QClaw从工具选型到代码落地：文创团队AI内容生产全流程复盘与实战

import openai

def generate_topics(keyword, api_key):
    client = openai.OpenAI(api_key=api_key)
    prompt = f"作为资深新媒体主编，请围绕'{keyword}'，结合当下热点，生成10个具有爆款潜质的选题标题。要求：包含数字、情绪词或反差感。"
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7
    )
    return response.choices[0].message.content

# 使用示例
# print(generate_topics("开源硬件", "YOUR_API_KEY"))

def adapt_content(original_text, platform, api_key):
    client = openai.OpenAI(api_key=api_key)
    
    style_map = {
        "xiaohongshu": "小红书风格：多用Emoji，分段短促，语气亲切，强调种草感，加满话题标签。",
        "zhihu": "知乎风格：逻辑严密，数据支撑，客观理性，使用‘谢邀’体，深度解析。",
        "wechat": "公众号风格：结构化强，金句突出，适合深度阅读，排版清晰。"
    }
    
    prompt = f"请将以下内容改写为{style_map[platform]}：\n{original_text}"
    
    response = client.chat.completions.create(
        model="gpt-3.5-turbo", # 使用较小模型以节省成本
        messages=[{"role": "user", "content": prompt}],
        temperature=0.5
    )
    return response.choices[0].message.content

import re

def quality_check(text):
    # 模拟敏感词库
    sensitive_words = ["第一", "顶级", "绝对", "暴富"] 
    found_words = [word for word in sensitive_words if word in text]
    
    # 检测AI常用词（去AI味）
    ai_cliches = ["值得注意的是", "综上所述", "不仅...而且", "旨在"]
    ai_score = sum(1 for word in ai_cliches if word in text)
    
    result = {
        "risk_level": "高" if found_words else "低",
        "sensitive_words": found_words,
        "ai_flavor_score": f"{ai_score}/4 (分数越高AI味越重)"
    }
    return result

# 使用示例
# print(quality_check("这款产品旨在为用户提供顶级体验..."))