新增批量人物蒸馏脚本，精简排行榜相关接口

- batch-distill: 独立单文件脚本，按时间窗调用线上服务批量蒸馏人物档案
- 精简 admin_users / public_leaderboard 路由及前端排行榜页面

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

batch-distill/README.md

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+# 批量人物蒸馏 → llmwiki
+
+一个**完全独立的单文件脚本 + 内置定时器**：读 Excel 名单，串行调用线上 DeerFlow 服务问答接口，
+复用「女娲 / huashu-nuwa」技能逐个蒸馏人物，**由脚本抓取回复并保存**成 llmwiki 风格 markdown。
+
+- **不联网**：只用已配置的知识库查询技能（`knowledge-search-v2`）检索资料。
+- **定时执行**：每天 **23:00 ~ 次日 08:00** 自动跑，到点暂停、次日继续。
+- **md 由 py 保存**：agent 只产出内容，脚本抓最终回复写成 `.md`（不让项目自己写文件）。
+
+## 你只需要做两件事
+
+1. **改一行地址**：打开 `distill_people.py`，把顶部 `BASE_URL = "..."` 改成你的线上服务地址。
+2. **放名单**：同目录放 `people.xlsx`（或 `people.csv`），**第一列 = 人名**，其余列=可选提示。
+
+其余（用户名 `distiller`、口令 `123ewq`、技能名、时间窗）都已内置，不用填。
+
+## 运行
+
+```bash
+python distill_people.py          # 常驻，按 23:00~08:00 窗口自动执行（推荐挂后台）
+python distill_people.py --now    # 忽略时间窗，立刻把名单跑完（测试用）
+python distill_people.py --redo   # 忽略已完成，全部重做
+```
+
+读 `.xlsx` 需要 `openpyxl`；用 `.csv` 则零额外依赖。
+
+## 输出（都在 output/ 一个文件夹）
+
+- `<人名>.md` —— 每个人的 llmwiki 档案
+- `manifest.json` —— 执行清单（状态/耗时/run_id）
+- `index.md` —— 可读清单表（谁完成/失败，点开看 md）
+
+**断点续跑**：已完成的人自动跳过；中途往名单里加人，下个执行窗会带上。

batch-distill/distill_people.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+批量人物蒸馏 → llmwiki markdown（完全独立的单文件客户端 + 内置定时器）
+
+特点
+----
+- 串行（一个一个来）调用正在运行的 DeerFlow 服务问答接口
+- 复用线上「女娲 / huashu-nuwa」技能做提炼；**不联网**，只用已配置的
+  知识库查询技能（knowledge-search-v2）检索资料
+- agent 只负责产出内容，**markdown 由本脚本抓取最终回复后自己保存**
+- 内置时间窗：每天 23:00 ~ 次日 08:00 才执行，到点自动暂停、次日继续
+- 断点续跑：已完成的人自动跳过；中途加名单也会被带上
+
+你只需要改一处：下面的 BASE_URL（线上服务地址）。其余都不用填。
+
+名单
+----
+脚本同目录放 people.xlsx 或 people.csv，**第一列 = 人名**，其余列=可选提示。
+
+运行
+----
+    python distill_people.py            # 常驻，按 23:00~08:00 窗口自动执行
+    python distill_people.py --now      # 忽略时间窗，立刻把名单跑完（测试用）
+    python distill_people.py --redo      # 忽略已完成，全部重做
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import re
+import sys
+import time
+from datetime import datetime, time as dtime
+from pathlib import Path
+
+try:
+    import requests
+except ImportError:
+    sys.exit("缺少依赖 requests：pip install requests")
+
+
+# ════════════════════════════════════════════════════════════════════════
+#  只改这一行：你的线上服务地址（不带结尾斜杠）
+# ════════════════════════════════════════════════════════════════════════
+BASE_URL = "http://localhost:8001"
+# ════════════════════════════════════════════════════════════════════════
+
+
+# ---- 以下都已内置，正常无需改动 ----
+USERNAME = "distiller"          # 免密自动注册
+PASSWORD = "123ewq"             # 共享口令（你们开了口令门）
+NUWA_SKILL = "huashu-nuwa"      # 女娲技能
+KB_SKILL = "knowledge-search-v2"  # 知识库查询技能（不联网，用它检索）
+DEPTH = "full"                  # full 完整 / fast 精简
+
+HERE = Path(__file__).resolve().parent
+OUTPUT_DIR = HERE / "output"
+PEOPLE_FILE_DEFAULT = HERE / "people.xlsx"
+
+# 执行时间窗：23:00 ~ 次日 08:00（跨午夜）
+WIN_START = dtime(23, 0)
+WIN_END = dtime(8, 0)
+
+PER_PERSON_TIMEOUT = 1800       # 单人最长等待（秒）
+POLL_INTERVAL = 5               # 轮询间隔（秒）
+HTTP_TIMEOUT = 60
+TERMINAL_STATUS = {"success", "error", "timeout", "interrupted"}
+
+
+# ================================ llmwiki 模板 ================================
+LLMWIKI_TEMPLATE = """\
+---
+name: <中文名/常用名>
+aliases: [<别名/英文名...>]
+type: person
+era: <在世 | 历史人物>
+domains: [<领域1>, <领域2>]
+distilled_at: <YYYY-MM-DD>
+sources_count: <检索到的来源条数>
+primary_source_ratio: <一手来源占比, 0~1 的小数>
+confidence: <high | medium | low>
+---
+
+# <人物名>
+
+> **一句话定位**：<此人看世界的独特方式，一句话>
+
+## 身份卡
+- **活跃年代**：
+- **核心身份**：
+- **自述(50字, 以其语气)**：
+
+## 核心观点（真信念 · 反复出现的主张）
+1. ...
+
+## 心智模型（3–7 个；每个含：一句话 / 证据场景≥2 / 应用 / 局限 / 来源）
+### 1. <模型名>
+- **一句话**：
+- **证据场景**：
+- **应用方式**：
+- **局限**：
+- **来源**：
+
+## 决策启发式（5–10 条 · 如果 X 则 Y，带案例）
+- 若 <X> → <Y>
+
+## 表达 DNA
+| 维度 | 特征 |
+|------|------|
+| 句式 | |
+| 高频词 | |
+| 幽默 | |
+| 确定性 | |
+| 引用习惯 | |
+
+## 价值观与反模式
+- **价值观排序**：
+- **明确反对**：
+
+## 内在张力（≥2 对矛盾）
+- <A> ↔ <B>
+
+## 经历时间线
+| 年份 | 事件 | 思想意义 |
+|------|------|---------|
+| | | |
+
+## 代表语录
+- “...” —（出处/年份，标一手/二手）
+
+## 智识谱系
+- **受影响于**：
+- **影响了**：
+
+## 外部评价与争议
+-
+
+## 诚实边界
+- <至少 3 条具体局限，含信息截止日期>
+
+## 调研来源
+**一手(N)**：
+**二手(N)**：
+"""
+
+
+def build_prompt(name: str, hints: str, depth: str) -> str:
+    depth_note = (
+        "本次走【完整模式】：尽量覆盖著作/对话/表达/他者评价/决策/时间线 6 个维度。"
+        if depth == "full"
+        else "本次走【精简模式】：重点覆盖核心观点、心智模型、时间线三块即可。"
+    )
+    hint_block = f"\n补充提示（来自名单其它列，可作为检索关键词）：{hints}\n" if hints.strip() else ""
+    return f"""\
+你的任务：把人物【{name}】蒸馏成一份 llmwiki 风格的人物档案 markdown。
+
+请复用本服务中已启用的「女娲 / {NUWA_SKILL}」技能的提炼方法论
+（心智模型三重验证、表达DNA分析、诚实边界）。
+
+⚠️ 本环境**无法联网，严禁使用 WebSearch 等任何联网工具**。
+所有资料**只能通过已配置的知识库查询技能「{KB_SKILL}」检索获取**。
+请充分调用该知识库技能，围绕该人物多角度检索（生平、观点、著作、语录、决策、评价等），
+基于检索到的内容做提炼。知识库里查不到的部分，如实在「诚实边界」标注，不要编造。
+
+{depth_note}
+{hint_block}
+输出要求：
+1. **不要写任何文件，不要调用 write_file / present_files**。
+2. 直接在你的回复正文里输出**完整中文 markdown 全文**，严格按下面模板结构
+   （保留各级标题，逐项填实，不要保留尖括号占位符），**不要任何额外解说、前言或结语**：
+
+------------------ 模板开始 ------------------
+{LLMWIKI_TEMPLATE}
+------------------ 模板结束 ------------------
+
+现在开始，独立完成，不要向我提问。"""
+
+
+# ================================ HTTP 客户端 ================================
+class DeerFlowClient:
+    def __init__(self, base_url: str):
+        self.base = base_url.rstrip("/")
+        self.s = requests.Session()
+        self.token: str | None = None
+
+    def _headers(self) -> dict:
+        h = {"Content-Type": "application/json"}
+        if self.token:
+            h["Authorization"] = f"Bearer {self.token}"  # 只用 Bearer，不带 cookie → 跳过 CSRF
+        return h
+
+    def login(self) -> bool:
+        url = f"{self.base}/api/v1/auth/login/username"
+        params = {"password": PASSWORD} if PASSWORD else None
+        try:
+            r = self.s.post(url, params=params, json={"username": USERNAME}, timeout=HTTP_TIMEOUT)
+        except requests.RequestException as e:
+            print(f"  [登录] 连接失败：{e}")
+            return False
+        if r.status_code == 200:
+            self.token = r.json().get("access_token")
+            self.s.cookies.clear()
+            print(f"  [登录] 成功，用户={USERNAME}")
+            return True
+        print(f"  [登录] 返回 {r.status_code}：{r.text[:200]}")
+        return False
+
+    def ensure_skill_enabled(self, name: str) -> None:
+        try:
+            r = self.s.get(f"{self.base}/api/skills", headers=self._headers(), timeout=HTTP_TIMEOUT)
+            if r.status_code == 200:
+                skills = r.json().get("skills", [])
+                hit = next((x for x in skills if x.get("name") == name), None)
+                if hit is None:
+                    print(f"  [技能] 警告：服务里没找到 '{name}'")
+                    return
+                if hit.get("enabled"):
+                    print(f"  [技能] '{name}' 已启用")
+                    return
+            pr = self.s.put(f"{self.base}/api/skills/{name}", headers=self._headers(),
+                            json={"enabled": True}, timeout=HTTP_TIMEOUT)
+            print(f"  [技能] 启用 '{name}' → {pr.status_code}")
+        except requests.RequestException as e:
+            print(f"  [技能] 检查 '{name}' 失败（忽略）：{e}")
+
+    def create_thread(self) -> str:
+        r = self.s.post(f"{self.base}/api/threads", headers=self._headers(), json={}, timeout=HTTP_TIMEOUT)
+        r.raise_for_status()
+        return r.json()["thread_id"]
+
+    def start_run(self, thread_id: str, prompt: str) -> str:
+        body = {
+            "input": {"messages": [{"role": "user", "content": prompt}]},
+            "excluded_tools": ["ask_clarification"],  # 禁澄清，无人值守不卡住
+        }
+        r = self.s.post(f"{self.base}/api/threads/{thread_id}/runs",
+                        headers=self._headers(), json=body, timeout=HTTP_TIMEOUT)
+        r.raise_for_status()
+        return r.json()["run_id"]
+
+    def poll_run(self, thread_id: str, run_id: str) -> str:
+        deadline = time.monotonic() + PER_PERSON_TIMEOUT
+        last = ""
+        while time.monotonic() < deadline:
+            try:
+                r = self.s.get(f"{self.base}/api/threads/{thread_id}/runs/{run_id}",
+                               headers=self._headers(), timeout=HTTP_TIMEOUT)
+                if r.status_code == 200:
+                    st = r.json().get("status", "")
+                    if st != last:
+                        print(f"    ...状态：{st}")
+                        last = st
+                    if st in TERMINAL_STATUS:
+                        return st
+            except requests.RequestException as e:
+                print(f"    轮询出错（重试）：{e}")
+            time.sleep(POLL_INTERVAL)
+        return "timeout"
+
+    def fetch_result_text(self, thread_id: str, run_id: str) -> str | None:
+        """抓取该 run 的最终 AI 回复文本（由 py 保存为 md）。"""
+        try:
+            r = self.s.get(f"{self.base}/api/threads/{thread_id}/runs/{run_id}/messages",
+                           headers=self._headers(), params={"limit": 200}, timeout=HTTP_TIMEOUT)
+            if r.status_code == 200:
+                data = r.json().get("data", [])
+                for msg in reversed(data):
+                    if msg.get("event_type") == "ai_message" or msg.get("type") == "ai":
+                        txt = _extract_text(msg)
+                        if txt and txt.strip():
+                            return txt
+        except requests.RequestException as e:
+            print(f"    取回复出错：{e}")
+        return None
+
+
+def _extract_text(msg: dict) -> str:
+    c = msg.get("content") or msg.get("text") or ""
+    if isinstance(c, str):
+        return c
+    if isinstance(c, list):
+        parts = []
+        for b in c:
+            if isinstance(b, dict):
+                parts.append(b.get("text") or b.get("content") or "")
+            else:
+                parts.append(str(b))
+        return "\n".join(p for p in parts if p)
+    return str(c)
+
+
+def _clean_markdown(text: str) -> str:
+    """去掉 agent 可能套上的 ```markdown ... ``` 围栏。"""
+    t = text.strip()
+    m = re.match(r"^```(?:markdown|md)?\s*\n(.*)\n```$", t, re.S)
+    if m:
+        t = m.group(1).strip()
+    return t
+
+
+# ================================ 名单读取 ================================
+def slugify(name: str) -> str:
+    s = re.sub(r'[\\/:*?"<>|]+', "", name).strip()
+    s = re.sub(r"\s+", "_", s)
+    return s or "unnamed"
+
+
+def read_people(path: Path) -> list[dict]:
+    if not path.exists():
+        return []
+    rows: list[list[str]] = []
+    if path.suffix.lower() in (".xlsx", ".xlsm"):
+        try:
+            import openpyxl
+        except ImportError:
+            sys.exit("读取 .xlsx 需要 openpyxl：pip install openpyxl（或把名单存成 .csv）")
+        wb = openpyxl.load_workbook(path, read_only=True, data_only=True)
+        for row in wb.active.iter_rows(values_only=True):
+            rows.append(["" if c is None else str(c).strip() for c in row])
+    elif path.suffix.lower() == ".csv":
+        with open(path, encoding="utf-8-sig", newline="") as f:
+            rows = [[c.strip() for c in r] for r in csv.reader(f)]
+    else:
+        sys.exit(f"不支持的名单格式：{path.suffix}（用 .xlsx 或 .csv）")
+
+    if rows and re.search(r"(姓名|名字|人物|name)", rows[0][0] if rows[0] else "", re.I):
+        rows = rows[1:]
+    people, seen = [], set()
+    for r in rows:
+        if not r or not r[0].strip():
+            continue
+        name = r[0].strip()
+        if name in seen:
+            continue
+        seen.add(name)
+        hints = " | ".join(x for x in r[1:] if x.strip())
+        people.append({"name": name, "hints": hints})
+    return people
+
+
+# ================================ 时间窗 ================================
+def in_window(now: datetime | None = None) -> bool:
+    now = now or datetime.now()
+    t = now.time()
+    return t >= WIN_START or t < WIN_END  # 跨午夜
+
+def seconds_until_window(now: datetime | None = None) -> int:
+    now = now or datetime.now()
+    if in_window(now):
+        return 0
+    target = now.replace(hour=WIN_START.hour, minute=WIN_START.minute, second=0, microsecond=0)
+    return max(1, int((target - now).total_seconds()))
+
+def sleep_interruptible(total: int) -> None:
+    end = time.monotonic() + total
+    while time.monotonic() < end:
+        time.sleep(min(30, max(1, int(end - time.monotonic()))))
+
+
+# ================================ 蒸馏单人 ================================
+def distill_one(client: DeerFlowClient, person: dict, out_dir: Path,
+                manifest: dict, save_manifest, depth: str) -> None:
+    name, hints = person["name"], person["hints"]
+    slug = slugify(name)
+    md_path = out_dir / f"{slug}.md"
+    t0 = time.time()
+
+    rec = {"name": name, "slug": slug, "status": "running",
+           "started_at": _now(), "file": f"{slug}.md"}
+    manifest[name] = rec
+    save_manifest()
+
+    try:
+        tid = client.create_thread()
+        run_id = client.start_run(tid, build_prompt(name, hints, depth))
+        status = client.poll_run(tid, run_id)
+        rec.update(thread_id=tid, run_id=run_id, run_status=status)
+
+        text = client.fetch_result_text(tid, run_id) if status == "success" else None
+        if status == "success" and text:
+            md = _clean_markdown(text)
+            md_path.write_text(md, encoding="utf-8")
+            rec["status"] = "succeeded"
+            rec["chars"] = len(md)
+            print(f"    ✅ 完成 → {md_path.name}（{len(md)} 字，耗时 {int(time.time()-t0)}s）")
+        else:
+            rec["status"] = "failed"
+            rec["error"] = f"run={status}, text={'有' if text else '无'}"
+            if text:
+                (out_dir / f"{slug}.partial.md").write_text(text, encoding="utf-8")
+            print(f"    ❌ 失败（run={status}）")
+    except Exception as e:
+        rec["status"] = "failed"
+        rec["error"] = str(e)
+        print(f"    ❌ 异常：{e}")
+
+    rec["finished_at"] = _now()
+    rec["seconds"] = int(time.time() - t0)
+    manifest[name] = rec
+    save_manifest()
+
+
+# ================================ 主流程 ================================
+def main():
+    ap = argparse.ArgumentParser(description="批量人物蒸馏 → llmwiki（定时窗 23:00~08:00）")
+    ap.add_argument("excel", nargs="?", default=str(PEOPLE_FILE_DEFAULT),
+                    help="名单文件（.xlsx 或 .csv），默认同目录 people.xlsx")
+    ap.add_argument("--now", action="store_true", help="忽略时间窗，立刻跑完整批（测试用）")
+    ap.add_argument("--redo", action="store_true", help="忽略已完成，全部重做")
+    args = ap.parse_args()
+
+    excel = Path(args.excel)
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    manifest_path = OUTPUT_DIR / "manifest.json"
+
+    manifest: dict = {}
+    if manifest_path.exists():
+        try:
+            manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+        except Exception:
+            manifest = {}
+
+    # 校验名单存在
+    if not read_people(excel):
+        sys.exit(f"名单为空或不存在：{excel}\n请放 people.xlsx（第一列写人名）。")
+
+    client = DeerFlowClient(BASE_URL)
+    if not client.login():
+        sys.exit("登录失败。请检查 BASE_URL 是否指向正在运行的服务、口令是否正确。")
+    client.ensure_skill_enabled(NUWA_SKILL)
+    client.ensure_skill_enabled(KB_SKILL)
+
+    def save_manifest():
+        manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
+        _write_index(OUTPUT_DIR, read_people(excel), manifest)
+
+    def pending() -> list[dict]:
+        people = read_people(excel)
+        out = []
+        for p in people:
+            r = manifest.get(p["name"], {})
+            done = (not args.redo) and r.get("status") == "succeeded" and (OUTPUT_DIR / f"{slugify(p['name'])}.md").exists()
+            if not done:
+                out.append(p)
+        return out
+
+    print(f"启动 | 服务={BASE_URL} | 输出={OUTPUT_DIR}")
+    print(f"模式={'立即跑完' if args.now else '定时窗 23:00~08:00'} | 名单={excel}")
+
+    try:
+        while True:
+            todo = pending()
+            if not todo:
+                print(f"[{_now()}] 全部已完成，待命中（每小时复查名单）...")
+                if args.now:
+                    break
+                sleep_interruptible(3600)
+                continue
+
+            if not args.now and not in_window():
+                wait = seconds_until_window()
+                nxt = datetime.now().replace(hour=WIN_START.hour, minute=0, second=0, microsecond=0)
+                print(f"[{_now()}] 不在执行窗口，剩 {len(todo)} 人待蒸；"
+                      f"休眠到 {nxt.strftime('%H:%M')}（约 {wait//60} 分钟）...")
+                sleep_interruptible(wait)
+                continue
+
+            # 在窗口内（或 --now）：处理一个人，然后回到循环重新判断窗口/名单
+            p = todo[0]
+            idx_total = len(read_people(excel))
+            done_n = sum(1 for v in manifest.values() if v.get("status") == "succeeded")
+            print(f"[{_now()}] ({done_n}/{idx_total} 已完成) 蒸馏：{p['name']}")
+            distill_one(client, p, OUTPUT_DIR, manifest, save_manifest, DEPTH)
+    except KeyboardInterrupt:
+        print("\n已手动停止。重跑脚本会从未完成处继续。")
+
+    ok = sum(1 for r in manifest.values() if r.get("status") == "succeeded")
+    fail = sum(1 for r in manifest.values() if r.get("status") == "failed")
+    print(f"\n结束：成功 {ok} / 失败 {fail}")
+    print(f"结果目录：{OUTPUT_DIR} | 清单：{manifest_path} | 索引：{OUTPUT_DIR/'index.md'}")
+
+
+def _now() -> str:
+    return time.strftime("%Y-%m-%d %H:%M:%S")
+
+
+def _write_index(out_dir: Path, people: list[dict], manifest: dict) -> None:
+    lines = ["# 蒸馏执行清单", "", f"更新时间：{_now()}", "",
+             "| # | 人物 | 状态 | 字数 | 耗时 | 文件 |",
+             "|---|------|------|------|------|------|"]
+    badge = {"succeeded": "✅完成", "failed": "❌失败", "running": "⏳进行中"}
+    for i, p in enumerate(people, 1):
+        r = manifest.get(p["name"], {})
+        st = badge.get(r.get("status", ""), "⌛排队")
+        f = r.get("file", "")
+        link = f"[{f}]({f})" if r.get("status") == "succeeded" and f else "-"
+        lines.append(f"| {i} | {p['name']} | {st} | {r.get('chars','-')} | {r.get('seconds','-')} | {link} |")
+    (out_dir / "index.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+if __name__ == "__main__":
+    main()

batch-distill/people.example.csv

@@ -0,0 +1,4 @@
+姓名,领域(可选提示),资料链接或备注(可选)
+查理·芒格,投资/多元思维,
+理查德·费曼,物理/教学,
+纳瓦尔·拉维坎特,创业/杠杆,https://nav.al/

frontend-web/src/core/admin/api.ts

@@ -93,15 +93,6 @@ export interface AdminLeaderboardDimension {
   last_seen_at: string;
 }
 
-export interface AdminLeaderboardQuestion {
-  text: string;
-  user_id: string;
-  email: string;
-  thread_id: string;
-  run_id: string;
-  asked_at: string;
-}
-
 export interface AdminLeaderboardTrendPoint {
   date: string;
   runs: number;
@@ -138,8 +129,6 @@ export interface AdminLeaderboardResponse {
   models: AdminLeaderboardDimension[];
   agents: AdminLeaderboardDimension[];
   run_statuses: AdminLeaderboardDimension[];
-  recent_questions: AdminLeaderboardQuestion[];
-  frequent_questions: AdminLeaderboardQuestion[];
   trends: AdminLeaderboardTrendPoint[];
 }
 
@@ -154,7 +143,6 @@ export interface AdminUserAnalyticsResponse {
   models: AdminLeaderboardDimension[];
   agents: AdminLeaderboardDimension[];
   run_statuses: AdminLeaderboardDimension[];
-  recent_questions: AdminLeaderboardQuestion[];
   trends: AdminLeaderboardTrendPoint[];
 }
 

frontend-web/src/pages/AdminUserLeaderboardPage.tsx

@@ -49,7 +49,6 @@ import { MarkdownContent } from "@/components/workspace/messages/markdown-conten
 import type {
   AdminLeaderboardDimension,
   AdminLeaderboardOverview,
-  AdminLeaderboardQuestion,
   AdminLeaderboardResponse,
   AdminLeaderboardSkill,
   AdminLeaderboardTrendPoint,
@@ -66,7 +65,7 @@ type UserRankTab = "activity" | "questions" | "tokens" | "skills" | "efficiency"
 type RangeState = { since: string; until: string; days?: number };
 type LeaderboardAccent = "crimson" | "rose" | "amber" | "teal";
 const DEFAULT_REPORT_MODEL = "__default__";
-const LEADERBOARD_PAGE_SIZE = 20;
+const LEADERBOARD_PAGE_SIZE = 10;
 
 const accentClasses: Record<LeaderboardAccent, { soft: string; text: string; bar: string; border: string }> = {
   crimson: {
@@ -132,11 +131,6 @@ function fmtTime(value?: string | null): string {
   return Number.isNaN(date.getTime()) ? value : date.toLocaleString();
 }
 
-function clampText(value: string, max = 120): string {
-  const text = value.trim();
-  return text.length > max ? `${text.slice(0, max)}...` : text;
-}
-
 function userScore(user: AdminLeaderboardUser, tab: UserRankTab): number {
   if (tab === "questions") return user.qa_count;
   if (tab === "tokens") return user.total_tokens;
@@ -207,12 +201,6 @@ function markdownList(items: string[]): string {
 
 function buildReportFocusMarkdown(data: AdminLeaderboardResponse | undefined, range: RangeState): string {
   if (!data) return "";
-  const recentQuestions = data.recent_questions
-    .slice(0, 8)
-    .map((item) => `${clampText(item.text, 120)}（${fmtTime(item.asked_at)}）`);
-  const frequentQuestions = data.frequent_questions
-    .slice(0, 8)
-    .map((item) => clampText(item.text, 120));
   const frequentUsage = [
     ...data.skills.slice(0, 5).map((item) => `技能：${item.name}，${fmtNumber(item.call_count)} 次调用`),
     ...data.tools.slice(0, 5).map((item) => `工具：${item.name}，${fmtNumber(item.call_count)} 次调用`),
@@ -223,12 +211,6 @@ function buildReportFocusMarkdown(data: AdminLeaderboardResponse | undefined, ra
     "",
     `统计周期：${range.since} 至 ${range.until}`,
     "",
-    "## 最近关注",
-    markdownList(recentQuestions),
-    "",
-    "## 高频问题",
-    markdownList(frequentQuestions),
-    "",
     "## 使用频率重点",
     markdownList(frequentUsage),
   ].join("\n");
@@ -905,43 +887,6 @@ function DimensionRanking({
   );
 }
 
-function QuestionPanel({
-  title,
-  items,
-  showUser = true,
-}: {
-  title: string;
-  items: AdminLeaderboardQuestion[];
-  showUser?: boolean;
-}) {
-  return (
-    <section className="flex h-[430px] flex-col overflow-hidden rounded-lg border border-[#F1D6D6] bg-white shadow-sm dark:border-[#213D5E] dark:bg-[#02244C]">
-      <div className="flex items-center justify-between gap-3 border-b border-[#F1D6D6] px-4 py-3 dark:border-[#213D5E]">
-        <div className="flex items-center gap-2">
-          <MessageSquareText className="size-4 text-[#5F6368] dark:text-slate-300" />
-          <h2 className="font-semibold text-[#202124] dark:text-white">{title}</h2>
-        </div>
-        <Badge variant="outline">{items.length}</Badge>
-      </div>
-      <ScrollArea className="min-h-0 flex-1">
-        <div className="divide-y divide-[#F1D6D6] dark:divide-[#213D5E]">
-          {items.length === 0 && <div className="py-12 text-center text-sm text-[#5F6368] dark:text-slate-400">暂无问题记录</div>}
-          {items.map((item, index) => (
-            <div key={`${item.run_id || item.text}-${index}`} className="px-4 py-3">
-              <div className="text-sm leading-6 text-[#202124] dark:text-slate-100">{clampText(item.text, 180)}</div>
-              <div className="mt-2 flex flex-wrap gap-x-3 gap-y-1 text-xs text-[#5F6368] dark:text-slate-400">
-                {showUser && <span>{item.email || item.user_id || "未知用户"}</span>}
-                <span>{fmtTime(item.asked_at)}</span>
-                {item.thread_id && <span>会话 {item.thread_id.slice(0, 8)}</span>}
-              </div>
-            </div>
-          ))}
-        </div>
-      </ScrollArea>
-    </section>
-  );
-}
-
 function Metric({ label, value }: { label: string; value: number }) {
   return (
     <div className="min-w-0 border-r border-[#F1D6D6] px-3 py-2 last:border-r-0 dark:border-[#213D5E]">
@@ -1006,16 +951,12 @@ function UserDetailPanel({
           </div>
         </section>
 
-        <Tabs defaultValue="questions" className="space-y-3">
+        <Tabs defaultValue="trend" className="space-y-3">
           <TabsList className="mb-3">
-            <TabsTrigger value="questions">提问</TabsTrigger>
             <TabsTrigger value="trend">趋势</TabsTrigger>
             <TabsTrigger value="skills">技能</TabsTrigger>
             <TabsTrigger value="dimensions">维度</TabsTrigger>
           </TabsList>
-          <TabsContent value="questions" className="min-h-[420px]">
-            <QuestionPanel title="最近提问" items={data.recent_questions} showUser={false} />
-          </TabsContent>
           <TabsContent value="trend" className="min-h-[420px]">
             <div className="mb-3 flex items-center gap-2 font-semibold text-[#231815] dark:text-white">
               <TrendingUp className="size-4 text-[#C1121F] dark:text-[#7FACF2]" />
@@ -1354,36 +1295,7 @@ export default function AdminUserLeaderboardPage() {
                     </Button>
                   </div>
                   {data && reportStatus === "idle" && !reportText && (
-                    <div className="mx-6 mt-4 grid shrink-0 gap-3 md:grid-cols-3">
-                      <section className="rounded-lg border border-[#F1D6D6] bg-white p-3 dark:border-[#213D5E] dark:bg-[#02244C]">
-                        <div className="mb-2 flex items-center gap-2 text-sm font-semibold text-[#231815] dark:text-white">
-                          <MessageSquareText className="size-4 text-[#C1121F] dark:text-[#7FACF2]" />
-                          最近关注
-                        </div>
-                        <div className="space-y-2">
-                          {data.recent_questions.slice(0, 3).map((item, index) => (
-                            <div key={`${item.run_id || item.text}-${index}`} className="text-xs leading-5 text-[#5F6368] dark:text-slate-300">
-                              <span className="font-medium text-[#C1121F] dark:text-[#7FACF2]">{fmtTime(item.asked_at)}</span>
-                              <span className="ml-1">{clampText(item.text, 58)}</span>
-                            </div>
-                          ))}
-                          {data.recent_questions.length === 0 && <div className="text-xs text-[#8B6B6B] dark:text-[#7FACF2]">暂无数据</div>}
-                        </div>
-                      </section>
-                      <section className="rounded-lg border border-[#F1D6D6] bg-white p-3 dark:border-[#213D5E] dark:bg-[#02244C]">
-                        <div className="mb-2 flex items-center gap-2 text-sm font-semibold text-[#231815] dark:text-white">
-                          <TrendingUp className="size-4 text-[#C1121F] dark:text-[#7FACF2]" />
-                          高频问题
-                        </div>
-                        <div className="space-y-2">
-                          {data.frequent_questions.slice(0, 3).map((item, index) => (
-                            <div key={`${item.run_id || item.text}-${index}`} className="text-xs leading-5 text-[#5F6368] dark:text-slate-300">
-                              {index + 1}. {clampText(item.text, 72)}
-                            </div>
-                          ))}
-                          {data.frequent_questions.length === 0 && <div className="text-xs text-[#8B6B6B] dark:text-[#7FACF2]">暂无数据</div>}
-                        </div>
-                      </section>
+                    <div className="mx-6 mt-4 grid shrink-0 gap-3">
                       <section className="rounded-lg border border-[#F1D6D6] bg-white p-3 dark:border-[#213D5E] dark:bg-[#02244C]">
                         <div className="mb-2 flex items-center gap-2 text-sm font-semibold text-[#231815] dark:text-white">
                           <Sparkles className="size-4 text-[#C1121F] dark:text-[#7FACF2]" />
@@ -1645,12 +1557,6 @@ export default function AdminUserLeaderboardPage() {
                   <DimensionRanking title="Agent 使用排行" icon={Bot} items={data.agents} empty="当前时间段暂无 Agent 运行" />
                   <DimensionRanking title="运行状态分布" icon={Gauge} items={data.run_statuses} empty="当前时间段暂无运行状态" />
                 </div>
-
-
-                <div className="grid gap-5 xl:grid-cols-2">
-                  <QuestionPanel title="最近经常问的问题" items={data.frequent_questions} />
-                  <QuestionPanel title="最新问题" items={data.recent_questions} />
-                </div>
               </>
             )}
           </div>

frontend-web/src/pages/PublicLeaderboardPage.tsx

@@ -34,12 +34,10 @@ import {
 } from "@/components/ui/dialog";
 import { Input } from "@/components/ui/input";
 import { Pagination } from "@/components/ui/pagination";
-import { ScrollArea } from "@/components/ui/scroll-area";
 import { Tabs, TabsList, TabsTrigger } from "@/components/ui/tabs";
 import type {
   AdminLeaderboardDimension,
   AdminLeaderboardOverview,
-  AdminLeaderboardQuestion,
   AdminLeaderboardResponse,
   AdminLeaderboardSkill,
   AdminLeaderboardTrendPoint,
@@ -52,8 +50,10 @@ type UserRankTab = "activity" | "questions" | "tokens" | "skills" | "efficiency"
 type LeaderboardAccent = "crimson" | "rose" | "amber" | "teal";
 
 const RANK_COLORS = ["#C1121F", "#E11D48", "#F59E0B", "#991B1B", "#B45309"] as const;
-const LEADERBOARD_PAGE_SIZE = 20;
-const RANGE_DAYS = 30;
+const LEADERBOARD_PAGE_SIZE = 10;
+// 公开页只统计近 7 天:跨日快照在每次请求时合并,天数越多合并越慢(尤其常问问题的
+// 模糊聚合是 O(n^2))。7 天足够展示近期热度,同时把请求耗时压到最低。
+const RANGE_DAYS = 7;
 
 const accentClasses: Record<LeaderboardAccent, { soft: string; text: string; bar: string; border: string }> = {
   crimson: {
@@ -98,11 +98,6 @@ function fmtDate(value?: string | null): string {
   return Number.isNaN(date.getTime()) ? "" : date.toLocaleString("zh-CN", { hour12: false });
 }
 
-function clampText(value: string, max = 120): string {
-  const text = value.trim();
-  return text.length > max ? `${text.slice(0, max)}...` : text;
-}
-
 function userScore(user: AdminLeaderboardUser, tab: UserRankTab): number {
   if (tab === "questions") return user.qa_count;
   if (tab === "tokens") return user.total_tokens;
@@ -367,12 +362,6 @@ export default function PublicLeaderboardPage() {
               <DimensionRanking title="Agent 使用排行" icon={Bot} items={data.agents} empty="当前时间段暂无 Agent 运行" />
               <DimensionRanking title="运行状态分布" icon={Gauge} items={data.run_statuses} empty="当前时间段暂无运行状态" />
             </div>
-
-            {/* 问题 */}
-            <div className="grid gap-5 xl:grid-cols-2">
-              <QuestionPanel title="最近经常问的问题" items={data.frequent_questions} />
-              <QuestionPanel title="最新问题" items={data.recent_questions} />
-            </div>
           </div>
         )}
 
@@ -688,40 +677,6 @@ function DimensionRanking({
   );
 }
 
-function QuestionPanel({
-  title,
-  items,
-}: {
-  title: string;
-  items: AdminLeaderboardQuestion[];
-}) {
-  return (
-    <section className="flex h-[430px] flex-col overflow-hidden rounded-lg border border-[#F1D6D6] bg-white shadow-sm dark:border-[#213D5E] dark:bg-[#02244C]">
-      <div className="flex items-center justify-between gap-3 border-b border-[#F1D6D6] px-4 py-3 dark:border-[#213D5E]">
-        <div className="flex items-center gap-2">
-          <MessageSquareText className="size-4 text-[#5F6368] dark:text-slate-300" />
-          <h2 className="font-semibold text-[#202124] dark:text-white">{title}</h2>
-        </div>
-        <Badge variant="outline">{items.length}</Badge>
-      </div>
-      <ScrollArea className="min-h-0 flex-1">
-        <div className="divide-y divide-[#F1D6D6] dark:divide-[#213D5E]">
-          {items.length === 0 && <div className="py-12 text-center text-sm text-[#5F6368] dark:text-slate-400">暂无问题记录</div>}
-          {items.map((item, index) => (
-            <div key={`${item.run_id || item.text}-${index}`} className="px-4 py-3">
-              <div className="text-sm leading-6 text-[#202124] dark:text-slate-100">{clampText(item.text, 180)}</div>
-              <div className="mt-2 flex flex-wrap gap-x-3 gap-y-1 text-xs text-[#5F6368] dark:text-slate-400">
-                <span>{item.email || item.user_id || "未知用户"}</span>
-                <span>{fmtTime(item.asked_at)}</span>
-              </div>
-            </div>
-          ))}
-        </div>
-      </ScrollArea>
-    </section>
-  );
-}
-
 function TrendBars({ points }: { points: AdminLeaderboardTrendPoint[] }) {
   type TrendMetric = keyof Pick<AdminLeaderboardTrendPoint, "runs" | "questions" | "users" | "tokens">;
   const [metric, setMetric] = useState<"all" | TrendMetric>("all");

offline-backend-20260512/backend/app/gateway/routers/admin_users.py

@@ -20,7 +20,6 @@ import os
 import re
 from collections.abc import AsyncIterator
 from contextvars import ContextVar
-from difflib import SequenceMatcher
 from hashlib import sha256
 from time import perf_counter
 from typing import Any
@@ -269,15 +268,6 @@ class AdminLeaderboardDimension(BaseModel):
     last_seen_at: str = ""
 
 
-class AdminLeaderboardQuestion(BaseModel):
-    text: str
-    user_id: str = ""
-    email: str = ""
-    thread_id: str = ""
-    run_id: str = ""
-    asked_at: str = ""
-
-
 class AdminLeaderboardTrendPoint(BaseModel):
     date: str
     runs: int = 0
@@ -314,8 +304,6 @@ class AdminLeaderboardResponse(BaseModel):
     models: list[AdminLeaderboardDimension] = Field(default_factory=list)
     agents: list[AdminLeaderboardDimension] = Field(default_factory=list)
     run_statuses: list[AdminLeaderboardDimension] = Field(default_factory=list)
-    recent_questions: list[AdminLeaderboardQuestion] = Field(default_factory=list)
-    frequent_questions: list[AdminLeaderboardQuestion] = Field(default_factory=list)
     trends: list[AdminLeaderboardTrendPoint] = Field(default_factory=list)
 
 
@@ -330,7 +318,6 @@ class AdminUserAnalyticsResponse(BaseModel):
     models: list[AdminLeaderboardDimension] = Field(default_factory=list)
     agents: list[AdminLeaderboardDimension] = Field(default_factory=list)
     run_statuses: list[AdminLeaderboardDimension] = Field(default_factory=list)
-    recent_questions: list[AdminLeaderboardQuestion] = Field(default_factory=list)
     trends: list[AdminLeaderboardTrendPoint] = Field(default_factory=list)
 
 
@@ -1014,129 +1001,6 @@ async def _query_model_rankings(
     ]
 
 
-async def _query_recent_questions(
-    session,
-    since: datetime,
-    until: datetime,
-    *,
-    user_id: str | None,
-    limit: int,
-    exclude_user_ids: set[str] | None = None,
-    include_scheduled: bool = True,
-    include_admins: bool = True,
-    include_failed: bool = True,
-    cleanup_system_questions: bool = True,
-) -> list[AdminLeaderboardQuestion]:
-    exclude_user_ids = exclude_user_ids or set()
-    stmt = (
-        select(RunRow.first_human_message, RunRow.user_id, UserRow.email, RunRow.thread_id, RunRow.run_id, RunRow.created_at)
-        .outerjoin(UserRow, UserRow.id == RunRow.user_id)
-        .where(_run_time_filter(since, until), RunRow.first_human_message.is_not(None), RunRow.first_human_message != "")
-        .order_by(RunRow.created_at.desc())
-        .limit(limit)
-    )
-    if user_id:
-        stmt = stmt.where(RunRow.user_id == user_id)
-    stmt = _apply_run_scope(
-        stmt,
-        user_column=RunRow.user_id,
-        run_id_column=RunRow.run_id,
-        exclude_user_ids=exclude_user_ids,
-        include_admins=include_admins,
-        include_failed=include_failed,
-        include_scheduled=include_scheduled,
-    )
-    rows = (await _execute_leaderboard_query(session, "recent_questions", stmt)).all()
-    questions: list[AdminLeaderboardQuestion] = []
-    for r in rows:
-        text = _clean_question_text(r[0]) if cleanup_system_questions else str(r[0] or "").strip()
-        if cleanup_system_questions and not _is_displayable_question(text):
-            continue
-        questions.append(
-            AdminLeaderboardQuestion(
-                text=text[:500],
-                user_id=r[1] or "",
-                email=r[2] or "",
-                thread_id=r[3] or "",
-                run_id=r[4] or "",
-                asked_at=_iso(r[5]),
-            )
-        )
-    return questions
-
-
-async def _query_frequent_questions(
-    session,
-    since: datetime,
-    until: datetime,
-    limit: int,
-    exclude_user_ids: set[str] | None = None,
-    include_scheduled: bool = True,
-    include_admins: bool = True,
-    include_failed: bool = True,
-    cleanup_system_questions: bool = True,
-) -> list[AdminLeaderboardQuestion]:
-    exclude_user_ids = exclude_user_ids or set()
-    stmt = (
-        select(
-            RunRow.first_human_message,
-            RunRow.user_id,
-            UserRow.email,
-            RunRow.created_at,
-        )
-        .outerjoin(UserRow, UserRow.id == RunRow.user_id)
-        .where(_run_time_filter(since, until), RunRow.first_human_message.is_not(None), RunRow.first_human_message != "")
-        .order_by(RunRow.created_at.desc())
-        .limit(min(max(limit * 20, 200), 1000))
-    )
-    stmt = _apply_run_scope(
-        stmt,
-        user_column=RunRow.user_id,
-        run_id_column=RunRow.run_id,
-        exclude_user_ids=exclude_user_ids,
-        include_admins=include_admins,
-        include_failed=include_failed,
-        include_scheduled=include_scheduled,
-    )
-    rows = (await _execute_leaderboard_query(session, "frequent_questions", stmt)).all()
-    groups: list[dict] = []
-    for r in rows:
-        text = _clean_question_text(r[0]) if cleanup_system_questions else str(r[0] or "").strip()
-        if cleanup_system_questions and not _is_displayable_question(text):
-            continue
-        key = _question_similarity_key(text)
-        if not key:
-            continue
-        target = None
-        for group in groups:
-            score = SequenceMatcher(None, key, group["key"]).ratio()
-            if score >= 0.72 or key in group["key"] or group["key"] in key:
-                target = group
-                break
-        if target is None:
-            groups.append({"key": key, "text": text, "count": 1, "user_id": r[1] or "", "email": r[2] or "", "asked_at": r[3]})
-        else:
-            target["count"] += 1
-            if r[3] and (target.get("asked_at") is None or r[3] > target["asked_at"]):
-                target.update({"text": text, "user_id": r[1] or "", "email": r[2] or "", "asked_at": r[3]})
-
-    groups.sort(key=lambda item: (item["count"], _iso(item.get("asked_at"))), reverse=True)
-    questions: list[AdminLeaderboardQuestion] = []
-    for group in groups[:limit]:
-        text = group["text"][:460]
-        if group["count"] > 1:
-            text = f"{text}（相似问题 {group['count']} 次）"
-        questions.append(
-            AdminLeaderboardQuestion(
-                text=text,
-                user_id=group["user_id"],
-                email=group["email"],
-                asked_at=_iso(group.get("asked_at")),
-            )
-        )
-    return questions
-
-
 async def _query_trends(
     session,
     since: datetime,
@@ -1306,10 +1170,6 @@ async def _build_user_leaderboard_response(
             "include_scheduled": include_scheduled,
             "include_admins": include_admins,
         }
-        question_common = {
-            **common,
-            "cleanup_system_questions": settings.cleanup_system_questions,
-        }
         overview = await _query_overview(session, since_dt, until_dt, **common)
         await _leaderboard_pause()
         users = await _query_user_leaderboard(session, since_dt, until_dt, **common)
@@ -1326,10 +1186,6 @@ async def _build_user_leaderboard_response(
         await _leaderboard_pause()
         run_statuses = await _query_run_dimensions(session, since_dt, until_dt, user_id=None, field="status", limit=limit, **common)
         await _leaderboard_pause()
-        recent_questions = await _query_recent_questions(session, since_dt, until_dt, user_id=None, limit=limit, **question_common)
-        await _leaderboard_pause()
-        frequent_questions = await _query_frequent_questions(session, since_dt, until_dt, limit=limit, **question_common)
-        await _leaderboard_pause()
         trends = await _query_trends(session, since_dt, until_dt, **common)
 
     return AdminLeaderboardResponse(
@@ -1347,8 +1203,6 @@ async def _build_user_leaderboard_response(
         models=models,
         agents=agents,
         run_statuses=run_statuses,
-        recent_questions=recent_questions,
-        frequent_questions=frequent_questions,
         trends=trends,
     )
 
@@ -1572,95 +1426,6 @@ def _merge_dimensions_from_daily(
     return sorted(merged_items, key=lambda item: (item.count, _sort_datetime(item.last_seen_at)), reverse=True)[:limit]
 
 
-def _merge_recent_questions_from_daily(
-    responses: list[AdminLeaderboardResponse],
-    limit: int,
-) -> list[AdminLeaderboardQuestion]:
-    questions: list[AdminLeaderboardQuestion] = []
-    seen: set[str] = set()
-    for response in responses:
-        for question in response.recent_questions:
-            key = question.run_id or f"{question.user_id}:{question.asked_at}:{question.text}"
-            if key in seen:
-                continue
-            seen.add(key)
-            questions.append(question)
-    questions.sort(key=lambda item: _sort_datetime(item.asked_at), reverse=True)
-    return questions[:limit]
-
-
-_FREQUENT_QUESTION_COUNT_RE = re.compile(r"（相似问题\s+(\d+)\s+次）$")
-
-
-def _split_frequent_question_count(text: str) -> tuple[str, int]:
-    match = _FREQUENT_QUESTION_COUNT_RE.search(text)
-    if not match:
-        return text, 1
-    return _FREQUENT_QUESTION_COUNT_RE.sub("", text).strip(), max(1, int(match.group(1)))
-
-
-def _merge_frequent_questions_from_daily(
-    responses: list[AdminLeaderboardResponse],
-    limit: int,
-) -> list[AdminLeaderboardQuestion]:
-    groups: list[dict] = []
-    for response in responses:
-        for question in response.frequent_questions:
-            base_text, count = _split_frequent_question_count(question.text)
-            key = _question_similarity_key(base_text)
-            if not key:
-                continue
-            target = None
-            for group in groups:
-                score = SequenceMatcher(None, key, group["key"]).ratio()
-                if score >= 0.72 or key in group["key"] or group["key"] in key:
-                    target = group
-                    break
-            if target is None:
-                groups.append(
-                    {
-                        "key": key,
-                        "text": base_text,
-                        "count": count,
-                        "user_id": question.user_id,
-                        "email": question.email,
-                        "thread_id": question.thread_id,
-                        "run_id": question.run_id,
-                        "asked_at": question.asked_at,
-                    }
-                )
-            else:
-                target["count"] += count
-                if _sort_datetime(question.asked_at) > _sort_datetime(target.get("asked_at")):
-                    target.update(
-                        {
-                            "text": base_text,
-                            "user_id": question.user_id,
-                            "email": question.email,
-                            "thread_id": question.thread_id,
-                            "run_id": question.run_id,
-                            "asked_at": question.asked_at,
-                        }
-                    )
-    groups.sort(key=lambda item: (item["count"], _sort_datetime(item.get("asked_at"))), reverse=True)
-    questions: list[AdminLeaderboardQuestion] = []
-    for group in groups[:limit]:
-        text_value = group["text"][:460]
-        if group["count"] > 1:
-            text_value = f"{text_value}（相似问题 {group['count']} 次）"
-        questions.append(
-            AdminLeaderboardQuestion(
-                text=text_value,
-                user_id=group["user_id"],
-                email=group["email"],
-                thread_id=group["thread_id"],
-                run_id=group["run_id"],
-                asked_at=group["asked_at"],
-            )
-        )
-    return questions
-
-
 def _merge_trends_from_daily(responses: list[AdminLeaderboardResponse]) -> list[AdminLeaderboardTrendPoint]:
     trends_by_date: dict[str, AdminLeaderboardTrendPoint] = {}
     for response in responses:
@@ -1719,8 +1484,6 @@ def _merge_daily_leaderboard_payloads(
         models=_merge_dimensions_from_daily(responses, "models", limit),
         agents=_merge_dimensions_from_daily(responses, "agents", limit),
         run_statuses=_merge_dimensions_from_daily(responses, "run_statuses", limit),
-        recent_questions=_merge_recent_questions_from_daily(responses, limit),
-        frequent_questions=_merge_frequent_questions_from_daily(responses, limit),
         trends=_merge_trends_from_daily(responses),
     )
 
@@ -2267,8 +2030,6 @@ async def _build_leaderboard_report_prompt(
     models = snapshot.models
     agents = snapshot.agents
     run_statuses = snapshot.run_statuses
-    recent_questions = snapshot.recent_questions
-    frequent_questions = snapshot.frequent_questions
     trends = snapshot.trends
     top_users = snapshot.users_by_activity[:12]
     context = {
@@ -2280,8 +2041,6 @@ async def _build_leaderboard_report_prompt(
         "top_models": [m.model_dump() for m in models[:12]],
         "agents": [a.model_dump() for a in agents[:12]],
         "run_statuses": [s.model_dump() for s in run_statuses[:12]],
-        "frequent_questions": [q.model_dump() for q in frequent_questions[:30]],
-        "recent_questions": [q.model_dump() for q in recent_questions[:40]],
         "trends": [t.model_dump() for t in trends[-21:]],
     }
     return (
@@ -2289,12 +2048,10 @@ async def _build_leaderboard_report_prompt(
         "报告需要有结论，不要只罗列数字；请使用 Markdown 标题、列表和表格，让前端可以直接渲染。"
         "重点要求："
         "1) 你是专门负责分析用户行为的专家，不要自称企业 AI 应用运营分析师；"
-        "2) 优先突出用户最近常用问题、最近关注的实时问题、事件、主题和业务方向，必须引用 recent_questions 中的近期问题作为依据；"
-        "3) 单独分析 frequent_questions，归纳高频关注簇，说明这些高频问题反映出的需求强度和重复频率；"
-        "4) 结合 top_users、top_skills、top_tools、top_models 和 trends，说明使用频率、使用增长、常用能力和重点用户行为画像；"
-        "5) 可以提及使用强度、关注变化和行为机会，但不要把报告重点放在系统本身的问题、故障、失败率或回答质量缺陷上；"
-        "6) 给管理员 3-5 条可执行建议，建议应围绕用户关注主题、资源配置、内容供给、培训引导和高频能力优化。"
-        "输出结构：摘要、最近关注、高频问题与频率、重点用户行为、常用能力、趋势观察、行动建议。"
+        "2) 结合 top_users、top_skills、top_tools、top_models 和 trends，说明使用频率、使用增长、常用能力和重点用户行为画像；"
+        "3) 可以提及使用强度、关注变化和行为机会，但不要把报告重点放在系统本身的问题、故障、失败率或回答质量缺陷上；"
+        "4) 给管理员 3-5 条可执行建议，建议应围绕资源配置、内容供给、培训引导和高频能力优化。"
+        "输出结构：摘要、重点用户行为、常用能力、趋势观察、行动建议。"
         "\n\n统计数据如下：\n"
         f"{json.dumps(context, ensure_ascii=False, default=str)}"
     )
@@ -2452,10 +2209,6 @@ async def get_user_analytics(
         "include_scheduled": settings.include_scheduled,
         "include_admins": settings.include_admins,
     }
-    question_common = {
-        **common,
-        "cleanup_system_questions": settings.cleanup_system_questions,
-    }
     since_dt, until_dt, range_days = _resolve_range(days, since, until)
     async with session_factory() as session:
         await _set_analytics_read_timeout(session)
@@ -2470,7 +2223,6 @@ async def get_user_analytics(
             models = await _query_run_dimensions(session, since_dt, until_dt, user_id=user_id, field="model", limit=limit, **common)
         agents = await _query_run_dimensions(session, since_dt, until_dt, user_id=user_id, field="agent", limit=limit, **common)
         run_statuses = await _query_run_dimensions(session, since_dt, until_dt, user_id=user_id, field="status", limit=limit, **common)
-        recent_questions = await _query_recent_questions(session, since_dt, until_dt, user_id=user_id, limit=limit, **question_common)
         trends = await _query_trends(session, since_dt, until_dt, user_id=user_id, **common)
 
     return AdminUserAnalyticsResponse(
@@ -2484,7 +2236,6 @@ async def get_user_analytics(
         models=models,
         agents=agents,
         run_statuses=run_statuses,
-        recent_questions=recent_questions,
         trends=trends,
     )
 

offline-backend-20260512/backend/app/gateway/routers/public_leaderboard.py

@@ -62,13 +62,6 @@ def _anonymize(resp: AdminLeaderboardResponse) -> AdminLeaderboardResponse:
             user.email = _mask_email(user.email)
             user.user_id = hashed(user.user_id)
 
-    for bucket in (resp.recent_questions, resp.frequent_questions):
-        for question in bucket:
-            question.email = _mask_email(question.email)
-            question.user_id = hashed(question.user_id)
-            question.thread_id = ""
-            question.run_id = ""
-
     return resp