315 lines
13 KiB
Python
315 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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AI 问题归纳脚本
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读取 cluster_context_{date}.json，调用 LLM 为每个问题簇生成精炼的问题描述，
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输出 ai_descriptions_{date}.json，然后回写到飞书知识库文档。
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用法:
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python3 ai_summarize_feedback.py [--date YYYY-MM-DD] [--dry-run]
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crontab:
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5 10 * * * python3 .../ai_summarize_feedback.py >> /var/log/xiaokui_ai_summarize.log 2>&1
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"""
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import sys, os, json, argparse, re, subprocess, urllib.request
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from datetime import datetime, date, timedelta
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# === 配置 ===
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DEEPSEEK_API_KEY = "sk-7cf94305fb12473b956fd2ed2a6db05b"
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DEEPSEEK_BASE_URL = "https://api.deepseek.com/v1"
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DEEPSEEK_MODEL = "deepseek-v4-pro"
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CONTEXT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "output", "daily_feedback")
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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SKILL_SCRIPT_DIR = os.path.join(SCRIPT_DIR, "..", "skills", "feishu-feedback-sync", "scripts")
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sys.path.insert(0, SKILL_SCRIPT_DIR)
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import sync_feishu_feedback # noqa: E402 — 用于 fallback 关键词规则
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SYSTEM_PROMPT = """你是一个游戏产品的问题归纳助手。你的任务是：
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阅读一段来自测试群的多人对话（可能包含多个发言人、多轮讨论），
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从中提炼出他们正在讨论的「具体问题是什么」，用一句中文描述清楚。
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要求：
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1. 只描述问题本身，不要评价或建议
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2. 包含关键要素：在哪个端、哪个环节、什么表现
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3. 如果对话中有多种说法，优先采用最后确认的描述
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4. 输出仅一句中文，不要加任何前缀、编号、引号或换行
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5. 如果对话全是无实质内容的闲聊（如"好的""收到"），输出"无明确问题"
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6. **严禁**在问题描述中出现任何员工姓名（如江涛、张骜等），人名用"相关人员"替代
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输出格式（严格）：直接输出问题描述，无任何额外文字。"""
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def load_context(date_str, channel="feishu"):
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"""加载指定日期的 cluster_context JSON"""
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prefix = "wechat_cluster_context" if channel == "wechat" else "cluster_context"
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path = os.path.join(CONTEXT_DIR, f"{prefix}_{date_str}.json")
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if not os.path.exists(path):
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print(f" ⚠️ 无上下文文件: {path}")
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return None
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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def build_user_prompt(cluster):
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"""为单个问题簇构建 LLM prompt"""
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lines = []
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lines.append(f"优先级: {cluster.get('priority', '?')}")
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lines.append(f"分类: {cluster.get('category', '?')}")
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lines.append(f"当前排查结论: {cluster.get('conclusion', '无')}")
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lines.append("")
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lines.append("--- 对话记录 ---")
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for msg in cluster.get("messages", []):
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sender = msg.get("sender", "?")
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content = msg.get("content", "")
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mtype = msg.get("msg_type", "text")
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time = msg.get("time", "")
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# 跳过纯媒体消息（无有效文本）
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if mtype in ("image", "post_image", "media", "file", "sticker") and not content.strip():
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continue
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if not content.strip():
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continue
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# 截断过长内容
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if len(content) > 200:
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content = content[:197] + "..."
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lines.append(f"[{time}] {sender}: {content}")
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return "\n".join(lines)
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def call_deepseek(system_prompt, user_prompt, max_retries=2):
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"""调用 DeepSeek API 生成问题描述"""
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body = json.dumps({
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"model": DEEPSEEK_MODEL,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": 0.3,
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"max_tokens": 1024,
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}).encode()
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for attempt in range(max_retries + 1):
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try:
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req = urllib.request.Request(
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f"{DEEPSEEK_BASE_URL}/chat/completions",
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data=body,
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headers={
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"Authorization": f"Bearer {DEEPSEEK_API_KEY}",
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"Content-Type": "application/json",
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},
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method="POST",
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)
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resp = urllib.request.urlopen(req, timeout=60)
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data = json.loads(resp.read())
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content = data["choices"][0]["message"]["content"].strip()
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# 清理常见的引号/前缀
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content = content.strip('"\'""'' \n')
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return content
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except Exception as e:
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if attempt < max_retries:
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print(f" ⚠️ API 调用重试 {attempt + 1}: {e}")
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import time
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time.sleep(2)
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else:
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raise
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def generate_fallback_description(cluster):
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"""AI 返回空描述时的回退：调用 sync_feishu_feedback.py 的关键词规则生成"""
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# 将 context JSON 消息格式转换为 sync_feishu_feedback 期望的数据库行格式
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# 数据库行: (msg_id, sender, msg_type, content, media_url, quote_id, time, timestamp)
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converted = []
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for m in cluster.get("messages", []):
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converted.append((
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m.get("message_id", ""),
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m.get("sender", ""),
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m.get("msg_type", "text"),
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m.get("content", ""),
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m.get("media_url", ""),
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m.get("quote_message_id", ""),
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m.get("time", ""),
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0,
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))
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idx = cluster.get("index", 0)
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location = sync_feishu_feedback.extract_location_elements(converted)
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root_text = converted[0][3] if converted else ""
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return sync_feishu_feedback.generate_problem_description(converted, location, root_text, ai_placeholder=False, placeholder_idx=idx)
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def strip_names(text, cluster=None):
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"""移除问题描述中的员工姓名（后处理兜底）。
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1. 优先使用簇中实际发送者姓名做精确替换
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2. 然后对常见姓氏+1字做保守匹配（排除已知内容词）
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"""
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import re
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if not text:
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return text
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# 1. 精确替换：簇中出现的发送者姓名
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if cluster:
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sender_names = set()
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for m in cluster.get("messages", []):
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name = m.get("sender", "").strip()
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if name and len(name) >= 2:
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sender_names.add(name)
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for name in sorted(sender_names, key=len, reverse=True):
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text = text.replace(name, '相关人员')
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# 2. 保守模式：姓氏 + 1个中文字符（两字名），排除已知内容词
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surnames = '李王张刘陈杨赵黄周吴徐孙胡朱高林何郭马罗梁宋郑谢韩唐冯于董萧程曹袁邓许傅沈曾彭吕苏卢蒋蔡贾丁魏薛叶阎余潘杜戴夏钟汪田任姜范方石姚谭廖邹熊金陆郝孔白崔康毛邱秦江史顾侯邵孟龙万段雷钱汤尹黎易常武乔贺赖龚文'
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pattern = '[' + surnames + '][一-鿿]'
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# 需要排除的已知内容词
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content_words = {
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'文件','资源','存在','动画','角色','设计','问题','音频','显示',
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'界面','关卡','课程','内容','配置','重点','引导','模型',
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'测试','环境','部署','灰度','版本','组件','数据','命名',
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'图片','视频','格式','选项','处理','结果','玩家','游戏',
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'开发','项目','报告','任务','状态','进度','确认','反馈',
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'功能','系统','后台','前端','服务','需要','可能','正常',
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'异常','错误','修复','解决','检查','查看','说明','登录',
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'注册','打开','关闭','更新','调试','运行','启动','停止',
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'通过','失败','成功','完成','开始','结束','使用','操作',
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'调整','优化','修改','增加','删除','添加','移除','切换',
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}
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def _replace(m):
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name = m.group(0)
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return '相关人员' if name not in content_words else name
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text = re.sub(pattern, _replace, text)
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return text
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def generate_descriptions(context_data, dry_run=False):
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"""为所有问题簇生成 AI 描述"""
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clusters = context_data.get("clusters", [])
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if not clusters:
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print(" ⚠️ 无问题簇数据")
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return None
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descriptions = []
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for cluster in clusters:
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idx = cluster.get("index", 0)
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print(f" 🤖 处理簇 #{idx}...")
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user_prompt = build_user_prompt(cluster)
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if dry_run:
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print(f" [DRY-RUN] Prompt 长度: {len(user_prompt)} chars")
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# 输出前 200 字符预览
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print(f" [DRY-RUN] 对话预览: {user_prompt[:200]}...")
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description = f"[DRY-RUN] 问题{idx}"
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else:
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try:
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description = call_deepseek(SYSTEM_PROMPT, user_prompt)
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except Exception as e:
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print(f" ❌ 簇 #{idx} API 调用失败: {e}")
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description = f"[API调用失败: {str(e)[:50]}]"
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# AI 返回空描述时回退
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if not description or not description.strip():
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description = generate_fallback_description(cluster)
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print(f" ⚠️ AI 返回空，回退: {description}")
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else:
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print(f" 📝 描述: {description}")
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# 脱敏：移除员工姓名
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description = strip_names(description, cluster=cluster)
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descriptions.append({"index": idx, "description": description})
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return descriptions
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def apply_descriptions(date_str, descriptions, channel="feishu"):
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"""调用 sync_*_feedback.py --apply-ai 回写文档
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channel: "feishu" 或 "wechat"
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"""
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sys.path.insert(0, SKILL_SCRIPT_DIR)
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# 渠道前缀
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prefix = "wechat_" if channel == "wechat" else ""
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# 先保存描述 JSON
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desc_path = os.path.join(CONTEXT_DIR, f"ai_descriptions_{channel}_{date_str}.json")
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payload = {"date": date_str, "descriptions": descriptions}
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with open(desc_path, "w", encoding="utf-8") as f:
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json.dump(payload, f, ensure_ascii=False, indent=2)
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print(f" 💾 描述已保存: {desc_path}")
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# 调用 --apply-ai
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if channel == "wechat":
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sync_script = os.path.join(SCRIPT_DIR, "sync_wechat_feedback.py")
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else:
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sync_script = os.path.join(SKILL_SCRIPT_DIR, "sync_feishu_feedback.py")
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env = os.environ.copy()
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env["LARKSUITE_CLI_CONFIG_DIR"] = "/root/.openclaw/credentials/xiaokui"
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env["HOME"] = "/root"
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env["PATH"] = "/root/.nvm/versions/node/v24.14.0/bin:" + env.get("PATH", "")
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cmd = ["python3", sync_script, "--date", date_str, "--apply-ai", desc_path]
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result = subprocess.run(
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cmd,
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capture_output=True, text=True, timeout=60, env=env
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)
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if "AI 描述已应用" in result.stdout or "✅" in result.stdout:
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print(f" ✅ AI 描述已回写到知识库文档")
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# 回写成功后清理上下文文件，避免心跳重复处理
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ctx_prefix = "wechat_cluster_context" if channel == "wechat" else "cluster_context"
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context_path = os.path.join(CONTEXT_DIR, f"{ctx_prefix}_{date_str}.json")
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if os.path.exists(context_path):
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os.remove(context_path)
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print(f" 🗑️ 已清理上下文文件: {context_path}")
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return True
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else:
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print(f" ❌ 回写失败: {result.stdout[:300]}")
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if result.stderr:
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print(f" stderr: {result.stderr[:300]}")
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return False
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def main():
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parser = argparse.ArgumentParser(description="AI 问题归纳")
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parser.add_argument("--date", help="日期 YYYY-MM-DD，默认昨天")
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parser.add_argument("--dry-run", action="store_true", help="仅预览不实际调用 API")
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parser.add_argument("--channel", default="feishu", choices=["feishu", "wechat"],
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help="数据渠道（默认 feishu）")
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args = parser.parse_args()
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if args.date:
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date_str = args.date
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else:
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date_str = (date.today() - timedelta(days=1)).strftime("%Y-%m-%d")
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channel = args.channel
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label = "微信" if channel == "wechat" else "飞书"
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print(f"📋 AI 问题归纳 - {date_str} [{label}]")
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os.makedirs(CONTEXT_DIR, exist_ok=True)
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context = load_context(date_str, channel=channel)
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if not context:
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print(" ℹ️ 无待处理数据，退出")
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return
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descriptions = generate_descriptions(context, dry_run=args.dry_run)
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if not descriptions:
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return
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if args.dry_run:
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desc_path = os.path.join(CONTEXT_DIR, f"ai_descriptions_{channel}_{date_str}.json")
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payload = {"date": date_str, "descriptions": descriptions}
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with open(desc_path, "w", encoding="utf-8") as f:
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json.dump(payload, f, ensure_ascii=False, indent=2)
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print(f"[DRY-RUN] 描述已保存到 {desc_path}，未回写文档")
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return
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apply_descriptions(date_str, descriptions, channel=channel)
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if __name__ == "__main__":
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main()
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