Criar um alias de modelo

POST

api

llm

models

aliases

Go (SDK)

package main

import(
	"context"
	mka1 "github.com/MeetKai/mka1-sdks/go"
	"github.com/MeetKai/mka1-sdks/go/optionalnullable"
	"github.com/MeetKai/mka1-sdks/go/models/components"
	"log"
)

func main() {
    ctx := context.Background()

    s := mka1.New(
        mka1.WithSecurity("<YOUR_BEARER_TOKEN_HERE>"),
    )

    res, err := s.Llm.Models.CreateAlias(ctx, components.CreateModelAliasRequest{
        Name: "best-math-model",
        SuiteID: "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
        Metric: "accuracy",
        CandidateModelIds: []string{
            "meetkai:functionary-pt",
            "meetkai:functionary-es",
        },
        DefaultModelID: optionalnullable.From(mka1.Pointer("meetkai:functionary-pt")),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.ModelAlias != nil {
        // handle response
    }
}

using MeetKai.MKA1;
using MeetKai.MKA1.Types.Components;
using System.Collections.Generic;

var sdk = new SDK(bearerAuth: "<YOUR_BEARER_TOKEN_HERE>");

CreateModelAliasRequest req = new CreateModelAliasRequest() {
    Name = "best-math-model",
    SuiteId = "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
    Metric = "accuracy",
    CandidateModelIds = new List<string>() {
        "meetkai:functionary-pt",
        "meetkai:functionary-es",
    },
    DefaultModelId = "meetkai:functionary-pt",
};

var res = await sdk.Llm.Models.CreateAliasAsync(req);

// handle response

from meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    res = sdk.llm.models.create_alias(name="best-math-model", suite_id="eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a", metric="accuracy", candidate_model_ids=[
        "meetkai:functionary-pt",
        "meetkai:functionary-es",
    ], default_model_id="meetkai:functionary-pt")

    # Handle response
    print(res)

import { SDK } from "@meetkai/mka1";

const sdk = new SDK({
  bearerAuth: "<YOUR_BEARER_TOKEN_HERE>",
});

async function run() {
  const result = await sdk.llm.models.createAlias({
    name: "best-math-model",
    suiteId: "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
    metric: "accuracy",
    candidateModelIds: [
      "meetkai:functionary-pt",
      "meetkai:functionary-es",
    ],
    defaultModelId: "meetkai:functionary-pt",
  });

  console.log(result);
}

run();

curl --request POST \
  --url https://apigw.mka1.com/api/v1/llm/models/aliases \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "best-math-model",
  "suite_id": "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
  "metric": "accuracy",
  "candidate_model_ids": [
    "meetkai:functionary-pt",
    "meetkai:functionary-es"
  ],
  "default_model_id": "meetkai:functionary-pt"
}
'

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: 'best-math-model',
    suite_id: 'eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a',
    metric: 'accuracy',
    candidate_model_ids: ['meetkai:functionary-pt', 'meetkai:functionary-es'],
    default_model_id: 'meetkai:functionary-pt'
  })
};

fetch('https://apigw.mka1.com/api/v1/llm/models/aliases', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://apigw.mka1.com/api/v1/llm/models/aliases",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => 'best-math-model',
    'suite_id' => 'eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a',
    'metric' => 'accuracy',
    'candidate_model_ids' => [
        'meetkai:functionary-pt',
        'meetkai:functionary-es'
    ],
    'default_model_id' => 'meetkai:functionary-pt'
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

HttpResponse<String> response = Unirest.post("https://apigw.mka1.com/api/v1/llm/models/aliases")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"best-math-model\",\n  \"suite_id\": \"eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a\",\n  \"metric\": \"accuracy\",\n  \"candidate_model_ids\": [\n    \"meetkai:functionary-pt\",\n    \"meetkai:functionary-es\"\n  ],\n  \"default_model_id\": \"meetkai:functionary-pt\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://apigw.mka1.com/api/v1/llm/models/aliases")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"best-math-model\",\n  \"suite_id\": \"eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a\",\n  \"metric\": \"accuracy\",\n  \"candidate_model_ids\": [\n    \"meetkai:functionary-pt\",\n    \"meetkai:functionary-es\"\n  ],\n  \"default_model_id\": \"meetkai:functionary-pt\"\n}"

response = http.request(request)
puts response.read_body

{
  "object": "model.alias",
  "name": "best-math-model",
  "suite_id": "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
  "suite_version": 3,
  "metric": "accuracy",
  "higher_is_better": true,
  "task_id": null,
  "candidate_model_ids": [
    "meetkai:functionary-pt",
    "meetkai:functionary-es"
  ],
  "api_format": "responses",
  "default_model_id": "meetkai:functionary-pt",
  "pinned_model_id": null,
  "current_model_id": "meetkai:functionary-es",
  "served_model_id": "meetkai:functionary-es",
  "served_because": "benchmark",
  "state": "ok",
  "resolvable": true,
  "standings": [
    {
      "model_id": "meetkai:functionary-pt",
      "score": 0.81,
      "run_id": "eval_run_5e1f7c3a-92b4-4d68-8f0a-b3c9d2e6a1f4",
      "completed_at": "2026-11-09T02:14:00.000Z",
      "sample_count": 500,
      "failed_count": 0,
      "eligible": true,
      "ineligible_reason": null
    },
    {
      "model_id": "meetkai:functionary-es",
      "score": 0.86,
      "run_id": "eval_run_5e1f7c3a-92b4-4d68-8f0a-b3c9d2e6a1f4",
      "completed_at": "2026-11-09T02:14:00.000Z",
      "sample_count": 500,
      "failed_count": 0,
      "eligible": true,
      "ineligible_reason": null
    }
  ],
  "evaluated_at": "2026-11-09T02:14:05.000Z",
  "created_by": "user-org-admin-1",
  "created_at": "2026-11-01T18:00:00.000Z",
  "updated_by": "user-org-admin-1",
  "updated_at": "2026-11-01T18:00:00.000Z"
}

Autorizações

Authorization

string

header

obrigatório

Send Authorization: Bearer , using an MKA1 API key for automation or an OAuth API access token for a signed-in application. Use a dynamic security callback to refresh OAuth access tokens. Browser session-only operations still require their declared cookie.

Corpo

application/json

name

string

obrigatório

O nome do modelo que os chamadores usam. Ele é reivindicado no registro da organização, portanto não deve já estar ativo.

Required string length: 1 - 255

suite_id

string

obrigatório

Uma suíte de avaliação pertencente a esta organização.

Required string length: 1 - 255

metric

string

obrigatório

Um id de métrica que a versão ativa da suíte declara. Seu higher_is_better decide qual pontuação vence.

Required string length: 1 - 128

candidate_model_ids

string[]

obrigatório

Dois a 16 modelos ativos de um formato de API. O alias sempre atende apenas um destes. A ordem desempata: o modelo anterior vence em caso de pontuação igual.

Required array length: 2 - 16 elements

Required string length: 1 - 255

task_id

string | null

Uma tarefa da suíte para ranquear. Null classifica a métrica média em todas as tarefas, contando apenas execuções que cobrem todas as tarefas da versão.

Required string length: 1 - 255

default_model_id

string | null

Um candidato para servir até que exista um primeiro vencedor. Null deixa o alias sem resolução até então.

Required string length: 1 - 255

Resposta

200 - application/json

object

any

obrigatório

name

string

obrigatório

suite_id

string

obrigatório

suite_version

integer | null

obrigatório

A versão da suíte que a última recomputação classificou. Nulo antes que uma pudesse ser lida.

Intervalo obrigatório: -9007199254740991 <= x <= 9007199254740991

metric

string

obrigatório

higher_is_better

boolean | null

obrigatório

task_id

string | null

obrigatório

candidate_model_ids

string[]

obrigatório

api_format

enum<string>

obrigatório

Opções disponíveis:

responses,

completions,

embeddings,

images,

transcriptions,

tts,

rerank

default_model_id

string | null

obrigatório

pinned_model_id

string | null

obrigatório

current_model_id

string | null

obrigatório

O vencedor do benchmark. Nulo até que exista um.

served_model_id

string | null

obrigatório

A solicitação para o alias é atendida por: o PIN, senão o vencedor, senão o padrão.

served_because

enum<string> | null

obrigatório

Opções disponíveis:

pinned,

benchmark,

default

state

enum<string>

obrigatório

ok: servindo um vencedor ranqueado. waiting_for_results: ainda não há um resultado completo para ranquear, então o último vencedor ou o padrão permanece. metric_missing: a versão ativa da suíte não declara mais a métrica ou a tarefa. suite_missing: a suíte foi excluída. Os dois últimos mantêm o último vencedor até que o alias seja editado.

Opções disponíveis:

ok,

waiting_for_results,

metric_missing,

suite_missing

resolvable

boolean

obrigatório

Se uma solicitação para o alias é resolvida no momento: ela atende a um modelo que ainda está ativo no registro.

standings

object[]

obrigatório

Show child attributes

evaluated_at

string<date-time> | null

obrigatório

created_by

string

obrigatório

created_at

string<date-time>

obrigatório

updated_by

string

obrigatório

updated_at

string<date-time>

obrigatório

⌘I

Go (SDK)

package main

import(
	"context"
	mka1 "github.com/MeetKai/mka1-sdks/go"
	"github.com/MeetKai/mka1-sdks/go/optionalnullable"
	"github.com/MeetKai/mka1-sdks/go/models/components"
	"log"
)

func main() {
    ctx := context.Background()

    s := mka1.New(
        mka1.WithSecurity("<YOUR_BEARER_TOKEN_HERE>"),
    )

    res, err := s.Llm.Models.CreateAlias(ctx, components.CreateModelAliasRequest{
        Name: "best-math-model",
        SuiteID: "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
        Metric: "accuracy",
        CandidateModelIds: []string{
            "meetkai:functionary-pt",
            "meetkai:functionary-es",
        },
        DefaultModelID: optionalnullable.From(mka1.Pointer("meetkai:functionary-pt")),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.ModelAlias != nil {
        // handle response
    }
}

using MeetKai.MKA1;
using MeetKai.MKA1.Types.Components;
using System.Collections.Generic;

var sdk = new SDK(bearerAuth: "<YOUR_BEARER_TOKEN_HERE>");

CreateModelAliasRequest req = new CreateModelAliasRequest() {
    Name = "best-math-model",
    SuiteId = "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
    Metric = "accuracy",
    CandidateModelIds = new List<string>() {
        "meetkai:functionary-pt",
        "meetkai:functionary-es",
    },
    DefaultModelId = "meetkai:functionary-pt",
};

var res = await sdk.Llm.Models.CreateAliasAsync(req);

// handle response

from meetkai_mka1 import SDK


with SDK(
    bearer_auth="<YOUR_BEARER_TOKEN_HERE>",
) as sdk:

    res = sdk.llm.models.create_alias(name="best-math-model", suite_id="eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a", metric="accuracy", candidate_model_ids=[
        "meetkai:functionary-pt",
        "meetkai:functionary-es",
    ], default_model_id="meetkai:functionary-pt")

    # Handle response
    print(res)

import { SDK } from "@meetkai/mka1";

const sdk = new SDK({
  bearerAuth: "<YOUR_BEARER_TOKEN_HERE>",
});

async function run() {
  const result = await sdk.llm.models.createAlias({
    name: "best-math-model",
    suiteId: "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
    metric: "accuracy",
    candidateModelIds: [
      "meetkai:functionary-pt",
      "meetkai:functionary-es",
    ],
    defaultModelId: "meetkai:functionary-pt",
  });

  console.log(result);
}

run();

curl --request POST \
  --url https://apigw.mka1.com/api/v1/llm/models/aliases \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "name": "best-math-model",
  "suite_id": "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
  "metric": "accuracy",
  "candidate_model_ids": [
    "meetkai:functionary-pt",
    "meetkai:functionary-es"
  ],
  "default_model_id": "meetkai:functionary-pt"
}
'

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: 'best-math-model',
    suite_id: 'eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a',
    metric: 'accuracy',
    candidate_model_ids: ['meetkai:functionary-pt', 'meetkai:functionary-es'],
    default_model_id: 'meetkai:functionary-pt'
  })
};

fetch('https://apigw.mka1.com/api/v1/llm/models/aliases', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://apigw.mka1.com/api/v1/llm/models/aliases",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => 'best-math-model',
    'suite_id' => 'eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a',
    'metric' => 'accuracy',
    'candidate_model_ids' => [
        'meetkai:functionary-pt',
        'meetkai:functionary-es'
    ],
    'default_model_id' => 'meetkai:functionary-pt'
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

HttpResponse<String> response = Unirest.post("https://apigw.mka1.com/api/v1/llm/models/aliases")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"best-math-model\",\n  \"suite_id\": \"eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a\",\n  \"metric\": \"accuracy\",\n  \"candidate_model_ids\": [\n    \"meetkai:functionary-pt\",\n    \"meetkai:functionary-es\"\n  ],\n  \"default_model_id\": \"meetkai:functionary-pt\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://apigw.mka1.com/api/v1/llm/models/aliases")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"best-math-model\",\n  \"suite_id\": \"eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a\",\n  \"metric\": \"accuracy\",\n  \"candidate_model_ids\": [\n    \"meetkai:functionary-pt\",\n    \"meetkai:functionary-es\"\n  ],\n  \"default_model_id\": \"meetkai:functionary-pt\"\n}"

response = http.request(request)
puts response.read_body

{
  "object": "model.alias",
  "name": "best-math-model",
  "suite_id": "eval_suite_0b9c2a1e-7d43-4f0e-9a51-2c6f8e1d4b7a",
  "suite_version": 3,
  "metric": "accuracy",
  "higher_is_better": true,
  "task_id": null,
  "candidate_model_ids": [
    "meetkai:functionary-pt",
    "meetkai:functionary-es"
  ],
  "api_format": "responses",
  "default_model_id": "meetkai:functionary-pt",
  "pinned_model_id": null,
  "current_model_id": "meetkai:functionary-es",
  "served_model_id": "meetkai:functionary-es",
  "served_because": "benchmark",
  "state": "ok",
  "resolvable": true,
  "standings": [
    {
      "model_id": "meetkai:functionary-pt",
      "score": 0.81,
      "run_id": "eval_run_5e1f7c3a-92b4-4d68-8f0a-b3c9d2e6a1f4",
      "completed_at": "2026-11-09T02:14:00.000Z",
      "sample_count": 500,
      "failed_count": 0,
      "eligible": true,
      "ineligible_reason": null
    },
    {
      "model_id": "meetkai:functionary-es",
      "score": 0.86,
      "run_id": "eval_run_5e1f7c3a-92b4-4d68-8f0a-b3c9d2e6a1f4",
      "completed_at": "2026-11-09T02:14:00.000Z",
      "sample_count": 500,
      "failed_count": 0,
      "eligible": true,
      "ineligible_reason": null
    }
  ],
  "evaluated_at": "2026-11-09T02:14:05.000Z",
  "created_by": "user-org-admin-1",
  "created_at": "2026-11-01T18:00:00.000Z",
  "updated_by": "user-org-admin-1",
  "updated_at": "2026-11-01T18:00:00.000Z"
}

Responses and conversations

Models and routing

Images and audio

Files and retrieval

Agents

Tools and sandbox

Evals and feedback

Compute

Repositories

Dataset generations

Organizations and access

Usage and budgets

Application utilities

Analytics

Legacy APIs

Additional APIs

Criar um alias de modelo

Autorizações

Corpo

Resposta