Classifique os candidatos de um alias de modelo agora

package main

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

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

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

    res, err := s.Llm.Models.RecomputeAlias(ctx, "best-math-model")
    if err != nil {
        log.Fatal(err)
    }
    if res.ModelAlias != nil {
        // handle response
    }
}

using MeetKai.MKA1;
using MeetKai.MKA1.Types.Components;

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

var res = await sdk.Llm.Models.RecomputeAliasAsync(name: "best-math-model");

// handle response

from meetkai_mka1 import SDK


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

    res = sdk.llm.models.recompute_alias(name="best-math-model")

    # 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.recomputeAlias({
    name: "best-math-model",
  });

  console.log(result);
}

run();

curl --request POST \
  --url https://apigw.mka1.com/api/v1/llm/models/aliases/recompute \
  --header 'Authorization: Bearer <token>'

const options = {method: 'POST', headers: {Authorization: 'Bearer <token>'}};

fetch('https://apigw.mka1.com/api/v1/llm/models/aliases/recompute', 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/recompute",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>"
  ],
]);

$response = curl_exec($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/recompute")
  .header("Authorization", "Bearer <token>")
  .asString();

require 'uri'
require 'net/http'

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

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'

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"
}