Package google.cloud.aiplatform.v1beta1

Index

EvaluationService (interface)
ExtensionExecutionService (interface)
ExtensionRegistryService (interface)
GenAiCacheConfigService (interface)
GenAiCacheService (interface)
GenAiTuningService (interface)
PredictionService (interface)
ReasoningEngineExecutionService (interface)
ReasoningEngineService (interface)
VertexRagDataService (interface)
VertexRagService (interface)
ApiAuth (message)
ApiAuth.ApiKeyConfig (message)
AugmentPromptRequest (message)
AugmentPromptRequest.Model (message)
AugmentPromptResponse (message)
AuthConfig (message)
AuthConfig.ApiKeyConfig (message)
AuthConfig.GoogleServiceAccountConfig (message)
AuthConfig.HttpBasicAuthConfig (message)
AuthConfig.OauthConfig (message)
AuthConfig.OidcConfig (message)
AuthType (enum)
BigQueryDestination (message)
BleuInput (message)
BleuInstance (message)
BleuMetricValue (message)
BleuResults (message)
BleuSpec (message)
Blob (message)
CacheConfig (message)
CachedContent (message)
CachedContent.UsageMetadata (message)
CancelTuningJobRequest (message)
Candidate (message)
Candidate.FinishReason (enum)
ChatCompletionsRequest (message)
Citation (message)
CitationMetadata (message)
Claim (message)
CodeExecutionResult (message)
CodeExecutionResult.Outcome (enum)
CoherenceInput (message)
CoherenceInstance (message)
CoherenceResult (message)
CoherenceSpec (message)
Content (message)
CorpusStatus (message)
CorpusStatus.State (enum)
CorroborateContentRequest (message)
CorroborateContentRequest.Parameters (message)
CorroborateContentResponse (message)
CountTokensRequest (message)
CountTokensResponse (message)
CreateCachedContentRequest (message)
CreateRagCorpusOperationMetadata (message)
CreateRagCorpusRequest (message)
CreateReasoningEngineOperationMetadata (message)
CreateReasoningEngineRequest (message)
CreateTuningJobRequest (message)
DatasetDistribution (message)
DatasetDistribution.DistributionBucket (message)
DatasetStats (message)
DeleteCachedContentRequest (message)
DeleteExtensionRequest (message)
DeleteOperationMetadata (message)
DeleteRagCorpusRequest (message)
DeleteRagFileRequest (message)
DeleteReasoningEngineRequest (message)
DirectUploadSource (message)
DistillationDataStats (message)
DistillationHyperParameters (message)
DistillationSpec (message)
DynamicRetrievalConfig (message)
DynamicRetrievalConfig.Mode (enum)
EncryptionSpec (message)
EvaluateInstancesRequest (message)
EvaluateInstancesResponse (message)
ExactMatchInput (message)
ExactMatchInstance (message)
ExactMatchMetricValue (message)
ExactMatchResults (message)
ExactMatchSpec (message)
ExecutableCode (message)
ExecutableCode.Language (enum)
ExecuteExtensionRequest (message)
ExecuteExtensionResponse (message)
Extension (message)
ExtensionManifest (message)
ExtensionManifest.ApiSpec (message)
ExtensionOperation (message)
ExtensionPrivateServiceConnectConfig (message)
Fact (message)
FileData (message)
FileStatus (message)
FileStatus.State (enum)
FluencyInput (message)
FluencyInstance (message)
FluencyResult (message)
FluencySpec (message)
FulfillmentInput (message)
FulfillmentInstance (message)
FulfillmentResult (message)
FulfillmentSpec (message)
FunctionCall (message)
FunctionCallingConfig (message)
FunctionCallingConfig.Mode (enum)
FunctionDeclaration (message)
FunctionResponse (message)
GcsDestination (message)
GcsSource (message)
GenerateContentRequest (message)
GenerateContentResponse (message)
GenerateContentResponse.PromptFeedback (message)
GenerateContentResponse.PromptFeedback.BlockedReason (enum)
GenerateContentResponse.UsageMetadata (message)
GenerationConfig (message)
GenerationConfig.RoutingConfig (message)
GenerationConfig.RoutingConfig.AutoRoutingMode (message)
GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference (enum)
GenerationConfig.RoutingConfig.ManualRoutingMode (message)
GenericOperationMetadata (message)
GetCacheConfigRequest (message)
GetCachedContentRequest (message)
GetExtensionRequest (message)
GetRagCorpusRequest (message)
GetRagFileRequest (message)
GetReasoningEngineRequest (message)
GetTuningJobRequest (message)
GoogleDriveSource (message)
GoogleDriveSource.ResourceId (message)
GoogleDriveSource.ResourceId.ResourceType (enum)
GoogleSearchRetrieval (message)
GroundednessInput (message)
GroundednessInstance (message)
GroundednessResult (message)
GroundednessSpec (message)
GroundingChunk (message)
GroundingChunk.RetrievedContext (message)
GroundingChunk.Web (message)
GroundingMetadata (message)
GroundingSupport (message)
HarmCategory (enum)
HttpElementLocation (enum)
ImportExtensionOperationMetadata (message)
ImportExtensionRequest (message)
ImportRagFilesConfig (message)
ImportRagFilesOperationMetadata (message)
ImportRagFilesRequest (message)
ImportRagFilesResponse (message)
JiraSource (message)
JiraSource.JiraQueries (message)
JobState (enum)
ListCachedContentsRequest (message)
ListCachedContentsResponse (message)
ListExtensionsRequest (message)
ListExtensionsResponse (message)
ListRagCorporaRequest (message)
ListRagCorporaResponse (message)
ListRagFilesRequest (message)
ListRagFilesResponse (message)
ListReasoningEnginesRequest (message)
ListReasoningEnginesResponse (message)
ListTuningJobsRequest (message)
ListTuningJobsResponse (message)
LogprobsResult (message)
LogprobsResult.Candidate (message)
LogprobsResult.TopCandidates (message)
PairwiseChoice (enum)
PairwiseMetricInput (message)
PairwiseMetricInstance (message)
PairwiseMetricResult (message)
PairwiseMetricSpec (message)
PairwiseQuestionAnsweringQualityInput (message)
PairwiseQuestionAnsweringQualityInstance (message)
PairwiseQuestionAnsweringQualityResult (message)
PairwiseQuestionAnsweringQualitySpec (message)
PairwiseSummarizationQualityInput (message)
PairwiseSummarizationQualityInstance (message)
PairwiseSummarizationQualityResult (message)
PairwiseSummarizationQualitySpec (message)
Part (message)
PartnerModelTuningSpec (message)
PointwiseMetricInput (message)
PointwiseMetricInstance (message)
PointwiseMetricResult (message)
PointwiseMetricSpec (message)
PredictRequest (message)
PredictResponse (message)
QueryExtensionRequest (message)
QueryExtensionResponse (message)
QueryReasoningEngineRequest (message)
QueryReasoningEngineResponse (message)
QuestionAnsweringCorrectnessInput (message)
QuestionAnsweringCorrectnessInstance (message)
QuestionAnsweringCorrectnessResult (message)
QuestionAnsweringCorrectnessSpec (message)
QuestionAnsweringHelpfulnessInput (message)
QuestionAnsweringHelpfulnessInstance (message)
QuestionAnsweringHelpfulnessResult (message)
QuestionAnsweringHelpfulnessSpec (message)
QuestionAnsweringQualityInput (message)
QuestionAnsweringQualityInstance (message)
QuestionAnsweringQualityResult (message)
QuestionAnsweringQualitySpec (message)
QuestionAnsweringRelevanceInput (message)
QuestionAnsweringRelevanceInstance (message)
QuestionAnsweringRelevanceResult (message)
QuestionAnsweringRelevanceSpec (message)
RagContexts (message)
RagContexts.Context (message)
RagCorpus (message)
RagEmbeddingModelConfig (message)
RagEmbeddingModelConfig.HybridSearchConfig (message)
RagEmbeddingModelConfig.SparseEmbeddingConfig (message)
RagEmbeddingModelConfig.SparseEmbeddingConfig.Bm25 (message)
RagEmbeddingModelConfig.VertexPredictionEndpoint (message)
RagFile (message)
RagFile.RagFileType (enum)
RagFileChunkingConfig (message)
RagFileParsingConfig (message)
RagQuery (message)
RagQuery.Ranking (message)
RagVectorDbConfig (message)
RagVectorDbConfig.Pinecone (message)
RagVectorDbConfig.RagManagedDb (message)
RagVectorDbConfig.VertexFeatureStore (message)
RagVectorDbConfig.VertexVectorSearch (message)
RagVectorDbConfig.Weaviate (message)
ReasoningEngine (message)
ReasoningEngineSpec (message)
ReasoningEngineSpec.PackageSpec (message)
RebaseTunedModelOperationMetadata (message)
RebaseTunedModelRequest (message)
Retrieval (message)
RetrievalMetadata (message)
RetrieveContextsRequest (message)
RetrieveContextsRequest.VertexRagStore (message)
RetrieveContextsRequest.VertexRagStore.RagResource (message)
RetrieveContextsResponse (message)
RougeInput (message)
RougeInstance (message)
RougeMetricValue (message)
RougeResults (message)
RougeSpec (message)
RuntimeConfig (message)
RuntimeConfig.CodeInterpreterRuntimeConfig (message)
RuntimeConfig.VertexAISearchRuntimeConfig (message)
SafetyInput (message)
SafetyInstance (message)
SafetyRating (message)
SafetyRating.HarmProbability (enum)
SafetyRating.HarmSeverity (enum)
SafetyResult (message)
SafetySetting (message)
SafetySetting.HarmBlockMethod (enum)
SafetySetting.HarmBlockThreshold (enum)
SafetySpec (message)
Schema (message)
SearchEntryPoint (message)
Segment (message)
SharePointSources (message)
SharePointSources.SharePointSource (message)
SlackSource (message)
SlackSource.SlackChannels (message)
SlackSource.SlackChannels.SlackChannel (message)
StreamDirectPredictRequest (message)
StreamDirectPredictResponse (message)
StreamDirectRawPredictRequest (message)
StreamDirectRawPredictResponse (message)
StreamingPredictRequest (message)
StreamingPredictResponse (message)
StreamingRawPredictRequest (message)
StreamingRawPredictResponse (message)
SummarizationHelpfulnessInput (message)
SummarizationHelpfulnessInstance (message)
SummarizationHelpfulnessResult (message)
SummarizationHelpfulnessSpec (message)
SummarizationQualityInput (message)
SummarizationQualityInstance (message)
SummarizationQualityResult (message)
SummarizationQualitySpec (message)
SummarizationVerbosityInput (message)
SummarizationVerbosityInstance (message)
SummarizationVerbosityResult (message)
SummarizationVerbositySpec (message)
SupervisedHyperParameters (message)
SupervisedHyperParameters.AdapterSize (enum)
SupervisedTuningDataStats (message)
SupervisedTuningDatasetDistribution (message)
SupervisedTuningDatasetDistribution.DatasetBucket (message)
SupervisedTuningSpec (message)
Tensor (message)
Tensor.DataType (enum)
Tool (message)
Tool.CodeExecution (message)
ToolCallValidInput (message)
ToolCallValidInstance (message)
ToolCallValidMetricValue (message)
ToolCallValidResults (message)
ToolCallValidSpec (message)
ToolConfig (message)
ToolNameMatchInput (message)
ToolNameMatchInstance (message)
ToolNameMatchMetricValue (message)
ToolNameMatchResults (message)
ToolNameMatchSpec (message)
ToolParameterKVMatchInput (message)
ToolParameterKVMatchInstance (message)
ToolParameterKVMatchMetricValue (message)
ToolParameterKVMatchResults (message)
ToolParameterKVMatchSpec (message)
ToolParameterKeyMatchInput (message)
ToolParameterKeyMatchInstance (message)
ToolParameterKeyMatchMetricValue (message)
ToolParameterKeyMatchResults (message)
ToolParameterKeyMatchSpec (message)
ToolUseExample (message)
ToolUseExample.ExtensionOperation (message)
TunedModel (message)
TunedModelRef (message)
TuningDataStats (message)
TuningJob (message)
Type (enum)
UpdateCacheConfigRequest (message)
UpdateCachedContentRequest (message)
UpdateExtensionRequest (message)
UpdateRagCorpusOperationMetadata (message)
UpdateRagCorpusRequest (message)
UpdateReasoningEngineOperationMetadata (message)
UpdateReasoningEngineRequest (message)
UploadRagFileConfig (message)
VertexAISearch (message)
VertexRagStore (message)
VertexRagStore.RagResource (message)
VideoMetadata (message)

EvaluationService

Vertex AI Online Evaluation Service.

EvaluateInstances

EvaluateInstances
`rpc EvaluateInstances(EvaluateInstancesRequest) returns (EvaluateInstancesResponse)` Evaluates instances based on a given metric.

rpc EvaluateInstances(EvaluateInstancesRequest) returns (EvaluateInstancesResponse)

Evaluates instances based on a given metric.

ExtensionExecutionService

A service for Extension execution.

ExecuteExtension

ExecuteExtension
`rpc ExecuteExtension(ExecuteExtensionRequest) returns (ExecuteExtensionResponse)` Executes the request against a given extension. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.extensions.execute` For more information, see the IAM documentation.

rpc ExecuteExtension(ExecuteExtensionRequest) returns (ExecuteExtensionResponse)

Executes the request against a given extension.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.extensions.execute

For more information, see the IAM documentation.

QueryExtension

QueryExtension
`rpc QueryExtension(QueryExtensionRequest) returns (QueryExtensionResponse)` Queries an extension with a default controller. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.extensions.execute` For more information, see the IAM documentation.

rpc QueryExtension(QueryExtensionRequest) returns (QueryExtensionResponse)

Queries an extension with a default controller.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.extensions.execute

For more information, see the IAM documentation.

ExtensionRegistryService

A service for managing Vertex AI's Extension registry.

DeleteExtension

DeleteExtension
`rpc DeleteExtension(DeleteExtensionRequest) returns (Operation)` Deletes an Extension. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.extensions.delete` For more information, see the IAM documentation.

rpc DeleteExtension(DeleteExtensionRequest) returns (Operation)

Deletes an Extension.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.extensions.delete

For more information, see the IAM documentation.

GetExtension

GetExtension
`rpc GetExtension(GetExtensionRequest) returns (Extension)` Gets an Extension. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.extensions.get` For more information, see the IAM documentation.

rpc GetExtension(GetExtensionRequest) returns (Extension)

Gets an Extension.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.extensions.get

For more information, see the IAM documentation.

ImportExtension

ImportExtension
`rpc ImportExtension(ImportExtensionRequest) returns (Operation)` Imports an Extension. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.extensions.import` For more information, see the IAM documentation.

rpc ImportExtension(ImportExtensionRequest) returns (Operation)

Imports an Extension.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.extensions.import

For more information, see the IAM documentation.

ListExtensions

ListExtensions
`rpc ListExtensions(ListExtensionsRequest) returns (ListExtensionsResponse)` Lists Extensions in a location. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.extensions.list` For more information, see the IAM documentation.

rpc ListExtensions(ListExtensionsRequest) returns (ListExtensionsResponse)

Lists Extensions in a location.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.extensions.list

For more information, see the IAM documentation.

UpdateExtension

UpdateExtension
`rpc UpdateExtension(UpdateExtensionRequest) returns (Extension)` Updates an Extension. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.extensions.update` For more information, see the IAM documentation.

rpc UpdateExtension(UpdateExtensionRequest) returns (Extension)

Updates an Extension.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.extensions.update

For more information, see the IAM documentation.

GenAiCacheConfigService

Service for GenAI Cache Config.

GetCacheConfig

GetCacheConfig
`rpc GetCacheConfig(GetCacheConfigRequest) returns (CacheConfig)` Gets a GenAI cache config. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.cacheConfigs.get` For more information, see the IAM documentation.

rpc GetCacheConfig(GetCacheConfigRequest) returns (CacheConfig)

Gets a GenAI cache config.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.cacheConfigs.get

For more information, see the IAM documentation.

UpdateCacheConfig

UpdateCacheConfig
`rpc UpdateCacheConfig(UpdateCacheConfigRequest) returns (Operation)` Updates a cache config. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.cacheConfigs.update` For more information, see the IAM documentation.

rpc UpdateCacheConfig(UpdateCacheConfigRequest) returns (Operation)

Updates a cache config.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.cacheConfigs.update

For more information, see the IAM documentation.

GenAiCacheService

Service for managing Vertex AI's CachedContent resource.

CreateCachedContent

CreateCachedContent
`rpc CreateCachedContent(CreateCachedContentRequest) returns (CachedContent)` Creates cached content, this call will initialize the cached content in the data storage, and users need to pay for the cache data storage. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.cachedContents.create` For more information, see the IAM documentation.

rpc CreateCachedContent(CreateCachedContentRequest) returns (CachedContent)

Creates cached content, this call will initialize the cached content in the data storage, and users need to pay for the cache data storage.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.cachedContents.create

For more information, see the IAM documentation.

DeleteCachedContent

DeleteCachedContent
`rpc DeleteCachedContent(DeleteCachedContentRequest) returns (Empty)` Deletes cached content IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.cachedContents.delete` For more information, see the IAM documentation.

rpc DeleteCachedContent(DeleteCachedContentRequest) returns (Empty)

Deletes cached content

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.cachedContents.delete

For more information, see the IAM documentation.

GetCachedContent

GetCachedContent
`rpc GetCachedContent(GetCachedContentRequest) returns (CachedContent)` Gets cached content configurations IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.cachedContents.get` For more information, see the IAM documentation.

rpc GetCachedContent(GetCachedContentRequest) returns (CachedContent)

Gets cached content configurations

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.cachedContents.get

For more information, see the IAM documentation.

ListCachedContents

ListCachedContents
`rpc ListCachedContents(ListCachedContentsRequest) returns (ListCachedContentsResponse)` Lists cached contents in a project IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.cachedContents.list` For more information, see the IAM documentation.

rpc ListCachedContents(ListCachedContentsRequest) returns (ListCachedContentsResponse)

Lists cached contents in a project

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.cachedContents.list

For more information, see the IAM documentation.

UpdateCachedContent

UpdateCachedContent
`rpc UpdateCachedContent(UpdateCachedContentRequest) returns (CachedContent)` Updates cached content configurations IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.cachedContents.update` For more information, see the IAM documentation.

rpc UpdateCachedContent(UpdateCachedContentRequest) returns (CachedContent)

Updates cached content configurations

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.cachedContents.update

For more information, see the IAM documentation.

GenAiTuningService

A service for creating and managing GenAI Tuning Jobs.

CancelTuningJob

CancelTuningJob
`rpc CancelTuningJob(CancelTuningJobRequest) returns (Empty)` Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use `GenAiTuningService.GetTuningJob` or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a `TuningJob.error` value with a `google.rpc.Status.code` of 1, corresponding to `Code.CANCELLED`, and `TuningJob.state` is set to `CANCELLED`. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.tuningJobs.cancel` For more information, see the IAM documentation.

rpc CancelTuningJob(CancelTuningJobRequest) returns (Empty)

Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and TuningJob.state is set to CANCELLED.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.tuningJobs.cancel

For more information, see the IAM documentation.

CreateTuningJob

CreateTuningJob
`rpc CreateTuningJob(CreateTuningJobRequest) returns (TuningJob)` Creates a TuningJob. A created TuningJob right away will be attempted to be run. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.tuningJobs.create` For more information, see the IAM documentation.

rpc CreateTuningJob(CreateTuningJobRequest) returns (TuningJob)

Creates a TuningJob. A created TuningJob right away will be attempted to be run.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.tuningJobs.create

For more information, see the IAM documentation.

GetTuningJob

GetTuningJob
`rpc GetTuningJob(GetTuningJobRequest) returns (TuningJob)` Gets a TuningJob. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.tuningJobs.get` For more information, see the IAM documentation.

rpc GetTuningJob(GetTuningJobRequest) returns (TuningJob)

Gets a TuningJob.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.tuningJobs.get

For more information, see the IAM documentation.

ListTuningJobs

ListTuningJobs
`rpc ListTuningJobs(ListTuningJobsRequest) returns (ListTuningJobsResponse)` Lists TuningJobs in a Location. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.tuningJobs.list` For more information, see the IAM documentation.

rpc ListTuningJobs(ListTuningJobsRequest) returns (ListTuningJobsResponse)

Lists TuningJobs in a Location.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.tuningJobs.list

For more information, see the IAM documentation.

RebaseTunedModel

RebaseTunedModel
`rpc RebaseTunedModel(RebaseTunedModelRequest) returns (Operation)` Rebase a TunedModel. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.tuningJobs.create` For more information, see the IAM documentation.

rpc RebaseTunedModel(RebaseTunedModelRequest) returns (Operation)

Rebase a TunedModel.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.tuningJobs.create

For more information, see the IAM documentation.

PredictionService

A service for online predictions and explanations.

ChatCompletions

ChatCompletions
`rpc ChatCompletions(ChatCompletionsRequest) returns (HttpBody)` Exposes an OpenAI-compatible endpoint for chat completions. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc ChatCompletions(ChatCompletionsRequest) returns (HttpBody)

Exposes an OpenAI-compatible endpoint for chat completions.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

CountTokens

CountTokens
`rpc CountTokens(CountTokensRequest) returns (CountTokensResponse)` Perform a token counting. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc CountTokens(CountTokensRequest) returns (CountTokensResponse)

Perform a token counting.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

GenerateContent

GenerateContent
`rpc GenerateContent(GenerateContentRequest) returns (GenerateContentResponse)` Generate content with multimodal inputs. IAM Permissions Requires the following IAM permission on the `model` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc GenerateContent(GenerateContentRequest) returns (GenerateContentResponse)

Generate content with multimodal inputs.

IAM Permissions

Requires the following IAM permission on the model resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

Predict

Predict
`rpc Predict(PredictRequest) returns (PredictResponse)` Perform an online prediction. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc Predict(PredictRequest) returns (PredictResponse)

Perform an online prediction.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

ServerStreamingPredict

ServerStreamingPredict
`rpc ServerStreamingPredict(StreamingPredictRequest) returns (StreamingPredictResponse)` Perform a server-side streaming online prediction request for Vertex LLM streaming. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc ServerStreamingPredict(StreamingPredictRequest) returns (StreamingPredictResponse)

Perform a server-side streaming online prediction request for Vertex LLM streaming.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

StreamDirectPredict

StreamDirectPredict
`rpc StreamDirectPredict(StreamDirectPredictRequest) returns (StreamDirectPredictResponse)` Perform a streaming online prediction request to a gRPC model server for Vertex first-party products and frameworks. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc StreamDirectPredict(StreamDirectPredictRequest) returns (StreamDirectPredictResponse)

Perform a streaming online prediction request to a gRPC model server for Vertex first-party products and frameworks.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

StreamDirectRawPredict

StreamDirectRawPredict
`rpc StreamDirectRawPredict(StreamDirectRawPredictRequest) returns (StreamDirectRawPredictResponse)` Perform a streaming online prediction request to a gRPC model server for custom containers. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc StreamDirectRawPredict(StreamDirectRawPredictRequest) returns (StreamDirectRawPredictResponse)

Perform a streaming online prediction request to a gRPC model server for custom containers.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

StreamGenerateContent

StreamGenerateContent
`rpc StreamGenerateContent(GenerateContentRequest) returns (GenerateContentResponse)` Generate content with multimodal inputs with streaming support. IAM Permissions Requires the following IAM permission on the `model` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc StreamGenerateContent(GenerateContentRequest) returns (GenerateContentResponse)

Generate content with multimodal inputs with streaming support.

IAM Permissions

Requires the following IAM permission on the model resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

StreamingPredict

StreamingPredict
`rpc StreamingPredict(StreamingPredictRequest) returns (StreamingPredictResponse)` Perform a streaming online prediction request for Vertex first-party products and frameworks. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc StreamingPredict(StreamingPredictRequest) returns (StreamingPredictResponse)

Perform a streaming online prediction request for Vertex first-party products and frameworks.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

StreamingRawPredict

StreamingRawPredict
`rpc StreamingRawPredict(StreamingRawPredictRequest) returns (StreamingRawPredictResponse)` Perform a streaming online prediction request through gRPC. IAM Permissions Requires the following IAM permission on the `endpoint` resource: `aiplatform.endpoints.predict` For more information, see the IAM documentation.

rpc StreamingRawPredict(StreamingRawPredictRequest) returns (StreamingRawPredictResponse)

Perform a streaming online prediction request through gRPC.

IAM Permissions

Requires the following IAM permission on the endpoint resource:

aiplatform.endpoints.predict

For more information, see the IAM documentation.

ReasoningEngineExecutionService

A service for executing queries on Reasoning Engine.

QueryReasoningEngine

QueryReasoningEngine
`rpc QueryReasoningEngine(QueryReasoningEngineRequest) returns (QueryReasoningEngineResponse)` Queries using a reasoning engine. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.reasoningEngines.query` For more information, see the IAM documentation.

rpc QueryReasoningEngine(QueryReasoningEngineRequest) returns (QueryReasoningEngineResponse)

Queries using a reasoning engine.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.reasoningEngines.query

For more information, see the IAM documentation.

ReasoningEngineService

A service for managing Vertex AI's Reasoning Engines.

CreateReasoningEngine

CreateReasoningEngine
`rpc CreateReasoningEngine(CreateReasoningEngineRequest) returns (Operation)` Creates a reasoning engine. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.reasoningEngines.create` For more information, see the IAM documentation.

rpc CreateReasoningEngine(CreateReasoningEngineRequest) returns (Operation)

Creates a reasoning engine.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.reasoningEngines.create

For more information, see the IAM documentation.

DeleteReasoningEngine

DeleteReasoningEngine
`rpc DeleteReasoningEngine(DeleteReasoningEngineRequest) returns (Operation)` Deletes a reasoning engine. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.reasoningEngines.delete` For more information, see the IAM documentation.

rpc DeleteReasoningEngine(DeleteReasoningEngineRequest) returns (Operation)

Deletes a reasoning engine.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.reasoningEngines.delete

For more information, see the IAM documentation.

GetReasoningEngine

GetReasoningEngine
`rpc GetReasoningEngine(GetReasoningEngineRequest) returns (ReasoningEngine)` Gets a reasoning engine. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.reasoningEngines.get` For more information, see the IAM documentation.

rpc GetReasoningEngine(GetReasoningEngineRequest) returns (ReasoningEngine)

Gets a reasoning engine.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.reasoningEngines.get

For more information, see the IAM documentation.

ListReasoningEngines

ListReasoningEngines
`rpc ListReasoningEngines(ListReasoningEnginesRequest) returns (ListReasoningEnginesResponse)` Lists reasoning engines in a location. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.reasoningEngines.list` For more information, see the IAM documentation.

rpc ListReasoningEngines(ListReasoningEnginesRequest) returns (ListReasoningEnginesResponse)

Lists reasoning engines in a location.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.reasoningEngines.list

For more information, see the IAM documentation.

UpdateReasoningEngine

UpdateReasoningEngine
`rpc UpdateReasoningEngine(UpdateReasoningEngineRequest) returns (Operation)` Updates a reasoning engine. IAM Permissions Requires the following IAM permission on the `name` resource: `aiplatform.reasoningEngines.update` For more information, see the IAM documentation.

rpc UpdateReasoningEngine(UpdateReasoningEngineRequest) returns (Operation)

Updates a reasoning engine.

IAM Permissions

Requires the following IAM permission on the name resource:

aiplatform.reasoningEngines.update

For more information, see the IAM documentation.

VertexRagDataService

A service for managing user data for RAG.

CreateRagCorpus

CreateRagCorpus
`rpc CreateRagCorpus(CreateRagCorpusRequest) returns (Operation)` Creates a RagCorpus.

rpc CreateRagCorpus(CreateRagCorpusRequest) returns (Operation)

Creates a RagCorpus.

DeleteRagCorpus

DeleteRagCorpus
`rpc DeleteRagCorpus(DeleteRagCorpusRequest) returns (Operation)` Deletes a RagCorpus.

rpc DeleteRagCorpus(DeleteRagCorpusRequest) returns (Operation)

Deletes a RagCorpus.

DeleteRagFile

DeleteRagFile
`rpc DeleteRagFile(DeleteRagFileRequest) returns (Operation)` Deletes a RagFile.

rpc DeleteRagFile(DeleteRagFileRequest) returns (Operation)

Deletes a RagFile.

GetRagCorpus

GetRagCorpus
`rpc GetRagCorpus(GetRagCorpusRequest) returns (RagCorpus)` Gets a RagCorpus.

rpc GetRagCorpus(GetRagCorpusRequest) returns (RagCorpus)

Gets a RagCorpus.

GetRagFile

GetRagFile
`rpc GetRagFile(GetRagFileRequest) returns (RagFile)` Gets a RagFile.

rpc GetRagFile(GetRagFileRequest) returns (RagFile)

Gets a RagFile.

ImportRagFiles

ImportRagFiles
`rpc ImportRagFiles(ImportRagFilesRequest) returns (Operation)` Import files from Google Cloud Storage or Google Drive into a RagCorpus.

rpc ImportRagFiles(ImportRagFilesRequest) returns (Operation)

Import files from Google Cloud Storage or Google Drive into a RagCorpus.

ListRagCorpora

ListRagCorpora
`rpc ListRagCorpora(ListRagCorporaRequest) returns (ListRagCorporaResponse)` Lists RagCorpora in a Location.

rpc ListRagCorpora(ListRagCorporaRequest) returns (ListRagCorporaResponse)

Lists RagCorpora in a Location.

ListRagFiles

ListRagFiles
`rpc ListRagFiles(ListRagFilesRequest) returns (ListRagFilesResponse)` Lists RagFiles in a RagCorpus.

rpc ListRagFiles(ListRagFilesRequest) returns (ListRagFilesResponse)

Lists RagFiles in a RagCorpus.

UpdateRagCorpus

UpdateRagCorpus
`rpc UpdateRagCorpus(UpdateRagCorpusRequest) returns (Operation)` Updates a RagCorpus.

rpc UpdateRagCorpus(UpdateRagCorpusRequest) returns (Operation)

Updates a RagCorpus.

VertexRagService

A service for retrieving relevant contexts.

AugmentPrompt

AugmentPrompt
`rpc AugmentPrompt(AugmentPromptRequest) returns (AugmentPromptResponse)` Given an input prompt, it returns augmented prompt from vertex rag store to guide LLM towards generating grounded responses. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.locations.get` For more information, see the IAM documentation.

rpc AugmentPrompt(AugmentPromptRequest) returns (AugmentPromptResponse)

Given an input prompt, it returns augmented prompt from vertex rag store to guide LLM towards generating grounded responses.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.locations.get

For more information, see the IAM documentation.

CorroborateContent

CorroborateContent
`rpc CorroborateContent(CorroborateContentRequest) returns (CorroborateContentResponse)` Given an input text, it returns a score that evaluates the factuality of the text. It also extracts and returns claims from the text and provides supporting facts. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.locations.get` For more information, see the IAM documentation.

rpc CorroborateContent(CorroborateContentRequest) returns (CorroborateContentResponse)

Given an input text, it returns a score that evaluates the factuality of the text. It also extracts and returns claims from the text and provides supporting facts.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.locations.get

For more information, see the IAM documentation.

RetrieveContexts

RetrieveContexts
`rpc RetrieveContexts(RetrieveContextsRequest) returns (RetrieveContextsResponse)` Retrieves relevant contexts for a query. IAM Permissions Requires the following IAM permission on the `parent` resource: `aiplatform.locations.get` For more information, see the IAM documentation.

rpc RetrieveContexts(RetrieveContextsRequest) returns (RetrieveContextsResponse)

Retrieves relevant contexts for a query.

IAM Permissions

Requires the following IAM permission on the parent resource:

aiplatform.locations.get

For more information, see the IAM documentation.

ApiAuth

The generic reusable api auth config.

Fields

Union field auth_config. The auth config. auth_config can be only one of the following:

api_key_config ApiKeyConfig

The API secret.

ApiKeyConfig

The API secret.

Fields

api_key_secret_version string

Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}

AugmentPromptRequest

Request message for AugmentPrompt.

Fields

parent string

Required. The resource name of the Location from which to augment prompt. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.

contents[] Content

Optional. Input content to augment, only text format is supported for now.

model Model

Optional. Metadata of the backend deployed model.

Union field data_source. The data source for retrieving contexts. data_source can be only one of the following:

vertex_rag_store VertexRagStore

Optional. Retrieves contexts from the Vertex RagStore.

Model

Metadata of the backend deployed model.

Fields

model string

Optional. The model that the user will send the augmented prompt for content generation.

model_version string

Optional. The model version of the backend deployed model.

AugmentPromptResponse

Response message for AugmentPrompt.

Fields

augmented_prompt[] Content

Augmented prompt, only text format is supported for now.

facts[] Fact

Retrieved facts from RAG data sources.

AuthConfig

Auth configuration to run the extension.

Fields

auth_type AuthType

Type of auth scheme.

Union field auth_config.

auth_config can be only one of the following:

api_key_config ApiKeyConfig

Config for API key auth.

http_basic_auth_config HttpBasicAuthConfig

Config for HTTP Basic auth.

google_service_account_config GoogleServiceAccountConfig

Config for Google Service Account auth.

oauth_config OauthConfig

Config for user oauth.

oidc_config OidcConfig

Config for user OIDC auth.

ApiKeyConfig

Config for authentication with API key.

Fields

name string

Required. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.

api_key_secret string

Required. The name of the SecretManager secret version resource storing the API key. Format: projects/{project}/secrets/{secrete}/versions/{version}

If specified, the secretmanager.versions.access permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.

http_element_location HttpElementLocation

Required. The location of the API key.

GoogleServiceAccountConfig

Config for Google Service Account Authentication.

Fields

service_account string

Optional. The service account that the extension execution service runs as.

If the service account is specified, the iam.serviceAccounts.getAccessToken permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account.
If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.

HttpBasicAuthConfig

Config for HTTP Basic Authentication.

Fields

credential_secret string

Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: projects/{project}/secrets/{secrete}/versions/{version}

If specified, the secretmanager.versions.access permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.

OauthConfig

Config for user oauth.

Fields

Union field oauth_config.

oauth_config can be only one of the following:

access_token string

Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.

service_account string

The service account used to generate access tokens for executing the Extension.

If the service account is specified, the iam.serviceAccounts.getAccessToken permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.

OidcConfig

Config for user OIDC auth.

Fields

Union field oidc_config.

oidc_config can be only one of the following:

id_token string

OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.

service_account string

The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc).

The audience for the token will be set to the URL in the server url defined in the OpenApi spec.
If the service account is provided, the service account should grant iam.serviceAccounts.getOpenIdToken permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).

AuthType

Type of Auth.

Enums
`AUTH_TYPE_UNSPECIFIED`
`NO_AUTH`	No Auth.
`API_KEY_AUTH`	API Key Auth.
`HTTP_BASIC_AUTH`	HTTP Basic Auth.
`GOOGLE_SERVICE_ACCOUNT_AUTH`	Google Service Account Auth.
`OAUTH`	OAuth auth.
`OIDC_AUTH`	OpenID Connect (OIDC) Auth.

BigQueryDestination

The BigQuery location for the output content.

Fields

output_uri string

Required. BigQuery URI to a project or table, up to 2000 characters long.

When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist.

Accepted forms:

BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

BleuInput

Input for bleu metric.

Fields

metric_spec BleuSpec

Required. Spec for bleu score metric.

instances[] BleuInstance

Required. Repeated bleu instances.

BleuInstance

Spec for bleu instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

BleuMetricValue

Bleu metric value for an instance.

Fields

score float

Output only. Bleu score.

BleuResults

Results for bleu metric.

Fields

bleu_metric_values[] BleuMetricValue

Output only. Bleu metric values.

BleuSpec

Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.

Fields

use_effective_order bool

Optional. Whether to use_effective_order to compute bleu score.

Blob

Content blob.

It's preferred to send as text directly rather than raw bytes.

Fields

mime_type string

Required. The IANA standard MIME type of the source data.

data bytes

Required. Raw bytes.

CacheConfig

Config of GenAI caching features. This is a singleton resource.

Fields

name string

Identifier. Name of the cache config. Format: - projects/{project}/cacheConfig.

disable_cache bool

If set to true, disables GenAI caching. Otherwise caching is enabled.

CachedContent

A resource used in LLM queries for users to explicitly specify what to cache and how to cache.

Fields

name string

Immutable. Identifier. The server-generated resource name of the cached content Format: projects/{project}/locations/{location}/cachedContents/{cached_content}

display_name string

Optional. Immutable. The user-generated meaningful display name of the cached content.

model string

Immutable. The name of the publisher model to use for cached content. Format: projects/{project}/locations/{location}/publishers/{publisher}/models/{model}

system_instruction Content

Optional. Input only. Immutable. Developer set system instruction. Currently, text only

contents[] Content

Optional. Input only. Immutable. The content to cache

tools[] Tool

Optional. Input only. Immutable. A list of Tools the model may use to generate the next response

tool_config ToolConfig

Optional. Input only. Immutable. Tool config. This config is shared for all tools

create_time Timestamp

Output only. Creatation time of the cache entry.

update_time Timestamp

Output only. When the cache entry was last updated in UTC time.

usage_metadata UsageMetadata

Output only. Metadata on the usage of the cached content.

Union field expiration. Expiration time of the cached content. expiration can be only one of the following:

expire_time Timestamp

Timestamp of when this resource is considered expired. This is always provided on output, regardless of what was sent on input.

ttl Duration

Input only. The TTL for this resource. The expiration time is computed: now + TTL.

UsageMetadata

Metadata on the usage of the cached content.

Fields

total_token_count int32

Total number of tokens that the cached content consumes.

text_count int32

Number of text characters.

image_count int32

Number of images.

video_duration_seconds int32

Duration of video in seconds.

audio_duration_seconds int32

Duration of audio in seconds.

CancelTuningJobRequest

Request message for GenAiTuningService.CancelTuningJob.

Fields

name string

Required. The name of the TuningJob to cancel. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

Candidate

A response candidate generated from the model.

Fields

index int32

Output only. Index of the candidate.

content Content

Output only. Content parts of the candidate.

avg_logprobs double

Output only. Average log probability score of the candidate.

logprobs_result LogprobsResult

Output only. Log-likelihood scores for the response tokens and top tokens

finish_reason FinishReason

Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

safety_ratings[] SafetyRating

Output only. List of ratings for the safety of a response candidate.

There is at most one rating per category.

citation_metadata CitationMetadata

Output only. Source attribution of the generated content.

grounding_metadata GroundingMetadata

Output only. Metadata specifies sources used to ground generated content.

finish_message string

Output only. Describes the reason the mode stopped generating tokens in more detail. This is only filled when finish_reason is set.

FinishReason

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

Enums
`FINISH_REASON_UNSPECIFIED`	The finish reason is unspecified.
`STOP`	Token generation reached a natural stopping point or a configured stop sequence.
`MAX_TOKENS`	Token generation reached the configured maximum output tokens.
`SAFETY`	Token generation stopped because the content potentially contains safety violations. NOTE: When streaming, `content` is empty if content filters blocks the output.
`RECITATION`	Token generation stopped because the content potentially contains copyright violations.
`OTHER`	All other reasons that stopped the token generation.
`BLOCKLIST`	Token generation stopped because the content contains forbidden terms.
`PROHIBITED_CONTENT`	Token generation stopped for potentially containing prohibited content.
`SPII`	Token generation stopped because the content potentially contains Sensitive Personally Identifiable Information (SPII).
`MALFORMED_FUNCTION_CALL`	The function call generated by the model is invalid.

ChatCompletionsRequest

Request message for [PredictionService.ChatCompletions]

Fields

endpoint string

Required. The name of the endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

http_body HttpBody

Optional. The prediction input. Supports HTTP headers and arbitrary data payload.

Citation

Source attributions for content.

Fields

start_index int32

Output only. Start index into the content.

end_index int32

Output only. End index into the content.

uri string

Output only. Url reference of the attribution.

title string

Output only. Title of the attribution.

license string

Output only. License of the attribution.

publication_date Date

Output only. Publication date of the attribution.

CitationMetadata

A collection of source attributions for a piece of content.

Fields

citations[] Citation

Output only. List of citations.

Claim

Claim that is extracted from the input text and facts that support it.

Fields

fact_indexes[] int32

Indexes of the facts supporting this claim.

start_index int32

Index in the input text where the claim starts (inclusive).

end_index int32

Index in the input text where the claim ends (exclusive).

score float

Confidence score of this corroboration.

CodeExecutionResult

Result of executing the [ExecutableCode].

Always follows a part containing the [ExecutableCode].

Fields

outcome Outcome

Required. Outcome of the code execution.

output string

Optional. Contains stdout when code execution is successful, stderr or other description otherwise.

Outcome

Enumeration of possible outcomes of the code execution.

Enums
`OUTCOME_UNSPECIFIED`	Unspecified status. This value should not be used.
`OUTCOME_OK`	Code execution completed successfully.
`OUTCOME_FAILED`	Code execution finished but with a failure. `stderr` should contain the reason.
`OUTCOME_DEADLINE_EXCEEDED`	Code execution ran for too long, and was cancelled. There may or may not be a partial output present.

CoherenceInput

Input for coherence metric.

Fields

metric_spec CoherenceSpec

Required. Spec for coherence score metric.

instance CoherenceInstance

Required. Coherence instance.

CoherenceInstance

Spec for coherence instance.

Fields

prediction string

Required. Output of the evaluated model.

CoherenceResult

Spec for coherence result.

Fields

explanation string

Output only. Explanation for coherence score.

score float

Output only. Coherence score.

confidence float

Output only. Confidence for coherence score.

CoherenceSpec

Spec for coherence score metric.

Fields

version int32

Optional. Which version to use for evaluation.

Content

The base structured datatype containing multi-part content of a message.

A Content includes a role field designating the producer of the Content and a parts field containing multi-part data that contains the content of the message turn.

Fields

role string

Optional. The producer of the content. Must be either 'user' or 'model'.

Useful to set for multi-turn conversations, otherwise can be left blank or unset.

parts[] Part

Required. Ordered Parts that constitute a single message. Parts may have different IANA MIME types.

CorpusStatus

RagCorpus status.

Fields

state State

Output only. RagCorpus life state.

error_status string

Output only. Only when the state field is ERROR.

State

RagCorpus life state.

Enums
`UNKNOWN`	This state is not supposed to happen.
`INITIALIZED`	RagCorpus resource entry is initialized, but hasn't done validation.
`ACTIVE`	RagCorpus is provisioned successfully and is ready to serve.
`ERROR`	RagCorpus is in a problematic situation. See `error_message` field for details.

CorroborateContentRequest

Request message for CorroborateContent.

Fields

parent string

Required. The resource name of the Location from which to corroborate text. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.

facts[] Fact

Optional. Facts used to generate the text can also be used to corroborate the text.

parameters Parameters

Optional. Parameters that can be set to override default settings per request.

content Content

Optional. Input content to corroborate, only text format is supported for now.

Parameters

Parameters that can be overrided per request.

Fields

citation_threshold double

Optional. Only return claims with citation score larger than the threshold.

CorroborateContentResponse

Response message for CorroborateContent.

Fields

claims[] Claim

Claims that are extracted from the input content and facts that support the claims.

corroboration_score float

Confidence score of corroborating content. Value is [0,1] with 1 is the most confidence.

CountTokensRequest

Request message for PredictionService.CountTokens.

Fields

endpoint string

Required. The name of the Endpoint requested to perform token counting. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

model string

Optional. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*

instances[] Value

Optional. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model.

contents[] Content

Optional. Input content.

tools[] Tool

Optional. A list of Tools the model may use to generate the next response.

A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.

system_instruction Content

Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.

generation_config GenerationConfig

Optional. Generation config that the model will use to generate the response.

CountTokensResponse

Response message for PredictionService.CountTokens.

Fields

total_tokens int32

The total number of tokens counted across all instances from the request.

total_billable_characters int32

The total number of billable characters counted across all instances from the request.

CreateCachedContentRequest

Request message for GenAiCacheService.CreateCachedContent.

Fields

parent string

Required. The parent resource where the cached content will be created

cached_content CachedContent

Required. The cached content to create

CreateRagCorpusOperationMetadata

Runtime operation information for VertexRagDataService.CreateRagCorpus.

Fields

generic_metadata GenericOperationMetadata

The operation generic information.

CreateRagCorpusRequest

Request message for VertexRagDataService.CreateRagCorpus.

Fields

parent string

Required. The resource name of the Location to create the RagCorpus in. Format: projects/{project}/locations/{location}

rag_corpus RagCorpus

Required. The RagCorpus to create.

CreateReasoningEngineOperationMetadata

Details of ReasoningEngineService.CreateReasoningEngine operation.

Fields

generic_metadata GenericOperationMetadata

The common part of the operation metadata.

CreateReasoningEngineRequest

Request message for ReasoningEngineService.CreateReasoningEngine.

Fields

parent string

Required. The resource name of the Location to create the ReasoningEngine in. Format: projects/{project}/locations/{location}

reasoning_engine ReasoningEngine

Required. The ReasoningEngine to create.

CreateTuningJobRequest

Request message for GenAiTuningService.CreateTuningJob.

Fields

parent string

Required. The resource name of the Location to create the TuningJob in. Format: projects/{project}/locations/{location}

tuning_job TuningJob

Required. The TuningJob to create.

DatasetDistribution

Distribution computed over a tuning dataset.

Fields

sum double

Output only. Sum of a given population of values.

min double

Output only. The minimum of the population values.

max double

Output only. The maximum of the population values.

mean double

Output only. The arithmetic mean of the values in the population.

median double

Output only. The median of the values in the population.

p5 double

Output only. The 5th percentile of the values in the population.

p95 double

Output only. The 95th percentile of the values in the population.

buckets[] DistributionBucket

Output only. Defines the histogram bucket.

DistributionBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

Fields

count int64

Output only. Number of values in the bucket.

left double

Output only. Left bound of the bucket.

right double

Output only. Right bound of the bucket.

DatasetStats

Statistics computed over a tuning dataset.

Fields

tuning_dataset_example_count int64

Output only. Number of examples in the tuning dataset.

total_tuning_character_count int64

Output only. Number of tuning characters in the tuning dataset.

total_billable_character_count int64

Output only. Number of billable characters in the tuning dataset.

tuning_step_count int64

Output only. Number of tuning steps for this Tuning Job.

user_input_token_distribution DatasetDistribution

Output only. Dataset distributions for the user input tokens.

user_message_per_example_distribution DatasetDistribution

Output only. Dataset distributions for the messages per example.

user_dataset_examples[] Content

Output only. Sample user messages in the training dataset uri.

user_output_token_distribution DatasetDistribution

Output only. Dataset distributions for the user output tokens.

DeleteCachedContentRequest

Request message for GenAiCacheService.DeleteCachedContent.

Fields

name string

Required. The resource name referring to the cached content

DeleteExtensionRequest

Request message for ExtensionRegistryService.DeleteExtension.

Fields

name string

Required. The name of the Extension resource to be deleted. Format: projects/{project}/locations/{location}/extensions/{extension}

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

Fields

generic_metadata GenericOperationMetadata

The common part of the operation metadata.

DeleteRagCorpusRequest

Request message for VertexRagDataService.DeleteRagCorpus.

Fields

name string

Required. The name of the RagCorpus resource to be deleted. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

force bool

Optional. If set to true, any RagFiles in this RagCorpus will also be deleted. Otherwise, the request will only work if the RagCorpus has no RagFiles.

DeleteRagFileRequest

Request message for VertexRagDataService.DeleteRagFile.

Fields

name string

Required. The name of the RagFile resource to be deleted. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}

DeleteReasoningEngineRequest

Request message for ReasoningEngineService.DeleteReasoningEngine.

Fields

name string

Required. The name of the ReasoningEngine resource to be deleted. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}

DirectUploadSource

This type has no fields.

The input content is encapsulated and uploaded in the request.

DistillationDataStats

Statistics computed for datasets used for distillation.

Fields

training_dataset_stats DatasetStats

Output only. Statistics computed for the training dataset.

DistillationHyperParameters

Hyperparameters for Distillation.

Fields

adapter_size AdapterSize

Optional. Adapter size for distillation.

epoch_count int64

Optional. Number of complete passes the model makes over the entire training dataset during training.

learning_rate_multiplier double

Optional. Multiplier for adjusting the default learning rate.

DistillationSpec

Tuning Spec for Distillation.

Fields

training_dataset_uri string

Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

hyper_parameters DistillationHyperParameters

Optional. Hyperparameters for Distillation.

student_model string

The student model that is being tuned, e.g., "google/gemma-2b-1.1-it".

pipeline_root_directory string

Required. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.

Union field teacher_model. The teacher model that is being distilled from, e.g., "gemini-1.0-pro-002". teacher_model can be only one of the following:

base_teacher_model string

The base teacher model that is being distilled, e.g., "gemini-1.0-pro-002".

tuned_teacher_model_source string

The resource name of the Tuned teacher model. Format: projects/{project}/locations/{location}/models/{model}.

validation_dataset_uri string

Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

DynamicRetrievalConfig

Describes the options to customize dynamic retrieval.

Fields

mode Mode

The mode of the predictor to be used in dynamic retrieval.

dynamic_threshold float

Optional. The threshold to be used in dynamic retrieval. If not set, a system default value is used.

Mode

The mode of the predictor to be used in dynamic retrieval.

Enums
`MODE_UNSPECIFIED`	Always trigger retrieval.
`MODE_DYNAMIC`	Run retrieval only when system decides it is necessary.

EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Fields

kms_key_name string

Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

EvaluateInstancesRequest

Request message for EvaluationService.EvaluateInstances.

Fields

location string

Required. The resource name of the Location to evaluate the instances. Format: projects/{project}/locations/{location}

Union field metric_inputs. Instances and specs for evaluation metric_inputs can be only one of the following:

exact_match_input ExactMatchInput

Auto metric instances. Instances and metric spec for exact match metric.

bleu_input BleuInput

Instances and metric spec for bleu metric.

rouge_input RougeInput

Instances and metric spec for rouge metric.

fluency_input FluencyInput

LLM-based metric instance. General text generation metrics, applicable to other categories. Input for fluency metric.

coherence_input CoherenceInput

Input for coherence metric.

safety_input SafetyInput

Input for safety metric.

groundedness_input GroundednessInput

Input for groundedness metric.

fulfillment_input FulfillmentInput

Input for fulfillment metric.

summarization_quality_input SummarizationQualityInput

Input for summarization quality metric.

pairwise_summarization_quality_input PairwiseSummarizationQualityInput

Input for pairwise summarization quality metric.

summarization_helpfulness_input SummarizationHelpfulnessInput

Input for summarization helpfulness metric.

summarization_verbosity_input SummarizationVerbosityInput

Input for summarization verbosity metric.

question_answering_quality_input QuestionAnsweringQualityInput

Input for question answering quality metric.

pairwise_question_answering_quality_input PairwiseQuestionAnsweringQualityInput

Input for pairwise question answering quality metric.

question_answering_relevance_input QuestionAnsweringRelevanceInput

Input for question answering relevance metric.

question_answering_helpfulness_input QuestionAnsweringHelpfulnessInput

Input for question answering helpfulness metric.

question_answering_correctness_input QuestionAnsweringCorrectnessInput

Input for question answering correctness metric.

pointwise_metric_input PointwiseMetricInput

Input for pointwise metric.

pairwise_metric_input PairwiseMetricInput

Input for pairwise metric.

tool_call_valid_input ToolCallValidInput

Tool call metric instances. Input for tool call valid metric.

tool_name_match_input ToolNameMatchInput

Input for tool name match metric.

tool_parameter_key_match_input ToolParameterKeyMatchInput

Input for tool parameter key match metric.

tool_parameter_kv_match_input ToolParameterKVMatchInput

Input for tool parameter key value match metric.

EvaluateInstancesResponse

Response message for EvaluationService.EvaluateInstances.

Fields

Union field evaluation_results. Evaluation results will be served in the same order as presented in EvaluationRequest.instances. evaluation_results can be only one of the following:

exact_match_results ExactMatchResults

Auto metric evaluation results. Results for exact match metric.

bleu_results BleuResults

Results for bleu metric.

rouge_results RougeResults

Results for rouge metric.

fluency_result FluencyResult

LLM-based metric evaluation result. General text generation metrics, applicable to other categories. Result for fluency metric.

coherence_result CoherenceResult

Result for coherence metric.

safety_result SafetyResult

Result for safety metric.

groundedness_result GroundednessResult

Result for groundedness metric.

fulfillment_result FulfillmentResult

Result for fulfillment metric.

summarization_quality_result SummarizationQualityResult

Summarization only metrics. Result for summarization quality metric.

pairwise_summarization_quality_result PairwiseSummarizationQualityResult

Result for pairwise summarization quality metric.

summarization_helpfulness_result SummarizationHelpfulnessResult

Result for summarization helpfulness metric.

summarization_verbosity_result SummarizationVerbosityResult

Result for summarization verbosity metric.

question_answering_quality_result QuestionAnsweringQualityResult

Question answering only metrics. Result for question answering quality metric.

pairwise_question_answering_quality_result PairwiseQuestionAnsweringQualityResult

Result for pairwise question answering quality metric.

question_answering_relevance_result QuestionAnsweringRelevanceResult

Result for question answering relevance metric.

question_answering_helpfulness_result QuestionAnsweringHelpfulnessResult

Result for question answering helpfulness metric.

question_answering_correctness_result QuestionAnsweringCorrectnessResult

Result for question answering correctness metric.

pointwise_metric_result PointwiseMetricResult

Generic metrics. Result for pointwise metric.

pairwise_metric_result PairwiseMetricResult

Result for pairwise metric.

tool_call_valid_results ToolCallValidResults

Tool call metrics. Results for tool call valid metric.

tool_name_match_results ToolNameMatchResults

Results for tool name match metric.

tool_parameter_key_match_results ToolParameterKeyMatchResults

Results for tool parameter key match metric.

tool_parameter_kv_match_results ToolParameterKVMatchResults

Results for tool parameter key value match metric.

ExactMatchInput

Input for exact match metric.

Fields

metric_spec ExactMatchSpec

Required. Spec for exact match metric.

instances[] ExactMatchInstance

Required. Repeated exact match instances.

ExactMatchInstance

Spec for exact match instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

ExactMatchMetricValue

Exact match metric value for an instance.

Fields

score float

Output only. Exact match score.

ExactMatchResults

Results for exact match metric.

Fields

exact_match_metric_values[] ExactMatchMetricValue

Output only. Exact match metric values.

ExactMatchSpec

This type has no fields.

Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0.

ExecutableCode

Code generated by the model that is meant to be executed, and the result returned to the model.

Generated when using the [FunctionDeclaration] tool and [FunctionCallingConfig] mode is set to [Mode.CODE].

Fields

language Language

Required. Programming language of the code.

code string

Required. The code to be executed.

Language

Supported programming languages for the generated code.

Enums
`LANGUAGE_UNSPECIFIED`	Unspecified language. This value should not be used.
`PYTHON`	Python >= 3.10, with numpy and simpy available.

ExecuteExtensionRequest

Request message for ExtensionExecutionService.ExecuteExtension.

Fields

name string

Required. Name (identifier) of the extension; Format: projects/{project}/locations/{location}/extensions/{extension}

operation_id string

Required. The desired ID of the operation to be executed in this extension as defined in ExtensionOperation.operation_id.

operation_params Struct

Optional. Request parameters that will be used for executing this operation.

The struct should be in a form of map with param name as the key and actual param value as the value. E.g. If this operation requires a param "name" to be set to "abc". you can set this to something like {"name": "abc"}.

runtime_auth_config AuthConfig

Optional. Auth config provided at runtime to override the default value in [Extension.manifest.auth_config][]. The AuthConfig.auth_type should match the value in [Extension.manifest.auth_config][].

ExecuteExtensionResponse

Response message for ExtensionExecutionService.ExecuteExtension.

Fields

content string

Response content from the extension. The content should be conformant to the response.content schema in the extension's manifest/OpenAPI spec.

Extension

Extensions are tools for large language models to access external data, run computations, etc.

Fields

name string

Identifier. The resource name of the Extension.

display_name string

Required. The display name of the Extension. The name can be up to 128 characters long and can consist of any UTF-8 characters.

description string

Optional. The description of the Extension.

create_time Timestamp

Output only. Timestamp when this Extension was created.

update_time Timestamp

Output only. Timestamp when this Extension was most recently updated.

etag string

Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

manifest ExtensionManifest

Required. Manifest of the Extension.

extension_operations[] ExtensionOperation

Output only. Supported operations.

runtime_config RuntimeConfig

Optional. Runtime config controlling the runtime behavior of this Extension.

tool_use_examples[] ToolUseExample

Optional. Examples to illustrate the usage of the extension as a tool.

private_service_connect_config ExtensionPrivateServiceConnectConfig

Optional. The PrivateServiceConnect config for the extension. If specified, the service endpoints associated with the Extension should be registered with private network access in the provided Service Directory (https://cloud.google.com/service-directory/docs/configuring-private-network-access).

If the service contains more than one endpoint with a network, the service will arbitrarilty choose one of the endpoints to use for extension execution.

ExtensionManifest

Manifest spec of an Extension needed for runtime execution.

Fields

name string

Required. Extension name shown to the LLM. The name can be up to 128 characters long.

description string

Required. The natural language description shown to the LLM. It should describe the usage of the extension, and is essential for the LLM to perform reasoning. e.g., if the extension is a data store, you can let the LLM know what data it contains.

api_spec ApiSpec

Required. Immutable. The API specification shown to the LLM.

auth_config AuthConfig

Required. Immutable. Type of auth supported by this extension.

ApiSpec

The API specification shown to the LLM.

Fields

Union field api_spec.

api_spec can be only one of the following:

open_api_yaml string

The API spec in Open API standard and YAML format.

open_api_gcs_uri string

Cloud Storage URI pointing to the OpenAPI spec.

ExtensionOperation

Operation of an extension.

Fields

operation_id string

Operation ID that uniquely identifies the operations among the extension. See: "Operation Object" in https://swagger.io/specification/.

This field is parsed from the OpenAPI spec. For HTTP extensions, if it does not exist in the spec, we will generate one from the HTTP method and path.

function_declaration FunctionDeclaration

Output only. Structured representation of a function declaration as defined by the OpenAPI Spec.

ExtensionPrivateServiceConnectConfig

PrivateExtensionConfig configuration for the extension.

Fields

service_directory string

Required. The Service Directory resource name in which the service endpoints associated to the extension are registered. Format: projects/{project_id}/locations/{location_id}/namespaces/{namespace_id}/services/{service_id}

The Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) should be granted servicedirectory.viewer and servicedirectory.pscAuthorizedService roles on the resource.

Fact

The fact used in grounding.

Fields

query string

Query that is used to retrieve this fact.

title string

If present, it refers to the title of this fact.

uri string

If present, this uri links to the source of the fact.

summary string

If present, the summary/snippet of the fact.

vector_distance double

If present, the distance between the query vector and this fact vector.

FileData

URI based data.

Fields

mime_type string

Required. The IANA standard MIME type of the source data.

file_uri string

Required. URI.

FileStatus

RagFile status.

Fields

state State

Output only. RagFile state.

error_status string

Output only. Only when the state field is ERROR.

State

RagFile state.

Enums
`STATE_UNSPECIFIED`	RagFile state is unspecified.
`ACTIVE`	RagFile resource has been created and indexed successfully.
`ERROR`	RagFile resource is in a problematic state. See `error_message` field for details.

FluencyInput

Input for fluency metric.

Fields

metric_spec FluencySpec

Required. Spec for fluency score metric.

instance FluencyInstance

Required. Fluency instance.

FluencyInstance

Spec for fluency instance.

Fields

prediction string

Required. Output of the evaluated model.

FluencyResult

Spec for fluency result.

Fields

explanation string

Output only. Explanation for fluency score.

score float

Output only. Fluency score.

confidence float

Output only. Confidence for fluency score.

FluencySpec

Spec for fluency score metric.

Fields

version int32

Optional. Which version to use for evaluation.

FulfillmentInput

Input for fulfillment metric.

Fields

metric_spec FulfillmentSpec

Required. Spec for fulfillment score metric.

instance FulfillmentInstance

Required. Fulfillment instance.

FulfillmentInstance

Spec for fulfillment instance.

Fields

prediction string

Required. Output of the evaluated model.

instruction string

Required. Inference instruction prompt to compare prediction with.

FulfillmentResult

Spec for fulfillment result.

Fields

explanation string

Output only. Explanation for fulfillment score.

score float

Output only. Fulfillment score.

confidence float

Output only. Confidence for fulfillment score.

FulfillmentSpec

Spec for fulfillment metric.

Fields

version int32

Optional. Which version to use for evaluation.

FunctionCall

A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values.

Fields

name string

Required. The name of the function to call. Matches [FunctionDeclaration.name].

args Struct

Optional. Required. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.

FunctionCallingConfig

Function calling config.

Fields

mode Mode

Optional. Function calling mode.

allowed_function_names[] string

Optional. Function names to call. Only set when the Mode is ANY. Function names should match [FunctionDeclaration.name]. With mode set to ANY, model will predict a function call from the set of function names provided.

Mode

Function calling mode.

Enums
`MODE_UNSPECIFIED`	Unspecified function calling mode. This value should not be used.
`AUTO`	Default model behavior, model decides to predict either function calls or natural language response.
`ANY`	Model is constrained to always predicting function calls only. If "allowed_function_names" are set, the predicted function calls will be limited to any one of "allowed_function_names", else the predicted function calls will be any one of the provided "function_declarations".
`NONE`	Model will not predict any function calls. Model behavior is same as when not passing any function declarations.

FunctionDeclaration

Structured representation of a function declaration as defined by the OpenAPI 3.0 specification. Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.

Fields

name string

Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64.

description string

Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function.

parameters Schema

Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1

response Schema

Optional. Describes the output from this function in JSON Schema format. Reflects the Open API 3.03 Response Object. The Schema defines the type used for the response value of the function.

FunctionResponse

The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction.

Fields

name string

Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].

response Struct

Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.

GcsDestination

The Google Cloud Storage location where the output is to be written to.

Fields

output_uri_prefix string

Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GcsSource

The Google Cloud Storage location for the input content.

Fields

uris[] string

Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

GenerateContentRequest

Request message for [PredictionService.GenerateContent].

Fields

model string

Required. The fully qualified name of the publisher model or tuned model endpoint to use.

Publisher model format: projects/{project}/locations/{location}/publishers/*/models/*

Tuned model endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}

contents[] Content

Required. The content of the current conversation with the model.

For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.

cached_content string

Optional. The name of the cached content used as context to serve the prediction. Note: only used in explicit caching, where users can have control over caching (e.g. what content to cache) and enjoy guaranteed cost savings. Format: projects/{project}/locations/{location}/cachedContents/{cachedContent}

tools[] Tool

Optional. A list of Tools the model may use to generate the next response.

A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.

tool_config ToolConfig

Optional. Tool config. This config is shared for all tools provided in the request.

labels map<string, string>

Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only.

Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.

safety_settings[] SafetySetting

Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.

generation_config GenerationConfig

Optional. Generation config.

system_instruction Content

Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.

GenerateContentResponse

Response message for [PredictionService.GenerateContent].

Fields

candidates[] Candidate

Output only. Generated candidates.

model_version string

Output only. The model version used to generate the response.

prompt_feedback PromptFeedback

Output only. Content filter results for a prompt sent in the request. Note: Sent only in the first stream chunk. Only happens when no candidates were generated due to content violations.

usage_metadata UsageMetadata

Usage metadata about the response(s).

PromptFeedback

Content filter results for a prompt sent in the request.

Fields

block_reason BlockedReason

Output only. Blocked reason.

safety_ratings[] SafetyRating

Output only. Safety ratings.

block_reason_message string

Output only. A readable block reason message.

BlockedReason

Blocked reason enumeration.

Enums
`BLOCKED_REASON_UNSPECIFIED`	Unspecified blocked reason.
`SAFETY`	Candidates blocked due to safety.
`OTHER`	Candidates blocked due to other reason.
`BLOCKLIST`	Candidates blocked due to the terms which are included from the terminology blocklist.
`PROHIBITED_CONTENT`	Candidates blocked due to prohibited content.

UsageMetadata

Usage metadata about response(s).

Fields

prompt_token_count int32

Number of tokens in the request. When cached_content is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.

candidates_token_count int32

Number of tokens in the response(s).

total_token_count int32

Total token count for prompt and response candidates.

cached_content_token_count int32

Output only. Number of tokens in the cached part in the input (the cached content).

GenerationConfig

Generation config.

Fields

stop_sequences[] string

Optional. Stop sequences.

response_mime_type string

Optional. Output response mimetype of the generated candidate text. Supported mimetype: - text/plain: (default) Text output. - application/json: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.

temperature float

Optional. Controls the randomness of predictions.

top_p float

Optional. If specified, nucleus sampling will be used.

top_k float

Optional. If specified, top-k sampling will be used.

candidate_count int32

Optional. Number of candidates to generate.

max_output_tokens int32

Optional. The maximum number of output tokens to generate per message.

response_logprobs bool

Optional. If true, export the logprobs results in response.

logprobs int32

Optional. Logit probabilities.

presence_penalty float

Optional. Positive penalties.

frequency_penalty float

Optional. Frequency penalties.

seed int32

Optional. Seed.

response_schema Schema

Optional. The Schema object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an OpenAPI 3.0 schema object. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response.

routing_config RoutingConfig

Optional. Routing configuration.

audio_timestamp bool

Optional. If enabled, audio timestamp will be included in the request to the model.

RoutingConfig

The configuration for routing the request to a specific model.

Fields

Union field routing_config. Routing mode. routing_config can be only one of the following:

auto_mode AutoRoutingMode

Automated routing.

manual_mode ManualRoutingMode

Manual routing.

AutoRoutingMode

When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference.

Fields

model_routing_preference ModelRoutingPreference

The model routing preference.

ModelRoutingPreference

The model routing preference.

Enums
`UNKNOWN`	Unspecified model routing preference.
`PRIORITIZE_QUALITY`	Prefer higher quality over low cost.
`BALANCED`	Balanced model routing preference.
`PRIORITIZE_COST`	Prefer lower cost over higher quality.

ManualRoutingMode

When manual routing is set, the specified model will be used directly.

Fields

model_name string

The model name to use. Only the public LLM models are accepted. e.g. 'gemini-1.5-pro-001'.

GenericOperationMetadata

Generic Metadata shared by all operations.

Fields

partial_failures[] Status

Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard Google Cloud error details.

create_time Timestamp

Output only. Time when the operation was created.

update_time Timestamp

Output only. Time when the operation was updated for the last time. If the operation has finished (successfully or not), this is the finish time.

GetCacheConfigRequest

Request message for getting a cache config.

Fields

name string

Required. Name of the cache config. Format: - projects/{project}/cacheConfig.

GetCachedContentRequest

Request message for GenAiCacheService.GetCachedContent.

Fields

name string

Required. The resource name referring to the cached content

GetExtensionRequest

Request message for ExtensionRegistryService.GetExtension.

Fields

name string

Required. The name of the Extension resource. Format: projects/{project}/locations/{location}/extensions/{extension}

GetRagCorpusRequest

Request message for VertexRagDataService.GetRagCorpus

Fields

name string

Required. The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

GetRagFileRequest

Request message for VertexRagDataService.GetRagFile

Fields

name string

Required. The name of the RagFile resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}

GetReasoningEngineRequest

Request message for ReasoningEngineService.GetReasoningEngine.

Fields

name string

Required. The name of the ReasoningEngine resource. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}

GetTuningJobRequest

Request message for GenAiTuningService.GetTuningJob.

Fields

name string

Required. The name of the TuningJob resource. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

GoogleDriveSource

The Google Drive location for the input content.

Fields

resource_ids[] ResourceId

Required. Google Drive resource IDs.

ResourceId

The type and ID of the Google Drive resource.

Fields

resource_type ResourceType

Required. The type of the Google Drive resource.

resource_id string

Required. The ID of the Google Drive resource.

ResourceType

The type of the Google Drive resource.

Enums
`RESOURCE_TYPE_UNSPECIFIED`	Unspecified resource type.
`RESOURCE_TYPE_FILE`	File resource type.
`RESOURCE_TYPE_FOLDER`	Folder resource type.

GoogleSearchRetrieval

Tool to retrieve public web data for grounding, powered by Google.

Fields

dynamic_retrieval_config DynamicRetrievalConfig

Specifies the dynamic retrieval configuration for the given source.

GroundednessInput

Input for groundedness metric.

Fields

metric_spec GroundednessSpec

Required. Spec for groundedness metric.

instance GroundednessInstance

Required. Groundedness instance.

GroundednessInstance

Spec for groundedness instance.

Fields

prediction string

Required. Output of the evaluated model.

context string

Required. Background information provided in context used to compare against the prediction.

GroundednessResult

Spec for groundedness result.

Fields

explanation string

Output only. Explanation for groundedness score.

score float

Output only. Groundedness score.

confidence float

Output only. Confidence for groundedness score.

GroundednessSpec

Spec for groundedness metric.

Fields

version int32

Optional. Which version to use for evaluation.

GroundingChunk

Grounding chunk.

Fields

Union field chunk_type. Chunk type. chunk_type can be only one of the following:

web Web

Grounding chunk from the web.

retrieved_context RetrievedContext

Grounding chunk from context retrieved by the retrieval tools.

RetrievedContext

Chunk from context retrieved by the retrieval tools.

Fields

uri string

URI reference of the attribution.

title string

Title of the attribution.

text string

Text of the attribution.

Web

Chunk from the web.

Fields

uri string

URI reference of the chunk.

title string

Title of the chunk.

GroundingMetadata

Metadata returned to client when grounding is enabled.

Fields

web_search_queries[] string

Optional. Web search queries for the following-up web search.

retrieval_queries[] string

Optional. Queries executed by the retrieval tools.

grounding_chunks[] GroundingChunk

List of supporting references retrieved from specified grounding source.

grounding_supports[] GroundingSupport

Optional. List of grounding support.

search_entry_point SearchEntryPoint

Optional. Google search entry for the following-up web searches.

retrieval_metadata RetrievalMetadata

Optional. Output only. Retrieval metadata.

GroundingSupport

Grounding support.

Fields

grounding_chunk_indices[] int32

A list of indices (into 'grounding_chunk') specifying the citations associated with the claim. For instance [1,3,4] means that grounding_chunk[1], grounding_chunk[3], grounding_chunk[4] are the retrieved content attributed to the claim.

confidence_scores[] float

Confidence score of the support references. Ranges from 0 to 1. 1 is the most confident. This list must have the same size as the grounding_chunk_indices.

segment Segment

Segment of the content this support belongs to.

HarmCategory

Harm categories that will block the content.

Enums
`HARM_CATEGORY_UNSPECIFIED`	The harm category is unspecified.
`HARM_CATEGORY_HATE_SPEECH`	The harm category is hate speech.
`HARM_CATEGORY_DANGEROUS_CONTENT`	The harm category is dangerous content.
`HARM_CATEGORY_HARASSMENT`	The harm category is harassment.
`HARM_CATEGORY_SEXUALLY_EXPLICIT`	The harm category is sexually explicit content.
`HARM_CATEGORY_CIVIC_INTEGRITY`	The harm category is civic integrity.

HttpElementLocation

Enum of location an HTTP element can be.

Enums
`HTTP_IN_UNSPECIFIED`
`HTTP_IN_QUERY`	Element is in the HTTP request query.
`HTTP_IN_HEADER`	Element is in the HTTP request header.
`HTTP_IN_PATH`	Element is in the HTTP request path.
`HTTP_IN_BODY`	Element is in the HTTP request body.
`HTTP_IN_COOKIE`	Element is in the HTTP request cookie.

ImportExtensionOperationMetadata

Details of ExtensionRegistryService.ImportExtension operation.

Fields

generic_metadata GenericOperationMetadata

The common part of the operation metadata.

ImportExtensionRequest

Request message for ExtensionRegistryService.ImportExtension.

Fields

parent string

Required. The resource name of the Location to import the Extension in. Format: projects/{project}/locations/{location}

extension Extension

Required. The Extension to import.

ImportRagFilesConfig

Config for importing RagFiles.

Fields

rag_file_chunking_config RagFileChunkingConfig

Specifies the size and overlap of chunks after importing RagFiles.

rag_file_parsing_config RagFileParsingConfig

Specifies the parsing config for RagFiles.

max_embedding_requests_per_min int32

Optional. The max number of queries per minute that this job is allowed to make to the embedding model specified on the corpus. This value is specific to this job and not shared across other import jobs. Consult the Quotas page on the project to set an appropriate value here. If unspecified, a default value of 1,000 QPM would be used.

Union field import_source. The source of the import. import_source can be only one of the following:

gcs_source GcsSource

Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - gs://bucket_name/my_directory/object_name/my_file.txt - gs://bucket_name/my_directory

google_drive_source GoogleDriveSource

Google Drive location. Supports importing individual files as well as Google Drive folders.

slack_source SlackSource

Slack channels with their corresponding access tokens.

jira_source JiraSource

Jira queries with their corresponding authentication.

share_point_sources SharePointSources

SharePoint sources.

Union field partial_failure_sink. Optional. If provided, all partial failures are written to the sink. Deprecated. Prefer to use the import_result_sink. partial_failure_sink can be only one of the following:

partial_failure_gcs_sink
(deprecated)

GcsDestination

The Cloud Storage path to write partial failures to. Deprecated. Prefer to use import_result_gcs_sink.

partial_failure_bigquery_sink
(deprecated)

BigQueryDestination

The BigQuery destination to write partial failures to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table. Deprecated. Prefer to use import_result_bq_sink.

ImportRagFilesOperationMetadata

Runtime operation information for VertexRagDataService.ImportRagFiles.

Fields

generic_metadata GenericOperationMetadata

The operation generic information.

rag_corpus_id int64

The resource ID of RagCorpus that this operation is executed on.

import_rag_files_config ImportRagFilesConfig

Output only. The config that was passed in the ImportRagFilesRequest.

progress_percentage int32

The progress percentage of the operation. Value is in the range [0, 100]. This percentage is calculated as follows: progress_percentage = 100 * (successes + failures + skips) / total

ImportRagFilesRequest

Request message for VertexRagDataService.ImportRagFiles.

Fields

parent string

Required. The name of the RagCorpus resource into which to import files. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

import_rag_files_config ImportRagFilesConfig

Required. The config for the RagFiles to be synced and imported into the RagCorpus. VertexRagDataService.ImportRagFiles.

ImportRagFilesResponse

Response message for VertexRagDataService.ImportRagFiles.

Fields

imported_rag_files_count int64

The number of RagFiles that had been imported into the RagCorpus.

failed_rag_files_count int64

The number of RagFiles that had failed while importing into the RagCorpus.

skipped_rag_files_count int64

The number of RagFiles that was skipped while importing into the RagCorpus.

Union field partial_failure_sink. The location into which the partial failures were written. partial_failure_sink can be only one of the following:

partial_failures_gcs_path string

The Google Cloud Storage path into which the partial failures were written.

partial_failures_bigquery_table string

The BigQuery table into which the partial failures were written.

JiraSource

The Jira source for the ImportRagFilesRequest.

Fields

jira_queries[] JiraQueries

Required. The Jira queries.

JiraQueries

JiraQueries contains the Jira queries and corresponding authentication.

Fields

projects[] string

A list of Jira projects to import in their entirety.

custom_queries[] string

A list of custom Jira queries to import. For information about JQL (Jira Query Language), see https://support.atlassian.com/jira-service-management-cloud/docs/use-advanced-search-with-jira-query-language-jql/

email string

Required. The Jira email address.

server_uri string

Required. The Jira server URI.

api_key_config ApiKeyConfig

Required. The SecretManager secret version resource name (e.g. projects/{project}/secrets/{secret}/versions/{version}) storing the Jira API key (https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account).

JobState

Describes the state of a job.

Enums
`JOB_STATE_UNSPECIFIED`	The job state is unspecified.
`JOB_STATE_QUEUED`	The job has been just created or resumed and processing has not yet begun.
`JOB_STATE_PENDING`	The service is preparing to run the job.
`JOB_STATE_RUNNING`	The job is in progress.
`JOB_STATE_SUCCEEDED`	The job completed successfully.
`JOB_STATE_FAILED`	The job failed.
`JOB_STATE_CANCELLING`	The job is being cancelled. From this state the job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
`JOB_STATE_CANCELLED`	The job has been cancelled.
`JOB_STATE_PAUSED`	The job has been stopped, and can be resumed.
`JOB_STATE_EXPIRED`	The job has expired.
`JOB_STATE_UPDATING`	The job is being updated. Only jobs in the `RUNNING` state can be updated. After updating, the job goes back to the `RUNNING` state.
`JOB_STATE_PARTIALLY_SUCCEEDED`	The job is partially succeeded, some results may be missing due to errors.

ListCachedContentsRequest

Request to list CachedContents.

Fields

parent string

Required. The parent, which owns this collection of cached contents.

page_size int32

Optional. The maximum number of cached contents to return. The service may return fewer than this value. If unspecified, some default (under maximum) number of items will be returned. The maximum value is 1000; values above 1000 will be coerced to 1000.

page_token string

Optional. A page token, received from a previous ListCachedContents call. Provide this to retrieve the subsequent page.

When paginating, all other parameters provided to ListCachedContents must match the call that provided the page token.

ListCachedContentsResponse

Response with a list of CachedContents.

Fields

cached_contents[] CachedContent

List of cached contents.

next_page_token string

A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListExtensionsRequest

Request message for ExtensionRegistryService.ListExtensions.

Fields

parent string

Required. The resource name of the Location to list the Extensions from. Format: projects/{project}/locations/{location}

filter string

Optional. The standard list filter. Supported fields: * display_name * create_time * update_time

More detail in AIP-160.

page_size int32

Optional. The standard list page size.

page_token string

Optional. The standard list page token.

order_by string

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * display_name * create_time * update_time

Example: display_name, create_time desc.

ListExtensionsResponse

Response message for ExtensionRegistryService.ListExtensions

Fields

extensions[] Extension

List of Extension in the requested page.

next_page_token string

A token to retrieve the next page of results. Pass to ListExtensionsRequest.page_token to obtain that page.

ListRagCorporaRequest

Request message for VertexRagDataService.ListRagCorpora.

Fields

parent string

Required. The resource name of the Location from which to list the RagCorpora. Format: projects/{project}/locations/{location}

page_size int32

Optional. The standard list page size.

page_token string

Optional. The standard list page token. Typically obtained via ListRagCorporaResponse.next_page_token of the previous VertexRagDataService.ListRagCorpora call.

ListRagCorporaResponse

Response message for VertexRagDataService.ListRagCorpora.

Fields

rag_corpora[] RagCorpus

List of RagCorpora in the requested page.

next_page_token string

A token to retrieve the next page of results. Pass to ListRagCorporaRequest.page_token to obtain that page.

ListRagFilesRequest

Request message for VertexRagDataService.ListRagFiles.

Fields

parent string

Required. The resource name of the RagCorpus from which to list the RagFiles. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

page_size int32

Optional. The standard list page size.

page_token string

Optional. The standard list page token. Typically obtained via ListRagFilesResponse.next_page_token of the previous VertexRagDataService.ListRagFiles call.

ListRagFilesResponse

Response message for VertexRagDataService.ListRagFiles.

Fields

rag_files[] RagFile

List of RagFiles in the requested page.

next_page_token string

A token to retrieve the next page of results. Pass to ListRagFilesRequest.page_token to obtain that page.

ListReasoningEnginesRequest

Request message for ReasoningEngineService.ListReasoningEngines.

Fields

parent string

Required. The resource name of the Location to list the ReasoningEngines from. Format: projects/{project}/locations/{location}

filter string

Optional. The standard list filter. More detail in AIP-160.

page_size int32

Optional. The standard list page size.

page_token string

Optional. The standard list page token.

ListReasoningEnginesResponse

Response message for ReasoningEngineService.ListReasoningEngines

Fields

reasoning_engines[] ReasoningEngine

List of ReasoningEngines in the requested page.

next_page_token string

A token to retrieve the next page of results. Pass to ListReasoningEnginesRequest.page_token to obtain that page.

ListTuningJobsRequest

Request message for GenAiTuningService.ListTuningJobs.

Fields

parent string

Required. The resource name of the Location to list the TuningJobs from. Format: projects/{project}/locations/{location}

filter string

Optional. The standard list filter.

page_size int32

Optional. The standard list page size.

page_token string

Optional. The standard list page token. Typically obtained via [ListTuningJob.next_page_token][] of the previous GenAiTuningService.ListTuningJob][] call.

ListTuningJobsResponse

Response message for GenAiTuningService.ListTuningJobs

Fields

tuning_jobs[] TuningJob

List of TuningJobs in the requested page.

next_page_token string

A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.

LogprobsResult

Logprobs Result

Fields

top_candidates[] TopCandidates

Length = total number of decoding steps.

chosen_candidates[] Candidate

Length = total number of decoding steps. The chosen candidates may or may not be in top_candidates.

Candidate

Candidate for the logprobs token and score.

Fields

token string

The candidate's token string value.

token_id int32

The candidate's token id value.

log_probability float

The candidate's log probability.

TopCandidates

Candidates with top log probabilities at each decoding step.

Fields

candidates[] Candidate

Sorted by log probability in descending order.

PairwiseChoice

Pairwise prediction autorater preference.

Enums
`PAIRWISE_CHOICE_UNSPECIFIED`	Unspecified prediction choice.
`BASELINE`	Baseline prediction wins
`CANDIDATE`	Candidate prediction wins
`TIE`	Winner cannot be determined

PairwiseMetricInput

Input for pairwise metric.

Fields

metric_spec PairwiseMetricSpec

Required. Spec for pairwise metric.

instance PairwiseMetricInstance

Required. Pairwise metric instance.

PairwiseMetricInstance

Pairwise metric instance. Usually one instance corresponds to one row in an evaluation dataset.

Fields

Union field instance. Instance for pairwise metric. instance can be only one of the following:

json_instance string

Instance specified as a json string. String key-value pairs are expected in the json_instance to render PairwiseMetricSpec.instance_prompt_template.

PairwiseMetricResult

Spec for pairwise metric result.

Fields

pairwise_choice PairwiseChoice

Output only. Pairwise metric choice.

explanation string

Output only. Explanation for pairwise metric score.

PairwiseMetricSpec

Spec for pairwise metric.

Fields

metric_prompt_template string

Required. Metric prompt template for pairwise metric.

PairwiseQuestionAnsweringQualityInput

Input for pairwise question answering quality metric.

Fields

metric_spec PairwiseQuestionAnsweringQualitySpec

Required. Spec for pairwise question answering quality score metric.

instance PairwiseQuestionAnsweringQualityInstance

Required. Pairwise question answering quality instance.

PairwiseQuestionAnsweringQualityInstance

Spec for pairwise question answering quality instance.

Fields

prediction string

Required. Output of the candidate model.

baseline_prediction string

Required. Output of the baseline model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Required. Text to answer the question.

instruction string

Required. Question Answering prompt for LLM.

PairwiseQuestionAnsweringQualityResult

Spec for pairwise question answering quality result.

Fields

pairwise_choice PairwiseChoice

Output only. Pairwise question answering prediction choice.

explanation string

Output only. Explanation for question answering quality score.

confidence float

Output only. Confidence for question answering quality score.

PairwiseQuestionAnsweringQualitySpec

Spec for pairwise question answering quality score metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute question answering quality.

version int32

Optional. Which version to use for evaluation.

PairwiseSummarizationQualityInput

Input for pairwise summarization quality metric.

Fields

metric_spec PairwiseSummarizationQualitySpec

Required. Spec for pairwise summarization quality score metric.

instance PairwiseSummarizationQualityInstance

Required. Pairwise summarization quality instance.

PairwiseSummarizationQualityInstance

Spec for pairwise summarization quality instance.

Fields

prediction string

Required. Output of the candidate model.

baseline_prediction string

Required. Output of the baseline model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Required. Text to be summarized.

instruction string

Required. Summarization prompt for LLM.

PairwiseSummarizationQualityResult

Spec for pairwise summarization quality result.

Fields

pairwise_choice PairwiseChoice

Output only. Pairwise summarization prediction choice.

explanation string

Output only. Explanation for summarization quality score.

confidence float

Output only. Confidence for summarization quality score.

PairwiseSummarizationQualitySpec

Spec for pairwise summarization quality score metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute pairwise summarization quality.

version int32

Optional. Which version to use for evaluation.

Part

A datatype containing media that is part of a multi-part Content message.

A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in Part.data.

A Part must have a fixed IANA MIME type identifying the type and subtype of the media if inline_data or file_data field is filled with raw bytes.

Fields

Union field data.

data can be only one of the following:

text string

Optional. Text part (can be code).

inline_data Blob

Optional. Inlined bytes data.

file_data FileData

Optional. URI based data.

function_call FunctionCall

Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.

function_response FunctionResponse

Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model.

executable_code ExecutableCode

Optional. Code generated by the model that is meant to be executed.

code_execution_result CodeExecutionResult

Optional. Result of executing the [ExecutableCode].

Union field metadata.

metadata can be only one of the following:

video_metadata VideoMetadata

Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.

PartnerModelTuningSpec

Tuning spec for Partner models.

Fields

training_dataset_uri string

Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

validation_dataset_uri string

Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

hyper_parameters map<string, Value>

Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.

PointwiseMetricInput

Input for pointwise metric.

Fields

metric_spec PointwiseMetricSpec

Required. Spec for pointwise metric.

instance PointwiseMetricInstance

Required. Pointwise metric instance.

PointwiseMetricInstance

Pointwise metric instance. Usually one instance corresponds to one row in an evaluation dataset.

Fields

Union field instance. Instance for pointwise metric. instance can be only one of the following:

json_instance string

Instance specified as a json string. String key-value pairs are expected in the json_instance to render PointwiseMetricSpec.instance_prompt_template.

PointwiseMetricResult

Spec for pointwise metric result.

Fields

explanation string

Output only. Explanation for pointwise metric score.

score float

Output only. Pointwise metric score.

PointwiseMetricSpec

Spec for pointwise metric.

Fields

metric_prompt_template string

Required. Metric prompt template for pointwise metric.

PredictRequest

Request message for PredictionService.Predict.

Fields

endpoint string

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

instances[] Value

Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

parameters Value

The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

PredictResponse

Response message for PredictionService.Predict.

Fields

predictions[] Value

The predictions that are the output of the predictions call. The schema of any single prediction may be specified via Endpoint's DeployedModels' Model's PredictSchemata's prediction_schema_uri.

deployed_model_id string

ID of the Endpoint's DeployedModel that served this prediction.

model string

Output only. The resource name of the Model which is deployed as the DeployedModel that this prediction hits.

model_version_id string

Output only. The version ID of the Model which is deployed as the DeployedModel that this prediction hits.

model_display_name string

Output only. The display name of the Model which is deployed as the DeployedModel that this prediction hits.

metadata Value

Output only. Request-level metadata returned by the model. The metadata type will be dependent upon the model implementation.

QueryExtensionRequest

Request message for ExtensionExecutionService.QueryExtension.

Fields

name string

Required. Name (identifier) of the extension; Format: projects/{project}/locations/{location}/extensions/{extension}

contents[] Content

Required. The content of the current conversation with the model.

For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.

QueryExtensionResponse

Response message for ExtensionExecutionService.QueryExtension.

Fields

steps[] Content

Steps of extension or LLM interaction, can contain function call, function response, or text response. The last step contains the final response to the query.

failure_message string

Failure message if any.

QueryReasoningEngineRequest

Request message for [ReasoningEngineExecutionService.Query][].

Fields

name string

Required. The name of the ReasoningEngine resource to use. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}

input Struct

Optional. Input content provided by users in JSON object format. Examples include text query, function calling parameters, media bytes, etc.

QueryReasoningEngineResponse

Response message for [ReasoningEngineExecutionService.Query][]

Fields

output Value

Response provided by users in JSON object format.

QuestionAnsweringCorrectnessInput

Input for question answering correctness metric.

Fields

metric_spec QuestionAnsweringCorrectnessSpec

Required. Spec for question answering correctness score metric.

instance QuestionAnsweringCorrectnessInstance

Required. Question answering correctness instance.

QuestionAnsweringCorrectnessInstance

Spec for question answering correctness instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Optional. Text provided as context to answer the question.

instruction string

Required. The question asked and other instruction in the inference prompt.

QuestionAnsweringCorrectnessResult

Spec for question answering correctness result.

Fields

explanation string

Output only. Explanation for question answering correctness score.

score float

Output only. Question Answering Correctness score.

confidence float

Output only. Confidence for question answering correctness score.

QuestionAnsweringCorrectnessSpec

Spec for question answering correctness metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute question answering correctness.

version int32

Optional. Which version to use for evaluation.

QuestionAnsweringHelpfulnessInput

Input for question answering helpfulness metric.

Fields

metric_spec QuestionAnsweringHelpfulnessSpec

Required. Spec for question answering helpfulness score metric.

instance QuestionAnsweringHelpfulnessInstance

Required. Question answering helpfulness instance.

QuestionAnsweringHelpfulnessInstance

Spec for question answering helpfulness instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Optional. Text provided as context to answer the question.

instruction string

Required. The question asked and other instruction in the inference prompt.

QuestionAnsweringHelpfulnessResult

Spec for question answering helpfulness result.

Fields

explanation string

Output only. Explanation for question answering helpfulness score.

score float

Output only. Question Answering Helpfulness score.

confidence float

Output only. Confidence for question answering helpfulness score.

QuestionAnsweringHelpfulnessSpec

Spec for question answering helpfulness metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute question answering helpfulness.

version int32

Optional. Which version to use for evaluation.

QuestionAnsweringQualityInput

Input for question answering quality metric.

Fields

metric_spec QuestionAnsweringQualitySpec

Required. Spec for question answering quality score metric.

instance QuestionAnsweringQualityInstance

Required. Question answering quality instance.

QuestionAnsweringQualityInstance

Spec for question answering quality instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Required. Text to answer the question.

instruction string

Required. Question Answering prompt for LLM.

QuestionAnsweringQualityResult

Spec for question answering quality result.

Fields

explanation string

Output only. Explanation for question answering quality score.

score float

Output only. Question Answering Quality score.

confidence float

Output only. Confidence for question answering quality score.

QuestionAnsweringQualitySpec

Spec for question answering quality score metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute question answering quality.

version int32

Optional. Which version to use for evaluation.

QuestionAnsweringRelevanceInput

Input for question answering relevance metric.

Fields

metric_spec QuestionAnsweringRelevanceSpec

Required. Spec for question answering relevance score metric.

instance QuestionAnsweringRelevanceInstance

Required. Question answering relevance instance.

QuestionAnsweringRelevanceInstance

Spec for question answering relevance instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Optional. Text provided as context to answer the question.

instruction string

Required. The question asked and other instruction in the inference prompt.

QuestionAnsweringRelevanceResult

Spec for question answering relevance result.

Fields

explanation string

Output only. Explanation for question answering relevance score.

score float

Output only. Question Answering Relevance score.

confidence float

Output only. Confidence for question answering relevance score.

QuestionAnsweringRelevanceSpec

Spec for question answering relevance metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute question answering relevance.

version int32

Optional. Which version to use for evaluation.

RagContexts

Relevant contexts for one query.

Fields

contexts[] Context

All its contexts.

Context

A context of the query.

Fields

source_uri string

If the file is imported from Cloud Storage or Google Drive, source_uri will be original file URI in Cloud Storage or Google Drive; if file is uploaded, source_uri will be file display name.

text string

The text chunk.

distance double

The distance between the query dense embedding vector and the context text vector.

sparse_distance double

The distance between the query sparse embedding vector and the context text vector.

RagCorpus

A RagCorpus is a RagFile container and a project can have multiple RagCorpora.

Fields

name string

Output only. The resource name of the RagCorpus.

display_name string

Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters.

description string

Optional. The description of the RagCorpus.

rag_embedding_model_config RagEmbeddingModelConfig

Optional. Immutable. The embedding model config of the RagCorpus.

rag_vector_db_config RagVectorDbConfig

Optional. Immutable. The Vector DB config of the RagCorpus.

create_time Timestamp

Output only. Timestamp when this RagCorpus was created.

update_time Timestamp

Output only. Timestamp when this RagCorpus was last updated.

corpus_status CorpusStatus

Output only. RagCorpus state.

RagEmbeddingModelConfig

Config for the embedding model to use for RAG.

Fields

Union field model_config. The model config to use. model_config can be only one of the following:

vertex_prediction_endpoint VertexPredictionEndpoint

The Vertex AI Prediction Endpoint that either refers to a publisher model or an endpoint that is hosting a 1P fine-tuned text embedding model. Endpoints hosting non-1P fine-tuned text embedding models are currently not supported. This is used for dense vector search.

hybrid_search_config HybridSearchConfig

Configuration for hybrid search.

HybridSearchConfig

Config for hybrid search.

Fields

sparse_embedding_config SparseEmbeddingConfig

Optional. The configuration for sparse embedding generation. This field is optional the default behavior depends on the vector database choice on the RagCorpus.

dense_embedding_model_prediction_endpoint VertexPredictionEndpoint

Required. The Vertex AI Prediction Endpoint that hosts the embedding model for dense embedding generations.

SparseEmbeddingConfig

Configuration for sparse emebdding generation.

Fields

Union field model. The model to use for sparse embedding generation. model can be only one of the following:

bm25 Bm25

Use BM25 scoring algorithm.

Bm25

Message for BM25 parameters.

Fields

multilingual bool

Optional. Use multilingual tokenizer if set to true.

k1 float

Optional. The parameter to control term frequency saturation. It determines the scaling between the matching term frequency and final score. k1 is in the range of [1.2, 3]. The default value is 1.2.

b float

Optional. The parameter to control document length normalization. It determines how much the document length affects the final score. b is in the range of [0, 1]. The default value is 0.75.

VertexPredictionEndpoint

Config representing a model hosted on Vertex Prediction Endpoint.

Fields

endpoint string

Required. The endpoint resource name. Format: projects/{project}/locations/{location}/publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/endpoints/{endpoint}

model string

Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: projects/{project}/locations/{location}/models/{model}

model_version_id string

Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model.

RagFile

A RagFile contains user data for chunking, embedding and indexing.

Fields

name string

Output only. The resource name of the RagFile.

display_name string

Required. The display name of the RagFile. The name can be up to 128 characters long and can consist of any UTF-8 characters.

description string

Optional. The description of the RagFile.

size_bytes int64

Output only. The size of the RagFile in bytes.

rag_file_type RagFileType

Output only. The type of the RagFile.

create_time Timestamp

Output only. Timestamp when this RagFile was created.

update_time Timestamp

Output only. Timestamp when this RagFile was last updated.

file_status FileStatus

Output only. State of the RagFile.

Union field rag_file_source. The origin location of the RagFile if it is imported from Google Cloud Storage or Google Drive. rag_file_source can be only one of the following:

gcs_source GcsSource

Output only. Google Cloud Storage location of the RagFile. It does not support wildcards in the Cloud Storage uri for now.

google_drive_source GoogleDriveSource

Output only. Google Drive location. Supports importing individual files as well as Google Drive folders.

direct_upload_source DirectUploadSource

Output only. The RagFile is encapsulated and uploaded in the UploadRagFile request.

slack_source SlackSource

The RagFile is imported from a Slack channel.

jira_source JiraSource

The RagFile is imported from a Jira query.

share_point_sources SharePointSources

The RagFile is imported from a SharePoint source.

RagFileType

The type of the RagFile.

Enums
`RAG_FILE_TYPE_UNSPECIFIED`	RagFile type is unspecified.
`RAG_FILE_TYPE_TXT`	RagFile type is TXT.
`RAG_FILE_TYPE_PDF`	RagFile type is PDF.

RagFileChunkingConfig

Specifies the size and overlap of chunks for RagFiles.

Fields

chunk_size int32

The size of the chunks.

chunk_overlap int32

The overlap between chunks.

RagFileParsingConfig

Specifies the parsing config for RagFiles.

Fields

use_advanced_pdf_parsing bool

Whether to use advanced PDF parsing.

RagQuery

A query to retrieve relevant contexts.

Fields

similarity_top_k int32

Optional. The number of contexts to retrieve.

ranking Ranking

Optional. Configurations for hybrid search results ranking.

Union field query. The query to retrieve contexts. Currently only text query is supported. query can be only one of the following:

text string

Optional. The query in text format to get relevant contexts.

Ranking

Configurations for hybrid search results ranking.

Fields

alpha float

Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.

RagVectorDbConfig

Config for the Vector DB to use for RAG.

Fields

api_auth ApiAuth

Authentication config for the chosen Vector DB.

Union field vector_db. The config for the Vector DB. vector_db can be only one of the following:

rag_managed_db RagManagedDb

The config for the RAG-managed Vector DB.

weaviate Weaviate

The config for the Weaviate.

pinecone Pinecone

The config for the Pinecone.

vertex_feature_store VertexFeatureStore

The config for the Vertex Feature Store.

vertex_vector_search VertexVectorSearch

The config for the Vertex Vector Search.

Pinecone

The config for the Pinecone.

Fields

index_name string

Pinecone index name. This value cannot be changed after it's set.

RagManagedDb

This type has no fields.

The config for the default RAG-managed Vector DB.

VertexFeatureStore

The config for the Vertex Feature Store.

Fields

feature_view_resource_name string

The resource name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

VertexVectorSearch

The config for the Vertex Vector Search.

Fields

index_endpoint string

The resource name of the Index Endpoint. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

index string

The resource name of the Index. Format: projects/{project}/locations/{location}/indexes/{index}

Weaviate

The config for the Weaviate.

Fields

http_endpoint string

Weaviate DB instance HTTP endpoint. e.g. 34.56.78.90:8080 Vertex RAG only supports HTTP connection to Weaviate. This value cannot be changed after it's set.

collection_name string

The corresponding collection this corpus maps to. This value cannot be changed after it's set.

ReasoningEngine

ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.

Fields

name string

Identifier. The resource name of the ReasoningEngine.

display_name string

Required. The display name of the ReasoningEngine.

description string

Optional. The description of the ReasoningEngine.

spec ReasoningEngineSpec

Required. Configurations of the ReasoningEngine

create_time Timestamp

Output only. Timestamp when this ReasoningEngine was created.

update_time Timestamp

Output only. Timestamp when this ReasoningEngine was most recently updated.

etag string

Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

ReasoningEngineSpec

ReasoningEngine configurations

Fields

package_spec PackageSpec

Required. User provided package spec of the ReasoningEngine.

class_methods[] Struct

Optional. Declarations for object class methods.

PackageSpec

User provided package spec like pickled object and package requirements.

Fields

pickle_object_gcs_uri string

Optional. The Cloud Storage URI of the pickled python object.

dependency_files_gcs_uri string

Optional. The Cloud Storage URI of the dependency files in tar.gz format.

requirements_gcs_uri string

Optional. The Cloud Storage URI of the requirements.txt file

python_version string

Optional. The Python version. Currently support 3.8, 3.9, 3.10, 3.11. If not specified, default value is 3.10.

RebaseTunedModelOperationMetadata

Runtime operation information for GenAiTuningService.RebaseTunedModel.

Fields

generic_metadata GenericOperationMetadata

The common part of the operation generic information.

RebaseTunedModelRequest

Request message for GenAiTuningService.RebaseTunedModel.

Fields

parent string

Required. The resource name of the Location into which to rebase the Model. Format: projects/{project}/locations/{location}

tuned_model_ref TunedModelRef

Required. TunedModel reference to retrieve the legacy model information.

tuning_job TuningJob

Optional. The TuningJob to be updated. Users can use this TuningJob field to overwrite tuning configs.

artifact_destination GcsDestination

Optional. The Google Cloud Storage location to write the artifacts.

deploy_to_same_endpoint bool

Optional. By default, bison to gemini migration will always create new model/endpoint, but for gemini-1.0 to gemini-1.5 migration, we default deploy to the same endpoint. See details in this Section.

Retrieval

Defines a retrieval tool that model can call to access external knowledge.

Fields

disable_attribution
(deprecated)

bool

Optional. Deprecated. This option is no longer supported.

Union field source. The source of the retrieval. source can be only one of the following:

vertex_ai_search VertexAISearch

Set to use data source powered by Vertex AI Search.

vertex_rag_store VertexRagStore

Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.

RetrievalMetadata

Metadata related to retrieval in the grounding flow.

Fields

google_search_dynamic_retrieval_score float

Optional. Score indicating how likely information from Google Search could help answer the prompt. The score is in the range [0, 1], where 0 is the least likely and 1 is the most likely. This score is only populated when Google Search grounding and dynamic retrieval is enabled. It will be compared to the threshold to determine whether to trigger Google Search.

RetrieveContextsRequest

Request message for VertexRagService.RetrieveContexts.

Fields

parent string

Required. The resource name of the Location from which to retrieve RagContexts. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.

query RagQuery

Required. Single RAG retrieve query.

Union field data_source. Data Source to retrieve contexts. data_source can be only one of the following:

vertex_rag_store VertexRagStore

The data source for Vertex RagStore.

VertexRagStore

The data source for Vertex RagStore.

Fields

rag_corpora[]
(deprecated)

string

Optional. Deprecated. Please use rag_resources to specify the data source.

rag_resources[] RagResource

Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.

vector_distance_threshold double

Optional. Only return contexts with vector distance smaller than the threshold.

RagResource

The definition of the Rag resource.

Fields

rag_corpus string

Optional. RagCorpora resource name. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

rag_file_ids[] string

Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.

RetrieveContextsResponse

Response message for VertexRagService.RetrieveContexts.

Fields

contexts RagContexts

The contexts of the query.

RougeInput

Input for rouge metric.

Fields

metric_spec RougeSpec

Required. Spec for rouge score metric.

instances[] RougeInstance

Required. Repeated rouge instances.

RougeInstance

Spec for rouge instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

RougeMetricValue

Rouge metric value for an instance.

Fields

score float

Output only. Rouge score.

RougeResults

Results for rouge metric.

Fields

rouge_metric_values[] RougeMetricValue

Output only. Rouge metric values.

RougeSpec

Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1.

Fields

rouge_type string

Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.

use_stemmer bool

Optional. Whether to use stemmer to compute rouge score.

split_summaries bool

Optional. Whether to split summaries while using rougeLsum.

RuntimeConfig

Runtime configuration to run the extension.

Fields

default_params Struct

Optional. Default parameters that will be set for all the execution of this extension. If specified, the parameter values can be overridden by values in [[ExecuteExtensionRequest.operation_params]] at request time.

Union field GoogleFirstPartyExtensionConfig. Runtime configurations for Google first party extensions. GoogleFirstPartyExtensionConfig can be only one of the following:

code_interpreter_runtime_config CodeInterpreterRuntimeConfig

Code execution runtime configurations for code interpreter extension.

vertex_ai_search_runtime_config VertexAISearchRuntimeConfig

Runtime configuration for Vertex AI Search extension.

CodeInterpreterRuntimeConfig

Fields

file_input_gcs_bucket string

Optional. The Cloud Storage bucket for file input of this Extension. If specified, support input from the Cloud Storage bucket. Vertex Extension Custom Code Service Agent should be granted file reader to this bucket. If not specified, the extension will only accept file contents from request body and reject Cloud Storage file inputs.

file_output_gcs_bucket string

Optional. The Cloud Storage bucket for file output of this Extension. If specified, write all output files to the Cloud Storage bucket. Vertex Extension Custom Code Service Agent should be granted file writer to this bucket. If not specified, the file content will be output in response body.

VertexAISearchRuntimeConfig

Fields

serving_config_name string

Optional. Vertex AI Search serving config name. Format: projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}

engine_id string

Optional. Vertex AI Search engine ID. This is used to construct the search request. By setting this engine_id, API will construct the serving config using the default value to call search API for the user. The engine_id and serving_config_name cannot both be empty at the same time.

SafetyInput

Input for safety metric.

Fields

metric_spec SafetySpec

Required. Spec for safety metric.

instance SafetyInstance

Required. Safety instance.

SafetyInstance

Spec for safety instance.

Fields

prediction string

Required. Output of the evaluated model.

SafetyRating

Safety rating corresponding to the generated content.

Fields

category HarmCategory

Output only. Harm category.

probability HarmProbability

Output only. Harm probability levels in the content.

probability_score float

Output only. Harm probability score.

severity HarmSeverity

Output only. Harm severity levels in the content.

severity_score float

Output only. Harm severity score.

blocked bool

Output only. Indicates whether the content was filtered out because of this rating.

HarmProbability

Harm probability levels in the content.

Enums
`HARM_PROBABILITY_UNSPECIFIED`	Harm probability unspecified.
`NEGLIGIBLE`	Negligible level of harm.
`LOW`	Low level of harm.
`MEDIUM`	Medium level of harm.
`HIGH`	High level of harm.

HarmSeverity

Harm severity levels.

Enums
`HARM_SEVERITY_UNSPECIFIED`	Harm severity unspecified.
`HARM_SEVERITY_NEGLIGIBLE`	Negligible level of harm severity.
`HARM_SEVERITY_LOW`	Low level of harm severity.
`HARM_SEVERITY_MEDIUM`	Medium level of harm severity.
`HARM_SEVERITY_HIGH`	High level of harm severity.

SafetyResult

Spec for safety result.

Fields

explanation string

Output only. Explanation for safety score.

score float

Output only. Safety score.

confidence float

Output only. Confidence for safety score.

SafetySetting

Safety settings.

Fields

category HarmCategory

Required. Harm category.

threshold HarmBlockThreshold

Required. The harm block threshold.

method HarmBlockMethod

Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score.

HarmBlockMethod

Probability vs severity.

Enums
`HARM_BLOCK_METHOD_UNSPECIFIED`	The harm block method is unspecified.
`SEVERITY`	The harm block method uses both probability and severity scores.
`PROBABILITY`	The harm block method uses the probability score.

HarmBlockThreshold

Probability based thresholds levels for blocking.

Enums
`HARM_BLOCK_THRESHOLD_UNSPECIFIED`	Unspecified harm block threshold.
`BLOCK_LOW_AND_ABOVE`	Block low threshold and above (i.e. block more).
`BLOCK_MEDIUM_AND_ABOVE`	Block medium threshold and above.
`BLOCK_ONLY_HIGH`	Block only high threshold (i.e. block less).
`BLOCK_NONE`	Block none.
`OFF`	Turn off the safety filter.

SafetySpec

Spec for safety metric.

Fields

version int32

Optional. Which version to use for evaluation.

Schema

Schema is used to define the format of input/output data. Represents a select subset of an OpenAPI 3.0 schema object. More fields may be added in the future as needed.

Fields

type Type

Optional. The type of the data.

format string

Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc

title string

Optional. The title of the Schema.

description string

Optional. The description of the data.

nullable bool

Optional. Indicates if the value may be null.

default Value

Optional. Default value of the data.

items Schema

Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.

min_items int64

Optional. Minimum number of the elements for Type.ARRAY.

max_items int64

Optional. Maximum number of the elements for Type.ARRAY.

enum[] string

Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}

properties map<string, Schema>

Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.

property_ordering[] string

Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.

required[] string

Optional. Required properties of Type.OBJECT.

min_properties int64

Optional. Minimum number of the properties for Type.OBJECT.

max_properties int64

Optional. Maximum number of the properties for Type.OBJECT.

minimum double

Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER

maximum double

Optional. Maximum value of the Type.INTEGER and Type.NUMBER

min_length int64

Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING

max_length int64

Optional. Maximum length of the Type.STRING

pattern string

Optional. Pattern of the Type.STRING to restrict a string to a regular expression.

example Value

Optional. Example of the object. Will only populated when the object is the root.

any_of[] Schema

Optional. The value should be validated against any (one or more) of the subschemas in the list.

SearchEntryPoint

Google search entry point.

Fields

rendered_content string

Optional. Web content snippet that can be embedded in a web page or an app webview.

sdk_blob bytes

Optional. Base64 encoded JSON representing array of <search term, search url> tuple.

Segment

Segment of the content.

Fields

part_index int32

Output only. The index of a Part object within its parent Content object.

start_index int32

Output only. Start index in the given Part, measured in bytes. Offset from the start of the Part, inclusive, starting at zero.

end_index int32

Output only. End index in the given Part, measured in bytes. Offset from the start of the Part, exclusive, starting at zero.

text string

Output only. The text corresponding to the segment from the response.

SharePointSources

The SharePointSources to pass to ImportRagFiles.

Fields

share_point_sources[] SharePointSource

The SharePoint sources.

SharePointSource

An individual SharePointSource.

Fields

client_id string

The Application ID for the app registered in Microsoft Azure Portal. The application must also be configured with MS Graph permissions "Files.ReadAll", "Sites.ReadAll" and BrowserSiteLists.Read.All.

client_secret ApiKeyConfig

The application secret for the app registered in Azure.

tenant_id string

Unique identifier of the Azure Active Directory Instance.

sharepoint_site_name string

The name of the SharePoint site to download from. This can be the site name or the site id.

file_id string

Output only. The SharePoint file id. Output only.

Union field folder_source. The SharePoint folder source. If not provided, uses "root". folder_source can be only one of the following:

sharepoint_folder_path string

The path of the SharePoint folder to download from.

sharepoint_folder_id string

The ID of the SharePoint folder to download from.

Union field drive_source. The SharePoint drive source. drive_source can be only one of the following:

drive_name string

The name of the drive to download from.

drive_id string

The ID of the drive to download from.

SlackSource

The Slack source for the ImportRagFilesRequest.

Fields

channels[] SlackChannels

Required. The Slack channels.

SlackChannels

SlackChannels contains the Slack channels and corresponding access token.

Fields

channels[] SlackChannel

Required. The Slack channel IDs.

api_key_config ApiKeyConfig

Required. The SecretManager secret version resource name (e.g. projects/{project}/secrets/{secret}/versions/{version}) storing the Slack channel access token that has access to the slack channel IDs. See: https://api.slack.com/tutorials/tracks/getting-a-token.

SlackChannel

SlackChannel contains the Slack channel ID and the time range to import.

Fields

channel_id string

Required. The Slack channel ID.

start_time Timestamp

Optional. The starting timestamp for messages to import.

end_time Timestamp

Optional. The ending timestamp for messages to import.

StreamDirectPredictRequest

Request message for PredictionService.StreamDirectPredict.

The first message must contain endpoint field and optionally [input][]. The subsequent messages must contain [input][].

Fields

endpoint string

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

inputs[] Tensor

Optional. The prediction input.

parameters Tensor

Optional. The parameters that govern the prediction.

StreamDirectPredictResponse

Response message for PredictionService.StreamDirectPredict.

Fields

outputs[] Tensor

The prediction output.

parameters Tensor

The parameters that govern the prediction.

StreamDirectRawPredictRequest

Request message for PredictionService.StreamDirectRawPredict.

The first message must contain endpoint and method_name fields and optionally input. The subsequent messages must contain input. method_name in the subsequent messages have no effect.

Fields

endpoint string

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

method_name string

Optional. Fully qualified name of the API method being invoked to perform predictions.

Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict

input bytes

Optional. The prediction input.

StreamDirectRawPredictResponse

Response message for PredictionService.StreamDirectRawPredict.

Fields

output bytes

The prediction output.

StreamingPredictRequest

Request message for PredictionService.StreamingPredict.

The first message must contain endpoint field and optionally [input][]. The subsequent messages must contain [input][].

Fields

endpoint string

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

inputs[] Tensor

The prediction input.

parameters Tensor

The parameters that govern the prediction.

StreamingPredictResponse

Response message for PredictionService.StreamingPredict.

Fields

outputs[] Tensor

The prediction output.

parameters Tensor

The parameters that govern the prediction.

StreamingRawPredictRequest

Request message for PredictionService.StreamingRawPredict.

The first message must contain endpoint and method_name fields and optionally input. The subsequent messages must contain input. method_name in the subsequent messages have no effect.

Fields

endpoint string

Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

method_name string

Fully qualified name of the API method being invoked to perform predictions.

Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict

input bytes

The prediction input.

StreamingRawPredictResponse

Response message for PredictionService.StreamingRawPredict.

Fields

output bytes

The prediction output.

SummarizationHelpfulnessInput

Input for summarization helpfulness metric.

Fields

metric_spec SummarizationHelpfulnessSpec

Required. Spec for summarization helpfulness score metric.

instance SummarizationHelpfulnessInstance

Required. Summarization helpfulness instance.

SummarizationHelpfulnessInstance

Spec for summarization helpfulness instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Required. Text to be summarized.

instruction string

Optional. Summarization prompt for LLM.

SummarizationHelpfulnessResult

Spec for summarization helpfulness result.

Fields

explanation string

Output only. Explanation for summarization helpfulness score.

score float

Output only. Summarization Helpfulness score.

confidence float

Output only. Confidence for summarization helpfulness score.

SummarizationHelpfulnessSpec

Spec for summarization helpfulness score metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute summarization helpfulness.

version int32

Optional. Which version to use for evaluation.

SummarizationQualityInput

Input for summarization quality metric.

Fields

metric_spec SummarizationQualitySpec

Required. Spec for summarization quality score metric.

instance SummarizationQualityInstance

Required. Summarization quality instance.

SummarizationQualityInstance

Spec for summarization quality instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Required. Text to be summarized.

instruction string

Required. Summarization prompt for LLM.

SummarizationQualityResult

Spec for summarization quality result.

Fields

explanation string

Output only. Explanation for summarization quality score.

score float

Output only. Summarization Quality score.

confidence float

Output only. Confidence for summarization quality score.

SummarizationQualitySpec

Spec for summarization quality score metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute summarization quality.

version int32

Optional. Which version to use for evaluation.

SummarizationVerbosityInput

Input for summarization verbosity metric.

Fields

metric_spec SummarizationVerbositySpec

Required. Spec for summarization verbosity score metric.

instance SummarizationVerbosityInstance

Required. Summarization verbosity instance.

SummarizationVerbosityInstance

Spec for summarization verbosity instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Optional. Ground truth used to compare against the prediction.

context string

Required. Text to be summarized.

instruction string

Optional. Summarization prompt for LLM.

SummarizationVerbosityResult

Spec for summarization verbosity result.

Fields

explanation string

Output only. Explanation for summarization verbosity score.

score float

Output only. Summarization Verbosity score.

confidence float

Output only. Confidence for summarization verbosity score.

SummarizationVerbositySpec

Spec for summarization verbosity score metric.

Fields

use_reference bool

Optional. Whether to use instance.reference to compute summarization verbosity.

version int32

Optional. Which version to use for evaluation.

SupervisedHyperParameters

Hyperparameters for SFT.

Fields

epoch_count int64

Optional. Number of complete passes the model makes over the entire training dataset during training.

learning_rate_multiplier double

Optional. Multiplier for adjusting the default learning rate.

adapter_size AdapterSize

Optional. Adapter size for tuning.

AdapterSize

Supported adapter sizes for tuning.

Enums
`ADAPTER_SIZE_UNSPECIFIED`	Adapter size is unspecified.
`ADAPTER_SIZE_ONE`	Adapter size 1.
`ADAPTER_SIZE_FOUR`	Adapter size 4.
`ADAPTER_SIZE_EIGHT`	Adapter size 8.
`ADAPTER_SIZE_SIXTEEN`	Adapter size 16.
`ADAPTER_SIZE_THIRTY_TWO`	Adapter size 32.

SupervisedTuningDataStats

Tuning data statistics for Supervised Tuning.

Fields

tuning_dataset_example_count int64

Output only. Number of examples in the tuning dataset.

total_tuning_character_count int64

Output only. Number of tuning characters in the tuning dataset.

total_billable_character_count
(deprecated)

int64

Output only. Number of billable characters in the tuning dataset.

total_billable_token_count int64

Output only. Number of billable tokens in the tuning dataset.

tuning_step_count int64

Output only. Number of tuning steps for this Tuning Job.

user_input_token_distribution SupervisedTuningDatasetDistribution

Output only. Dataset distributions for the user input tokens.

user_output_token_distribution SupervisedTuningDatasetDistribution

Output only. Dataset distributions for the user output tokens.

user_message_per_example_distribution SupervisedTuningDatasetDistribution

Output only. Dataset distributions for the messages per example.

user_dataset_examples[] Content

Output only. Sample user messages in the training dataset uri.

total_truncated_example_count int64

The number of examples in the dataset that have been truncated by any amount.

truncated_example_indices[] int64

A partial sample of the indices (starting from 1) of the truncated examples.

SupervisedTuningDatasetDistribution

Dataset distribution for Supervised Tuning.

Fields

sum int64

Output only. Sum of a given population of values.

billable_sum int64

Output only. Sum of a given population of values that are billable.

min double

Output only. The minimum of the population values.

max double

Output only. The maximum of the population values.

mean double

Output only. The arithmetic mean of the values in the population.

median double

Output only. The median of the values in the population.

p5 double

Output only. The 5th percentile of the values in the population.

p95 double

Output only. The 95th percentile of the values in the population.

buckets[] DatasetBucket

Output only. Defines the histogram bucket.

DatasetBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

Fields

count double

Output only. Number of values in the bucket.

left double

Output only. Left bound of the bucket.

right double

Output only. Right bound of the bucket.

SupervisedTuningSpec

Tuning Spec for Supervised Tuning for first party models.

Fields

training_dataset_uri string

Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

validation_dataset_uri string

Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

hyper_parameters SupervisedHyperParameters

Optional. Hyperparameters for SFT.

Tensor

A tensor value type.

Fields

dtype DataType

The data type of tensor.

shape[] int64

Shape of the tensor.

bool_val[] bool

Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order.

[BOOL][google.aiplatform.master.Tensor.DataType.BOOL]

string_val[] string

[STRING][google.aiplatform.master.Tensor.DataType.STRING]

bytes_val[] bytes

[STRING][google.aiplatform.master.Tensor.DataType.STRING]

float_val[] float

[FLOAT][google.aiplatform.master.Tensor.DataType.FLOAT]

double_val[] double

[DOUBLE][google.aiplatform.master.Tensor.DataType.DOUBLE]

int_val[] int32

[INT_8][google.aiplatform.master.Tensor.DataType.INT8] [INT_16][google.aiplatform.master.Tensor.DataType.INT16] [INT_32][google.aiplatform.master.Tensor.DataType.INT32]

int64_val[] int64

[INT64][google.aiplatform.master.Tensor.DataType.INT64]

uint_val[] uint32

[UINT8][google.aiplatform.master.Tensor.DataType.UINT8] [UINT16][google.aiplatform.master.Tensor.DataType.UINT16] [UINT32][google.aiplatform.master.Tensor.DataType.UINT32]

uint64_val[] uint64

[UINT64][google.aiplatform.master.Tensor.DataType.UINT64]

list_val[] Tensor

A list of tensor values.

struct_val map<string, Tensor>

A map of string to tensor.

tensor_val bytes

Serialized raw tensor content.

DataType

Data type of the tensor.

Enums
`DATA_TYPE_UNSPECIFIED`	Not a legal value for DataType. Used to indicate a DataType field has not been set.
`BOOL`	Data types that all computation devices are expected to be capable to support.
`STRING`
`FLOAT`
`DOUBLE`
`INT8`
`INT16`
`INT32`
`INT64`
`UINT8`
`UINT16`
`UINT32`
`UINT64`

Tool

Tool details that the model may use to generate response.

A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).

Fields

function_declarations[] FunctionDeclaration

Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating [FunctionCall][content.part.function_call] in the response. User should provide a [FunctionResponse][content.part.function_response] for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 128 function declarations can be provided.

retrieval Retrieval

Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.

google_search_retrieval GoogleSearchRetrieval

Optional. GoogleSearchRetrieval tool type. Specialized retrieval tool that is powered by Google search.

code_execution CodeExecution

Optional. CodeExecution tool type. Enables the model to execute code as part of generation. This field is only used by the Gemini Developer API services.

CodeExecution

This type has no fields.

Tool that executes code generated by the model, and automatically returns the result to the model.

See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool.

ToolCallValidInput

Input for tool call valid metric.

Fields

metric_spec ToolCallValidSpec

Required. Spec for tool call valid metric.

instances[] ToolCallValidInstance

Required. Repeated tool call valid instances.

ToolCallValidInstance

Spec for tool call valid instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

ToolCallValidMetricValue

Tool call valid metric value for an instance.

Fields

score float

Output only. Tool call valid score.

ToolCallValidResults

Results for tool call valid metric.

Fields

tool_call_valid_metric_values[] ToolCallValidMetricValue

Output only. Tool call valid metric values.

ToolCallValidSpec

This type has no fields.

Spec for tool call valid metric.

ToolConfig

Tool config. This config is shared for all tools provided in the request.

Fields

function_calling_config FunctionCallingConfig

Optional. Function calling config.

ToolNameMatchInput

Input for tool name match metric.

Fields

metric_spec ToolNameMatchSpec

Required. Spec for tool name match metric.

instances[] ToolNameMatchInstance

Required. Repeated tool name match instances.

ToolNameMatchInstance

Spec for tool name match instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

ToolNameMatchMetricValue

Tool name match metric value for an instance.

Fields

score float

Output only. Tool name match score.

ToolNameMatchResults

Results for tool name match metric.

Fields

tool_name_match_metric_values[] ToolNameMatchMetricValue

Output only. Tool name match metric values.

ToolNameMatchSpec

This type has no fields.

Spec for tool name match metric.

ToolParameterKVMatchInput

Input for tool parameter key value match metric.

Fields

metric_spec ToolParameterKVMatchSpec

Required. Spec for tool parameter key value match metric.

instances[] ToolParameterKVMatchInstance

Required. Repeated tool parameter key value match instances.

ToolParameterKVMatchInstance

Spec for tool parameter key value match instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

ToolParameterKVMatchMetricValue

Tool parameter key value match metric value for an instance.

Fields

score float

Output only. Tool parameter key value match score.

ToolParameterKVMatchResults

Results for tool parameter key value match metric.

Fields

tool_parameter_kv_match_metric_values[] ToolParameterKVMatchMetricValue

Output only. Tool parameter key value match metric values.

ToolParameterKVMatchSpec

Spec for tool parameter key value match metric.

Fields

use_strict_string_match bool

Optional. Whether to use STRICT string match on parameter values.

ToolParameterKeyMatchInput

Input for tool parameter key match metric.

Fields

metric_spec ToolParameterKeyMatchSpec

Required. Spec for tool parameter key match metric.

instances[] ToolParameterKeyMatchInstance

Required. Repeated tool parameter key match instances.

ToolParameterKeyMatchInstance

Spec for tool parameter key match instance.

Fields

prediction string

Required. Output of the evaluated model.

reference string

Required. Ground truth used to compare against the prediction.

ToolParameterKeyMatchMetricValue

Tool parameter key match metric value for an instance.

Fields

score float

Output only. Tool parameter key match score.

ToolParameterKeyMatchResults

Results for tool parameter key match metric.

Fields

tool_parameter_key_match_metric_values[] ToolParameterKeyMatchMetricValue

Output only. Tool parameter key match metric values.

ToolParameterKeyMatchSpec

This type has no fields.

Spec for tool parameter key match metric.

ToolUseExample

A single example of the tool usage.

Fields

display_name string

Required. The display name for example.

query string

Required. Query that should be routed to this tool.

request_params Struct

Request parameters used for executing this tool.

response_params Struct

Response parameters generated by this tool.

response_summary string

Summary of the tool response to the user query.

Union field Target. Target tool to use. Target can be only one of the following:

extension_operation ExtensionOperation

Extension operation to call.

function_name string

Function name to call.

ExtensionOperation

Identifies one operation of the extension.

Fields

extension string

Resource name of the extension.

operation_id string

Required. Operation ID of the extension.

TunedModel

The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob.

Fields

model string

Output only. The resource name of the TunedModel. Format: projects/{project}/locations/{location}/models/{model}.

endpoint string

Output only. A resource name of an Endpoint. Format: projects/{project}/locations/{location}/endpoints/{endpoint}.

TunedModelRef

TunedModel Reference for legacy model migration.

Fields

Union field tuned_model_ref. The Tuned Model Reference for the model. tuned_model_ref can be only one of the following:

tuned_model string

Support migration from model registry.

tuning_job string

Support migration from tuning job list page, from gemini-1.0-pro-002 to 1.5 and above.

pipeline_job string

Support migration from tuning job list page, from bison model to gemini model.

TuningDataStats

The tuning data statistic values for TuningJob.

Fields

Union field tuning_data_stats.

tuning_data_stats can be only one of the following:

supervised_tuning_data_stats SupervisedTuningDataStats

The SFT Tuning data stats.

distillation_data_stats DistillationDataStats

Output only. Statistics for distillation.

TuningJob

Represents a TuningJob that runs with Google owned models.

Fields

name string

Output only. Identifier. Resource name of a TuningJob. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

tuned_model_display_name string

Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.

description string

Optional. The description of the TuningJob.

state JobState

Output only. The detailed state of the job.

create_time Timestamp

Output only. Time when the TuningJob was created.

start_time Timestamp

Output only. Time when the TuningJob for the first time entered the JOB_STATE_RUNNING state.

end_time Timestamp

Output only. Time when the TuningJob entered any of the following JobStates: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED, JOB_STATE_EXPIRED.

update_time Timestamp

Output only. Time when the TuningJob was most recently updated.

error Status

Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

labels map<string, string>

Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint.

Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information and examples of labels.

experiment string

Output only. The Experiment associated with this TuningJob.

tuned_model TunedModel

Output only. The tuned model resources assiociated with this TuningJob.

tuning_data_stats TuningDataStats

Output only. The tuning data statistics associated with this TuningJob.

pipeline_job string

Output only. The resource name of the PipelineJob associated with the TuningJob. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}.

encryption_spec EncryptionSpec

Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.

Union field source_model.

source_model can be only one of the following:

base_model string

The base model that is being tuned, e.g., "gemini-1.0-pro-002".

Union field tuning_spec.

tuning_spec can be only one of the following:

supervised_tuning_spec SupervisedTuningSpec

Tuning Spec for Supervised Fine Tuning.

distillation_spec DistillationSpec

Tuning Spec for Distillation.

partner_model_tuning_spec PartnerModelTuningSpec

Tuning Spec for open sourced and third party Partner models.

Type

Type contains the list of OpenAPI data types as defined by https://swagger.io/docs/specification/data-models/data-types/

Enums
`TYPE_UNSPECIFIED`	Not specified, should not be used.
`STRING`	OpenAPI string type
`NUMBER`	OpenAPI number type
`INTEGER`	OpenAPI integer type
`BOOLEAN`	OpenAPI boolean type
`ARRAY`	OpenAPI array type
`OBJECT`	OpenAPI object type

UpdateCacheConfigRequest

Request message for updating a cache config.

Fields

cache_config CacheConfig

Required. The cache config to be updated. cache_config.name is used to identify the cache config. Format: - projects/{project}/cacheConfig.

UpdateCachedContentRequest

Request message for GenAiCacheService.UpdateCachedContent. Only expire_time or ttl can be updated.

Fields

cached_content CachedContent

Required. The cached content to update

update_mask FieldMask

Required. The list of fields to update.

UpdateExtensionRequest

Request message for ExtensionRegistryService.UpdateExtension.

Fields

extension Extension

Required. The Extension which replaces the resource on the server.

update_mask FieldMask

Required. Mask specifying which fields to update. Supported fields:

display_name
description
runtime_config
tool_use_examples
manifest.description

UpdateRagCorpusOperationMetadata

Runtime operation information for VertexRagDataService.UpdateRagCorpus.

Fields

generic_metadata GenericOperationMetadata

The operation generic information.

UpdateRagCorpusRequest

Request message for VertexRagDataService.UpdateRagCorpus.

Fields

rag_corpus RagCorpus

Required. The RagCorpus which replaces the resource on the server.

UpdateReasoningEngineOperationMetadata

Details of ReasoningEngineService.UpdateReasoningEngine operation.

Fields

generic_metadata GenericOperationMetadata

The common part of the operation metadata.

UpdateReasoningEngineRequest

Request message for ReasoningEngineService.UpdateReasoningEngine.

Fields

reasoning_engine ReasoningEngine

Required. The ReasoningEngine which replaces the resource on the server.

update_mask FieldMask

Optional. Mask specifying which fields to update.

UploadRagFileConfig

Config for uploading RagFile.

Fields

rag_file_chunking_config RagFileChunkingConfig

Specifies the size and overlap of chunks after uploading RagFile.

VertexAISearch

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/products/agent-builder

Fields

datastore string

Required. Fully-qualified Vertex AI Search data store resource ID. Format: projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}

VertexRagStore

Retrieve from Vertex RAG Store for grounding.

Fields

rag_corpora[]
(deprecated)

string

Optional. Deprecated. Please use rag_resources instead.

rag_resources[] RagResource

similarity_top_k int32

Optional. Number of top k results to return from the selected corpora.

vector_distance_threshold double

Optional. Only return results with vector distance smaller than the threshold.

RagResource

The definition of the Rag resource.

Fields

rag_corpus string

Optional. RagCorpora resource name. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

rag_file_ids[] string

Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.

VideoMetadata

Metadata describes the input video content.

Fields

start_offset Duration

Optional. The start offset of the video.

end_offset Duration

Optional. The end offset of the video.