Package google.cloud.aiplatform.v1beta1

Index

• EvaluationService (interface)
• ExtensionExecutionService (interface)
• ExtensionRegistryService (interface)
• GenAiCacheConfigService (interface)
• GenAiCacheService (interface)
• GenAiTuningService (interface)
• LlmBidiService (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)
• BidiGenerateContentClientContent (message)
• BidiGenerateContentClientMessage (message)
• BidiGenerateContentRealtimeInput (message)
• BidiGenerateContentServerContent (message)
• BidiGenerateContentServerMessage (message)
• BidiGenerateContentSetup (message)
• BidiGenerateContentSetupComplete (message)
• BidiGenerateContentToolCall (message)
• BidiGenerateContentToolCallCancellation (message)
• BidiGenerateContentToolResponse (message)
• 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)
• CometInput (message)
• CometInstance (message)
• CometResult (message)
• CometSpec (message)
• CometSpec.CometVersion (enum)
• 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)
• FetchPredictOperationRequest (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.MediaResolution (enum)
• GenerationConfig.Modality (enum)
• 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)
• MetricxInput (message)
• MetricxInstance (message)
• MetricxResult (message)
• MetricxSpec (message)
• MetricxSpec.MetricxVersion (enum)
• 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)
• PrebuiltVoiceConfig (message)
• PredictLongRunningRequest (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)
• RagFileChunkingConfig.FixedLengthChunking (message)
• RagFileParsingConfig (message)
• RagFileParsingConfig.AdvancedParser (message)
• RagFileParsingConfig.LayoutParser (message)
• RagFileParsingConfig.LlmParser (message)
• RagFileTransformationConfig (message)
• RagQuery (message)
• RagQuery.Ranking (message)
• RagRetrievalConfig (message)
• RagRetrievalConfig.Filter (message)
• RagRetrievalConfig.HybridSearch (message)
• RagRetrievalConfig.Ranking (message)
• RagRetrievalConfig.Ranking.LlmRanker (message)
• RagRetrievalConfig.Ranking.RankService (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)
• SpeechConfig (message)
• StreamDirectPredictRequest (message)
• StreamDirectPredictResponse (message)
• StreamDirectRawPredictRequest (message)
• StreamDirectRawPredictResponse (message)
• StreamQueryReasoningEngineRequest (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)
• Tool.GoogleSearch (message)
• ToolCall (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)
• Trajectory (message)
• TrajectoryAnyOrderMatchInput (message)
• TrajectoryAnyOrderMatchInstance (message)
• TrajectoryAnyOrderMatchMetricValue (message)
• TrajectoryAnyOrderMatchResults (message)
• TrajectoryAnyOrderMatchSpec (message)
• TrajectoryExactMatchInput (message)
• TrajectoryExactMatchInstance (message)
• TrajectoryExactMatchMetricValue (message)
• TrajectoryExactMatchResults (message)
• TrajectoryExactMatchSpec (message)
• TrajectoryInOrderMatchInput (message)
• TrajectoryInOrderMatchInstance (message)
• TrajectoryInOrderMatchMetricValue (message)
• TrajectoryInOrderMatchResults (message)
• TrajectoryInOrderMatchSpec (message)
• TrajectoryPrecisionInput (message)
• TrajectoryPrecisionInstance (message)
• TrajectoryPrecisionMetricValue (message)
• TrajectoryPrecisionResults (message)
• TrajectoryPrecisionSpec (message)
• TrajectoryRecallInput (message)
• TrajectoryRecallInstance (message)
• TrajectoryRecallMetricValue (message)
• TrajectoryRecallResults (message)
• TrajectoryRecallSpec (message)
• TrajectorySingleToolUseInput (message)
• TrajectorySingleToolUseInstance (message)
• TrajectorySingleToolUseMetricValue (message)
• TrajectorySingleToolUseResults (message)
• TrajectorySingleToolUseSpec (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)
• VertexAiSearchConfig (message)
• VertexRagStore (message)
• VertexRagStore.RagResource (message)
• VideoMetadata (message)
• VoiceConfig (message)

EvaluationService

Vertex AI Online Evaluation Service.

EvaluateInstances
rpc EvaluateInstances(EvaluateInstancesRequest) returns (EvaluateInstancesResponse) Evaluates instances based on a given metric. IAM Permissions Requires the following IAM permission on the location resource: aiplatform.locations.evaluateInstances For more information, see the IAM documentation.

ExtensionExecutionService

A service for Extension execution.

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.

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.

ExtensionRegistryService

A service for managing Vertex AI's Extension registry.

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.

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.

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.

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.

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.

GenAiCacheConfigService

Service for GenAI Cache Config.

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.

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.

GenAiCacheService

Service for managing Vertex AI's CachedContent resource.

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.

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.

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.

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.

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.

GenAiTuningService

A service for creating and managing GenAI Tuning Jobs.

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.

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.

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.

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.

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.

LlmBidiService

A service for LLM related bidirectional low latency APIs.

BidiGenerateContent
rpc BidiGenerateContent(BidiGenerateContentClientMessage) returns (BidiGenerateContentServerMessage) Bidirectional streaming predict. IAM Permissions Requires the following IAM permission on the model resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

PredictionService

A service for online predictions and explanations.

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.

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.

FetchPredictOperation
rpc FetchPredictOperation(FetchPredictOperationRequest) returns (Operation) Fetch an asynchronous online prediction operation. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

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.

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.

PredictLongRunning
rpc PredictLongRunning(PredictLongRunningRequest) returns (Operation) IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

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.

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.

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.

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.

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.

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.

ReasoningEngineExecutionService

A service for executing queries on Reasoning Engine.

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.

StreamQueryReasoningEngine
rpc StreamQueryReasoningEngine(StreamQueryReasoningEngineRequest) returns (HttpBody) Streams 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
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
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
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
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
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
rpc CreateRagCorpus(CreateRagCorpusRequest) returns (Operation) Creates a RagCorpus. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.create For more information, see the IAM documentation.

DeleteRagCorpus
rpc DeleteRagCorpus(DeleteRagCorpusRequest) returns (Operation) Deletes a RagCorpus. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragCorpora.delete For more information, see the IAM documentation.

DeleteRagFile
rpc DeleteRagFile(DeleteRagFileRequest) returns (Operation) Deletes a RagFile. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragFiles.delete For more information, see the IAM documentation.

GetRagCorpus
rpc GetRagCorpus(GetRagCorpusRequest) returns (RagCorpus) Gets a RagCorpus. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragCorpora.get For more information, see the IAM documentation.

GetRagFile
rpc GetRagFile(GetRagFileRequest) returns (RagFile) Gets a RagFile. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragFiles.get For more information, see the IAM documentation.

ImportRagFiles
rpc ImportRagFiles(ImportRagFilesRequest) returns (Operation) Import files from Google Cloud Storage or Google Drive into a RagCorpus. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragFiles.import For more information, see the IAM documentation.

ListRagCorpora
rpc ListRagCorpora(ListRagCorporaRequest) returns (ListRagCorporaResponse) Lists RagCorpora in a Location. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.list For more information, see the IAM documentation.

ListRagFiles
rpc ListRagFiles(ListRagFilesRequest) returns (ListRagFilesResponse) Lists RagFiles in a RagCorpus. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragFiles.list For more information, see the IAM documentation.

UpdateRagCorpus
rpc UpdateRagCorpus(UpdateRagCorpusRequest) returns (Operation) Updates a RagCorpus. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragCorpora.delete For more information, see the IAM documentation.

VertexRagService

A service for retrieving relevant contexts.

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.ragCorpora.get For more information, see the IAM documentation.

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.ragCorpora.get For more information, see the IAM documentation.

RetrieveContexts
rpc RetrieveContexts(RetrieveContextsRequest) returns (RetrieveContextsResponse) Retrieves relevant contexts for a query. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.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 Optional. The name of the SecretManager secret version resource storing the API key. Format: projects/{project}/secrets/{secrete}/versions/{version} If both api_key_secret and api_key_string are specified, this field takes precedence over api_key_string. 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.
api_key_string	string Optional. The API key to be used in the request directly.
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.

BidiGenerateContentClientContent

Incremental update of the current conversation delivered from the client. All the content here will unconditionally be appended to the conversation history and used as part of the prompt to the model to generate content.

A message here will interrupt any current model generation.

Fields
turns[]	Content Optional. The content appended to 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.
turn_complete	bool Optional. If true, indicates that the server content generation should start with the currently accumulated prompt. Otherwise, the server will await additional messages before starting generation.

BidiGenerateContentClientMessage

Messages sent by the client in the BidiGenerateContent RPC call.

Fields
Union field message_type. The type of the message. message_type can be only one of the following:
setup	BidiGenerateContentSetup Optional. Message to be sent in the first and only first client message.
client_content	BidiGenerateContentClientContent Optional. Incremental update of the current conversation delivered from the client.
realtime_input	BidiGenerateContentRealtimeInput Optional. User input that is sent in real time.
tool_response	BidiGenerateContentToolResponse Optional. Response to a ToolCallMessage received from the server.

BidiGenerateContentRealtimeInput

User input that is sent in real time.

This is different from ClientContentUpdate in a few ways: - Can be sent continuously without interruption the model generation. - If there is a need to mix data interleaved across the ClientContentUpdate and the RealtimeUpdate, server will attempt to optimize for best response, but there are no guarnatees. - End of turn is not explicitly specified, but is rather derived from user activity, e.g. end of speech. - Even before the end of turn, the data will be processed incrementally to optimize for a fast start of the response from the model. - Is always assumed to be the user's input (cannot be used to populate conversation history).

Fields
media_chunks[]	Blob Optional. Inlined bytes data for media input.

BidiGenerateContentServerContent

Incremental server update generated by the model in response to client messages.

Content is generated as quickly as possible, and not in realtime. Clients may choose to buffer and play it out in realtime.

Fields
turn_complete	bool Output only. If true, indicates that the model is done generating. Generation will only start in response to additional client messages. Can be set alongside content, indicating that the content is the last in the turn.
interrupted	bool Output only. If true, indicates that a client message has interrupted current model generation. If the client is playing out the content in realtime, this is a good signal to stop and empty the current queue. If the client is playing out the content in realtime, this is a good signal to stop and empty the current playback queue.
grounding_metadata	GroundingMetadata Output only. Metadata specifies sources used to ground generated content.
model_turn	Content Output only. The content that the model has generated as part of the current conversation with the user.

BidiGenerateContentServerMessage

Response message for BidiGenerateContent RPC call.

Fields
Union field message_type. The type of the message. message_type can be only one of the following:
setup_complete	BidiGenerateContentSetupComplete Output only. Sent in response to a BidiGenerateContentSetup message from the client.
server_content	BidiGenerateContentServerContent Output only. Content generated by the model in response to client messages.
tool_call	BidiGenerateContentToolCall Output only. Request for the client to execute the function_calls and return the responses with the matching ids.
tool_call_cancellation	BidiGenerateContentToolCallCancellation Output only. Notification for the client that a previously issued ToolCallMessage with the specified ids should have been not executed and should be cancelled.

BidiGenerateContentSetup

Message to be sent in the first and only first BidiGenerateContentClientMessage. Contains configuration that will apply for the duration of the streaming RPC.

Clients should wait for a BidiGenerateContentSetupComplete message before sending any additional messages.

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/*
generation_config	GenerationConfig Optional. Generation config. The following fields are not supported: - response_logprobs - response_mime_type - logprobs - response_schema - stop_sequence - routing_config - audio_timestamp
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.
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.

BidiGenerateContentSetupComplete

This type has no fields.

Sent in response to a BidiGenerateContentSetup message from the client.

BidiGenerateContentToolCall

Request for the client to execute the function_calls and return the responses with the matching ids.

Fields
function_calls[]	FunctionCall Output only. The function call to be executed.

BidiGenerateContentToolCallCancellation

Notification for the client that a previously issued ToolCallMessage with the specified ids should have been not executed and should be cancelled. If there were side-effects to those tool calls, clients may attempt to undo the tool calls. This message occurs only in cases where the clients interrupt server turns.

Fields
ids[]	string Output only. The ids of the tool calls to be cancelled.

BidiGenerateContentToolResponse

Client generated response to a ToolCall received from the server. Individual FunctionResponse objects are matched to the responective FunctionCall objects by the id field.

Note that in the unary and server-streaming GenerateContent APIs function calling happens by exchanging the Content parts, while in the bidi GenerateContent APIs function calling happens over these dedicated set of messages.

Fields
function_responses[]	FunctionResponse Optional. The response to the function calls.

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.

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	The token generation stopped because of potential recitation.
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.

CometInput

Input for Comet metric.

Fields
metric_spec	CometSpec Required. Spec for comet metric.
instance	CometInstance Required. Comet instance.

CometInstance

Spec for Comet instance - The fields used for evaluation are dependent on the comet version.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.
source	string Optional. Source text in original language.

CometResult

Spec for Comet result - calculates the comet score for the given instance using the version specified in the spec.

Fields
score	float Output only. Comet score. Range depends on version.

CometSpec

Spec for Comet metric.

Fields
source_language	string Optional. Source language in BCP-47 format.
target_language	string Optional. Target language in BCP-47 format. Covers both prediction and reference.
version	CometVersion Required. Which version to use for evaluation.

CometVersion

Comet version options.

Enums
COMET_VERSION_UNSPECIFIED	Comet version unspecified.
COMET_22_SRC_REF	Comet 22 for translation + source + reference (source-reference-combined).

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 (deprecated)	string This item is deprecated! Deprecated. 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 (deprecated)	string This item is deprecated! The student model that is being tuned, e.g., "google/gemma-2b-1.1-it". Deprecated. Use base_model instead.
pipeline_root_directory (deprecated)	string This item is deprecated! Deprecated. 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-1.5-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.
comet_input	CometInput Translation metrics. Input for Comet metric.
metricx_input	MetricxInput Input for Metricx metric.
trajectory_exact_match_input	TrajectoryExactMatchInput Input for trajectory exact match metric.
trajectory_in_order_match_input	TrajectoryInOrderMatchInput Input for trajectory in order match metric.
trajectory_any_order_match_input	TrajectoryAnyOrderMatchInput Input for trajectory match any order metric.
trajectory_precision_input	TrajectoryPrecisionInput Input for trajectory precision metric.
trajectory_recall_input	TrajectoryRecallInput Input for trajectory recall metric.
trajectory_single_tool_use_input	TrajectorySingleToolUseInput Input for trajectory single tool use 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.
comet_result	CometResult Translation metrics. Result for Comet metric.
metricx_result	MetricxResult Result for Metricx metric.
trajectory_exact_match_results	TrajectoryExactMatchResults Result for trajectory exact match metric.
trajectory_in_order_match_results	TrajectoryInOrderMatchResults Result for trajectory in order match metric.
trajectory_any_order_match_results	TrajectoryAnyOrderMatchResults Result for trajectory any order match metric.
trajectory_precision_results	TrajectoryPrecisionResults Result for trajectory precision metric.
trajectory_recall_results	TrajectoryRecallResults Results for trajectory recall metric.
trajectory_single_tool_use_results	TrajectorySingleToolUseResults Results for trajectory single tool use 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. 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.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

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 (deprecated)	double This item is deprecated! If present, the distance between the query vector and this fact vector.
score	double If present, according to the underlying Vector DB and the selected metric type, the score can be either the distance or the similarity between the query and the fact and its range depends on the metric type. For example, if the metric type is COSINE_DISTANCE, it represents the distance between the query and the fact. The larger the distance, the less relevant the fact is to the query. The range is [0, 2], while 0 means the most relevant and 2 means the least relevant.

FetchPredictOperationRequest

Request message for PredictionService.FetchPredictOperation.

Fields
endpoint	string Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
operation_name	string Required. The server-assigned name for the operation.

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, description, parameters and response type. 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.
response_modalities[]	Modality Optional. The modalities of the response.
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.
media_resolution	MediaResolution Optional. If specified, the media resolution specified will be used.
speech_config	SpeechConfig Optional. The speech generation config.

MediaResolution

Media resolution for the input media.

Enums
MEDIA_RESOLUTION_UNSPECIFIED	Media resolution has not been set.
MEDIA_RESOLUTION_LOW	Media resolution set to low (64 tokens).
MEDIA_RESOLUTION_MEDIUM	Media resolution set to medium (256 tokens).
MEDIA_RESOLUTION_HIGH	Media resolution set to high (zoomed reframing with 256 tokens).

Modality

The modalities of the response.

Enums
MODALITY_UNSPECIFIED	Unspecified modality. Will be processed as text.
TEXT	Text modality.
IMAGE	Image modality.
AUDIO	Audio modality.

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 (deprecated)	RagFileChunkingConfig This item is deprecated! Specifies the size and overlap of chunks after importing RagFiles.
rag_file_transformation_config	RagFileTransformationConfig Specifies the transformation config for RagFiles.
rag_file_parsing_config	RagFileParsingConfig Optional. Specifies the parsing config for RagFiles. RAG will use the default parser if this field is not set.
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 This item is deprecated! The Cloud Storage path to write partial failures to. Deprecated. Prefer to use import_result_gcs_sink.
partial_failure_bigquery_sink (deprecated)	BigQueryDestination This item is deprecated! 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. See 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 ListTuningJobsResponse.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.

MetricxInput

Input for MetricX metric.

Fields
metric_spec	MetricxSpec Required. Spec for Metricx metric.
instance	MetricxInstance Required. Metricx instance.

MetricxInstance

Spec for MetricX instance - The fields used for evaluation are dependent on the MetricX version.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.
source	string Optional. Source text in original language.

MetricxResult

Spec for MetricX result - calculates the MetricX score for the given instance using the version specified in the spec.

Fields
score	float Output only. MetricX score. Range depends on version.

MetricxSpec

Spec for MetricX metric.

Fields
source_language	string Optional. Source language in BCP-47 format.
target_language	string Optional. Target language in BCP-47 format. Covers both prediction and reference.
version	MetricxVersion Required. Which version to use for evaluation.

MetricxVersion

MetricX Version options.

Enums
METRICX_VERSION_UNSPECIFIED	MetricX version unspecified.
METRICX_24_REF	MetricX 2024 (2.6) for translation + reference (reference-based).
METRICX_24_SRC	MetricX 2024 (2.6) for translation + source (QE).
METRICX_24_SRC_REF	MetricX 2024 (2.6) for translation + source + reference (source-reference-combined).

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.

PrebuiltVoiceConfig

The configuration for the prebuilt speaker to use.

Fields
voice_name	string The name of the preset voice to use.

PredictLongRunningRequest

Request message for PredictionService.PredictLongRunning.

Fields
endpoint	string Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
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 Optional. 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.

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.
class_method	string Optional. Class method to be used for the query. It is optional and defaults to "query" if unspecified.

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.
source_display_name	string The file display name.
text	string The text chunk.
distance (deprecated)	double This item is deprecated! The distance between the query dense embedding vector and the context text vector.
sparse_distance (deprecated)	double This item is deprecated! The distance between the query sparse embedding vector and the context text vector.
score	double According to the underlying Vector DB and the selected metric type, the score can be either the distance or the similarity between the query and the context and its range depends on the metric type. For example, if the metric type is COSINE_DISTANCE, it represents the distance between the query and the context. The larger the distance, the less relevant the context is to the query. The range is [0, 2], while 0 means the most relevant and 2 means the least relevant.

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 (deprecated)	RagEmbeddingModelConfig This item is deprecated! Optional. Immutable. The embedding model config of the RagCorpus.
rag_vector_db_config (deprecated)	RagVectorDbConfig This item is deprecated! 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.
Union field backend_config. The backend config of the RagCorpus. It can be data store and/or retrieval engine. backend_config can be only one of the following:
vector_db_config	RagVectorDbConfig Optional. Immutable. The config for the Vector DBs.
vertex_ai_search_config	VertexAiSearchConfig Optional. Immutable. The config for the Vertex AI Search.

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 (deprecated)	int32 This item is deprecated! The size of the chunks.
chunk_overlap (deprecated)	int32 This item is deprecated! The overlap between chunks.
Union field chunking_config. Specifies the chunking config for RagFiles. chunking_config can be only one of the following:
fixed_length_chunking	FixedLengthChunking Specifies the fixed length chunking config.

FixedLengthChunking

Specifies the fixed length chunking config.

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 (deprecated)	bool This item is deprecated! Whether to use advanced PDF parsing.
Union field parser. The parser to use for RagFiles. parser can be only one of the following:
advanced_parser	AdvancedParser The Advanced Parser to use for RagFiles.
layout_parser	LayoutParser The Layout Parser to use for RagFiles.
llm_parser	LlmParser The LLM Parser to use for RagFiles.

AdvancedParser

Specifies the advanced parsing for RagFiles.

Fields
use_advanced_pdf_parsing	bool Whether to use advanced PDF parsing.

LayoutParser

Document AI Layout Parser config.

Fields
processor_name	string The full resource name of a Document AI processor or processor version. The processor must have type LAYOUT_PARSER_PROCESSOR. If specified, the additional_config.parse_as_scanned_pdf field must be false. Format: * projects/{project_id}/locations/{location}/processors/{processor_id} * projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}
max_parsing_requests_per_min	int32 The maximum number of requests the job is allowed to make to the Document AI processor per minute. Consult https://cloud.google.com/document-ai/quotas and the Quota page for your project to set an appropriate value here. If unspecified, a default value of 120 QPM would be used.

LlmParser

Specifies the advanced parsing for RagFiles.

Fields
model_name	string The name of a LLM model used for parsing. Format: gemini-1.5-pro-002
max_parsing_requests_per_min	int32 The maximum number of requests the job is allowed to make to the LLM model per minute. Consult https://cloud.google.com/vertex-ai/generative-ai/docs/quotas and your document size to set an appropriate value here. If unspecified, a default value of 5000 QPM would be used.
custom_parsing_prompt	string The prompt to use for parsing. If not specified, a default prompt will be used.

RagFileTransformationConfig

Specifies the transformation config for RagFiles.

Fields
rag_file_chunking_config	RagFileChunkingConfig Specifies the chunking config for RagFiles.

RagQuery

A query to retrieve relevant contexts.

Fields
similarity_top_k (deprecated)	int32 This item is deprecated! Optional. The number of contexts to retrieve.
ranking (deprecated)	Ranking This item is deprecated! Optional. Configurations for hybrid search results ranking.
rag_retrieval_config	RagRetrievalConfig Optional. The retrieval config for the query.
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.

RagRetrievalConfig

Specifies the context retrieval config.

Fields
top_k	int32 Optional. The number of contexts to retrieve.
hybrid_search	HybridSearch Optional. Config for Hybrid Search.
filter	Filter Optional. Config for filters.
ranking	Ranking Optional. Config for ranking and reranking.

Filter

Config for filters.

Fields
metadata_filter	string Optional. String for metadata filtering.
Union field vector_db_threshold. Filter contexts retrieved from the vector DB based on either vector distance or vector similarity. vector_db_threshold can be only one of the following:
vector_distance_threshold	double Optional. Only returns contexts with vector distance smaller than the threshold.
vector_similarity_threshold	double Optional. Only returns contexts with vector similarity larger than the threshold.

HybridSearch

Config for Hybrid Search.

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.

Ranking

Config for ranking and reranking.

Fields
Union field ranking_config. Config options for ranking. Currently only Rank Service is supported. ranking_config can be only one of the following:
rank_service	RankService Optional. Config for Rank Service.
llm_ranker	LlmRanker Optional. Config for LlmRanker.

LlmRanker

Config for LlmRanker.

Fields
model_name	string Optional. The model name used for ranking. Format: gemini-1.5-pro

RankService

Config for Rank Service.

Fields
model_name	string Optional. The model name of the rank service. Format: semantic-ranker-512@latest

RagVectorDbConfig

Config for the Vector DB to use for RAG.

Fields
api_auth	ApiAuth Authentication config for the chosen Vector DB.
rag_embedding_model_config	RagEmbeddingModelConfig Optional. Immutable. The embedding model config of the 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 in OpenAPI specification format.

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 This item is deprecated! 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 This item is deprecated! 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 (deprecated)	double This item is deprecated! 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. 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"}.
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.

SpeechConfig

The speech generation config.

Fields
voice_config	VoiceConfig The configuration for the speaker to use.

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.

StreamQueryReasoningEngineRequest

Request message for [ReasoningEngineExecutionService.StreamQuery][].

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.
class_method	string Optional. Class method to be used for the stream query. It is optional and defaults to "stream_query" if unspecified.

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 This item is deprecated! 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
string_val[]	string STRING
bytes_val[]	bytes STRING
float_val[]	float FLOAT
double_val[]	double DOUBLE
int_val[]	int32 INT_8 INT_16 INT_32
int64_val[]	int64 INT64
uint_val[]	uint32 UINT8 UINT16 UINT32
uint64_val[]	uint64 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 in the response. User should provide a FunctionResponse 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	GoogleSearch Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
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.

GoogleSearch

This type has no fields.

GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.

ToolCall

Spec for tool call.

Fields
tool_name	string Required. Spec for tool name
tool_input	string Optional. Spec for tool input

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.

Trajectory

Spec for trajectory.

Fields
tool_calls[]	ToolCall Required. Tool calls in the trajectory.

TrajectoryAnyOrderMatchInput

Instances and metric spec for TrajectoryAnyOrderMatch metric.

Fields
metric_spec	TrajectoryAnyOrderMatchSpec Required. Spec for TrajectoryAnyOrderMatch metric.
instances[]	TrajectoryAnyOrderMatchInstance Required. Repeated TrajectoryAnyOrderMatch instance.

TrajectoryAnyOrderMatchInstance

Spec for TrajectoryAnyOrderMatch instance.

Fields
predicted_trajectory	Trajectory Required. Spec for predicted tool call trajectory.
reference_trajectory	Trajectory Required. Spec for reference tool call trajectory.

TrajectoryAnyOrderMatchMetricValue

TrajectoryAnyOrderMatch metric value for an instance.

Fields
score	float Output only. TrajectoryAnyOrderMatch score.

TrajectoryAnyOrderMatchResults

Results for TrajectoryAnyOrderMatch metric.

Fields
trajectory_any_order_match_metric_values[]	TrajectoryAnyOrderMatchMetricValue Output only. TrajectoryAnyOrderMatch metric values.

TrajectoryAnyOrderMatchSpec

This type has no fields.

Spec for TrajectoryAnyOrderMatch metric - returns 1 if all tool calls in the reference trajectory appear in the predicted trajectory in any order, else 0.

TrajectoryExactMatchInput

Instances and metric spec for TrajectoryExactMatch metric.

Fields
metric_spec	TrajectoryExactMatchSpec Required. Spec for TrajectoryExactMatch metric.
instances[]	TrajectoryExactMatchInstance Required. Repeated TrajectoryExactMatch instance.

TrajectoryExactMatchInstance

Spec for TrajectoryExactMatch instance.

Fields
predicted_trajectory	Trajectory Required. Spec for predicted tool call trajectory.
reference_trajectory	Trajectory Required. Spec for reference tool call trajectory.

TrajectoryExactMatchMetricValue

TrajectoryExactMatch metric value for an instance.

Fields
score	float Output only. TrajectoryExactMatch score.

TrajectoryExactMatchResults

Results for TrajectoryExactMatch metric.

Fields
trajectory_exact_match_metric_values[]	TrajectoryExactMatchMetricValue Output only. TrajectoryExactMatch metric values.

TrajectoryExactMatchSpec

This type has no fields.

Spec for TrajectoryExactMatch metric - returns 1 if tool calls in the reference trajectory exactly match the predicted trajectory, else 0.

TrajectoryInOrderMatchInput

Instances and metric spec for TrajectoryInOrderMatch metric.

Fields
metric_spec	TrajectoryInOrderMatchSpec Required. Spec for TrajectoryInOrderMatch metric.
instances[]	TrajectoryInOrderMatchInstance Required. Repeated TrajectoryInOrderMatch instance.

TrajectoryInOrderMatchInstance

Spec for TrajectoryInOrderMatch instance.

Fields
predicted_trajectory	Trajectory Required. Spec for predicted tool call trajectory.
reference_trajectory	Trajectory Required. Spec for reference tool call trajectory.

TrajectoryInOrderMatchMetricValue

TrajectoryInOrderMatch metric value for an instance.

Fields
score	float Output only. TrajectoryInOrderMatch score.

TrajectoryInOrderMatchResults

Results for TrajectoryInOrderMatch metric.

Fields
trajectory_in_order_match_metric_values[]	TrajectoryInOrderMatchMetricValue Output only. TrajectoryInOrderMatch metric values.

TrajectoryInOrderMatchSpec

This type has no fields.

Spec for TrajectoryInOrderMatch metric - returns 1 if tool calls in the reference trajectory appear in the predicted trajectory in the same order, else 0.

TrajectoryPrecisionInput

Instances and metric spec for TrajectoryPrecision metric.

Fields
metric_spec	TrajectoryPrecisionSpec Required. Spec for TrajectoryPrecision metric.
instances[]	TrajectoryPrecisionInstance Required. Repeated TrajectoryPrecision instance.

TrajectoryPrecisionInstance

Spec for TrajectoryPrecision instance.

Fields
predicted_trajectory	Trajectory Required. Spec for predicted tool call trajectory.
reference_trajectory	Trajectory Required. Spec for reference tool call trajectory.

TrajectoryPrecisionMetricValue

TrajectoryPrecision metric value for an instance.

Fields
score	float Output only. TrajectoryPrecision score.

TrajectoryPrecisionResults

Results for TrajectoryPrecision metric.

Fields
trajectory_precision_metric_values[]	TrajectoryPrecisionMetricValue Output only. TrajectoryPrecision metric values.

TrajectoryPrecisionSpec

This type has no fields.

Spec for TrajectoryPrecision metric - returns a float score based on average precision of individual tool calls.

TrajectoryRecallInput

Instances and metric spec for TrajectoryRecall metric.

Fields
metric_spec	TrajectoryRecallSpec Required. Spec for TrajectoryRecall metric.
instances[]	TrajectoryRecallInstance Required. Repeated TrajectoryRecall instance.

TrajectoryRecallInstance

Spec for TrajectoryRecall instance.

Fields
predicted_trajectory	Trajectory Required. Spec for predicted tool call trajectory.
reference_trajectory	Trajectory Required. Spec for reference tool call trajectory.

TrajectoryRecallMetricValue

TrajectoryRecall metric value for an instance.

Fields
score	float Output only. TrajectoryRecall score.

TrajectoryRecallResults

Results for TrajectoryRecall metric.

Fields
trajectory_recall_metric_values[]	TrajectoryRecallMetricValue Output only. TrajectoryRecall metric values.

TrajectoryRecallSpec

This type has no fields.

Spec for TrajectoryRecall metric - returns a float score based on average recall of individual tool calls.

TrajectorySingleToolUseInput

Instances and metric spec for TrajectorySingleToolUse metric.

Fields
metric_spec	TrajectorySingleToolUseSpec Required. Spec for TrajectorySingleToolUse metric.
instances[]	TrajectorySingleToolUseInstance Required. Repeated TrajectorySingleToolUse instance.

TrajectorySingleToolUseInstance

Spec for TrajectorySingleToolUse instance.

Fields
predicted_trajectory	Trajectory Required. Spec for predicted tool call trajectory.

TrajectorySingleToolUseMetricValue

TrajectorySingleToolUse metric value for an instance.

Fields
score	float Output only. TrajectorySingleToolUse score.

TrajectorySingleToolUseResults

Results for TrajectorySingleToolUse metric.

Fields
trajectory_single_tool_use_metric_values[]	TrajectorySingleToolUseMetricValue Output only. TrajectorySingleToolUse metric values.

TrajectorySingleToolUseSpec

Spec for TrajectorySingleToolUse metric - returns 1 if tool is present in the predicted trajectory, else 0.

Fields
tool_name	string Required. Spec for tool name to be checked for in the predicted trajectory.

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 (deprecated)	string This item is deprecated! 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.
service_account	string The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the iam.serviceAccounts.actAs permission on this service account.
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 (deprecated)	RagFileChunkingConfig This item is deprecated! Specifies the size and overlap of chunks after uploading RagFile.
rag_file_transformation_config	RagFileTransformationConfig Specifies the transformation config for RagFiles.

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}

VertexAiSearchConfig

Config for the Vertex AI Search.

Fields
serving_config	string Vertex AI Search Serving Config resource full name. For example, projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config} or projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}.

VertexRagStore

Retrieve from Vertex RAG Store for grounding.

Fields
rag_corpora[] (deprecated)	string This item is deprecated! Optional. Deprecated. Please use rag_resources instead.
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.
rag_retrieval_config	RagRetrievalConfig Optional. The retrieval config for the Rag query.
similarity_top_k (deprecated)	int32 This item is deprecated! Optional. Number of top k results to return from the selected corpora.
vector_distance_threshold (deprecated)	double This item is deprecated! 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.

VoiceConfig

The configuration for the voice to use.

Fields
Union field voice_config. The configuration for the speaker to use. voice_config can be only one of the following:
prebuilt_voice_config	PrebuiltVoiceConfig The configuration for the prebuilt voice to use.