مستندات درواره

pip install openai

from openai import OpenAI

client = OpenAI(
    base_url="https://api.darvareh.ir/v1",
    api_key="sk-darvareh-YOUR_KEY"
)

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "سلام! خودت رو معرفی کن."}
    ]
)

print(response.choices[0].message.content)

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4",
    messages=[
        {"role": "system", "content": "You are a senior Python developer."},
        {"role": "user", "content": "یک تابع فیبوناچی بهینه بنویس."}
    ],
    temperature=0.7,
    max_tokens=1024
)

print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")

stream = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "یک داستان کوتاه بنویس."}],
    stream=True
)

for chunk in stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

# Async streaming
import asyncio
from openai import AsyncOpenAI

async_client = AsyncOpenAI(
    base_url="https://api.darvareh.ir/v1",
    api_key="sk-darvareh-YOUR_KEY"
)

async def stream_response():
    stream = await async_client.chat.completions.create(
        model="openai/gpt-4o",
        messages=[{"role": "user", "content": "یک شعر بنویس."}],
        stream=True
    )
    async for chunk in stream:
        content = chunk.choices[0].delta.content
        if content:
            print(content, end="", flush=True)

asyncio.run(stream_response())

response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="سلام دنیا"
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")
print(f"First 5: {embedding[:5]}")

# Batch embeddings
response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input=["متن اول", "متن دوم", "متن سوم"]
)

for item in response.data:
    print(f"[{item.index}] {len(item.embedding)} dims")

import base64, requests

r = requests.post("https://api.darvareh.ir/v1/images/generations",
    headers={"Authorization": "Bearer sk-darvareh-YOUR_KEY"},
    json={
        "model": "google/gemini-3-pro-image",
        "prompt": "A futuristic city at sunset, cyberpunk style",
        "size": "1024x1024",
    }).json()

# پاسخ به‌صورت base64 برمی‌گردد
open("output.png", "wb").write(base64.b64decode(r["data"][0]["b64_json"]))

import time, requests

BASE = "https://api.darvareh.ir/v1"
HEAD = {"Authorization": "Bearer sk-darvareh-YOUR_KEY"}

# ۱) ثبت درخواست → دریافت شناسه‌ی کار
job = requests.post(f"{BASE}/videos", headers=HEAD, json={
    "model": "bytedance/seedance-2.0-fast",
    "prompt": "a cat playing with a ball in a garden",
    "duration": 5,
}).json()
job_id = job["id"]

# ۲) poll تا آماده شدن (حدود ۱ تا ۳ دقیقه)
while True:
    s = requests.get(f"{BASE}/videos/{job_id}", headers=HEAD).json()
    if s["status"] == "completed":
        break
    if s["status"] == "failed":
        raise RuntimeError(s.get("error"))
    time.sleep(5)

# ۳) دانلود ویدیو
mp4 = requests.get(f"{BASE}/videos/{job_id}/content", headers=HEAD).content
open("output.mp4", "wb").write(mp4)
print("هزینه:", s.get("cost_toman"), "تومان")

audio_file = open("recording.mp3", "rb")

transcript = client.audio.transcriptions.create(
    file=audio_file,
    model="google/gemini-3.5-flash",
    language="fa"
)

print(transcript.text)

models = client.models.list()

for model in models.data:
    print(f"{model.id} — {model.owned_by}")

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="openai/gpt-4o",
    base_url="https://api.darvareh.ir/v1",
    api_key="sk-darvareh-YOUR_KEY",
    temperature=0.7
)

response = llm.invoke("سلام! خودت رو معرفی کن.")
print(response.content)

# With streaming
for chunk in llm.stream("یک داستان کوتاه بنویس."):
    print(chunk.content, end="")

# Embeddings
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="openai/text-embedding-3-small",
    base_url="https://api.darvareh.ir/v1",
    api_key="sk-darvareh-YOUR_KEY"
)

result = embeddings.embed_query("سلام دنیا")
print(f"Dimensions: {len(result)}")

from openai import OpenAI, APIError, AuthenticationError, RateLimitError

client = OpenAI(
    base_url="https://api.darvareh.ir/v1",
    api_key="sk-darvareh-YOUR_KEY"
)

try:
    response = client.chat.completions.create(
        model="openai/gpt-4o",
        messages=[{"role": "user", "content": "سلام!"}]
    )
except AuthenticationError:
    print("کلید API نامعتبر است")
except RateLimitError:
    print("محدودیت نرخ — کمی صبر کنید")
except APIError as e:
    if e.status_code == 402:
        print("اعتبار ناکافی — حساب را شارژ کنید")
    elif e.status_code == 403:
        print("این مدل برای کلید شما مجاز نیست")
    else:
        print(f"خطا: {e.message}")

curl https://api.darvareh.ir/api/v1/billing/balance \
  -H "Authorization: Bearer YOUR_JWT_TOKEN"

# پاسخ:
# {
#   "user_id": "...",
#   "balance": "8,750,000",
#   "total_credits_used": "26,250,000"
# }