curl --request GET \
  --url https://api.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog

import requests

url = "https://api.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog"

response = requests.get(url)

print(response.text)

const options = {method: 'GET'};

fetch('https://api.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog', 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.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
]);

$response = curl_exec($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.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog"

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

	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.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.orbit.devotel.io/api/v1/cdp/external-models/feature-catalog")

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

request = Net::HTTP::Get.new(url)

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

{
  "data": {
    "features": [
      {
        "name": "recency_days",
        "description": "Days since the profile was last contacted (clamped at zero)."
      }
    ],
    "populations": [
      {
        "key": "all_profiles",
        "label": "All active profiles"
      }
    ],
    "spec": {
      "description": "Score profiles with a linear / logistic model you trained off-platform.",
      "kinds": [
        "classification",
        "regression"
      ]
    }
  },
  "meta": {
    "request_id": "req_4b8d2e7c"
  }
}

{
  "error": {
    "code": "<string>",
    "message": "<string>",
    "status": 123,
    "details": {}
  },
  "meta": {
    "request_id": "<string>",
    "timestamp": "2023-11-07T05:31:56Z",
    "docs_url": "<string>"
  }
}

{
  "error": {
    "code": "<string>",
    "message": "<string>",
    "status": 123,
    "details": {}
  },
  "meta": {
    "request_id": "<string>",
    "timestamp": "2023-11-07T05:31:56Z",
    "docs_url": "<string>"
  }
}

{
  "error": {
    "code": "<string>",
    "message": "<string>",
    "status": 123,
    "details": {}
  },
  "meta": {
    "request_id": "<string>",
    "timestamp": "2023-11-07T05:31:56Z",
    "docs_url": "<string>"
  }
}

{
  "error": {
    "code": "RATE_LIMITED",
    "message": "<string>",
    "status": 429,
    "retry_after": 2
  },
  "meta": {
    "request_id": "<string>",
    "timestamp": "2023-11-07T05:31:56Z",
    "docs_url": "<string>"
  }
}

{
  "error": {
    "code": "<string>",
    "message": "<string>",
    "status": 123,
    "details": {}
  },
  "meta": {
    "request_id": "<string>",
    "timestamp": "2023-11-07T05:31:56Z",
    "docs_url": "<string>"
  }
}