朱雀大模型检测 API 调用教程：开发者必备技能

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

import requests

url = "https://api.matrix.tencent.com/ai-detect/text"
api_key = "YOUR_API_KEY"
text = "这是一段待检测的文本内容。"

payload = {
    "content": text,
    "type": "text",
    "sensitive": 0.8
}

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

response = requests.post(url, json=payload, headers=headers)
result = response.json()

print("AI生成概率：", result["probability"])
print("检测结果：", "AI生成" if result["is_ai"] else "人工创作")

{
    "code": ,
    "message": "success",
    "data": {
        "probability": 0.92,
        "is_ai": true,
        "model": "gpt-4"
    }
}

import requests

url = "https://api.matrix.tencent.com/ai-detect/image"
api_key = "YOUR_API_KEY"
image_path = "test.jpg"

with open(image_path, "rb") as f:
    files = {"image": f}
    headers = {
        "Authorization": f"Bearer {api_key}"
    }
    response = requests.post(url, files=files, headers=headers)

result = response.json()
print("AI生成概率：", result["probability"])
print("检测结果：", "AI生成" if result["is_ai"] else "人工创作")

{
    "code": ,
    "message": "success",
    "data": {
        "probability": 0.86,
        "is_ai": true,
        "model": "midjourney-v5"
    }
}

import requests
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(), wait=wait_exponential(multiplier=, min=, max=))
def call_api(url, payload, headers):
    response = requests.post(url, json=payload, headers=headers)
    response.raise_for_status()
    return response.json()