feat: 每日热点问题共享库(定时生成+管理员手动更新+点击埋点)

数据库:
- daily_topics表:每天每场景一条热点问题记录
- sample_clicks表:记录用户点击示例问题行为

后端:
- DailyTopicStore: CRUD + 埋点 + 点击统计
- generate_daily_topics: AI生成4场景各25-30个问题
- 定时任务:每天凌晨3点自动生成(后台线程轮询)
- GET /topics/daily: 获取当天热点(公开)
- POST /topics/click: 点击埋点
- GET /admin/daily-topics: 管理端查看
- POST /admin/daily-topics: 管理员手动更新
- POST /admin/daily-topics/generate: 手动触发AI生成
- GET /admin/sample-clicks: 点击统计

前端:
- ChatPanel挂载时取当天热点,无则用硬编码兜底
- 点击示例问题时记录埋点
- AdminDailyTopics管理页面:查看/编辑/手动生成
- 路由 /admin/daily-topics + 导航配置

Generated with [Devin](https://devin.ai)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

bossai.py

@@ -3170,6 +3170,211 @@ def build_profile_context(profile: UserProfile) -> str:
     return profile.to_context()
 
 
+# ===== 每日热点问题库 =====
+
+class DailyTopicStore:
+    """每日热点问题库:每天 AI 生成或管理员手动更新。
+
+    每个场景(legal/decision/life/emotion)每天一条记录,
+    questions 为 JSONB 数组,前端取当天问题展示给用户。
+    """
+
+    SCENES = ["legal", "decision", "life", "emotion"]
+
+    def __init__(self, dsn: str | None = None):
+        self.dsn = dsn or os.getenv(
+            "DATABASE_URL", "host=localhost port=5432 dbname=mcp user=postgres"
+        )
+
+    def _conn(self):
+        return psycopg2.connect(self.dsn)
+
+    def get_today(self, scene: str) -> list[str]:
+        """获取当天热点问题,无记录返回空列表。"""
+        from datetime import date
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "SELECT questions FROM daily_topics "
+                    "WHERE date = %s AND scene = %s",
+                    (date.today(), scene),
+                )
+                r = cur.fetchone()
+        return r[0] if r else []
+
+    def get_by_date(self, date_str: str) -> list[dict]:
+        """获取指定日期所有场景的热点问题(管理端用)。"""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "SELECT id, date, scene, questions, source, created_at "
+                    "FROM daily_topics WHERE date = %s ORDER BY scene",
+                    (date_str,),
+                )
+                rows = cur.fetchall()
+        return [
+            {"id": r[0], "date": str(r[1]), "scene": r[2],
+             "questions": r[3], "source": r[4], "created_at": str(r[5])}
+            for r in rows
+        ]
+
+    def upsert(self, scene: str, questions: list[str],
+               source: str = "ai", date_str: str | None = None) -> dict:
+        """插入或更新当天(或指定日期)的热点问题。
+
+        Args:
+            scene: 场景 legal/decision/life/emotion
+            questions: 问题列表
+            source: ai(自动生成) / admin(管理员手动)
+            date_str: 指定日期(YYYY-MM-DD),默认今天
+
+        Returns:
+            更新后的记录 dict
+        """
+        from datetime import date
+        import json
+        target_date = date_str if date_str else str(date.today())
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "INSERT INTO daily_topics (date, scene, questions, source) "
+                    "VALUES (%s, %s, %s, %s) "
+                    "ON CONFLICT (date, scene) DO UPDATE "
+                    "SET questions = EXCLUDED.questions, source = EXCLUDED.source, "
+                    "created_at = now() "
+                    "RETURNING id, date, scene, questions, source, created_at",
+                    (target_date, scene, json.dumps(questions), source),
+                )
+                r = cur.fetchone()
+        return {"id": r[0], "date": str(r[1]), "scene": r[2],
+                "questions": r[3], "source": r[4], "created_at": str(r[5])}
+
+    def record_click(self, scene: str, question: str,
+                     user_phone: str | None = None) -> bool:
+        """记录用户点击示例问题(埋点)。"""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "INSERT INTO sample_clicks (user_phone, scene, question) "
+                    "VALUES (%s, %s, %s)",
+                    (user_phone, scene, question),
+                )
+                return cur.rowcount > 0
+
+    def click_stats(self, days: int = 14) -> list[dict]:
+        """获取近 N 天示例问题点击统计(管理端用)。"""
+        with self._conn() as conn:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "SELECT scene, question, COUNT(*) as clicks "
+                    "FROM sample_clicks "
+                    "WHERE clicked_at >= now() - interval '%s days' "
+                    "GROUP BY scene, question ORDER BY clicks DESC LIMIT 100",
+                    (days,),
+                )
+                rows = cur.fetchall()
+        return [
+            {"scene": r[0], "question": r[1], "clicks": r[2]}
+            for r in rows
+        ]
+
+
+def generate_daily_topics(kimi_client, store: DailyTopicStore) -> dict:
+    """AI 生成当天 4 个场景的热点问题并存库。
+
+    每个场景生成 20-30 个问题,聚焦老板焦虑和关注点。
+    AI 不可用时跳过(保留已有数据)。
+
+    Returns:
+        各场景生成结果 {scene: {count, source}}
+    """
+    from datetime import datetime
+
+    scene_desc = {
+        "legal": "法律/法务问题,聚焦中小企业老板的焦虑:劳动用工风险、合同纠纷、债权债务、股东纠纷、税务风险、诉讼仲裁、婚姻财产分割、股权分配、知识产权、行政处罚等",
+        "decision": "经营决策问题,聚焦中小企业老板的焦虑:扩张收缩、项目承接、定价策略、招人裁员、融资合伙、业务转型、现金流管理、竞争应对、投资决策等",
+        "life": "个人生活问题,聚焦中小企业老板的焦虑:家庭与事业平衡、子女教育规划、父母养老、健康风险、睡眠压力、时间管理、财富传承等",
+        "emotion": "情绪与心理问题,聚焦中小企业老板的焦虑:创业压力、合伙人冲突、孤独感、决策焦虑、拖延症、自我怀疑、危机应对等",
+    }
+
+    # 获取当前日期用于 prompt
+    today_str = datetime.now().strftime("%Y年%m月%d日")
+    # 获取当前热点事件提示(简单版:用日期季节推断)
+    month = datetime.now().month
+    season_hints = {
+        1: "年初:年度规划、去年总结、新政策生效、春节前后用工",
+        2: "春节后:招工难、复工、年度目标分解",
+        3: "两会政策、年报截止、个税汇算",
+        4: "Q1复盘、社保基数调整预告、春招",
+        5: "劳动节、用工合规检查、年中冲刺",
+        6: "年中复盘、半年报、高温补贴、用电高峰",
+        7: "下半年规划、社保基数调整、学生毕业季",
+        8: "秋招准备、Q3冲刺、税务自查",
+        9: "Q3复盘、中秋、国庆备货、年度预算",
+        10: "国庆后冲刺、年终规划、个税专项附加扣除",
+        11: "年底冲刺、年终奖规划、来年预算、欠薪风险",
+        12: "年终总结、欠薪清查、税务清算、年度复盘、春节前用工",
+    }
+    season = season_hints.get(month, "")
+
+    results = {}
+    for scene, desc in scene_desc.items():
+        prompt = f"""你是左右(BossAi)的内容编辑,负责为中小企业老板生成今日热点问题。
+
+当前日期:{today_str}
+季节性背景:{season}
+
+场景:{desc}
+
+请生成25-30个该场景下老板最焦虑、最关注的热点问题。
+
+要求:
+1. 问题必须从老板/雇主视角出发(BossAi 用户是企业老板)
+2. 结合当前季节性和时事热点(如政策变化、节日前后、经济形势)
+3. 问题要具体、有场景感、能直接开问,不要空泛
+4. 涵盖该场景下不同方向,不要集中在某一类
+5. 问题长度 10-30 字
+6. 返回JSON数组格式:["问题1","问题2",...]
+7. 只返回JSON,不要其他文字"""
+
+        try:
+            answer = kimi_client.chat(
+                [{"role": "user", "content": prompt}],
+                temperature=0.8, timeout=90,
+            )
+            answer = answer.strip()
+            if answer.startswith("```"):
+                answer = answer.split("\n", 1)[1] if "\n" in answer else answer[3:]
+            if answer.endswith("```"):
+                answer = answer[:-3]
+            answer = answer.strip().replace("\u201c", '"').replace("\u201d", '"')
+            start = answer.find("[")
+            end = answer.rfind("]")
+            if start == -1 or end == -1:
+                results[scene] = {"count": 0, "source": "ai", "error": "JSON解析失败"}
+                continue
+            questions = json.loads(answer[start:end + 1])
+            # 过滤和去重
+            valid = []
+            seen = set()
+            for q in questions:
+                q = str(q).strip()
+                if q and q not in seen and len(q) <= 50:
+                    valid.append(q)
+                    seen.add(q)
+            if len(valid) < 10:
+                results[scene] = {"count": 0, "source": "ai", "error": "有效问题不足10个"}
+                continue
+            store.upsert(scene, valid[:30], source="ai")
+            results[scene] = {"count": len(valid[:30]), "source": "ai"}
+        except Exception as e:
+            import logging
+            logging.warning(f"generate_daily_topics[{scene}] 失败: {type(e).__name__}: {e}")
+            results[scene] = {"count": 0, "source": "ai", "error": str(e)}
+
+    return results
+
+
 # ===== 对话历史落库 =====
 
 class CryptoUtil: