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 Model | Base URL | Format |
|---|---|---|
| LLM (Chat) | https://api.atlascloud.ai/v1 | Kompatibel dengan OpenAI |
| Pembuatan Gambar | https://api.atlascloud.ai/api/v1 | Atlas Cloud API |
| Pembuatan Video | https://api.atlascloud.ai/api/v1 | Atlas Cloud API |
| Upload Media | https://api.atlascloud.ai/api/v1 | Atlas 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
- Buka Pengaturan -> Tambah Penyedia Kustom
- Atur API Host ke
https://api.atlascloud.ai/v1(akhiran/v1wajib) - Masukkan API Key Anda
- Pilih nama model dari Perpustakaan Model
- 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.