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python-aiplatform の API リファレンス

python-aiplatform (googleapis/python-aiplatform) の公開 API 400 件 —— クラス 208、関数 65、メソッド 127。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: googleapis/python-aiplatform

種別件数
クラス208
関数65
メソッド127

API 一覧

classagentplatform._genai._agent_engines_utils.Cloneable
Protocol for Agent Engines that can be cloned.
methodagentplatform._genai._agent_engines_utils.Cloneable.clone() -> Any
Return a clone of the object.
classagentplatform._genai._agent_engines_utils.ModuleAgent
Agent that is defined by a module and an agent name.
methodagentplatform._genai._agent_engines_utils.ModuleAgent.clone() -> 'ModuleAgent'
Return a clone of the agent.
classagentplatform._genai._agent_engines_utils.Queryable
Protocol for Agent Engines that can be queried.
funcagentplatform._genai._agent_engines_utils.dump_event_for_json(event:BaseModel) -> Dict[str, Any]
Dumps an ADK event to a JSON-serializable dictionary.
classagentplatform._genai._bigquery_utils.BigQueryUtils
Handles BigQuery operations.
funcagentplatform._genai._datasets_utils.create_from_response(model_type:Type[T], response:dict[str, Any], config:Any | None=None) -> T
Creates a model from a response.
classagentplatform._genai._evals_data_converters.EvalDatasetSchema
Represents the schema of an evaluation dataset.
funcagentplatform._genai._evals_data_converters.auto_detect_dataset_schema(raw_dataset:list[dict[str, Any]]) -> Union[EvalDatasetSchema, str]
Detects the schema of a raw dataset.
classagentplatform._genai._evals_metric_handlers.ComputationMetricHandler
Metric handler for computation metrics.
classagentplatform._genai._evals_metric_handlers.CustomMetricHandler
Metric handler for custom metrics.
classagentplatform._genai._evals_metric_handlers.EvaluationRunConfig
Configuration for an evaluation run.
classagentplatform._genai._evals_metric_handlers.LLMMetricHandler
Metric handler for LLM metrics.
classagentplatform._genai._evals_metric_handlers.MetricHandler
Abstract base class for metric handlers.
classagentplatform._genai._evals_metric_handlers.PredefinedMetricHandler
Metric handler for predefined metrics.
classagentplatform._genai._evals_metric_handlers.RegisteredMetricHandler
Metric handler for registered metrics.
classagentplatform._genai._evals_metric_handlers.TranslationMetricHandler
Metric handler for translation metrics.
funcagentplatform._genai._evals_metric_loaders.CodeExecutionMetric(name:str, custom_function:str, **kwargs:Any) -> 'types.Metric'
Instantiates a code execution metric.
classagentplatform._genai._evals_utils.BatchEvaluateRequestPreparer
Prepares data for requests.
classagentplatform._genai._evals_utils.EvalDataConverter
Abstract base class for dataset converters.
funcagentplatform._genai._skills_utils.zip_directory(directory_path:pathlib.Path | str) -> bytes
Zips a directory into memory and returns the bytes.
funcagentplatform._genai._transformers.t_metric_sources(metrics:list[Any]) -> list[dict[str, Any]]
Prepares the MetricSource payload.
classagentplatform._genai.client.AsyncClient
Async Gen AI Client for the Vertex SDK.
methodagentplatform._genai.client.AsyncClient.aclose() -> None
Closes the async client explicitly.
classagentplatform._genai.client.Client
Gen AI Client for the Vertex SDK.
methodagentplatform._genai.evals.AsyncEvals.create_evaluation_set(*evaluation_items:list[str], *display_name:Optional[str]=None, *config:Optional[types.CreateEvaluationSetConfigOrDict]=None) -> types.EvaluationSet
Creates an EvaluationSet.
methodagentplatform._genai.evals.AsyncEvals.delete_evaluation_metric(*metric_resource_name:str, *config:Optional[types.DeleteEvaluationMetricConfigOrDict]=None) -> None
Deletes an EvaluationMetric.
methodagentplatform._genai.evals.AsyncEvals.evaluate_instances(*metric_config:types._EvaluateInstancesRequestParameters) -> types.EvaluateInstancesResponse
Evaluates an instance of a model.
methodagentplatform._genai.evals.AsyncEvals.list_evaluation_experiments(*config:Optional[types.ListEvaluationExperimentsConfigOrDict]=None) -> types.ListEvaluationExperimentsResponse
Lists EvaluationExperiments.
methodagentplatform._genai.evals.AsyncEvals.update_evaluation_experiment(*name:str, *config:Optional[types.UpdateEvaluationExperimentConfigOrDict]=None) -> types.EvaluationExperiment
Updates an EvaluationExperiment.
methodagentplatform._genai.evals.Evals.create_evaluation_set(*evaluation_items:list[str], *display_name:Optional[str]=None, *config:Optional[types.CreateEvaluationSetConfigOrDict]=None) -> types.EvaluationSet
Creates an EvaluationSet.
methodagentplatform._genai.evals.Evals.delete_evaluation_metric(*metric_resource_name:str, *config:Optional[types.DeleteEvaluationMetricConfigOrDict]=None) -> None
Deletes an EvaluationMetric.
methodagentplatform._genai.evals.Evals.evaluate_instances(*metric_config:types._EvaluateInstancesRequestParameters) -> types.EvaluateInstancesResponse
Evaluates an instance of a model.
methodagentplatform._genai.evals.Evals.list_evaluation_experiments(*config:Optional[types.ListEvaluationExperimentsConfigOrDict]=None) -> types.ListEvaluationExperimentsResponse
Lists EvaluationExperiments.
methodagentplatform._genai.evals.Evals.update_evaluation_experiment(*name:str, *config:Optional[types.UpdateEvaluationExperimentConfigOrDict]=None) -> types.EvaluationExperiment
Updates an EvaluationExperiment.
classagentplatform._genai.live.AsyncLive
[Preview] AsyncLive.
classagentplatform._genai.live_agent_engines.AsyncLiveAgentEngineSession
AsyncLiveAgentEngineSession.
methodagentplatform._genai.live_agent_engines.AsyncLiveAgentEngineSession.close() -> None
Close the connection.
methodagentplatform._genai.live_agent_engines.AsyncLiveAgentEngineSession.receive() -> Any
Receive one response from the Agent.
methodagentplatform._genai.live_agent_engines.AsyncLiveAgentEngineSession.send(query_input:Dict[str, Any]) -> None
Send a query input to the Agent.
classagentplatform._genai.live_agent_engines.AsyncLiveAgentEngines
AsyncLiveAgentEngines.
classagentplatform._genai.model_garden.AsyncModelGarden
Model Garden module.
classagentplatform._genai.model_garden.ModelGarden
Model Garden module.
methodagentplatform._genai.model_garden.ModelGarden.list_models(config:Optional[types.ListModelGardenModelsConfigOrDict]=None) -> list[str]
Lists all models available in Model Garden.
classagentplatform._genai.prompt_optimizer.AsyncPromptOptimizer
Prompt Optimizer
classagentplatform._genai.prompt_optimizer.PromptOptimizer
Prompt Optimizer
methodagentplatform._genai.prompt_optimizer.PromptOptimizer.optimize(method:types.PromptOptimizerMethod, config:types.PromptOptimizerConfigOrDict) -> types.CustomJob
Call PO-Data optimizer.
methodagentplatform._genai.prompts.Prompts.delete(*prompt_id:str, *config:Optional[types.DeletePromptConfig]=None) -> None
Deletes a prompt resource.
methodagentplatform._genai.prompts.Prompts.delete_version(*prompt_id:str, *version_id:str, *config:Optional[types.DeletePromptConfig]=None) -> None
Deletes a prompt version resource.
methodagentplatform._genai.prompts.Prompts.get(*prompt_id:str, *config:Optional[types.GetPromptConfig]=None) -> types.Prompt
Gets a prompt resource from a Vertex Dataset.
methodagentplatform._genai.prompts.Prompts.launch_optimization_job(method:types.PromptOptimizerMethod, config:types.PromptOptimizerConfigOrDict) -> types.CustomJob
Call PO-Data optimizer.
methodagentplatform._genai.prompts.Prompts.list(*config:Optional[types.ListPromptsConfigOrDict]=None) -> Iterator[types.PromptRef]
Lists prompt resources in a project.
classagentplatform._genai.sandbox_snapshots.AsyncSandboxSnapshots
Sandbox environment snapshot commands.
classagentplatform._genai.sandbox_snapshots.SandboxSnapshots
Sandbox environment snapshot commands.
classagentplatform._genai.sandbox_templates.AsyncSandboxTemplates
Sandbox environment templates commands.
classagentplatform._genai.sandbox_templates.SandboxTemplates
Sandbox environment templates commands.
methodagentplatform._genai.session_events.SessionEvents.list(*name:str, *config:Optional[types.ListAgentEngineSessionEventsConfigOrDict]=None) -> Iterator[types.SessionEvent]
Lists Agent Engine session events.
classagentplatform._genai.skill_revisions.SkillRevisions
Class for managing Skill Revisions in the Skill Registry.
methodagentplatform._genai.skill_revisions.SkillRevisions.get(*name:str, *config:Optional[types.GetSkillRevisionConfigOrDict]=None) -> types.SkillRevision
Gets a Skill Revision.
methodagentplatform._genai.skill_revisions.SkillRevisions.list(*name:str, *config:Optional[types.ListSkillRevisionsConfigOrDict]=None) -> types.ListSkillRevisionsResponse
Lists Skill Revisions.
classagentplatform._genai.skills.AsyncSkills
Class for managing Skills in the Skill Registry.
methodagentplatform._genai.skills.AsyncSkills.delete(*name:str, *config:Optional[types.DeleteSkillConfigOrDict]=None) -> Optional[types.DeleteSkillOperation]
Deletes a Skill asynchronously.
methodagentplatform._genai.skills.AsyncSkills.get(*name:str, *config:Optional[types.GetSkillConfigOrDict]=None) -> types.Skill
Gets a Skill.
classagentplatform._genai.skills.Skills
Class for managing Skills in the Skill Registry.
methodagentplatform._genai.skills.Skills.create(*skill_id:str, *display_name:str, *description:str, *config:Optional[types.CreateSkillConfigOrDict]=None) -> Union[types.Skill, types.SkillOperation]
Creates a new Skill.
methodagentplatform._genai.skills.Skills.delete(*name:str, *config:Optional[types.DeleteSkillConfigOrDict]=None) -> Optional[types.DeleteSkillOperation]
Deletes a Skill.
methodagentplatform._genai.skills.Skills.get(*name:str, *config:Optional[types.GetSkillConfigOrDict]=None) -> types.Skill
Gets a Skill.
methodagentplatform._genai.skills.Skills.list(*config:Optional[types.ListSkillsConfigOrDict]=None) -> Pager[types.Skill]
Lists Skills in the Skill Registry.
methodagentplatform._genai.skills.Skills.revisions() -> 'skill_revisions_module.SkillRevisions'
Returns the revisions sub-module.
methodagentplatform._genai.skills.Skills.update(*name:str, *config:Optional[types.UpdateSkillConfigOrDict]=None) -> Union[types.Skill, types.SkillOperation]
Updates an existing Skill.
classagentplatform._genai.types.evals.AgentConfig
Represents configuration for an Agent.
methodagentplatform._genai.types.evals.AgentConfig.from_agent(agent:Any) -> 'AgentConfig'
Creates an AgentConfig from an ADK agent.
classagentplatform._genai.types.evals.AgentConfigDict
Represents configuration for an Agent.
methodagentplatform._genai.types.evals.AgentData.from_session(agent:Any, session_history:list[Any]) -> 'AgentData'
Creates an AgentData object from a session history.
classagentplatform._genai.types.evals.AgentEvent
A single event in the execution trace.
classagentplatform._genai.types.evals.AgentEventDict
A single event in the execution trace.
methodagentplatform._genai.types.evals.AgentInfo.load_from_agent(agent:Any) -> 'AgentInfo'
Loads agent info from an ADK agent.
classagentplatform._genai.types.evals.CandidateResult
Result for a single candidate.
classagentplatform._genai.types.evals.CandidateResultDict
Result for a single candidate.
classagentplatform._genai.types.evals.Importance
Importance level of the rubric.
classagentplatform._genai.types.evals.Message
Represents a single message turn in a conversation.
classagentplatform._genai.types.evals.MessageDict
Represents a single message turn in a conversation.
classagentplatform._genai.types.evals.RubricContentProperty
Defines criteria based on a specific property.
classagentplatform._genai.types.evals.RubricContentPropertyDict
Defines criteria based on a specific property.
classagentplatform._genai.types.evals.UserScenarioGenerationConfig
User scenario generation configuration.
classagentplatform._genai.types.evals.UserScenarioGenerationConfigDict
User scenario generation configuration.
classagentplatform._genai.types.evals.UserSimulatorConfig
Configuration for a user simulator.
classagentplatform._genai.types.evals.UserSimulatorConfigDict
Configuration for a user simulator.
classagentplatform._genai.types.prompt_optimizer.ParsedResponse
Response for the optimize_prompt method.
classagentplatform._genai.types.prompt_optimizer.ParsedResponseDict
Response for the optimize_prompt method.
classagentplatform._genai.types.prompt_optimizer.ParsedResponseFewShot
Response for the optimize_prompt method.
classagentplatform._genai.types.prompt_optimizer.ParsedResponseFewShotDict
Response for the optimize_prompt method.
classagentplatform._genai.types.prompts.ParsedResponse
Response for the optimize_prompt method.
classagentplatform._genai.types.prompts.ParsedResponseDict
Response for the optimize_prompt method.
classagentplatform._genai.types.prompts.ParsedResponseFewShot
Response for the optimize_prompt method.
classagentplatform._genai.types.prompts.ParsedResponseFewShotDict
Response for the optimize_prompt method.
classagentplatform.agent_engines._agent_engines.AgentEngine
Represents a Vertex AI Agent Engine resource.
methodagentplatform.agent_engines._agent_engines.AgentEngine.delete(*force:bool=False, **kwargs) -> None
Deletes the ReasoningEngine.
methodagentplatform.agent_engines._agent_engines.AgentEngine.resource_name() -> str
Fully-qualified resource name.
classagentplatform.agent_engines._agent_engines.Cloneable
Protocol for Agent Engines that can be cloned.
methodagentplatform.agent_engines._agent_engines.Cloneable.clone() -> Any
Return a clone of the object.
classagentplatform.agent_engines._agent_engines.ModuleAgent
Agent that is defined by a module and an agent name.
classagentplatform.agent_engines._agent_engines.Queryable
Protocol for Agent Engines that can be queried.
funcagentplatform.agent_engines.delete(resource_name:str, *force:bool=False, **kwargs) -> None
Delete an Agent Engine resource.
funcagentplatform.agent_engines.get(resource_name:str) -> AgentEngine
Retrieves an Agent Engine resource.
funcagentplatform.agent_engines.list(*filter:str='') -> Iterable[AgentEngine]
List all instances of Agent Engine matching the filter.
classagentplatform.agent_engines.templates.a2a.HelloWorldAgentExecutor
Hello World Agent Executor.
funcagentplatform.agent_engines.templates.a2a.default_a2a_agent() -> 'A2aAgent'
Creates a default A2aAgent instance.
classagentplatform.agent_engines.templates.adk.AdkApp
An ADK Application.
methodagentplatform.agent_engines.templates.adk.AdkApp.async_add_session_to_memory(*session:Dict[str, Any])
Generates memories.
methodagentplatform.agent_engines.templates.adk.AdkApp.async_create_session(*user_id:str, *session_id:Optional[str]=None, *state:Optional[Dict[str, Any]]=None, **kwargs)
Creates a new session.
methodagentplatform.agent_engines.templates.adk.AdkApp.async_delete_artifact(*user_id:str, *filename:str, *session_id:Optional[str]=None, **kwargs)
Deletes an artifact.
methodagentplatform.agent_engines.templates.adk.AdkApp.async_get_session(*user_id:str, *session_id:str, **kwargs)
Get a session for the given user.
methodagentplatform.agent_engines.templates.adk.AdkApp.async_list_sessions(*user_id:str, **kwargs)
List sessions for the given user.
methodagentplatform.agent_engines.templates.adk.AdkApp.create_session(*user_id:str, *session_id:Optional[str]=None, *state:Optional[Dict[str, Any]]=None, **kwargs)
Deprecated.
methodagentplatform.agent_engines.templates.adk.AdkApp.delete_session(*user_id:str, *session_id:str, **kwargs)
Deprecated.
methodagentplatform.agent_engines.templates.adk.AdkApp.get_session(*user_id:str, *session_id:str, **kwargs)
Deprecated.
methodagentplatform.agent_engines.templates.adk.AdkApp.list_sessions(*user_id:str, **kwargs)
Deprecated.
methodagentplatform.agent_engines.templates.adk.AdkApp.set_up()
Sets up the ADK application.
funcagentplatform.agent_engines.templates.adk.get_adk_version() -> Optional[str]
Returns the version of the ADK package.
funcagentplatform.agent_engines.templates.adk.is_version_sufficient(version_to_check:str) -> bool
Compares the existing version of ADK with the required version.
classagentplatform.agent_engines.templates.ag2.AG2Agent
An AG2 Agent.
methodagentplatform.agent_engines.templates.ag2.AG2Agent.clone() -> 'AG2Agent'
Returns a clone of the AG2Agent.
methodagentplatform.agent_engines.templates.ag2.AG2Agent.query(*input:Union[str, Mapping[str, Any]], *max_turns:Optional[int]=None, **kwargs:Any) -> Dict[str, Any]
Queries the Agent with the given input.
classagentplatform.agent_engines.templates.langchain.LangchainAgent
A Langchain Agent.
methodagentplatform.agent_engines.templates.langchain.LangchainAgent.clone() -> 'LangchainAgent'
Returns a clone of the LangchainAgent.
classagentplatform.agent_engines.templates.langgraph.LanggraphAgent
A LangGraph Agent.
methodagentplatform.agent_engines.templates.langgraph.LanggraphAgent.clone() -> 'LanggraphAgent'
Returns a clone of the LanggraphAgent.
methodagentplatform.agent_engines.templates.langgraph.LanggraphAgent.get_state(config:Optional[dict[str, Any]]=None, **kwargs:Any) -> Dict[str, Any]
Gets the current state of the Agent.
methodagentplatform.agent_engines.templates.langgraph.LanggraphAgent.get_state_history(config:Optional[dict[str, Any]]=None, **kwargs:Any) -> Iterable[Any]
Gets the state history of the Agent.
methodagentplatform.agent_engines.templates.langgraph.LanggraphAgent.update_state(config:Optional[dict[str, Any]]=None, **kwargs:Any) -> Dict[str, Any]
Updates the state of the Agent.
classagentplatform.agent_engines.templates.llama_index.LlamaIndexQueryPipelineAgent
A LlamaIndex Query Pipeline Agent.
classagentplatform.model_garden._model_garden.CustomModel
Represents a Model Garden Custom model.
classagentplatform.model_garden._model_garden.Model
Represents a Model Garden model.
methodagentplatform.model_garden._model_garden.Model.deploy(**kwargs) -> aiplatform.Endpoint
Deploys the model to an endpoint.
classagentplatform.model_garden._model_garden.OpenModel
Represents a Model Garden Open model.
classagentplatform.model_garden._model_garden.PartnerModel
Represents a Model Garden Partner model.
funcagentplatform.model_garden._model_garden.list_models(*list_hf_models:bool=False, *model_filter:Optional[str]=None) -> List[str]
Lists the models in Model Garden.
funcagentplatform.preview.rag.rag_data.batch_create_data_schemas(corpus_name:str, requests:Sequence[RagDataSchema], timeout:int=600) -> Sequence[RagDataSchema]
Batch creates RagDataSchema resources.
funcagentplatform.preview.rag.rag_data.batch_create_metadata(corpus_name:str, file_name:str, requests:Sequence[RagMetadata], timeout:int=600) -> Sequence[RagMetadata]
Batch creates RagMetadata resources.
funcagentplatform.preview.rag.rag_data.batch_delete_data_schemas(corpus_name:str, names:Sequence[str], timeout:int=600) -> None
Batch deletes RagDataSchema resources.
funcagentplatform.preview.rag.rag_data.batch_delete_metadata(corpus_name:str, file_name:str, names:Sequence[str], timeout:int=600) -> None
Batch deletes RagMetadata resources.
funcagentplatform.preview.rag.rag_data.delete_corpus(name:str) -> None
Delete an existing RagCorpus.
funcagentplatform.preview.rag.rag_data.delete_file(name:str, corpus_name:Optional[str]=None) -> None
Delete RagFile from an existing RagCorpus.
funcagentplatform.preview.rag.rag_data.get_corpus(name:str) -> RagCorpus
Get an existing RagCorpus.
funcagentplatform.preview.rag.rag_data.get_file(name:str, corpus_name:Optional[str]=None) -> RagFile
Get an existing RagFile.
funcagentplatform.preview.rag.rag_data.get_rag_engine_config(name:str) -> RagEngineConfig
Get an existing RagEngineConfig.
funcagentplatform.preview.rag.rag_data.list_files(corpus_name:str, page_size:Optional[int]=None, page_token:Optional[str]=None) -> ListRagFilesPager
List all RagFiles in an existing RagCorpus.
funcagentplatform.preview.rag.rag_data.update_metadata(rag_metadata:RagMetadata) -> RagMetadata
Updates a RagMetadata resource.
funcagentplatform.preview.rag.rag_data.update_rag_engine_config(rag_engine_config:RagEngineConfig, timeout:int=600) -> RagEngineConfig
Update RagEngineConfig.
classagentplatform.preview.rag.rag_store.VertexRagStore
Retrieve from Vertex RAG Store.
funcagentplatform.preview.rag.utils._gapic_utils.convert_gapic_to_rag_corpus(gapic_rag_corpus:GapicRagCorpus) -> RagCorpus
Convert GapicRagCorpus to RagCorpus.
funcagentplatform.preview.rag.utils._gapic_utils.convert_gapic_to_rag_file(gapic_rag_file:GapicRagFile) -> RagFile
Convert GapicRagFile to RagFile.
funcagentplatform.preview.rag.utils._gapic_utils.convert_gapic_to_rag_metadata(gapic_rag_metadata:GapicRagDataTypes.RagMetadata) -> RagMetadata
Convert Gapic RagMetadata to RagMetadata.
funcagentplatform.preview.rag.utils._gapic_utils.convert_json_to_rag_file(upload_rag_file_response:Dict[str, Any]) -> RagFile
Converts a JSON response to a RagFile.
funcagentplatform.preview.rag.utils._gapic_utils.convert_rag_metadata_to_gapic(rag_metadata:RagMetadata) -> GapicRagDataTypes.RagMetadata
Convert RagMetadata to Gapic RagMetadata.
funcagentplatform.preview.rag.utils._gapic_utils.get_data_schema_name(name:str, corpus_name:str) -> str
Get the full resource name for a RagDataSchema.
funcagentplatform.preview.rag.utils._gapic_utils.get_metadata_name(name:str, corpus_name:str, file_name:str) -> str
Get the full resource name for a RagMetadata.
funcagentplatform.preview.rag.utils._gapic_utils.set_corpus_type_config(corpus_type_config:RagCorpusTypeConfig, rag_corpus:GapicRagCorpus) -> None
Set corpus type config in GapicRagCorpus.
funcagentplatform.preview.rag.utils._gapic_utils.set_encryption_spec(encryption_spec:EncryptionSpec, rag_corpus:GapicRagCorpus) -> None
Sets the encryption spec for the rag corpus.
classagentplatform.preview.rag.utils.resources.ANN
Config for ANN search.
classagentplatform.preview.rag.utils.resources.ChunkingConfig
ChunkingConfig.
classagentplatform.preview.rag.utils.resources.DocumentCorpus
DocumentCorpus.
classagentplatform.preview.rag.utils.resources.EmbeddingModelConfig
EmbeddingModelConfig.
classagentplatform.preview.rag.utils.resources.Filter
Filter.
classagentplatform.preview.rag.utils.resources.HybridSearch
HybridSearch.
classagentplatform.preview.rag.utils.resources.JiraQuery
JiraQuery.
classagentplatform.preview.rag.utils.resources.JiraSource
JiraSource.
classagentplatform.preview.rag.utils.resources.KNN
Config for KNN search.
classagentplatform.preview.rag.utils.resources.LlmParserConfig
Configuration for the LLM Parser Processor.
classagentplatform.preview.rag.utils.resources.LlmRanker
LlmRanker.
classagentplatform.preview.rag.utils.resources.MemoryCorpus
MemoryCorpus.
classagentplatform.preview.rag.utils.resources.MetadataValue
The value of metadata.
classagentplatform.preview.rag.utils.resources.Pinecone
Pinecone.
classagentplatform.preview.rag.utils.resources.RagCorpus
RAG corpus(output only).
classagentplatform.preview.rag.utils.resources.RagCorpusTypeConfig
CorpusTypeConfig.
classagentplatform.preview.rag.utils.resources.RagDataSchema
The schema of the user specified metadata.
classagentplatform.preview.rag.utils.resources.RagEmbeddingModelConfig
RagEmbeddingModelConfig.
classagentplatform.preview.rag.utils.resources.RagEngineConfig
RagEngineConfig.
classagentplatform.preview.rag.utils.resources.RagFile
RAG file (output only).
classagentplatform.preview.rag.utils.resources.RagManagedDb
RagManagedDb.
classagentplatform.preview.rag.utils.resources.RagManagedDbConfig
RagManagedDbConfig.
classagentplatform.preview.rag.utils.resources.RagManagedVertexVectorSearch
RagManagedVertexVectorSearch.
classagentplatform.preview.rag.utils.resources.RagMetadata
Metadata for RagFile provided by users.
classagentplatform.preview.rag.utils.resources.RagMetadataSchemaDetails.ListConfig
Config for List data type.
classagentplatform.preview.rag.utils.resources.RagResource
RagResource.
classagentplatform.preview.rag.utils.resources.RagRetrievalConfig
RagRetrievalConfig.
classagentplatform.preview.rag.utils.resources.RagVectorDbConfig
RagVectorDbConfig.
classagentplatform.preview.rag.utils.resources.RankService
RankService.
classagentplatform.preview.rag.utils.resources.Ranking
Ranking.
classagentplatform.preview.rag.utils.resources.SharePointSource
SharePointSource.
classagentplatform.preview.rag.utils.resources.SharePointSources
SharePointSources.
classagentplatform.preview.rag.utils.resources.SlackChannel
SlackChannel.
classagentplatform.preview.rag.utils.resources.SlackChannelsSource
SlackChannelsSource.
classagentplatform.preview.rag.utils.resources.TransformationConfig
TransformationConfig.
classagentplatform.preview.rag.utils.resources.UserSpecifiedMetadata
Metadata provided by users.
classagentplatform.preview.rag.utils.resources.VertexAiSearchConfig
VertexAiSearchConfig.
classagentplatform.preview.rag.utils.resources.VertexFeatureStore
VertexFeatureStore.
classagentplatform.preview.rag.utils.resources.VertexPredictionEndpoint
VertexPredictionEndpoint.
classagentplatform.preview.rag.utils.resources.VertexVectorSearch
VertexVectorSearch.
classagentplatform.preview.rag.utils.resources.Weaviate
Weaviate.
funcagentplatform.rag.rag_data.delete_corpus(name:str) -> None
Delete an existing RagCorpus.
funcagentplatform.rag.rag_data.delete_file(name:str, corpus_name:Optional[str]=None) -> None
Delete RagFile from an existing RagCorpus.
funcagentplatform.rag.rag_data.get_corpus(name:str) -> RagCorpus
Get an existing RagCorpus.
funcagentplatform.rag.rag_data.get_file(name:str, corpus_name:Optional[str]=None) -> RagFile
Get an existing RagFile.
funcagentplatform.rag.rag_data.get_rag_engine_config(name:str) -> RagEngineConfig
Get an existing RagEngineConfig.
funcagentplatform.rag.rag_data.list_files(corpus_name:str, page_size:Optional[int]=None, page_token:Optional[str]=None) -> ListRagFilesPager
List all RagFiles in an existing RagCorpus.
funcagentplatform.rag.rag_data.update_rag_engine_config(rag_engine_config:RagEngineConfig, timeout:int=600) -> RagEngineConfig
Update RagEngineConfig.
classagentplatform.rag.rag_store.VertexRagStore
Retrieve from Vertex RAG Store.
funcagentplatform.rag.utils._gapic_utils.convert_gapic_to_rag_corpus(gapic_rag_corpus:GapicRagCorpus) -> RagCorpus
Convert GapicRagCorpus to RagCorpus.
funcagentplatform.rag.utils._gapic_utils.convert_gapic_to_rag_file(gapic_rag_file:GapicRagFile) -> RagFile
Convert GapicRagFile to RagFile.
funcagentplatform.rag.utils._gapic_utils.convert_json_to_rag_file(upload_rag_file_response:Dict[str, Any]) -> RagFile
Converts a JSON response to a RagFile.
funcagentplatform.rag.utils._gapic_utils.set_encryption_spec(encryption_spec:EncryptionSpec, rag_corpus:GapicRagCorpus) -> None
Sets the encryption spec for the rag corpus.
classagentplatform.rag.utils.resources.ChunkingConfig
ChunkingConfig.
classagentplatform.rag.utils.resources.Filter
Filter.
classagentplatform.rag.utils.resources.JiraQuery
JiraQuery.
classagentplatform.rag.utils.resources.JiraSource
JiraSource.
classagentplatform.rag.utils.resources.LlmRanker
LlmRanker.
classagentplatform.rag.utils.resources.Pinecone
Pinecone.
classagentplatform.rag.utils.resources.RagCitedGenerationResponse
RagCitedGenerationResponse.
classagentplatform.rag.utils.resources.RagCorpus
RAG corpus(output only).
classagentplatform.rag.utils.resources.RagEmbeddingModelConfig
RagEmbeddingModelConfig.
classagentplatform.rag.utils.resources.RagEngineConfig
RagEngineConfig.
classagentplatform.rag.utils.resources.RagFile
RAG file (output only).
classagentplatform.rag.utils.resources.RagManagedDb
RagManagedDb.
classagentplatform.rag.utils.resources.RagManagedDbConfig
RagManagedDbConfig.
classagentplatform.rag.utils.resources.RagResource
RagResource.
classagentplatform.rag.utils.resources.RagRetrievalConfig
RagRetrievalConfig.
classagentplatform.rag.utils.resources.RagVectorDbConfig
RagVectorDbConfig.
classagentplatform.rag.utils.resources.RankService
RankService.
classagentplatform.rag.utils.resources.Ranking
Ranking.
classagentplatform.rag.utils.resources.SharePointSource
SharePointSource.
classagentplatform.rag.utils.resources.SharePointSources
SharePointSources.
classagentplatform.rag.utils.resources.SlackChannel
SlackChannel.
classagentplatform.rag.utils.resources.SlackChannelsSource
SlackChannelsSource.
classagentplatform.rag.utils.resources.TransformationConfig
TransformationConfig.
classagentplatform.rag.utils.resources.VertexAiSearchConfig
VertexAiSearchConfig.
classagentplatform.rag.utils.resources.VertexFeatureStore
VertexFeatureStore.
classagentplatform.rag.utils.resources.VertexPredictionEndpoint
VertexPredictionEndpoint.
classagentplatform.rag.utils.resources.VertexVectorSearch
VertexVectorSearch.
classagentplatform.rag.utils.resources.Weaviate
Weaviate.
classagentplatform.resources.preview.feature_store.feature.Feature
Class for managing Feature resources.
methodagentplatform.resources.preview.feature_store.feature.Feature.description() -> str
The description of the feature.
methodagentplatform.resources.preview.feature_store.feature.Feature.point_of_contact() -> str
The point of contact for the feature.
classagentplatform.resources.preview.feature_store.feature_group.FeatureGroup
Class for managing Feature Group resources.
methodagentplatform.resources.preview.feature_store.feature_group.FeatureGroup.delete(force:bool=False, sync:bool=True) -> None
Deletes this feature group.
classagentplatform.resources.preview.feature_store.feature_view.FeatureView
Class for managing Feature View resources.
methodagentplatform.resources.preview.feature_store.feature_view.FeatureView.delete(sync:bool=True) -> None
Deletes this feature view.
classagentplatform.resources.preview.feature_store.utils.FeatureGroupBigQuerySource
BigQuery source for the Feature Group.
classagentplatform.resources.preview.feature_store.utils.PublicEndpointNotFoundError
Public endpoint has not been created yet.
classagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor
Initializer for ModelMonitor.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.delete(force:bool=False, sync:bool=True) -> None
Force delete the model monitor.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.delete_model_monitoring_job(model_monitoring_job_name:str) -> None
Delete a model monitoring job.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.delete_schedule(schedule_name:str) -> None
Deletes an existing Schedule.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.get_schedule(schedule_name:str) -> 'gca_schedule.Schedule'
Gets an existing Schedule.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.list_jobs(page_size:Optional[int]=None, page_token:Optional[str]=None) -> 'ListJobsResponse.list_jobs'
List ModelMonitoringJobs.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.list_schedules(filter:Optional[str]=None, page_size:Optional[int]=None, page_token:Optional[str]=None) -> 'ListSchedulesResponse.list_schedules'
List Schedules.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.pause_schedule(schedule_name:str) -> None
Pauses an existing Schedule.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitor.resume_schedule(schedule_name:str) -> None
Resumes an existing Schedule.
classagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob
Initializer for ModelMonitoringJob.
methodagentplatform.resources.preview.ml_monitoring.model_monitors.ModelMonitoringJob.delete() -> None
Deletes an Model Monitoring Job.
classagentplatform.resources.preview.ml_monitoring.spec.notification.NotificationSpec
Initializer for NotificationSpec.
classagentplatform.resources.preview.ml_monitoring.spec.objective.DataDriftSpec
Data drift monitoring spec.
classagentplatform.resources.preview.ml_monitoring.spec.objective.FeatureAttributionSpec
Feature attribution spec.
classagentplatform.resources.preview.ml_monitoring.spec.objective.MonitoringInput
Model monitoring data input spec.
classagentplatform.resources.preview.ml_monitoring.spec.objective.ObjectiveSpec
Initializer for ObjectiveSpec.
classagentplatform.resources.preview.ml_monitoring.spec.objective.TabularObjective
Initializer for TabularObjective.
classagentplatform.resources.preview.ml_monitoring.spec.output.OutputSpec
Initializer for OutputSpec.
classagentplatform.resources.preview.ml_monitoring.spec.schema.FieldSchema
Field Schema.
classagentplatform.resources.preview.ml_monitoring.spec.schema.ModelMonitoringSchema
Initializer for ModelMonitoringSchema.
funcgoogle.cloud.aiplatform._streaming_prediction.tensor_to_value(tensor_pb:aiplatform_types.Tensor) -> Any
Converts `Tensor` to a Python value.
funcgoogle.cloud.aiplatform._streaming_prediction.value_to_tensor(value:Any) -> aiplatform_types.Tensor
Converts a Python value to `Tensor`.
classgoogle.cloud.aiplatform.base.FutureManager
Tracks concurrent futures against this object.
classgoogle.cloud.aiplatform.base.VertexAiResourceNoun
Base class the Vertex AI resource nouns.
methodgoogle.cloud.aiplatform.base.VertexAiResourceNoun.create_time() -> datetime.datetime
Time this resource was created.
methodgoogle.cloud.aiplatform.base.VertexAiResourceNoun.display_name() -> str
Display name of this resource.
methodgoogle.cloud.aiplatform.base.VertexAiResourceNoun.name() -> str
Name of this resource.
methodgoogle.cloud.aiplatform.base.VertexAiResourceNoun.resource_name() -> str
Full qualified resource name.
methodgoogle.cloud.aiplatform.base.VertexAiResourceNoun.to_dict() -> Dict[str, Any]
Returns the resource proto as a dictionary.
methodgoogle.cloud.aiplatform.base.VertexAiResourceNoun.update_time() -> datetime.datetime
Time this resource was last updated.
classgoogle.cloud.aiplatform.base.VertexLogger
Logging wrapper class with high level helper methods.
methodgoogle.cloud.aiplatform.base.VertexLogger.log_create_with_lro(cls:Type['VertexAiResourceNoun'], lro:Optional[operation.Operation]=None)
Logs create event with LRO.
methodgoogle.cloud.aiplatform.base.VertexLogger.log_delete_complete(resource:Type['VertexAiResourceNoun'])
Logs delete event is complete.
methodgoogle.cloud.aiplatform.base.VertexLogger.log_delete_with_lro(resource:Type['VertexAiResourceNoun'], lro:Optional[operation.Operation]=None)
Logs delete event with LRO.
funcgoogle.cloud.aiplatform.base.get_annotation_class(annotation:type) -> type
Helper method to retrieve type annotation.
funcgoogle.cloud.aiplatform.base.wrapper(*args, **kwargs)
Wraps method.
classgoogle.cloud.aiplatform.datasets._datasources.Datasource
An abstract class that sets dataset_metadata.
methodgoogle.cloud.aiplatform.datasets._datasources.Datasource.dataset_metadata()
Dataset Metadata.
classgoogle.cloud.aiplatform.datasets.image_dataset.ImageDataset
A managed image dataset resource for Vertex AI.
classgoogle.cloud.aiplatform.datasets.text_dataset.TextDataset
A managed text dataset resource for Vertex AI.
classgoogle.cloud.aiplatform.datasets.video_dataset.VideoDataset
A managed video dataset resource for Vertex AI.
classgoogle.cloud.aiplatform.docker_utils.errors.Error
A base exception for all user recoverable errors.
funcgoogle.cloud.aiplatform.docker_utils.local_util.execute_command(cmd:List[str], input_str:Optional[str]=None) -> int
Executes commands in subprocess.
funcgoogle.cloud.aiplatform.docker_utils.run.print_container_logs(container:docker.models.containers.Container, start_index:Optional[int]=None, message:Optional[str]=None) -> int
Prints container logs.
funcgoogle.cloud.aiplatform.docker_utils.utils.check_image_exists_locally(image_name:str) -> bool
Checks if an image exists locally.
funcgoogle.cloud.aiplatform.explain.lit.create_lit_dataset(dataset:pd.DataFrame, column_types:'OrderedDict[str, lit_types.LitType]') -> lit_dataset.Dataset
Creates a LIT Dataset object.
classgoogle.cloud.aiplatform.explain.metadata.metadata_builder.MetadataBuilder
Abstract base class for metadata builders.
classgoogle.cloud.aiplatform.featurestore.feature.Feature
Managed feature resource for Vertex AI.
classgoogle.cloud.aiplatform.featurestore.featurestore.Featurestore
Managed featurestore resource for Vertex AI.
methodgoogle.cloud.aiplatform.featurestore.featurestore.Featurestore.delete(sync:bool=True, force:bool=False) -> None
Deletes this Featurestore resource.
methodgoogle.cloud.aiplatform.jobs.BatchPredictionJob.partial_failures() -> Optional[Sequence[status_pb2.Status]]
Partial failures encountered.
methodgoogle.cloud.aiplatform.jobs.BatchPredictionJob.wait_for_resource_creation() -> None
Waits until resource has been created.
classgoogle.cloud.aiplatform.jobs.CustomJob
Vertex AI Custom Job.
classgoogle.cloud.aiplatform.jobs.HyperparameterTuningJob
Vertex AI Hyperparameter Tuning Job.
classgoogle.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob
Vertex AI Model Deployment Monitoring Job.
methodgoogle.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob.delete() -> None
Deletes an MDM job.
methodgoogle.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob.pause() -> 'ModelDeploymentMonitoringJob'
Pause a running MDM job.
methodgoogle.cloud.aiplatform.jobs.ModelDeploymentMonitoringJob.resume() -> 'ModelDeploymentMonitoringJob'
Resumes a paused MDM job.
classgoogle.cloud.aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery
Hybrid query.
classgoogle.cloud.aiplatform.metadata.artifact.Artifact
Metadata Artifact resource for Vertex AI
methodgoogle.cloud.aiplatform.metadata.artifact.Artifact.state() -> Optional[gca_artifact.Artifact.State]
The State for this Artifact.
methodgoogle.cloud.aiplatform.metadata.artifact.Artifact.uri() -> Optional[str]
Uri for this Artifact.
classgoogle.cloud.aiplatform.metadata.context.Context
Metadata Context resource for Vertex AI
classgoogle.cloud.aiplatform.metadata.execution.Execution
Metadata Execution resource for Vertex AI
methodgoogle.cloud.aiplatform.metadata.execution.Execution.state() -> gca_execution.Execution.State
State of this Execution.
classgoogle.cloud.aiplatform.metadata.experiment_resources.Experiment
Represents a Vertex AI Experiment resource.
funcgoogle.cloud.aiplatform.metadata.experiment_resources.Experiment.column_sort_key(key:str) -> int
Helper method to reorder columns.
methodgoogle.cloud.aiplatform.metadata.experiment_resources.Experiment.dashboard_url() -> Optional[str]
Cloud console URL for this resource.
methodgoogle.cloud.aiplatform.metadata.experiment_resources.Experiment.name() -> str
The name of this experiment.
classgoogle.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun
A Vertex AI Experiment run.
methodgoogle.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun.get_state() -> gca_execution.Execution.State
The state of this run.
methodgoogle.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun.state() -> gca_execution.Execution.State
The state of this run.
classgoogle.cloud.aiplatform.metadata.schema.base_artifact.BaseArtifactSchema
Base class for Metadata Artifact types.
classgoogle.cloud.aiplatform.metadata.schema.base_context.BaseContextSchema
Base class for Metadata Context schema.
classgoogle.cloud.aiplatform.metadata.schema.base_execution.BaseExecutionSchema
Base class for Metadata Execution schema.
classgoogle.cloud.aiplatform.metadata.schema.google.artifact_schema.VertexDataset
An artifact representing a Vertex Dataset.
classgoogle.cloud.aiplatform.metadata.schema.google.artifact_schema.VertexModel
An artifact representing a Vertex Model.
classgoogle.cloud.aiplatform.metadata.schema.system.artifact_schema.Artifact
A generic artifact.
classgoogle.cloud.aiplatform.metadata.schema.system.artifact_schema.Dataset
An artifact representing a system Dataset.
classgoogle.cloud.aiplatform.metadata.schema.system.artifact_schema.Metrics
Artifact schema for scalar metrics.
classgoogle.cloud.aiplatform.metadata.schema.system.artifact_schema.Model
Artifact type for model.
classgoogle.cloud.aiplatform.metadata.schema.system.context_schema.Experiment
Context schema for a Experiment context.
classgoogle.cloud.aiplatform.metadata.schema.system.context_schema.Pipeline
Context schema for a Pipeline context.
classgoogle.cloud.aiplatform.metadata.schema.system.context_schema.PipelineRun
Context schema for a PipelineRun context.
classgoogle.cloud.aiplatform.metadata.schema.system.execution_schema.Run
Execution schema for root run execution.
classgoogle.cloud.aiplatform.metadata.schema.utils.ConfidenceMetric
A class that represents a Confidence Metric.
classgoogle.cloud.aiplatform.metadata.schema.utils.ConfusionMatrix
A class that represents a Confusion Matrix.
classgoogle.cloud.aiplatform.metadata.schema.utils.ContainerSpec
Container configuration for the model.
funcgoogle.cloud.aiplatform.metadata.schema.utils.create_uri_from_resource_name(resource_name:str) -> str
Construct the service URI for a given resource_name.
classgoogle.cloud.aiplatform.model_monitoring.objective.ExplanationConfig
A class that enables Vertex Explainable AI.
methodgoogle.cloud.aiplatform.persistent_resource.PersistentResource.reboot(sync:Optional[bool]=True) -> None
Reboots this Persistent Resource.
methodgoogle.cloud.aiplatform.pipeline_jobs.PipelineJob.has_failed() -> bool
Returns True if pipeline has failed.
methodgoogle.cloud.aiplatform.pipeline_jobs.PipelineJob.state() -> Optional[gca_pipeline_state.PipelineState]
Current pipeline state.
methodgoogle.cloud.aiplatform.pipeline_jobs.PipelineJob.wait_for_resource_creation() -> None
Waits until resource has been created.
methodgoogle.cloud.aiplatform.prediction.handler.Handler.handle(request:Request) -> Response
Handles a prediction request.
funcgoogle.cloud.aiplatform.prediction.handler_utils.get_accept_from_headers(headers:Optional[starlette.datastructures.Headers]) -> str
Gets accept from headers.
funcgoogle.cloud.aiplatform.prediction.handler_utils.get_content_type_from_headers(headers:Optional[starlette.datastructures.Headers]) -> Optional[str]
Gets content type from headers.
funcgoogle.cloud.aiplatform.prediction.handler_utils.parse_accept_header(accept_header:Optional[str]) -> Dict[str, float]
Parses the accept header with quality factors.
classgoogle.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint
Class that represents a local endpoint.
methodgoogle.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint.get_container_status() -> str
Gets the container status.
methodgoogle.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint.predict(request:Optional[Any]=None, request_file:Optional[str]=None, headers:Optional[Dict]=None, verbose:bool=True) -> requests.models.Response
Executes a prediction.
methodgoogle.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint.print_container_logs(show_all:bool=False, message:Optional[str]=None) -> None
Prints container logs.
methodgoogle.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint.run_health_check(verbose:bool=True) -> requests.models.Response
Runs a health check.
methodgoogle.cloud.aiplatform.prediction.local_endpoint.LocalEndpoint.stop() -> None
Explicitly stops the container.
classgoogle.cloud.aiplatform.prediction.local_model.LocalModel
Class that represents a local model.
methodgoogle.cloud.aiplatform.prediction.local_model.LocalModel.copy_image(dst_image_uri:str) -> 'LocalModel'
Copies the image to another image uri.
methodgoogle.cloud.aiplatform.prediction.local_model.LocalModel.push_image() -> None
Pushes the image to a registry.
classgoogle.cloud.aiplatform.prediction.model_server.CprModelServer
Model server to do custom prediction routines.
methodgoogle.cloud.aiplatform.prediction.model_server.CprModelServer.predict(request:Request) -> Response
Executes a prediction.
methodgoogle.cloud.aiplatform.prediction.predictor.Predictor.load(artifacts_uri:str, **kwargs) -> None
Loads the model artifact.
methodgoogle.cloud.aiplatform.prediction.predictor.Predictor.postprocess(prediction_results:Any) -> Any
Postprocesses the prediction results.
methodgoogle.cloud.aiplatform.prediction.predictor.Predictor.predict(instances:Any) -> Any
Performs prediction.
methodgoogle.cloud.aiplatform.prediction.serializer.Serializer.deserialize(data:Any, content_type:Optional[str]) -> Any
Deserializes the request data.
methodgoogle.cloud.aiplatform.prediction.serializer.Serializer.serialize(prediction:Any, accept:Optional[str]) -> Any
Serializes the prediction results.
classgoogle.cloud.aiplatform.preview.datasets.GeminiExample
A class representing a Gemini example.
methodgoogle.cloud.aiplatform.preview.datasets.GeminiExample.cached_content() -> Optional[str]
The cached content of the GeminiExample.
methodgoogle.cloud.aiplatform.preview.datasets.GeminiExample.contents() -> Optional[List[Content]]
The contents of the GeminiExample.
methodgoogle.cloud.aiplatform.preview.datasets.GeminiExample.from_prompt(prompt:prompts.Prompt) -> 'GeminiExample'
Creates a GeminiExample from a Prompt.
methodgoogle.cloud.aiplatform.preview.datasets.GeminiExample.model() -> Optional[str]
The model to use for the GeminiExample.
methodgoogle.cloud.aiplatform.preview.datasets.GeminiExample.tool_config() -> Optional[ToolConfig]
The tool config of the GeminiExample.
methodgoogle.cloud.aiplatform.preview.datasets.GeminiExample.tools() -> Optional[List[Tool]]
The tools of the GeminiExample.
classgoogle.cloud.aiplatform.preview.datasets.GeminiTemplateConfig
A class representing a Gemini template config.
classgoogle.cloud.aiplatform.preview.featurestore.entity_type.EntityType
Preview EntityType resource for Vertex AI.
classgoogle.cloud.aiplatform.preview.jobs.BatchPredictionJob
Vertex AI Batch Prediction Job.
classgoogle.cloud.aiplatform.preview.jobs.CustomJob
Deprecated.
classgoogle.cloud.aiplatform.preview.jobs.HyperparameterTuningJob
Deprecated.
classgoogle.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard
Managed tensorboard resource for Vertex AI.
classgoogle.cloud.aiplatform.tensorboard.tensorboard_resource.TensorboardRun
Managed tensorboard resource for Vertex AI.
classgoogle.cloud.aiplatform.tensorboard.upload_tracker.UploadStats
Statistics of uploading.
methodgoogle.cloud.aiplatform.tensorboard.upload_tracker.UploadStats.add_blob(blob_bytes, is_skipped)
Add a blob.
methodgoogle.cloud.aiplatform.tensorboard.upload_tracker.UploadStats.add_plugin(plugin_name)
Add a plugin.
classgoogle.cloud.aiplatform.tensorboard.upload_tracker.UploadTracker
Tracker for uploader progress and status.
classgoogle.cloud.aiplatform.training_utils.cloud_profiler.plugins.tensorflow.tf_profiler.TFProfiler
Handler for Tensorflow Profiling.
classgoogle.cloud.aiplatform.training_utils.cloud_profiler.webserver.WebServer
A basic web server for handling requests.
methodgoogle.cloud.aiplatform.training_utils.cloud_profiler.webserver.WebServer.dispatch_request(environ:wsgi_types.Environment, start_response:wsgi_types.StartResponse) -> Response
Handles the routing of requests.
methodgoogle.cloud.aiplatform.training_utils.cloud_profiler.webserver.WebServer.wsgi_app(environ:wsgi_types.Environment, start_response:wsgi_types.StartResponse) -> Response
Entrypoint for wsgi application.
funcgoogle.cloud.aiplatform.utils.featurestore_utils.validate_feature_id(feature_id:str) -> None
Validates feature ID.
funcgoogle.cloud.aiplatform.utils.featurestore_utils.validate_id(resource_id:str) -> None
Validates feature store resource ID pattern.
funcgoogle.cloud.aiplatform.utils.featurestore_utils.validate_value_type(value_type:str) -> None
Validates user provided feature value_type string.
funcgoogle.cloud.aiplatform.utils.gcs_utils.blob_from_uri(uri:str, client:storage.Client) -> storage.Blob
Create a Blob from a GCS URI, compatible with v2 and v3.
funcgoogle.cloud.aiplatform.utils.gcs_utils.validate_gcs_path(gcs_path:str) -> None
Validates a GCS path.
funcgoogle.cloud.aiplatform.utils.get_timestamp_proto(time:Optional[datetime.datetime]=None) -> timestamp_pb2.Timestamp
Gets timestamp proto of a given time.
funcgoogle.cloud.aiplatform.utils.mrep_endpoint(service_base_path:str, location:str) -> str
Returns the mREP host for a jurisdiction.
classgoogle.cloud.aiplatform.utils.pipeline_utils.PipelineRuntimeConfigBuilder
Pipeline RuntimeConfig builder.
methodgoogle.cloud.aiplatform.utils.pipeline_utils.PipelineRuntimeConfigBuilder.build() -> Dict[str, Any]
Build a RuntimeConfig proto.
methodgoogle.cloud.aiplatform.utils.pipeline_utils.PipelineRuntimeConfigBuilder.update_default_runtime(default_runtime:Dict[str, Any]) -> None
Merges default runtime.
methodgoogle.cloud.aiplatform.utils.pipeline_utils.PipelineRuntimeConfigBuilder.update_failure_policy(failure_policy:Optional[str]=None) -> None
Merges runtime failure policy.
methodgoogle.cloud.aiplatform.utils.pipeline_utils.PipelineRuntimeConfigBuilder.update_input_artifacts(input_artifacts:Optional[Mapping[str, str]]) -> None
Merges runtime input artifacts.
methodgoogle.cloud.aiplatform.utils.pipeline_utils.PipelineRuntimeConfigBuilder.update_pipeline_root(pipeline_root:Optional[str]) -> None
Updates pipeline_root value.

この情報について

掲載しているシグネチャは googleapis/python-aiplatform の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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