curl --request GET \
  --url https://api.llmstore.ru/v1/embeddings/models \
  --header 'Authorization: Bearer <token>'

import requests

url = "https://api.llmstore.ru/v1/embeddings/models"

headers = {"Authorization": "Bearer <token>"}

response = requests.get(url, headers=headers)

print(response.text)

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

fetch('https://api.llmstore.ru/v1/embeddings/models', 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://api.llmstore.ru/v1/embeddings/models",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  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;
}

package main

import (
	"fmt"
	"net/http"
	"io"
)

func main() {

	url := "https://api.llmstore.ru/v1/embeddings/models"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("Authorization", "Bearer <token>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.llmstore.ru/v1/embeddings/models")
  .header("Authorization", "Bearer <token>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.llmstore.ru/v1/embeddings/models")

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

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

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

{
  "data": [
    {
      "architecture": {
        "input_modalities": [
          "text"
        ],
        "instruct_type": null,
        "modality": "text->text",
        "output_modalities": [
          "embeddings"
        ],
        "tokenizer": "GPT"
      },
      "canonical_slug": "openai/text-embedding-3-small",
      "context_length": 8192,
      "created": 1692901234,
      "default_parameters": null,
      "description": "OpenAI text embedding model optimized for performance.",
      "expiration_date": null,
      "id": "openai/text-embedding-3-small",
      "knowledge_cutoff": null,
      "links": {
        "details": "/api/v1/models/openai/text-embedding-3-small/endpoints"
      },
      "name": "Text Embedding 3 Small",
      "per_request_limits": null,
      "pricing": {
        "completion": "0",
        "image": "0",
        "prompt": "0.00000002",
        "request": "0"
      },
      "supported_parameters": [],
      "supported_voices": null,
      "top_provider": {
        "context_length": 8192,
        "is_moderated": false,
        "max_completion_tokens": null
      }
    }
  ]
}

{
  "error": {
    "code": 400,
    "message": "Invalid request parameters"
  }
}

{
  "error": {
    "code": 500,
    "message": "Internal Server Error"
  }
}