"腾讯混元 API 完整指南 2026：开发者接入教程"

# 安装：pip install tencentcloud-sdk-python-hunyuan
from tencentcloud.common import credential
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.hunyuan.v20230901 import hunyuan_client, models

def chat_with_hunyuan(message: str, system_prompt: str = None) -> str:
    """使用腾讯云 SDK 调用混元 API"""
    
    # 初始化认证
    cred = credential.Credential(
        "your-secret-id",
        "your-secret-key"
    )
    
    # 配置HTTP选项
    httpProfile = HttpProfile()
    httpProfile.endpoint = "hunyuan.tencentcloudapi.com"
    
    clientProfile = ClientProfile()
    clientProfile.httpProfile = httpProfile
    
    client = hunyuan_client.HunyuanClient(cred, "", clientProfile)
    
    req = models.ChatCompletionsRequest()
    
    # 构建消息
    messages = []
    if system_prompt:
        msg = models.Message()
        msg.Role = "system"
        msg.Content = system_prompt
        messages.append(msg)
    
    user_msg = models.Message()
    user_msg.Role = "user"
    user_msg.Content = message
    messages.append(user_msg)
    
    req.Model = "hunyuan-turbos-20241224"
    req.Messages = messages
    req.Stream = False
    
    resp = client.ChatCompletions(req)
    return resp.Choices[0].Message.Content

# 使用示例
result = chat_with_hunyuan(
    "请分析一下电商直播的商业模式",
    system_prompt="你是一个商业分析师，请给出专业、有深度的分析"
)
print(result)

def stream_hunyuan(message: str):
    """流式输出，适合长文本生成"""
    
    cred = credential.Credential("your-secret-id", "your-secret-key")
    client = hunyuan_client.HunyuanClient(cred, "", ClientProfile())
    
    req = models.ChatCompletionsRequest()
    
    msg = models.Message()
    msg.Role = "user"
    msg.Content = message
    
    req.Model = "hunyuan-turbos-20241224"
    req.Messages = [msg]
    req.Stream = True  # 开启流式
    
    resp = client.ChatCompletions(req)
    
    for event in resp:
        if hasattr(event, 'Choices') and event.Choices:
            delta = event.Choices[0].Delta
            if hasattr(delta, 'Content') and delta.Content:
                print(delta.Content, end="", flush=True)
    print()

from openai import OpenAI

# Crazyrouter 提供 OpenAI 兼容接口，无需学习新 SDK
client = OpenAI(
    api_key="your-crazyrouter-key",
    base_url="https://crazyrouter.com/v1"
)

response = client.chat.completions.create(
    model="hunyuan-turbos",
    messages=[
        {
            "role": "system",
            "content": "你是一个专业的中文写作助手。"
        },
        {
            "role": "user",
            "content": "帮我写一篇关于AI对零售业影响的500字分析文章"
        }
    ],
    max_tokens=1024,
    temperature=0.7
)

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

# 通过 Crazyrouter 调用（OpenAI 兼容格式）
curl https://crazyrouter.com/v1/chat/completions \
  -H "Authorization: Bearer your-crazyrouter-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "hunyuan-turbos",
    "messages": [
      {"role": "user", "content": "用简单的语言解释量子计算"}
    ],
    "max_tokens": 1024
  }'

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.CRAZYROUTER_API_KEY,
  baseURL: 'https://crazyrouter.com/v1',
});

async function hunyuanChat(question, systemPrompt = '你是一个专业助手') {
  const response = await client.chat.completions.create({
    model: 'hunyuan-turbos',
    messages: [
      { role: 'system', content: systemPrompt },
      { role: 'user', content: question }
    ],
    temperature: 0.7,
    max_tokens: 2048,
  });
  
  return response.choices[0].message.content;
}

// 使用示例
const analysis = await hunyuanChat(
  '分析小红书平台的用户增长策略',
  '你是一个专注于中国互联网的商业分析师'
);
console.log(analysis);

ENTERPRISE_SERVICE_PROMPT = """你是一个专业的企业客服助手。
规则：
1. 使用礼貌、专业的中文
2. 直接回答问题，不要废话
3. 不确定的内容不要猜测
4. 复杂问题引导转人工
5. 记录用户意图，方便后续跟进"""

def enterprise_service_bot(user_message: str, context: dict = None) -> str:
    messages = [
        {"role": "system", "content": ENTERPRISE_SERVICE_PROMPT}
    ]
    
    # 加入历史上下文
    if context and context.get("history"):
        messages.extend(context["history"][-6:])  # 最近3轮
    
    messages.append({"role": "user", "content": user_message})
    
    response = client.chat.completions.create(
        model="hunyuan-turbos",
        messages=messages,
        max_tokens=512,
        temperature=0.3  # 客服场景用低温度保持稳定性
    )
    
    return response.choices[0].message.content

def content_moderation(content: str) -> dict:
    """中文内容合规审核"""
    
    response = client.chat.completions.create(
        model="hunyuan-standard",  # 审核任务用标准版即可
        messages=[
            {
                "role": "system",
                "content": "你是内容审核专家。分析内容是否合规，返回JSON格式。"
            },
            {
                "role": "user",
                "content": f"""审核以下内容，返回JSON：
{{
  "is_safe": true/false,
  "risk_level": "low/medium/high",
  "issues": ["问题1", "问题2"],
  "reason": "简要说明"
}}

内容：{content}"""
            }
        ],
        temperature=0.1
    )
    
    import json
    try:
        return json.loads(response.choices[0].message.content)
    except:
        return {"is_safe": True, "risk_level": "low", "issues": [], "reason": "解析失败"}

# 企业微信消息 + 混元AI 处理
import hmac
import hashlib
import xml.etree.ElementTree as ET

def process_wechat_enterprise_message(xml_body: str) -> str:
    """处理企业微信消息并用混元 AI 回复"""
    
    root = ET.fromstring(xml_body)
    content = root.find('Content').text
    from_user = root.find('FromUserName').text
    
    # 调用混元 AI
    ai_response = client.chat.completions.create(
        model="hunyuan-turbos",
        messages=[
            {
                "role": "system",
                "content": "你是公司的智能助手，请根据员工问题给出简洁有用的回答。"
            },
            {"role": "user", "content": content}
        ],
        max_tokens=512
    )
    
    reply_content = ai_response.choices[0].message.content
    return reply_content