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# 推荐
Authorization: Bearer sk-ah-xxxxxxxxxxxxxxxx

# 也支持
x-api-key: sk-ah-xxxxxxxxxxxxxxxx

curl — \
  -H "Authorization: Bearer $AI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gem-3.7-flash",
    "messages": [{"role": "user", "content": "你好"}]
  }'

from openai import OpenAI

client = OpenAI(api_key="sk-ah-…", base_url=—)
r = client.chat.completions.create(
    model="gem-3.7-flash",
    messages=[{"role": "user", "content": "你好"}])
print(r.choices[0].message.content)

import OpenAI from 'openai'

const client = new OpenAI({ apiKey: 'sk-ah-…', baseURL: — })

const r = await client.chat.completions.create({
  model: 'gem-3.7-flash',
  messages: [{ role: 'user', content: '你好' }]
})

data: {"_billing":{"cost":0.0123,"balance":12.3456}}

curl — \
  -H "Authorization: Bearer $AI_KEY"

export AI_KEY='替换为你的本站API密钥'
export AI_BASE_URL='—/v1'
export AI_MODEL='替换为实际对话模型ID'

curl -sS "$AI_BASE_URL/models" -H "Authorization: Bearer $AI_KEY"

$env:AI_KEY = '替换为你的本站API密钥'
$env:AI_BASE_URL = '—/v1'
$env:AI_MODEL = '替换为实际对话模型ID'
Invoke-RestMethod -Uri "$env:AI_BASE_URL/models" -Headers @{Authorization="Bearer $env:AI_KEY"}

{
  "model": "替换为实际对话模型ID",
  "messages": [
    {"role": "system", "content": "你是一位简洁的中文助手。"},
    {"role": "user", "content": "请解释浏览器缓存。"},
    {"role": "assistant", "content": "浏览器会保存部分资源，减少重复下载。"},
    {"role": "user", "content": "再举一个例子。"}
  ],
  "stream": false
}

{
  "id": "上游返回的响应ID",
  "object": "chat.completion",
  "choices": [{"index": 0, "message": {"role": "assistant", "content": "你好！"}, "finish_reason": "stop"}],
  "usage": {"prompt_tokens": 12, "completion_tokens": 3, "total_tokens": 15},
  "_billing": {"cost": 0.001, "balance": 10.999, "inputTokens": 12, "outputTokens": 3}
}

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["AI_KEY"],
                base_url=os.environ["AI_BASE_URL"],
                timeout=180.0, max_retries=0)
response = client.chat.completions.create(
    model=os.environ["AI_MODEL"],
    messages=[{"role": "user", "content": "你好，请简短介绍你自己。"}])
print(response.choices[0].message.content)

import OpenAI from 'openai';
const client = new OpenAI({
  apiKey: process.env.AI_KEY, baseURL: process.env.AI_BASE_URL,
  timeout: 180000, maxRetries: 0
});
const response = await client.chat.completions.create({
  model: process.env.AI_MODEL,
  messages: [{role: 'user', content: '你好，请简短介绍你自己。'}]
});
console.log(response.choices[0]?.message?.content);

curl -sS --max-time 180 "$AI_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $AI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"替换为实际对话模型ID","messages":[{"role":"user","content":"你好"}]}'

import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["AI_KEY"], base_url=os.environ["AI_BASE_URL"],
                timeout=180.0, max_retries=0)
for chunk in client.chat.completions.create(
    model=os.environ["AI_MODEL"], stream=True,
    messages=[{"role": "user", "content": "写一段简短的欢迎词。"}]):
    if chunk.choices:
        text = chunk.choices[0].delta.content
        if text:
            print(text, end="", flush=True)
print()

data: {"choices":[{"index":0,"delta":{"content":"你好"}}]}

data: {"choices":[],"usage":{"prompt_tokens":12,"completion_tokens":3,"total_tokens":15}}

data: [DONE]

data: {"_billing":{"cost":0.001,"balance":10.999,"inputTokens":12,"outputTokens":3}}

{
  "model": "替换为支持视觉的对话模型ID",
  "messages": [{"role": "user", "content": [
    {"type": "text", "text": "请描述这张图片。"},
    {"type": "image_url", "image_url": {"url": "https://你的素材域名/example.jpg"}}
  ]}]
}

{
  "error": {
    "message": "请求过于频繁",
    "code": "rate_limited",
    "retryAfter": 3
  }
}

curl -sS "$AI_BASE_URL/images/generations" \
  -H "Authorization: Bearer $AI_KEY" -H "Content-Type: application/json" \
  -d '{"model":"替换为图片模型ID","prompt":"清晨的海边，写实摄影","params":{}}'

# POST /v1/videos/generations
{"model":"替换为视频模型ID","prompt":"镜头缓慢推进，海浪拍打岸边","params":{}}

# POST /v1/audio/generations
{"model":"替换为音频模型ID","prompt":"你好，欢迎使用本站。","params":{}}

{
  "task": {
    "id": "本站任务ID", "object": "media.task",
    "model": "实际模型ID", "type": "image",
    "state": "submitted", "status": "已提交", "progress": "0",
    "is_final": false, "result_url": "", "result_type": "",
    "error": null, "created_at": "时间", "completed_at": null,
    "_billing": {"settled": false, "cost": 0}
  }
}

import os, json, time
from urllib.request import Request, urlopen
from urllib.error import HTTPError

base = os.environ["AI_BASE_URL"].rstrip("/")
key = os.environ["AI_KEY"]
def request(path, body=None):
    payload = None if body is None else json.dumps(body).encode()
    req = Request(base + path, data=payload, headers={
        "Authorization": "Bearer " + key, "Content-Type": "application/json"})
    try:
        with urlopen(req, timeout=120) as response:
            return json.load(response)
    except HTTPError as e:
        raise RuntimeError(str(e.code) + " " + e.read().decode()) from e

# AI_MEDIA_MODEL 设置为实际图片模型 ID；视频/音频替换对应提交路径和模型。
task = request("/images/generations", {
    "model": os.environ["AI_MEDIA_MODEL"], "prompt": "清晨的海边，写实摄影", "params": {}
})["task"]
task_id = task["id"]
deadline = time.monotonic() + 1800
while not task["is_final"]:
    if time.monotonic() > deadline:
        raise TimeoutError("客户端停止等待，请保留任务ID后续查询：" + task_id)
    time.sleep(5)
    task = request("/tasks/" + task_id)["task"]
if task.get("error") or not task.get("result_url"):
    raise RuntimeError(str(task))
print(task["result_url"])
print(task["_billing"])

import os, json
from pathlib import Path
import requests

path = Path("reference.png")
with path.open("rb") as source:
    response = requests.post(os.environ["AI_BASE_URL"].rstrip("/") + "/images/generations",
        headers={"Authorization": "Bearer " + os.environ["AI_KEY"]},
        data={"model": os.environ["AI_MEDIA_MODEL"],
              "prompt": "根据参考素材生成画面", "params": json.dumps({})},
        # 将 reference_parameter 替换为所选模型实际接收的参考素材参数名。
        files={"reference_parameter": (path.name, source, "image/png")}, timeout=180)
response.raise_for_status()
task_id = response.json()["task"]["id"]
print(task_id)  # 使用 /tasks/{id} 查询结果