Package Methods (1.117.0)

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

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

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

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

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

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

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

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

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 )

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

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

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

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

create_version( prompt: vertexai.prompts._prompts.Prompt, prompt_id: typing.Optional[str] = None, version_name: typing.Optional[str] = None, ) -> vertexai.prompts._prompts.Prompt

delete(prompt_id: str) -> None

get( prompt_id: str, version_id: typing.Optional[str] = None ) -> vertexai.prompts._prompts.Prompt

list() -> list[vertexai.prompts._prompt_management.PromptMetadata]

list_versions( prompt_id: str, ) -> list[vertexai.prompts._prompt_management.PromptVersionMetadata]

restore_version( prompt_id: str, version_id: str ) -> vertexai.prompts._prompt_management.PromptVersionMetadata

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

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

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

create_version( prompt: vertexai.prompts._prompts.Prompt, prompt_id: typing.Optional[str] = None, version_name: typing.Optional[str] = None, ) -> vertexai.prompts._prompts.Prompt

delete(prompt_id: str) -> None

get( prompt_id: str, version_id: typing.Optional[str] = None ) -> vertexai.prompts._prompts.Prompt

list_prompts() -> list[vertexai.prompts._prompt_management.PromptMetadata]

list_versions( prompt_id: str, ) -> list[vertexai.prompts._prompt_management.PromptVersionMetadata]

restore_version( prompt_id: str, version_id: str ) -> vertexai.prompts._prompt_management.PromptVersionMetadata

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

evaluate( *, dataset: typing.Union[ vertexai._genai.types.EvaluationDataset, vertexai._genai.types.EvaluationDatasetDict, list[ typing.Union[ vertexai._genai.types.EvaluationDataset, vertexai._genai.types.EvaluationDatasetDict, ] ], ], metrics: typing.Optional[ list[ typing.Union[vertexai._genai.types.Metric, vertexai._genai.types.MetricDict] ] ] = None, config: typing.Optional[ typing.Union[ vertexai._genai.types.EvaluateMethodConfig, vertexai._genai.types.EvaluateMethodConfigDict, ] ] = None ) -> vertexai._genai.types.EvaluationResult

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

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

run() -> vertexai._genai.types.EvaluateInstancesResponse

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

optimize( method: str, config: typing.Union[ vertexai._genai.types.PromptOptimizerVAPOConfig, vertexai._genai.types.PromptOptimizerVAPOConfigDict, ], ) -> vertexai._genai.types.CustomJob

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

optimize( method: str, config: typing.Union[ vertexai._genai.types.PromptOptimizerVAPOConfig, vertexai._genai.types.PromptOptimizerVAPOConfigDict, ], ) -> vertexai._genai.types.CustomJob

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

AG2Agent( model: str, runnable_name: str, *, api_type: typing.Optional[str] = None, llm_config: typing.Optional[typing.Mapping[str, typing.Any]] = None, system_instruction: typing.Optional[str] = None, runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, runnable_builder: typing.Optional[typing.Callable[[...], ConversableAgent]] = None, tools: typing.Optional[typing.Sequence[typing.Callable[[...], typing.Any]]] = None, enable_tracing: bool = False, instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None )

clone() -> vertexai.agent_engines.templates.ag2.AG2Agent

query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], max_turns: typing.Optional[int] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]

set_up()

AdkApp( *, agent: BaseAgent, app_name: typing.Optional[str] = None, plugins: typing.Optional[typing.List[BasePlugin]] = None, enable_tracing: bool = False, session_service_builder: typing.Optional[ typing.Callable[[...], BaseSessionService] ] = None, artifact_service_builder: typing.Optional[ typing.Callable[[...], BaseArtifactService] ] = None, memory_service_builder: typing.Optional[ typing.Callable[[...], BaseMemoryService] ] = None, instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None )

async_add_session_to_memory(*, session: typing.Dict[str, typing.Any])

async_create_session( *, user_id: str, session_id: typing.Optional[str] = None, state: typing.Optional[typing.Dict[str, typing.Any]] = None, **kwargs )

async_delete_session(*, user_id: str, session_id: str, **kwargs)

async_get_session(*, user_id: str, session_id: str, **kwargs)

async_list_sessions(*, user_id: str, **kwargs)

async_search_memory(*, user_id: str, query: str)

async_stream_query( *, message: typing.Union[str, typing.Dict[str, typing.Any]], user_id: str, session_id: typing.Optional[str] = None, run_config: typing.Optional[typing.Dict[str, typing.Any]] = None, **kwargs ) -> typing.AsyncIterable[typing.Dict[str, typing.Any]]

clone()

register_operations() -> typing.Dict[str, typing.List[str]]

set_up()

streaming_agent_run_with_events(request_json: str)

AgentEngine(resource_name: str)

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

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

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]

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

to_dict() -> typing.Dict[str, typing.Any]

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

wait()

async_query(**kwargs) -> typing.Coroutine[typing.Any, typing.Any, typing.Any]

async_stream_query(**kwargs) -> typing.AsyncIterable[typing.Any]

clone() -> typing.Any

LangchainAgent( model: str, *, system_instruction: typing.Optional[str] = None, 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, instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None )

clone() -> vertexai.agent_engines.templates.langchain.LangchainAgent

query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]

set_up()

stream_query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs ) -> typing.Iterable[typing.Any]

LanggraphAgent( model: str, *, tools: typing.Optional[typing.Sequence[_ToolLike]] = None, model_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, model_tool_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, model_builder: typing.Optional[typing.Callable[[...], BaseLanguageModel]] = None, runnable_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, runnable_builder: typing.Optional[ typing.Callable[[...], RunnableSerializable] ] = None, checkpointer_kwargs: typing.Optional[typing.Mapping[str, typing.Any]] = None, checkpointer_builder: typing.Optional[ typing.Callable[[...], BaseCheckpointSaver] ] = None, enable_tracing: bool = False, instrumentor_builder: typing.Optional[typing.Callable[[...], typing.Any]] = None )

clone() -> vertexai.agent_engines.templates.langgraph.LanggraphAgent

get_state( config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]

get_state_history( config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Iterable[typing.Any]

query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]

register_operations() -> typing.Mapping[str, typing.Sequence[str]]

set_up()

stream_query( *, input: typing.Union[str, typing.Mapping[str, typing.Any]], config: typing.Optional[RunnableConfig] = None, **kwargs ) -> typing.Iterable[typing.Any]

update_state( config: typing.Optional[RunnableConfig] = None, **kwargs: typing.Any ) -> typing.Dict[str, typing.Any]

ModuleAgent( *, module_name: str, agent_name: str, register_operations: typing.Dict[str, typing.Sequence[str]], sys_paths: typing.Optional[typing.Sequence[str]] = None, agent_framework: typing.Optional[str] = None )