Memanggil model pertama

Mulai dengan API Model Atlas Cloud dalam hitungan menit. Panduan ini mencakup pengaturan API key, melakukan panggilan API, dan menggunakan alat pihak ketiga.

Prasyarat

Ringkasan API

Atlas Cloud menyediakan endpoint API berbeda untuk jenis model yang berbeda:

Jenis ModelBase URLFormat
LLM (Chat)https://api.atlascloud.ai/v1Kompatibel dengan OpenAI
Pembuatan Gambarhttps://api.atlascloud.ai/api/v1Atlas Cloud API
Pembuatan Videohttps://api.atlascloud.ai/api/v1Atlas Cloud API
Upload Mediahttps://api.atlascloud.ai/api/v1Atlas Cloud API

LLM / Chat Completions

API LLM sepenuhnya kompatibel dengan OpenAI. Gunakan OpenAI SDK dengan base URL Atlas Cloud.

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.atlascloud.ai/v1"
)

# Non-streaming
response = client.chat.completions.create(
    model="deepseek-v3",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ]
)
print(response.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="deepseek-v3",
    messages=[
        {"role": "user", "content": "Write a short poem about AI."}
    ],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Node.js / TypeScript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "your-api-key",
  baseURL: "https://api.atlascloud.ai/v1",
});

// Non-streaming
const response = await client.chat.completions.create({
  model: "deepseek-v3",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Explain quantum computing in simple terms." },
  ],
});
console.log(response.choices[0].message.content);

// Streaming
const stream = await client.chat.completions.create({
  model: "deepseek-v3",
  messages: [{ role: "user", content: "Write a short poem about AI." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

cURL

curl https://api.atlascloud.ai/v1/chat/completions \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ]
  }'

Pembuatan Gambar

import requests

response = requests.post(
    "https://api.atlascloud.ai/api/v1/model/generateImage",
    headers={
        "Authorization": "Bearer your-api-key",
        "Content-Type": "application/json"
    },
    json={
        "model": "seedream-3.0",
        "prompt": "A futuristic cityscape at sunset, cyberpunk style"
    }
)

result = response.json()
prediction_id = result["data"]["id"]
print(f"Prediction ID: {prediction_id}")

Pembuatan Video

import requests

response = requests.post(
    "https://api.atlascloud.ai/api/v1/model/generateVideo",
    headers={
        "Authorization": "Bearer your-api-key",
        "Content-Type": "application/json"
    },
    json={
        "model": "kling-v2.0",
        "prompt": "A timelapse of flowers blooming in a garden"
    }
)

result = response.json()
prediction_id = result["data"]["id"]
print(f"Prediction ID: {prediction_id}")

Upload Media

Upload file lokal untuk mendapatkan URL sementara untuk alur kerja image-to-video, pengeditan gambar, dan proses multi-langkah lainnya:

import requests

response = requests.post(
    "https://api.atlascloud.ai/api/v1/model/uploadMedia",
    headers={"Authorization": "Bearer your-api-key"},
    files={"file": open("photo.jpg", "rb")}
)

url = response.json().get("url")
print(f"Uploaded file URL: {url}")

File yang diupload ditujukan untuk penggunaan sementara dengan tugas pembuatan Atlas Cloud. File mungkin dibersihkan secara berkala.

Dapatkan Hasil Asinkron

Tugas pembuatan gambar dan video berjalan secara asinkron. Lakukan polling untuk hasil menggunakan prediction ID:

import requests
import time

def wait_for_result(prediction_id, api_key, interval=5):
    while True:
        resp = requests.get(
            f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}",
            headers={"Authorization": f"Bearer {api_key}"}
        )
        data = resp.json()
        status = data["data"]["status"]

        if status == "completed":
            return data["data"]["outputs"][0]
        elif status == "failed":
            raise Exception(f"Task failed: {data['data'].get('error')}")

        print(f"Status: {status}. Waiting...")
        time.sleep(interval)

result = wait_for_result(prediction_id, "your-api-key")
print(f"Result: {result}")

Menggunakan Alat Pihak Ketiga

Chatbox / Cherry Studio

  1. Buka Pengaturan -> Tambah Penyedia Kustom
  2. Atur API Host ke https://api.atlascloud.ai/v1 (akhiran /v1 wajib)
  3. Masukkan API Key Anda
  4. Pilih nama model dari Perpustakaan Model
  5. Mulai mengobrol

OpenWebUI

Konfigurasikan koneksi kompatibel OpenAI dengan base URL https://api.atlascloud.ai/v1 dan API key Anda.

Integrasi IDE

Gunakan MCP Server untuk mengakses model Atlas Cloud langsung dari IDE Anda (Cursor, Claude Desktop, Claude Code, VS Code, dll.).

Jelajahi Model

Telusuri semua 400+ model di Perpustakaan Model. Setiap halaman model mencakup:

  • Playground interaktif untuk pengujian dengan parameter berbeda
  • API View yang menampilkan format permintaan dan parameter yang tepat
  • Informasi harga

Untuk referensi API detail, lihat Referensi API.