Get Evaluation

client.evaluations.retrieve(, ?, ?): Evaluation { id, created_at, created_by, 12 more }

GET/v5/evaluations/{evaluation_id}

Retrieve a single evaluation by ID.

Returns the evaluation with its datasets, async-job progress, metadata, and task-error count. Archived evaluations are excluded unless include_archived is set. Pass the tasks view to include the evaluation’s task configurations in the response.

ParametersExpand Collapse

evaluationID: string

query: EvaluationRetrieveParams { include_archived, views }

include_archived?: boolean

views?: Array<EvaluationViews { } >

ReturnsExpand Collapse

Evaluation { id, created_at, created_by, 12 more }

id: string

The unique identifier of the entity.

created_at: string

The date and time when the entity was created in ISO format.

formatdate-time

created_by: Identity { id, type, object }

The identity that created the entity.

id: string

type: "user" | "service_account"

One of the following:

"user"

"service_account"

object?: "identity"

datasets: Array<Dataset { id, created_at, created_by, 6 more } > | null

id: string

The unique identifier of the entity.

created_at: string

The date and time when the entity was created in ISO format.

formatdate-time

created_by: Identity { id, type, object }

The identity that created the entity.

id: string

type: "user" | "service_account"

One of the following:

"user"

"service_account"

object?: "identity"

current_version_num: number

tags: Array<string> | null

The tags associated with the entity

archived_at?: string

The date and time when the entity was archived in ISO format.

formatdate-time

description?: string

object?: "dataset"

status: "failed" | "completed" | "running"

One of the following:

"failed"

"completed"

"running"

tags: Array<string> | null

The tags associated with the entity

archived_at?: string

The date and time when the entity was archived in ISO format.

formatdate-time

description?: string

error_count?: number

Number of task errors across all items in this evaluation.

metadata?: Record<string, unknown>

Metadata key-value pairs for the evaluation

object?: "evaluation"

progress?: EvaluationTasksProgressSchema { items, workflows }

Progress of the evaluation’s underlying async job

items?: Items { failed, pending, successful, 2 more }

failed: number

pending: number

successful: number

total: number

failed_items?: Array<FailedItem>

item_id: string

error?: string

error_type?: string

workflows?: Workflows { completed, failed, pending, total }

completed: number

failed: number

pending: number

total: number

status_reason?: string

Reason for evaluation status

tasks?: Array<EvaluationTask>

Tasks executed during evaluation. Populated with optional task view.

One of the following:

ChatCompletionEvaluationTask { configuration, alias, task_type }

configuration: Configuration { messages, model, audio, 24 more }

messages: Array<Record<string, unknown>> | ItemLocator

openai standard message format

One of the following:

Array<Record<string, unknown>>

ItemLocator = string

model: string

model specified as model_vendor/model, for example openai/gpt-4o

audio?: Record<string, unknown> | ItemLocator

Parameters for audio output. Required when audio output is requested with modalities: [‘audio’].

One of the following:

Record<string, unknown>

ItemLocator = string

frequency_penalty?: number | ItemLocator

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far.

One of the following:

number

ItemLocator = string

function_call?: Record<string, unknown> | ItemLocator

Deprecated in favor of tool_choice. Controls which function is called by the model.

One of the following:

Record<string, unknown>

ItemLocator = string

functions?: Array<Record<string, unknown>> | ItemLocator

Deprecated in favor of tools. A list of functions the model may generate JSON inputs for.

One of the following:

Array<Record<string, unknown>>

ItemLocator = string

logit_bias?: Record<string, number> | ItemLocator

Modify the likelihood of specified tokens appearing in the completion. Maps tokens to bias values from -100 to 100.

One of the following:

Record<string, number>

ItemLocator = string

logprobs?: boolean | ItemLocator

Whether to return log probabilities of the output tokens or not.

One of the following:

boolean

ItemLocator = string

max_completion_tokens?: number | ItemLocator

An upper bound for the number of tokens that can be generated, including visible output tokens and reasoning tokens.

One of the following:

number

ItemLocator = string

max_tokens?: number | ItemLocator

Deprecated in favor of max_completion_tokens. The maximum number of tokens to generate.

One of the following:

number

ItemLocator = string

metadata?: Record<string, string> | ItemLocator

Developer-defined tags and values used for filtering completions in the dashboard.

One of the following:

Record<string, string>

ItemLocator = string

modalities?: Array<string> | ItemLocator

Output types that you would like the model to generate for this request.

One of the following:

Array<string>

ItemLocator = string

n?: number | ItemLocator

How many chat completion choices to generate for each input message.

One of the following:

number

ItemLocator = string

parallel_tool_calls?: boolean | ItemLocator

Whether to enable parallel function calling during tool use.

One of the following:

boolean

ItemLocator = string

prediction?: Record<string, unknown> | ItemLocator

Static predicted output content, such as the content of a text file being regenerated.

One of the following:

Record<string, unknown>

ItemLocator = string

presence_penalty?: number | ItemLocator

Number between -2.0 and 2.0. Positive values penalize tokens based on whether they appear in the text so far.

One of the following:

number

ItemLocator = string

reasoning_effort?: string

For o1 models only. Constrains effort on reasoning. Values: low, medium, high.

response_format?: Record<string, unknown> | ItemLocator

An object specifying the format that the model must output.

One of the following:

Record<string, unknown>

ItemLocator = string

seed?: number | ItemLocator

If specified, system will attempt to sample deterministically for repeated requests with same seed.

One of the following:

number

ItemLocator = string

stop?: string | Array<string>

Up to 4 sequences where the API will stop generating further tokens.

One of the following:

string

Array<string>

store?: boolean | ItemLocator

Whether to store the output for use in model distillation or evals products.

One of the following:

boolean

ItemLocator = string

temperature?: number | ItemLocator

What sampling temperature to use. Higher values make output more random, lower more focused.

One of the following:

number

ItemLocator = string

tool_choice?: string | Record<string, unknown>

Controls which tool is called by the model. Values: none, auto, required, or specific tool.

One of the following:

string

Record<string, unknown>

tools?: Array<Record<string, unknown>> | ItemLocator

A list of tools the model may call. Currently, only functions are supported. Max 128 functions.

One of the following:

Array<Record<string, unknown>>

ItemLocator = string

top_k?: number | ItemLocator

Only sample from the top K options for each subsequent token

One of the following:

number

ItemLocator = string

top_logprobs?: number | ItemLocator

Number of most likely tokens to return at each position, with associated log probability.

One of the following:

number

ItemLocator = string

top_p?: number | ItemLocator

Alternative to temperature. Only tokens comprising top_p probability mass are considered.

One of the following:

number

ItemLocator = string

alias?: string

Alias to title the results column. Defaults to the chat_completion

task_type?: "chat_completion"

GenericInferenceEvaluationTask { configuration, alias, task_type }

configuration: Configuration { model, args, inference_configuration }

model: string

model specified as vendor/name (ex. openai/gpt-5)

args?: Record<string, unknown> | ItemLocator

Arguments passed into model

One of the following:

Record<string, unknown>

ItemLocator = string

inference_configuration?: LaunchInferenceConfiguration { num_retries, timeout_seconds } | ItemLocator

Vendor specific configuration

One of the following:

LaunchInferenceConfiguration { num_retries, timeout_seconds }

num_retries?: number

timeout_seconds?: number

ItemLocator = string

alias?: string

Alias to title the results column. Defaults to the inference

task_type?: "inference"

ApplicationVariantV1EvaluationTask { configuration, alias, task_type }

configuration: Configuration { application_variant_id, inputs, history, 2 more }

application_variant_id: string

inputs: Record<string, unknown> | ItemLocator

Input data for the application. For agents service variants, you must provide inputs as a mapping from {input_name: input_value}. For V0 variants, you must specify the node your input should be passed to, structuring your input as {node_id: {input_name: input_value}}.

One of the following:

Record<string, unknown>

ItemLocator = string

history?: Array<ApplicationRequestResponsePairArray> | ItemLocator

History of the application

One of the following:

Array<ApplicationRequestResponsePairArray>

request: string

Request inputs

response: string

Response outputs

session_data?: Record<string, unknown>

Session data corresponding to the request response pair

ItemLocator = string

operation_metadata?: Record<string, unknown> | ItemLocator

Arbitrary user-defined metadata that can be attached to the process operations and will be registered in the interaction.

One of the following:

Record<string, unknown>

ItemLocator = string

overrides?: AgenticApplicationOverrides { concurrent, initial_state, partial_trace, 2 more } | Record<string, KnowledgeBaseNodeOverride> | ItemLocator

Optional overrides for the application

One of the following:

AgenticApplicationOverrides { concurrent, initial_state, partial_trace, 2 more }

Execution override options for agentic applications

concurrent?: boolean

initial_state?: InitialState { current_node, state }

current_node: string

state: Record<string, unknown>

partial_trace?: Array<PartialTrace>

duration_ms: number

node_id: string

operation_input: string

operation_output: string

operation_type: string

start_timestamp: string

workflow_id: string

operation_metadata?: Record<string, unknown>

return_span?: boolean

use_channels?: boolean

Record<string, KnowledgeBaseNodeOverride>

artifact_ids_filter?: Array<string>

artifact_name_regex?: Array<string>

type?: "knowledge_base_schema"

ItemLocator = string

alias?: string

Alias to title the results column. Defaults to the application_variant

task_type?: "application_variant"

AgentexOutputEvaluationTask { configuration, alias, task_type }

configuration: Configuration { agentex_agent_id, input_column, agent_task_params, 6 more }

agentex_agent_id: string

The ID of the Agentex agent to use

input_column: string | Record<string, unknown> | Array<unknown>

The dataset column to use as input for the agent

One of the following:

string

Record<string, unknown>

Array<unknown>

agent_task_params?: Record<string, unknown> | ItemLocator

Extra params merged into the Agentex task/create call’s params object and forwarded verbatim to the agent. Required by agents that demand configuration at task creation — the golden agent, for example, rejects any task whose params omit config_id. SGP always pins is_eval: true; a caller-supplied description overrides the SGP default. Nested item.-prefixed strings and {{item.x}} templates are resolved per evaluation item, so a per-row config_id can come from a dataset column.

One of the following:

Record<string, unknown>

ItemLocator = string

completion_mode?: "first_message" | "turn_quiescence"

How the agent’s first turn is judged finished. first_message (the default) grades the first non-empty agent text message after the input, which is cheap but grades a streaming harness on whatever text block streamed first. turn_quiescence keeps listening while the agent is still producing messages and grades once at least one agent text message exists and nothing new has arrived for quiescence_seconds — the right choice for tool-using agents. Neither mode requires the agent to mark the task complete; a terminal task status always ends the wait, and timeout_seconds always bounds it.

One of the following:

"first_message"

"turn_quiescence"

deployment_id?: string

Optional Agentex deployment ID to pin the eval to a specific deployment. When set, RPC traffic routes through /agents/{agent_id}/deployments/{deployment_id}/rpc. When unset, traffic uses the agent’s default RPC endpoint, which resolves through the agent’s current routing rules on the Agentex side.

include_traces?: boolean | ItemLocator

Whether to include trace data in the evaluation results. Traces are read from SGP’s own span store for the agent’s trace, not from Agentex.

One of the following:

boolean

ItemLocator = string

input_mode?: "text" | "data"

How the resolved input_column is delivered to the agent. text (the default) sends a TextContent message with the value stringified. data sends a DataContent message whose data is the value as a JSON object; the resolved value must be an object, or a string that parses to one. Most agents accept text only and reject data.

One of the following:

"text"

"data"

quiescence_seconds?: number | ItemLocator

Seconds of no new messages before completion_mode: turn_quiescence considers the turn finished. Ignored in first_message mode. Should exceed the agent’s longest expected gap between messages (a slow tool call), or the turn is graded early.

One of the following:

number

ItemLocator = string

timeout_seconds?: number | ItemLocator

Maximum seconds to wait for the agent’s first-turn response per item. If not set, the server-side default of 600s applies. Capped at 1500s to stay within the evaluation item activity’s 1800s start-to-close budget.

One of the following:

number

ItemLocator = string

alias?: string

Alias to title the results column. Defaults to the agentex_output

task_type?: "agentex_output"

MetricEvaluationTask { configuration, alias, task_type }

configuration: BleuScorerConfigWithItemLocator { candidate, reference, type } | MeteorScorerConfigWithItemLocator { candidate, reference, type } | CosineSimilarityScorerConfigWithItemLocator { candidate, reference, type } | 4 more

One of the following:

BleuScorerConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "bleu"

MeteorScorerConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "meteor"

CosineSimilarityScorerConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "cosine_similarity"

F1ScorerConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "f1"

RougeScorer1ConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "rouge1"

RougeScorer2ConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "rouge2"

RougeScorerLConfigWithItemLocator { candidate, reference, type }

candidate: string

reference: string

type: "rougeL"

alias?: string

Alias to title the results column. Defaults to the metric type specified in the configuration

task_type?: "metric"

AutoEvaluationQuestionTask { configuration, alias, task_type }

configuration: Configuration { model, prompt, question_id }

model: string

model specified as model_vendor/model_name

prompt: string

question_id: string

question to be evaluated

alias?: string

Alias to title the results column. Defaults to the auto_evaluation_question

task_type?: "auto_evaluation.question"

AutoEvaluationGuidedDecodingEvaluationTask { configuration, alias, task_type }

configuration: AutoEvaluationStructuredOutputTaskRequestWithItemLocator { model, prompt, response_format, 3 more } | AutoEvaluationGuidedDecodingTaskRequestWithItemLocator { choices, model, prompt, 3 more } | AutoEvaluationAgentTaskRequestWithItemLocator { definition, name, output_rules, 6 more }

One of the following:

AutoEvaluationStructuredOutputTaskRequestWithItemLocator { model, prompt, response_format, 3 more }

model: string

model specified as model_vendor/model_name

prompt: string

response_format: Record<string, unknown>

JSON schema used for structuring the model response

inference_args?: Record<string, unknown>

Additional arguments to pass to the inference request

run_condition?: ConstEvaluationRunCondition { op, value } | VarEvaluationRunCondition { path, op } | EqEvaluationRunCondition { left, right, op } | 12 more

One of the following:

ConstEvaluationRunCondition { op, value }

op?: "const"

value?: string | number | boolean

One of the following:

string

number

boolean

VarEvaluationRunCondition { path, op }

path: string

op?: "var"

EqEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "eq"

NeEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "ne"

LtEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "lt"

LteEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "lte"

GtEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "gt"

GteEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "gte"

AndEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "and"

OrEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "or"

InEvaluationRunCondition { left, operands, op }

left: unknown

operands: Array<unknown>

op?: "in"

NotInEvaluationRunCondition { left, operands, op }

left: unknown

operands: Array<unknown>

op?: "not_in"

NotEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "not"

IsNullEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "is_null"

IsNotNullEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "is_not_null"

system_prompt?: string

AutoEvaluationGuidedDecodingTaskRequestWithItemLocator { choices, model, prompt, 3 more }

choices: Array<string>

Choices array cannot be empty

model: string

model specified as model_vendor/model_name

prompt: string

inference_args?: Record<string, unknown>

Additional arguments to pass to the inference request

run_condition?: ConstEvaluationRunCondition { op, value } | VarEvaluationRunCondition { path, op } | EqEvaluationRunCondition { left, right, op } | 12 more

One of the following:

ConstEvaluationRunCondition { op, value }

op?: "const"

value?: string | number | boolean

One of the following:

string

number

boolean

VarEvaluationRunCondition { path, op }

path: string

op?: "var"

EqEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "eq"

NeEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "ne"

LtEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "lt"

LteEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "lte"

GtEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "gt"

GteEvaluationRunCondition { left, right, op }

left: unknown

right: unknown

op?: "gte"

AndEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "and"

OrEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "or"

InEvaluationRunCondition { left, operands, op }

left: unknown

operands: Array<unknown>

op?: "in"

NotInEvaluationRunCondition { left, operands, op }

left: unknown

operands: Array<unknown>

op?: "not_in"

NotEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "not"

IsNullEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "is_null"

IsNotNullEvaluationRunCondition { operands, op }

operands: Array<unknown>

op?: "is_not_null"

system_prompt?: string

AutoEvaluationAgentTaskRequestWithItemLocator { definition, name, output_rules, 6 more }

definition: string

output_rules: Array<string>

data_fields?: Array<string>

designated_to?: ApeAgent { config, agent_name } | IfAgent { config, agent_name } | TruthfulnessAgent { config, agent_name } | BaseAgent { config, agent_name }

One of the following:

ApeAgent { config, agent_name }

config: Config { model, temperature }

model?: string

temperature?: number

agent_name?: "APEAgent"

IfAgent { config, agent_name }

config: Config { model }

model?: string

agent_name?: "IFAgent"

TruthfulnessAgent { config, agent_name }

config: Config { model }

model?: string

agent_name?: "TruthfulnessAgent"

BaseAgent { config, agent_name }

config: Config { model }

model?: string

agent_name?: "BaseAgent"

output_type?: "text" | "integer" | "float" | "boolean"

One of the following:

"text"

"integer"

"float"

"boolean"

output_values?: Array<string | number | boolean>

One of the following:

string

number

boolean

rubric_id?: string

rubric_version?: number

alias?: string

Alias to title the results column. Defaults to the auto_evaluation_guided_decoding

task_type?: "auto_evaluation.guided_decoding"

AutoEvaluationAgentEvaluationTask { configuration, alias, task_type }

configuration: AutoEvaluationAgentTaskRequestWithItemLocator { definition, name, output_rules, 6 more }

definition: string

output_rules: Array<string>

data_fields?: Array<string>

designated_to?: ApeAgent { config, agent_name } | IfAgent { config, agent_name } | TruthfulnessAgent { config, agent_name } | BaseAgent { config, agent_name }

One of the following:

ApeAgent { config, agent_name }

config: Config { model, temperature }

model?: string

temperature?: number

agent_name?: "APEAgent"

IfAgent { config, agent_name }

config: Config { model }

model?: string

agent_name?: "IFAgent"

TruthfulnessAgent { config, agent_name }

config: Config { model }

model?: string

agent_name?: "TruthfulnessAgent"

BaseAgent { config, agent_name }

config: Config { model }

model?: string

agent_name?: "BaseAgent"

output_type?: "text" | "integer" | "float" | "boolean"

One of the following:

"text"

"integer"

"float"

"boolean"

output_values?: Array<string | number | boolean>

One of the following:

string

number

boolean

rubric_id?: string

rubric_version?: number

alias?: string

Alias to title the results column. Defaults to the auto_evaluation_agent

task_type?: "auto_evaluation.agent"

ContributorEvaluationQuestionTask { configuration, alias, task_type }

configuration: Configuration { layout, question_id, prefill_from, 3 more }

children: Array<Container { children, direction } | Component { data, label } >

The children to be displayed within the container

One of the following:

Container = Container { children, direction }

Component { data, label }

data: ItemLocator

A pointer to the data in each evaluation item to be displayed within the component

label?: string

direction?: "row" | "column"

The axis that children are placed in the container. Based on CSS flex-direction (see: https://developer.mozilla.org/en-US/docs/Web/CSS/flex-direction)

One of the following:

"row"

"column"

question_id: string

prefill_from?: string

Dataset column to prefill contributor question task result

minLength1

queue_id?: string

The contributor annotation queue to include this task in. Defaults to default

maxLength100

required?: boolean

Whether the question is required to be answered

rubric_id?: string

ID of the rubric to use for scoring this evaluation question

alias?: string

Alias to title the results column. Defaults to the contributor_evaluation_question

task_type?: "contributor_evaluation.question"

CustomFunctionEvaluationTask { configuration, alias, task_type }

configuration: Configuration { function_source, arg_mapping, config_args, outputs }

Configuration for a custom Python function evaluation task.

function_source: string

Python function source code

maxLength10000

arg_mapping?: Record<string, string>

Mapping of function parameter names to item locators (e.g. item.field). Auto-derived from function signature if not provided.

config_args?: Record<string, unknown>

Literal argument values for function parameters, such as thresholds or RNG seeds. Serialized JSON must be at most 10000 characters.

outputs?: Array<Output>

Optional output paths to materialize as separate result columns. If omitted, the function return value is stored only under the task alias/data key.

path: string

Dot path in the custom function return value to materialize.

minLength1

alias?: string

Result column alias. Defaults to path with dots replaced by underscores.

minLength1

alias?: string

Alias to title the results column. Defaults to the function name.

task_type?: "custom_function"

Get Evaluation

import SGPClient from 'scale-gp';

const client = new SGPClient({
  accountID: 'My Account ID',
  apiKey: process.env['SGP_API_KEY'], // This is the default and can be omitted
});

const evaluation = await client.evaluations.retrieve('evaluation_id');

console.log(evaluation.id);

{
  "id": "id",
  "created_at": "2019-12-27T18:11:19.117Z",
  "created_by": {
    "id": "id",
    "type": "user",
    "object": "identity"
  },
  "datasets": [
    {
      "id": "id",
      "created_at": "2019-12-27T18:11:19.117Z",
      "created_by": {
        "id": "id",
        "type": "user",
        "object": "identity"
      },
      "current_version_num": 0,
      "name": "name",
      "tags": [
        "string"
      ],
      "archived_at": "2019-12-27T18:11:19.117Z",
      "description": "description",
      "object": "dataset"
    }
  ],
  "name": "name",
  "status": "failed",
  "tags": [
    "string"
  ],
  "archived_at": "2019-12-27T18:11:19.117Z",
  "description": "description",
  "error_count": 0,
  "metadata": {
    "foo": "bar"
  },
  "object": "evaluation",
  "progress": {
    "items": {
      "failed": 0,
      "pending": 0,
      "successful": 0,
      "total": 0,
      "failed_items": [
        {
          "item_id": "item_id",
          "error": "error",
          "error_type": "error_type"
        }
      ]
    },
    "workflows": {
      "completed": 0,
      "failed": 0,
      "pending": 0,
      "total": 0
    }
  },
  "status_reason": "status_reason",
  "tasks": [
    {
      "configuration": {
        "messages": [
          {
            "foo": "bar"
          }
        ],
        "model": "model",
        "audio": {
          "foo": "bar"
        },
        "frequency_penalty": -2,
        "function_call": {
          "foo": "bar"
        },
        "functions": [
          {
            "foo": "bar"
          }
        ],
        "logit_bias": {
          "foo": 0
        },
        "logprobs": true,
        "max_completion_tokens": 0,
        "max_tokens": 0,
        "metadata": {
          "foo": "string"
        },
        "modalities": [
          "string"
        ],
        "n": 0,
        "parallel_tool_calls": true,
        "prediction": {
          "foo": "bar"
        },
        "presence_penalty": -2,
        "reasoning_effort": "reasoning_effort",
        "response_format": {
          "foo": "bar"
        },
        "seed": 0,
        "stop": "string",
        "store": true,
        "temperature": 0,
        "tool_choice": "string",
        "tools": [
          {
            "foo": "bar"
          }
        ],
        "top_k": 0,
        "top_logprobs": 0,
        "top_p": 0
      },
      "alias": "alias",
      "task_type": "chat_completion"
    }
  ]
}

Returns Examples

{
  "id": "id",
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