REST Resource: projects.locations.evaluationRuns

Resource: EvaluationRun

EvaluationRun is a resource that represents a single evaluation run, which includes a set of prompts, model responses, evaluation configuration and the resulting metrics.

Fields
name string

Identifier. The resource name of the EvaluationRun. This is a unique identifier. Format: projects/{project}/locations/{location}/evaluationRuns/{evaluationRun}

displayName string

Required. The display name of the Evaluation Run.

metadata value (Value format)

Optional. metadata about the evaluation run, can be used by the caller to store additional tracking information about the evaluation run.

labels map (key: string, value: string)

Optional. Labels for the evaluation run.

dataSource object (DataSource)

Required. The data source for the evaluation run.

inferenceConfigs map (key: string, value: object (InferenceConfig))

Optional. The candidate to inference config map for the evaluation run. The candidate can be up to 128 characters long and can consist of any UTF-8 characters.

evaluationConfig object (EvaluationConfig)

Required. The configuration used for the evaluation.

state enum (State)

Output only. The state of the evaluation run.

error object (Status)

Output only. Only populated when the evaluation run's state is FAILED or CANCELLED.

evaluationResults object (EvaluationResults)

Output only. The results of the evaluation run. Only populated when the evaluation run's state is SUCCEEDED.

createTime string (Timestamp format)

Output only. time when the evaluation run was created.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

completionTime string (Timestamp format)

Output only. time when the evaluation run was completed.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

evaluationSetSnapshot string

Output only. The specific evaluation set of the evaluation run. For runs with an evaluation set input, this will be that same set. For runs with BigQuery input, it's the sampled BigQuery dataset.

JSON representation 
{
  "name": string,
  "displayName": string,
  "metadata": value,
  "labels": {
    string: string,
    ...
  },
  "dataSource": {
    object (DataSource)
  },
  "inferenceConfigs": {
    string: {
      object (InferenceConfig)
    },
    ...
  },
  "evaluationConfig": {
    object (EvaluationConfig)
  },
  "state": enum (State),
  "error": {
    object (Status)
  },
  "evaluationResults": {
    object (EvaluationResults)
  },
  "createTime": string,
  "completionTime": string,
  "evaluationSetSnapshot": string
}

DataSource

The data source for the evaluation run.

Fields
source Union type
One of multiple supported sources. source can be only one of the following:
evaluationSet string

The EvaluationSet resource name. Format: projects/{project}/locations/{location}/evaluationSets/{evaluationSet}

bigqueryRequestSet object (BigQueryRequestSet)

Evaluation data in bigquery.

JSON representation 
{

  // source
  "evaluationSet": string,
  "bigqueryRequestSet": {
    object (BigQueryRequestSet)
  }
  // Union type
}

BigQueryRequestSet

The request set for the evaluation run.

Fields
uri string

Required. The URI of a BigQuery table. e.g. bq://projectId.bqDatasetId.bqTableId

promptColumn string

Optional. The name of the column that contains the requests to evaluate. This will be in evaluationItem.EvalPrompt format.

rubricsColumn string

Optional. The name of the column that contains the rubrics. This is in evaluation_rubric.RubricGroup format.

candidateResponseColumns map (key: string, value: string)

Optional. Map of candidate name to candidate response column name. The column will be in evaluationItem.CandidateResponse format.

samplingConfig object (SamplingConfig)

Optional. The sampling config for the bigquery resource.

JSON representation 
{
  "uri": string,
  "promptColumn": string,
  "rubricsColumn": string,
  "candidateResponseColumns": {
    string: string,
    ...
  },
  "samplingConfig": {
    object (SamplingConfig)
  }
}

SamplingConfig

The sampling config.

Fields
samplingCount integer

Optional. The total number of logged data to import. If available data is less than the sampling count, all data will be imported. Default is 100.

samplingMethod enum (SamplingMethod)

Optional. The sampling method to use.

samplingDuration string (Duration format)

Optional. How long to wait before sampling data from the BigQuery table. If not specified, defaults to 0.

A duration in seconds with up to nine fractional digits, ending with 's'. Example: "3.5s".

JSON representation 
{
  "samplingCount": integer,
  "samplingMethod": enum (SamplingMethod),
  "samplingDuration": string
}

SamplingMethod

The sampling method to use.

Enums
SAMPLING_METHOD_UNSPECIFIED Unspecified sampling method.
RANDOM Random sampling.

InferenceConfig

An inference config used for model inference during the evaluation run.

Fields
model string

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

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

Endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}

model_config Union type
Configuration for the LLM. model_config can be only one of the following:
generationConfig object (GenerationConfig)

Optional. Generation config.

JSON representation 
{
  "model": string,

  // model_config
  "generationConfig": {
    object (GenerationConfig)
  }
  // Union type
}

GenerationConfig

Generation config.

Fields
stopSequences[] string

Optional. Stop sequences.

responseMimeType 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.

responseModalities[] enum (Modality)

Optional. The modalities of the response.

thinkingConfig object (ThinkingConfig)

Optional. Config for thinking features. An error will be returned if this field is set for models that don't support thinking.

modelConfig
(deprecated) object (ModelConfig)

Optional. Config for model selection.

temperature number

Optional. Controls the randomness of predictions.

topP number

Optional. If specified, nucleus sampling will be used.

topK number

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

candidateCount integer

Optional. Number of candidates to generate.

maxOutputTokens integer

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

responseLogprobs boolean

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

logprobs integer

Optional. Logit probabilities.

presencePenalty number

Optional. Positive penalties.

frequencyPenalty number

Optional. Frequency penalties.

seed integer

Optional. Seed.

responseSchema object (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 responseMimeType must also be set. Compatible mimetypes: application/json: Schema for JSON response.

responseJsonSchema value (Value format)

Optional. Output schema of the generated response. This is an alternative to responseSchema that accepts JSON Schema.

If set, responseSchema must be omitted, but responseMimeType is required.

While the full JSON Schema may be sent, not all features are supported. Specifically, only the following properties are supported:

  • $id
  • $defs
  • $ref
  • $anchor
  • type
  • format
  • title
  • description
  • enum (for strings and numbers)
  • items
  • prefixItems
  • minItems
  • maxItems
  • minimum
  • maximum
  • anyOf
  • oneOf (interpreted the same as anyOf)
  • properties
  • additionalProperties
  • required

The non-standard propertyOrdering property may also be set.

Cyclic references are unrolled to a limited degree and, as such, may only be used within non-required properties. (Nullable properties are not sufficient.) If $ref is set on a sub-schema, no other properties, except for than those starting as a $, may be set.

routingConfig object (RoutingConfig)

Optional. Routing configuration.

audioTimestamp boolean

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

mediaResolution enum (MediaResolution)

Optional. If specified, the media resolution specified will be used.

speechConfig object (SpeechConfig)

Optional. The speech generation config.

enableAffectiveDialog boolean

Optional. If enabled, the model will detect emotions and adapt its responses accordingly.

JSON representation 
{
  "stopSequences": [
    string
  ],
  "responseMimeType": string,
  "responseModalities": [
    enum (Modality)
  ],
  "thinkingConfig": {
    object (ThinkingConfig)
  },
  "modelConfig": {
    object (ModelConfig)
  },
  "temperature": number,
  "topP": number,
  "topK": number,
  "candidateCount": integer,
  "maxOutputTokens": integer,
  "responseLogprobs": boolean,
  "logprobs": integer,
  "presencePenalty": number,
  "frequencyPenalty": number,
  "seed": integer,
  "responseSchema": {
    object (Schema)
  },
  "responseJsonSchema": value,
  "routingConfig": {
    object (RoutingConfig)
  },
  "audioTimestamp": boolean,
  "mediaResolution": enum (MediaResolution),
  "speechConfig": {
    object (SpeechConfig)
  },
  "enableAffectiveDialog": boolean
}

RoutingConfig

The configuration for routing the request to a specific model.

Fields
routing_config Union type
Routing mode. routing_config can be only one of the following:
autoMode object (AutoRoutingMode)

Automated routing.

manualMode object (ManualRoutingMode)

Manual routing.

JSON representation 
{

  // routing_config
  "autoMode": {
    object (AutoRoutingMode)
  },
  "manualMode": {
    object (ManualRoutingMode)
  }
  // Union type
}

AutoRoutingMode

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

Fields
modelRoutingPreference enum (ModelRoutingPreference)

The model routing preference.

JSON representation 
{
  "modelRoutingPreference": enum (ModelRoutingPreference)
}

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
modelName string

The model name to use. Only the public LLM models are accepted. See Supported models.

JSON representation 
{
  "modelName": string
}

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.

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).

SpeechConfig

The speech generation config.

Fields
voiceConfig object (VoiceConfig)

The configuration for the speaker to use.

languageCode string

Optional. Language code (ISO 639. e.g. en-US) for the speech synthesization.

JSON representation 
{
  "voiceConfig": {
    object (VoiceConfig)
  },
  "languageCode": string
}

VoiceConfig

The configuration for the voice to use.

Fields
voice_config Union type
The configuration for the speaker to use. voice_config can be only one of the following:
prebuiltVoiceConfig object (PrebuiltVoiceConfig)

The configuration for the prebuilt voice to use.

JSON representation 
{

  // voice_config
  "prebuiltVoiceConfig": {
    object (PrebuiltVoiceConfig)
  }
  // Union type
}

PrebuiltVoiceConfig

The configuration for the prebuilt speaker to use.

Fields
voiceName string

The name of the preset voice to use.

JSON representation 
{
  "voiceName": string
}

ThinkingConfig

Config for thinking features.

Fields
includeThoughts boolean

Optional. Indicates whether to include thoughts in the response. If true, thoughts are returned only when available.

thinkingBudget integer

Optional. Indicates the thinking budget in tokens.

JSON representation 
{
  "includeThoughts": boolean,
  "thinkingBudget": integer
}

ModelConfig

Config for model selection.

Fields
featureSelectionPreference enum (FeatureSelectionPreference)

Required. feature selection preference.

JSON representation 
{
  "featureSelectionPreference": enum (FeatureSelectionPreference)
}

FeatureSelectionPreference

Options for feature selection preference.

Enums
FEATURE_SELECTION_PREFERENCE_UNSPECIFIED Unspecified feature selection preference.
PRIORITIZE_QUALITY Prefer higher quality over lower cost.
BALANCED Balanced feature selection preference.
PRIORITIZE_COST Prefer lower cost over higher quality.

EvaluationConfig

The Evalution configuration used for the evaluation run.

Fields
metrics[] object (EvaluationRunMetric)

Required. The metrics to be calculated in the evaluation run.

rubricConfigs[] object (EvaluationRubricConfig)

Optional. The rubric configs for the evaluation run. They are used to generate rubrics which can be used by rubric-based metrics. Multiple rubric configs can be specified for rubric generation but only one rubric config can be used for a rubric-based metric. If more than one rubric config is provided, the evaluation metric must specify a rubric group key. Note that if a generation spec is specified on both a rubric config and an evaluation metric, the rubrics generated for the metric will be used for evaluation.

outputConfig object (OutputConfig)

Optional. The output config for the evaluation run.

autoraterConfig object (AutoraterConfig)

Optional. The autorater config for the evaluation run.

promptTemplate object (PromptTemplate)

The prompt template used for inference. The values for variables in the prompt template are defined in EvaluationItem.EvaluationPrompt.PromptTemplateData.values.

JSON representation 
{
  "metrics": [
    {
      object (EvaluationRunMetric)
    }
  ],
  "rubricConfigs": [
    {
      object (EvaluationRubricConfig)
    }
  ],
  "outputConfig": {
    object (OutputConfig)
  },
  "autoraterConfig": {
    object (AutoraterConfig)
  },
  "promptTemplate": {
    object (PromptTemplate)
  }
}

EvaluationRunMetric

The metric used for evaluation runs.

Fields
metric string

Required. The name of the metric.

metric_spec Union type
The metric spec used for evaluation. metric_spec can be only one of the following:
rubricBasedMetricSpec object (RubricBasedMetricSpec)

Spec for rubric based metric.

predefinedMetricSpec object (PredefinedMetricSpec)

Spec for a pre-defined metric.

llmBasedMetricSpec object (LLMBasedMetricSpec)

Spec for an LLM based metric.

JSON representation 
{
  "metric": string,

  // metric_spec
  "rubricBasedMetricSpec": {
    object (RubricBasedMetricSpec)
  },
  "predefinedMetricSpec": {
    object (PredefinedMetricSpec)
  },
  "llmBasedMetricSpec": {
    object (LLMBasedMetricSpec)
  }
  // Union type
}

RubricBasedMetricSpec

Specification for a metric that is based on rubrics.

Fields
metricPromptTemplate string

Optional. Template for the prompt used by the judge model to evaluate against rubrics.

rubrics_source Union type
Source of the rubrics to be used for evaluation. rubrics_source can be only one of the following:
inlineRubrics object (RepeatedRubrics)

Use rubrics provided directly in the spec.

rubricGroupKey string

Use a pre-defined group of rubrics associated with the input content. This refers to a key in the rubricGroups map of RubricEnhancedContents.

rubricGenerationSpec object (RubricGenerationSpec)

Dynamically generate rubrics for evaluation using this specification.

judgeAutoraterConfig object (AutoraterConfig)

Optional. Optional configuration for the judge LLM (Autorater). The definition of AutoraterConfig needs to be provided.

JSON representation 
{
  "metricPromptTemplate": string,

  // rubrics_source
  "inlineRubrics": {
    object (RepeatedRubrics)
  },
  "rubricGroupKey": string,
  "rubricGenerationSpec": {
    object (RubricGenerationSpec)
  }
  // Union type
  "judgeAutoraterConfig": {
    object (AutoraterConfig)
  }
}

RepeatedRubrics

Defines a list of rubrics, used when providing rubrics inline.

Fields
rubrics[] object (Rubric)

The list of rubrics.

JSON representation 
{
  "rubrics": [
    {
      object (Rubric)
    }
  ]
}

RubricGenerationSpec

Specification for how rubrics should be generated.

Fields
promptTemplate string

Optional. Template for the prompt used to generate rubrics. The details should be updated based on the most-recent recipe requirements.

rubricContentType enum (RubricContentType)

Optional. The type of rubric content to be generated.

rubricTypeOntology[] string

Optional. An optional, pre-defined list of allowed types for generated rubrics. If this field is provided, it implies include_rubric_type should be true, and the generated rubric types should be chosen from this ontology.

modelConfig object (AutoraterConfig)

Optional. Configuration for the model used in rubric generation. Configs including sampling count and base model can be specified here. Flipping is not supported for rubric generation.

JSON representation 
{
  "promptTemplate": string,
  "rubricContentType": enum (RubricContentType),
  "rubricTypeOntology": [
    string
  ],
  "modelConfig": {
    object (AutoraterConfig)
  }
}

AutoraterConfig

The autorater config used for the evaluation run.

Fields
autoraterModel string

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

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

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

generationConfig object (GenerationConfig)

Optional. Configuration options for model generation and outputs.

sampleCount integer

Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.

JSON representation 
{
  "autoraterModel": string,
  "generationConfig": {
    object (GenerationConfig)
  },
  "sampleCount": integer
}

RubricContentType

Specifies the type of rubric content to generate.

Enums
RUBRIC_CONTENT_TYPE_UNSPECIFIED The content type to generate is not specified.
PROPERTY Generate rubrics based on properties.
NL_QUESTION_ANSWER Generate rubrics in an NL question answer format.
PYTHON_CODE_ASSERTION Generate rubrics in a unit test format.

PredefinedMetricSpec

Specification for a pre-defined metric.

Fields
metricSpecName string

Required. The name of a pre-defined metric, such as "instruction_following_v1" or "text_quality_v1".

parameters object (Struct format)

Optional. The parameters needed to run the pre-defined metric.

JSON representation 
{
  "metricSpecName": string,
  "parameters": {
    object
  }
}

LLMBasedMetricSpec

Specification for an LLM based metric.

Fields
rubrics_source Union type
Source of the rubrics to be used for evaluation. rubrics_source can be only one of the following:
rubricGroupKey string

Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubricGroups map of EvaluationInstance.

rubricGenerationSpec object (RubricGenerationSpec)

Dynamically generate rubrics using this specification.

predefinedRubricGenerationSpec object (PredefinedMetricSpec)

Dynamically generate rubrics using a predefined spec.

metricPromptTemplate string

Required. Template for the prompt sent to the judge model.

systemInstruction string

Optional. System instructions for the judge model.

judgeAutoraterConfig object (AutoraterConfig)

Optional. Optional configuration for the judge LLM (Autorater).

additionalConfig object (Struct format)

Optional. Optional additional configuration for the metric.

JSON representation 
{

  // rubrics_source
  "rubricGroupKey": string,
  "rubricGenerationSpec": {
    object (RubricGenerationSpec)
  },
  "predefinedRubricGenerationSpec": {
    object (PredefinedMetricSpec)
  }
  // Union type
  "metricPromptTemplate": string,
  "systemInstruction": string,
  "judgeAutoraterConfig": {
    object (AutoraterConfig)
  },
  "additionalConfig": {
    object
  }
}

EvaluationRubricConfig

Configuration for a rubric group to be generated/saved for evaluation.

Fields
rubricGroupKey string

Required. The key used to save the generated rubrics. If a generation spec is provided, this key will be used for the name of the generated rubric group. Otherwise, this key will be used to look up the existing rubric group on the evaluation item. Note that if a rubric group key is specified on both a rubric config and an evaluation metric, the key from the metric will be used to select the rubrics for evaluation.

generation_config Union type
The configuration for generating rubrics. generation_config can be only one of the following:
rubricGenerationSpec object (RubricGenerationSpec)

Dynamically generate rubrics using this specification.

predefinedRubricGenerationSpec object (PredefinedMetricSpec)

Dynamically generate rubrics using a predefined spec.

JSON representation 
{
  "rubricGroupKey": string,

  // generation_config
  "rubricGenerationSpec": {
    object (RubricGenerationSpec)
  },
  "predefinedRubricGenerationSpec": {
    object (PredefinedMetricSpec)
  }
  // Union type
}

OutputConfig

The output config for the evaluation run.

Fields
bigqueryDestination object (BigQueryDestination)

BigQuery destination for evaluation output.

gcsDestination object (GcsDestination)

Cloud Storage destination for evaluation output.

JSON representation 
{
  "bigqueryDestination": {
    object (BigQueryDestination)
  },
  "gcsDestination": {
    object (GcsDestination)
  }
}

BigQueryDestination

The BigQuery location for the output content.

Fields
outputUri 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.
JSON representation 
{
  "outputUri": string
}

PromptTemplate

Prompt template used for inference.

Fields
source Union type
The source of the prompt template. source can be only one of the following:
promptTemplate string

Inline prompt template. Template variables should be in the format "{var_name}". Example: "Translate the following from {source_lang} to {target_lang}: {text}"

gcsUri string

Prompt template stored in Cloud Storage. Format: "gs://my-bucket/file-name.txt".

JSON representation 
{

  // source
  "promptTemplate": string,
  "gcsUri": string
  // Union type
}

State

The state of the evaluation run.

Enums
STATE_UNSPECIFIED Unspecified state.
PENDING The evaluation run is pending.
RUNNING The evaluation run is running.
SUCCEEDED The evaluation run has succeeded.
FAILED The evaluation run has failed.
CANCELLED The evaluation run has been cancelled.
INFERENCE The evaluation run is performing inference.
GENERATING_RUBRICS The evaluation run is performing rubric generation.

EvaluationResults

The results of the evaluation run.

Fields
summaryMetrics object (SummaryMetrics)

Optional. The summary metrics for the evaluation run.

evaluationSet string

The evaluation set where item level results are stored.

JSON representation 
{
  "summaryMetrics": {
    object (SummaryMetrics)
  },
  "evaluationSet": string
}

SummaryMetrics

The summary metrics for the evaluation run.

Fields
metrics map (key: string, value: value (Value format))

Optional. Map of metric name to metric value.

totalItems integer

Optional. The total number of items that were evaluated.

failedItems integer

Optional. The number of items that failed to be evaluated.

JSON representation 
{
  "metrics": {
    string: value,
    ...
  },
  "totalItems": integer,
  "failedItems": integer
}

Methods

cancel

 Cancels an Evaluation Run.

create

 Creates an Evaluation Run.

delete

 Deletes an Evaluation Run.

get

 Gets an Evaluation Run.

list

 Lists Evaluation Runs.