Следующие шаги

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"Write a comprehensive blog post about {topic}",
        "email": f"Write a professional email about {topic}",
        "tweet": f"Write an engaging tweet about {topic}"
    }

    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"Write {language} code to {task}. Include comments and error handling."

    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.2  # Более низкая температура для более детерминированного кода
    )

    return response.choices[0].message.content

def analyze_data(data_description):
    prompt = f"""
    Analyze this data and provide insights:
    {data_description}

    Provide:
    1. Key findings
    2. Trends
    3. Recommendations
    """

    response = client.chat.completions.create(
        model="claude-opus-4-1-20250805",  # Лучше всего для анализа
        messages=[{"role": "user", "content": prompt}]
    )

    return response.choices[0].message.content