{{ t('requestParams') }}

{{ t('parameter') }}	{{ t('type') }}	{{ t('required') }}	{{ t('description') }}
model	string	否	模型ID，默认cloud-llm
messages	array	是	对话消息列表，包含role和content
stream	boolean	否	是否流式返回，默认false
max_tokens	integer	否	最大生成Token数，默认512
temperature	float	否	温度采样，范围(0, 2)，默认0.7
top_p	float	否	top_p采样，范围(0, 1]
frequency_penalty	float	否	频率惩罚，范围[-2, 2]
presence_penalty	float	否	存在惩罚，范围[-2.0, 2.0]
stop	string/array	否	停止标识

{{ t('messagesFormat') }}

[
  {"role": "system", "content": "你是一个有帮助的助手"},
  {"role": "user", "content": "你好"},
  {"role": "assistant", "content": "你好！有什么可以帮助你的吗？"}
]

{{ t('responseExample') }}

{{ t('supportedModels') }}

{{ t('modelId') }}	{{ t('modelName') }}	{{ t('inputPrice') }}	{{ t('outputPrice') }}
cloud-llm	DeepSeek-V3	¥1.00/百万	¥4.00/百万
glm-4	GLM-4	¥1.60/百万	¥1.60/百万
glm-4-flash	GLM-4-Flash	¥0.16/百万	¥0.16/百万

{{ t('curlExample') }}

curl -X POST https://nebulai.top/api/proxy/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cloud-llm",
    "messages": [{"role": "user", "content": "你好"}],
    "temperature": 0.7,
    "max_tokens": 512
  }'

{{ t('pythonExample') }}

import requests

API_BASE = "https://nebulai.top/api/proxy"
API_KEY = "sk-your-api-key-here"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

data = {
    "model": "cloud-llm",
    "messages": [{"role": "user", "content": "你好"}],
    "temperature": 0.7,
    "max_tokens": 1024
}

response = requests.post(
    f"{API_BASE}/chat/completions",
    headers=headers,
    json=data,
    timeout=60
)

result = response.json()
print("AI回复:", result['choices'][0]['message']['content'])
print("费用:", result['billing']['cost'])

{{ t('requestParams') }}

{{ t('parameter') }}	{{ t('type') }}	{{ t('required') }}	{{ t('description') }}
model	string	否	向量化模型ID，默认text-embedding-v3
input	string/array	是	要向量化的文本或文本列表
encoding_format	string	否	编码格式：float或base64

{{ t('curlExample') }}

curl -X POST https://nebulai.top/api/proxy/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "这是一段需要向量化的文本"
  }'

{{ t('batchEmbedding') }}

curl -X POST https://nebulai.top/api/proxy/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["第一段文本", "第二段文本", "第三段文本"]
  }'

{{ t('requestParams') }}

{{ t('parameter') }}	{{ t('type') }}	{{ t('required') }}	{{ t('description') }}
prompt	string	是	图像描述文本
model	string	否	图像生成模型
n	integer	否	生成图片数量，默认1
size	string	否	图片尺寸，如"1024x1024"
response_format	string	否	响应格式：url或b64_json

{{ t('curlExample') }}

curl -X POST https://nebulai.top/api/proxy/images/generate \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "一只可爱的橘猫在草地上玩耍",
    "n": 1,
    "size": "512x512"
  }'

{{ t('requestParams') }}

{{ t('parameter') }}	{{ t('type') }}	{{ t('required') }}	{{ t('description') }}
model	string	是	视频生成模型（Sdance2.0 或 Sdance2.0-fast）
content	array	是	内容输入，包含type和text字段
ratio	string	否	视频比例，默认16:9
duration	integer	否	视频时长（秒），默认5
watermark	boolean	否	是否添加水印，默认false

{{ t('curlExample') }}

curl -X POST https://nebulai.top/api/proxy/video/generate \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Sdance2.0-fast",
    "content": [
      {
        "type": "text",
        "text": "写实风格，晴朗的蓝天之下，一大片白色的雏菊花田"
      }
    ],
    "ratio": "16:9",
    "duration": 5
  }'

{{ t('responseExample') }}

{{ t('curlExample') }}

curl "https://nebulai.top/api/proxy/video/tasks/video-task-12345?apiKey=YOUR_API_KEY"

{{ t('responseExample') }}

{{ t('statusDescription') }}

{{ t('status') }}	{{ t('description') }}
pending	{{ t('pending') }}
running	{{ t('running') }}
completed	{{ t('completed') }}
failed	{{ t('failed') }}

{{ t('requestParams') }}

{{ t('parameter') }}	{{ t('type') }}	{{ t('required') }}	{{ t('description') }}
model	string	否	重排序模型ID，默认bge-reranker
query	string	是	查询文本
documents	array	是	候选文档列表
top_n	integer	否	返回前N个结果，默认全部返回
return_documents	boolean	否	是否返回原始文档内容，默认true

调用示例（curl）

curl -X POST https://nebulai.top/v1/rerank \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "人工智能最新进展",
    "documents": [
      "深度学习是机器学习的一个分支...",
      "大语言模型在自然语言处理中取得了重大突破...",
      "计算机视觉技术近年来发展迅速..."
    ],
    "top_n": 2
  }'

{{ t('requestParams') }}

{{ t('parameter') }}	{{ t('type') }}	{{ t('required') }}	{{ t('description') }}
type	string	否	模型类型过滤：text/embedding/vision/rerank/all，默认all

{{ t('curlExample') }}

curl -X GET "https://nebulai.top/v1/models" \
  -H "Authorization: Bearer YOUR_API_KEY"

{{ t('pythonExample') }}

import requests

API_BASE = "https://nebulai.top"
API_KEY = "sk-your-api-key-here"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# 获取所有模型
response = requests.get(f"{API_BASE}/v1/models", headers=headers)
models = response.json()

# 获取文本模型
response = requests.get(f"{API_BASE}/v1/models?type=text", headers=headers)
text_models = response.json()

print("可用模型列表:")
for model in models['data']:
    print(f"- {model['id']}: {model['name']} ({model['type']})")

响应示例

{{ t('curlExample') }}

curl "https://nebulai.top/api/proxy/balance/user123"

{{ t('curlExample') }}

curl "https://nebulai.top/api/proxy/usage/user123?period=week"

{{ t('javascript') }}

const axios = require('axios');

const API_BASE = 'https://nebulai.top/api/proxy';
const API_KEY = 'sk-your-api-key-here';

async function chat() {
  const response = await axios.post(
    `${API_BASE}/chat/completions`,
    {
      model: 'cloud-llm',
      messages: [{ role: 'user', content: '你好' }],
      temperature: 0.7,
      max_tokens: 512
    },
    {
      headers: {
        'Authorization': `Bearer ${API_KEY}`,
        'Content-Type': 'application/json'
      }
    }
  );
  
  console.log('AI回复:', response.data.choices[0].message.content);
  console.log('费用:', response.data.billing.cost);
}

async function getEmbedding(text) {
  const response = await axios.post(
    `${API_BASE}/embeddings`,
    { input: text },
    {
      headers: {
        'Authorization': `Bearer ${API_KEY}`,
        'Content-Type': 'application/json'
      }
    }
  );
  return response.data.data[0].embedding;
}

chat();

{{ t('python') }}

import requests

API_BASE = "https://nebulai.top/api/proxy"
API_KEY = "sk-your-api-key-here"

def chat():
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    data = {
        "model": "cloud-llm",
        "messages": [{"role": "user", "content": "你好"}],
        "temperature": 0.7,
        "max_tokens": 512
    }
    
    response = requests.post(f"{API_BASE}/chat/completions", headers=headers, json=data)
    result = response.json()
    
    print("AI回复:", result['choices'][0]['message']['content'])
    print("费用:", result['billing']['cost'])

def get_embedding(text):
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    data = {"input": text}
    response = requests.post(f"{API_BASE}/embeddings", headers=headers, json=data)
    return response.json()['data'][0]['embedding']

if __name__ == "__main__":
    chat()

curl

# 对话接口
curl -X POST https://nebulai.top/api/proxy/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cloud-llm",
    "messages": [{"role": "user", "content": "你好"}],
    "temperature": 0.7
  }'

# 向量化接口
curl -X POST https://nebulai.top/api/proxy/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"input": "这是一段文本"}'

# 查询余额
curl "https://nebulai.top/api/proxy/balance/user123"

• {{ t('apiKeySecurity') }}
• {{ t('timeoutSetting') }}
• {{ t('errorRetry') }}
• {{ t('tokenLimit') }}
• {{ t('permissionControl') }}
• {{ t('requestFrequency') }}
• {{ t('billingMethod') }}
• {{ t('contactUs') }}: {{ t('contactEmail') }}