下一步

from openai import OpenAI

client = OpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-key"
)

def chatbot(user_message, history=[]):
    history.append({"role": "user", "content": user_message})

    response = client.chat.completions.create(
        model="gpt-4",
        messages=history
    )

    assistant_message = response.choices[0].message.content
    history.append({"role": "assistant", "content": assistant_message})

    return assistant_message, history

def generate_content(topic, content_type="blog"):
    prompts = {
        "blog": f"写一篇关于 {topic} 的综合博客文章",
        "email": f"写一封关于 {topic} 的专业电子邮件",
        "tweet": f"写一条关于 {topic} 的引人入胜的推文"
    }

    response = client.chat.completions.create(
        model="claude-3.5-sonnet",
        messages=[{"role": "user", "content": prompts[content_type]}],
        temperature=0.7
    )

    return response.choices[0].message.content

def code_assistant(task, language="python"):
    prompt = f"编写 {language} 代码来 {task}。包括注释和错误处理。"

    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.2  # 较低的 temperature 以获得更确定的代码
    )

    return response.choices[0].message.content

def analyze_data(data_description):
    prompt = f"""
    分析这些数据并提供见解:
    {data_description}

    提供:
    1. 关键发现
    2. 趋势
    3. 建议
    """

    response = client.chat.completions.create(
        model="claude-opus-4-1-20250805",  # 最适合分析
        messages=[{"role": "user", "content": prompt}]
    )

    return response.choices[0].message.content