1. 触发 -> 定时任务每周一9:00触发
2. 数据收集 -> 并行从各数据源收集数据
3. 数据整理 -> 整理收集到的原始数据
4. 内容生成 -> GPT-5.4生成周报内容
5. 格式排版 -> 转换为指定格式
6. 发送 -> 发送给相关人员
7. 归档 -> 保存归档版本

import openai
import requests
import json
from datetime import datetime, timedelta
import smtplib
from email.mime.text import MIMEText

# 配置
OPENAI_API_KEY = "your-api-key"
openai.api_key = OPENAI_API_KEY

# 收集Jira任务数据
def collect_jira_data(start_date, end_date):
    # Jira API调用获取已完成任务
    jira_url = "https://your-domain.atlassian.net/rest/api/3/search"
    jql = f"project = YOUR_PROJECT AND status = Done AND updatedDate >= {start_date} AND updatedDate <= {end_date}"
    # 认证和请求
    response = requests.get(
        jira_url,
        params={"jql": jql, "fields": "summary,description,assignee"},
        auth=("your-email", "your-api-token")
    )
    return response.json()

# 收集GitHub提交数据
def collect_github_commits(start_date, end_date):
    # GitHub API调用获取提交记录
    headers = {"Authorization": f"token YOUR_GITHUB_TOKEN"}
    url = f"https://api.github.com/repos/OWNER/REPO/commits"
    params = {"since": start_date, "until": end_date}
    response = requests.get(url, headers=headers, params=params)
    return response.json()

# 使用GPT-5.4生成周报内容
def generate_weekly_report(jira_data, github_data, meeting_notes):
    prompt = f"""
    请根据以下数据生成一份专业的项目周报：

    ## Jira已完成任务
    {json.dumps(jira_data, indent=2, ensure_ascii=False)}

    ## GitHub代码提交
    {json.dumps(github_data, indent=2, ensure_ascii=False)}

    ## 会议纪要要点
    {meeting_notes}

    请按照以下格式生成周报：
    1. 本周工作总结
    2. 完成任务清单
    3. 代码变更统计
    4. 下周工作计划
    5. 风险和问题

    要求语言专业，条理清晰，重点突出。
    """

    response = openai.ChatCompletion.create(
        model="gpt-5.4",
        messages=[
            {"role": "system", "content": "你是一个专业的项目经理，负责生成清晰专业的项目周报。"},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7
    )

    return response.choices[0].message.content

# 发送邮件
def send_email(report_content, recipients):
    msg = MIMEText(report_content, 'plain', 'utf-8')
    msg['Subject'] = f"项目周报 - {datetime.now().strftime('%Y年%m月%d日')}"
    msg['From'] = "ai-automation@your-company.com"
    msg['To'] = ', '.join(recipients)

    with smtplib.SMTP_SSL('smtp.your-company.com', 465) as server:
        server.login("your-email", "your-password")
        server.send_message(msg)

# 主工作流
def main():
    # 计算时间范围
    end_date = datetime.now()
    start_date = end_date - timedelta(days=7)

    # 1. 收集数据
    jira_data = collect_jira_data(start_date, end_date)
    github_data = collect_github_commits(start_date, end_date)
    meeting_notes = "此处填入本周会议纪要要点"

    # 2. 生成报告
    report = generate_weekly_report(jira_data, github_data, meeting_notes)

    # 3. 发送报告
    recipients = ["manager@company.com", "team@company.com"]
    send_email(report, recipients)

    # 4. 归档
    with open(f"weekly_reports/report_{end_date.strftime('%Y%m%d')}.md", "w") as f:
        f.write(report)

    print("周报生成并发送成功！")

if __name__ == "__main__":
    main()

选题Agent -> 大纲Agent -> 写作Agent -> 校对Agent -> 排版Agent -> SEO优化Agent

# 定义多个代理角色
agents = {
    "选题": {
        "system_prompt": "你是经验丰富的内容主编，负责挑选有流量潜力的选题...",
        "model": "gpt-5.4"
    },
    "写作": {
        "system_prompt": "你是专业的技术作者，擅长写出清晰易懂的技术文章...",
        "model": "gpt-5.4"  
    },
    "校对": {
        "system_prompt": "你是专业编辑，负责检查文章中的错误和优化表达...",
        "model": "gpt-5.4"
    }
}

# 工作流执行
def content_pipeline(topic):
    # 选题阶段
    selected_topic = run_agent("选题", topic)

    # 大纲阶段  
    outline = run_agent("大纲", selected_topic)

    # 写作阶段
    article = run_agent("写作", outline)

    # 校对阶段
    final_article = run_agent("校对", article)

    return final_article

from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_gpt_call(prompt):
    return openai.ChatCompletion.create(
        model="gpt-5.4",
        messages=[{"role": "user", "content": prompt}]
    )