Package Methods (2.4.0)

2.4.0 (latest) 2.3.0 2.2.0 2.1.3 2.0.1 1.165.1 1.164.0 1.163.0 1.162.0 1.161.0 1.160.0 1.159.0 1.158.0 1.157.0 1.156.0 1.155.0 1.154.0

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.agent_engines.create

create(
    agent_engine: typing.Union[
        None,
        vertexai.agent_engines.AsyncQueryable,
        vertexai.agent_engines.AsyncStreamQueryable,
        vertexai.agent_engines._agent_engines.BidiStreamQueryable,
        vertexai.agent_engines.OperationRegistrable,
        vertexai.agent_engines.Queryable,
        vertexai.agent_engines.StreamQueryable,
    ] = None,
    *,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    env_vars: typing.Optional[
        typing.Union[
            typing.Sequence[str],
            typing.Dict[
                str,
                typing.Union[str, google.cloud.aiplatform_v1.types.env_var.SecretRef],
            ],
        ]
    ] = None,
    build_options: typing.Optional[typing.Dict[str, typing.Sequence[str]]] = None,
    service_account: typing.Optional[str] = None,
    identity_type: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1.types.reasoning_engine.ReasoningEngineSpec.IdentityType,
        ]
    ] = None,
    psc_interface_config: typing.Optional[
        google.cloud.aiplatform_v1.types.service_networking.PscInterfaceConfig
    ] = None,
    min_instances: typing.Optional[int] = None,
    max_instances: typing.Optional[int] = None,
    resource_limits: typing.Optional[typing.Dict[str, str]] = None,
    container_concurrency: typing.Optional[int] = None,
    encryption_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec
    ] = None
) -> vertexai.agent_engines.AgentEngine

Creates a new Agent Engine.

See more: vertexai.agent_engines.create

vertexai.agent_engines.delete

delete(resource_name: str, *, force: bool = False, **kwargs) -> None

Delete an Agent Engine resource.

See more: vertexai.agent_engines.delete

vertexai.agent_engines.get

get(resource_name: str) -> vertexai.agent_engines.AgentEngine

Retrieves an Agent Engine resource.

See more: vertexai.agent_engines.get

vertexai.agent_engines.list

list(*, filter: str = "") -> typing.Iterable[vertexai.agent_engines.AgentEngine]

List all instances of Agent Engine matching the filter.

See more: vertexai.agent_engines.list

vertexai.agent_engines.update

update(
    resource_name: str,
    *,
    agent_engine: typing.Optional[
        typing.Union[
            vertexai.agent_engines.Queryable,
            vertexai.agent_engines.OperationRegistrable,
        ]
    ] = None,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    env_vars: typing.Optional[
        typing.Union[
            typing.Sequence[str],
            typing.Dict[
                str,
                typing.Union[str, google.cloud.aiplatform_v1.types.env_var.SecretRef],
            ],
        ]
    ] = None,
    build_options: typing.Optional[typing.Dict[str, typing.Sequence[str]]] = None,
    service_account: typing.Optional[str] = None,
    identity_type: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1.types.reasoning_engine.ReasoningEngineSpec.IdentityType,
        ]
    ] = None,
    psc_interface_config: typing.Optional[
        google.cloud.aiplatform_v1.types.service_networking.PscInterfaceConfig
    ] = None,
    min_instances: typing.Optional[int] = None,
    max_instances: typing.Optional[int] = None,
    resource_limits: typing.Optional[typing.Dict[str, str]] = None,
    container_concurrency: typing.Optional[int] = None,
    encryption_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec
    ] = None
) -> vertexai.agent_engines.AgentEngine

Updates an existing Agent Engine.

See more: vertexai.agent_engines.update

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.preview_train

preview_train(
    *,
    source_model: typing.Union[
        str,
        vertexai.generative_models.GenerativeModel,
        vertexai.preview.tuning._tuning.SourceModel,
    ],
    train_dataset: typing.Union[
        str, google.cloud.aiplatform.preview.datasets.MultimodalDataset
    ],
    validation_dataset: typing.Optional[
        typing.Union[str, google.cloud.aiplatform.preview.datasets.MultimodalDataset]
    ] = None,
    tuned_model_display_name: typing.Optional[str] = None,
    tuning_mode: typing.Optional[typing.Literal["FULL", "PEFT_ADAPTER"]] = None,
    epochs: typing.Optional[int] = None,
    learning_rate: typing.Optional[float] = None,
    learning_rate_multiplier: typing.Optional[float] = None,
    adapter_size: typing.Optional[typing.Literal[1, 4, 8, 16, 32]] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    output_uri: typing.Optional[str] = None
) -> vertexai.preview.tuning._supervised_tuning.SupervisedTuningJob

Tunes a model using supervised training.

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

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,
        vertexai.tuning._tuning.SourceModel,
    ],
    train_dataset: typing.Union[
        str, google.cloud.aiplatform.preview.datasets.MultimodalDataset
    ],
    validation_dataset: typing.Optional[
        typing.Union[str, google.cloud.aiplatform.preview.datasets.MultimodalDataset]
    ] = None,
    tuned_model_display_name: typing.Optional[str] = None,
    tuning_mode: typing.Optional[typing.Literal["FULL", "PEFT_ADAPTER"]] = None,
    epochs: typing.Optional[int] = None,
    learning_rate: typing.Optional[float] = None,
    learning_rate_multiplier: typing.Optional[float] = None,
    adapter_size: typing.Optional[typing.Literal[1, 4, 8, 16, 32]] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    output_uri: typing.Optional[str] = None
) -> vertexai.tuning._supervised_tuning.SupervisedTuningJob

Tunes a model using supervised training.

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

vertexai.Client

Client(
    *,
    api_key: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    debug_config: typing.Optional[google.genai.client.DebugConfig] = None,
    http_options: typing.Optional[
        typing.Union[google.genai.types.HttpOptions, google.genai.types.HttpOptionsDict]
    ] = None
)

Initializes the client.

See more: vertexai.Client

vertexai._genai.agent_engines.AgentEngines.append_session_event

append_session_event(
    *,
    name: str,
    author: str,
    invocation_id: str,
    timestamp: datetime.datetime,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfig,
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AppendAgentEngineSessionEventResponse

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.append_session_event

vertexai._genai.agent_engines.AgentEngines.cancel_query_job

cancel_query_job(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CancelQueryJobAgentEngineConfig,
            vertexai._genai.types.common.CancelQueryJobAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.CancelQueryJobResult

Cancels a long-running query job on an Agent Engine.

See more: vertexai._genai.agent_engines.AgentEngines.cancel_query_job

vertexai._genai.agent_engines.AgentEngines.check_query_job

check_query_job(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CheckQueryJobAgentEngineConfig,
            vertexai._genai.types.common.CheckQueryJobAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.CheckQueryJobResult

Checks a query job on an agent engine and optionally returns the results.

See more: vertexai._genai.agent_engines.AgentEngines.check_query_job

vertexai._genai.agent_engines.AgentEngines.create

create(
    *,
    agent_engine: typing.Optional[typing.Any] = None,
    agent: typing.Optional[typing.Any] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AgentEngineConfig,
            vertexai._genai.types.common.AgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngine

Creates an agent engine.

See more: vertexai._genai.agent_engines.AgentEngines.create

vertexai._genai.agent_engines.AgentEngines.create_memory

create_memory(
    *,
    name: str,
    fact: str,
    scope: dict[str, str],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AgentEngineMemoryConfig,
            vertexai._genai.types.common.AgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineMemoryOperation

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.create_memory

vertexai._genai.agent_engines.AgentEngines.create_session

create_session(
    *,
    name: str,
    user_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateAgentEngineSessionConfig,
            vertexai._genai.types.common.CreateAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineSessionOperation

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.create_session

vertexai._genai.agent_engines.AgentEngines.delete

delete(
    *,
    name: str,
    force: typing.Optional[bool] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineConfig,
            vertexai._genai.types.common.DeleteAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineOperation

Delete an Agent Engine resource.

See more: vertexai._genai.agent_engines.AgentEngines.delete

vertexai._genai.agent_engines.AgentEngines.delete_memory

delete_memory(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfig,
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineMemoryOperation

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.delete_memory

vertexai._genai.agent_engines.AgentEngines.delete_session

delete_session(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineSessionConfig,
            vertexai._genai.types.common.DeleteAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineSessionOperation

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.delete_session

vertexai._genai.agent_engines.AgentEngines.generate_memories

generate_memories(
    *,
    name: str,
    vertex_session_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestVertexSessionSource,
            vertexai._genai.types.common.GenerateMemoriesRequestVertexSessionSourceDict,
        ]
    ] = None,
    direct_contents_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestDirectContentsSource,
            vertexai._genai.types.common.GenerateMemoriesRequestDirectContentsSourceDict,
        ]
    ] = None,
    direct_memories_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestDirectMemoriesSource,
            vertexai._genai.types.common.GenerateMemoriesRequestDirectMemoriesSourceDict,
        ]
    ] = None,
    scope: typing.Optional[dict[str, str]] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateAgentEngineMemoriesConfig,
            vertexai._genai.types.common.GenerateAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineGenerateMemoriesOperation

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.generate_memories

vertexai._genai.agent_engines.AgentEngines.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineConfig,
            vertexai._genai.types.common.GetAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngine

Gets an agent engine.

See more: vertexai._genai.agent_engines.AgentEngines.get

vertexai._genai.agent_engines.AgentEngines.get_memory

get_memory(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineMemoryConfig,
            vertexai._genai.types.common.GetAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Memory

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.get_memory

vertexai._genai.agent_engines.AgentEngines.get_session

get_session(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineSessionConfig,
            vertexai._genai.types.common.GetAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Session

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.get_session

vertexai._genai.agent_engines.AgentEngines.list

list(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineConfig,
            vertexai._genai.types.common.ListAgentEngineConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.AgentEngine]

List all instances of Agent Engine matching the filter.

See more: vertexai._genai.agent_engines.AgentEngines.list

vertexai._genai.agent_engines.AgentEngines.list_memories

list_memories(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineMemoryConfig,
            vertexai._genai.types.common.ListAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.Memory]

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.list_memories

vertexai._genai.agent_engines.AgentEngines.list_session_events

list_session_events(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSessionEventsConfig,
            vertexai._genai.types.common.ListAgentEngineSessionEventsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.SessionEvent]

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.list_session_events

vertexai._genai.agent_engines.AgentEngines.list_sessions

list_sessions(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSessionsConfig,
            vertexai._genai.types.common.ListAgentEngineSessionsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.Session]

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.list_sessions

vertexai._genai.agent_engines.AgentEngines.retrieve_memories

retrieve_memories(
    *,
    name: str,
    scope: dict[str, str],
    similarity_search_params: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoriesRequestSimilaritySearchParams,
            vertexai._genai.types.common.RetrieveMemoriesRequestSimilaritySearchParamsDict,
        ]
    ] = None,
    simple_retrieval_params: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoriesRequestSimpleRetrievalParams,
            vertexai._genai.types.common.RetrieveMemoriesRequestSimpleRetrievalParamsDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveAgentEngineMemoriesConfig,
            vertexai._genai.types.common.RetrieveAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> typing.Iterator[
    vertexai._genai.types.common.RetrieveMemoriesResponseRetrievedMemory
]

Deprecated.

See more: vertexai._genai.agent_engines.AgentEngines.retrieve_memories

vertexai._genai.agent_engines.AgentEngines.run_query_job

run_query_job(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RunQueryJobAgentEngineConfig,
            vertexai._genai.types.common.RunQueryJobAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.RunQueryJobResult

Launches a long-running query job on an Agent Engine .

See more: vertexai._genai.agent_engines.AgentEngines.run_query_job

vertexai._genai.agent_engines.AgentEngines.update

update(
    *,
    name: str,
    agent: typing.Optional[typing.Any] = None,
    agent_engine: typing.Optional[typing.Any] = None,
    config: typing.Union[
        vertexai._genai.types.common.AgentEngineConfig,
        vertexai._genai.types.common.AgentEngineConfigDict,
    ]
) -> vertexai._genai.types.common.AgentEngine

Updates an existing Agent Engine.

See more: vertexai._genai.agent_engines.AgentEngines.update

vertexai._genai.agent_engines.AsyncAgentEngines.append_session_event

append_session_event(
    *,
    name: str,
    author: str,
    invocation_id: str,
    timestamp: datetime.datetime,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfig,
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AppendAgentEngineSessionEventResponse

Deprecated.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.append_session_event

vertexai._genai.agent_engines.AsyncAgentEngines.cancel_query_job

cancel_query_job(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CancelQueryJobAgentEngineConfig,
            vertexai._genai.types.common.CancelQueryJobAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.CancelQueryJobResult

Cancels a long-running query job on an Agent Engine.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.cancel_query_job

vertexai._genai.agent_engines.AsyncAgentEngines.delete

delete(
    *,
    name: str,
    force: typing.Optional[bool] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineConfig,
            vertexai._genai.types.common.DeleteAgentEngineConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineOperation

Delete an Agent Engine resource.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.delete

vertexai._genai.agent_engines.AsyncAgentEngines.delete_memory

delete_memory(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfig,
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineMemoryOperation

Deprecated.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.delete_memory

vertexai._genai.agent_engines.AsyncAgentEngines.delete_session

delete_session(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineSessionConfig,
            vertexai._genai.types.common.DeleteAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineSessionOperation

Deprecated.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.delete_session

vertexai._genai.agent_engines.AsyncAgentEngines.get_memory

get_memory(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineMemoryConfig,
            vertexai._genai.types.common.GetAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Memory

Deprecated.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.get_memory

vertexai._genai.agent_engines.AsyncAgentEngines.get_session

get_session(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineSessionConfig,
            vertexai._genai.types.common.GetAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Session

Deprecated.

See more: vertexai._genai.agent_engines.AsyncAgentEngines.get_session

vertexai._genai.evals.AsyncEvals.batch_evaluate

batch_evaluate(
    *,
    dataset: typing.Union[
        vertexai._genai.types.common.EvaluationDataset,
        vertexai._genai.types.common.EvaluationDatasetDict,
    ],
    metrics: list[
        typing.Union[
            vertexai._genai.types.common.Metric, vertexai._genai.types.common.MetricDict
        ]
    ],
    dest: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.EvaluateDatasetConfig,
            vertexai._genai.types.common.EvaluateDatasetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluateDatasetOperation

Evaluates a dataset based on a set of given metrics.

See more: vertexai._genai.evals.AsyncEvals.batch_evaluate

vertexai._genai.evals.AsyncEvals.create_evaluation_experiment

create_evaluation_experiment(
    *,
    display_name: typing.Optional[str] = None,
    labels: typing.Optional[dict[str, str]] = None,
    merge_strategy: typing.Optional[
        vertexai._genai.types.common.EvaluationExperimentMergeStrategy
    ] = None,
    metadata: typing.Optional[dict[str, typing.Any]] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationExperimentConfig,
            vertexai._genai.types.common.CreateEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationExperiment

Creates an EvaluationExperiment.

See more: vertexai._genai.evals.AsyncEvals.create_evaluation_experiment

vertexai._genai.evals.AsyncEvals.create_evaluation_item

create_evaluation_item(
    *,
    evaluation_item_type: vertexai._genai.types.common.EvaluationItemType,
    gcs_uri: str,
    display_name: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationItemConfig,
            vertexai._genai.types.common.CreateEvaluationItemConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationItem

Creates an EvaluationItem.

See more: vertexai._genai.evals.AsyncEvals.create_evaluation_item

vertexai._genai.evals.AsyncEvals.create_evaluation_metric

create_evaluation_metric(
    *,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    metric: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.Metric, vertexai._genai.types.common.MetricDict
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationMetricConfig,
            vertexai._genai.types.common.CreateEvaluationMetricConfigDict,
        ]
    ] = None
) -> str

Creates an EvaluationMetric.

See more: vertexai._genai.evals.AsyncEvals.create_evaluation_metric

vertexai._genai.evals.AsyncEvals.create_evaluation_run

create_evaluation_run(
    *,
    dataset: typing.Union[
        vertexai._genai.types.common.EvaluationRunDataSource,
        vertexai._genai.types.common.EvaluationDataset,
    ],
    dest: str,
    metrics: list[
        typing.Union[
            vertexai._genai.types.common.EvaluationRunMetric,
            vertexai._genai.types.common.EvaluationRunMetricDict,
        ]
    ],
    name: typing.Optional[str] = None,
    display_name: typing.Optional[str] = None,
    evaluation_experiment: typing.Optional[str] = None,
    agent_info: typing.Optional[vertexai._genai.types.evals.AgentInfo] = None,
    agent: typing.Optional[str] = None,
    user_simulator_config: typing.Optional[
        typing.Union[
            vertexai._genai.types.evals.UserSimulatorConfig,
            vertexai._genai.types.evals.UserSimulatorConfigDict,
        ]
    ] = None,
    inference_configs: typing.Optional[
        dict[
            str,
            typing.Union[
                vertexai._genai.types.common.EvaluationRunInferenceConfig,
                vertexai._genai.types.common.EvaluationRunInferenceConfigDict,
            ],
        ]
    ] = None,
    red_teaming_config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RedTeamingAnalysisConfig,
            vertexai._genai.types.common.RedTeamingAnalysisConfigDict,
        ]
    ] = None,
    labels: typing.Optional[dict[str, str]] = None,
    loss_analysis_metrics: typing.Optional[
        list[
            typing.Union[
                str,
                vertexai._genai.types.common.Metric,
                vertexai._genai.types.common.MetricDict,
            ]
        ]
    ] = None,
    loss_analysis_configs: typing.Optional[
        list[
            typing.Union[
                vertexai._genai.types.common.LossAnalysisConfig,
                vertexai._genai.types.common.LossAnalysisConfigDict,
            ]
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationRunConfig,
            vertexai._genai.types.common.CreateEvaluationRunConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationRun

Creates an EvaluationRun.

See more: vertexai._genai.evals.AsyncEvals.create_evaluation_run

vertexai._genai.evals.AsyncEvals.create_evaluation_set

create_evaluation_set(
    *,
    evaluation_items: list[str],
    display_name: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationSetConfig,
            vertexai._genai.types.common.CreateEvaluationSetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationSet

Creates an EvaluationSet.

See more: vertexai._genai.evals.AsyncEvals.create_evaluation_set

vertexai._genai.evals.AsyncEvals.delete_evaluation_experiment

delete_evaluation_experiment(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteEvaluationExperimentConfig,
            vertexai._genai.types.common.DeleteEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteEvaluationExperimentOperation

Deletes an EvaluationExperiment.

See more: vertexai._genai.evals.AsyncEvals.delete_evaluation_experiment

vertexai._genai.evals.AsyncEvals.delete_evaluation_metric

delete_evaluation_metric(
    *,
    metric_resource_name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteEvaluationMetricConfig,
            vertexai._genai.types.common.DeleteEvaluationMetricConfigDict,
        ]
    ] = None
) -> None

Deletes an EvaluationMetric.

See more: vertexai._genai.evals.AsyncEvals.delete_evaluation_metric

vertexai._genai.evals.AsyncEvals.evaluate_instances

evaluate_instances(
    *, metric_config: vertexai._genai.types.common._EvaluateInstancesRequestParameters
) -> vertexai._genai.types.common.EvaluateInstancesResponse

Evaluates an instance of a model.

See more: vertexai._genai.evals.AsyncEvals.evaluate_instances

vertexai._genai.evals.AsyncEvals.generate_conversation_scenarios

generate_conversation_scenarios(
    *,
    agent_info: typing.Union[
        vertexai._genai.types.evals.AgentInfo, vertexai._genai.types.evals.AgentInfoDict
    ],
    config: typing.Union[
        vertexai._genai.types.evals.UserScenarioGenerationConfig,
        vertexai._genai.types.evals.UserScenarioGenerationConfigDict,
    ],
    allow_cross_region_model: typing.Optional[bool] = None
) -> vertexai._genai.types.common.EvaluationDataset

Generates an evaluation dataset with user scenarios, which helps to generate conversations between a simulated user and the agent under test.

See more: vertexai._genai.evals.AsyncEvals.generate_conversation_scenarios

vertexai._genai.evals.AsyncEvals.generate_loss_clusters

generate_loss_clusters(
    *,
    eval_result: vertexai._genai.types.common.EvaluationResult,
    metric: typing.Optional[
        typing.Union[
            str,
            vertexai._genai.types.common.Metric,
            vertexai._genai.types.common.MetricDict,
        ]
    ] = None,
    candidate: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.LossAnalysisConfig,
            vertexai._genai.types.common.LossAnalysisConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.GenerateLossClustersResponse

Generates loss clusters from evaluation results.

See more: vertexai._genai.evals.AsyncEvals.generate_loss_clusters

vertexai._genai.evals.AsyncEvals.get_evaluation_experiment

get_evaluation_experiment(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationExperimentConfig,
            vertexai._genai.types.common.GetEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationExperiment

Retrieves an EvaluationExperiment from the resource name.

See more: vertexai._genai.evals.AsyncEvals.get_evaluation_experiment

vertexai._genai.evals.AsyncEvals.get_evaluation_item

get_evaluation_item(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationItemConfig,
            vertexai._genai.types.common.GetEvaluationItemConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationItem

Retrieves an EvaluationItem from the resource name.

See more: vertexai._genai.evals.AsyncEvals.get_evaluation_item

vertexai._genai.evals.AsyncEvals.get_evaluation_metric

get_evaluation_metric(
    *,
    metric_resource_name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationMetricConfig,
            vertexai._genai.types.common.GetEvaluationMetricConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationMetric

Retrieves an EvaluationMetric from the resource name.

See more: vertexai._genai.evals.AsyncEvals.get_evaluation_metric

vertexai._genai.evals.AsyncEvals.get_evaluation_run

get_evaluation_run(
    *,
    name: str,
    include_evaluation_items: bool = False,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationRunConfig,
            vertexai._genai.types.common.GetEvaluationRunConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationRun

Retrieves the EvaluationRun from the resource name.

See more: vertexai._genai.evals.AsyncEvals.get_evaluation_run

vertexai._genai.evals.AsyncEvals.get_evaluation_set

get_evaluation_set(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationSetConfig,
            vertexai._genai.types.common.GetEvaluationSetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationSet

Retrieves an EvaluationSet from the resource name.

See more: vertexai._genai.evals.AsyncEvals.get_evaluation_set

vertexai._genai.evals.AsyncEvals.list_evaluation_experiments

list_evaluation_experiments(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListEvaluationExperimentsConfig,
            vertexai._genai.types.common.ListEvaluationExperimentsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ListEvaluationExperimentsResponse

Lists EvaluationExperiments.

See more: vertexai._genai.evals.AsyncEvals.list_evaluation_experiments

vertexai._genai.evals.AsyncEvals.list_evaluation_metrics

list_evaluation_metrics(
    *,
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListEvaluationMetricsConfig,
            vertexai._genai.types.common.ListEvaluationMetricsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ListEvaluationMetricsResponse

Lists EvaluationMetrics.

See more: vertexai._genai.evals.AsyncEvals.list_evaluation_metrics

vertexai._genai.evals.AsyncEvals.update_evaluation_experiment

update_evaluation_experiment(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.UpdateEvaluationExperimentConfig,
            vertexai._genai.types.common.UpdateEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationExperiment

Updates an EvaluationExperiment.

See more: vertexai._genai.evals.AsyncEvals.update_evaluation_experiment

vertexai._genai.evals.Evals.batch_evaluate

batch_evaluate(
    *,
    dataset: typing.Union[
        vertexai._genai.types.common.EvaluationDataset,
        vertexai._genai.types.common.EvaluationDatasetDict,
    ],
    metrics: list[
        typing.Union[
            vertexai._genai.types.common.Metric, vertexai._genai.types.common.MetricDict
        ]
    ],
    dest: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.EvaluateDatasetConfig,
            vertexai._genai.types.common.EvaluateDatasetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluateDatasetOperation

Evaluates a dataset based on a set of given metrics.

See more: vertexai._genai.evals.Evals.batch_evaluate

vertexai._genai.evals.Evals.create_evaluation_experiment

create_evaluation_experiment(
    *,
    display_name: typing.Optional[str] = None,
    labels: typing.Optional[dict[str, str]] = None,
    merge_strategy: typing.Optional[
        vertexai._genai.types.common.EvaluationExperimentMergeStrategy
    ] = None,
    metadata: typing.Optional[dict[str, typing.Any]] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationExperimentConfig,
            vertexai._genai.types.common.CreateEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationExperiment

Creates an EvaluationExperiment.

See more: vertexai._genai.evals.Evals.create_evaluation_experiment

vertexai._genai.evals.Evals.create_evaluation_item

create_evaluation_item(
    *,
    evaluation_item_type: vertexai._genai.types.common.EvaluationItemType,
    gcs_uri: str,
    display_name: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationItemConfig,
            vertexai._genai.types.common.CreateEvaluationItemConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationItem

Creates an EvaluationItem.

See more: vertexai._genai.evals.Evals.create_evaluation_item

vertexai._genai.evals.Evals.create_evaluation_metric

create_evaluation_metric(
    *,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    metric: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.Metric, vertexai._genai.types.common.MetricDict
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationMetricConfig,
            vertexai._genai.types.common.CreateEvaluationMetricConfigDict,
        ]
    ] = None
) -> str

Creates an EvaluationMetric.

See more: vertexai._genai.evals.Evals.create_evaluation_metric

vertexai._genai.evals.Evals.create_evaluation_run

create_evaluation_run(
    *,
    dataset: typing.Union[
        vertexai._genai.types.common.EvaluationRunDataSource,
        vertexai._genai.types.common.EvaluationDataset,
    ],
    dest: str,
    metrics: list[
        typing.Union[
            vertexai._genai.types.common.EvaluationRunMetric,
            vertexai._genai.types.common.EvaluationRunMetricDict,
        ]
    ],
    name: typing.Optional[str] = None,
    display_name: typing.Optional[str] = None,
    evaluation_experiment: typing.Optional[str] = None,
    agent_info: typing.Optional[
        typing.Union[
            vertexai._genai.types.evals.AgentInfo,
            vertexai._genai.types.evals.AgentInfoDict,
        ]
    ] = None,
    agent: typing.Optional[str] = None,
    user_simulator_config: typing.Optional[
        typing.Union[
            vertexai._genai.types.evals.UserSimulatorConfig,
            vertexai._genai.types.evals.UserSimulatorConfigDict,
        ]
    ] = None,
    inference_configs: typing.Optional[
        dict[
            str,
            typing.Union[
                vertexai._genai.types.common.EvaluationRunInferenceConfig,
                vertexai._genai.types.common.EvaluationRunInferenceConfigDict,
            ],
        ]
    ] = None,
    labels: typing.Optional[dict[str, str]] = None,
    loss_analysis_metrics: typing.Optional[
        list[
            typing.Union[
                str,
                vertexai._genai.types.common.Metric,
                vertexai._genai.types.common.MetricDict,
            ]
        ]
    ] = None,
    loss_analysis_configs: typing.Optional[
        list[
            typing.Union[
                vertexai._genai.types.common.LossAnalysisConfig,
                vertexai._genai.types.common.LossAnalysisConfigDict,
            ]
        ]
    ] = None,
    red_teaming_config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RedTeamingAnalysisConfig,
            vertexai._genai.types.common.RedTeamingAnalysisConfigDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationRunConfig,
            vertexai._genai.types.common.CreateEvaluationRunConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationRun

Creates an EvaluationRun.

See more: vertexai._genai.evals.Evals.create_evaluation_run

vertexai._genai.evals.Evals.create_evaluation_set

create_evaluation_set(
    *,
    evaluation_items: list[str],
    display_name: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateEvaluationSetConfig,
            vertexai._genai.types.common.CreateEvaluationSetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationSet

Creates an EvaluationSet.

See more: vertexai._genai.evals.Evals.create_evaluation_set

vertexai._genai.evals.Evals.delete_evaluation_experiment

delete_evaluation_experiment(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteEvaluationExperimentConfig,
            vertexai._genai.types.common.DeleteEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteEvaluationExperimentOperation

Deletes an EvaluationExperiment.

See more: vertexai._genai.evals.Evals.delete_evaluation_experiment

vertexai._genai.evals.Evals.delete_evaluation_metric

delete_evaluation_metric(
    *,
    metric_resource_name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteEvaluationMetricConfig,
            vertexai._genai.types.common.DeleteEvaluationMetricConfigDict,
        ]
    ] = None
) -> None

Deletes an EvaluationMetric.

See more: vertexai._genai.evals.Evals.delete_evaluation_metric

vertexai._genai.evals.Evals.evaluate

evaluate(
    *,
    dataset: typing.Union[
        pandas.core.frame.DataFrame,
        vertexai._genai.types.common.EvaluationDataset,
        vertexai._genai.types.common.EvaluationDatasetDict,
        list[
            typing.Union[
                vertexai._genai.types.common.EvaluationDataset,
                vertexai._genai.types.common.EvaluationDatasetDict,
            ]
        ],
    ],
    metrics: typing.Optional[
        list[
            typing.Union[
                vertexai._genai.types.common.Metric,
                vertexai._genai.types.common.MetricDict,
            ]
        ]
    ] = None,
    location: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.EvaluateMethodConfig,
            vertexai._genai.types.common.EvaluateMethodConfigDict,
        ]
    ] = None,
    **kwargs: typing.Any
) -> vertexai._genai.types.common.EvaluationResult

Evaluates candidate responses in the provided dataset(s) using the specified metrics.

See more: vertexai._genai.evals.Evals.evaluate

vertexai._genai.evals.Evals.evaluate_instances

evaluate_instances(
    *, metric_config: vertexai._genai.types.common._EvaluateInstancesRequestParameters
) -> vertexai._genai.types.common.EvaluateInstancesResponse

Evaluates an instance of a model.

See more: vertexai._genai.evals.Evals.evaluate_instances

vertexai._genai.evals.Evals.generate_conversation_scenarios

generate_conversation_scenarios(
    *,
    agent_info: typing.Union[
        vertexai._genai.types.evals.AgentInfo, vertexai._genai.types.evals.AgentInfoDict
    ],
    config: typing.Union[
        vertexai._genai.types.evals.UserScenarioGenerationConfig,
        vertexai._genai.types.evals.UserScenarioGenerationConfigDict,
    ],
    allow_cross_region_model: typing.Optional[bool] = None
) -> vertexai._genai.types.common.EvaluationDataset

Generates an evaluation dataset with user scenarios, which helps to generate conversations between a simulated user and the agent under test.

See more: vertexai._genai.evals.Evals.generate_conversation_scenarios

vertexai._genai.evals.Evals.generate_loss_clusters

generate_loss_clusters(
    *,
    eval_result: vertexai._genai.types.common.EvaluationResult,
    metric: typing.Optional[
        typing.Union[
            str,
            vertexai._genai.types.common.Metric,
            vertexai._genai.types.common.MetricDict,
        ]
    ] = None,
    candidate: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.LossAnalysisConfig,
            vertexai._genai.types.common.LossAnalysisConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.GenerateLossClustersResponse

Generates loss clusters from evaluation results.

See more: vertexai._genai.evals.Evals.generate_loss_clusters

vertexai._genai.evals.Evals.generate_rubrics

generate_rubrics(
    *,
    src: typing.Union[
        str, pd.DataFrame, vertexai._genai.types.common.EvaluationDataset
    ],
    rubric_group_name: str,
    prompt_template: typing.Optional[str] = None,
    generator_model_config: typing.Optional[genai_types.AutoraterConfigOrDict] = None,
    rubric_content_type: typing.Optional[types.RubricContentType] = None,
    rubric_type_ontology: typing.Optional[list[str]] = None,
    predefined_spec_name: typing.Optional[
        typing.Union[str, types.PrebuiltMetric]
    ] = None,
    metric_spec_parameters: typing.Optional[dict[str, typing.Any]] = None,
    metric: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.Metric, vertexai._genai.types.common.MetricDict
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RubricGenerationConfig,
            vertexai._genai.types.common.RubricGenerationConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationDataset

Generates rubrics for each prompt in the source and adds them as a new column structured as a dictionary.

See more: vertexai._genai.evals.Evals.generate_rubrics

vertexai._genai.evals.Evals.get_evaluation_experiment

get_evaluation_experiment(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationExperimentConfig,
            vertexai._genai.types.common.GetEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationExperiment

Retrieves an EvaluationExperiment from the resource name.

See more: vertexai._genai.evals.Evals.get_evaluation_experiment

vertexai._genai.evals.Evals.get_evaluation_item

get_evaluation_item(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationItemConfig,
            vertexai._genai.types.common.GetEvaluationItemConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationItem

Retrieves an EvaluationItem from the resource name.

See more: vertexai._genai.evals.Evals.get_evaluation_item

vertexai._genai.evals.Evals.get_evaluation_metric

get_evaluation_metric(
    *,
    metric_resource_name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationMetricConfig,
            vertexai._genai.types.common.GetEvaluationMetricConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationMetric

Retrieves an EvaluationMetric from the resource name.

See more: vertexai._genai.evals.Evals.get_evaluation_metric

vertexai._genai.evals.Evals.get_evaluation_run

get_evaluation_run(
    *,
    name: str,
    include_evaluation_items: bool = False,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationRunConfig,
            vertexai._genai.types.common.GetEvaluationRunConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationRun

Retrieves an EvaluationRun from the resource name.

See more: vertexai._genai.evals.Evals.get_evaluation_run

vertexai._genai.evals.Evals.get_evaluation_set

get_evaluation_set(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetEvaluationSetConfig,
            vertexai._genai.types.common.GetEvaluationSetConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationSet

Retrieves an EvaluationSet from the resource name.

See more: vertexai._genai.evals.Evals.get_evaluation_set

vertexai._genai.evals.Evals.list_evaluation_experiments

list_evaluation_experiments(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListEvaluationExperimentsConfig,
            vertexai._genai.types.common.ListEvaluationExperimentsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ListEvaluationExperimentsResponse

Lists EvaluationExperiments.

See more: vertexai._genai.evals.Evals.list_evaluation_experiments

vertexai._genai.evals.Evals.list_evaluation_metrics

list_evaluation_metrics(
    *,
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListEvaluationMetricsConfig,
            vertexai._genai.types.common.ListEvaluationMetricsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ListEvaluationMetricsResponse

Lists EvaluationMetrics.

See more: vertexai._genai.evals.Evals.list_evaluation_metrics

vertexai._genai.evals.Evals.run_inference

run_inference(
    *,
    src: typing.Union[
        str, pandas.core.frame.DataFrame, vertexai._genai.types.common.EvaluationDataset
    ],
    model: typing.Optional[
        typing.Union[str, typing.Callable[[typing.Any], typing.Any]]
    ] = None,
    agent: typing.Optional[
        typing.Union[str, vertexai._genai.types.common.AgentEngine]
    ] = None,
    location: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.EvalRunInferenceConfig,
            vertexai._genai.types.common.EvalRunInferenceConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationDataset

Runs inference on a dataset for evaluation.

See more: vertexai._genai.evals.Evals.run_inference

vertexai._genai.evals.Evals.update_evaluation_experiment

update_evaluation_experiment(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.UpdateEvaluationExperimentConfig,
            vertexai._genai.types.common.UpdateEvaluationExperimentConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.EvaluationExperiment

Updates an EvaluationExperiment.

See more: vertexai._genai.evals.Evals.update_evaluation_experiment

vertexai._genai.memories.AsyncMemories.create

create(
    *,
    name: str,
    fact: str,
    scope: dict[str, str],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AgentEngineMemoryConfig,
            vertexai._genai.types.common.AgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineMemoryOperation

Creates a new memory in the Agent Engine.

See more: vertexai._genai.memories.AsyncMemories.create

vertexai._genai.memories.AsyncMemories.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfig,
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineMemoryOperation

Delete an Agent Engine memory.

See more: vertexai._genai.memories.AsyncMemories.delete

vertexai._genai.memories.AsyncMemories.generate

generate(
    *,
    name: str,
    vertex_session_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestVertexSessionSource,
            vertexai._genai.types.common.GenerateMemoriesRequestVertexSessionSourceDict,
        ]
    ] = None,
    direct_contents_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestDirectContentsSource,
            vertexai._genai.types.common.GenerateMemoriesRequestDirectContentsSourceDict,
        ]
    ] = None,
    direct_memories_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestDirectMemoriesSource,
            vertexai._genai.types.common.GenerateMemoriesRequestDirectMemoriesSourceDict,
        ]
    ] = None,
    scope: typing.Optional[dict[str, str]] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateAgentEngineMemoriesConfig,
            vertexai._genai.types.common.GenerateAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineGenerateMemoriesOperation

Generates memories for the agent engine.

See more: vertexai._genai.memories.AsyncMemories.generate

vertexai._genai.memories.AsyncMemories.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineMemoryConfig,
            vertexai._genai.types.common.GetAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Memory

Gets an agent engine memory.

See more: vertexai._genai.memories.AsyncMemories.get

vertexai._genai.memories.AsyncMemories.ingest_events

ingest_events(
    *,
    name: str,
    scope: dict[str, str],
    stream_id: str = "",
    direct_contents_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.IngestionDirectContentsSource,
            vertexai._genai.types.common.IngestionDirectContentsSourceDict,
        ]
    ] = None,
    generation_trigger_config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.MemoryGenerationTriggerConfig,
            vertexai._genai.types.common.MemoryGenerationTriggerConfigDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.IngestEventsConfig,
            vertexai._genai.types.common.IngestEventsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.MemoryBankIngestEventsOperation

Ingests events into an Agent Engine.

See more: vertexai._genai.memories.AsyncMemories.ingest_events

vertexai._genai.memories.AsyncMemories.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineMemoryConfig,
            vertexai._genai.types.common.ListAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> google.genai.pagers.AsyncPager[vertexai._genai.types.common.Memory]

Lists Agent Engine memories.

See more: vertexai._genai.memories.AsyncMemories.list

vertexai._genai.memories.AsyncMemories.purge

purge(
    *,
    name: str,
    filter: typing.Optional[str] = None,
    filter_groups: typing.Optional[
        typing.List[vertexai._genai.types.common.MemoryConjunctionFilter]
    ] = None,
    force: bool = False,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.PurgeAgentEngineMemoriesConfig,
            vertexai._genai.types.common.PurgeAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEnginePurgeMemoriesOperation

Purges memories from an Agent Engine.

See more: vertexai._genai.memories.AsyncMemories.purge

vertexai._genai.memories.AsyncMemories.retrieve

retrieve(
    *,
    name: str,
    scope: dict[str, str],
    similarity_search_params: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoriesRequestSimilaritySearchParams,
            vertexai._genai.types.common.RetrieveMemoriesRequestSimilaritySearchParamsDict,
        ]
    ] = None,
    simple_retrieval_params: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoriesRequestSimpleRetrievalParams,
            vertexai._genai.types.common.RetrieveMemoriesRequestSimpleRetrievalParamsDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveAgentEngineMemoriesConfig,
            vertexai._genai.types.common.RetrieveAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> google.genai.pagers.AsyncPager[
    vertexai._genai.types.common.RetrieveMemoriesResponseRetrievedMemory
]

Retrieves memories for the agent.

See more: vertexai._genai.memories.AsyncMemories.retrieve

vertexai._genai.memories.AsyncMemories.retrieve_profiles

retrieve_profiles(
    *,
    name: str,
    scope: dict[str, str],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoryProfilesConfig,
            vertexai._genai.types.common.RetrieveMemoryProfilesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.RetrieveProfilesResponse

Retrieves memory profiles for an Agent Engine.

See more: vertexai._genai.memories.AsyncMemories.retrieve_profiles

vertexai._genai.memories.AsyncMemories.rollback

rollback(
    *,
    name: str,
    target_revision_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RollbackAgentEngineMemoryConfig,
            vertexai._genai.types.common.RollbackAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineRollbackMemoryOperation

Rolls back a memory to a previous revision.

See more: vertexai._genai.memories.AsyncMemories.rollback

vertexai._genai.memories.Memories.create

create(
    *,
    name: str,
    fact: str,
    scope: dict[str, str],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AgentEngineMemoryConfig,
            vertexai._genai.types.common.AgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineMemoryOperation

Creates a new memory in the Agent Engine.

See more: vertexai._genai.memories.Memories.create

vertexai._genai.memories.Memories.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfig,
            vertexai._genai.types.common.DeleteAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineMemoryOperation

Delete an Agent Engine memory.

See more: vertexai._genai.memories.Memories.delete

vertexai._genai.memories.Memories.generate

generate(
    *,
    name: str,
    vertex_session_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestVertexSessionSource,
            vertexai._genai.types.common.GenerateMemoriesRequestVertexSessionSourceDict,
        ]
    ] = None,
    direct_contents_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestDirectContentsSource,
            vertexai._genai.types.common.GenerateMemoriesRequestDirectContentsSourceDict,
        ]
    ] = None,
    direct_memories_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateMemoriesRequestDirectMemoriesSource,
            vertexai._genai.types.common.GenerateMemoriesRequestDirectMemoriesSourceDict,
        ]
    ] = None,
    scope: typing.Optional[dict[str, str]] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GenerateAgentEngineMemoriesConfig,
            vertexai._genai.types.common.GenerateAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineGenerateMemoriesOperation

Generates memories for the agent engine.

See more: vertexai._genai.memories.Memories.generate

vertexai._genai.memories.Memories.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineMemoryConfig,
            vertexai._genai.types.common.GetAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Memory

Gets an agent engine memory.

See more: vertexai._genai.memories.Memories.get

vertexai._genai.memories.Memories.ingest_events

ingest_events(
    *,
    name: str,
    scope: dict[str, str],
    stream_id: str = "",
    direct_contents_source: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.IngestionDirectContentsSource,
            vertexai._genai.types.common.IngestionDirectContentsSourceDict,
        ]
    ] = None,
    generation_trigger_config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.MemoryGenerationTriggerConfig,
            vertexai._genai.types.common.MemoryGenerationTriggerConfigDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.IngestEventsConfig,
            vertexai._genai.types.common.IngestEventsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.MemoryBankIngestEventsOperation

Ingests events into an Agent Engine.

See more: vertexai._genai.memories.Memories.ingest_events

vertexai._genai.memories.Memories.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineMemoryConfig,
            vertexai._genai.types.common.ListAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.Memory]

Lists Agent Engine memories.

See more: vertexai._genai.memories.Memories.list

vertexai._genai.memories.Memories.purge

purge(
    *,
    name: str,
    filter: typing.Optional[str] = None,
    filter_groups: typing.Optional[
        typing.List[vertexai._genai.types.common.MemoryConjunctionFilter]
    ] = None,
    force: bool = False,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.PurgeAgentEngineMemoriesConfig,
            vertexai._genai.types.common.PurgeAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEnginePurgeMemoriesOperation

Purges memories from an Agent Engine.

See more: vertexai._genai.memories.Memories.purge

vertexai._genai.memories.Memories.retrieve

retrieve(
    *,
    name: str,
    scope: dict[str, str],
    similarity_search_params: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoriesRequestSimilaritySearchParams,
            vertexai._genai.types.common.RetrieveMemoriesRequestSimilaritySearchParamsDict,
        ]
    ] = None,
    simple_retrieval_params: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoriesRequestSimpleRetrievalParams,
            vertexai._genai.types.common.RetrieveMemoriesRequestSimpleRetrievalParamsDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveAgentEngineMemoriesConfig,
            vertexai._genai.types.common.RetrieveAgentEngineMemoriesConfigDict,
        ]
    ] = None
) -> typing.Iterator[
    vertexai._genai.types.common.RetrieveMemoriesResponseRetrievedMemory
]

Retrieves memories for the agent.

See more: vertexai._genai.memories.Memories.retrieve

vertexai._genai.memories.Memories.retrieve_profiles

retrieve_profiles(
    *,
    name: str,
    scope: dict[str, str],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveMemoryProfilesConfig,
            vertexai._genai.types.common.RetrieveMemoryProfilesConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.RetrieveProfilesResponse

Retrieves memory profiles for an Agent Engine.

See more: vertexai._genai.memories.Memories.retrieve_profiles

vertexai._genai.memories.Memories.rollback

rollback(
    *,
    name: str,
    target_revision_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RollbackAgentEngineMemoryConfig,
            vertexai._genai.types.common.RollbackAgentEngineMemoryConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineRollbackMemoryOperation

Rolls back a memory to a previous revision.

See more: vertexai._genai.memories.Memories.rollback

vertexai._genai.memory_revisions.AsyncMemoryRevisions.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineMemoryRevisionConfig,
            vertexai._genai.types.common.GetAgentEngineMemoryRevisionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.MemoryRevision

Gets an agent engine memory revision.

See more: vertexai._genai.memory_revisions.AsyncMemoryRevisions.get

vertexai._genai.memory_revisions.AsyncMemoryRevisions.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineMemoryRevisionsConfig,
            vertexai._genai.types.common.ListAgentEngineMemoryRevisionsConfigDict,
        ]
    ] = None
) -> google.genai.pagers.AsyncPager[vertexai._genai.types.common.MemoryRevision]

Lists Agent Engine memory revisions.

See more: vertexai._genai.memory_revisions.AsyncMemoryRevisions.list

vertexai._genai.memory_revisions.MemoryRevisions.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineMemoryRevisionConfig,
            vertexai._genai.types.common.GetAgentEngineMemoryRevisionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.MemoryRevision

Gets an agent engine memory revision.

See more: vertexai._genai.memory_revisions.MemoryRevisions.get

vertexai._genai.memory_revisions.MemoryRevisions.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineMemoryRevisionsConfig,
            vertexai._genai.types.common.ListAgentEngineMemoryRevisionsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.MemoryRevision]

Lists Agent Engine memory revisions.

See more: vertexai._genai.memory_revisions.MemoryRevisions.list

vertexai._genai.prompt_optimizer.AsyncPromptOptimizer.optimize

optimize(
    method: vertexai._genai.types.common.PromptOptimizerMethod,
    config: typing.Union[
        vertexai._genai.types.common.PromptOptimizerConfig,
        vertexai._genai.types.common.PromptOptimizerConfigDict,
    ],
) -> vertexai._genai.types.common.CustomJob

Call async Vertex AI Prompt Optimizer (VAPO).

See more: vertexai._genai.prompt_optimizer.AsyncPromptOptimizer.optimize

vertexai._genai.prompt_optimizer.AsyncPromptOptimizer.optimize_prompt

optimize_prompt(
    *,
    prompt: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.OptimizeConfig,
            vertexai._genai.types.common.OptimizeConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.OptimizeResponse

Makes an async request to _optimize_prompt and returns an optimized prompt.

See more: vertexai._genai.prompt_optimizer.AsyncPromptOptimizer.optimize_prompt

vertexai._genai.prompt_optimizer.PromptOptimizer.optimize

optimize(
    method: vertexai._genai.types.common.PromptOptimizerMethod,
    config: typing.Union[
        vertexai._genai.types.common.PromptOptimizerConfig,
        vertexai._genai.types.common.PromptOptimizerConfigDict,
    ],
) -> vertexai._genai.types.common.CustomJob

Call PO-Data optimizer.

See more: vertexai._genai.prompt_optimizer.PromptOptimizer.optimize

vertexai._genai.prompt_optimizer.PromptOptimizer.optimize_prompt

optimize_prompt(
    *,
    prompt: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.OptimizeConfig,
            vertexai._genai.types.common.OptimizeConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.OptimizeResponse

Makes an API request to _optimize_prompt and returns the parsed response.

See more: vertexai._genai.prompt_optimizer.PromptOptimizer.optimize_prompt

vertexai._genai.prompts.AsyncPrompts.create

create(
    *,
    prompt: typing.Union[
        vertexai._genai.types.common.Prompt, vertexai._genai.types.common.PromptDict
    ],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreatePromptConfig,
            vertexai._genai.types.common.CreatePromptConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Prompt

Creates a new prompt in a Vertex Dataset resource.

See more: vertexai._genai.prompts.AsyncPrompts.create

vertexai._genai.prompts.AsyncPrompts.create_version

create_version(
    *,
    prompt: typing.Union[
        vertexai._genai.types.common.Prompt, vertexai._genai.types.common.PromptDict
    ],
    prompt_id: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreatePromptVersionConfig,
            vertexai._genai.types.common.CreatePromptVersionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Prompt

Creates a prompt resource and an initial prompt version.

See more: vertexai._genai.prompts.AsyncPrompts.create_version

vertexai._genai.prompts.AsyncPrompts.delete

delete(
    *,
    prompt_id: str,
    config: typing.Optional[vertexai._genai.types.common.DeletePromptConfig] = None
) -> None

Deletes a prompt resource.

See more: vertexai._genai.prompts.AsyncPrompts.delete

vertexai._genai.prompts.AsyncPrompts.delete_version

delete_version(
    *,
    prompt_id: str,
    version_id: str,
    config: typing.Optional[vertexai._genai.types.common.DeletePromptConfig] = None
) -> None

Deletes a prompt version resource.

See more: vertexai._genai.prompts.AsyncPrompts.delete_version

vertexai._genai.prompts.AsyncPrompts.get

get(
    *,
    prompt_id: str,
    config: typing.Optional[vertexai._genai.types.common.GetPromptConfig] = None
) -> vertexai._genai.types.common.Prompt

Gets a prompt resource from a Vertex Dataset.

See more: vertexai._genai.prompts.AsyncPrompts.get

vertexai._genai.prompts.AsyncPrompts.get_version

get_version(
    *,
    prompt_id: str,
    version_id: str,
    config: typing.Optional[vertexai._genai.types.common.GetPromptConfig] = None
) -> vertexai._genai.types.common.Prompt

Gets a prompt resource from a Vertex Dataset.

See more: vertexai._genai.prompts.AsyncPrompts.get_version

vertexai._genai.prompts.AsyncPrompts.launch_optimization_job

launch_optimization_job(
    method: vertexai._genai.types.common.PromptOptimizerMethod,
    config: typing.Union[
        vertexai._genai.types.common.PromptOptimizerConfig,
        vertexai._genai.types.common.PromptOptimizerConfigDict,
    ],
) -> vertexai._genai.types.common.CustomJob

Call async Vertex AI Prompt Optimizer (VAPO).

See more: vertexai._genai.prompts.AsyncPrompts.launch_optimization_job

vertexai._genai.prompts.AsyncPrompts.list

list(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListPromptsConfig,
            vertexai._genai.types.common.ListPromptsConfigDict,
        ]
    ] = None
) -> typing.AsyncIterator[vertexai._genai.types.common.PromptRef]

Lists prompt resources in a project.

See more: vertexai._genai.prompts.AsyncPrompts.list

vertexai._genai.prompts.AsyncPrompts.list_versions

list_versions(
    *,
    prompt_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListPromptsConfig,
            vertexai._genai.types.common.ListPromptsConfigDict,
        ]
    ] = None
) -> typing.AsyncIterator[vertexai._genai.types.common.PromptVersionRef]

Lists prompt version resources for a provided prompt_id.

See more: vertexai._genai.prompts.AsyncPrompts.list_versions

vertexai._genai.prompts.AsyncPrompts.optimize

optimize(
    *,
    prompt: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.OptimizeConfig,
            vertexai._genai.types.common.OptimizeConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.OptimizeResponse

Makes an async request to the optimizePrompt endpoint and returns an optimized prompt.

See more: vertexai._genai.prompts.AsyncPrompts.optimize

vertexai._genai.prompts.AsyncPrompts.restore_version

restore_version(
    *,
    prompt_id: str,
    version_id: str,
    config: typing.Optional[vertexai._genai.types.common.RestoreVersionConfig] = None
) -> vertexai._genai.types.common.Prompt

Restores the provided prompt version to the latest version.

See more: vertexai._genai.prompts.AsyncPrompts.restore_version

vertexai._genai.prompts.AsyncPrompts.update

update(
    *,
    prompt_id: str,
    prompt: typing.Union[
        vertexai._genai.types.common.Prompt, vertexai._genai.types.common.PromptDict
    ],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.UpdatePromptConfig,
            vertexai._genai.types.common.UpdatePromptConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Prompt

Updates an existing prompt and creates a new version for the prompt associated with the provided prompt_id.

See more: vertexai._genai.prompts.AsyncPrompts.update

vertexai._genai.prompts.Prompts.create

create(
    *,
    prompt: typing.Union[
        vertexai._genai.types.common.Prompt, vertexai._genai.types.common.PromptDict
    ],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreatePromptConfig,
            vertexai._genai.types.common.CreatePromptConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Prompt

Creates a new prompt in a Vertex Dataset resource.

See more: vertexai._genai.prompts.Prompts.create

vertexai._genai.prompts.Prompts.create_version

create_version(
    *,
    prompt: typing.Union[
        vertexai._genai.types.common.Prompt, vertexai._genai.types.common.PromptDict
    ],
    prompt_id: typing.Optional[str] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreatePromptVersionConfig,
            vertexai._genai.types.common.CreatePromptVersionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Prompt

Creates a prompt resource and an initial prompt version.

See more: vertexai._genai.prompts.Prompts.create_version

vertexai._genai.prompts.Prompts.delete

delete(
    *,
    prompt_id: str,
    config: typing.Optional[vertexai._genai.types.common.DeletePromptConfig] = None
) -> None

Deletes a prompt resource.

See more: vertexai._genai.prompts.Prompts.delete

vertexai._genai.prompts.Prompts.delete_version

delete_version(
    *,
    prompt_id: str,
    version_id: str,
    config: typing.Optional[vertexai._genai.types.common.DeletePromptConfig] = None
) -> None

Deletes a prompt version resource.

See more: vertexai._genai.prompts.Prompts.delete_version

vertexai._genai.prompts.Prompts.get

get(
    *,
    prompt_id: str,
    config: typing.Optional[vertexai._genai.types.common.GetPromptConfig] = None
) -> vertexai._genai.types.common.Prompt

Gets a prompt resource from a Vertex Dataset.

See more: vertexai._genai.prompts.Prompts.get

vertexai._genai.prompts.Prompts.get_version

get_version(
    *,
    prompt_id: str,
    version_id: str,
    config: typing.Optional[vertexai._genai.types.common.GetPromptConfig] = None
) -> vertexai._genai.types.common.Prompt

Gets a prompt resource from a Vertex Dataset.

See more: vertexai._genai.prompts.Prompts.get_version

vertexai._genai.prompts.Prompts.launch_optimization_job

launch_optimization_job(
    method: vertexai._genai.types.common.PromptOptimizerMethod,
    config: typing.Union[
        vertexai._genai.types.common.PromptOptimizerConfig,
        vertexai._genai.types.common.PromptOptimizerConfigDict,
    ],
) -> vertexai._genai.types.common.CustomJob

Call PO-Data optimizer.

See more: vertexai._genai.prompts.Prompts.launch_optimization_job

vertexai._genai.prompts.Prompts.list

list(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListPromptsConfig,
            vertexai._genai.types.common.ListPromptsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.PromptRef]

Lists prompt resources in a project.

See more: vertexai._genai.prompts.Prompts.list

vertexai._genai.prompts.Prompts.list_versions

list_versions(
    *,
    prompt_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListPromptsConfig,
            vertexai._genai.types.common.ListPromptsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.PromptVersionRef]

Lists prompt version resources for a provided prompt_id.

See more: vertexai._genai.prompts.Prompts.list_versions

vertexai._genai.prompts.Prompts.optimize

optimize(
    *,
    prompt: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.OptimizeConfig,
            vertexai._genai.types.common.OptimizeConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.OptimizeResponse

Makes an API request to optimize a prompt and returns the parsed response.

See more: vertexai._genai.prompts.Prompts.optimize

vertexai._genai.prompts.Prompts.restore_version

restore_version(
    *,
    prompt_id: str,
    version_id: str,
    config: typing.Optional[vertexai._genai.types.common.RestoreVersionConfig] = None
) -> vertexai._genai.types.common.Prompt

Restores the provided prompt version to the latest version.

See more: vertexai._genai.prompts.Prompts.restore_version

vertexai._genai.prompts.Prompts.update

update(
    *,
    prompt_id: str,
    prompt: typing.Union[
        vertexai._genai.types.common.Prompt, vertexai._genai.types.common.PromptDict
    ],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.UpdatePromptConfig,
            vertexai._genai.types.common.UpdatePromptConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Prompt

Updates an existing prompt and creates a new version for the prompt associated with the provided prompt_id.

See more: vertexai._genai.prompts.Prompts.update

vertexai._genai.sandboxes.Sandboxes.create

create(
    *,
    name: str,
    poll_interval_seconds: float = 0.1,
    spec: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.SandboxEnvironmentSpec,
            vertexai._genai.types.common.SandboxEnvironmentSpecDict,
        ]
    ] = None,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateAgentEngineSandboxConfig,
            vertexai._genai.types.common.CreateAgentEngineSandboxConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineSandboxOperation

Creates a new sandbox in the Agent Engine.

See more: vertexai._genai.sandboxes.Sandboxes.create

vertexai._genai.sandboxes.Sandboxes.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineSandboxConfig,
            vertexai._genai.types.common.DeleteAgentEngineSandboxConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineSandboxOperation

Deletes an agent engine sandbox.

See more: vertexai._genai.sandboxes.Sandboxes.delete

vertexai._genai.sandboxes.Sandboxes.execute_code

execute_code(
    *,
    name: str,
    input_data: dict[str, typing.Any],
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ExecuteCodeAgentEngineSandboxConfig,
            vertexai._genai.types.common.ExecuteCodeAgentEngineSandboxConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ExecuteSandboxEnvironmentResponse

Executes code in the Agent Engine sandbox.

See more: vertexai._genai.sandboxes.Sandboxes.execute_code

vertexai._genai.sandboxes.Sandboxes.generate_access_token

generate_access_token(service_account_email: str, timeout: int = 3600) -> str

Signs a JWT with a Google Cloud service account.

See more: vertexai._genai.sandboxes.Sandboxes.generate_access_token

vertexai._genai.sandboxes.Sandboxes.generate_browser_ws_headers

generate_browser_ws_headers(
    sandbox_environment: vertexai._genai.types.common.SandboxEnvironment,
    service_account_email: str,
    port: str = "8080",
    timeout: int = 3600,
    psc_endpoint: typing.Optional[str] = None,
) -> tuple[str, dict[str, str]]

Generates the websocket upgrade headers for the browser.

See more: vertexai._genai.sandboxes.Sandboxes.generate_browser_ws_headers

vertexai._genai.sandboxes.Sandboxes.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineSandboxConfig,
            vertexai._genai.types.common.GetAgentEngineSandboxConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.SandboxEnvironment

Gets an agent engine sandbox.

See more: vertexai._genai.sandboxes.Sandboxes.get

vertexai._genai.sandboxes.Sandboxes.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSandboxesConfig,
            vertexai._genai.types.common.ListAgentEngineSandboxesConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.SandboxEnvironment]

Lists Agent Engine sandboxes.

See more: vertexai._genai.sandboxes.Sandboxes.list

vertexai._genai.sandboxes.Sandboxes.send_command

send_command(
    *,
    http_method: str,
    access_token: str,
    sandbox_environment: vertexai._genai.types.common.SandboxEnvironment,
    port: str = "8080",
    path: typing.Optional[str] = None,
    query_params: typing.Optional[dict[str, object]] = None,
    headers: typing.Optional[dict[str, str]] = None,
    request_dict: typing.Optional[dict[str, object]] = None,
    psc_endpoint: typing.Optional[str] = None
) -> google.genai.types.HttpResponse

Sends a command to the sandbox.

See more: vertexai._genai.sandboxes.Sandboxes.send_command

vertexai._genai.session_events.AsyncSessionEvents.append

append(
    *,
    name: str,
    author: str,
    invocation_id: str,
    timestamp: datetime.datetime,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfig,
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AppendAgentEngineSessionEventResponse

Appends Agent Engine session event.

See more: vertexai._genai.session_events.AsyncSessionEvents.append

vertexai._genai.session_events.AsyncSessionEvents.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSessionEventsConfig,
            vertexai._genai.types.common.ListAgentEngineSessionEventsConfigDict,
        ]
    ] = None
) -> google.genai.pagers.AsyncPager[vertexai._genai.types.common.SessionEvent]

Lists Agent Engine session events.

See more: vertexai._genai.session_events.AsyncSessionEvents.list

vertexai._genai.session_events.SessionEvents.append

append(
    *,
    name: str,
    author: str,
    invocation_id: str,
    timestamp: datetime.datetime,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfig,
            vertexai._genai.types.common.AppendAgentEngineSessionEventConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AppendAgentEngineSessionEventResponse

Appends Agent Engine session event.

See more: vertexai._genai.session_events.SessionEvents.append

vertexai._genai.session_events.SessionEvents.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSessionEventsConfig,
            vertexai._genai.types.common.ListAgentEngineSessionEventsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.SessionEvent]

Lists Agent Engine session events.

See more: vertexai._genai.session_events.SessionEvents.list

vertexai._genai.sessions.AsyncSessions.create

create(
    *,
    name: str,
    user_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateAgentEngineSessionConfig,
            vertexai._genai.types.common.CreateAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineSessionOperation

Creates a new session in the Agent Engine.

See more: vertexai._genai.sessions.AsyncSessions.create

vertexai._genai.sessions.AsyncSessions.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineSessionConfig,
            vertexai._genai.types.common.DeleteAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineSessionOperation

Delete an Agent Engine session.

See more: vertexai._genai.sessions.AsyncSessions.delete

vertexai._genai.sessions.AsyncSessions.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineSessionConfig,
            vertexai._genai.types.common.GetAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Session

Gets an agent engine session.

See more: vertexai._genai.sessions.AsyncSessions.get

vertexai._genai.sessions.AsyncSessions.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSessionsConfig,
            vertexai._genai.types.common.ListAgentEngineSessionsConfigDict,
        ]
    ] = None
) -> google.genai.pagers.AsyncPager[vertexai._genai.types.common.Session]

Lists Agent Engine sessions.

See more: vertexai._genai.sessions.AsyncSessions.list

vertexai._genai.sessions.Sessions.create

create(
    *,
    name: str,
    user_id: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateAgentEngineSessionConfig,
            vertexai._genai.types.common.CreateAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.AgentEngineSessionOperation

Creates a new session in the Agent Engine.

See more: vertexai._genai.sessions.Sessions.create

vertexai._genai.sessions.Sessions.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineSessionConfig,
            vertexai._genai.types.common.DeleteAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.DeleteAgentEngineSessionOperation

Delete an Agent Engine session.

See more: vertexai._genai.sessions.Sessions.delete

vertexai._genai.sessions.Sessions.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetAgentEngineSessionConfig,
            vertexai._genai.types.common.GetAgentEngineSessionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Session

Gets an agent engine session.

See more: vertexai._genai.sessions.Sessions.get

vertexai._genai.sessions.Sessions.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListAgentEngineSessionsConfig,
            vertexai._genai.types.common.ListAgentEngineSessionsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.Session]

Lists Agent Engine sessions.

See more: vertexai._genai.sessions.Sessions.list

vertexai._genai.skill_revisions.AsyncSkillRevisions.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetSkillRevisionConfig,
            vertexai._genai.types.common.GetSkillRevisionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.SkillRevision

Gets a Skill Revision.

See more: vertexai._genai.skill_revisions.AsyncSkillRevisions.get

vertexai._genai.skill_revisions.AsyncSkillRevisions.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListSkillRevisionsConfig,
            vertexai._genai.types.common.ListSkillRevisionsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ListSkillRevisionsResponse

Lists Skill Revisions.

See more: vertexai._genai.skill_revisions.AsyncSkillRevisions.list

vertexai._genai.skill_revisions.SkillRevisions.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetSkillRevisionConfig,
            vertexai._genai.types.common.GetSkillRevisionConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.SkillRevision

Gets a Skill Revision.

See more: vertexai._genai.skill_revisions.SkillRevisions.get

vertexai._genai.skill_revisions.SkillRevisions.list

list(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListSkillRevisionsConfig,
            vertexai._genai.types.common.ListSkillRevisionsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.ListSkillRevisionsResponse

Lists Skill Revisions.

See more: vertexai._genai.skill_revisions.SkillRevisions.list

vertexai._genai.skills.AsyncSkills.create

create(
    *,
    display_name: str,
    description: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateSkillConfig,
            vertexai._genai.types.common.CreateSkillConfigDict,
        ]
    ] = None
) -> typing.Union[
    vertexai._genai.types.common.Skill, vertexai._genai.types.common.SkillOperation
]

Creates a new Skill asynchronously.

See more: vertexai._genai.skills.AsyncSkills.create

vertexai._genai.skills.AsyncSkills.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteSkillConfig,
            vertexai._genai.types.common.DeleteSkillConfigDict,
        ]
    ] = None
) -> typing.Optional[vertexai._genai.types.common.DeleteSkillOperation]

Deletes a Skill asynchronously.

See more: vertexai._genai.skills.AsyncSkills.delete

vertexai._genai.skills.AsyncSkills.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetSkillConfig,
            vertexai._genai.types.common.GetSkillConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Skill

Gets a Skill.

See more: vertexai._genai.skills.AsyncSkills.get

vertexai._genai.skills.AsyncSkills.list

list(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListSkillsConfig,
            vertexai._genai.types.common.ListSkillsConfigDict,
        ]
    ] = None
) -> google.genai.pagers.AsyncPager[vertexai._genai.types.common.Skill]

Lists Skills in the Skill Registry asynchronously.

See more: vertexai._genai.skills.AsyncSkills.list

vertexai._genai.skills.AsyncSkills.retrieve

retrieve(
    *,
    query: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveSkillsConfig,
            vertexai._genai.types.common.RetrieveSkillsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.RetrieveSkillsResponse

Retrieves skills semantically matched to a query.

See more: vertexai._genai.skills.AsyncSkills.retrieve

vertexai._genai.skills.AsyncSkills.update

update(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.UpdateSkillConfig,
            vertexai._genai.types.common.UpdateSkillConfigDict,
        ]
    ] = None
) -> typing.Union[
    vertexai._genai.types.common.Skill, vertexai._genai.types.common.SkillOperation
]

Updates an existing Skill asynchronously.

See more: vertexai._genai.skills.AsyncSkills.update

vertexai._genai.skills.Skills.create

create(
    *,
    display_name: str,
    description: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.CreateSkillConfig,
            vertexai._genai.types.common.CreateSkillConfigDict,
        ]
    ] = None
) -> typing.Union[
    vertexai._genai.types.common.Skill, vertexai._genai.types.common.SkillOperation
]

Creates a new Skill.

See more: vertexai._genai.skills.Skills.create

vertexai._genai.skills.Skills.delete

delete(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteSkillConfig,
            vertexai._genai.types.common.DeleteSkillConfigDict,
        ]
    ] = None
) -> typing.Optional[vertexai._genai.types.common.DeleteSkillOperation]

Deletes a Skill.

See more: vertexai._genai.skills.Skills.delete

vertexai._genai.skills.Skills.get

get(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.GetSkillConfig,
            vertexai._genai.types.common.GetSkillConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.Skill

Gets a Skill.

See more: vertexai._genai.skills.Skills.get

vertexai._genai.skills.Skills.list

list(
    *,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.ListSkillsConfig,
            vertexai._genai.types.common.ListSkillsConfigDict,
        ]
    ] = None
) -> typing.Iterator[vertexai._genai.types.common.Skill]

Lists Skills in the Skill Registry.

See more: vertexai._genai.skills.Skills.list

vertexai._genai.skills.Skills.retrieve

retrieve(
    *,
    query: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.RetrieveSkillsConfig,
            vertexai._genai.types.common.RetrieveSkillsConfigDict,
        ]
    ] = None
) -> vertexai._genai.types.common.RetrieveSkillsResponse

Retrieves skills semantically matched to a query.

See more: vertexai._genai.skills.Skills.retrieve

vertexai._genai.skills.Skills.update

update(
    *,
    name: str,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.UpdateSkillConfig,
            vertexai._genai.types.common.UpdateSkillConfigDict,
        ]
    ] = None
) -> typing.Union[
    vertexai._genai.types.common.Skill, vertexai._genai.types.common.SkillOperation
]

Updates an existing Skill.

See more: vertexai._genai.skills.Skills.update

vertexai._genai.types.A2aTask

A2aTask(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.A2aTask

vertexai._genai.types.AgentEngine

AgentEngine(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngine

vertexai._genai.types.AgentEngine.delete

delete(
    force: bool = False,
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineConfig,
            vertexai._genai.types.common.DeleteAgentEngineConfigDict,
        ]
    ] = None,
) -> None

Deletes the agent engine.

See more: vertexai._genai.types.AgentEngine.delete

vertexai._genai.types.AgentEngine.operation_schemas

operation_schemas() -> typing.Optional[list[typing.Dict[str, typing.Any]]]

Returns the schemas of all registered operations for the agent.

See more: vertexai._genai.types.AgentEngine.operation_schemas

vertexai._genai.types.AgentEngineConfig

AgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineConfig

vertexai._genai.types.AgentEngineGenerateMemoriesOperation

AgentEngineGenerateMemoriesOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineGenerateMemoriesOperation

vertexai._genai.types.AgentEngineMemoryConfig

AgentEngineMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineMemoryConfig

vertexai._genai.types.AgentEngineMemoryOperation

AgentEngineMemoryOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineMemoryOperation

vertexai._genai.types.AgentEngineOperation

AgentEngineOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineOperation

vertexai._genai.types.AgentEnginePurgeMemoriesOperation

AgentEnginePurgeMemoriesOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEnginePurgeMemoriesOperation

vertexai._genai.types.AgentEngineRollbackMemoryOperation

AgentEngineRollbackMemoryOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineRollbackMemoryOperation

vertexai._genai.types.AgentEngineRuntimeRevision

AgentEngineRuntimeRevision(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineRuntimeRevision

vertexai._genai.types.AgentEngineRuntimeRevision.delete

delete(
    config: typing.Optional[
        typing.Union[
            vertexai._genai.types.common.DeleteAgentEngineRuntimeRevisionConfig,
            vertexai._genai.types.common.DeleteAgentEngineRuntimeRevisionConfigDict,
        ]
    ] = None,
) -> None

Deletes the agent engine runtime revision.

See more: vertexai._genai.types.AgentEngineRuntimeRevision.delete

vertexai._genai.types.AgentEngineRuntimeRevision.operation_schemas

operation_schemas() -> typing.Optional[list[typing.Dict[str, typing.Any]]]

Returns the schemas of all registered operations for the agent.

See more: vertexai._genai.types.AgentEngineRuntimeRevision.operation_schemas

vertexai._genai.types.AgentEngineSandboxOperation

AgentEngineSandboxOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineSandboxOperation

vertexai._genai.types.AgentEngineSandboxSnapshotOperation

AgentEngineSandboxSnapshotOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineSandboxSnapshotOperation

vertexai._genai.types.AgentEngineSessionOperation

AgentEngineSessionOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentEngineSessionOperation

vertexai._genai.types.AgentRunConfig

AgentRunConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AgentRunConfig

vertexai._genai.types.AggregatedMetricResult

AggregatedMetricResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AggregatedMetricResult

vertexai._genai.types.AnalysisConfig

AnalysisConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AnalysisConfig

vertexai._genai.types.AppendAgentEngineSessionEventConfig

AppendAgentEngineSessionEventConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AppendAgentEngineSessionEventConfig

vertexai._genai.types.AppendAgentEngineSessionEventResponse

AppendAgentEngineSessionEventResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AppendAgentEngineSessionEventResponse

vertexai._genai.types.AppendAgentEngineTaskEventConfig

AppendAgentEngineTaskEventConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AppendAgentEngineTaskEventConfig

vertexai._genai.types.AppendAgentEngineTaskEventResponse

AppendAgentEngineTaskEventResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AppendAgentEngineTaskEventResponse

vertexai._genai.types.AssembleDataset

AssembleDataset(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AssembleDataset

vertexai._genai.types.AssembleDatasetConfig

AssembleDatasetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AssembleDatasetConfig

vertexai._genai.types.AssessDatasetConfig

AssessDatasetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AssessDatasetConfig

vertexai._genai.types.AttackCategoryResult

AttackCategoryResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.AttackCategoryResult

vertexai._genai.types.BatchPredictionResourceUsageAssessmentConfig

BatchPredictionResourceUsageAssessmentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BatchPredictionResourceUsageAssessmentConfig

vertexai._genai.types.BatchPredictionResourceUsageAssessmentResult

BatchPredictionResourceUsageAssessmentResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BatchPredictionResourceUsageAssessmentResult

vertexai._genai.types.BatchPredictionValidationAssessmentConfig

BatchPredictionValidationAssessmentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BatchPredictionValidationAssessmentConfig

vertexai._genai.types.BatchPredictionValidationAssessmentResult

BatchPredictionValidationAssessmentResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BatchPredictionValidationAssessmentResult

vertexai._genai.types.BigQueryRequestSet

BigQueryRequestSet(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BigQueryRequestSet

vertexai._genai.types.BleuInstance

BleuInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BleuInstance

vertexai._genai.types.BleuResults

BleuResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.BleuResults

vertexai._genai.types.CancelQueryJobAgentEngineConfig

CancelQueryJobAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CancelQueryJobAgentEngineConfig

vertexai._genai.types.CancelQueryJobResult

CancelQueryJobResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CancelQueryJobResult

vertexai._genai.types.CandidateResponse

CandidateResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CandidateResponse

vertexai._genai.types.CandidateResult

CandidateResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CandidateResult

vertexai._genai.types.CheckQueryJobAgentEngineConfig

CheckQueryJobAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CheckQueryJobAgentEngineConfig

vertexai._genai.types.CheckQueryJobResponse

CheckQueryJobResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CheckQueryJobResponse

vertexai._genai.types.CheckQueryJobResult

CheckQueryJobResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CheckQueryJobResult

vertexai._genai.types.Chunk

Chunk(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Chunk

vertexai._genai.types.CodeExecutionMetric

CodeExecutionMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CodeExecutionMetric

vertexai._genai.types.CodeExecutionMetric.model_post_init

model_post_init(context: Any, /) -> None

This function is meant to behave like a BaseModel method to initialize private attributes.

See more: vertexai._genai.types.CodeExecutionMetric.model_post_init

vertexai._genai.types.CodeExecutionMetric.validate_code

validate_code(value: typing.Optional[str]) -> typing.Optional[str]

API documentation for validate_code method.

See more: vertexai._genai.types.CodeExecutionMetric.validate_code

vertexai._genai.types.CometResult

CometResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CometResult

vertexai._genai.types.ContainerSpec

ContainerSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ContainerSpec

vertexai._genai.types.ContentMap

ContentMap(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ContentMap

vertexai._genai.types.ContentMapContents

ContentMapContents(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ContentMapContents

vertexai._genai.types.CreateAgentEngineConfig

CreateAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateAgentEngineConfig

vertexai._genai.types.CreateAgentEngineSandboxConfig

CreateAgentEngineSandboxConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateAgentEngineSandboxConfig

vertexai._genai.types.CreateAgentEngineSandboxSnapshotConfig

CreateAgentEngineSandboxSnapshotConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateAgentEngineSandboxSnapshotConfig

vertexai._genai.types.CreateAgentEngineSessionConfig

CreateAgentEngineSessionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateAgentEngineSessionConfig

vertexai._genai.types.CreateAgentEngineTaskConfig

CreateAgentEngineTaskConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateAgentEngineTaskConfig

vertexai._genai.types.CreateDatasetConfig

CreateDatasetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateDatasetConfig

vertexai._genai.types.CreateDatasetVersionConfig

CreateDatasetVersionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateDatasetVersionConfig

vertexai._genai.types.CreateEvaluationExperimentConfig

CreateEvaluationExperimentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateEvaluationExperimentConfig

vertexai._genai.types.CreateEvaluationItemConfig

CreateEvaluationItemConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateEvaluationItemConfig

vertexai._genai.types.CreateEvaluationMetricConfig

CreateEvaluationMetricConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateEvaluationMetricConfig

vertexai._genai.types.CreateEvaluationRunConfig

CreateEvaluationRunConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateEvaluationRunConfig

vertexai._genai.types.CreateEvaluationSetConfig

CreateEvaluationSetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateEvaluationSetConfig

vertexai._genai.types.CreateMultimodalDatasetConfig

CreateMultimodalDatasetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateMultimodalDatasetConfig

vertexai._genai.types.CreatePromptConfig

CreatePromptConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreatePromptConfig

vertexai._genai.types.CreatePromptVersionConfig

CreatePromptVersionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreatePromptVersionConfig

vertexai._genai.types.CreateSandboxEnvironmentTemplateConfig

CreateSandboxEnvironmentTemplateConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateSandboxEnvironmentTemplateConfig

vertexai._genai.types.CreateSkillConfig

CreateSkillConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CreateSkillConfig

vertexai._genai.types.CustomCodeExecutionSpec

CustomCodeExecutionSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CustomCodeExecutionSpec

vertexai._genai.types.CustomJob

CustomJob(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CustomJob

vertexai._genai.types.CustomJobSpec

CustomJobSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.CustomJobSpec

vertexai._genai.types.Dataset

Dataset(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Dataset

vertexai._genai.types.DatasetOperation

DatasetOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DatasetOperation

vertexai._genai.types.DatasetVersion

DatasetVersion(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DatasetVersion

vertexai._genai.types.DeleteAgentEngineConfig

DeleteAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineConfig

vertexai._genai.types.DeleteAgentEngineMemoryConfig

DeleteAgentEngineMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineMemoryConfig

vertexai._genai.types.DeleteAgentEngineMemoryOperation

DeleteAgentEngineMemoryOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineMemoryOperation

vertexai._genai.types.DeleteAgentEngineOperation

DeleteAgentEngineOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineOperation

vertexai._genai.types.DeleteAgentEngineRuntimeRevisionConfig

DeleteAgentEngineRuntimeRevisionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineRuntimeRevisionConfig

vertexai._genai.types.DeleteAgentEngineRuntimeRevisionOperation

DeleteAgentEngineRuntimeRevisionOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineRuntimeRevisionOperation

vertexai._genai.types.DeleteAgentEngineSandboxConfig

DeleteAgentEngineSandboxConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineSandboxConfig

vertexai._genai.types.DeleteAgentEngineSandboxOperation

DeleteAgentEngineSandboxOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineSandboxOperation

vertexai._genai.types.DeleteAgentEngineSessionConfig

DeleteAgentEngineSessionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineSessionConfig

vertexai._genai.types.DeleteAgentEngineSessionOperation

DeleteAgentEngineSessionOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineSessionOperation

vertexai._genai.types.DeleteAgentEngineTaskConfig

DeleteAgentEngineTaskConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteAgentEngineTaskConfig

vertexai._genai.types.DeleteEvaluationExperimentConfig

DeleteEvaluationExperimentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteEvaluationExperimentConfig

vertexai._genai.types.DeleteEvaluationExperimentOperation

DeleteEvaluationExperimentOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteEvaluationExperimentOperation

vertexai._genai.types.DeleteEvaluationMetricConfig

DeleteEvaluationMetricConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteEvaluationMetricConfig

vertexai._genai.types.DeleteEvaluationMetricOperation

DeleteEvaluationMetricOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteEvaluationMetricOperation

vertexai._genai.types.DeletePromptConfig

DeletePromptConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeletePromptConfig

vertexai._genai.types.DeletePromptOperation

DeletePromptOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeletePromptOperation

vertexai._genai.types.DeletePromptVersionOperation

DeletePromptVersionOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeletePromptVersionOperation

vertexai._genai.types.DeleteSandboxEnvironmentSnapshotConfig

DeleteSandboxEnvironmentSnapshotConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteSandboxEnvironmentSnapshotConfig

vertexai._genai.types.DeleteSandboxEnvironmentSnapshotOperation

DeleteSandboxEnvironmentSnapshotOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteSandboxEnvironmentSnapshotOperation

vertexai._genai.types.DeleteSandboxEnvironmentTemplateConfig

DeleteSandboxEnvironmentTemplateConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteSandboxEnvironmentTemplateConfig

vertexai._genai.types.DeleteSandboxEnvironmentTemplateOperation

DeleteSandboxEnvironmentTemplateOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteSandboxEnvironmentTemplateOperation

vertexai._genai.types.DeleteSkillConfig

DeleteSkillConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteSkillConfig

vertexai._genai.types.DeleteSkillOperation

DeleteSkillOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DeleteSkillOperation

vertexai._genai.types.DiskSpec

DiskSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DiskSpec

vertexai._genai.types.DnsPeeringConfig

DnsPeeringConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.DnsPeeringConfig

vertexai._genai.types.EnvVar

EnvVar(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EnvVar

vertexai._genai.types.EvalCase

EvalCase(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvalCase

vertexai._genai.types.EvalCaseMetricResult

EvalCaseMetricResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvalCaseMetricResult

vertexai._genai.types.EvalCaseResult

EvalCaseResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvalCaseResult

vertexai._genai.types.EvalRunInferenceConfig

EvalRunInferenceConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvalRunInferenceConfig

vertexai._genai.types.EvaluateDatasetConfig

EvaluateDatasetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluateDatasetConfig

vertexai._genai.types.EvaluateDatasetRequestParameters

EvaluateDatasetRequestParameters(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluateDatasetRequestParameters

vertexai._genai.types.EvaluateInstancesConfig

EvaluateInstancesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluateInstancesConfig

vertexai._genai.types.EvaluateInstancesResponse

EvaluateInstancesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluateInstancesResponse

vertexai._genai.types.EvaluateMethodConfig

EvaluateMethodConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluateMethodConfig

vertexai._genai.types.EvaluationDataset

EvaluationDataset(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationDataset

vertexai._genai.types.EvaluationDataset.load_from_adk_eval_set

load_from_adk_eval_set(
    eval_set: typing.Union[str, os.PathLike[str], dict[str, typing.Any]],
) -> vertexai._genai.types.common.EvaluationDataset

Loads an ADK eval set (.evalset.json) into an EvaluationDataset.

See more: vertexai._genai.types.EvaluationDataset.load_from_adk_eval_set

vertexai._genai.types.EvaluationDataset.load_from_observability_eval_cases

load_from_observability_eval_cases(
    cases: list["ObservabilityEvalCase"],
) -> vertexai._genai.types.common.EvaluationDataset

Fetches GenAI Observability data from GCS and parses into a DataFrame.

See more: vertexai._genai.types.EvaluationDataset.load_from_observability_eval_cases

vertexai._genai.types.EvaluationDataset.show

show() -> None

Shows the evaluation dataset.

See more: vertexai._genai.types.EvaluationDataset.show

vertexai._genai.types.EvaluationExperiment

EvaluationExperiment(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationExperiment

vertexai._genai.types.EvaluationInstance

EvaluationInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationInstance

vertexai._genai.types.EvaluationItem

EvaluationItem(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationItem

vertexai._genai.types.EvaluationItemRequest

EvaluationItemRequest(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationItemRequest

vertexai._genai.types.EvaluationItemResult

EvaluationItemResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationItemResult

vertexai._genai.types.EvaluationMetric

EvaluationMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationMetric

vertexai._genai.types.EvaluationPrompt

EvaluationPrompt(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationPrompt

vertexai._genai.types.EvaluationResult

EvaluationResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationResult

vertexai._genai.types.EvaluationResult.show

show(candidate_names: typing.Optional[typing.List[str]] = None) -> None

Shows the evaluation result.

See more: vertexai._genai.types.EvaluationResult.show

vertexai._genai.types.EvaluationRun

EvaluationRun(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRun

vertexai._genai.types.EvaluationRun.show

show() -> None

Shows the evaluation result.

See more: vertexai._genai.types.EvaluationRun.show

vertexai._genai.types.EvaluationRunAgentConfig

EvaluationRunAgentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunAgentConfig

vertexai._genai.types.EvaluationRunConfig

EvaluationRunConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunConfig

vertexai._genai.types.EvaluationRunDataSource

EvaluationRunDataSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunDataSource

vertexai._genai.types.EvaluationRunInferenceConfig

EvaluationRunInferenceConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunInferenceConfig

vertexai._genai.types.EvaluationRunMetadata

EvaluationRunMetadata(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunMetadata

vertexai._genai.types.EvaluationRunMetric

EvaluationRunMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunMetric

vertexai._genai.types.EvaluationRunPromptTemplate

EvaluationRunPromptTemplate(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunPromptTemplate

vertexai._genai.types.EvaluationRunResults

EvaluationRunResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationRunResults

vertexai._genai.types.EvaluationSet

EvaluationSet(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EvaluationSet

vertexai._genai.types.Event

Event(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Event

vertexai._genai.types.EventActions

EventActions(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EventActions

vertexai._genai.types.EventMetadata

EventMetadata(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.EventMetadata

vertexai._genai.types.ExactMatchInstance

ExactMatchInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ExactMatchInstance

vertexai._genai.types.ExactMatchResults

ExactMatchResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ExactMatchResults

vertexai._genai.types.ExactMatchSpec

ExactMatchSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ExactMatchSpec

vertexai._genai.types.ExecuteCodeAgentEngineSandboxConfig

ExecuteCodeAgentEngineSandboxConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ExecuteCodeAgentEngineSandboxConfig

vertexai._genai.types.ExecuteSandboxEnvironmentResponse

ExecuteSandboxEnvironmentResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ExecuteSandboxEnvironmentResponse

vertexai._genai.types.FailedRubric

FailedRubric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.FailedRubric

vertexai._genai.types.GeminiExample

GeminiExample(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GeminiExample

vertexai._genai.types.GeminiRequestReadConfig

GeminiRequestReadConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GeminiRequestReadConfig

vertexai._genai.types.GeminiRequestReadConfig.single_turn_template

single_turn_template(
    *,
    prompt: str,
    response: typing.Optional[str] = None,
    system_instruction: typing.Optional[str] = None,
    model: typing.Optional[str] = None,
    cached_content: typing.Optional[str] = None,
    tools: typing.Optional[
        list[typing.Union[google.genai.types.Tool, dict[str, typing.Any]]]
    ] = None,
    tool_config: typing.Optional[
        typing.Union[google.genai.types.ToolConfig, dict[str, typing.Any]]
    ] = None,
    safety_settings: typing.Optional[
        list[typing.Union[google.genai.types.SafetySetting, dict[str, typing.Any]]]
    ] = None,
    generation_config: typing.Optional[
        typing.Union[google.genai.types.GenerationConfig, dict[str, typing.Any]]
    ] = None,
    field_mapping: typing.Optional[dict[str, str]] = None
) -> vertexai._genai.types.common.GeminiRequestReadConfig

Constructs a GeminiRequestReadConfig object for single-turn cases.

See more: vertexai._genai.types.GeminiRequestReadConfig.single_turn_template

vertexai._genai.types.GeminiTemplateConfig

GeminiTemplateConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GeminiTemplateConfig

vertexai._genai.types.GenerateAgentEngineMemoriesConfig

GenerateAgentEngineMemoriesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateAgentEngineMemoriesConfig

vertexai._genai.types.GenerateInstanceRubricsResponse

GenerateInstanceRubricsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateInstanceRubricsResponse

vertexai._genai.types.GenerateLossClustersConfig

GenerateLossClustersConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateLossClustersConfig

vertexai._genai.types.GenerateLossClustersOperation

GenerateLossClustersOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateLossClustersOperation

vertexai._genai.types.GenerateLossClustersResponse

GenerateLossClustersResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateLossClustersResponse

vertexai._genai.types.GenerateLossClustersResponse.show

show() -> None

Shows the loss pattern analysis report with rich HTML visualization.

See more: vertexai._genai.types.GenerateLossClustersResponse.show

vertexai._genai.types.GenerateMemoriesRequestDirectContentsSource

GenerateMemoriesRequestDirectContentsSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateMemoriesRequestDirectContentsSource

vertexai._genai.types.GenerateMemoriesRequestDirectMemoriesSource

GenerateMemoriesRequestDirectMemoriesSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateMemoriesRequestDirectMemoriesSource

vertexai._genai.types.GenerateMemoriesRequestDirectMemoriesSourceDirectMemory

GenerateMemoriesRequestDirectMemoriesSourceDirectMemory(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateMemoriesRequestDirectMemoriesSourceDirectMemory

vertexai._genai.types.GenerateMemoriesRequestVertexSessionSource

GenerateMemoriesRequestVertexSessionSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateMemoriesRequestVertexSessionSource

vertexai._genai.types.GenerateMemoriesResponse

GenerateMemoriesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateMemoriesResponse

vertexai._genai.types.GenerateMemoriesResponseGeneratedMemory

GenerateMemoriesResponseGeneratedMemory(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateMemoriesResponseGeneratedMemory

vertexai._genai.types.GenerateUserScenariosResponse

GenerateUserScenariosResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GenerateUserScenariosResponse

vertexai._genai.types.GetAgentEngineConfig

GetAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineConfig

vertexai._genai.types.GetAgentEngineMemoryConfig

GetAgentEngineMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineMemoryConfig

vertexai._genai.types.GetAgentEngineMemoryRevisionConfig

GetAgentEngineMemoryRevisionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineMemoryRevisionConfig

vertexai._genai.types.GetAgentEngineRuntimeRevisionConfig

GetAgentEngineRuntimeRevisionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineRuntimeRevisionConfig

vertexai._genai.types.GetAgentEngineSandboxConfig

GetAgentEngineSandboxConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineSandboxConfig

vertexai._genai.types.GetAgentEngineSessionConfig

GetAgentEngineSessionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineSessionConfig

vertexai._genai.types.GetAgentEngineTaskConfig

GetAgentEngineTaskConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetAgentEngineTaskConfig

vertexai._genai.types.GetDatasetOperationConfig

GetDatasetOperationConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetDatasetOperationConfig

vertexai._genai.types.GetEvaluationExperimentConfig

GetEvaluationExperimentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetEvaluationExperimentConfig

vertexai._genai.types.GetEvaluationItemConfig

GetEvaluationItemConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetEvaluationItemConfig

vertexai._genai.types.GetEvaluationMetricConfig

GetEvaluationMetricConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetEvaluationMetricConfig

vertexai._genai.types.GetEvaluationRunConfig

GetEvaluationRunConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetEvaluationRunConfig

vertexai._genai.types.GetEvaluationSetConfig

GetEvaluationSetConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetEvaluationSetConfig

vertexai._genai.types.GetMultimodalDatasetOperationConfig

GetMultimodalDatasetOperationConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetMultimodalDatasetOperationConfig

vertexai._genai.types.GetPromptConfig

GetPromptConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetPromptConfig

vertexai._genai.types.GetSandboxEnvironmentSnapshotConfig

GetSandboxEnvironmentSnapshotConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetSandboxEnvironmentSnapshotConfig

vertexai._genai.types.GetSandboxEnvironmentTemplateConfig

GetSandboxEnvironmentTemplateConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetSandboxEnvironmentTemplateConfig

vertexai._genai.types.GetSkillConfig

GetSkillConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.GetSkillConfig

vertexai._genai.types.IngestEventsConfig

IngestEventsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.IngestEventsConfig

vertexai._genai.types.IngestionDirectContentsSource

IngestionDirectContentsSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.IngestionDirectContentsSource

vertexai._genai.types.IngestionDirectContentsSourceEvent

IngestionDirectContentsSourceEvent(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.IngestionDirectContentsSourceEvent

vertexai._genai.types.IntermediateExtractedMemory

IntermediateExtractedMemory(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.IntermediateExtractedMemory

vertexai._genai.types.KeepAliveProbe

KeepAliveProbe(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.KeepAliveProbe

vertexai._genai.types.KeepAliveProbeHttpGet

KeepAliveProbeHttpGet(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.KeepAliveProbeHttpGet

vertexai._genai.types.LLMMetric

LLMMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LLMMetric

vertexai._genai.types.LLMMetric.load

load(
    config_path: str, client: typing.Optional[typing.Any] = None
) -> vertexai._genai.types.common.LLMMetric

Loads a metric configuration from a YAML or JSON file.

See more: vertexai._genai.types.LLMMetric.load

vertexai._genai.types.LLMMetric.model_post_init

model_post_init(context: Any, /) -> None

This function is meant to behave like a BaseModel method to initialize private attributes.

See more: vertexai._genai.types.LLMMetric.model_post_init

vertexai._genai.types.LLMMetric.validate_judge_model_sampling_count

validate_judge_model_sampling_count(
    value: typing.Optional[int],
) -> typing.Optional[int]

Validates judge_model_sampling_count to be between 1 and 32.

See more: vertexai._genai.types.LLMMetric.validate_judge_model_sampling_count

vertexai._genai.types.LLMMetric.validate_prompt_template

validate_prompt_template(
    value: typing.Union[str, vertexai._genai.types.common.MetricPromptBuilder],
) -> str

Validates prompt template to be a non-empty string.

See more: vertexai._genai.types.LLMMetric.validate_prompt_template

vertexai._genai.types.ListAgentEngineConfig

ListAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineConfig

vertexai._genai.types.ListAgentEngineMemoryConfig

ListAgentEngineMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineMemoryConfig

vertexai._genai.types.ListAgentEngineMemoryRevisionsConfig

ListAgentEngineMemoryRevisionsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineMemoryRevisionsConfig

vertexai._genai.types.ListAgentEngineMemoryRevisionsResponse

ListAgentEngineMemoryRevisionsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineMemoryRevisionsResponse

vertexai._genai.types.ListAgentEngineRuntimeRevisionsConfig

ListAgentEngineRuntimeRevisionsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineRuntimeRevisionsConfig

vertexai._genai.types.ListAgentEngineSandboxesConfig

ListAgentEngineSandboxesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineSandboxesConfig

vertexai._genai.types.ListAgentEngineSandboxesResponse

ListAgentEngineSandboxesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineSandboxesResponse

vertexai._genai.types.ListAgentEngineSessionEventsConfig

ListAgentEngineSessionEventsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineSessionEventsConfig

vertexai._genai.types.ListAgentEngineSessionEventsResponse

ListAgentEngineSessionEventsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineSessionEventsResponse

vertexai._genai.types.ListAgentEngineSessionsConfig

ListAgentEngineSessionsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineSessionsConfig

vertexai._genai.types.ListAgentEngineTaskEventsConfig

ListAgentEngineTaskEventsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineTaskEventsConfig

vertexai._genai.types.ListAgentEngineTaskEventsResponse

ListAgentEngineTaskEventsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineTaskEventsResponse

vertexai._genai.types.ListAgentEngineTasksConfig

ListAgentEngineTasksConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineTasksConfig

vertexai._genai.types.ListAgentEngineTasksResponse

ListAgentEngineTasksResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListAgentEngineTasksResponse

vertexai._genai.types.ListDatasetVersionsResponse

ListDatasetVersionsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListDatasetVersionsResponse

vertexai._genai.types.ListDatasetsResponse

ListDatasetsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListDatasetsResponse

vertexai._genai.types.ListEvaluationExperimentsConfig

ListEvaluationExperimentsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListEvaluationExperimentsConfig

vertexai._genai.types.ListEvaluationExperimentsResponse

ListEvaluationExperimentsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListEvaluationExperimentsResponse

vertexai._genai.types.ListEvaluationMetricsConfig

ListEvaluationMetricsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListEvaluationMetricsConfig

vertexai._genai.types.ListEvaluationMetricsResponse

ListEvaluationMetricsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListEvaluationMetricsResponse

vertexai._genai.types.ListMultimodalDatasetsConfig

ListMultimodalDatasetsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListMultimodalDatasetsConfig

vertexai._genai.types.ListMultimodalDatasetsResponse

ListMultimodalDatasetsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListMultimodalDatasetsResponse

vertexai._genai.types.ListPromptsConfig

ListPromptsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListPromptsConfig

vertexai._genai.types.ListReasoningEnginesMemoriesResponse

ListReasoningEnginesMemoriesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListReasoningEnginesMemoriesResponse

vertexai._genai.types.ListReasoningEnginesResponse

ListReasoningEnginesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListReasoningEnginesResponse

vertexai._genai.types.ListReasoningEnginesRuntimeRevisionsResponse

ListReasoningEnginesRuntimeRevisionsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListReasoningEnginesRuntimeRevisionsResponse

vertexai._genai.types.ListReasoningEnginesSessionsResponse

ListReasoningEnginesSessionsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListReasoningEnginesSessionsResponse

vertexai._genai.types.ListSandboxEnvironmentSnapshotsConfig

ListSandboxEnvironmentSnapshotsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListSandboxEnvironmentSnapshotsConfig

vertexai._genai.types.ListSandboxEnvironmentSnapshotsResponse

ListSandboxEnvironmentSnapshotsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListSandboxEnvironmentSnapshotsResponse

vertexai._genai.types.ListSandboxEnvironmentTemplatesConfig

ListSandboxEnvironmentTemplatesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListSandboxEnvironmentTemplatesConfig

vertexai._genai.types.ListSandboxEnvironmentTemplatesResponse

ListSandboxEnvironmentTemplatesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListSandboxEnvironmentTemplatesResponse

vertexai._genai.types.ListSkillsConfig

ListSkillsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListSkillsConfig

vertexai._genai.types.ListSkillsResponse

ListSkillsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ListSkillsResponse

vertexai._genai.types.LossAnalysisConfig

LossAnalysisConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LossAnalysisConfig

vertexai._genai.types.LossAnalysisResult

LossAnalysisResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LossAnalysisResult

vertexai._genai.types.LossAnalysisResult.show

show() -> None

Shows the loss analysis result with rich HTML visualization.

See more: vertexai._genai.types.LossAnalysisResult.show

vertexai._genai.types.LossCluster

LossCluster(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LossCluster

vertexai._genai.types.LossExample

LossExample(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LossExample

vertexai._genai.types.LossTaxonomyEntry

LossTaxonomyEntry(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LossTaxonomyEntry

vertexai._genai.types.LustreMount

LustreMount(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.LustreMount

vertexai._genai.types.MachineSpec

MachineSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MachineSpec

vertexai._genai.types.MapInstance

MapInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MapInstance

vertexai._genai.types.Memory

Memory(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Memory

vertexai._genai.types.MemoryBankCustomizationConfig

MemoryBankCustomizationConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfig

vertexai._genai.types.MemoryBankCustomizationConfigConsolidationConfig

MemoryBankCustomizationConfigConsolidationConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigConsolidationConfig

vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExample

MemoryBankCustomizationConfigGenerateMemoriesExample(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExample

vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExampleConversationSource

MemoryBankCustomizationConfigGenerateMemoriesExampleConversationSource(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExampleConversationSource

vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExampleConversationSourceEvent

MemoryBankCustomizationConfigGenerateMemoriesExampleConversationSourceEvent(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExampleConversationSourceEvent

vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExampleGeneratedMemory

MemoryBankCustomizationConfigGenerateMemoriesExampleGeneratedMemory(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigGenerateMemoriesExampleGeneratedMemory

vertexai._genai.types.MemoryBankCustomizationConfigMemoryTopic

MemoryBankCustomizationConfigMemoryTopic(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigMemoryTopic

vertexai._genai.types.MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic

MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic

vertexai._genai.types.MemoryBankCustomizationConfigMemoryTopicManagedMemoryTopic

MemoryBankCustomizationConfigMemoryTopicManagedMemoryTopic(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankCustomizationConfigMemoryTopicManagedMemoryTopic

vertexai._genai.types.MemoryBankIngestEventsOperation

MemoryBankIngestEventsOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryBankIngestEventsOperation

vertexai._genai.types.MemoryConjunctionFilter

MemoryConjunctionFilter(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryConjunctionFilter

vertexai._genai.types.MemoryFilter

MemoryFilter(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryFilter

vertexai._genai.types.MemoryGenerationTriggerConfig

MemoryGenerationTriggerConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryGenerationTriggerConfig

vertexai._genai.types.MemoryGenerationTriggerConfigGenerationTriggerRule

MemoryGenerationTriggerConfigGenerationTriggerRule(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryGenerationTriggerConfigGenerationTriggerRule

vertexai._genai.types.MemoryMetadataValue

MemoryMetadataValue(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryMetadataValue

vertexai._genai.types.MemoryProfile

MemoryProfile(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryProfile

vertexai._genai.types.MemoryRevision

MemoryRevision(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryRevision

vertexai._genai.types.MemoryStructuredContent

MemoryStructuredContent(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryStructuredContent

vertexai._genai.types.MemoryTopicId

MemoryTopicId(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MemoryTopicId

vertexai._genai.types.Message

Message(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Message

vertexai._genai.types.Metadata

Metadata(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Metadata

vertexai._genai.types.Metric

Metric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Metric

vertexai._genai.types.Metric.model_post_init

model_post_init(context: Any, /) -> None

This function is meant to behave like a BaseModel method to initialize private attributes.

See more: vertexai._genai.types.Metric.model_post_init

vertexai._genai.types.Metric.to_yaml_file

to_yaml_file(file_path: str, version: typing.Optional[str] = None) -> None

Dumps the metric object to a YAML file.

See more: vertexai._genai.types.Metric.to_yaml_file

vertexai._genai.types.Metric.validate_name

validate_name(
    model: vertexai._genai.types.common.Metric,
) -> vertexai._genai.types.common.Metric

API documentation for validate_name method.

See more: vertexai._genai.types.Metric.validate_name

vertexai._genai.types.MetricPromptBuilder

MetricPromptBuilder(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MetricPromptBuilder

vertexai._genai.types.MetricPromptBuilder.__str__

__str__() -> str

Returns the fully constructed prompt template text.

See more: vertexai.genai.types.MetricPromptBuilder._str

vertexai._genai.types.MetricResult

MetricResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MetricResult

vertexai._genai.types.MetricSource

MetricSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MetricSource

vertexai._genai.types.MetricxResult

MetricxResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MetricxResult

vertexai._genai.types.MultimodalDataset

MultimodalDataset(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MultimodalDataset

vertexai._genai.types.MultimodalDataset.set_bigquery_uri

set_bigquery_uri(bigquery_uri: str) -> None

Sets the bigquery uri in the dataset metadata.

See more: vertexai._genai.types.MultimodalDataset.set_bigquery_uri

vertexai._genai.types.MultimodalDataset.set_read_config

set_read_config(
    *,
    read_config: typing.Union[
        vertexai._genai.types.common.GeminiRequestReadConfig,
        vertexai._genai.types.common.GeminiRequestReadConfigDict,
    ]
) -> None

Sets the read config in the dataset metadata.

See more: vertexai._genai.types.MultimodalDataset.set_read_config

vertexai._genai.types.MultimodalDataset.to_bigframes

to_bigframes() -> bigframes.pandas.DataFrame

Converts the multimodal dataset to a BigFrames dataframe.

See more: vertexai._genai.types.MultimodalDataset.to_bigframes

vertexai._genai.types.MultimodalDatasetOperation

MultimodalDatasetOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.MultimodalDatasetOperation

vertexai._genai.types.NfsMount

NfsMount(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.NfsMount

vertexai._genai.types.ObservabilityEvalCase

ObservabilityEvalCase(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ObservabilityEvalCase

vertexai._genai.types.OptimizeConfig

OptimizeConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.OptimizeConfig

vertexai._genai.types.OptimizeJobConfig

OptimizeJobConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.OptimizeJobConfig

vertexai._genai.types.OptimizeResponse

OptimizeResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.OptimizeResponse

vertexai._genai.types.OptimizeResponseEndpoint

OptimizeResponseEndpoint(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.OptimizeResponseEndpoint

vertexai._genai.types.PairwiseMetricInput

PairwiseMetricInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PairwiseMetricInput

vertexai._genai.types.PairwiseMetricInstance

PairwiseMetricInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PairwiseMetricInstance

vertexai._genai.types.PointwiseMetricInput

PointwiseMetricInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PointwiseMetricInput

vertexai._genai.types.PointwiseMetricInstance

PointwiseMetricInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PointwiseMetricInstance

vertexai._genai.types.Prompt

Prompt(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Prompt

vertexai._genai.types.Prompt.assemble_contents

assemble_contents() -> list[google.genai.types.Content]

Transforms a Prompt object into a list with a single genai_types.Content object.

See more: vertexai._genai.types.Prompt.assemble_contents

vertexai._genai.types.Prompt.model_post_init

model_post_init(context: Any, /) -> None

This function is meant to behave like a BaseModel method to initialize private attributes.

See more: vertexai._genai.types.Prompt.model_post_init

vertexai._genai.types.PromptOptimizerConfig

PromptOptimizerConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PromptOptimizerConfig

vertexai._genai.types.PromptRef

PromptRef(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PromptRef

vertexai._genai.types.PromptTemplate

PromptTemplate(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PromptTemplate

vertexai._genai.types.PromptTemplate.assemble

assemble(**kwargs: typing.Any) -> str

Assembles the prompt template with the given keyword arguments.

See more: vertexai._genai.types.PromptTemplate.assemble

vertexai._genai.types.PromptTemplate.text_must_not_be_empty

text_must_not_be_empty(value: str) -> str

API documentation for text_must_not_be_empty method.

See more: vertexai._genai.types.PromptTemplate.text_must_not_be_empty

vertexai._genai.types.PromptTemplateData

PromptTemplateData(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PromptTemplateData

vertexai._genai.types.PromptVersionRef

PromptVersionRef(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PromptVersionRef

vertexai._genai.types.PscInterfaceConfig

PscInterfaceConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PscInterfaceConfig

vertexai._genai.types.PurgeAgentEngineMemoriesConfig

PurgeAgentEngineMemoriesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PurgeAgentEngineMemoriesConfig

vertexai._genai.types.PurgeMemoriesResponse

PurgeMemoriesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PurgeMemoriesResponse

vertexai._genai.types.PythonPackageSpec

PythonPackageSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.PythonPackageSpec

vertexai._genai.types.QueryAgentEngineConfig

QueryAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.QueryAgentEngineConfig

vertexai._genai.types.QueryAgentEngineRuntimeRevisionConfig

QueryAgentEngineRuntimeRevisionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.QueryAgentEngineRuntimeRevisionConfig

vertexai._genai.types.QueryReasoningEngineResponse

QueryReasoningEngineResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.QueryReasoningEngineResponse

vertexai._genai.types.ReasoningEngine

ReasoningEngine(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngine

vertexai._genai.types.ReasoningEngineContextSpec

ReasoningEngineContextSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineContextSpec

vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfig

ReasoningEngineContextSpecMemoryBankConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfig

vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigGenerationConfig

ReasoningEngineContextSpecMemoryBankConfigGenerationConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigGenerationConfig

vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigSimilaritySearchConfig

ReasoningEngineContextSpecMemoryBankConfigSimilaritySearchConfig(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigSimilaritySearchConfig

vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigTtlConfig

ReasoningEngineContextSpecMemoryBankConfigTtlConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigTtlConfig

vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigTtlConfigGranularTtlConfig

ReasoningEngineContextSpecMemoryBankConfigTtlConfigGranularTtlConfig(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineContextSpecMemoryBankConfigTtlConfigGranularTtlConfig

vertexai._genai.types.ReasoningEngineRuntimeRevision

ReasoningEngineRuntimeRevision(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineRuntimeRevision

vertexai._genai.types.ReasoningEngineSpec

ReasoningEngineSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpec

vertexai._genai.types.ReasoningEngineSpecBuildSpec

ReasoningEngineSpecBuildSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecBuildSpec

vertexai._genai.types.ReasoningEngineSpecContainerSpec

ReasoningEngineSpecContainerSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecContainerSpec

vertexai._genai.types.ReasoningEngineSpecDeploymentSpec

ReasoningEngineSpecDeploymentSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecDeploymentSpec

vertexai._genai.types.ReasoningEngineSpecDeploymentSpecAgentGatewayConfig

ReasoningEngineSpecDeploymentSpecAgentGatewayConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecDeploymentSpecAgentGatewayConfig

vertexai._genai.types.ReasoningEngineSpecDeploymentSpecAgentGatewayConfigAgentToAnywhereConfig

ReasoningEngineSpecDeploymentSpecAgentGatewayConfigAgentToAnywhereConfig(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecDeploymentSpecAgentGatewayConfigAgentToAnywhereConfig

vertexai._genai.types.ReasoningEngineSpecDeploymentSpecAgentGatewayConfigClientToAgentConfig

ReasoningEngineSpecDeploymentSpecAgentGatewayConfigClientToAgentConfig(
    **data: typing.Any,
)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecDeploymentSpecAgentGatewayConfigClientToAgentConfig

vertexai._genai.types.ReasoningEngineSpecPackageSpec

ReasoningEngineSpecPackageSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecPackageSpec

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpec

ReasoningEngineSpecSourceCodeSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpec

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecAgentConfigSource

ReasoningEngineSpecSourceCodeSpecAgentConfigSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecAgentConfigSource

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecAgentConfigSourceAdkConfig

ReasoningEngineSpecSourceCodeSpecAgentConfigSourceAdkConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecAgentConfigSourceAdkConfig

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecDeveloperConnectConfig

ReasoningEngineSpecSourceCodeSpecDeveloperConnectConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecDeveloperConnectConfig

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecDeveloperConnectSource

ReasoningEngineSpecSourceCodeSpecDeveloperConnectSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecDeveloperConnectSource

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecImageSpec

ReasoningEngineSpecSourceCodeSpecImageSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecImageSpec

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecInlineSource

ReasoningEngineSpecSourceCodeSpecInlineSource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecInlineSource

vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecPythonSpec

ReasoningEngineSpecSourceCodeSpecPythonSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineSpecSourceCodeSpecPythonSpec

vertexai._genai.types.ReasoningEngineTrafficConfig

ReasoningEngineTrafficConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineTrafficConfig

vertexai._genai.types.ReasoningEngineTrafficConfigTrafficSplitAlwaysLatest

ReasoningEngineTrafficConfigTrafficSplitAlwaysLatest(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineTrafficConfigTrafficSplitAlwaysLatest

vertexai._genai.types.ReasoningEngineTrafficConfigTrafficSplitManual

ReasoningEngineTrafficConfigTrafficSplitManual(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineTrafficConfigTrafficSplitManual

vertexai._genai.types.ReasoningEngineTrafficConfigTrafficSplitManualTarget

ReasoningEngineTrafficConfigTrafficSplitManualTarget(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReasoningEngineTrafficConfigTrafficSplitManualTarget

vertexai._genai.types.RedTeamingAnalysisConfig

RedTeamingAnalysisConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RedTeamingAnalysisConfig

vertexai._genai.types.RedTeamingAnalysisResult

RedTeamingAnalysisResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RedTeamingAnalysisResult

vertexai._genai.types.ReservationAffinity

ReservationAffinity(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ReservationAffinity

vertexai._genai.types.ResponseCandidate

ResponseCandidate(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ResponseCandidate

vertexai._genai.types.ResponseCandidateResult

ResponseCandidateResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ResponseCandidateResult

vertexai._genai.types.RestoreVersionConfig

RestoreVersionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RestoreVersionConfig

vertexai._genai.types.RestoreVersionOperation

RestoreVersionOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RestoreVersionOperation

vertexai._genai.types.RetrieveAgentEngineMemoriesConfig

RetrieveAgentEngineMemoriesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveAgentEngineMemoriesConfig

vertexai._genai.types.RetrieveMemoriesRequestSimilaritySearchParams

RetrieveMemoriesRequestSimilaritySearchParams(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveMemoriesRequestSimilaritySearchParams

vertexai._genai.types.RetrieveMemoriesRequestSimpleRetrievalParams

RetrieveMemoriesRequestSimpleRetrievalParams(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveMemoriesRequestSimpleRetrievalParams

vertexai._genai.types.RetrieveMemoriesResponse

RetrieveMemoriesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveMemoriesResponse

vertexai._genai.types.RetrieveMemoriesResponseRetrievedMemory

RetrieveMemoriesResponseRetrievedMemory(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveMemoriesResponseRetrievedMemory

vertexai._genai.types.RetrieveMemoryProfilesConfig

RetrieveMemoryProfilesConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveMemoryProfilesConfig

vertexai._genai.types.RetrieveProfilesResponse

RetrieveProfilesResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveProfilesResponse

vertexai._genai.types.RetrieveSkillsConfig

RetrieveSkillsConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveSkillsConfig

vertexai._genai.types.RetrieveSkillsResponse

RetrieveSkillsResponse(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrieveSkillsResponse

vertexai._genai.types.RetrievedSkill

RetrievedSkill(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RetrievedSkill

vertexai._genai.types.RollbackAgentEngineMemoryConfig

RollbackAgentEngineMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RollbackAgentEngineMemoryConfig

vertexai._genai.types.RougeInput

RougeInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RougeInput

vertexai._genai.types.RougeInstance

RougeInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RougeInstance

vertexai._genai.types.RougeResults

RougeResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RougeResults

vertexai._genai.types.Rubric

Rubric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Rubric

vertexai._genai.types.RubricBasedMetricInput

RubricBasedMetricInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricBasedMetricInput

vertexai._genai.types.RubricBasedMetricInstance

RubricBasedMetricInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricBasedMetricInstance

vertexai._genai.types.RubricBasedMetricResult

RubricBasedMetricResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricBasedMetricResult

vertexai._genai.types.RubricBasedMetricSpec

RubricBasedMetricSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricBasedMetricSpec

vertexai._genai.types.RubricContent

RubricContent(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricContent

vertexai._genai.types.RubricContentProperty

RubricContentProperty(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricContentProperty

vertexai._genai.types.RubricEnhancedContents

RubricEnhancedContents(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricEnhancedContents

vertexai._genai.types.RubricGenerationConfig

RubricGenerationConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricGenerationConfig

vertexai._genai.types.RubricGroup

RubricGroup(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricGroup

vertexai._genai.types.RubricVerdict

RubricVerdict(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RubricVerdict

vertexai._genai.types.RunQueryJobAgentEngineConfig

RunQueryJobAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RunQueryJobAgentEngineConfig

vertexai._genai.types.RunQueryJobResult

RunQueryJobResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.RunQueryJobResult

vertexai._genai.types.SamplingConfig

SamplingConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SamplingConfig

vertexai._genai.types.SandboxEnvironment

SandboxEnvironment(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironment

vertexai._genai.types.SandboxEnvironmentConnectionInfo

SandboxEnvironmentConnectionInfo(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentConnectionInfo

vertexai._genai.types.SandboxEnvironmentSnapshot

SandboxEnvironmentSnapshot(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentSnapshot

vertexai._genai.types.SandboxEnvironmentSpec

SandboxEnvironmentSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentSpec

vertexai._genai.types.SandboxEnvironmentSpecCodeExecutionEnvironment

SandboxEnvironmentSpecCodeExecutionEnvironment(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentSpecCodeExecutionEnvironment

vertexai._genai.types.SandboxEnvironmentSpecComputerUseEnvironment

SandboxEnvironmentSpecComputerUseEnvironment(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentSpecComputerUseEnvironment

vertexai._genai.types.SandboxEnvironmentTemplate

SandboxEnvironmentTemplate(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplate

vertexai._genai.types.SandboxEnvironmentTemplateCustomContainerEnvironment

SandboxEnvironmentTemplateCustomContainerEnvironment(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateCustomContainerEnvironment

vertexai._genai.types.SandboxEnvironmentTemplateCustomContainerSpec

SandboxEnvironmentTemplateCustomContainerSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateCustomContainerSpec

vertexai._genai.types.SandboxEnvironmentTemplateDefaultContainerEnvironment

SandboxEnvironmentTemplateDefaultContainerEnvironment(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateDefaultContainerEnvironment

vertexai._genai.types.SandboxEnvironmentTemplateEgressControlConfig

SandboxEnvironmentTemplateEgressControlConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateEgressControlConfig

vertexai._genai.types.SandboxEnvironmentTemplateNetworkPort

SandboxEnvironmentTemplateNetworkPort(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateNetworkPort

vertexai._genai.types.SandboxEnvironmentTemplateOperation

SandboxEnvironmentTemplateOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateOperation

vertexai._genai.types.SandboxEnvironmentTemplateResourceRequirements

SandboxEnvironmentTemplateResourceRequirements(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SandboxEnvironmentTemplateResourceRequirements

vertexai._genai.types.SavedQuery

SavedQuery(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SavedQuery

vertexai._genai.types.Scheduling

Scheduling(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Scheduling

vertexai._genai.types.SchemaPredictParamsGroundingConfig

SchemaPredictParamsGroundingConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPredictParamsGroundingConfig

vertexai._genai.types.SchemaPredictParamsGroundingConfigSourceEntry

SchemaPredictParamsGroundingConfigSourceEntry(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPredictParamsGroundingConfigSourceEntry

vertexai._genai.types.SchemaPromptApiSchema

SchemaPromptApiSchema(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptApiSchema

vertexai._genai.types.SchemaPromptInstancePromptExecution

SchemaPromptInstancePromptExecution(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptInstancePromptExecution

vertexai._genai.types.SchemaPromptInstanceVariableValue

SchemaPromptInstanceVariableValue(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptInstanceVariableValue

vertexai._genai.types.SchemaPromptSpecAppBuilderData

SchemaPromptSpecAppBuilderData(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecAppBuilderData

vertexai._genai.types.SchemaPromptSpecAppBuilderDataLinkedResource

SchemaPromptSpecAppBuilderDataLinkedResource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecAppBuilderDataLinkedResource

vertexai._genai.types.SchemaPromptSpecInteractionData

SchemaPromptSpecInteractionData(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecInteractionData

vertexai._genai.types.SchemaPromptSpecMultimodalPrompt

SchemaPromptSpecMultimodalPrompt(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecMultimodalPrompt

vertexai._genai.types.SchemaPromptSpecPartList

SchemaPromptSpecPartList(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecPartList

vertexai._genai.types.SchemaPromptSpecPromptMessage

SchemaPromptSpecPromptMessage(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecPromptMessage

vertexai._genai.types.SchemaPromptSpecReferenceSentencePair

SchemaPromptSpecReferenceSentencePair(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecReferenceSentencePair

vertexai._genai.types.SchemaPromptSpecReferenceSentencePairList

SchemaPromptSpecReferenceSentencePairList(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecReferenceSentencePairList

vertexai._genai.types.SchemaPromptSpecStructuredPrompt

SchemaPromptSpecStructuredPrompt(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecStructuredPrompt

vertexai._genai.types.SchemaPromptSpecTranslationExample

SchemaPromptSpecTranslationExample(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecTranslationExample

vertexai._genai.types.SchemaPromptSpecTranslationOption

SchemaPromptSpecTranslationOption(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecTranslationOption

vertexai._genai.types.SchemaPromptSpecTranslationPrompt

SchemaPromptSpecTranslationPrompt(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaPromptSpecTranslationPrompt

vertexai._genai.types.SchemaTablesDatasetMetadata

SchemaTablesDatasetMetadata(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaTablesDatasetMetadata

vertexai._genai.types.SchemaTablesDatasetMetadataBigQuerySource

SchemaTablesDatasetMetadataBigQuerySource(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaTablesDatasetMetadataBigQuerySource

vertexai._genai.types.SchemaTablesDatasetMetadataInputConfig

SchemaTablesDatasetMetadataInputConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaTablesDatasetMetadataInputConfig

vertexai._genai.types.SchemaTextPromptDatasetMetadata

SchemaTextPromptDatasetMetadata(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SchemaTextPromptDatasetMetadata

vertexai._genai.types.SecretEnvVar

SecretEnvVar(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SecretEnvVar

vertexai._genai.types.SecretRef

SecretRef(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SecretRef

vertexai._genai.types.Session

Session(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Session

vertexai._genai.types.SessionEvent

SessionEvent(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SessionEvent

vertexai._genai.types.Skill

Skill(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.Skill

vertexai._genai.types.SkillOperation

SkillOperation(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SkillOperation

vertexai._genai.types.StructuredMemoryConfig

StructuredMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.StructuredMemoryConfig

vertexai._genai.types.StructuredMemorySchemaConfig

StructuredMemorySchemaConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.StructuredMemorySchemaConfig

vertexai._genai.types.SummaryMetric

SummaryMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.SummaryMetric

vertexai._genai.types.TaskArtifact

TaskArtifact(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskArtifact

vertexai._genai.types.TaskArtifactChange

TaskArtifactChange(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskArtifactChange

vertexai._genai.types.TaskEvent

TaskEvent(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskEvent

vertexai._genai.types.TaskEventData

TaskEventData(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskEventData

vertexai._genai.types.TaskMessage

TaskMessage(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskMessage

vertexai._genai.types.TaskMetadataChange

TaskMetadataChange(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskMetadataChange

vertexai._genai.types.TaskOutput

TaskOutput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskOutput

vertexai._genai.types.TaskOutputChange

TaskOutputChange(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskOutputChange

vertexai._genai.types.TaskStateChange

TaskStateChange(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskStateChange

vertexai._genai.types.TaskStatusDetails

TaskStatusDetails(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskStatusDetails

vertexai._genai.types.TaskStatusDetailsChange

TaskStatusDetailsChange(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TaskStatusDetailsChange

vertexai._genai.types.ToolCallValidInput

ToolCallValidInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolCallValidInput

vertexai._genai.types.ToolCallValidInstance

ToolCallValidInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolCallValidInstance

vertexai._genai.types.ToolCallValidMetricValue

ToolCallValidMetricValue(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolCallValidMetricValue

vertexai._genai.types.ToolCallValidResults

ToolCallValidResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolCallValidResults

vertexai._genai.types.ToolCallValidSpec

ToolCallValidSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolCallValidSpec

vertexai._genai.types.ToolNameMatchInput

ToolNameMatchInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolNameMatchInput

vertexai._genai.types.ToolNameMatchInstance

ToolNameMatchInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolNameMatchInstance

vertexai._genai.types.ToolNameMatchMetricValue

ToolNameMatchMetricValue(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolNameMatchMetricValue

vertexai._genai.types.ToolNameMatchResults

ToolNameMatchResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolNameMatchResults

vertexai._genai.types.ToolNameMatchSpec

ToolNameMatchSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolNameMatchSpec

vertexai._genai.types.ToolParameterKVMatchInput

ToolParameterKVMatchInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKVMatchInput

vertexai._genai.types.ToolParameterKVMatchInstance

ToolParameterKVMatchInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKVMatchInstance

vertexai._genai.types.ToolParameterKVMatchMetricValue

ToolParameterKVMatchMetricValue(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKVMatchMetricValue

vertexai._genai.types.ToolParameterKVMatchResults

ToolParameterKVMatchResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKVMatchResults

vertexai._genai.types.ToolParameterKVMatchSpec

ToolParameterKVMatchSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKVMatchSpec

vertexai._genai.types.ToolParameterKeyMatchInput

ToolParameterKeyMatchInput(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKeyMatchInput

vertexai._genai.types.ToolParameterKeyMatchInstance

ToolParameterKeyMatchInstance(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKeyMatchInstance

vertexai._genai.types.ToolParameterKeyMatchMetricValue

ToolParameterKeyMatchMetricValue(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKeyMatchMetricValue

vertexai._genai.types.ToolParameterKeyMatchResults

ToolParameterKeyMatchResults(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKeyMatchResults

vertexai._genai.types.ToolParameterKeyMatchSpec

ToolParameterKeyMatchSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.ToolParameterKeyMatchSpec

vertexai._genai.types.TuningResourceUsageAssessmentConfig

TuningResourceUsageAssessmentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TuningResourceUsageAssessmentConfig

vertexai._genai.types.TuningResourceUsageAssessmentResult

TuningResourceUsageAssessmentResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TuningResourceUsageAssessmentResult

vertexai._genai.types.TuningValidationAssessmentConfig

TuningValidationAssessmentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TuningValidationAssessmentConfig

vertexai._genai.types.TuningValidationAssessmentResult

TuningValidationAssessmentResult(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.TuningValidationAssessmentResult

vertexai._genai.types.UnifiedMetric

UnifiedMetric(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UnifiedMetric

vertexai._genai.types.UpdateAgentEngineConfig

UpdateAgentEngineConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UpdateAgentEngineConfig

vertexai._genai.types.UpdateAgentEngineMemoryConfig

UpdateAgentEngineMemoryConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UpdateAgentEngineMemoryConfig

vertexai._genai.types.UpdateAgentEngineSessionConfig

UpdateAgentEngineSessionConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UpdateAgentEngineSessionConfig

vertexai._genai.types.UpdateEvaluationExperimentConfig

UpdateEvaluationExperimentConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UpdateEvaluationExperimentConfig

vertexai._genai.types.UpdatePromptConfig

UpdatePromptConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UpdatePromptConfig

vertexai._genai.types.UpdateSkillConfig

UpdateSkillConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.UpdateSkillConfig

vertexai._genai.types.VertexBaseConfig

VertexBaseConfig(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.VertexBaseConfig

vertexai._genai.types.VulnerableTool

VulnerableTool(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.VulnerableTool

vertexai._genai.types.WinRateStats

WinRateStats(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.WinRateStats

vertexai._genai.types.WorkerPoolSpec

WorkerPoolSpec(**data: typing.Any)

Create a new model by parsing and validating input data from keyword arguments.

See more: vertexai._genai.types.WorkerPoolSpec

vertexai.agent_engines.AG2Agent

AG2Agent(
    model: str,
    runnable_name: str,
    *,
    api_type: typing.Optional[str] = None,
    llm_config: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    system_instruction: typing.Optional[str] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[typing.Callable[[...], ConversableAgent]] = None,
    tools: typing.Optional[typing.Sequence[typing.Callable[[...], typing.Any]]] = None,
    enable_tracing: bool = False,
    instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None
)

Initializes the AG2 Agent.

See more: vertexai.agent_engines.AG2Agent

vertexai.agent_engines.AG2Agent.clone

clone() -> vertexai.agent_engines.templates.ag2.AG2Agent

Returns a clone of the AG2Agent.

See more: vertexai.agent_engines.AG2Agent.clone

vertexai.agent_engines.AG2Agent.query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    max_turns: typing.Optional[int] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input.

See more: vertexai.agent_engines.AG2Agent.query

vertexai.agent_engines.AG2Agent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.agent_engines.AG2Agent.set_up

vertexai.agent_engines.AdkApp

AdkApp(
    *,
    app: App = None,
    agent: BaseAgent = None,
    app_name: typing.Optional[str] = None,
    plugins: typing.Optional[typing.List[BasePlugin]] = None,
    enable_tracing: typing.Optional[bool] = None,
    session_service_builder: typing.Optional[
        typing.Callable[[...], BaseSessionService]
    ] = None,
    artifact_service_builder: typing.Optional[
        typing.Callable[[...], BaseArtifactService]
    ] = None,
    memory_service_builder: typing.Optional[
        typing.Callable[[...], BaseMemoryService]
    ] = None,
    instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None
)

An ADK Application.

See more: vertexai.agent_engines.AdkApp

vertexai.agent_engines.AdkApp.async_add_session_to_memory

async_add_session_to_memory(*, session: typing.Dict[str, typing.Any])

Generates memories.

See more: vertexai.agent_engines.AdkApp.async_add_session_to_memory

vertexai.agent_engines.AdkApp.async_create_session

async_create_session(
    *,
    user_id: str,
    session_id: typing.Optional[str] = None,
    state: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
)

Creates a new session.

See more: vertexai.agent_engines.AdkApp.async_create_session

vertexai.agent_engines.AdkApp.async_delete_session

async_delete_session(*, user_id: str, session_id: str, **kwargs)

Deletes a session for the given user.

See more: vertexai.agent_engines.AdkApp.async_delete_session

vertexai.agent_engines.AdkApp.async_get_session

async_get_session(*, user_id: str, session_id: str, **kwargs)

Get a session for the given user.

See more: vertexai.agent_engines.AdkApp.async_get_session

vertexai.agent_engines.AdkApp.async_list_sessions

async_list_sessions(*, user_id: str, **kwargs)

List sessions for the given user.

See more: vertexai.agent_engines.AdkApp.async_list_sessions

vertexai.agent_engines.AdkApp.async_search_memory

async_search_memory(*, user_id: str, query: str)

Searches memories for the given user.

See more: vertexai.agent_engines.AdkApp.async_search_memory

vertexai.agent_engines.AdkApp.async_stream_query

async_stream_query(
    *,
    message: typing.Union[str, typing.Dict[str, typing.Any]],
    user_id: str,
    session_id: typing.Optional[str] = None,
    session_events: typing.Optional[typing.List[typing.Dict[str, typing.Any]]] = None,
    run_config: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
) -> typing.AsyncIterable[typing.Dict[str, typing.Any]]

Streams responses asynchronously from the ADK application.

See more: vertexai.agent_engines.AdkApp.async_stream_query

vertexai.agent_engines.AdkApp.clone

clone()

Returns a clone of the ADK application.

See more: vertexai.agent_engines.AdkApp.clone

vertexai.agent_engines.AdkApp.create_session

create_session(
    *,
    user_id: str,
    session_id: typing.Optional[str] = None,
    state: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
)

Deprecated.

See more: vertexai.agent_engines.AdkApp.create_session

vertexai.agent_engines.AdkApp.delete_session

delete_session(*, user_id: str, session_id: str, **kwargs)

Deprecated.

See more: vertexai.agent_engines.AdkApp.delete_session

vertexai.agent_engines.AdkApp.get_session

get_session(*, user_id: str, session_id: str, **kwargs)

Deprecated.

See more: vertexai.agent_engines.AdkApp.get_session

vertexai.agent_engines.AdkApp.list_sessions

list_sessions(*, user_id: str, **kwargs)

Deprecated.

See more: vertexai.agent_engines.AdkApp.list_sessions

vertexai.agent_engines.AdkApp.register_operations

register_operations() -> typing.Dict[str, typing.List[str]]

Registers the operations of the ADK application.

See more: vertexai.agent_engines.AdkApp.register_operations

vertexai.agent_engines.AdkApp.set_up

set_up()

Sets up the ADK application.

See more: vertexai.agent_engines.AdkApp.set_up

vertexai.agent_engines.AdkApp.stream_query

stream_query(
    *,
    message: typing.Union[str, typing.Dict[str, typing.Any]],
    user_id: str,
    session_id: typing.Optional[str] = None,
    run_config: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
)

Deprecated.

See more: vertexai.agent_engines.AdkApp.stream_query

vertexai.agent_engines.AdkApp.streaming_agent_run_with_events

streaming_agent_run_with_events(request_json: str)

Streams responses asynchronously from the ADK application.

See more: vertexai.agent_engines.AdkApp.streaming_agent_run_with_events

vertexai.agent_engines.AgentEngine

AgentEngine(resource_name: str)

Retrieves an Agent Engine resource.

See more: vertexai.agent_engines.AgentEngine

vertexai.agent_engines.AgentEngine.create

create(
    agent_engine: typing.Union[
        None,
        vertexai.agent_engines.AsyncQueryable,
        vertexai.agent_engines.AsyncStreamQueryable,
        vertexai.agent_engines._agent_engines.BidiStreamQueryable,
        vertexai.agent_engines.OperationRegistrable,
        vertexai.agent_engines.Queryable,
        vertexai.agent_engines.StreamQueryable,
    ] = None,
    *,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    env_vars: typing.Optional[
        typing.Union[
            typing.Sequence[str],
            typing.Dict[
                str,
                typing.Union[str, google.cloud.aiplatform_v1.types.env_var.SecretRef],
            ],
        ]
    ] = None,
    build_options: typing.Optional[typing.Dict[str, typing.Sequence[str]]] = None,
    service_account: typing.Optional[str] = None,
    identity_type: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1.types.reasoning_engine.ReasoningEngineSpec.IdentityType,
        ]
    ] = None,
    psc_interface_config: typing.Optional[
        google.cloud.aiplatform_v1.types.service_networking.PscInterfaceConfig
    ] = None,
    min_instances: typing.Optional[int] = None,
    max_instances: typing.Optional[int] = None,
    resource_limits: typing.Optional[typing.Dict[str, str]] = None,
    container_concurrency: typing.Optional[int] = None,
    encryption_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec
    ] = None
) -> vertexai.agent_engines.AgentEngine

Creates a new Agent Engine.

See more: vertexai.agent_engines.AgentEngine.create

vertexai.agent_engines.AgentEngine.delete

delete(*, force: bool = False, **kwargs) -> None

Deletes the ReasoningEngine.

See more: vertexai.agent_engines.AgentEngine.delete

vertexai.agent_engines.AgentEngine.list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    parent: typing.Optional[str] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Vertex AI Resource.

See more: vertexai.agent_engines.AgentEngine.list

vertexai.agent_engines.AgentEngine.operation_schemas

operation_schemas() -> typing.Sequence[typing.Dict[str, typing.Any]]

Returns the (Open)API schemas for the Agent Engine.

See more: vertexai.agent_engines.AgentEngine.operation_schemas

vertexai.agent_engines.AgentEngine.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.agent_engines.AgentEngine.to_dict

vertexai.agent_engines.AgentEngine.update

update(
    *,
    agent_engine: typing.Union[
        None,
        vertexai.agent_engines.AsyncQueryable,
        vertexai.agent_engines.AsyncStreamQueryable,
        vertexai.agent_engines._agent_engines.BidiStreamQueryable,
        vertexai.agent_engines.OperationRegistrable,
        vertexai.agent_engines.Queryable,
        vertexai.agent_engines.StreamQueryable,
    ] = None,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    env_vars: typing.Optional[
        typing.Union[
            typing.Sequence[str],
            typing.Dict[
                str,
                typing.Union[str, google.cloud.aiplatform_v1.types.env_var.SecretRef],
            ],
        ]
    ] = None,
    build_options: typing.Optional[typing.Dict[str, typing.Sequence[str]]] = None,
    service_account: typing.Optional[str] = None,
    identity_type: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1.types.reasoning_engine.ReasoningEngineSpec.IdentityType,
        ]
    ] = None,
    psc_interface_config: typing.Optional[
        google.cloud.aiplatform_v1.types.service_networking.PscInterfaceConfig
    ] = None,
    min_instances: typing.Optional[int] = None,
    max_instances: typing.Optional[int] = None,
    resource_limits: typing.Optional[typing.Dict[str, str]] = None,
    container_concurrency: typing.Optional[int] = None,
    encryption_spec: typing.Optional[
        google.cloud.aiplatform_v1.types.encryption_spec.EncryptionSpec
    ] = None
) -> vertexai.agent_engines.AgentEngine

Updates an existing Agent Engine.

See more: vertexai.agent_engines.AgentEngine.update

vertexai.agent_engines.AgentEngine.wait

wait()

Helper method that blocks until all futures are complete.

See more: vertexai.agent_engines.AgentEngine.wait

vertexai.agent_engines.AsyncQueryable.async_query

async_query(**kwargs) -> typing.Coroutine[typing.Any, typing.Any, typing.Any]

Runs the Agent Engine to serve the user query asynchronously.

See more: vertexai.agent_engines.AsyncQueryable.async_query

vertexai.agent_engines.AsyncStreamQueryable.async_stream_query

async_stream_query(**kwargs) -> typing.AsyncIterable[typing.Any]

Asynchronously stream responses to serve the user query.

See more: vertexai.agent_engines.AsyncStreamQueryable.async_stream_query

vertexai.agent_engines.Cloneable.clone

clone() -> typing.Any

Return a clone of the object.

See more: vertexai.agent_engines.Cloneable.clone

vertexai.agent_engines.LangchainAgent

LangchainAgent(
    model: str,
    *,
    system_instruction: typing.Optional[str] = None,
    prompt: typing.Optional[RunnableSerializable] = None,
    tools: typing.Optional[_ToolsType] = None,
    output_parser: typing.Optional[RunnableSerializable] = None,
    chat_history: typing.Optional[GetSessionHistoryCallable] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    agent_executor_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable] = None,
    runnable_builder: typing.Optional[typing.Callable] = None,
    enable_tracing: bool = False,
    instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None
)

Initializes the LangchainAgent.

See more: vertexai.agent_engines.LangchainAgent

vertexai.agent_engines.LangchainAgent.clone

clone() -> vertexai.agent_engines.templates.langchain.LangchainAgent

Returns a clone of the LangchainAgent.

See more: vertexai.agent_engines.LangchainAgent.clone

vertexai.agent_engines.LangchainAgent.query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input and config.

See more: vertexai.agent_engines.LangchainAgent.query

vertexai.agent_engines.LangchainAgent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.agent_engines.LangchainAgent.set_up

vertexai.agent_engines.LangchainAgent.stream_query

stream_query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs
) -> typing.Iterable[typing.Any]

Stream queries the Agent with the given input and config.

See more: vertexai.agent_engines.LangchainAgent.stream_query

vertexai.agent_engines.LanggraphAgent

LanggraphAgent(
    model: str,
    *,
    tools: typing.Optional[typing.Sequence[_ToolLike]] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable[[...], BaseLanguageModel]] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None,
    checkpointer_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    checkpointer_builder: typing.Optional[
        typing.Callable[[...], BaseCheckpointSaver]
    ] = None,
    enable_tracing: bool = False,
    instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None
)

Initializes the LangGraph Agent.

See more: vertexai.agent_engines.LanggraphAgent

vertexai.agent_engines.LanggraphAgent.clone

clone() -> vertexai.agent_engines.templates.langgraph.LanggraphAgent

Returns a clone of the LanggraphAgent.

See more: vertexai.agent_engines.LanggraphAgent.clone

vertexai.agent_engines.LanggraphAgent.get_state

get_state(
    config: typing.Optional[dict[str, typing.Any]] = None, **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Gets the current state of the Agent.

See more: vertexai.agent_engines.LanggraphAgent.get_state

vertexai.agent_engines.LanggraphAgent.get_state_history

get_state_history(
    config: typing.Optional[dict[str, typing.Any]] = None, **kwargs: typing.Any
) -> typing.Iterable[typing.Any]

Gets the state history of the Agent.

See more: vertexai.agent_engines.LanggraphAgent.get_state_history

vertexai.agent_engines.LanggraphAgent.query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[dict[str, typing.Any]] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input and config.

See more: vertexai.agent_engines.LanggraphAgent.query

vertexai.agent_engines.LanggraphAgent.register_operations

register_operations() -> typing.Mapping[str, typing.Sequence[str]]

Registers the operations of the Agent.

See more: vertexai.agent_engines.LanggraphAgent.register_operations

vertexai.agent_engines.LanggraphAgent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.agent_engines.LanggraphAgent.set_up

vertexai.agent_engines.LanggraphAgent.stream_query

stream_query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[dict[str, typing.Any]] = None,
    **kwargs
) -> typing.Iterable[typing.Any]

Stream queries the Agent with the given input and config.

See more: vertexai.agent_engines.LanggraphAgent.stream_query

vertexai.agent_engines.LanggraphAgent.update_state

update_state(
    config: typing.Optional[dict[str, typing.Any]] = None, **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Updates the state of the Agent.

See more: vertexai.agent_engines.LanggraphAgent.update_state

vertexai.agent_engines.ModuleAgent

ModuleAgent(
    *,
    module_name: str,
    agent_name: str,
    register_operations: typing.Dict[str, typing.Sequence[str]],
    sys_paths: typing.Optional[typing.Sequence[str]] = None,
    agent_framework: typing.Optional[str] = None
)

Initializes a module-based agent.

See more: vertexai.agent_engines.ModuleAgent

vertexai.agent_engines.ModuleAgent.clone

clone()

Return a clone of the agent.

See more: vertexai.agent_engines.ModuleAgent.clone

vertexai.agent_engines.ModuleAgent.register_operations

register_operations(**kwargs) -> typing.Dict[str, typing.Sequence[str]]

Register the user provided operations (modes and methods).

See more: vertexai.agent_engines.ModuleAgent.register_operations

vertexai.agent_engines.ModuleAgent.set_up

set_up() -> None

Sets up the agent for execution of queries at runtime.

See more: vertexai.agent_engines.ModuleAgent.set_up

vertexai.agent_engines.OperationRegistrable.register_operations

register_operations(**kwargs) -> typing.Dict[str, typing.Sequence[str]]

Register the user provided operations (modes and methods).

See more: vertexai.agent_engines.OperationRegistrable.register_operations

vertexai.agent_engines.Queryable.query

query(**kwargs) -> typing.Any

Runs the Agent Engine to serve the user query.

See more: vertexai.agent_engines.Queryable.query

vertexai.agent_engines.StreamQueryable.stream_query

stream_query(**kwargs) -> typing.Iterable[typing.Any]

Stream responses to serve the user query.

See more: vertexai.agent_engines.StreamQueryable.stream_query

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[
            str, 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[
            str, 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,
    response_modalities: typing.Optional[typing.List[GenerationConfig.Modality]] = None,
    model_config: typing.Optional[GenerationConfig.ModelConfig] = None
)

Constructs a GenerationConfig object.

See more: vertexai.generative_models.GenerationConfig

vertexai.generative_models.GenerationConfig.ModelConfig.__delattr__

__delattr__(key)

Delete the value on the given field.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.delattr

vertexai.generative_models.GenerationConfig.ModelConfig.__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.eq

vertexai.generative_models.GenerationConfig.ModelConfig.__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.ne

vertexai.generative_models.GenerationConfig.ModelConfig.__setattr__

__setattr__(key, value)

Set the value on the given field.

See more: vertexai.generative_models.GenerationConfig.ModelConfig.setattr

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: ContentsType,
) -> 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: ContentsType,
) -> 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: ContentsType,
    *,
    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: ContentsType,
    *,
    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.from_cached_content

from_cached_content(
    cached_content: typing.Union[str, CachedContent],
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = None
) -> _GenerativeModel

Creates a model from cached content.

See more: vertexai.generative_models.GenerativeModel.from_cached_content

vertexai.generative_models.GenerativeModel.generate_content

generate_content(
    contents: ContentsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = 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: ContentsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = 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.language_models.TextGenerationResponse

Asynchronously gets model response for a single prompt.

See more: vertexai.language_models.CodeGenerationModel.predict_async

vertexai.language_models.CodeGenerationModel.predict_streaming

predict_streaming(
    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
) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]

Predicts the code based on previous code.

See more: vertexai.language_models.CodeGenerationModel.predict_streaming

vertexai.language_models.CodeGenerationModel.predict_streaming_async

predict_streaming_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
) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]

Asynchronously predicts the code based on previous code.

See more: vertexai.language_models.CodeGenerationModel.predict_streaming_async

vertexai.language_models.CodeGenerationModel.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,
    tuning_evaluation_spec: typing.Optional[
        vertexai.language_models.TuningEvaluationSpec
    ] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    max_context_length: typing.Optional[str] = None
) -> vertexai.language_models._language_models._LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.CodeGenerationModel.tune_model

vertexai.language_models.TextEmbeddingModel.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.TextEmbeddingModel.batch_predict

vertexai.language_models.TextEmbeddingModel.count_tokens

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

Counts the tokens and billable characters for a given prompt.

See more: vertexai.language_models.TextEmbeddingModel.count_tokens

vertexai.language_models.TextEmbeddingModel.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.

See more: vertexai.language_models.TextEmbeddingModel.deploy_tuned_model

vertexai.language_models.TextEmbeddingModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.language_models.TextEmbeddingModel.from_pretrained

vertexai.language_models.TextEmbeddingModel.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.

See more: vertexai.language_models.TextEmbeddingModel.get_embeddings

vertexai.language_models.TextEmbeddingModel.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.

See more: vertexai.language_models.TextEmbeddingModel.get_embeddings_async

vertexai.language_models.TextEmbeddingModel.get_tuned_model

get_tuned_model(*args, **kwargs)

Loads the specified tuned language model.

See more: vertexai.language_models.TextEmbeddingModel.get_tuned_model

vertexai.language_models.TextEmbeddingModel.list_tuned_model_names

list_tuned_model_names() -> typing.Sequence[str]

Lists the names of tuned models.

See more: vertexai.language_models.TextEmbeddingModel.list_tuned_model_names

vertexai.language_models.TextEmbeddingModel.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.

See more: vertexai.language_models.TextEmbeddingModel.tune_model

vertexai.language_models.TextGenerationModel.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.TextGenerationModel.batch_predict

vertexai.language_models.TextGenerationModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.language_models.TextGenerationModel.from_pretrained

vertexai.language_models.TextGenerationModel.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.TextGenerationModel.get_tuned_model

vertexai.language_models.TextGenerationModel.list_tuned_model_names

list_tuned_model_names() -> typing.Sequence[str]

Lists the names of tuned models.

See more: vertexai.language_models.TextGenerationModel.list_tuned_model_names

vertexai.language_models.TextGenerationModel.predict

predict(
    prompt: str,
    *,
    max_output_tokens: typing.Optional[int] = 128,
    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,
    logprobs: typing.Optional[int] = None,
    presence_penalty: typing.Optional[float] = None,
    frequency_penalty: typing.Optional[float] = None,
    logit_bias: typing.Optional[typing.Dict[str, float]] = None,
    seed: typing.Optional[int] = None
) -> vertexai.language_models.MultiCandidateTextGenerationResponse

Gets model response for a single prompt.

See more: vertexai.language_models.TextGenerationModel.predict

vertexai.language_models.TextGenerationModel.predict_async

predict_async(
    prompt: str,
    *,
    max_output_tokens: typing.Optional[int] = 128,
    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,
    logprobs: typing.Optional[int] = None,
    presence_penalty: typing.Optional[float] = None,
    frequency_penalty: typing.Optional[float] = None,
    logit_bias: typing.Optional[typing.Dict[str, float]] = None,
    seed: typing.Optional[int] = None
) -> vertexai.language_models.MultiCandidateTextGenerationResponse

Asynchronously gets model response for a single prompt.

See more: vertexai.language_models.TextGenerationModel.predict_async

vertexai.language_models.TextGenerationModel.predict_streaming

predict_streaming(
    prompt: str,
    *,
    max_output_tokens: int = 128,
    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,
    logprobs: typing.Optional[int] = None,
    presence_penalty: typing.Optional[float] = None,
    frequency_penalty: typing.Optional[float] = None,
    logit_bias: typing.Optional[typing.Dict[str, float]] = None,
    seed: typing.Optional[int] = None
) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]

Gets a streaming model response for a single prompt.

See more: vertexai.language_models.TextGenerationModel.predict_streaming

vertexai.language_models.TextGenerationModel.predict_streaming_async

predict_streaming_async(
    prompt: str,
    *,
    max_output_tokens: int = 128,
    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,
    logprobs: typing.Optional[int] = None,
    presence_penalty: typing.Optional[float] = None,
    frequency_penalty: typing.Optional[float] = None,
    logit_bias: typing.Optional[typing.Dict[str, float]] = None,
    seed: typing.Optional[int] = None
) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]

Asynchronously gets a streaming model response for a single prompt.

See more: vertexai.language_models.TextGenerationModel.predict_streaming_async

vertexai.language_models.TextGenerationModel.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,
    tuning_evaluation_spec: typing.Optional[
        vertexai.language_models.TuningEvaluationSpec
    ] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    max_context_length: typing.Optional[str] = None
) -> vertexai.language_models._language_models._LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.TextGenerationModel.tune_model

vertexai.language_models.TextGenerationModel.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.TextGenerationModel.tune_model_rlhf

vertexai.language_models._language_models._TunableModelMixin

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

Creates a LanguageModel.

See more: vertexai.language_models._language_models._TunableModelMixin

vertexai.language_models._language_models._TunableModelMixin.tune_model

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

Tunes a model based on training data.

See more: vertexai.language_models._language_models._TunableModelMixin.tune_model

vertexai.preview.generative_models.AutomaticFunctionCallingResponder

AutomaticFunctionCallingResponder(max_automatic_function_calls: int = 1)

Initializes the responder.

See more: vertexai.preview.generative_models.AutomaticFunctionCallingResponder

vertexai.preview.generative_models.CallableFunctionDeclaration

CallableFunctionDeclaration(
    name: str,
    function: typing.Callable[[...], typing.Any],
    parameters: typing.Dict[str, typing.Any],
    description: typing.Optional[str] = None,
)

Constructs a FunctionDeclaration.

See more: vertexai.preview.generative_models.CallableFunctionDeclaration

vertexai.preview.generative_models.CallableFunctionDeclaration.from_func

from_func(
    func: typing.Callable[[...], typing.Any],
) -> vertexai.generative_models._generative_models.CallableFunctionDeclaration

Automatically creates a CallableFunctionDeclaration from a Python function.

See more: vertexai.preview.generative_models.CallableFunctionDeclaration.from_func

vertexai.preview.generative_models.ChatSession.send_message

send_message(
    content: PartsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = 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.preview.generative_models.ChatSession.send_message

vertexai.preview.generative_models.ChatSession.send_message_async

send_message_async(
    content: PartsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    stream: bool = False
) -> typing.Union[
    typing.Awaitable[vertexai.generative_models._generative_models.GenerationResponse],
    typing.Awaitable[
        typing.AsyncIterable[
            vertexai.generative_models._generative_models.GenerationResponse
        ]
    ],
]

Generates content asynchronously.

See more: vertexai.preview.generative_models.ChatSession.send_message_async

vertexai.preview.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.preview.generative_models.FunctionDeclaration

vertexai.preview.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,
    response_modalities: typing.Optional[typing.List[GenerationConfig.Modality]] = None,
    model_config: typing.Optional[GenerationConfig.ModelConfig] = None
)

Constructs a GenerationConfig object.

See more: vertexai.preview.generative_models.GenerationConfig

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__delattr__

__delattr__(key)

Delete the value on the given field.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.delattr

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__eq__

__eq__(other)

Return True if the messages are equal, False otherwise.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.eq

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__ne__

__ne__(other)

Return True if the messages are unequal, False otherwise.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.ne

vertexai.preview.generative_models.GenerationConfig.ModelConfig.__setattr__

__setattr__(key, value)

Set the value on the given field.

See more: vertexai.preview.generative_models.GenerationConfig.ModelConfig.setattr

vertexai.preview.generative_models.GenerationConfig.RoutingConfig.AutoRoutingMode

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

AutoRouingMode constructor .

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

vertexai.preview.generative_models.GenerationConfig.RoutingConfig.ManualRoutingMode

ManualRoutingMode(*, model_name: str)

ManualRoutingMode constructor .

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

vertexai.preview.generative_models.GenerativeModel.compute_tokens

compute_tokens(
    contents: ContentsType,
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

Computes tokens.

See more: vertexai.preview.generative_models.GenerativeModel.compute_tokens

vertexai.preview.generative_models.GenerativeModel.compute_tokens_async

compute_tokens_async(
    contents: ContentsType,
) -> google.cloud.aiplatform_v1beta1.types.llm_utility_service.ComputeTokensResponse

Computes tokens asynchronously.

See more: vertexai.preview.generative_models.GenerativeModel.compute_tokens_async

vertexai.preview.generative_models.GenerativeModel.count_tokens

count_tokens(
    contents: ContentsType,
    *,
    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.preview.generative_models.GenerativeModel.count_tokens

vertexai.preview.generative_models.GenerativeModel.count_tokens_async

count_tokens_async(
    contents: ContentsType,
    *,
    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.preview.generative_models.GenerativeModel.count_tokens_async

vertexai.preview.generative_models.GenerativeModel.from_cached_content

from_cached_content(
    cached_content: typing.Union[str, CachedContent],
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = None
) -> _GenerativeModel

Creates a model from cached content.

See more: vertexai.preview.generative_models.GenerativeModel.from_cached_content

vertexai.preview.generative_models.GenerativeModel.generate_content

generate_content(
    contents: ContentsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = 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.preview.generative_models.GenerativeModel.generate_content

vertexai.preview.generative_models.GenerativeModel.generate_content_async

generate_content_async(
    contents: ContentsType,
    *,
    generation_config: typing.Optional[GenerationConfigType] = None,
    safety_settings: typing.Optional[SafetySettingsType] = 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.preview.generative_models.GenerativeModel.generate_content_async

vertexai.preview.generative_models.GenerativeModel.set_request_response_logging_config

set_request_response_logging_config(
    *,
    enabled: bool,
    sampling_rate: float,
    bigquery_destination: str,
    enable_otel_logging: typing.Optional[bool] = None
) -> typing.Union[
    google.cloud.aiplatform_v1beta1.types.endpoint.PublisherModelConfig,
    google.cloud.aiplatform_v1beta1.types.endpoint.Endpoint,
]

Sets the request/response logging config.

See more: vertexai.preview.generative_models.GenerativeModel.set_request_response_logging_config

vertexai.preview.generative_models.GenerativeModel.start_chat

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

Creates a stateful chat session.

See more: vertexai.preview.generative_models.GenerativeModel.start_chat

vertexai.preview.generative_models.Image.from_bytes

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

Loads image from image bytes.

See more: vertexai.preview.generative_models.Image.from_bytes

vertexai.preview.generative_models.Image.load_from_file

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

Loads image from file.

See more: vertexai.preview.generative_models.Image.load_from_file

vertexai.preview.generative_models.ResponseBlockedError.with_traceback

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

See more: vertexai.preview.generative_models.ResponseBlockedError.with_traceback

vertexai.preview.generative_models.ResponseValidationError.with_traceback

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

See more: vertexai.preview.generative_models.ResponseValidationError.with_traceback

vertexai.preview.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.preview.generative_models.SafetySetting

vertexai.preview.reasoning_engines.A2aAgent

A2aAgent(
    *,
    agent_card: AgentCard,
    task_store_builder: typing.Callable[[...], TaskStore] = None,
    task_store_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    agent_executor_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    agent_executor_builder: typing.Optional[
        typing.Callable[[...], AgentExecutor]
    ] = None,
    request_handler_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    request_handler_builder: typing.Optional[
        typing.Callable[[...], RequestHandler]
    ] = None,
    extended_agent_card: AgentCard = None
)

Initializes the A2A agent.

See more: vertexai.preview.reasoning_engines.A2aAgent

vertexai.preview.reasoning_engines.A2aAgent.clone

clone() -> vertexai.preview.reasoning_engines.templates.a2a.A2aAgent

Clones the A2A agent.

See more: vertexai.preview.reasoning_engines.A2aAgent.clone

vertexai.preview.reasoning_engines.A2aAgent.on_message_send_stream

on_message_send_stream(
    request: Request, context: ServerCallContext
) -> collections.abc.AsyncIterator[str]

Handles A2A streaming requests via SSE.

See more: vertexai.preview.reasoning_engines.A2aAgent.on_message_send_stream

vertexai.preview.reasoning_engines.A2aAgent.on_resubscribe_to_task

on_resubscribe_to_task(
    request: Request, context: ServerCallContext
) -> collections.abc.AsyncIterator[str]

Handles A2A task resubscription requests via SSE.

See more: vertexai.preview.reasoning_engines.A2aAgent.on_resubscribe_to_task

vertexai.preview.reasoning_engines.A2aAgent.register_operations

register_operations() -> typing.Dict[str, typing.List[str]]

Registers the operations of the A2A Agent.

See more: vertexai.preview.reasoning_engines.A2aAgent.register_operations

vertexai.preview.reasoning_engines.A2aAgent.set_up

set_up()

Sets up the A2A application.

See more: vertexai.preview.reasoning_engines.A2aAgent.set_up

vertexai.preview.reasoning_engines.AG2Agent

AG2Agent(
    model: str,
    runnable_name: str,
    *,
    api_type: typing.Optional[str] = None,
    llm_config: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    system_instruction: typing.Optional[str] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[typing.Callable[[...], ConversableAgent]] = None,
    tools: typing.Optional[typing.Sequence[typing.Callable[[...], typing.Any]]] = None,
    enable_tracing: bool = False
)

Initializes the AG2 Agent.

See more: vertexai.preview.reasoning_engines.AG2Agent

vertexai.preview.reasoning_engines.AG2Agent.clone

clone() -> vertexai.preview.reasoning_engines.templates.ag2.AG2Agent

Returns a clone of the AG2Agent.

See more: vertexai.preview.reasoning_engines.AG2Agent.clone

vertexai.preview.reasoning_engines.AG2Agent.query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    max_turns: typing.Optional[int] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input.

See more: vertexai.preview.reasoning_engines.AG2Agent.query

vertexai.preview.reasoning_engines.AG2Agent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.preview.reasoning_engines.AG2Agent.set_up

vertexai.preview.reasoning_engines.AdkApp

AdkApp(
    *,
    agent: BaseAgent,
    plugins: typing.Optional[typing.List[BasePlugin]] = None,
    enable_tracing: typing.Optional[bool] = None,
    session_service_builder: typing.Optional[
        typing.Callable[[...], BaseSessionService]
    ] = None,
    artifact_service_builder: typing.Optional[
        typing.Callable[[...], BaseArtifactService]
    ] = None,
    memory_service_builder: typing.Optional[
        typing.Callable[[...], BaseMemoryService]
    ] = None,
    credential_service_builder: typing.Optional[
        typing.Callable[[...], BaseCredentialService]
    ] = None,
    env_vars: typing.Optional[typing.Dict[str, str]] = None
)

An ADK Application.

See more: vertexai.preview.reasoning_engines.AdkApp

vertexai.preview.reasoning_engines.AdkApp.async_add_session_to_memory

async_add_session_to_memory(*, session: typing.Dict[str, typing.Any])

Generates memories.

See more: vertexai.preview.reasoning_engines.AdkApp.async_add_session_to_memory

vertexai.preview.reasoning_engines.AdkApp.async_create_session

async_create_session(
    *,
    user_id: str,
    session_id: typing.Optional[str] = None,
    state: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
)

Creates a new session.

See more: vertexai.preview.reasoning_engines.AdkApp.async_create_session

vertexai.preview.reasoning_engines.AdkApp.async_delete_session

async_delete_session(*, user_id: str, session_id: str, **kwargs)

Deletes a session for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.async_delete_session

vertexai.preview.reasoning_engines.AdkApp.async_get_session

async_get_session(*, user_id: str, session_id: str, **kwargs)

Get a session for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.async_get_session

vertexai.preview.reasoning_engines.AdkApp.async_list_sessions

async_list_sessions(*, user_id: str, **kwargs)

List sessions for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.async_list_sessions

vertexai.preview.reasoning_engines.AdkApp.async_search_memory

async_search_memory(*, user_id: str, query: str)

Searches memories for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.async_search_memory

vertexai.preview.reasoning_engines.AdkApp.async_stream_query

async_stream_query(
    *,
    message: typing.Union[str, typing.Dict[str, typing.Any]],
    user_id: str,
    session_id: typing.Optional[str] = None,
    run_config: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
) -> typing.AsyncIterable[typing.Dict[str, typing.Any]]

Streams responses asynchronously from the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.async_stream_query

vertexai.preview.reasoning_engines.AdkApp.bidi_stream_query

bidi_stream_query(request_queue: typing.Any) -> typing.AsyncIterable[typing.Any]

Bidi streaming query the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.bidi_stream_query

vertexai.preview.reasoning_engines.AdkApp.clone

clone()

Returns a clone of the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.clone

vertexai.preview.reasoning_engines.AdkApp.create_session

create_session(
    *,
    user_id: str,
    session_id: typing.Optional[str] = None,
    state: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
)

Creates a new session.

See more: vertexai.preview.reasoning_engines.AdkApp.create_session

vertexai.preview.reasoning_engines.AdkApp.delete_session

delete_session(*, user_id: str, session_id: str, **kwargs)

Deletes a session for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.delete_session

vertexai.preview.reasoning_engines.AdkApp.get_session

get_session(*, user_id: str, session_id: str, **kwargs)

Get a session for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.get_session

vertexai.preview.reasoning_engines.AdkApp.list_sessions

list_sessions(*, user_id: str, **kwargs)

List sessions for the given user.

See more: vertexai.preview.reasoning_engines.AdkApp.list_sessions

vertexai.preview.reasoning_engines.AdkApp.register_operations

register_operations() -> typing.Dict[str, typing.List[str]]

Registers the operations of the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.register_operations

vertexai.preview.reasoning_engines.AdkApp.set_up

set_up()

Sets up the ADK application.

See more: vertexai.preview.reasoning_engines.AdkApp.set_up

vertexai.preview.reasoning_engines.AdkApp.stream_query

stream_query(
    *,
    message: typing.Union[str, typing.Dict[str, typing.Any]],
    user_id: str,
    session_id: typing.Optional[str] = None,
    run_config: typing.Optional[typing.Dict[str, typing.Any]] = None,
    **kwargs
)

Streams responses from the ADK application in response to a message.

See more: vertexai.preview.reasoning_engines.AdkApp.stream_query

vertexai.preview.reasoning_engines.LangchainAgent

LangchainAgent(
    model: str,
    *,
    system_instruction: typing.Optional[str] = None,
    prompt: typing.Optional[RunnableSerializable] = None,
    tools: typing.Optional[_ToolsType] = None,
    output_parser: typing.Optional[RunnableSerializable] = None,
    chat_history: typing.Optional[GetSessionHistoryCallable] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    agent_executor_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable] = None,
    runnable_builder: typing.Optional[typing.Callable] = None,
    enable_tracing: bool = False
)

Initializes the LangchainAgent.

See more: vertexai.preview.reasoning_engines.LangchainAgent

vertexai.preview.reasoning_engines.LangchainAgent.clone

clone() -> vertexai.preview.reasoning_engines.templates.langchain.LangchainAgent

Returns a clone of the LangchainAgent.

See more: vertexai.preview.reasoning_engines.LangchainAgent.clone

vertexai.preview.reasoning_engines.LangchainAgent.query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input and config.

See more: vertexai.preview.reasoning_engines.LangchainAgent.query

vertexai.preview.reasoning_engines.LangchainAgent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.preview.reasoning_engines.LangchainAgent.set_up

vertexai.preview.reasoning_engines.LangchainAgent.stream_query

stream_query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs
) -> typing.Iterable[typing.Any]

Stream queries the Agent with the given input and config.

See more: vertexai.preview.reasoning_engines.LangchainAgent.stream_query

vertexai.preview.reasoning_engines.LanggraphAgent

LanggraphAgent(
    model: str,
    *,
    tools: typing.Optional[typing.Sequence[_ToolLike]] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable[[...], BaseLanguageModel]] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[
        typing.Callable[[...], RunnableSerializable]
    ] = None,
    checkpointer_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    checkpointer_builder: typing.Optional[
        typing.Callable[[...], BaseCheckpointSaver]
    ] = None,
    enable_tracing: bool = False
)

Initializes the LangGraph Agent.

See more: vertexai.preview.reasoning_engines.LanggraphAgent

vertexai.preview.reasoning_engines.LanggraphAgent.clone

clone() -> vertexai.preview.reasoning_engines.templates.langgraph.LanggraphAgent

Returns a clone of the LanggraphAgent.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.clone

vertexai.preview.reasoning_engines.LanggraphAgent.get_state

get_state(
    config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Gets the current state of the Agent.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.get_state

vertexai.preview.reasoning_engines.LanggraphAgent.get_state_history

get_state_history(
    config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any
) -> typing.Iterable[typing.Any]

Gets the state history of the Agent.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.get_state_history

vertexai.preview.reasoning_engines.LanggraphAgent.query

query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Queries the Agent with the given input and config.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.query

vertexai.preview.reasoning_engines.LanggraphAgent.register_operations

register_operations() -> typing.Mapping[str, typing.Sequence[str]]

Registers the operations of the Agent.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.register_operations

vertexai.preview.reasoning_engines.LanggraphAgent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.set_up

vertexai.preview.reasoning_engines.LanggraphAgent.stream_query

stream_query(
    *,
    input: typing.Union[str, typing.Mapping[str, typing.Any]],
    config: typing.Optional[RunnableConfig] = None,
    **kwargs
) -> typing.Iterable[typing.Any]

Stream queries the Agent with the given input and config.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.stream_query

vertexai.preview.reasoning_engines.LanggraphAgent.update_state

update_state(
    config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any
) -> typing.Dict[str, typing.Any]

Updates the state of the Agent.

See more: vertexai.preview.reasoning_engines.LanggraphAgent.update_state

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent

LlamaIndexQueryPipelineAgent(
    model: str,
    *,
    system_instruction: typing.Optional[str] = None,
    prompt: typing.Optional[QueryComponent] = None,
    model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    model_builder: typing.Optional[typing.Callable[[...], FunctionCallingLLM]] = None,
    retriever_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    retriever_builder: typing.Optional[typing.Callable[[...], QueryComponent]] = None,
    response_synthesizer_kwargs: typing.Optional[
        typing.Mapping[str, typing.Any]
    ] = None,
    response_synthesizer_builder: typing.Optional[
        typing.Callable[[...], QueryComponent]
    ] = None,
    runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None,
    runnable_builder: typing.Optional[typing.Callable[[...], QueryPipeline]] = None,
    enable_tracing: bool = False
)

Initializes the LlamaIndexQueryPipelineAgent.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.clone

clone() -> (
    vertexai.preview.reasoning_engines.templates.llama_index.LlamaIndexQueryPipelineAgent
)

Returns a clone of the LlamaIndexQueryPipelineAgent.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.clone

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.query

query(
    input: typing.Union[str, typing.Mapping[str, typing.Any]], **kwargs: typing.Any
) -> typing.Union[
    str,
    typing.Dict[str, typing.Any],
    typing.Sequence[typing.Union[str, typing.Dict[str, typing.Any]]],
]

Queries the Agent with the given input and config.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.query

vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.set_up

set_up()

Sets up the agent for execution of queries at runtime.

See more: vertexai.preview.reasoning_engines.LlamaIndexQueryPipelineAgent.set_up

vertexai.preview.reasoning_engines.Queryable.query

query(**kwargs)

Runs the Reasoning Engine to serve the user query.

See more: vertexai.preview.reasoning_engines.Queryable.query

vertexai.preview.reasoning_engines.ReasoningEngine

ReasoningEngine(reasoning_engine_name: str)

Retrieves a Reasoning Engine resource.

See more: vertexai.preview.reasoning_engines.ReasoningEngine

vertexai.preview.reasoning_engines.ReasoningEngine.create

create(
    reasoning_engine: typing.Union[
        vertexai.reasoning_engines._reasoning_engines.Queryable,
        vertexai.reasoning_engines._reasoning_engines.OperationRegistrable,
    ],
    *,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    reasoning_engine_name: typing.Optional[str] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: str = "reasoning_engine",
    sys_version: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    service_account: typing.Optional[str] = None,
    identity_type: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1beta1.types.reasoning_engine.ReasoningEngineSpec.IdentityType,
        ]
    ] = None
) -> vertexai.reasoning_engines._reasoning_engines.ReasoningEngine

Creates a new ReasoningEngine.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.create

vertexai.preview.reasoning_engines.ReasoningEngine.delete

delete(sync: bool = True) -> None

Deletes this Vertex AI resource.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.delete

vertexai.preview.reasoning_engines.ReasoningEngine.list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    parent: typing.Optional[str] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Vertex AI Resource.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.list

vertexai.preview.reasoning_engines.ReasoningEngine.operation_schemas

operation_schemas() -> typing.Sequence[typing.Dict[str, typing.Any]]

Returns the (Open)API schemas for the Reasoning Engine.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.operation_schemas

vertexai.preview.reasoning_engines.ReasoningEngine.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.to_dict

vertexai.preview.reasoning_engines.ReasoningEngine.update

update(
    *,
    reasoning_engine: typing.Optional[
        typing.Union[
            vertexai.reasoning_engines._reasoning_engines.Queryable,
            vertexai.reasoning_engines._reasoning_engines.OperationRegistrable,
        ]
    ] = None,
    requirements: typing.Optional[typing.Union[str, typing.Sequence[str]]] = None,
    display_name: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    gcs_dir_name: str = "reasoning_engine",
    sys_version: typing.Optional[str] = None,
    extra_packages: typing.Optional[typing.Sequence[str]] = None,
    service_account: typing.Optional[str] = None,
    identity_type: typing.Optional[
        typing.Union[
            str,
            google.cloud.aiplatform_v1beta1.types.reasoning_engine.ReasoningEngineSpec.IdentityType,
        ]
    ] = None
) -> vertexai.reasoning_engines._reasoning_engines.ReasoningEngine

Updates an existing ReasoningEngine.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.update

vertexai.preview.reasoning_engines.ReasoningEngine.wait

wait()

Helper method that blocks until all futures are complete.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.wait

vertexai.preview.tuning.SourceModel

SourceModel(base_model: str, custom_base_model: str = "")

Initializes SourceModel.

See more: vertexai.preview.tuning.SourceModel

vertexai.preview.tuning.TuningJob

TuningJob(tuning_job_name: str)

Initializes class with project, location, and api_client.

See more: vertexai.preview.tuning.TuningJob

vertexai.preview.tuning.TuningJob.list

list(
    filter: typing.Optional[str] = None,
) -> typing.List[vertexai.tuning._tuning.TuningJob]

Lists TuningJobs.

See more: vertexai.preview.tuning.TuningJob.list

vertexai.preview.tuning.TuningJob.refresh

refresh() -> vertexai.tuning._tuning.TuningJob

Refreshed the tuning job from the service.

See more: vertexai.preview.tuning.TuningJob.refresh

vertexai.preview.tuning.TuningJob.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.preview.tuning.TuningJob.to_dict

vertexai.preview.tuning.sft.SupervisedTuningJob.list

list(
    filter: typing.Optional[str] = None,
) -> typing.List[vertexai.tuning._tuning.TuningJob]

Lists TuningJobs.

See more: vertexai.preview.tuning.sft.SupervisedTuningJob.list

vertexai.preview.tuning.sft.SupervisedTuningJob.refresh

refresh() -> vertexai.tuning._tuning.TuningJob

Refreshed the tuning job from the service.

See more: vertexai.preview.tuning.sft.SupervisedTuningJob.refresh

vertexai.preview.tuning.sft.SupervisedTuningJob.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.preview.tuning.sft.SupervisedTuningJob.to_dict

vertexai.preview.vision_models.ControlReferenceImage

ControlReferenceImage(
    reference_id,
    image: typing.Optional[
        typing.Union[bytes, vertexai.vision_models.Image, str]
    ] = None,
    control_type: typing.Optional[
        typing.Literal["default", "scribble", "face_mesh", "canny"]
    ] = None,
    enable_control_image_computation: typing.Optional[bool] = False,
)

Creates a ControlReferenceImage object.

See more: vertexai.preview.vision_models.ControlReferenceImage

vertexai.preview.vision_models.GeneratedImage

GeneratedImage(
    image_bytes: typing.Optional[bytes],
    generation_parameters: typing.Dict[str, typing.Any],
    gcs_uri: typing.Optional[str] = None,
)

Creates a GeneratedImage object.

See more: vertexai.preview.vision_models.GeneratedImage

vertexai.preview.vision_models.GeneratedImage.load_from_file

load_from_file(location: str) -> vertexai.preview.vision_models.GeneratedImage

Loads image from file.

See more: vertexai.preview.vision_models.GeneratedImage.load_from_file

vertexai.preview.vision_models.GeneratedImage.save

save(location: str, include_generation_parameters: bool = True)

Saves image to a file.

See more: vertexai.preview.vision_models.GeneratedImage.save

vertexai.preview.vision_models.GeneratedImage.show

show()

Shows the image.

See more: vertexai.preview.vision_models.GeneratedImage.show

vertexai.preview.vision_models.GeneratedMask

GeneratedMask(
    image_bytes: typing.Optional[bytes],
    gcs_uri: typing.Optional[str] = None,
    labels: typing.Optional[
        typing.List[vertexai.preview.vision_models.EntityLabel]
    ] = None,
)

Creates a GeneratedMask object.

See more: vertexai.preview.vision_models.GeneratedMask

vertexai.preview.vision_models.GeneratedMask.load_from_file

load_from_file(location: str) -> vertexai.vision_models.Image

Loads image from local file or Google Cloud Storage.

See more: vertexai.preview.vision_models.GeneratedMask.load_from_file

vertexai.preview.vision_models.GeneratedMask.save

save(location: str)

Saves image to a file.

See more: vertexai.preview.vision_models.GeneratedMask.save

vertexai.preview.vision_models.GeneratedMask.show

show()

Shows the image.

See more: vertexai.preview.vision_models.GeneratedMask.show

vertexai.preview.vision_models.Image

Image(
    image_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None
)

Creates an Image object.

See more: vertexai.preview.vision_models.Image

vertexai.preview.vision_models.Image.load_from_file

load_from_file(location: str) -> vertexai.vision_models.Image

Loads image from local file or Google Cloud Storage.

See more: vertexai.preview.vision_models.Image.load_from_file

vertexai.preview.vision_models.Image.save

save(location: str)

Saves image to a file.

See more: vertexai.preview.vision_models.Image.save

vertexai.preview.vision_models.Image.show

show()

Shows the image.

See more: vertexai.preview.vision_models.Image.show

vertexai.preview.vision_models.ImageCaptioningModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageCaptioningModel

vertexai.preview.vision_models.ImageCaptioningModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageCaptioningModel.from_pretrained

vertexai.preview.vision_models.ImageCaptioningModel.get_captions

get_captions(
    image: vertexai.vision_models.Image,
    *,
    number_of_results: int = 1,
    language: str = "en",
    output_gcs_uri: typing.Optional[str] = None
) -> typing.List[str]

Generates captions for a given image.

See more: vertexai.preview.vision_models.ImageCaptioningModel.get_captions

vertexai.preview.vision_models.ImageGenerationModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageGenerationModel

vertexai.preview.vision_models.ImageGenerationModel.edit_image

edit_image(
    *,
    prompt: str,
    base_image: typing.Optional[vertexai.vision_models.Image] = None,
    mask: typing.Optional[vertexai.vision_models.Image] = None,
    reference_images: typing.Optional[
        typing.List[vertexai.vision_models.ReferenceImage]
    ] = None,
    negative_prompt: typing.Optional[str] = None,
    number_of_images: int = 1,
    guidance_scale: typing.Optional[float] = None,
    edit_mode: typing.Optional[
        typing.Literal[
            "inpainting-insert", "inpainting-remove", "outpainting", "product-image"
        ]
    ] = None,
    mask_mode: typing.Optional[
        typing.Literal["background", "foreground", "semantic"]
    ] = None,
    segmentation_classes: typing.Optional[typing.List[str]] = None,
    mask_dilation: typing.Optional[float] = None,
    product_position: typing.Optional[typing.Literal["fixed", "reposition"]] = None,
    output_mime_type: typing.Optional[typing.Literal["image/png", "image/jpeg"]] = None,
    compression_quality: typing.Optional[float] = None,
    language: typing.Optional[str] = None,
    seed: typing.Optional[int] = None,
    output_gcs_uri: typing.Optional[str] = None,
    safety_filter_level: typing.Optional[
        typing.Literal["block_most", "block_some", "block_few", "block_fewest"]
    ] = None,
    person_generation: typing.Optional[
        typing.Literal["dont_allow", "allow_adult", "allow_all"]
    ] = None
) -> vertexai.preview.vision_models.ImageGenerationResponse

Edits an existing image based on text prompt.

See more: vertexai.preview.vision_models.ImageGenerationModel.edit_image

vertexai.preview.vision_models.ImageGenerationModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageGenerationModel.from_pretrained

vertexai.preview.vision_models.ImageGenerationModel.generate_images

generate_images(
    prompt: str,
    *,
    negative_prompt: typing.Optional[str] = None,
    number_of_images: int = 1,
    aspect_ratio: typing.Optional[
        typing.Literal["1:1", "9:16", "16:9", "4:3", "3:4"]
    ] = None,
    guidance_scale: typing.Optional[float] = None,
    language: typing.Optional[str] = None,
    seed: typing.Optional[int] = None,
    output_gcs_uri: typing.Optional[str] = None,
    add_watermark: typing.Optional[bool] = True,
    safety_filter_level: typing.Optional[
        typing.Literal["block_most", "block_some", "block_few", "block_fewest"]
    ] = None,
    person_generation: typing.Optional[
        typing.Literal["dont_allow", "allow_adult", "allow_all"]
    ] = None
) -> vertexai.preview.vision_models.ImageGenerationResponse

Generates images from text prompt.

See more: vertexai.preview.vision_models.ImageGenerationModel.generate_images

vertexai.preview.vision_models.ImageGenerationModel.upscale_image

upscale_image(
    image: typing.Union[
        vertexai.vision_models.Image, vertexai.preview.vision_models.GeneratedImage
    ],
    new_size: typing.Optional[int] = 2048,
    upscale_factor: typing.Optional[typing.Literal["x2", "x4"]] = None,
    output_mime_type: typing.Optional[
        typing.Literal["image/png", "image/jpeg"]
    ] = "image/png",
    output_compression_quality: typing.Optional[int] = None,
    output_gcs_uri: typing.Optional[str] = None,
) -> vertexai.vision_models.Image

Upscales an image.

See more: vertexai.preview.vision_models.ImageGenerationModel.upscale_image

vertexai.preview.vision_models.ImageGenerationResponse.__getitem__

__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedImage

Gets the generated image by index.

See more: vertexai.preview.vision_models.ImageGenerationResponse.getitem

vertexai.preview.vision_models.ImageGenerationResponse.__iter__

__iter__() -> typing.Iterator[vertexai.preview.vision_models.GeneratedImage]

Iterates through the generated images.

See more: vertexai.preview.vision_models.ImageGenerationResponse.iter

vertexai.preview.vision_models.ImageQnAModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageQnAModel

vertexai.preview.vision_models.ImageQnAModel.ask_question

ask_question(
    image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1
) -> typing.List[str]

Answers questions about an image.

See more: vertexai.preview.vision_models.ImageQnAModel.ask_question

vertexai.preview.vision_models.ImageQnAModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageQnAModel.from_pretrained

vertexai.preview.vision_models.ImageSegmentationModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageSegmentationModel

vertexai.preview.vision_models.ImageSegmentationModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageSegmentationModel.from_pretrained

vertexai.preview.vision_models.ImageSegmentationModel.segment_image

segment_image(
    base_image: vertexai.vision_models.Image,
    prompt: typing.Optional[str] = None,
    scribble: typing.Optional[vertexai.preview.vision_models.Scribble] = None,
    mode: typing.Literal[
        "foreground", "background", "semantic", "prompt", "interactive"
    ] = "foreground",
    max_predictions: typing.Optional[int] = None,
    confidence_threshold: typing.Optional[float] = 0.1,
    mask_dilation: typing.Optional[float] = None,
    binary_color_threshold: typing.Optional[float] = None,
) -> vertexai.preview.vision_models.ImageSegmentationResponse

Segments an image.

See more: vertexai.preview.vision_models.ImageSegmentationModel.segment_image

vertexai.preview.vision_models.ImageSegmentationResponse.__getitem__

__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedMask

Gets the generated masks by index.

See more: vertexai.preview.vision_models.ImageSegmentationResponse.getitem

vertexai.preview.vision_models.ImageSegmentationResponse.__iter__

__iter__() -> typing.Iterator[vertexai.preview.vision_models.GeneratedMask]

Iterates through the generated masks.

See more: vertexai.preview.vision_models.ImageSegmentationResponse.iter

vertexai.preview.vision_models.ImageTextModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageTextModel

vertexai.preview.vision_models.ImageTextModel.ask_question

ask_question(
    image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1
) -> typing.List[str]

Answers questions about an image.

See more: vertexai.preview.vision_models.ImageTextModel.ask_question

vertexai.preview.vision_models.ImageTextModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.ImageTextModel.from_pretrained

vertexai.preview.vision_models.ImageTextModel.get_captions

get_captions(
    image: vertexai.vision_models.Image,
    *,
    number_of_results: int = 1,
    language: str = "en",
    output_gcs_uri: typing.Optional[str] = None
) -> typing.List[str]

Generates captions for a given image.

See more: vertexai.preview.vision_models.ImageTextModel.get_captions

vertexai.preview.vision_models.MaskReferenceImage

MaskReferenceImage(
    reference_id,
    image: typing.Optional[
        typing.Union[bytes, vertexai.vision_models.Image, str]
    ] = None,
    mask_mode: typing.Optional[
        typing.Literal[
            "default", "user_provided", "background", "foreground", "semantic"
        ]
    ] = None,
    dilation: typing.Optional[float] = None,
    segmentation_classes: typing.Optional[typing.List[int]] = None,
)

Creates a MaskReferenceImage object.

See more: vertexai.preview.vision_models.MaskReferenceImage

vertexai.preview.vision_models.MultiModalEmbeddingModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.MultiModalEmbeddingModel

vertexai.preview.vision_models.MultiModalEmbeddingModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.MultiModalEmbeddingModel.from_pretrained

vertexai.preview.vision_models.MultiModalEmbeddingModel.get_embeddings

get_embeddings(
    image: typing.Optional[vertexai.vision_models.Image] = None,
    video: typing.Optional[vertexai.vision_models.Video] = None,
    contextual_text: typing.Optional[str] = None,
    dimension: typing.Optional[int] = None,
    video_segment_config: typing.Optional[
        vertexai.vision_models.VideoSegmentConfig
    ] = None,
) -> vertexai.vision_models.MultiModalEmbeddingResponse

Gets embedding vectors from the provided image.

See more: vertexai.preview.vision_models.MultiModalEmbeddingModel.get_embeddings

vertexai.preview.vision_models.RawReferenceImage

RawReferenceImage(
    reference_id,
    image: typing.Optional[
        typing.Union[bytes, vertexai.vision_models.Image, str]
    ] = None,
)

Creates a ReferenceImage object.

See more: vertexai.preview.vision_models.RawReferenceImage

vertexai.preview.vision_models.ReferenceImage

ReferenceImage(
    reference_id,
    image: typing.Optional[
        typing.Union[bytes, vertexai.vision_models.Image, str]
    ] = None,
)

Creates a ReferenceImage object.

See more: vertexai.preview.vision_models.ReferenceImage

vertexai.preview.vision_models.Scribble

Scribble(image_bytes: typing.Optional[bytes], gcs_uri: typing.Optional[str] = None)

Creates a Scribble object.

See more: vertexai.preview.vision_models.Scribble

vertexai.preview.vision_models.StyleReferenceImage

StyleReferenceImage(
    reference_id,
    image: typing.Optional[
        typing.Union[bytes, vertexai.vision_models.Image, str]
    ] = None,
    style_description: typing.Optional[str] = None,
)

Creates a StyleReferenceImage object.

See more: vertexai.preview.vision_models.StyleReferenceImage

vertexai.preview.vision_models.SubjectReferenceImage

SubjectReferenceImage(
    reference_id,
    image: typing.Optional[
        typing.Union[bytes, vertexai.vision_models.Image, str]
    ] = None,
    subject_description: typing.Optional[str] = None,
    subject_type: typing.Optional[
        typing.Literal["default", "person", "animal", "product"]
    ] = None,
)

Creates a SubjectReferenceImage object.

See more: vertexai.preview.vision_models.SubjectReferenceImage

vertexai.preview.vision_models.Video

Video(
    video_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None
)

Creates a Video object.

See more: vertexai.preview.vision_models.Video

vertexai.preview.vision_models.Video.load_from_file

load_from_file(location: str) -> vertexai.vision_models.Video

Loads video from local file or Google Cloud Storage.

See more: vertexai.preview.vision_models.Video.load_from_file

vertexai.preview.vision_models.Video.save

save(location: str)

Saves video to a file.

See more: vertexai.preview.vision_models.Video.save

vertexai.preview.vision_models.VideoEmbedding

VideoEmbedding(
    start_offset_sec: int, end_offset_sec: int, embedding: typing.List[float]
)

Creates a VideoEmbedding object.

See more: vertexai.preview.vision_models.VideoEmbedding

vertexai.preview.vision_models.VideoSegmentConfig

VideoSegmentConfig(
    start_offset_sec: int = 0, end_offset_sec: int = 120, interval_sec: int = 16
)

Creates a VideoSegmentConfig object.

See more: vertexai.preview.vision_models.VideoSegmentConfig

vertexai.preview.vision_models.WatermarkVerificationModel

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

Creates a _ModelGardenModel.

See more: vertexai.preview.vision_models.WatermarkVerificationModel

vertexai.preview.vision_models.WatermarkVerificationModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.preview.vision_models.WatermarkVerificationModel.from_pretrained

vertexai.preview.vision_models.WatermarkVerificationModel.verify_image

verify_image(
    image: vertexai.vision_models.Image,
) -> vertexai.preview.vision_models.WatermarkVerificationResponse

Verifies the watermark of an image.

See more: vertexai.preview.vision_models.WatermarkVerificationModel.verify_image

vertexai.resources.preview.ml_monitoring.ModelMonitor

ModelMonitor(
    model_monitor_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Initializes class with project, location, and api_client.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor

vertexai.resources.preview.ml_monitoring.ModelMonitor.create

create(
    model_name: str,
    model_version_id: str,
    training_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    display_name: typing.Optional[str] = None,
    model_monitoring_schema: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema
    ] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    model_monitor_id: typing.Optional[str] = None,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitor

Creates a new ModelMonitor.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.create

vertexai.resources.preview.ml_monitoring.ModelMonitor.create_schedule

create_schedule(
    cron: str,
    target_dataset: vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_display_name: typing.Optional[str] = None,
    start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
) -> google.cloud.aiplatform_v1beta1.types.schedule.Schedule

Creates a new Scheduled run for model monitoring job.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.create_schedule

vertexai.resources.preview.ml_monitoring.ModelMonitor.delete

delete(force: bool = False, sync: bool = True) -> None

Force delete the model monitor.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.delete

vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_model_monitoring_job

delete_model_monitoring_job(model_monitoring_job_name: str) -> None

Delete a model monitoring job.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_model_monitoring_job

vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_schedule

delete_schedule(schedule_name: str) -> None

Deletes an existing Schedule.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.delete_schedule

vertexai.resources.preview.ml_monitoring.ModelMonitor.get_model_monitoring_job

get_model_monitoring_job(
    model_monitoring_job_name: str,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob

Get the specified ModelMonitoringJob.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.get_model_monitoring_job

vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schedule

get_schedule(
    schedule_name: str,
) -> google.cloud.aiplatform_v1beta1.types.schedule.Schedule

Gets an existing Schedule.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schedule

vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schema

get_schema() -> (
    google.cloud.aiplatform_v1beta1.types.model_monitor.ModelMonitoringSchema
)

Get the schema of the model monitor.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.get_schema

vertexai.resources.preview.ml_monitoring.ModelMonitor.list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    parent: typing.Optional[str] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Vertex AI Resource.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.list

vertexai.resources.preview.ml_monitoring.ModelMonitor.list_jobs

list_jobs(
    page_size: typing.Optional[int] = None, page_token: typing.Optional[str] = None
) -> ListJobsResponse.list_jobs

List ModelMonitoringJobs.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.list_jobs

vertexai.resources.preview.ml_monitoring.ModelMonitor.list_schedules

list_schedules(
    filter: typing.Optional[str] = None,
    page_size: typing.Optional[int] = None,
    page_token: typing.Optional[str] = None,
) -> ListSchedulesResponse.list_schedules

List Schedules.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.list_schedules

vertexai.resources.preview.ml_monitoring.ModelMonitor.pause_schedule

pause_schedule(schedule_name: str) -> None

Pauses an existing Schedule.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.pause_schedule

vertexai.resources.preview.ml_monitoring.ModelMonitor.resume_schedule

resume_schedule(schedule_name: str) -> None

Resumes an existing Schedule.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.resume_schedule

vertexai.resources.preview.ml_monitoring.ModelMonitor.run

run(
    target_dataset: vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_id: typing.Optional[str] = None,
    sync: typing.Optional[bool] = False,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob

Creates a new ModelMonitoringJob.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.run

vertexai.resources.preview.ml_monitoring.ModelMonitor.search_alerts

search_alerts(
    stats_name: typing.Optional[str] = None,
    objective_type: typing.Optional[str] = None,
    model_monitoring_job_name: typing.Optional[str] = None,
    start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    page_size: typing.Optional[int] = None,
    page_token: typing.Optional[str] = None,
) -> typing.Dict[str, typing.Any]

Search ModelMonitoringAlerts.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.search_alerts

vertexai.resources.preview.ml_monitoring.ModelMonitor.search_metrics

search_metrics(
    stats_name: typing.Optional[str] = None,
    objective_type: typing.Optional[str] = None,
    model_monitoring_job_name: typing.Optional[str] = None,
    schedule_name: typing.Optional[str] = None,
    algorithm: typing.Optional[str] = None,
    start_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
    page_size: typing.Optional[int] = None,
    page_token: typing.Optional[str] = None,
) -> MetricsSearchResponse.monitoring_stats

Search ModelMonitoringStats.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.search_metrics

vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_attribution_drift_stats

show_feature_attribution_drift_stats(model_monitoring_job_name: str) -> None

The method to visualize the feature attribution drift result from a model monitoring job as a histogram chart and a table.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_attribution_drift_stats

vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_drift_stats

show_feature_drift_stats(model_monitoring_job_name: str) -> None

The method to visualize the feature drift result from a model monitoring job as a histogram chart and a table.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.show_feature_drift_stats

vertexai.resources.preview.ml_monitoring.ModelMonitor.show_output_drift_stats

show_output_drift_stats(model_monitoring_job_name: str) -> None

The method to visualize the prediction output drift result from a model monitoring job as a histogram chart and a table.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.show_output_drift_stats

vertexai.resources.preview.ml_monitoring.ModelMonitor.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.to_dict

vertexai.resources.preview.ml_monitoring.ModelMonitor.update

update(
    display_name: typing.Optional[str] = None,
    training_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    model_monitoring_schema: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema
    ] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitor

Updates an existing ModelMonitor.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.update

vertexai.resources.preview.ml_monitoring.ModelMonitor.update_schedule

update_schedule(
    schedule_name: str,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_display_name: typing.Optional[str] = None,
    cron: typing.Optional[str] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    target_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
    end_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
) -> google.cloud.aiplatform_v1beta1.types.schedule.Schedule

Updates an existing Schedule.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.update_schedule

vertexai.resources.preview.ml_monitoring.ModelMonitor.wait

wait()

Helper method that blocks until all futures are complete.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitor.wait

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob

ModelMonitoringJob(
    model_monitoring_job_name: str,
    model_monitor_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Initializes class with project, location, and api_client.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.create

create(
    model_monitor_name: typing.Optional[str] = None,
    target_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    display_name: typing.Optional[str] = None,
    model_monitoring_job_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    baseline_dataset: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.MonitoringInput
    ] = None,
    tabular_objective_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.objective.TabularObjective
    ] = None,
    output_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.output.OutputSpec
    ] = None,
    notification_spec: typing.Optional[
        vertexai.resources.preview.ml_monitoring.spec.notification.NotificationSpec
    ] = None,
    explanation_spec: typing.Optional[
        google.cloud.aiplatform_v1beta1.types.explanation.ExplanationSpec
    ] = None,
    sync: bool = False,
) -> vertexai.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob

Creates a new ModelMonitoringJob.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.create

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.delete

delete() -> None

Deletes an Model Monitoring Job.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.delete

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.done

done() -> bool

Method indicating whether a job has completed.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.done

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    parent: typing.Optional[str] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

List all instances of this Vertex AI Resource.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.list

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.to_dict

vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.wait

wait()

Helper method that blocks until all futures are complete.

See more: vertexai.resources.preview.ml_monitoring.ModelMonitoringJob.wait

vertexai.resources.preview.ml_monitoring.spec.ModelMonitoringSchema.to_json

to_json(output_dir: typing.Optional[str] = None) -> str

Transform ModelMonitoringSchema to json format.

See more: vertexai.resources.preview.ml_monitoring.spec.ModelMonitoringSchema.to_json

vertexai.vision_models.GeneratedImage

GeneratedImage(
    image_bytes: typing.Optional[bytes],
    generation_parameters: typing.Dict[str, typing.Any],
    gcs_uri: typing.Optional[str] = None,
)

Creates a GeneratedImage object.

See more: vertexai.vision_models.GeneratedImage

vertexai.vision_models.GeneratedImage.load_from_file

load_from_file(location: str) -> vertexai.preview.vision_models.GeneratedImage

Loads image from file.

See more: vertexai.vision_models.GeneratedImage.load_from_file

vertexai.vision_models.GeneratedImage.save

save(location: str, include_generation_parameters: bool = True)

Saves image to a file.

See more: vertexai.vision_models.GeneratedImage.save

vertexai.vision_models.GeneratedImage.show

show()

Shows the image.

See more: vertexai.vision_models.GeneratedImage.show

vertexai.vision_models.Image

Image(
    image_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None
)

Creates an Image object.

See more: vertexai.vision_models.Image

vertexai.vision_models.Image.load_from_file

load_from_file(location: str) -> vertexai.vision_models.Image

Loads image from local file or Google Cloud Storage.

See more: vertexai.vision_models.Image.load_from_file

vertexai.vision_models.Image.save

save(location: str)

Saves image to a file.

See more: vertexai.vision_models.Image.save

vertexai.vision_models.Image.show

show()

Shows the image.

See more: vertexai.vision_models.Image.show

vertexai.vision_models.ImageCaptioningModel

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

Creates a _ModelGardenModel.

See more: vertexai.vision_models.ImageCaptioningModel

vertexai.vision_models.ImageCaptioningModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.vision_models.ImageCaptioningModel.from_pretrained

vertexai.vision_models.ImageCaptioningModel.get_captions

get_captions(
    image: vertexai.vision_models.Image,
    *,
    number_of_results: int = 1,
    language: str = "en",
    output_gcs_uri: typing.Optional[str] = None
) -> typing.List[str]

Generates captions for a given image.

See more: vertexai.vision_models.ImageCaptioningModel.get_captions

vertexai.vision_models.ImageGenerationModel

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

Creates a _ModelGardenModel.

See more: vertexai.vision_models.ImageGenerationModel

vertexai.vision_models.ImageGenerationModel.edit_image

edit_image(
    *,
    prompt: str,
    base_image: typing.Optional[vertexai.vision_models.Image] = None,
    mask: typing.Optional[vertexai.vision_models.Image] = None,
    reference_images: typing.Optional[
        typing.List[vertexai.vision_models.ReferenceImage]
    ] = None,
    negative_prompt: typing.Optional[str] = None,
    number_of_images: int = 1,
    guidance_scale: typing.Optional[float] = None,
    edit_mode: typing.Optional[
        typing.Literal[
            "inpainting-insert", "inpainting-remove", "outpainting", "product-image"
        ]
    ] = None,
    mask_mode: typing.Optional[
        typing.Literal["background", "foreground", "semantic"]
    ] = None,
    segmentation_classes: typing.Optional[typing.List[str]] = None,
    mask_dilation: typing.Optional[float] = None,
    product_position: typing.Optional[typing.Literal["fixed", "reposition"]] = None,
    output_mime_type: typing.Optional[typing.Literal["image/png", "image/jpeg"]] = None,
    compression_quality: typing.Optional[float] = None,
    language: typing.Optional[str] = None,
    seed: typing.Optional[int] = None,
    output_gcs_uri: typing.Optional[str] = None,
    safety_filter_level: typing.Optional[
        typing.Literal["block_most", "block_some", "block_few", "block_fewest"]
    ] = None,
    person_generation: typing.Optional[
        typing.Literal["dont_allow", "allow_adult", "allow_all"]
    ] = None
) -> vertexai.preview.vision_models.ImageGenerationResponse

Edits an existing image based on text prompt.

See more: vertexai.vision_models.ImageGenerationModel.edit_image

vertexai.vision_models.ImageGenerationModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.vision_models.ImageGenerationModel.from_pretrained

vertexai.vision_models.ImageGenerationModel.generate_images

generate_images(
    prompt: str,
    *,
    negative_prompt: typing.Optional[str] = None,
    number_of_images: int = 1,
    aspect_ratio: typing.Optional[
        typing.Literal["1:1", "9:16", "16:9", "4:3", "3:4"]
    ] = None,
    guidance_scale: typing.Optional[float] = None,
    language: typing.Optional[str] = None,
    seed: typing.Optional[int] = None,
    output_gcs_uri: typing.Optional[str] = None,
    add_watermark: typing.Optional[bool] = True,
    safety_filter_level: typing.Optional[
        typing.Literal["block_most", "block_some", "block_few", "block_fewest"]
    ] = None,
    person_generation: typing.Optional[
        typing.Literal["dont_allow", "allow_adult", "allow_all"]
    ] = None
) -> vertexai.preview.vision_models.ImageGenerationResponse

Generates images from text prompt.

See more: vertexai.vision_models.ImageGenerationModel.generate_images

vertexai.vision_models.ImageGenerationModel.upscale_image

upscale_image(
    image: typing.Union[
        vertexai.vision_models.Image, vertexai.preview.vision_models.GeneratedImage
    ],
    new_size: typing.Optional[int] = 2048,
    upscale_factor: typing.Optional[typing.Literal["x2", "x4"]] = None,
    output_mime_type: typing.Optional[
        typing.Literal["image/png", "image/jpeg"]
    ] = "image/png",
    output_compression_quality: typing.Optional[int] = None,
    output_gcs_uri: typing.Optional[str] = None,
) -> vertexai.vision_models.Image

Upscales an image.

See more: vertexai.vision_models.ImageGenerationModel.upscale_image

vertexai.vision_models.ImageGenerationResponse.__getitem__

__getitem__(idx: int) -> vertexai.preview.vision_models.GeneratedImage

Gets the generated image by index.

See more: vertexai.vision_models.ImageGenerationResponse.getitem

vertexai.vision_models.ImageGenerationResponse.__iter__

__iter__() -> typing.Iterator[vertexai.preview.vision_models.GeneratedImage]

Iterates through the generated images.

See more: vertexai.vision_models.ImageGenerationResponse.iter

vertexai.vision_models.ImageQnAModel

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

Creates a _ModelGardenModel.

See more: vertexai.vision_models.ImageQnAModel

vertexai.vision_models.ImageQnAModel.ask_question

ask_question(
    image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1
) -> typing.List[str]

Answers questions about an image.

See more: vertexai.vision_models.ImageQnAModel.ask_question

vertexai.vision_models.ImageQnAModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.vision_models.ImageQnAModel.from_pretrained

vertexai.vision_models.ImageTextModel

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

Creates a _ModelGardenModel.

See more: vertexai.vision_models.ImageTextModel

vertexai.vision_models.ImageTextModel.ask_question

ask_question(
    image: vertexai.vision_models.Image, question: str, *, number_of_results: int = 1
) -> typing.List[str]

Answers questions about an image.

See more: vertexai.vision_models.ImageTextModel.ask_question

vertexai.vision_models.ImageTextModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.vision_models.ImageTextModel.from_pretrained

vertexai.vision_models.ImageTextModel.get_captions

get_captions(
    image: vertexai.vision_models.Image,
    *,
    number_of_results: int = 1,
    language: str = "en",
    output_gcs_uri: typing.Optional[str] = None
) -> typing.List[str]

Generates captions for a given image.

See more: vertexai.vision_models.ImageTextModel.get_captions

vertexai.vision_models.MultiModalEmbeddingModel

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

Creates a _ModelGardenModel.

See more: vertexai.vision_models.MultiModalEmbeddingModel

vertexai.vision_models.MultiModalEmbeddingModel.from_pretrained

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

Loads a _ModelGardenModel.

See more: vertexai.vision_models.MultiModalEmbeddingModel.from_pretrained

vertexai.vision_models.MultiModalEmbeddingModel.get_embeddings

get_embeddings(
    image: typing.Optional[vertexai.vision_models.Image] = None,
    video: typing.Optional[vertexai.vision_models.Video] = None,
    contextual_text: typing.Optional[str] = None,
    dimension: typing.Optional[int] = None,
    video_segment_config: typing.Optional[
        vertexai.vision_models.VideoSegmentConfig
    ] = None,
) -> vertexai.vision_models.MultiModalEmbeddingResponse

Gets embedding vectors from the provided image.

See more: vertexai.vision_models.MultiModalEmbeddingModel.get_embeddings

vertexai.vision_models.Video

Video(
    video_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None
)

Creates a Video object.

See more: vertexai.vision_models.Video

vertexai.vision_models.Video.load_from_file

load_from_file(location: str) -> vertexai.vision_models.Video

Loads video from local file or Google Cloud Storage.

See more: vertexai.vision_models.Video.load_from_file

vertexai.vision_models.Video.save

save(location: str)

Saves video to a file.

See more: vertexai.vision_models.Video.save

vertexai.vision_models.VideoEmbedding

VideoEmbedding(
    start_offset_sec: int, end_offset_sec: int, embedding: typing.List[float]
)

Creates a VideoEmbedding object.

See more: vertexai.vision_models.VideoEmbedding

vertexai.vision_models.VideoSegmentConfig

VideoSegmentConfig(
    start_offset_sec: int = 0, end_offset_sec: int = 120, interval_sec: int = 16
)

Creates a VideoSegmentConfig object.

See more: vertexai.vision_models.VideoSegmentConfig