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Archive Evaluation

client.evaluations.archive(stringevaluationID, RequestOptionsoptions?): Evaluation { id, created_at, created_by, 12 more }
DELETE/v5/evaluations/{evaluation_id}

Archive (soft-delete) an evaluation.

Sets the evaluation’s archived timestamp rather than permanently deleting it, and cascades the archive to the evaluation’s items and dashboards while removing it from any evaluation groups. The evaluation can later be brought back with a restore request to the update endpoint.

ParametersExpand Collapse
evaluationID: string
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
name: string
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"
name: string
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
name: 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
name: 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 }
layout: Container { children, direction }
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 }

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"

Archive 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.archive('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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