Package google.cloud.aiplatform.v1

Index

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.

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.

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.

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.

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.

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.

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.

DeleteCachedContentRequest

Request message for GenAiCacheService.DeleteCachedContent.

Fields
name

string

Required. The resource name referring to the cached content

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.

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.

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

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

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.

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.

ImportRagFilesConfig

Config for importing RagFiles.

Fields
rag_file_transformation_config

RagFileTransformationConfig

Specifies the transformation config for RagFiles.

max_embedding_requests_per_min

int32

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

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

GcsSource

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

google_drive_source

GoogleDriveSource

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

slack_source

SlackSource

Slack channels with their corresponding access tokens.

jira_source

JiraSource

Jira queries with their corresponding authentication.

share_point_sources

SharePointSources

SharePoint sources.

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

GcsDestination

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

partial_failure_bigquery_sink
(deprecated)

BigQueryDestination

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

ImportRagFilesOperationMetadata

Runtime operation information for VertexRagDataService.ImportRagFiles.

Fields
generic_metadata

GenericOperationMetadata

The operation generic information.

rag_corpus_id

int64

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

import_rag_files_config

ImportRagFilesConfig

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

progress_percentage

int32

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

ImportRagFilesRequest

Request message for VertexRagDataService.ImportRagFiles.

Fields
parent

string

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

import_rag_files_config

ImportRagFilesConfig

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

ImportRagFilesResponse

Response message for VertexRagDataService.ImportRagFiles.

Fields
imported_rag_files_count

int64

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

failed_rag_files_count

int64

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

skipped_rag_files_count

int64

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

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

string

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

partial_failures_bigquery_table

string

The BigQuery table into which the partial failures were written.

JiraSource

The Jira source for the ImportRagFilesRequest.

Fields
jira_queries[]

JiraQueries

Required. The Jira queries.

JiraQueries

JiraQueries contains the Jira queries and corresponding authentication.

Fields
projects[]

string

A list of Jira projects to import in their entirety.

custom_queries[]

string

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

email

string

Required. The Jira email address.

server_uri

string

Required. The Jira server URI.

api_key_config

ApiKeyConfig

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

RagFileChunkingConfig

Specifies the size and overlap of chunks for RagFiles.

Fields
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.

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
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.

RagRetrievalConfig

Specifies the context retrieval config.

Fields
top_k

int32

Optional. The number of contexts to retrieve.

filter

Filter

Optional. Config for filters.

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.

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.

pinecone

Pinecone

The config for the Pinecone.

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.

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}

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

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_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_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

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.

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

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_retrieval

GoogleSearchRetrieval

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

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.

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.

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.

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.

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

UpdateCacheConfigOperationMetadata

Runtime operation information for GenAiCacheConfigService.UpdateCacheConfig.

Fields
generic_metadata

GenericOperationMetadata

The operation generic information.

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.

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_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}

VertexRagStore

Retrieve from Vertex RAG Store for grounding.

Fields
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

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

vector_distance_threshold
(deprecated)

double

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

RagResource

The definition of the Rag resource.

Fields
rag_corpus

string

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

rag_file_ids[]

string

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

VideoMetadata

Metadata describes the input video content.

Fields
start_offset

Duration

Optional. The start offset of the video.

end_offset

Duration

Optional. The end offset of the video.

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.