一个接口，连接全球顶尖 AI
让智能触手可及

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "你是一个有帮助的助手"},
        {"role": "user", "content": "介绍一下人工智能"}
    ],
    temperature=0.7,
    max_tokens=1000
)

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

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "写一首诗"}],
    stream=True
)

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

import anthropic

client = anthropic.Anthropic(
    api_key="your-api-key",
    base_url="https://new.aiskt.com"
)

message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system="你是一个有帮助的 AI 助手",
    messages=[
        {"role": "user", "content": "介绍一下人工智能"}
    ]
)

print(message.content[0].text)

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[
        {"role": "user", "content": "9.11 和 9.9 哪个更大？"}
    ]
)

# 推理过程
reasoning = response.choices[0].message.reasoning_content
print("推理过程:", reasoning)

# 最终答案
answer = response.choices[0].message.content
print("最终答案:", answer)

import google.generativeai as genai

genai.configure(
    api_key="your-api-key",
    transport="rest",
    client_options={"api_endpoint": "one.aiskt.com"}
)

model = genai.GenerativeModel('gemini-pro')

response = model.generate_content(
    "介绍一下人工智能",
    generation_config={
        "temperature": 0.7,
        "max_output_tokens": 1000
    }
)

print(response.text)

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

# 单个文本
response = client.embeddings.create(
    model="text-embedding-3-small",
    input="人工智能正在改变世界"
)
print(response.data[0].embedding)

# 批量文本
response = client.embeddings.create(
    model="text-embedding-3-small",
    input=["文本1", "文本2", "文本3"]
)
for item in response.data:
    print(f"索引 {item.index}: {len(item.embedding)} 维向量")

import requests

response = requests.post(
    "https://new.aiskt.com/v1/rerank",
    headers={
        "Authorization": "Bearer your-api-key",
        "Content-Type": "application/json"
    },
    json={
        "model": "jina-reranker-v2-base-multilingual",
        "query": "什么是人工智能？",
        "documents": [
            "人工智能是计算机科学的一个分支",
            "今天天气很好",
            "机器学习是人工智能的核心技术",
            "我喜欢吃苹果"
        ],
        "top_n": 2
    }
)

for result in response.json()["results"]:
    print(f"文档 {result['index']}: 分数 {result['relevance_score']}")

import cohere

co = cohere.Client(
    api_key="your-api-key",
    base_url="https://new.aiskt.com"
)

results = co.rerank(
    model="rerank-multilingual-v3.0",
    query="什么是人工智能？",
    documents=[
        "人工智能是计算机科学的一个分支",
        "今天天气很好",
        "机器学习是人工智能的核心技术"
    ],
    top_n=2
)

for result in results.results:
    print(f"文档 {result.index}: 分数 {result.relevance_score}")

import requests

response = requests.post(
    "https://new.aiskt.com/v1/rerank",
    headers={
        "Authorization": "Bearer your-api-key",
        "Content-Type": "application/json"
    },
    json={
        "model": "bge-reranker-v2-m3",
        "query": "什么是人工智能？",
        "documents": [
            "人工智能是计算机科学的一个分支",
            "机器学习是人工智能的核心技术"
        ],
        "return_documents": True
    }
)

print(response.json())

import asyncio
import websockets
import json
import base64

async def realtime_chat():
    url = "wss://one.aiskt.com/v1/realtime?model=gpt-4o-realtime-preview"
    headers = {
        "Authorization": "Bearer your-api-key",
        "OpenAI-Beta": "realtime=v1"
    }

    async with websockets.connect(url, extra_headers=headers) as ws:
        # 发送配置
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "modalities": ["text", "audio"],
                "voice": "alloy",
                "instructions": "你是一个有帮助的助手"
            }
        }))

        # 发送文本消息
        await ws.send(json.dumps({
            "type": "conversation.item.create",
            "item": {
                "type": "message",
                "role": "user",
                "content": [{"type": "input_text", "text": "你好"}]
            }
        }))

        # 接收响应
        async for message in ws:
            data = json.loads(message)
            print(f"收到: {data['type']}")

asyncio.run(realtime_chat())

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

response = client.images.generate(
    model="dall-e-3",
    prompt="一只可爱的猫咪在太空中漂浮，赛博朋克风格",
    size="1024x1024",
    quality="hd",
    n=1
)

print(response.data[0].url)

import requests

# 提交任务
response = requests.post(
    "https://new.aiskt.com/mj/submit/imagine",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "prompt": "a cute cat in space, cyberpunk style --ar 16:9 --v 6"
    }
)

task_id = response.json()["result"]

# 查询结果
result = requests.get(
    f"https://new.aiskt.com/mj/task/{task_id}/fetch",
    headers={"Authorization": "Bearer your-api-key"}
)

print(result.json())

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

with open("audio.mp3", "rb") as audio_file:
    transcript = client.audio.transcriptions.create(
        model="whisper-1",
        file=audio_file,
        language="zh"
    )

print(transcript.text)

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

response = client.audio.speech.create(
    model="tts-1",
    voice="alloy",
    input="你好，欢迎使用 AI 语音服务！"
)

response.stream_to_file("output.mp3")

import requests

response = requests.post(
    "https://new.aiskt.com/v1/music/generations",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "prompt": "一首轻快的流行歌曲，关于夏天和海滩",
        "tags": "pop, upbeat, summer",
        "make_instrumental": False
    }
)

task_id = response.json()["id"]
print(f"任务 ID: {task_id}")

import requests

response = requests.post(
    "https://new.aiskt.com/v1/video/generations",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "model": "kling-v1",
        "prompt": "一只猫咪在草地上奔跑，阳光明媚",
        "duration": 5
    }
)

task_id = response.json()["id"]
print(f"任务 ID: {task_id}")

import requests

response = requests.post(
    "https://new.aiskt.com/v1/video/generations",
    headers={"Authorization": "Bearer your-api-key"},
    json={
        "model": "sora-1.0",
        "prompt": "一个女孩在东京街头漫步，霓虹灯闪烁",
        "size": "1920x1080",
        "duration": 10
    }
)

print(response.json())

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://new.aiskt.com/v1"
)

models = client.models.list()

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

curl https://new.aiskt.com/api/token \
  -H "Authorization: Bearer YOUR_SESSION_TOKEN"

curl https://new.aiskt.com/api/token \
  -H "Authorization: Bearer YOUR_SESSION_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "我的 API 密钥",
    "remain_quota": 1000000,
    "unlimited_quota": false
  }'

curl -X PUT https://new.aiskt.com/api/token \
  -H "Authorization: Bearer YOUR_SESSION_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "id": 123,
    "name": "更新后的名称",
    "remain_quota": 2000000
  }'

curl -X DELETE https://new.aiskt.com/api/token/123 \
  -H "Authorization: Bearer YOUR_SESSION_TOKEN"

import requests

response = requests.get(
    "https://new.aiskt.com/v1/usage",
    headers={"Authorization": "Bearer your-api-key"},
    params={
        "start_date": "2024-01-01",
        "end_date": "2024-01-31"
    }
)

usage = response.json()
print(f"总消费: ${usage['total_cost']}")
print(f"总 Token: {usage['total_tokens']}")

{
  "id": "chatcmpl-8xYz1234567890",
  "object": "chat.completion",
  "created": 1704067200,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "人工智能是计算机科学的一个分支..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 150,
    "total_tokens": 170
  }
}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1704067200,"model":"gpt-4o","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1704067200,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"人工"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1704067200,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"智能"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1704067200,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.0023064255,
        -0.009327292,
        -0.0028842222,
        ...
      ]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

{
  "created": 1704067200,
  "data": [
    {
      "url": "https://example.com/image.png",
      "revised_prompt": "一只可爱的猫咪在太空中漂浮，赛博朋克风格"
    }
  ]
}

{
  "error": {
    "message": "Invalid API key provided",
    "type": "invalid_request_error",
    "param": null,
    "code": "invalid_api_key"
  }
}