Package Methods (1.59.0)

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_transport: typing.Optional[str] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = None
)

Updates common initialization parameters with provided options.

See more: vertexai.init

vertexai.preview.end_run

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

Ends the the current experiment run.

See more: vertexai.preview.end_run

vertexai.preview.get_experiment_df

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

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

See more: vertexai.preview.get_experiment_df

vertexai.preview.log_classification_metrics

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

Create an artifact for classification metrics and log to ExperimentRun.

See more: vertexai.preview.log_classification_metrics

vertexai.preview.log_metrics

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

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

See more: vertexai.preview.log_metrics

vertexai.preview.log_params

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

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

See more: vertexai.preview.log_params

vertexai.preview.log_time_series_metrics

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

Logs time series metrics to to this Experiment Run.

See more: vertexai.preview.log_time_series_metrics

vertexai.preview.start_run

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

Start a run to current session.

See more: vertexai.preview.start_run

vertexai.generative_models.ChatSession.send_message

send_message(
    content: typing.Union[
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    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.ChatSession.send_message

vertexai.generative_models.ChatSession.send_message_async

send_message_async(
    content: typing.Union[
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    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.generative_models.ChatSession.send_message_async

vertexai.generative_models.FunctionDeclaration

FunctionDeclaration(
    *,
    name: str,
    parameters: typing.Dict[str, typing.Any],
    description: typing.Optional[str] = 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
)

Constructs a GenerationConfig object.

See more: vertexai.generative_models.GenerationConfig

vertexai.generative_models.GenerativeModel.compute_tokens

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

Counts tokens.

See more: vertexai.generative_models.GenerativeModel.compute_tokens

vertexai.generative_models.GenerativeModel.compute_tokens_async

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

Counts tokens asynchronously.

See more: vertexai.generative_models.GenerativeModel.compute_tokens_async

vertexai.generative_models.GenerativeModel.count_tokens

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

Counts tokens.

See more: vertexai.generative_models.GenerativeModel.count_tokens

vertexai.generative_models.GenerativeModel.count_tokens_async

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

Counts tokens asynchronously.

See more: vertexai.generative_models.GenerativeModel.count_tokens_async

vertexai.generative_models.GenerativeModel.generate_content

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

Generates content.

See more: vertexai.generative_models.GenerativeModel.generate_content

vertexai.generative_models.GenerativeModel.generate_content_async

generate_content_async(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None,
    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.GoogleSearchRetrieval

GoogleSearchRetrieval()

Initializes a Google Search Retrieval tool.

See more: vertexai.generative_models.grounding.GoogleSearchRetrieval

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[TuningEvaluationSpec] = None
) -> _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[TuningEvaluationSpec] = None
) -> _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[TuningEvaluationSpec] = None
) -> _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[TuningEvaluationSpec] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    max_context_length: typing.Optional[str] = None
) -> _LanguageModelTuningJob

Tunes a model based on training data.

See more: vertexai.language_models.CodeGenerationModel.tune_model

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[int, 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[int, 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[int, 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[int, 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[TuningEvaluationSpec] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    max_context_length: typing.Optional[str] = None
) -> _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[TuningEvaluationSpec] = None
) -> _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[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
) -> _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: typing.Union[
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    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: typing.Union[
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    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
)

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
)

Constructs a GenerationConfig object.

See more: vertexai.preview.generative_models.GenerationConfig

vertexai.preview.generative_models.GenerativeModel.compute_tokens

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

Counts tokens.

See more: vertexai.preview.generative_models.GenerativeModel.compute_tokens

vertexai.preview.generative_models.GenerativeModel.compute_tokens_async

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

Counts tokens asynchronously.

See more: vertexai.preview.generative_models.GenerativeModel.compute_tokens_async

vertexai.preview.generative_models.GenerativeModel.count_tokens

count_tokens(
    contents: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ]
) -> 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: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ]
) -> google.cloud.aiplatform_v1beta1.types.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, caching.CachedContent],
    *,
    generation_config: typing.Optional[
        typing.Union[GenerationConfig, typing.Dict[str, typing.Any]]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = 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: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None,
    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: typing.Union[
        typing.List[vertexai.generative_models._generative_models.Content],
        typing.List[typing.Dict[str, typing.Any]],
        str,
        vertexai.generative_models._generative_models.Image,
        vertexai.generative_models._generative_models.Part,
        typing.List[
            typing.Union[
                str,
                vertexai.generative_models._generative_models.Image,
                vertexai.generative_models._generative_models.Part,
            ]
        ],
    ],
    *,
    generation_config: typing.Optional[
        typing.Union[
            vertexai.generative_models._generative_models.GenerationConfig,
            typing.Dict[str, typing.Any],
        ]
    ] = None,
    safety_settings: typing.Optional[
        typing.Union[
            typing.List[vertexai.generative_models._generative_models.SafetySetting],
            typing.Dict[
                google.cloud.aiplatform_v1beta1.types.content.HarmCategory,
                google.cloud.aiplatform_v1beta1.types.content.SafetySetting.HarmBlockThreshold,
            ],
        ]
    ] = None,
    tools: typing.Optional[
        typing.List[vertexai.generative_models._generative_models.Tool]
    ] = None,
    tool_config: typing.Optional[
        vertexai.generative_models._generative_models.ToolConfig
    ] = None,
    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.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.LangchainAgent

LangchainAgent(
    model: str,
    *,
    prompt: typing.Optional[RunnableSerializable] = None,
    tools: typing.Optional[typing.Sequence[_ToolLike]] = 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.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: vertexai.reasoning_engines._reasoning_engines.Queryable,
    *,
    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
) -> 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.query

query(**kwargs) -> typing.Dict[str, typing.Any]

Runs the Reasoning Engine to serve the user query.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.query

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.wait

wait()

Helper method that blocks until all futures are complete.

See more: vertexai.preview.reasoning_engines.ReasoningEngine.wait

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.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: vertexai.vision_models.Image,
    mask: typing.Optional[vertexai.vision_models.Image] = 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,
    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.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.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.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.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.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