str] = None,
    metric_column_mapping: typing.Optional[typing.Dict[str, str]] = None,
    output_uri_prefix: typing.Optional[str] = ""
)

Initializes an EvalTask.

See more: vertexai.evaluation.EvalTask

vertexai.evaluation.EvalTask.display_runs

display_runs()

Displays experiment runs associated with this EvalTask.

See more: vertexai.evaluation.EvalTask.display_runs

vertexai.evaluation.EvalTask.evaluate

evaluate(
    *,
    model: typing.Optional[
        typing.Union[
            vertexai.generative_models.GenerativeModel, typing.Callable[[str], str]
        ]
    ] = None,
    prompt_template: typing.Optional[str] = None,
    experiment_run_name: typing.Optional[str] = None,
    response_column_name: typing.Optional[str] = None,
    baseline_model_response_column_name: typing.Optional[str] = None,
    evaluation_service_qps: typing.Optional[float] = None,
    retry_timeout: float = 120.0,
    output_file_name: typing.Optional[str] = None
) -> vertexai.evaluation.EvalResult

Runs an evaluation for the EvalTask.

See more: vertexai.evaluation.EvalTask.evaluate

vertexai.evaluation.MetricPromptTemplateExamples.get_prompt_template

get_prompt_template(metric_name: str) -> str

Returns the prompt template for the given metric name.

See more: vertexai.evaluation.MetricPromptTemplateExamples.get_prompt_template

vertexai.evaluation.MetricPromptTemplateExamples.list_example_metric_names

list_example_metric_names() -> typing.List[str]

Returns a list of all metric prompt templates.

See more: vertexai.evaluation.MetricPromptTemplateExamples.list_example_metric_names

vertexai.evaluation.PairwiseMetric

PairwiseMetric(
    *,
    metric: str,
    metric_prompt_template: typing.Union[
        vertexai.evaluation.metrics.metric_prompt_template.PairwiseMetricPromptTemplate,
        str,
    ],
    baseline_model: typing.Optional[
        typing.Union[
            vertexai.generative_models.GenerativeModel, typing.Callable[[str], str]
        ]
    ] = None
)

Initializes a pairwise evaluation metric.

See more: vertexai.evaluation.PairwiseMetric

vertexai.evaluation.PairwiseMetricPromptTemplate

PairwiseMetricPromptTemplate(
    *,
    criteria: typing.Dict[str, str],
    rating_rubric: typing.Dict[str, str],
    input_variables: typing.Optional[typing.List[str]] = None,
    instruction: typing.Optional[str] = None,
    metric_definition: typing.Optional[str] = None,
    evaluation_steps: typing.Optional[typing.Dict[str, str]] = None,
    few_shot_examples: typing.Optional[typing.List[str]] = None
)

Initializes a pairwise metric prompt template.

See more: vertexai.evaluation.PairwiseMetricPromptTemplate

vertexai.evaluation.PairwiseMetricPromptTemplate.__str__

__str__()

Serializes the pairwise metric prompt template to a string.

See more: vertexai.evaluation.PairwiseMetricPromptTemplate.str

vertexai.evaluation.PairwiseMetricPromptTemplate.assemble

assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplate

Replaces only the provided variables in the template with specific values.

See more: vertexai.evaluation.PairwiseMetricPromptTemplate.assemble

vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_evaluation_steps

get_default_pairwise_evaluation_steps() -> typing.Dict[str, str]

Returns the default evaluation steps for the metric prompt template.

See more: vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_evaluation_steps

vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_instruction

get_default_pairwise_instruction() -> str

Returns the default instruction for the metric prompt template.

See more: vertexai.evaluation.PairwiseMetricPromptTemplate.get_default_pairwise_instruction

vertexai.evaluation.PointwiseMetric

PointwiseMetric(
    *,
    metric: str,
    metric_prompt_template: typing.Union[
        vertexai.evaluation.metrics.metric_prompt_template.PointwiseMetricPromptTemplate,
        str,
    ]
)

Initializes a pointwise evaluation metric.

See more: vertexai.evaluation.PointwiseMetric

vertexai.evaluation.PointwiseMetricPromptTemplate

PointwiseMetricPromptTemplate(
    *,
    criteria: typing.Dict[str, str],
    rating_rubric: typing.Dict[str, str],
    input_variables: typing.Optional[typing.List[str]] = None,
    instruction: typing.Optional[str] = None,
    metric_definition: typing.Optional[str] = None,
    evaluation_steps: typing.Optional[typing.Dict[str, str]] = None,
    few_shot_examples: typing.Optional[typing.List[str]] = None
)

Initializes a pointwise metric prompt template.

See more: vertexai.evaluation.PointwiseMetricPromptTemplate

vertexai.evaluation.PointwiseMetricPromptTemplate.__str__

__str__()

Serializes the pointwise metric prompt template to a string.

See more: vertexai.evaluation.PointwiseMetricPromptTemplate.str

vertexai.evaluation.PointwiseMetricPromptTemplate.assemble

assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplate

Replaces only the provided variables in the template with specific values.

See more: vertexai.evaluation.PointwiseMetricPromptTemplate.assemble

vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_evaluation_steps

get_default_pointwise_evaluation_steps() -> typing.Dict[str, str]

Returns the default evaluation steps for the metric prompt template.

See more: vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_evaluation_steps

vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_instruction

get_default_pointwise_instruction() -> str

Returns the default instruction for the metric prompt template.

See more: vertexai.evaluation.PointwiseMetricPromptTemplate.get_default_pointwise_instruction

vertexai.evaluation.PromptTemplate

PromptTemplate(template: str)

Initializes the PromptTemplate with a given template.

See more: vertexai.evaluation.PromptTemplate

vertexai.evaluation.PromptTemplate.__repr__

__repr__() -> str

Returns a string representation of the PromptTemplate.

See more: vertexai.evaluation.PromptTemplate.repr

vertexai.evaluation.PromptTemplate.__str__

__str__() -> str

Returns the template string.

See more: vertexai.evaluation.PromptTemplate.str

vertexai.evaluation.PromptTemplate.assemble

assemble(**kwargs) -> vertexai.evaluation.prompt_template.PromptTemplate

Replaces only the provided variables in the template with specific values.

See more: vertexai.evaluation.PromptTemplate.assemble

vertexai.evaluation.Rouge

Rouge(
    *,
    rouge_type: typing.Literal[
        "rouge1",
        "rouge2",
        "rouge3",
        "rouge4",
        "rouge5",
        "rouge6",
        "rouge7",
        "rouge8",
        "rouge9",
        "rougeL",
        "rougeLsum",
    ],
    use_stemmer: bool = False,
    split_summaries: bool = False
)

Initializes the ROUGE metric.

See more: vertexai.evaluation.Rouge

vertexai.generative_models.ChatSession.send_message

Generates content.

See more: vertexai.generative_models.ChatSession.send_message

vertexai.generative_models.ChatSession.send_message_async

Generates content asynchronously.

See more: vertexai.generative_models.ChatSession.send_message_async

vertexai.generative_models.FunctionDeclaration

FunctionDeclaration(
    *,
    name: str,
    parameters: typing.Dict[str, typing.Any],
    description: typing.Optional[str] = None,
    response: typing.Optional[typing.Dict[str, typing.Any]] = None
)

Constructs a FunctionDeclaration.

See more: vertexai.generative_models.FunctionDeclaration

vertexai.generative_models.GenerationConfig

GenerationConfig(
    *,
    temperature: typing.Optional[float] = None,
    top_p: typing.Optional[float] = None,
    top_k: typing.Optional[int] = None,
    candidate_count: typing.Optional[int] = None,
    max_output_tokens: typing.Optional[int] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None,
    presence_penalty: typing.Optional[float] = None,
    frequency_penalty: typing.Optional[float] = None,
    response_mime_type: typing.Optional[str] = None,
    response_schema: typing.Optional[typing.Dict[str, typing.Any]] = None,
    seed: typing.Optional[int] = None,
    audio_timestamp: typing.Optional[bool] = None,
    routing_config: typing.Optional[RoutingConfig] = None,
    logprobs: typing.Optional[int] = None,
    response_logprobs: typing.Optional[bool] = None
)

Constructs a GenerationConfig object.

See more: vertexai.generative_models.GenerationConfig

vertexai.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode

AutoRoutingMode(
    *,
    model_routing_preference: google.cloud.aiplatform_v1beta1.types.content.GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference
)

AutoRouingMode constructor .

See more: vertexai.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode

vertexai.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode

ManualRoutingMode(*, model_name: str)

ManualRoutingMode constructor .

See more: vertexai.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode

vertexai.generative_models.GenerativeModel.compute_tokens

compute_tokens(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ]
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

Computes tokens.

See more: vertexai.generative_models.GenerativeModel.compute_tokens

vertexai.generative_models.GenerativeModel.compute_tokens_async

compute_tokens_async(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ]
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

Computes tokens asynchronously.

See more: vertexai.generative_models.GenerativeModel.compute_tokens_async

vertexai.generative_models.GenerativeModel.count_tokens

count_tokens(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None
) -> google.cloud.aiplatform_v1beta1.types.prediction_service.CountTokensResponse

Counts tokens.

See more: vertexai.generative_models.GenerativeModel.count_tokens

vertexai.generative_models.GenerativeModel.count_tokens_async

count_tokens_async(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None
) -> google.cloud.aiplatform_v1beta1.types.prediction_service.CountTokensResponse

Counts tokens asynchronously.

See more: vertexai.generative_models.GenerativeModel.count_tokens_async

vertexai.generative_models.GenerativeModel.generate_content

generate_content(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    stream: bool = False
) -> typing.Union[
    vertexai.generative_models._generative_models.GenerationResponse,
    typing.Iterable[vertexai.generative_models._generative_models.GenerationResponse],
]

Generates content.

See more: vertexai.generative_models.GenerativeModel.generate_content

vertexai.generative_models.GenerativeModel.generate_content_async

generate_content_async(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    stream: bool = False
) -> typing.Union[
    vertexai.generative_models._generative_models.GenerationResponse,
    typing.AsyncIterable[
        vertexai.generative_models._generative_models.GenerationResponse
    ],
]

Generates content asynchronously.

See more: vertexai.generative_models.GenerativeModel.generate_content_async

vertexai.generative_models.GenerativeModel.start_chat

start_chat(
    *,
    history: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Content]
    ] = None,
    response_validation: bool = True
) -> vertexai.generative_models._generative_models.ChatSession

Creates a stateful chat session.

See more: vertexai.generative_models.GenerativeModel.start_chat

vertexai.generative_models.Image.from_bytes

from_bytes(data: bytes) -> vertexai.generative_models._generative_models.Image

Loads image from image bytes.

See more: vertexai.generative_models.Image.from_bytes

vertexai.generative_models.Image.load_from_file

load_from_file(
    location: str,
) -> vertexai.generative_models._generative_models.Image

Loads image from file.

See more: vertexai.generative_models.Image.load_from_file

vertexai.generative_models.ResponseValidationError.with_traceback

Exception.with_traceback(tb) -- set self.traceback to tb and return self.

See more: vertexai.generative_models.ResponseValidationError.with_traceback

vertexai.generative_models.SafetySetting

SafetySetting(
    *,
    category: google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
    threshold: google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
    method: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockMethod
    ] = None
)

Safety settings.

See more: vertexai.generative_models.SafetySetting

vertexai.generative_models.grounding.DynamicRetrievalConfig

DynamicRetrievalConfig(
    mode: google.cloud.aiplatform_v1beta1.types.tool.DynamicRetrievalConfig.Mode = Mode.MODE_UNSPECIFIED,
    dynamic_threshold: typing.Optional[float] = None,
)

Initializes a DynamicRetrievalConfig.

See more: vertexai.generative_models.grounding.DynamicRetrievalConfig

vertexai.generative_models.grounding.GoogleSearchRetrieval

GoogleSearchRetrieval(
    dynamic_retrieval_config: typing.Optional[
        vertexai.generative_models._generative_models.grounding.DynamicRetrievalConfig
    ] = None,
)

Initializes a Google Search Retrieval tool.

See more: vertexai.generative_models.grounding.GoogleSearchRetrieval

vertexai.generative_models.grounding.Retrieval

Retrieval(
    source: vertexai.generative_models._generative_models.grounding.VertexAISearch,
    disable_attribution: typing.Optional[bool] = None,
)

Initializes a Retrieval tool.

See more: vertexai.generative_models.grounding.Retrieval

vertexai.generative_models.grounding.VertexAISearch

VertexAISearch(
    datastore: str,
    *,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None
)

Initializes a Vertex AI Search tool.

See more: vertexai.generative_models.grounding.VertexAISearch

vertexai.language_models.ChatModel

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

Creates a LanguageModel.

See more: vertexai.language_models.ChatModel

vertexai.language_models.ChatModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.language_models.ChatModel.from_pretrained

vertexai.language_models.ChatModel.get_tuned_model

get_tuned_model(
    tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModel

Loads the specified tuned language model.

See more: vertexai.language_models.ChatModel.get_tuned_model

vertexai.language_models.ChatModel.list_tuned_model_names

list_tuned_model_names() -> typing.Sequence[str]

Lists the names of tuned models.

See more: vertexai.language_models.ChatModel.list_tuned_model_names

vertexai.language_models.ChatModel.start_chat

start_chat(
    *,
    context: typing.Optional[str] = None,
    examples: typing.Optional[
        typing.List[vertexai.language_models.InputOutputTextPair]
    ] = None,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    top_k: typing.Optional[int] = None,
    top_p: typing.Optional[float] = None,
    message_history: typing.Optional[
        typing.List[vertexai.language_models.ChatMessage]
    ] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None
) -> vertexai.language_models.ChatSession

Starts a chat session with the model.

See more: vertexai.language_models.ChatModel.start_chat

vertexai.language_models.ChatModel.tune_model

tune_model(
    training_data: typing.Union[str, pandas.core.frame.DataFrame],
    *,
    train_steps: typing.Optional[int] = None,
    learning_rate_multiplier: typing.Optional[float] = None,
    tuning_job_location: typing.Optional[str] = None,
    tuned_model_location: typing.Optional[str] = None,
    model_display_name: typing.Optional[str] = None,
    default_context: typing.Optional[str] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    tuning_evaluation_spec: typing.Optional[
        vertexai.language_models.TuningEvaluationSpec
    ] = None
) -> vertexai.language_models._language_models._LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.ChatModel.tune_model

vertexai.language_models.ChatModel.tune_model_rlhf

tune_model_rlhf(
    *,
    prompt_data: typing.Union[str, pandas.core.frame.DataFrame],
    preference_data: typing.Union[str, pandas.core.frame.DataFrame],
    model_display_name: typing.Optional[str] = None,
    prompt_sequence_length: typing.Optional[int] = None,
    target_sequence_length: typing.Optional[int] = None,
    reward_model_learning_rate_multiplier: typing.Optional[float] = None,
    reinforcement_learning_rate_multiplier: typing.Optional[float] = None,
    reward_model_train_steps: typing.Optional[int] = None,
    reinforcement_learning_train_steps: typing.Optional[int] = None,
    kl_coeff: typing.Optional[float] = None,
    default_context: typing.Optional[str] = None,
    tuning_job_location: typing.Optional[str] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    tuning_evaluation_spec: typing.Optional[
        vertexai.language_models.TuningEvaluationSpec
    ] = None
) -> vertexai.language_models._language_models._LanguageModelTuningJob

Tunes a model using reinforcement learning from human feedback.

See more: vertexai.language_models.ChatModel.tune_model_rlhf

vertexai.language_models.ChatSession.send_message

send_message(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    top_k: typing.Optional[int] = None,
    top_p: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None,
    candidate_count: typing.Optional[int] = None,
    grounding_source: typing.Optional[
        typing.Union[
            vertexai.language_models._language_models.WebSearch,
            vertexai.language_models._language_models.VertexAISearch,
            vertexai.language_models._language_models.InlineContext,
        ]
    ] = None
) -> vertexai.language_models.MultiCandidateTextGenerationResponse

Sends message to the language model and gets a response.

See more: vertexai.language_models.ChatSession.send_message

vertexai.language_models.ChatSession.send_message_async

send_message_async(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    top_k: typing.Optional[int] = None,
    top_p: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None,
    candidate_count: typing.Optional[int] = None,
    grounding_source: typing.Optional[
        typing.Union[
            vertexai.language_models._language_models.WebSearch,
            vertexai.language_models._language_models.VertexAISearch,
            vertexai.language_models._language_models.InlineContext,
        ]
    ] = None
) -> vertexai.language_models.MultiCandidateTextGenerationResponse

Asynchronously sends message to the language model and gets a response.

See more: vertexai.language_models.ChatSession.send_message_async

vertexai.language_models.ChatSession.send_message_streaming

send_message_streaming(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    top_k: typing.Optional[int] = None,
    top_p: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None
) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]

Sends message to the language model and gets a streamed response.

See more: vertexai.language_models.ChatSession.send_message_streaming

vertexai.language_models.ChatSession.send_message_streaming_async

send_message_streaming_async(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    top_k: typing.Optional[int] = None,
    top_p: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None
) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]

Asynchronously sends message to the language model and gets a streamed response.

See more: vertexai.language_models.ChatSession.send_message_streaming_async

vertexai.language_models.CodeChatModel

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

Creates a LanguageModel.

See more: vertexai.language_models.CodeChatModel

vertexai.language_models.CodeChatModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.language_models.CodeChatModel.from_pretrained

vertexai.language_models.CodeChatModel.get_tuned_model

get_tuned_model(
    tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModel

Loads the specified tuned language model.

See more: vertexai.language_models.CodeChatModel.get_tuned_model

vertexai.language_models.CodeChatModel.list_tuned_model_names

list_tuned_model_names() -> typing.Sequence[str]

Lists the names of tuned models.

See more: vertexai.language_models.CodeChatModel.list_tuned_model_names

vertexai.language_models.CodeChatModel.start_chat

start_chat(
    *,
    context: typing.Optional[str] = None,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    message_history: typing.Optional[
        typing.List[vertexai.language_models.ChatMessage]
    ] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None
) -> vertexai.language_models.CodeChatSession

Starts a chat session with the code chat model.

See more: vertexai.language_models.CodeChatModel.start_chat

vertexai.language_models.CodeChatModel.tune_model

tune_model(
    training_data: typing.Union[str, pandas.core.frame.DataFrame],
    *,
    train_steps: typing.Optional[int] = None,
    learning_rate_multiplier: typing.Optional[float] = None,
    tuning_job_location: typing.Optional[str] = None,
    tuned_model_location: typing.Optional[str] = None,
    model_display_name: typing.Optional[str] = None,
    default_context: typing.Optional[str] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    tuning_evaluation_spec: typing.Optional[
        vertexai.language_models.TuningEvaluationSpec
    ] = None
) -> vertexai.language_models._language_models._LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.CodeChatModel.tune_model

vertexai.language_models.CodeChatSession.send_message

send_message(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None,
    candidate_count: typing.Optional[int] = None
) -> vertexai.language_models.MultiCandidateTextGenerationResponse

Sends message to the code chat model and gets a response.

See more: vertexai.language_models.CodeChatSession.send_message

vertexai.language_models.CodeChatSession.send_message_async

send_message_async(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    candidate_count: typing.Optional[int] = None
) -> vertexai.language_models.MultiCandidateTextGenerationResponse

Asynchronously sends message to the code chat model and gets a response.

See more: vertexai.language_models.CodeChatSession.send_message_async

vertexai.language_models.CodeChatSession.send_message_streaming

send_message_streaming(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None
) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]

Sends message to the language model and gets a streamed response.

See more: vertexai.language_models.CodeChatSession.send_message_streaming

vertexai.language_models.CodeChatSession.send_message_streaming_async

send_message_streaming_async(
    message: str,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None
) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]

Asynchronously sends message to the language model and gets a streamed response.

See more: vertexai.language_models.CodeChatSession.send_message_streaming_async

vertexai.language_models.CodeGenerationModel.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.

See more: vertexai.language_models.CodeGenerationModel.batch_predict

vertexai.language_models.CodeGenerationModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.language_models.CodeGenerationModel.from_pretrained

vertexai.language_models.CodeGenerationModel.get_tuned_model

get_tuned_model(
    tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModel

Loads the specified tuned language model.

See more: vertexai.language_models.CodeGenerationModel.get_tuned_model

vertexai.language_models.CodeGenerationModel.list_tuned_model_names

list_tuned_model_names() -> typing.Sequence[str]

Lists the names of tuned models.

See more: vertexai.language_models.CodeGenerationModel.list_tuned_model_names

vertexai.language_models.CodeGenerationModel.predict

predict(
    prefix: str,
    suffix: typing.Optional[str] = None,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None,
    candidate_count: typing.Optional[int] = None
) -> vertexai.language_models.TextGenerationResponse

Gets model response for a single prompt.

See more: vertexai.language_models.CodeGenerationModel.predict

vertexai.language_models.CodeGenerationModel.predict_async

predict_async(
    prefix: str,
    suffix: typing.Optional[str] = None,
    *,
    max_output_tokens: typing.Optional[int] = None,
    temperature: typing.Optional[float] = None,
    stop_sequences: typing.Optional[typing.List[str]] = None,
    candidate_count: typing.Optional[int] = None
) -> vertexai