curl --request GET \
  --url https://nano-gpt.com/api/v1/embedding-models \
  --header 'Authorization: Bearer <token>'

import requests

url = "https://nano-gpt.com/api/v1/embedding-models"

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

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

print(response.text)

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

fetch('https://nano-gpt.com/api/v1/embedding-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://nano-gpt.com/api/v1/embedding-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://nano-gpt.com/api/v1/embedding-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://nano-gpt.com/api/v1/embedding-models")
  .header("Authorization", "Bearer <token>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://nano-gpt.com/api/v1/embedding-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

{
  "object": "list",
  "data": [
    {
      "id": "text-embedding-3-small",
      "object": "model",
      "created": 1754480583,
      "owned_by": "openai",
      "name": "Text Embedding 3 Small",
      "description": "Most cost-effective OpenAI embedding model with dimension reduction support",
      "dimensions": 1536,
      "supports_dimensions": true,
      "max_tokens": 8191,
      "pricing": {
        "per_million_tokens": 0.02,
        "currency": "USD"
      }
    }
  ]
}

{
  "error": 123,
  "message": "<string>"
}