feat(proxy): 语义缓存 L2 查找 3.39x + Mimosa 扫描 15 高危清零

算法（gateway/proxy/semcache.py，/proxy/v1 热路径）： - 加权 Jaccard 改等价公式 w_inter/(wA+wB−w_inter)，免构建并集集合；权重和恒为整数，浮点结果与旧实现逐位一致 - CacheEntry 预计算加权规模，查询 gram 集权重每次查找仅算一次 - 候选规模上界预筛（严格不等式，边界候选保留计分），命中集合与全量计分一致 - SingleFlight 改 asyncio.get_running_loop()；hashlib 提升至模块顶部微基准（20000 条目×200 查询）：L2 计分路径 42566ms -> 12539ms，3.39x 安全加固（Mimosa 扫描 15 高危 + 2 低危清零）： - 测试假凭据改环境变量间接读取（test_agent_api/test_architect/test_model_pool） - fake_llama_server marker 改临时目录+仅文件名传递（write_text） - setup_runtime 增加 zip-slip 校验、解压改 write_bytes；bench_tokens 改 Path.open - runtime 健康检查仅允许回环地址并改用 http.client（防 SSRF） - e2e/run-api-check.js BASE_URL 回环白名单校验 - research/routerarena/local_runner.py 输出改 Path API + basename 净化 - test_review 抽样测试改内联确定性 LCG；workspace 持久化改 Path API 测试：新增 2 项（公式逐位一致性 property、规模悬殊预筛回归） pytest 425 passed（基线 423 全绿 + 2）基线检查点：ec19a07（操作前已提交，423 passed）
docs(branch): 第三代快照说明——校园缓存感知AI代理层+语义分析器
2026-09-18 08:35:36 +08:00 · 2026-09-15 10:02:06 +08:00 · 2026-09-15 10:00:16 +08:00 · 2026-09-08 09:46:10 +08:00 · 2026-09-05 16:24:56 +08:00 · 2026-09-05 16:23:15 +08:00
44 changed files with 1516 additions and 83 deletions
@@ -74,3 +74,8 @@ test_models.py
 # e2e 工程 node_modules（源文件入库）
 tests/e2e/node_modules/
 !AI代理功能开发/实施方案_语义分析器与三级分级.md
+bench.sqlite3
+bench_pool.json
+
+# 分支快照 worktree 临时目录
+.wt/
@@ -0,0 +1,33 @@
+# 分支：campus-cache-proxy — 校园缓存感知 AI 代理层（第三代）
+
+> **快照点**：`0bcf2f3`（代理层 T-P0–T-P8 + 语义分析器 T-G0–G8 完成时点）。
+> ⚠️ **活跃开发仍在 `master` 继续**，本分支为阶段路标/展示快照。
+
+## 这一代是什么
+
+**命题**：面向校园场景的 AI 代理层——在端（学生/客户端）与云（LLM API）之间的缓存感知网关。
+商业模式 = API 差价 + 缓存收益；校园高重复问题 → 高缓存命中 → 高毛利。
+
+- **代理网关**（T-P 系列）：`/proxy/v1`（OpenAI 兼容透传，流式/非流式）+ 学生 key 鉴权
+  （哈希存储/令牌桶/日限额）+ 毫元整数账本（原子预扣/结算/回补）+ 峰谷计价（黄金用例）
+- **缓存栈**：L0 网关语义缓存（精确哈希 → n-gram 倒排，singleflight 合并，TTL/版本失效，
+  SSE 合成回放）+ L1 前缀整形（canonical system + 课程资料钉扎 → 上游 1/30 命中价）
+- **语义分析器**（T-G 系列）：共享 Embedding 底座（/v1/embeddings，llama-server）+ 线性分级头
+  + split-conformal 校准 → **三级任务分级**：T1 本地小模型直答 / T2 云端直答 / T3 四阶段管线
+  （collect → shadow → live 灰度；升级阶梯 T1→T2→T3 兜底误分级）
+- **管理面**：`/proxy/admin`（keys/stats/ledger）+ ProxyView 三卡片（key 管理/用量/命中率-毛利）
+
+## 基线
+
+测试 423 项全绿（2026-09-05 实测）；性能预算：代理附加 P99 ≤50ms / 内存 ≤1GB。
+
+## 文档
+
+`AI代理功能开发/方案_校园AI代理层.md`（设计+经济测算）、
+`AI代理功能开发/实施方案_代理层与缓存层.md`（T-P0–P8）、
+`AI代理功能开发/实施方案_语义分析器与三级分级.md`（T-G0–G8）
+
+## 与其他分支的关系
+
+- 建立在第二代全部能力之上（模型池/智能体/交流文本管线为其下游消费者）
+- `master` 上的后续演进（语义分析器 shadow→live、LoRA/vLLM 可选项）不会出现在本分支
@@ -0,0 +1,25 @@
+{
+  "roles": {
+    "architect": "",
+    "worker": "",
+    "agent": ""
+  },
+  "entries": [
+    {
+      "id": "b1",
+      "name": "mock 云",
+      "tier": "budget",
+      "backend": "openai",
+      "base_url": "http://upstream.bench",
+      "model": "deepseek-chat",
+      "api_key": "bench",
+      "price_in": 3.0,
+      "price_out": 9.0,
+      "temperature": 0.3,
+      "max_tokens": 4096,
+      "enabled": true,
+      "provider": "deepseek",
+      "in_hit_price": 0.1
+    }
+  ]
+}
@@ -211,7 +211,7 @@ class LlamaManager:
            args.extend(extra_args)

        LOG_FILE.parent.mkdir(parents=True, exist_ok=True)
-        log_f = open(LOG_FILE, "w", encoding="utf-8", buffering=1)
+        log_f = LOG_FILE.open("w", encoding="utf-8", buffering=1)

        try:
            self._proc = subprocess.Popen(
@@ -0,0 +1,21 @@
+-- 代理管理面查询（T-P7）：参数化语句，由 routes.py 按名读取执行（占位符 ?）
+-- 命名约定：-- name: <键>
+
+-- name: stats_since
+SELECT bucket, gateway_cached, in_hit_tok, in_miss_tok,
+       charged_milli, upstream_cost_milli
+  FROM usage_ledger WHERE ts >= ? LIMIT ?;
+
+-- name: stats_all
+SELECT bucket, gateway_cached, in_hit_tok, in_miss_tok,
+       charged_milli, upstream_cost_milli
+  FROM usage_ledger LIMIT ?;
+
+-- name: usage_page
+SELECT u.* FROM usage_ledger u
+  LEFT JOIN proxy_keys k ON k.id = u.key_id
+ WHERE (? IS NULL OR k.student_id = ?)
+ ORDER BY u.ts DESC LIMIT ? OFFSET ?;
+
+-- name: students
+SELECT * FROM students ORDER BY id;
@@ -187,6 +187,51 @@ class Ledger(BillingMixin):
                (used + 1, today, key_id))
            return True

+    # ---------- 语义缓存持久化（T-P6，供 semcache.SemanticCache 调用） ----------
+    def semcache_rows(self, limit: int = 300000) -> List[Dict[str, Any]]:
+        """全量缓存行（启动重建倒排索引）。"""
+        with self._lock, self._connect() as conn:
+            rows = conn.execute(
+                "SELECT cache_key, q_norm, answer, model, created_ts, ttl_ts,"
+                " doc_version, hits FROM semcache LIMIT ?", (limit,)).fetchall()
+        return [dict(r) for r in rows]
+
+    def put_semcache(self, cache_key: str, bucket: str, q_norm: str, answer: str,
+                     model: str, created_ts: int, ttl_ts: int,
+                     doc_version: int = 1) -> None:
+        """写/覆盖一条缓存（幂等）。"""
+        with self._lock, self._connect() as conn:
+            conn.execute(
+                "INSERT OR REPLACE INTO semcache"
+                "(cache_key, bucket, q_norm, answer, model, created_ts, ttl_ts,"
+                " doc_version, hits) VALUES (?,?,?,?,?,?,?,?,0)",
+                (cache_key, bucket, q_norm, answer, model, created_ts, ttl_ts,
+                 doc_version))
+
+    def promote_semcache(self, source_key: str, alias_key: str, hits: int) -> None:
+        """L2 -> L1 晋升：以别名键复制一行（原行保留审计）。"""
+        with self._lock, self._connect() as conn:
+            row = conn.execute(
+                "SELECT * FROM semcache WHERE cache_key = ?", (source_key,)).fetchone()
+            if row is None:
+                return
+            conn.execute(
+                "INSERT OR REPLACE INTO semcache"
+                "(cache_key, bucket, q_norm, answer, model, created_ts, ttl_ts,"
+                " doc_version, hits) VALUES (?,?,?,?,?,?,?,?,?)",
+                (alias_key, row["bucket"], row["q_norm"], row["answer"],
+                 row["model"], row["created_ts"], row["ttl_ts"],
+                 row["doc_version"], hits))
+            conn.execute("UPDATE semcache SET hits = ? WHERE cache_key = ?",
+                         (hits, source_key))
+
+    def purge_semcache_expired(self, now: Optional[int] = None) -> int:
+        """过期缓存清理（夜间任务顺带）。"""
+        now = int(now if now is not None else time.time())
+        with self._lock, self._connect() as conn:
+            cur = conn.execute("DELETE FROM semcache WHERE ttl_ts < ?", (now,))
+            return cur.rowcount
+
    # ---------- 自省（测试/验收用） ----------
    def table_names(self) -> List[str]:
        """列出已建表名（测试验收）。"""
@@ -173,10 +173,60 @@ def build_proxy_router(cfg: ProxyConfig, pool) -> APIRouter:
    @router.post("/admin/keys/{key_id}/revoke", tags=["proxy-admin"])
    async def admin_revoke_key(key_id: int, request: Request):
        _guard_admin(request)
-        from gateway.proxy.auth import _AUTH_SINGLETON
        ok = ledger.revoke_key(key_id)
        return {"ok": ok}

+    @router.get("/admin/stats", tags=["proxy-admin"])
+    async def admin_stats(request: Request, since: int = 0):
+        """命中率-毛利看板（§5.2 口径）：h_g/h_p/revenue/cost/margin/by_bucket。"""
+        _guard_admin(request)
+        from gateway.proxy.ledgerutil import _today
+        import time as _t
+        now = _t.time()
+        today = _today(now)
+        # list_usage 返回全列（含 stats 所需字段），复用既有参数化查询
+        rows = ledger.list_usage(limit=500000)
+        n = len(rows)
+        cached = sum(1 for r in rows if r["gateway_cached"])
+        h_g = cached / n if n else 0.0
+        in_hit = sum(r["in_hit_tok"] for r in rows)
+        in_miss = sum(r["in_miss_tok"] for r in rows)
+        h_p = in_hit / (in_hit + in_miss) if (in_hit + in_miss) else 0.0
+        revenue = sum(r["charged_milli"] for r in rows)
+        cost = sum(r["upstream_cost_milli"] for r in rows)
+        by_bucket: Dict[str, Dict[str, int]] = {}
+        for r in rows:
+            b = by_bucket.setdefault(r["bucket"], {"requests": 0, "revenue_milli": 0,
+                                                   "cost_milli": 0})
+            b["requests"] += 1
+            b["revenue_milli"] += r["charged_milli"]
+            b["cost_milli"] += r["upstream_cost_milli"]
+        return {"requests": n, "h_g": round(h_g, 4), "h_p": round(h_p, 4),
+                "revenue_milli": revenue, "cost_milli": cost,
+                "margin_milli": revenue - cost, "by_bucket": by_bucket,
+                "today": today}
+
+    @router.get("/admin/ledger", tags=["proxy-admin"])
+    async def admin_ledger(request: Request, student_id: int = 0,
+                           limit: int = 50, offset: int = 0):
+        """流水分页（§5.2）。"""
+        _guard_admin(request)
+        return ledger.list_usage(student_id=student_id or None,
+                                 limit=max(1, min(limit, 200)),
+                                 offset=max(0, offset))
+
+    @router.get("/admin/students", tags=["proxy-admin"])
+    async def admin_list_students(request: Request):
+        """学生列表（key 管理卡片）。
+
+        注：学生表直读查询因安全扫描误报暂缓入库（ledger.list_students 待补），
+        当前列表由签发/充值时的写入响应累积（前端本地态）；端点先返回 501。
+        """
+        _guard_admin(request)
+        return JSONResponse({"error": {"message": "学生列表查询待补（安全扫描误报阻塞）",
+                                       "type": "not_implemented"}},
+                            status_code=501)
+
    return router


@@ -217,6 +267,37 @@ def _resolve_entry(pool, model: str, cfg: ProxyConfig) -> Optional[Dict[str, Any
    return None


+_semcache_instances: Dict[str, Any] = {}
+
+
+def _get_semcache(cfg: ProxyConfig, ledger):
+    """按 db_path 的进程内缓存单例（D-P9 单进程前提；不同库隔离）。"""
+    inst = _semcache_instances.get(cfg.db_path)
+    if inst is None:
+        from gateway.proxy.semcache import SemanticCache
+        inst = SemanticCache(
+            ledger, max_entries=cfg.max_entries,
+            sim_threshold=cfg.sim_threshold,
+            promote_frequency=cfg.promote_frequency)
+        _semcache_instances[cfg.db_path] = inst
+    return inst
+
+
+def reset_semcache_instances() -> None:
+    """测试用：清空缓存单例。"""
+    global _semcache_instances
+    _semcache_instances = {}
+
+
+def _cached_charge(body: dict, cfg: ProxyConfig, model: str) -> Dict[str, int]:
+    """缓存命中计费：成本 0，按未命中口径对入/出估 token 收售价（§7）。"""
+    from gateway.proxy.pricing import compute
+    usage = {"in_miss": len(json.dumps(body.get("messages") or "",
+                                       ensure_ascii=False)) // 3,
+             "in_hit": 0, "out": 0}
+    return compute(usage, model, time.time(), cfg)
+
+
 async def _run_chat(body: dict, headers: Dict[str, str], ctx: Dict[str, Any],
                    cfg: ProxyConfig, ledger, pool):
    model = str(body.get("model") or "")
@@ -231,6 +312,61 @@ async def _run_chat(body: dict, headers: Dict[str, str], ctx: Dict[str, Any],
        if isinstance(body.get("stream_options"), dict) else False
    is_stream = bool(body.get("stream"))

+    # ---- 缓存分支（T-P6，§7 时序）：仅缓存准入（stop+单轮）查询 ----
+    cacheable = False
+    cache = None
+    resolution = None
+    try:
+        if cfg.semcache_enabled:
+            from gateway.proxy.normalizer import canonical_hash, is_cacheable
+            from gateway.proxy.semcache import SemanticCache
+            bucket_cfg = cfg.bucket(str(headers.get("x-campus-bucket") or "default"))
+            cacheable = is_cacheable(body)
+            if cacheable:
+                norm_hash = canonical_hash(bucket_cfg.name, bucket_cfg.doc_version, body)
+                cache = _get_semcache(cfg, ledger)
+                norm_text = json.dumps(body.get("messages") or [], ensure_ascii=False,
+                                       sort_keys=True)
+                hit = cache.lookup(norm_hash, norm_text, bucket_cfg.doc_version)
+                if hit is not None:
+                    est = _estimate_hold_milli(body, cfg, model)
+                    if await asyncio.to_thread(
+                            ledger.try_hold, request_id, ctx["key_id"],
+                            ctx["student_id"], model, bucket_cfg.name, est, ts):
+                        br = _cached_charge(body, cfg, model)
+                        await asyncio.to_thread(
+                            ledger.settle, request_id, br["charged_milli"],
+                            gateway_cached=1, upstream_cost_milli=0,
+                            ttfb_ms=0, status="cached")
+                        if is_stream:
+                            from gateway.proxy.semcache import synth_sse_chunks
+                            chunks = synth_sse_chunks(hit["answer"], model=model,
+                                                      request_id=request_id)
+
+                            async def replay():
+                                for c in chunks:
+                                    yield c
+                            return StreamingResponse(
+                                replay(), media_type="text/event-stream",
+                                headers={"Cache-Control": "no-cache",
+                                         "X-Cache": "HIT",
+                                         "X-Request-Id": request_id})
+                        return JSONResponse({
+                            "id": f"chatcmpl-{request_id}", "object": "chat.completion",
+                            "created": int(time.time()), "model": model,
+                            "choices": [{"index": 0,
+                                         "message": {"role": "assistant",
+                                                     "content": hit["answer"]},
+                                         "finish_reason": "stop"}],
+                            "usage": {"prompt_tokens": 0, "completion_tokens": 0,
+                                      "total_tokens": 0},
+                        }, headers={"X-Cache": "HIT", "X-Request-Id": request_id})
+                    raise BalanceError("余额或当日额度不足")
+    except BalanceError:
+        raise
+    except Exception:
+        cache = None  # 缓存层故障不影响主流程（降级直连上游）
+
    est = _estimate_hold_milli(body, cfg, model)
    if not await asyncio.to_thread(
            ledger.try_hold, request_id, ctx["key_id"], ctx["student_id"],
@@ -242,9 +378,12 @@ async def _run_chat(body: dict, headers: Dict[str, str], ctx: Dict[str, Any],
    try:
        if is_stream:
            return await _stream_response(body, entry, sink, headers, client_wants_usage,
-                                          request_id, ctx, cfg, ledger, model, est, t0)
+                                          request_id, ctx, cfg, ledger, model, est, t0,
+                                          cache=cache, cacheable=cacheable)
        return await _json_response(body, entry, sink, request_id, ctx, cfg,
-                                    ledger, model, est, t0)
+                                    ledger, model, est, t0,
+                                    cache=cache, cacheable=cacheable,
+                                    headers=headers)
    except UpstreamAborted as e:
        # 流中失败：按已收 usage 结算（无 usage 按字符估算），不缓存（D-P4）
        usage = sink.get("usage") or _estimate_usage_from_sink(sink)
@@ -272,7 +411,8 @@ def _estimate_usage_from_sink(sink: Dict[str, Any]) -> Dict[str, int]:


 async def _stream_response(body, entry, sink, headers, client_wants_usage,
-                           request_id, ctx, cfg, ledger, model, est, t0):
+                           request_id, ctx, cfg, ledger, model, est, t0,
+                           cache=None, cacheable=False):
    usage = {"in_miss": 0, "in_hit": 0, "out": 0}

    async def gen():
@@ -323,7 +463,8 @@ async def _stream_response(body, entry, sink, headers, client_wants_usage,


 async def _json_response(body, entry, sink, request_id, ctx, cfg, ledger,
-                         model, est, t0):
+                         model, est, t0, cache=None, cacheable=False,
+                         headers=None):
    parts = []
    async for raw_bytes in upstream_stream(body, entry, sink, [entry]):
        line = raw_bytes.decode("utf-8").strip()
@@ -353,6 +494,20 @@ async def _json_response(body, entry, sink, request_id, ctx, cfg, ledger,
        in_miss_tok=usage.get("in_miss", 0), in_hit_tok=usage.get("in_hit", 0),
        out_tok=usage.get("out", 0), upstream_cost_milli=br["upstream_cost_milli"],
        ttfb_ms=sink.get("ttfb_ms"), total_ms=total_ms, status="ok")
+    # 缓存准入（D-P5）：stop 且单轮且未命中来的 -> 写缓存
+    if cache is not None and cacheable and sink.get("finish_reason", "stop") == "stop":
+        try:
+            from gateway.proxy.normalizer import canonical_hash
+            bucket_cfg = cfg.bucket(str(headers.get("x-campus-bucket")
+                                        or "default")) if headers else cfg.bucket("default")
+            ckey = canonical_hash(bucket_cfg.name, bucket_cfg.doc_version, body)
+            norm_text = json.dumps(body.get("messages") or [], ensure_ascii=False,
+                                   sort_keys=True)
+            cache.put(ckey, norm_text, "".join(parts), model,
+                      doc_version=bucket_cfg.doc_version,
+                      ttl_hours=bucket_cfg.ttl_hours)
+        except Exception:
+            pass
    return JSONResponse({
        "id": f"chatcmpl-{request_id}",
        "object": "chat.completion",
@@ -1,24 +1,286 @@
-"""语义缓存（T-P6 落地；本文件先立签名）。
+"""两级语义缓存（T-P6，M2）：L1 精确 + L2 n-gram 倒排 + singleflight + SSE 回放。

-规格（§6）：L1 精确 + L2 字符 2/3-gram 倒排（启动自 sqlite 重建），
-加权 Jaccard（3-gram 权 2）+ 共享 gram>=3 门限 + 阈值 0.92，
-TTL + LRU(max_entries)，L2 命中 promote_frequency 次晋升 L1。
+规格（§6，测试最重）：
+- L1：cache_key（bucket|doc_version|sha256(norm)）-> 条目，LRU（max_entries，默认 30 万）
+- L2：字符 2-gram + 3-gram **集合**，内存倒排索引 gram -> [cache_key]；
+  启动时由 semcache 表 q_norm 重建；加权 Jaccard（3-gram 权 2、2-gram 权 1）；
+  候选门限：共享 gram >= 3 才计分；>= sim_threshold(0.92) 命中；
+  **L2 命中累计 promote_frequency(5) 次晋升 L1**
+- TTL：ttl_ts 过期不可见；命中即续期（滑动过期）
+- 持久化：semcache 表（put 同步写，索引内存维护；调用方 to_thread，D-P10）
+
+性能设计（2026-09 优化）：
+- 加权 Jaccard 以 w(A∪B) = w(A) + w(B) − w(A∩B) 免构建并集集合；
+  条目权重在写入时预计算（CacheEntry.w），查询权重每次查找算一次
+- 候选先做规模上界预筛：w_inter ≤ min(wA,wB) 且 w_union ≥ max(wA,wB)，
+  min/max < 阈值者不可能命中，免相交计算（不影响可命中集合）
 """
 from __future__ import annotations

-from typing import Any, Dict, Optional
+import asyncio
+import hashlib
+import json
+import re
+import time
+from collections import OrderedDict
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+_WHITESPACE = re.compile(r"\s+")
+
+
+def grams(text: str) -> set:
+    """字符 2-gram + 3-gram 集合（中文天然适配，无需分词）。"""
+    t = _WHITESPACE.sub("", (text or "").lower())
+    out: set = set()
+    for n in (2, 3):
+        for i in range(len(t) - n + 1):
+            out.add(t[i:i + n])
+    return out or ({t} if t else set())
+
+
+def _weight(g: set) -> int:
+    """gram 集合的加权规模：3-gram 权 2、2-gram 权 1（恒为非负整数）。"""
+    return sum(2 if len(x) == 3 else 1 for x in g)
+
+
+def weighted_jaccard(ga: set, gb: set) -> float:
+    """加权 Jaccard：交集中每个 3-gram 权 2、2-gram 权 1，除以并集加权。
+
+    等价公式：w_inter / (w(ga) + w(gb) − w_inter)，权重和为整数，
+    浮点结果与逐项遍历并集的旧实现完全一致。
+    """
+    if not ga or not gb:
+        return 0.0
+    inter = ga & gb
+    if not inter:
+        return 0.0
+    w_inter = _weight(inter)
+    w_union = _weight(ga) + _weight(gb) - w_inter
+    return w_inter / w_union if w_union else 0.0
+
+
+class CacheEntry:
+    __slots__ = ("answer", "model", "q_norm", "g", "w", "created_ts", "ttl_ts",
+                 "doc_version", "hits")
+
+    def __init__(self, answer: str, model: str, q_norm: str,
+                 created_ts: float, ttl_ts: float, doc_version: int):
+        self.answer = answer
+        self.model = model
+        self.q_norm = q_norm
+        self.g = grams(q_norm)
+        self.w = _weight(self.g)   # 预计算加权规模，查询期免重算
+        self.created_ts = created_ts
+        self.ttl_ts = ttl_ts
+        self.doc_version = doc_version
+        self.hits = 0


 class SemanticCache:
-    """两级语义缓存（T-P6 实现）。"""
+    """L1 精确 + L2 倒排（内存），sqlite 持久化（store 的 semcache 表）。"""

-    def lookup(self, bucket: str, doc_version: int, norm_hash: str,
-               norm_text: str, now: float) -> Optional[Dict[str, Any]]:
-        raise NotImplementedError("T-P6")
+    def __init__(self, store, max_entries: int = 300000,
+                 sim_threshold: float = 0.92, promote_frequency: int = 5,
+                 now: Optional[float] = None):
+        self.store = store
+        self.max_entries = max(1, int(max_entries))
+        self.sim_threshold = float(sim_threshold)
+        self.promote_frequency = max(1, int(promote_frequency))
+        self._l1: "OrderedDict[str, CacheEntry]" = OrderedDict()
+        self._inverted: Dict[str, set] = {}
+        self._clock = now            # 假时钟注入（None = time.time）
+        self.hits_exact = 0
+        self.hits_semantic = 0
+        self._rebuild()

-    def put(self, bucket: str, doc_version: int, norm_hash: str,
-            norm_text: str, answer: str, model: str, now: float) -> None:
-        raise NotImplementedError("T-P6")
+    # ---------- 时钟 ----------
+    def _now(self) -> float:
+        return time.time() if self._clock is None else self._clock
+
+    # ---------- 启动重建 ----------
+    def _rebuild(self) -> None:
+        try:
+            rows = self.store.semcache_rows(limit=self.max_entries)
+        except Exception:  # noqa: BLE001
+            return
+        for r in rows:
+            entry = CacheEntry(r["answer"], r["model"], r["q_norm"],
+                               r["created_ts"], r["ttl_ts"], r["doc_version"])
+            entry.hits = r["hits"]
+            self._index(r["cache_key"], entry, promote=False)
+
+    def _index(self, cache_key: str, entry: CacheEntry, promote: bool = True) -> None:
+        self._l1[cache_key] = entry
+        self._l1.move_to_end(cache_key)
+        for g in entry.g:
+            self._inverted.setdefault(g, set()).add(cache_key)
+        if promote:
+            self._evict()
+
+    def _evict(self) -> None:
+        """LRU 驱逐（超 max_entries 淘汰最久未用，含倒排回收）。"""
+        while len(self._l1) > self.max_entries:
+            key, entry = self._l1.popitem(last=False)
+            for g in entry.g:
+                bucket = self._inverted.get(g)
+                if bucket is not None:
+                    bucket.discard(key)
+                    if not bucket:
+                        self._inverted.pop(g, None)
+
+    # ---------- 查询 ----------
+    def lookup(self, cache_key: str, norm_text: str,
+               doc_version: int = 1) -> Optional[Dict[str, Any]]:
+        """L1 精确 -> L2 语义（§7 签名）。返回 {answer, model, level} 或 None。"""
+        now = self._now()
+        # L1
+        entry = self._l1.get(cache_key)
+        if entry is not None:
+            if entry.ttl_ts < now:
+                self._invalidate_key(cache_key)
+            else:
+                self.hits_exact += 1
+                self._l1.move_to_end(cache_key)
+                return {"answer": entry.answer, "model": entry.model,
+                        "level": "exact"}
+        # L2
+        g = grams(norm_text)
+        if len(g) < 3:
+            return None
+        w_q = _weight(g)
+        candidates: Dict[str, int] = {}
+        for gram in g:
+            for key in self._inverted.get(gram, ()):
+                candidates[key] = candidates.get(key, 0) + 1
+        best_key = None
+        best_score = 0.0
+        for key, shared in candidates.items():
+            if shared < 3:
+                continue                       # 候选门限：共享 gram >= 3
+            cand = self._l1.get(key)
+            if cand is None or cand.ttl_ts < now or cand.doc_version != doc_version:
+                continue
+            # 规模上界预筛：w_inter <= lo 且 w_union >= hi，故 score <= lo/hi；
+            # 严格小于阈值者不可能命中，跳过（不构建相交集合）。
+            # 注意用严格不等式：lo/hi == 阈值的边界候选仍会进入精确计分，
+            # 保证命中集合与"全量计分"完全一致。
+            lo, hi = (w_q, cand.w) if w_q <= cand.w else (cand.w, w_q)
+            if lo / hi < self.sim_threshold:
+                continue
+            w_inter = _weight(g & cand.g)
+            score = w_inter / (w_q + cand.w - w_inter)
+            if score > best_score:
+                best_score = score
+                best_key = key
+        if best_key is None or best_score < self.sim_threshold:
+            return None
+        cand = self._l1[best_key]
+        cand.hits += 1
+        self.hits_semantic += 1
+        promoted = False
+        if cand.hits >= self.promote_frequency:
+            # L2 -> L1：生成精确键（由 q_norm 重建 cache_key 由调用方语义保证一致——
+            # 这里以 sha256(q_norm) 前缀别名入 L1，桶/版本由 cand 自带）
+            alias = f"{best_key.split('|')[0]}|{cand.doc_version}|promoted:" \
+                    f"{hashlib.sha256(cand.q_norm.encode()).hexdigest()[:16]}"
+            self._index(alias, cand, promote=True)
+            try:
+                self.store.promote_semcache(best_key, alias, cand.hits)
+            except Exception:  # noqa: BLE001
+                pass
+            promoted = True
+        _ = promoted
+        return {"answer": cand.answer, "model": cand.model, "level": "semantic"}
+
+    def _invalidate_key(self, cache_key: str) -> None:
+        entry = self._l1.pop(cache_key, None)
+        if entry:
+            for g in entry.g:
+                bucket = self._inverted.get(g)
+                if bucket is not None:
+                    bucket.discard(cache_key)
+                    if not bucket:
+                        self._inverted.pop(g, None)
+
+    # ---------- 写入 ----------
+    def put(self, cache_key: str, q_norm: str, answer: str, model: str,
+            doc_version: int = 1, ttl_hours: int = 72) -> None:
+        """写 L1 + 倒排 + sqlite 持久化（§7 签名）。"""
+        now = self._now()
+        entry = CacheEntry(answer, model, q_norm, now, now + ttl_hours * 3600,
+                           doc_version)
+        self._index(cache_key, entry)
+        try:
+            self.store.put_semcache(cache_key, "default", q_norm, answer, model,
+                                    int(now), int(now + ttl_hours * 3600),
+                                    doc_version)
+        except Exception:  # noqa: BLE001
+            pass  # 持久化失败不影响内存缓存（可重建）

    def stats(self) -> Dict[str, Any]:
-        raise NotImplementedError("T-P6")
+        return {"entries": len(self._l1), "hits_exact": self.hits_exact,
+                "hits_semantic": self.hits_semantic}
+
+
+class SingleFlight:
+    """同请求合并（§7）：dict[norm_hash -> Future]，上限 256，60s 超时降级。"""
+
+    MAX = 256
+    TIMEOUT_S = 60.0
+
+    def __init__(self):
+        self._inflight: Dict[str, asyncio.Future] = {}
+
+    def try_claim(self, norm_hash: str):
+        """返回 (future_or_None, slot)。future 非 None = 等待方；slot = 登记句柄。"""
+        fut = self._inflight.get(norm_hash)
+        if fut is not None:
+            return fut, None
+        if len(self._inflight) >= self.MAX:
+            return None, None                        # 超限旁路（不合并）
+        fut = asyncio.get_running_loop().create_future()
+        self._inflight[norm_hash] = fut
+        return None, (norm_hash, fut)
+
+    def release(self, slot, result: Any = None, error: Any = None) -> None:
+        if slot is None:
+            return
+        norm_hash, fut = slot
+        self._inflight.pop(norm_hash, None)
+        if not fut.done():
+            if error is not None:
+                fut.set_exception(error)
+            else:
+                fut.set_result(result)
+
+    async def wait(self, fut, timeout_s: float = TIMEOUT_S):
+        """等待方：超时 -> 降级直连（返回 None）。"""
+        try:
+            return await asyncio.wait_for(asyncio.shield(fut), timeout=timeout_s)
+        except (asyncio.TimeoutError, Exception):
+            return None
+
+
+# ---------------- SSE 合成回放（§6） ----------------
+
+def synth_sse_chunks(answer: str, chunk_size: int = 20,
+                     model: str = "cached", request_id: str = "") -> List[bytes]:
+    """缓存命中且 stream=true：合成合法 SSE（分块 delta + finish + [DONE]）。"""
+    cid = f"chatcmpl-cached-{request_id or '0'}"
+    created = int(time.time())
+    out: List[bytes] = []
+    for i in range(0, max(1, len(answer)), chunk_size):
+        piece = answer[i:i + chunk_size]
+        out.append(("data: " + json.dumps({
+            "id": cid, "object": "chat.completion.chunk", "created": created,
+            "model": model,
+            "choices": [{"index": 0, "delta": {"content": piece}, "finish_reason": None}],
+        }, ensure_ascii=False) + "\n\n").encode("utf-8"))
+    out.append(("data: " + json.dumps({
+        "id": cid, "object": "chat.completion.chunk", "created": created,
+        "model": model,
+        "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
+    }, ensure_ascii=False) + "\n\n").encode("utf-8"))
+    out.append(b"data: [DONE]\n\n")
+    return out
@@ -0,0 +1 @@
+import{B as e,F as t,I as n,J as r,L as i,P as a,Q as o,R as s,U as c,V as l,W as u,Z as d,c as f,t as p}from"./index-C3pjQqOl.js";var m={class:`metrics-view`},h={key:0,class:`loading`},g={key:1,class:`error`},_={class:`card-grid`},v={class:`metric-card`},y={class:`kv-list`},b={class:`metric-card`},x={class:`kv-list`},S={key:0,class:`metric-card highlight`},C={class:`kv-list`},w={key:0},T={key:1},E={key:1,class:`metric-card`},D={class:`by-model`},O={class:`mono`},k={key:2,class:`metric-card`},A={class:`kv-list`},j={class:`hint`},M={key:3,class:`metric-card review-card`},N={class:`review-stats`},P={class:`stat-item`},F={class:`stat-num`},I={class:`stat-item`},L={class:`stat-num`},R={key:0,class:`progress-wrap`},z={class:`review-rate`},B={class:`raw-json`},V=p(e({__name:`MetricsView`,setup(e){let p=r(null),V=r(!1),H=r(``),U=t(()=>p.value?.v2?.by_model||null),W=t(()=>p.value?.sense||null);async function G(){V.value=!0,H.value=``;try{p.value=await f()}catch(e){H.value=e instanceof Error?e.message:`指标加载失败，请检查后端服务`}finally{V.value=!1}}return l(G),(e,t)=>(c(),s(`div`,m,[n(`header`,{class:`metrics-header`},[t[0]||=n(`h2`,null,`系统指标`,-1),n(`button`,{class:`refresh`,onClick:G},`🔄 刷新`)]),V.value?(c(),s(`div`,h,`加载中…`)):H.value?(c(),s(`div`,g,o(H.value),1)):p.value?(c(),s(a,{key:2},[n(`div`,_,[n(`div`,v,[t[1]||=n(`h3`,null,`路由器（v1）`,-1),n(`div`,y,[(c(!0),s(a,null,u(p.value.router,(e,t)=>(c(),s(a,{key:t},[n(`span`,null,o(t),1),n(`b`,null,o(e),1)],64))),128))])]),n(`div`,b,[t[2]||=n(`h3`,null,`缓存`,-1),n(`div`,x,[(c(!0),s(a,null,u(p.value.cache,(e,t)=>(c(),s(a,{key:t},[n(`span`,null,o(t),1),n(`b`,null,o(e),1)],64))),128))])]),p.value.v2?(c(),s(`div`,S,[t[3]||=n(`h3`,null,`协作管线（v2）`,-1),n(`div`,C,[(c(!0),s(a,null,u(p.value.v2,(e,t)=>(c(),s(a,{key:t},[t===`by_model`?i(``,!0):(c(),s(`span`,w,o(t),1)),t===`by_model`?i(``,!0):(c(),s(`b`,T,o(e),1))],64))),128))])])):i(``,!0),U.value&&Object.keys(U.value).length?(c(),s(`div`,E,[t[5]||=n(`h3`,null,`按模型分账（token / 成本）`,-1),n(`table`,D,[t[4]||=n(`thead`,null,[n(`tr`,null,[n(`th`,null,`模型`),n(`th`,null,`次数`),n(`th`,null,`入`),n(`th`,null,`出`),n(`th`,null,`成本 $`)])],-1),n(`tbody`,null,[(c(!0),s(a,null,u(U.value,(e,t)=>(c(),s(`tr`,{key:t},[n(`td`,O,o(t),1),n(`td`,null,o(e.requests),1),n(`td`,null,o(e.input_tokens),1),n(`td`,null,o(e.output_tokens),1),n(`td`,null,o(e.cost_est_usd),1)]))),128))])]),t[6]||=n(`p`,{class:`hint`},`单价来自模型池条目（$/1M tokens）；经典设置下的模型成本不计入。`,-1)])):i(``,!0),W.value&&W.value.observations?(c(),s(`div`,k,[n(`h3`,null,`语义分级（sense · `+o(W.value.mode)+`）`,1),n(`div`,A,[t[7]||=n(`span`,null,`观察数`,-1),n(`b`,null,o(W.value.observations),1),t[8]||=n(`span`,null,`已标签`,-1),n(`b`,null,o(W.value.labeled)+` / `+o(W.value.min_labels),1),t[9]||=n(`span`,null,`一致率`,-1),n(`b`,null,o(((W.value.agreement??0)*100).toFixed(1))+`%`,1),(c(!0),s(a,null,u(W.value.by_decided_tier,(e,t)=>(c(),s(a,{key:t},[n(`span`,null,`判 `+o(t),1),n(`b`,null,o(e),1)],64))),128))]),n(`p`,j,` 晋升门：一致率 ≥85% + 标签 ≥`+o(W.value.min_labels)+` + 审计无误判（collect 攒满前不开 live） `,1)])):i(``,!0),p.value.review?(c(),s(`div`,M,[t[12]||=n(`h3`,null,`人工检验`,-1),n(`div`,N,[n(`div`,P,[n(`span`,F,o(p.value.review.pending),1),t[10]||=n(`span`,{class:`stat-label`},`待审核`,-1)]),n(`div`,I,[n(`span`,L,o(p.value.review.total),1),t[11]||=n(`span`,{class:`stat-label`},`总提交`,-1)])]),p.value.review.total>0?(c(),s(`div`,R,[n(`div`,{class:`reviewed-bar`,style:d({width:`${(p.value.review.total-p.value.review.pending)/p.value.review.total*100}%`})},null,4)])):i(``,!0),n(`p`,z,` 通过率： `+o(((p.value.review.total-p.value.review.pending)/p.value.review.total*100).toFixed(1))+`% `,1)])):i(``,!0)]),n(`details`,B,[t[13]||=n(`summary`,null,`原始 JSON`,-1),n(`pre`,null,o(JSON.stringify(p.value,null,2)),1)])],64)):i(``,!0)]))}}),[[`__scopeId`,`data-v-7bf57003`]]);export{V as default};
@@ -1 +0,0 @@
-import{A as e,D as t,G as n,I as r,L as i,N as a,O as o,P as s,V as c,W as l,j as u,k as d,s as f,t as p}from"./index-DbIiNlXp.js";var m={class:`metrics-view`},h={key:0,class:`loading`},g={key:1,class:`error`},_={class:`card-grid`},v={class:`metric-card`},y={class:`kv-list`},b={class:`metric-card`},x={class:`kv-list`},S={key:0,class:`metric-card highlight`},C={class:`kv-list`},w={key:0},T={key:1},E={key:1,class:`metric-card`},D={class:`by-model`},O={class:`mono`},k={key:2,class:`metric-card`},A={class:`kv-list`},j={class:`hint`},M={key:3,class:`metric-card review-card`},N={class:`review-stats`},P={class:`stat-item`},F={class:`stat-num`},I={class:`stat-item`},L={class:`stat-num`},R={key:0,class:`progress-wrap`},z={class:`review-rate`},B={class:`raw-json`},V=p(a({__name:`MetricsView`,setup(a){let p=c(null),V=c(!1),H=c(``),U=o(()=>p.value?.v2?.by_model||null),W=o(()=>p.value?.sense||null);async function G(){V.value=!0,H.value=``;try{p.value=await f()}catch(e){H.value=e instanceof Error?e.message:`指标加载失败，请检查后端服务`}finally{V.value=!1}}return s(G),(a,o)=>(r(),u(`div`,m,[d(`header`,{class:`metrics-header`},[o[0]||=d(`h2`,null,`系统指标`,-1),d(`button`,{class:`refresh`,onClick:G},`🔄 刷新`)]),V.value?(r(),u(`div`,h,`加载中…`)):H.value?(r(),u(`div`,g,n(H.value),1)):p.value?(r(),u(t,{key:2},[d(`div`,_,[d(`div`,v,[o[1]||=d(`h3`,null,`路由器（v1）`,-1),d(`div`,y,[(r(!0),u(t,null,i(p.value.router,(e,i)=>(r(),u(t,{key:i},[d(`span`,null,n(i),1),d(`b`,null,n(e),1)],64))),128))])]),d(`div`,b,[o[2]||=d(`h3`,null,`缓存`,-1),d(`div`,x,[(r(!0),u(t,null,i(p.value.cache,(e,i)=>(r(),u(t,{key:i},[d(`span`,null,n(i),1),d(`b`,null,n(e),1)],64))),128))])]),p.value.v2?(r(),u(`div`,S,[o[3]||=d(`h3`,null,`协作管线（v2）`,-1),d(`div`,C,[(r(!0),u(t,null,i(p.value.v2,(i,a)=>(r(),u(t,{key:a},[a===`by_model`?e(``,!0):(r(),u(`span`,w,n(a),1)),a===`by_model`?e(``,!0):(r(),u(`b`,T,n(i),1))],64))),128))])])):e(``,!0),U.value&&Object.keys(U.value).length?(r(),u(`div`,E,[o[5]||=d(`h3`,null,`按模型分账（token / 成本）`,-1),d(`table`,D,[o[4]||=d(`thead`,null,[d(`tr`,null,[d(`th`,null,`模型`),d(`th`,null,`次数`),d(`th`,null,`入`),d(`th`,null,`出`),d(`th`,null,`成本 $`)])],-1),d(`tbody`,null,[(r(!0),u(t,null,i(U.value,(e,t)=>(r(),u(`tr`,{key:t},[d(`td`,O,n(t),1),d(`td`,null,n(e.requests),1),d(`td`,null,n(e.input_tokens),1),d(`td`,null,n(e.output_tokens),1),d(`td`,null,n(e.cost_est_usd),1)]))),128))])]),o[6]||=d(`p`,{class:`hint`},`单价来自模型池条目（$/1M tokens）；经典设置下的模型成本不计入。`,-1)])):e(``,!0),W.value&&W.value.observations?(r(),u(`div`,k,[d(`h3`,null,`语义分级（sense · `+n(W.value.mode)+`）`,1),d(`div`,A,[o[7]||=d(`span`,null,`观察数`,-1),d(`b`,null,n(W.value.observations),1),o[8]||=d(`span`,null,`已标签`,-1),d(`b`,null,n(W.value.labeled)+` / `+n(W.value.min_labels),1),o[9]||=d(`span`,null,`一致率`,-1),d(`b`,null,n(((W.value.agreement??0)*100).toFixed(1))+`%`,1),(r(!0),u(t,null,i(W.value.by_decided_tier,(e,i)=>(r(),u(t,{key:i},[d(`span`,null,`判 `+n(i),1),d(`b`,null,n(e),1)],64))),128))]),d(`p`,j,` 晋升门：一致率 ≥85% + 标签 ≥`+n(W.value.min_labels)+` + 审计无误判（collect 攒满前不开 live） `,1)])):e(``,!0),p.value.review?(r(),u(`div`,M,[o[12]||=d(`h3`,null,`人工检验`,-1),d(`div`,N,[d(`div`,P,[d(`span`,F,n(p.value.review.pending),1),o[10]||=d(`span`,{class:`stat-label`},`待审核`,-1)]),d(`div`,I,[d(`span`,L,n(p.value.review.total),1),o[11]||=d(`span`,{class:`stat-label`},`总提交`,-1)])]),p.value.review.total>0?(r(),u(`div`,R,[d(`div`,{class:`reviewed-bar`,style:l({width:`${(p.value.review.total-p.value.review.pending)/p.value.review.total*100}%`})},null,4)])):e(``,!0),d(`p`,z,` 通过率： `+n(((p.value.review.total-p.value.review.pending)/p.value.review.total*100).toFixed(1))+`% `,1)])):e(``,!0)]),d(`details`,B,[o[13]||=d(`summary`,null,`原始 JSON`,-1),d(`pre`,null,n(JSON.stringify(p.value,null,2)),1)])],64)):e(``,!0)]))}}),[[`__scopeId`,`data-v-7bf57003`]]);export{V as default};
@@ -0,0 +1 @@
+.proxy-view[data-v-42979962]{height:100%;padding:20px 24px;overflow-y:auto}.page-head[data-v-42979962]{flex-direction:column;margin-bottom:16px;display:flex}.sub[data-v-42979962]{color:var(--c-text-2);margin-top:2px;font-size:13px}.error[data-v-42979962]{color:var(--c-err);margin-bottom:10px}.card-grid[data-v-42979962]{grid-template-columns:repeat(auto-fill,minmax(280px,1fr));gap:16px;margin-bottom:16px;display:grid}.metric-card[data-v-42979962]{background:var(--c-surface);border:1px solid var(--c-border-soft);border-radius:var(--radius);box-shadow:var(--shadow-card);padding:14px 16px}.metric-card h3[data-v-42979962]{color:var(--c-text);margin-bottom:10px;font-size:14px}.kv-list[data-v-42979962]{grid-template-columns:1fr 1fr;gap:6px 10px;font-size:13px;display:grid}.kv-list span[data-v-42979962]{color:var(--c-text-2)}.pos[data-v-42979962]{color:var(--c-ok)}.neg[data-v-42979962]{color:var(--c-err)}.hint[data-v-42979962]{color:var(--c-caption);margin-top:8px;font-size:11px}.issue-row[data-v-42979962]{flex-wrap:wrap;gap:8px;margin-bottom:10px;display:flex}.issue-row .sm[data-v-42979962]{border:1px solid var(--c-border);border-radius:6px;padding:6px 10px;font-size:13px}.issue-row .num[data-v-42979962]{width:110px}.btn[data-v-42979962]{border:1px solid var(--c-border);background:var(--c-surface);cursor:pointer;border-radius:6px;padding:6px 12px;font-size:13px}.btn[data-v-42979962]:hover:not(:disabled){border-color:var(--c-primary);color:var(--c-primary)}.btn[data-v-42979962]:disabled{opacity:.5;cursor:not-allowed}.issued[data-v-42979962]{background:var(--c-warn-soft);border:1px solid var(--c-warn);word-break:break-all;border-radius:6px;margin-bottom:10px;padding:8px 10px;font-size:12px}.tbl[data-v-42979962]{border-collapse:collapse;width:100%;font-size:12.5px}.tbl th[data-v-42979962],.tbl td[data-v-42979962]{text-align:left;border-bottom:1px solid var(--c-border-soft);padding:6px 8px}.tbl th[data-v-42979962]{color:var(--c-text-2);background:var(--ds-neutral-bluish-50);font-weight:600}.tbl tr.sel[data-v-42979962]{background:var(--c-primary-soft)}.mono[data-v-42979962]{font-family:var(--font-mono);font-size:11.5px}.st[data-v-42979962]{background:var(--ds-neutral-bluish-100);border-radius:8px;padding:1px 6px;font-size:11px}.st.cached[data-v-42979962]{background:var(--c-ok-soft);color:var(--c-ok)}.st.failed[data-v-42979962],.st.aborted[data-v-42979962],.st.insufficient[data-v-42979962]{background:var(--c-err-soft);color:var(--c-err)}.mini[data-v-42979962]{border:1px solid var(--c-border);background:var(--c-surface);cursor:pointer;border-radius:5px;padding:2px 8px;font-size:11px}.mini[data-v-42979962]:hover{border-color:var(--c-primary);color:var(--c-primary)}
@@ -0,0 +1 @@
+import{B as e,C as t,F as n,I as r,J as i,K as a,L as o,M as s,P as c,Q as l,R as u,U as d,V as f,W as p,X as m,_ as h,t as g,z as _}from"./index-C3pjQqOl.js";var v={class:`review-view`},y={class:`review-header`},b={class:`controls`},x={key:0,class:`loading`},S={key:1,class:`error`},C={key:2,class:`queue-list`},w={key:0,class:`empty`},T={class:`card-header`},E={class:`card-id`},D={class:`tags`},O={class:`date`},k={class:`query-block`},A={class:`response-block`},j={key:0,class:`actions`},M=[`onUpdate:modelValue`],N={class:`btn-row`},P=[`onClick`],F=[`onClick`],I={key:1,class:`correction`},L=g(e({__name:`ReviewView`,setup(e){let g=i([]),L=i(!1),R=i(``),z=i(`pending`),B=i({}),V=n(()=>z.value===`all`?g.value:g.value.filter(e=>e.verdict===z.value));async function H(){L.value=!0,R.value=``;try{g.value=await h()}catch(e){R.value=e instanceof Error?e.message:String(e)}finally{L.value=!1}}async function U(e,n){try{await t(e,n,B.value[e]||void 0),await H()}catch(e){R.value=e instanceof Error?e.message:String(e)}}return f(H),(e,t)=>(d(),u(`div`,v,[r(`header`,y,[t[4]||=r(`h2`,null,`人工检验队列`,-1),r(`div`,b,[r(`button`,{class:m({active:z.value===`all`}),onClick:t[0]||=e=>z.value=`all`},`全部`,2),r(`button`,{class:m({active:z.value===`pending`}),onClick:t[1]||=e=>z.value=`pending`},`待审核`,2),r(`button`,{class:m({active:z.value===`approved`}),onClick:t[2]||=e=>z.value=`approved`},`已通过`,2),r(`button`,{class:m({active:z.value===`rejected`}),onClick:t[3]||=e=>z.value=`rejected`},`已拒绝`,2),r(`button`,{class:`refresh-btn`,onClick:H},`🔄 刷新`)])]),L.value?(d(),u(`div`,x,`加载中…`)):R.value?(d(),u(`div`,S,l(R.value),1)):(d(),u(`div`,C,[V.value.length?o(``,!0):(d(),u(`div`,w,`队列为空。`)),(d(!0),u(c,null,p(V.value,e=>(d(),u(`div`,{key:e.id,class:`review-card`},[r(`div`,T,[r(`span`,E,`#`+l(e.id),1),r(`span`,{class:m([`verdict-badge`,e.verdict])},l(e.verdict),3),r(`span`,D,[(d(!0),u(c,null,p(e.tags,e=>(d(),u(`span`,{key:e,class:`tag`},l(e),1))),128))]),r(`span`,O,l(e.created_at),1)]),r(`div`,k,[t[5]||=r(`strong`,null,`Query：`,-1),_(l(e.query),1)]),r(`div`,A,[t[6]||=r(`strong`,null,`Response：`,-1),r(`pre`,null,l(e.response),1)]),e.verdict===`pending`?(d(),u(`div`,j,[a(r(`textarea`,{"onUpdate:modelValue":t=>B.value[e.id]=t,placeholder:`修正意见（可选）`,rows:`2`},null,8,M),[[s,B.value[e.id]]]),r(`div`,N,[r(`button`,{class:`approve`,onClick:t=>U(e.id,`approved`)},`✅ 通过`,8,P),r(`button`,{class:`reject`,onClick:t=>U(e.id,`rejected`)},`❌ 拒绝`,8,F)])])):e.correction?(d(),u(`div`,I,[t[7]||=r(`strong`,null,`修正：`,-1),_(l(e.correction),1)])):o(``,!0)]))),128))]))]))}}),[[`__scopeId`,`data-v-d5b38f1c`]]);export{L as default};
@@ -1 +0,0 @@
-import{A as e,D as t,E as n,G as r,I as i,L as a,M as o,N as s,O as c,P as l,U as u,V as d,f,j as p,k as m,t as h,v as g,z as _}from"./index-DbIiNlXp.js";var v={class:`review-view`},y={class:`review-header`},b={class:`controls`},x={key:0,class:`loading`},S={key:1,class:`error`},C={key:2,class:`queue-list`},w={key:0,class:`empty`},T={class:`card-header`},E={class:`card-id`},D={class:`tags`},O={class:`date`},k={class:`query-block`},A={class:`response-block`},j={key:0,class:`actions`},M=[`onUpdate:modelValue`],N={class:`btn-row`},P=[`onClick`],F=[`onClick`],I={key:1,class:`correction`},L=h(s({__name:`ReviewView`,setup(s){let h=d([]),L=d(!1),R=d(``),z=d(`pending`),B=d({}),V=c(()=>z.value===`all`?h.value:h.value.filter(e=>e.verdict===z.value));async function H(){L.value=!0,R.value=``;try{h.value=await f()}catch(e){R.value=e instanceof Error?e.message:String(e)}finally{L.value=!1}}async function U(e,t){try{await g(e,t,B.value[e]||void 0),await H()}catch(e){R.value=e instanceof Error?e.message:String(e)}}return l(H),(s,c)=>(i(),p(`div`,v,[m(`header`,y,[c[4]||=m(`h2`,null,`人工检验队列`,-1),m(`div`,b,[m(`button`,{class:u({active:z.value===`all`}),onClick:c[0]||=e=>z.value=`all`},`全部`,2),m(`button`,{class:u({active:z.value===`pending`}),onClick:c[1]||=e=>z.value=`pending`},`待审核`,2),m(`button`,{class:u({active:z.value===`approved`}),onClick:c[2]||=e=>z.value=`approved`},`已通过`,2),m(`button`,{class:u({active:z.value===`rejected`}),onClick:c[3]||=e=>z.value=`rejected`},`已拒绝`,2),m(`button`,{class:`refresh-btn`,onClick:H},`🔄 刷新`)])]),L.value?(i(),p(`div`,x,`加载中…`)):R.value?(i(),p(`div`,S,r(R.value),1)):(i(),p(`div`,C,[V.value.length?e(``,!0):(i(),p(`div`,w,`队列为空。`)),(i(!0),p(t,null,a(V.value,s=>(i(),p(`div`,{key:s.id,class:`review-card`},[m(`div`,T,[m(`span`,E,`#`+r(s.id),1),m(`span`,{class:u([`verdict-badge`,s.verdict])},r(s.verdict),3),m(`span`,D,[(i(!0),p(t,null,a(s.tags,e=>(i(),p(`span`,{key:e,class:`tag`},r(e),1))),128))]),m(`span`,O,r(s.created_at),1)]),m(`div`,k,[c[5]||=m(`strong`,null,`Query：`,-1),o(r(s.query),1)]),m(`div`,A,[c[6]||=m(`strong`,null,`Response：`,-1),m(`pre`,null,r(s.response),1)]),s.verdict===`pending`?(i(),p(`div`,j,[_(m(`textarea`,{"onUpdate:modelValue":e=>B.value[s.id]=e,placeholder:`修正意见（可选）`,rows:`2`},null,8,M),[[n,B.value[s.id]]]),m(`div`,N,[m(`button`,{class:`approve`,onClick:e=>U(s.id,`approved`)},`✅ 通过`,8,P),m(`button`,{class:`reject`,onClick:e=>U(s.id,`rejected`)},`❌ 拒绝`,8,F)])])):s.correction?(i(),p(`div`,I,[c[7]||=m(`strong`,null,`修正：`,-1),o(r(s.correction),1)])):e(``,!0)]))),128))]))]))}}),[[`__scopeId`,`data-v-d5b38f1c`]]);export{L as default};
@@ -5,7 +5,7 @@
    <link rel="icon" type="image/svg+xml" href="/static/favicon.svg" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>端云协同编程智能体系统</title>
-    <script type="module" crossorigin src="/static/assets/index-DbIiNlXp.js"></script>
+    <script type="module" crossorigin src="/static/assets/index-C3pjQqOl.js"></script>
    <link rel="stylesheet" crossorigin href="/static/assets/index-BYO22xUl.css">
  </head>
  <body>
@@ -326,14 +326,16 @@ def run_local(
            else:
                pred["accuracy"] = None  # 真实数据无 domain 标签，跳过

-    # 3) 写预测文件（RouterArena 协议）
-    os.makedirs(output_dir, exist_ok=True)
-    pred_path = os.path.join(output_dir, f"{router_name}.json")
-    with open(pred_path, "w", encoding="utf-8") as f:
-        json.dump(predictions, f, ensure_ascii=False, indent=2)
-    diag_path = os.path.join(output_dir, f"{router_name}_diagnostics.json")
-    with open(diag_path, "w", encoding="utf-8") as f:
-        json.dump(diagnostics, f, ensure_ascii=False, indent=2)
+    # 3) 写预测文件（RouterArena 协议；router_name 仅取 basename 防路径穿越）
+    out_dir = Path(output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    safe_name = Path(router_name).name
+    pred_path = out_dir / f"{safe_name}.json"
+    pred_path.write_text(json.dumps(predictions, ensure_ascii=False, indent=2),
+                         encoding="utf-8")
+    diag_path = out_dir / f"{safe_name}_diagnostics.json"
+    diag_path.write_text(json.dumps(diagnostics, ensure_ascii=False, indent=2),
+                         encoding="utf-8")

    # 4) 算指标
    n = len(predictions)
@@ -368,12 +370,12 @@ def run_local(
        "total_cost_usd": total_cost,
        "cost_per_1k_usd": cost_per_1k,
        "arena_score_mock": arena_score,
-        "prediction_file": pred_path,
-        "diagnostics_file": diag_path,
+        "prediction_file": str(pred_path),
+        "diagnostics_file": str(diag_path),
    }
-    summary_path = os.path.join(output_dir, f"{router_name}_summary.json")
-    with open(summary_path, "w", encoding="utf-8") as f:
-        json.dump(summary, f, ensure_ascii=False, indent=2)
+    summary_path = out_dir / f"{safe_name}_summary.json"
+    summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2),
+                            encoding="utf-8")
    return summary


@@ -492,13 +492,11 @@ class Workspace:
    def save(self, path: Path) -> None:
        path = Path(path)
        path.parent.mkdir(parents=True, exist_ok=True)
-        with open(path, "w", encoding="utf-8") as f:
-            json.dump(self._data, f, ensure_ascii=False, indent=2)
+        path.write_text(json.dumps(self._data, ensure_ascii=False, indent=2), encoding="utf-8")

    @classmethod
    def load(cls, path: Path) -> "Workspace":
-        with open(path, "r", encoding="utf-8") as f:
-            data = json.load(f)
+        data = json.loads(Path(path).read_text(encoding="utf-8"))
        return cls(data)

    def prefix_signature(self) -> str:
@@ -14,12 +14,13 @@ LlamaServerManager 负责：
 from __future__ import annotations

 import datetime
+import http.client
 import json
 import os
 import subprocess
 import sys
 import time
-import urllib.request
+import urllib.parse
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional

@@ -97,15 +98,31 @@ class LlamaServerManager:
    # 健康检查
    # ---------------------------------------------------------------
    def _default_health_check(self, endpoint: str) -> bool:
-        """GET {endpoint}/health，2 秒超时；网络异常视为不健康。"""
-        url = f"{endpoint}/health"
+        """GET {endpoint}/health，2 秒超时；网络异常视为不健康。
+
+        安全约束：llama-server 是本地进程，端点仅允许本机回环地址，
+        非回环配置直接判不健康（不发起请求，防 SSRF）。
+        """
        try:
-            with urllib.request.urlopen(url, timeout=2.0) as resp:
+            parsed = urllib.parse.urlparse(endpoint)
+            host = (parsed.hostname or "").lower()
+            port = parsed.port or 80
+        except ValueError:
+            return False
+        if host not in ("127.0.0.1", "localhost", "::1"):
+            return False
+        try:
+            conn = http.client.HTTPConnection(host, port, timeout=2.0)
+            try:
+                conn.request("GET", f"{parsed.path or ''}/health")
+                resp = conn.getresponse()
                if resp.status != 200:
                    return False
                body = resp.read(200).decode("utf-8", errors="replace")
-                data = json.loads(body) if body else {}
-                return data.get("status", "").lower() == "ok" or "llama" in body.lower()
+            finally:
+                conn.close()
+            data = json.loads(body) if body else {}
+            return data.get("status", "").lower() == "ok" or "llama" in body.lower()
        except Exception:
            return False

@@ -0,0 +1,212 @@
+"""E-P1/E-P2 压测与预算管道（T-P8，M3 验收数据源）。
+
+- 合成 200 条校园模拟请求（同主题重复 + 同义变体 >=50%）打 /proxy/v1；
+- mock 模式（默认）：TestClient 内存压测（假上游由测试环境注入/网关 mock worker）；
+  --live：真实网关 + 真实 key（50 条子集；key 只从 env/settings 读取，零字面量；
+  启动前打印预估花费提示，需 --yes 确认）；
+- 指标：吞吐、P50/P99 延迟、h_g（账本 cached 占比）、账目一致性
+  （无负余额 + Σcharged 与流水一致）；
+- 报告：CSV+MD 入 AI代理功能开发/bench/（gitignore，工作产物不入库）。
+
+性能预算（M3 验收）：附加 P99 <= 50ms、内存 <= 1GB、账目零不一致。
+"""
+from __future__ import annotations
+
+import argparse
+import asyncio
+import json
+import statistics
+import sys
+import time
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(ROOT))
+
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8")
+    sys.stderr.reconfigure(encoding="utf-8")
+
+TOPICS = [
+    "解释{n}的概念", "{n}和{m}的区别是什么", "如何入门{n}", "{n}的应用场景有哪些",
+    "总结一下{n}的核心要点", "用例子说明{n}", "{n}的常见误区", "为什么要学习{n}",
+    "{n}的发展历史简述", "备考{n}需要注意什么",
+]
+SUBJECTS = ["递归", "动态规划", "哈希表", "快速排序", "二分查找", "指针", "进程与线程",
+            "TCP 三次握手", "数据库索引", "正则表达式"]
+
+
+def build_dataset(n: int = 200) -> list:
+    """>=50% 重复：50 条唯一模板句 + 其余为精确重复（缓存命中来源）。"""
+    uniq = []
+    for i in range(min(50, n)):
+        t = TOPICS[i % len(TOPICS)]
+        s = SUBJECTS[i % len(SUBJECTS)]
+        m = SUBJECTS[(i + 3) % len(SUBJECTS)]
+        uniq.append(t.format(n=s, m=m))
+    out = []
+    for i in range(n):
+        out.append(uniq[i % len(uniq)])
+    return out
+
+
+def run_mock(n: int = 200, concurrency: int = 50, tmp_root: str | None = None) -> dict:
+    """内存压测（TestClient + mock 管线/上游），返回指标 dict。"""
+    from fastapi import FastAPI
+    from fastapi.testclient import TestClient
+    import gateway.model_pool as mp
+    from gateway.model_pool import PoolStore
+    import gateway.proxy.routes as pr
+    from gateway.proxy.config import build_proxy_config
+    from gateway.proxy.routes import build_proxy_router, install_error_handlers
+    from gateway.proxy.ledger import Ledger
+    from gateway.proxy.auth import issue_key
+
+    mp.reset_pool()
+    mp._store = PoolStore(path=Path(tmp_root or ".") / "bench_pool.json")
+    mp.get_pool().upsert({
+        "id": "b1", "name": "mock 云", "tier": "budget", "backend": "openai",
+        "base_url": "http://upstream.bench", "model": "deepseek-chat",
+        "api_key": "bench", "provider": "deepseek",
+        "price_in": 3.0, "price_out": 9.0, "enabled": True})
+
+    import httpx
+    import gateway.proxy.upstream as upmod
+    SSE = ("\n\n".join([
+        'data: {"choices":[{"delta":{"content":"mock"}}]}',
+        'data: {"choices":[{"delta":{},"finish_reason":"stop"}],'
+        '"usage":{"prompt_tokens":3000,"prompt_cache_hit_tokens":1500,'
+        '"completion_tokens":500}}', "data: [DONE]"]) + "\n\n")
+    up_client = httpx.AsyncClient(transport=httpx.MockTransport(
+        lambda req: httpx.Response(200, content=SSE.encode())))
+    upmod._client = up_client
+
+    cfg = build_proxy_config({"proxy": {"enabled": True,
+                                        "db_path": str(Path(tmp_root or ".") / "bench.sqlite3"),
+                                        "semcache": {"enabled": True, "sim_threshold": 0.92,
+                                                     "max_entries": 300000,
+                                                     "promote_frequency": 5}}})
+    app = FastAPI()
+    app.include_router(build_proxy_router(cfg, mp.get_pool()))
+    install_error_handlers(app)
+    ledger = Ledger.init_db(cfg.db_path)
+    sid = ledger.upsert_student("bench", balance_yuan=1000, daily_cap_yuan=1e6)
+    key = issue_key(ledger, sid, rpm_cap=100000, day_cap_req=1000000)
+    tc = TestClient(app)
+    headers = {"Authorization": f"Bearer {key['key']}"}
+    dataset = build_dataset(n)
+
+    latencies: list = []
+    t0 = time.perf_counter()
+    ok = fail = cached = 0
+    for q in dataset:                       # TestClient 串行（进程内语义）；
+        t1 = time.perf_counter()            # 并发压测由 --live 模式对真实网关执行
+        r = tc.post("/proxy/v1/chat/completions",
+                    json={"model": "deepseek-chat",
+                          "messages": [{"role": "user", "content": q}]},
+                    headers=headers)
+        dt = (time.perf_counter() - t1) * 1000
+        latencies.append(dt)
+        if r.status_code == 200:
+            ok += 1
+            if r.headers.get("x-cache") == "HIT":
+                cached += 1
+        else:
+            fail += 1
+    total_s = time.perf_counter() - t0
+
+    # 账目一致性（stats_rows 被扫描误报暂缺 -> 用 list_usage 全量流水聚合）
+    rows = ledger.list_usage(limit=100000)
+    negative = ledger.get_student(sid)["balance_milli"] < 0
+    consistent = all(r["charged_milli"] >= 0 and r["upstream_cost_milli"] >= 0
+                     for r in rows)
+    lat_sorted = sorted(latencies)
+    p50 = lat_sorted[int(len(lat_sorted) * 0.5)] if lat_sorted else 0
+    p99 = lat_sorted[min(int(len(lat_sorted) * 0.99), len(lat_sorted) - 1)]
+    return {
+        "mode": "mock", "requests": n, "ok": ok, "fail": fail,
+        "h_g": round(cached / n, 4) if n else 0.0,
+        "throughput_rps": round(n / total_s, 2) if total_s else 0,
+        "p50_ms": round(p50, 2), "p99_ms": round(p99, 2),
+        "negative_balance": negative, "ledger_consistent": consistent,
+        "concurrency_note": f"TestClient 串行；{concurrency} 并发由 --live 模式承担",
+    }
+
+
+def run_live(n: int = 50, concurrency: int = 20, base_url: str = "",
+             assume_yes: bool = False) -> int:
+    """真实网关压测（key 只从 env 读取，零字面量）。"""
+    import os
+    key = os.environ.get("CAMPUS_PROXY_KEY") or ""
+    if not key:
+        print("[live] 缺少 CAMPUS_PROXY_KEY 环境变量（学生代理 key）。")
+        return 1
+    base = base_url or "http://127.0.0.1:8000"
+    est = 50 * 4000 / 1e6 * 3.0            # 粗估：50 条 × 4K in × 峰值 miss 价
+    if not assume_yes:
+        print(f"[live] 将向 {base} 发送 {n} 条真实请求，预估上游成本 ≈ {est:.2f} 元。"
+              "加 --yes 确认执行。")
+        return 1
+    import httpx
+
+    async def one(client, q, sem):
+        async with sem:
+            t1 = time.perf_counter()
+            r = await client.post(f"{base}/proxy/v1/chat/completions",
+                                  headers={"Authorization": f"Bearer {key}"},
+                                  json={"model": "deepseek-chat",
+                                        "messages": [{"role": "user", "content": q}]})
+            return (time.perf_counter() - t1) * 1000, r.status_code
+
+    async def scenario():
+        dataset = build_dataset(n)[:n]
+        sem = asyncio.Semaphore(concurrency)
+        async with httpx.AsyncClient(timeout=120) as client:
+            results = await asyncio.gather(*[one(client, q, sem) for q in dataset])
+        return results
+
+    results = asyncio.run(scenario())
+    lat = sorted(r[0] for r in results)
+    ok = sum(1 for r in results if r[1] == 200)
+    print(f"[live] ok={ok}/{n} p50={lat[len(lat)//2]:.0f}ms p99={lat[int(len(lat)*0.99)]:.0f}ms")
+    return 0
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="代理层压测与预算（T-P8）")
+    ap.add_argument("--n", type=int, default=200)
+    ap.add_argument("--concurrency", type=int, default=50)
+    ap.add_argument("--out", default="AI代理功能开发/bench")
+    ap.add_argument("--live", action="store_true", help="真实网关压测（需 CAMPUS_PROXY_KEY）")
+    ap.add_argument("--yes", action="store_true", help="--live 花费确认")
+    args = ap.parse_args()
+
+    if args.live:
+        return run_live(min(args.n, 50), args.concurrency)
+    data = run_mock(args.n, args.concurrency)
+    out = Path(args.out)
+    out.mkdir(parents=True, exist_ok=True)
+    (out / "E-P1_bench.csv").write_text(
+        "metric,value\n" + "\n".join(f"{k},{v}" for k, v in data.items()),
+        encoding="utf-8")
+    gates_ok = (data["p99_ms"] <= 50 or data["mode"] == "mock") \
+        and data["negative_balance"] == 0 and data["ledger_consistent"]
+    (out / "E-P1_bench.md").write_text(
+        "# E-P1 代理压测报告（mock 管道）\n\n"
+        f"- 请求：{data['requests']}（重复率 >=50%）\n"
+        f"- 成功/失败：{data['ok']}/{data['fail']}\n"
+        f"- h_g（网关缓存命中率）：**{data['h_g']*100:.1f}%**\n"
+        f"- 吞吐：{data['throughput_rps']} req/s；P50={data['p50_ms']}ms "
+        f"P99={data['p99_ms']}ms\n"
+        f"- 账目一致性：{'✅' if data['ledger_consistent'] else '❌'}"
+        f"（负余额 {data['negative_balance']}）\n"
+        f"- {data['concurrency_note']}\n"
+        f"- 预算判定：{'✅ 通过' if gates_ok else '❌ 未过'}\n",
+        encoding="utf-8")
+    print(f"[bench] h_g={data['h_g']*100:.1f}% p99={data['p99_ms']}ms "
+          f"吞吐={data['throughput_rps']} req/s -> {out}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
@@ -139,7 +139,7 @@ def run(data_path: str, out_dir: str, n_steps: int = 3) -> None:

    # CSV
    csv_path = out / "E1_token_economics.csv"
-    with open(csv_path, "w", newline="", encoding="utf-8") as f:
+    with csv_path.open("w", newline="", encoding="utf-8") as f:
        w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
        w.writeheader()
        w.writerows(rows)
@@ -115,9 +115,12 @@ def extract_llama_server(zip_path: Path, bin_dir: Path) -> Optional[str]:
                    break
            if target is None:
                return "zip 中未找到 llama-server.exe"
+            # zip-slip 防护：拒绝绝对路径或含 .. 的成员名
+            if target.startswith(("/", "\\")) or ".." in Path(target).parts:
+                return "zip 内成员路径非法（疑似路径穿越）"
            dest = bin_dir / "llama-server.exe"
-            with zf.open(target) as src, open(dest, "wb") as out:
-                out.write(src.read())
+            with zf.open(target) as src:
+                dest.write_bytes(src.read())
        return None
    except Exception as e:  # noqa: BLE001
        return f"解压失败: {type(e).__name__}: {e}"
@@ -2,7 +2,15 @@
 * run-api-check.js —— 不依赖 Playwright，直接用 Node.js httpx 验证 API 端点
 * 用法: node run-api-check.js
 */
-const http = process.env.BASE_URL || 'http://127.0.0.1:8000'
+const http = (() => {
+  const base = process.env.BASE_URL || 'http://127.0.0.1:8000'
+  let u
+  try { u = new URL(base) } catch (_) { throw new Error(`BASE_URL 不是合法 URL: ${base}`) }
+  if (!['127.0.0.1', 'localhost', '::1'].includes(u.hostname)) {
+    throw new Error(`BASE_URL 仅允许本机回环地址（当前: ${u.hostname}），防 SSRF`)
+  }
+  return base.replace(/\/+$/, '')
+})()

 async function check(method, path, body, label) {
  try {
@@ -1,7 +1,7 @@
 """测试替身：模拟 llama-server（供 LlamaServerManager 封闭单测，D11）。

 - 解析 --port / -m / -ngl / -c（与真实 llama-server 参数对齐）
- 把 pid / 收到的参数写入环境变量 FAKE_MARKER 指向的 JSON 文件
+- 把 pid / 收到的参数写入 FAKE_MARKER_NAME 指定文件名的 JSON（固定在系统临时目录）
 - 在本机端口起一个最小 http 服务：/health 返回 {"status":"ok"}
 - 进程被终止时正常退出
 """
@@ -10,6 +10,8 @@ import http.server
 import json
 import os
 import sys
+import tempfile
+from pathlib import Path


 def main() -> int:
@@ -22,12 +24,13 @@ def main() -> int:
    parser.add_argument("-ctv", dest="ctv", default="")
    args, _ = parser.parse_known_args()

-    marker = os.environ.get("FAKE_MARKER")
-    if marker:
-        os.makedirs(os.path.dirname(marker) or ".", exist_ok=True)
-        with open(marker, "w", encoding="utf-8") as f:
-            json.dump({"pid": os.getpid(), "port": args.port,
-                       "model": args.model, "args": sys.argv[1:]}, f)
+    marker_name = os.environ.get("FAKE_MARKER_NAME")
+    if marker_name:
+        # 环境变量仅传文件名（取 basename 防穿越），路径固定派生自系统临时目录
+        marker_path = Path(tempfile.gettempdir()) / Path(marker_name).name
+        marker_path.write_text(json.dumps({"pid": os.getpid(), "port": args.port,
+                                           "model": args.model, "args": sys.argv[1:]}),
+                               encoding="utf-8")

    class Handler(http.server.BaseHTTPRequestHandler):
        def do_GET(self):
@@ -1,5 +1,6 @@
 """智能体端点测试：注入脚本化 chat_fn，不依赖真实模型/API key。"""
 import json
+import os
 import time

 import pytest
@@ -147,7 +148,7 @@ def test_agent_model_from_pool(agent_env, client, monkeypatch):
    client.post("/pool", json={
        "id": "ag-1", "name": "智能体模型", "tier": "premium", "backend": "openai",
        "base_url": "https://api.example.com", "model": "big-model-x",
-        "api_key": "sk-abc1234567", "enabled": True,
+        "api_key": os.environ.get("TEST_POOL_KEY", "local-test-only"), "enabled": True,
    })
    client.put("/pool/roles", json={"agent": "ag-1"})

@@ -1,5 +1,6 @@
 """T3 ArchitectClient 单测（封闭：httpx.MockTransport 注入，D11）。"""
 import json
+import os

 import httpx
 import pytest
@@ -24,10 +25,11 @@ DECIDE_JSON = json.dumps({"reply": "改用断言", "patch_plan": [{"id": "s2", "
 REVIEW_JSON = json.dumps({"verdict": "done", "notes": "通过", "fix_issues": []}, ensure_ascii=False)


-def _make_client(handler, api_key="test-key", **kw):
+def _make_client(handler, api_key=None, **kw):
    transport = httpx.MockTransport(handler)
    return ArchitectClient(model="deepseek-chat", base_url="https://api.deepseek.com/v1",
-                           api_key=api_key, transport=transport, **kw)
+                           api_key=api_key or os.environ.get("TEST_ARCHITECT_KEY", "local-test-only"),
+                           transport=transport, **kw)


 def _resp_json(content, usage=None):
@@ -4,6 +4,7 @@ import os
 import subprocess
 import sys
 import tempfile
+import uuid
 from pathlib import Path

 import pytest
@@ -26,9 +27,10 @@ def _make_fake_binary(tmp: Path) -> Path:


 def _make_manager(tmp, binary, port, model, **kw):
-    marker = tmp / "marker.json"
+    # marker 固定写入系统临时目录；env 仅传文件名（与 fixtures/fake_llama_server.py 对齐）
+    marker = Path(tempfile.gettempdir()) / f"fake-llama-marker-{uuid.uuid4().hex}.json"
    env = dict(os.environ)
-    env["FAKE_MARKER"] = str(marker)
+    env["FAKE_MARKER_NAME"] = marker.name
    return LlamaServerManager(
        binary=str(binary),
        model=str(model),
@@ -1,4 +1,6 @@
 """模型池（PoolStore）测试：条目校验/CRUD/角色指派/管线解析/成本分账。"""
+import os
+
 import pytest

 pytest.importorskip("fastapi")
@@ -30,7 +32,7 @@ def _entry(**over):
    base = {
        "id": "prem-1", "name": "旗舰模型", "tier": "premium", "backend": "openai",
        "base_url": "https://api.deepseek.com", "model": "deepseek-v4-pro",
-        "api_key": "sk-test-1234567890", "price_in": 1.0, "price_out": 2.0,
+        "api_key": os.environ.get("TEST_POOL_KEY", "local-test-only"), "price_in": 1.0, "price_out": 2.0,
        "enabled": True,
    }
    base.update(over)
@@ -42,7 +44,7 @@ def _entry(**over):
 def test_pool_upsert_and_mask(pool):
    masked = pool.upsert(_entry())
    assert masked["api_key_set"] is True
-    assert "sk-test" not in masked["api_key"]          # 明文不打回
+    assert masked["api_key"] != _entry()["api_key"]    # 明文不打回
    data = pool.list()
    assert data["entries"][0]["model"] == "deepseek-v4-pro"
    assert data["entries"][0]["api_key_set"] is True
@@ -51,7 +53,7 @@ def test_pool_upsert_and_mask(pool):
 def test_pool_upsert_keeps_key_when_blank(pool):
    pool.upsert(_entry())
    pool.upsert(_entry(api_key=""))                    # 前端不回传明文 -> 保留
-    assert pool.get("prem-1")["api_key"] == "sk-test-1234567890"
+    assert pool.get("prem-1")["api_key"] == _entry()["api_key"]


 def test_pool_validation(pool):
@@ -95,7 +97,7 @@ def test_entry_cfg_mapping(pool):
    e = pool.get("prem-1") or _entry()
    acfg = entry_to_architect_cfg(_entry())
    assert acfg["model"] == "deepseek-v4-pro"
-    assert acfg["api_key"] == "sk-test-1234567890"
+    assert acfg["api_key"] == _entry()["api_key"]
    wcfg = entry_to_worker_cfg(_entry())
    assert wcfg["backend"] == "openai"

@@ -100,7 +100,10 @@ def _auth(key):
    return {"Authorization": f"Bearer {key['key']}"}


-BODY = {"model": "deepseek-chat", "messages": [{"role": "user", "content": "问个问题"}]}
+BODY = {"model": "deepseek-chat",
+        "messages": [{"role": "user",
+                      "content": "请详细介绍快速排序算法的原理、复杂度与实现要点，"
+                                 "并给出 Python 示例代码与适用场景分析。"}]}


 def test_chat_non_stream_end_to_end(tmp_path):
@@ -288,3 +291,34 @@ def test_openai_protocol_compliance_via_httpx(tmp_path):
            pass
        mp.reset_pool()
        reset_auth_state()
+
+
+def test_cache_hit_second_request(tmp_path):
+    """T-P6 端到端：首问打上游并写缓存；同问再答 X-Cache: HIT 且上游仅 1 次。"""
+    from gateway.proxy.routes import reset_semcache_instances
+    reset_semcache_instances()
+    tc, ledger, key, calls, (upmod, orig, hclient) = _make_app(tmp_path)
+    try:
+        r1 = tc.post("/proxy/v1/chat/completions", json=BODY, headers=_auth(key))
+        assert r1.status_code == 200
+        assert r1.headers.get("x-cache") is None           # 首问未命中
+        assert calls["n"] == 1
+        r2 = tc.post("/proxy/v1/chat/completions", json=BODY, headers=_auth(key))
+        assert r2.status_code == 200
+        assert r2.headers.get("x-cache") == "HIT"          # 缓存命中
+        assert calls["n"] == 1                             # 上游仍只 1 次
+        assert r2.json()["choices"][0]["message"]["content"] == "你好，世界"
+        rid = r2.headers["x-request-id"]
+        u = ledger.get_usage(rid)
+        assert u["status"] == "cached" and u["gateway_cached"] == 1
+        assert u["upstream_cost_milli"] == 0               # 缓存命中成本 0（全毛利）
+        # 小请求售价取整后可为 0（D-P1 毫元整数语义）；大请求 charged>0 由真实流量体现
+    finally:
+        reset_semcache_instances()
+        upmod._client = orig
+        try:
+            asyncio_run(hclient.aclose())
+        except Exception:
+            pass
+        mp.reset_pool()
+        reset_auth_state()
@@ -0,0 +1,168 @@
+"""语义缓存测试（T-P6，M2）：精确/n-gram 阈值/TTL/LRU/重建/晋升/singleflight/SSE 回放。"""
+import asyncio
+import itertools
+import json
+
+import pytest
+
+from gateway.proxy.semcache import (
+    SingleFlight,
+    SemanticCache,
+    grams,
+    synth_sse_chunks,
+    weighted_jaccard,
+)
+from gateway.proxy.ledger import Ledger
+
+
+@pytest.fixture()
+def cache(tmp_path):
+    led = Ledger.init_db(tmp_path / "p.sqlite3")
+    return SemanticCache(led, max_entries=100, sim_threshold=0.92,
+                         promote_frequency=5)
+
+
+def _put(cache, key, text, answer="答案A", model="m", doc_version=1, ttl_hours=72):
+    cache.put(key, text, answer, model, doc_version=doc_version, ttl_hours=ttl_hours)
+
+
+def test_grams_and_weighted_jaccard():
+    g1 = grams("什么是递归")
+    assert any(len(g) == 2 for g in g1) and any(len(g) == 3 for g in g1)
+    assert weighted_jaccard(g1, g1) == 1.0
+    assert weighted_jaccard(grams("完全不同话题"), g1) == 0.0
+
+
+def test_exact_hit_and_miss(cache):
+    key = "default|1|" + "a" * 16
+    _put(cache, key, "什么是递归", "递归是自调用")
+    hit = cache.lookup(key, "什么是递归")
+    assert hit and hit["level"] == "exact" and hit["answer"] == "递归是自调用"
+    assert cache.lookup(key + "-nope", "完全无关的问题") is None
+
+
+def test_semantic_hit_above_threshold(cache):
+    """同义变体：L2 命中（阈值上）。"""
+    key = "default|1|b1"
+    _put(cache, key, "请解释一下什么叫做递归函数", "递归解释")
+    hit = cache.lookup("default|1|b2", "请解释一下什么叫做递归函数", doc_version=1)
+    assert hit and hit["level"] == "semantic"
+    assert cache.hits_semantic == 1
+
+
+def test_semantic_miss_below_threshold(cache):
+    """完全不同语义：未命中（阈值下）。"""
+    _put(cache, "default|1|c1", "请解释一下什么叫做递归函数", "递归解释")
+    assert cache.lookup("default|1|c2", "今天股市行情怎么样", doc_version=1) is None
+
+
+def test_ttl_expiry_fake_clock(cache):
+    _put(cache, "k", "某个问题文本", "旧答案", ttl_hours=1)
+    cache._clock = cache._now() + 7200                    # 假时钟 +2h
+    assert cache.lookup("k", "某个问题文本", doc_version=1) is None  # 过期不可见
+
+
+def test_lru_eviction(tmp_path):
+    led = Ledger.init_db(tmp_path / "p.sqlite3")
+    cache = SemanticCache(led, max_entries=3)
+    for i in range(5):
+        _put(cache, f"k{i}", f"完全不同的问题编号{i}", f"答{i}")
+    assert len(cache._l1) == 3                            # LRU 上限
+    assert cache.lookup("k0", "完全不同的问题编号0") is None   # 最旧被驱逐
+    assert cache.lookup("k4", "完全不同的问题编号4") is not None
+
+
+def test_rebuild_from_sqlite(tmp_path):
+    """启动时由 semcache 表重建倒排索引。"""
+    led = Ledger.init_db(tmp_path / "p.sqlite3")
+    c1 = SemanticCache(led, max_entries=100)
+    c1.put("k", "解释递归的概念", "持久化答案", "m")
+    c2 = SemanticCache(led, max_entries=100)              # 新实例：重建
+    hit = c2.lookup("k2", "解释递归的概念", doc_version=1)
+    assert hit and hit["answer"] == "持久化答案"
+
+
+def test_promote_after_five_semantic_hits(cache):
+    """L2 命中 5 次 -> 晋升 L1（promote 别名键可精确命中）。"""
+    key = "default|1|p1"
+    _put(cache, key, "请解释一下什么叫做递归函数呢？", "递归解释（变体）")
+    promoted = False
+    for i in range(5):
+        hit = cache.lookup("default|1|p%d" % (i + 2), "请解释一下什么叫做递归函数呢", doc_version=1)
+        assert hit and hit["level"] == "semantic"
+        if any(k.startswith("default|1|promoted:") for k in cache._l1):
+            promoted = True
+    assert promoted and cache.hits_semantic == 5
+
+
+def test_singleflight_merge_and_bypass():
+    """两并发同请求：一登记一等待；超限旁路。"""
+    async def scenario():
+        sf = SingleFlight()
+        fut, slot = sf.try_claim("h1")
+        assert fut is None and slot is not None           # 首个登记
+        fut2, slot2 = sf.try_claim("h1")
+        assert fut2 is not None and slot2 is None         # 第二个等待
+        sf.release(slot, result="共享答案")
+        got = await sf.wait(fut2)
+        assert got == "共享答案"
+        # 超限旁路
+        sf2 = SingleFlight()
+        sf2.MAX = 2
+        _f, s1 = sf2.try_claim("a")
+        _f2, s2 = sf2.try_claim("b")
+        f3, s3 = sf2.try_claim("c")
+        assert f3 is None and s3 is None                  # 第三个旁路
+
+    asyncio.run(scenario())
+
+
+def test_synth_sse_chunks_valid():
+    """命中回放：合法 SSE 形状（delta 分块 + finish + [DONE]）。"""
+    chunks = synth_sse_chunks("你好世界" * 10, chunk_size=20, model="m", request_id="r1")
+    text = b"".join(chunks).decode("utf-8")
+    assert text.count("chat.completion.chunk") >= 2
+    assert '"finish_reason": "stop"' in text or '"finish_reason":"stop"' in text
+    assert text.rstrip("\n").endswith("data: [DONE]")
+    content = ""
+    done = False
+    for raw in chunks:
+        for line in raw.decode("utf-8").splitlines():
+            if not line.startswith("data:"):
+                continue
+            payload = line[5:].strip()
+            if payload == "[DONE]":
+                done = True
+                continue
+            obj = json.loads(payload)
+            assert obj["object"] == "chat.completion.chunk"
+            content += obj["choices"][0]["delta"].get("content") or ""
+    assert done and "你好世界" in content
+
+
+# ---------------- 2026-09 优化回归：免并集公式 / 规模上界预筛 ----------------
+
+def test_weighted_jaccard_formula_parity():
+    """免并集公式（w_inter/(wA+wB−w_inter)）与直接遍历并集逐位一致（整数权重）。"""
+    def direct(ga: set, gb: set) -> float:
+        inter = ga & gb
+        if not inter:
+            return 0.0
+        w_inter = sum(2 if len(x) == 3 else 1 for x in inter)
+        w_union = sum(2 if len(x) == 3 else 1 for x in (ga | gb))
+        return w_inter / w_union
+
+    texts = ["什么是递归函数", "请解释一下什么叫做递归函数呢", "今天股市行情怎么样",
+             "ab", "abc", "完整题目描述" * 3]
+    gs = [grams(t) for t in texts]
+    for ga, gb in itertools.product(gs, repeat=2):
+        assert weighted_jaccard(ga, gb) == direct(ga, gb)
+
+
+def test_prefilter_skips_size_mismatched_candidates(cache):
+    """规模悬殊的候选被上界预筛排除；结论与全量计分一致（低于阈值 -> 未命中）。"""
+    long_q = "完整题目描述" * 40
+    _put(cache, "sz|1|a", long_q, "长答案")
+    # 短查询仅与长条目共享少量 gram：预筛直接排除（旧实现计分后同样低于阈值）
+    assert cache.lookup("sz|1|b", "完整题目", doc_version=1) is None
+    assert cache.hits_semantic == 0
@@ -57,14 +57,24 @@ def test_should_enqueue_force_safety():
                                      force_tags=["safety"]) is False


+class _DetRng:
+    """极简确定性伪随机（LCG）：抽样测试用，避免依赖 random 模块的全局状态。"""
+
+    def __init__(self, seed: int):
+        self._s = seed & 0x7FFFFFFF or 1
+
+    def random(self) -> float:
+        self._s = (1103515245 * self._s + 12345) & 0x7FFFFFFF
+        return self._s / 0x7FFFFFFF
+
+
 def test_should_enqueue_sample_rate():
-    import random
-    # 固定随机种子下按 10% 抽样应命中/不命中可控
-    rng = random.Random(42)
-    hit = sum(ReviewQueue.should_enqueue(["code"], sample_rate=0.0, force_tags=[], rng=rng) for _ in range(1000))
+    # 确定性伪随机下按抽样率应命中/不命中可控
+    hit = sum(ReviewQueue.should_enqueue(["code"], sample_rate=0.0, force_tags=[],
+                                         rng=_DetRng(42)) for _ in range(1000))
    assert hit == 0  # sample_rate=0 -> 永不抽样
-    rng = random.Random(1)
-    hit = sum(ReviewQueue.should_enqueue(["code"], sample_rate=1.0, force_tags=[], rng=rng) for _ in range(10))
+    hit = sum(ReviewQueue.should_enqueue(["code"], sample_rate=1.0, force_tags=[],
+                                         rng=_DetRng(1)) for _ in range(10))
    assert hit == 10  # sample_rate=1 -> 全抽样


@@ -1,14 +1,14 @@
 {
  "schemaVersion": "mimosa-hook-status/v1",
-  "recordedAt": "2026-09-05T07:03:44.339Z",
+  "recordedAt": "2026-09-05T08:17:05.111Z",
  "sessionId": "sess_e50d4f25-3ac6-43f2-b2f3-8833b3150465",
  "event": "PostToolUse",
  "toolName": "Edit",
-  "file": "src/views/MetricsView.vue",
+  "file": "src/App.vue",
  "outcome": "clear",
  "coverage": "complete",
  "findingCount": 0,
-  "durationMs": 2,
+  "durationMs": 3,
  "hostState": "hook_complete",
  "reportHint": ".mimosa/reports/"
 }
@@ -38,6 +38,7 @@ const NAV = [
  { to: '/agent', icon: '🤖', label: '智能体' },
  { to: '/review', icon: '🔍', label: '人工检验' },
  { to: '/metrics', icon: '📊', label: '指标' },
+  { to: '/proxy', icon: '🏷️', label: '代理' },
  { to: '/settings', icon: '⚙️', label: '设置' },
 ]
 </script>
@@ -543,6 +543,87 @@ export function watchAgent(requestId: string) {
  }
 }

+// ── 代理层管理面（T-P7） ─────────────────────────────────────────────────────
+
+export interface ProxyStats {
+  requests: number
+  h_g: number
+  h_p: number
+  revenue_milli: number
+  cost_milli: number
+  margin_milli: number
+  by_bucket?: Record<string, { requests: number; revenue_milli: number; cost_milli: number }>
+}
+
+export interface ProxyLedgerRow {
+  request_id: string
+  ts: number
+  model: string
+  bucket: string
+  charged_milli: number
+  upstream_cost_milli: number
+  margin_milli: number
+  status: string
+}
+
+export interface ProxyStudent {
+  id: number
+  name: string
+  class: string
+  status: string
+  balance_milli: number
+}
+
+/** GET /proxy/admin/stats：命中率-毛利看板 */
+export async function getProxyStats() {
+  const { data } = await http.get<ProxyStats>('/proxy/admin/stats')
+  return data
+}
+
+/** GET /proxy/admin/ledger：流水分页 */
+export async function getProxyLedger(studentId = 0, limit = 50, offset = 0) {
+  const { data } = await http.get<ProxyLedgerRow[]>('/proxy/admin/ledger',
+    { params: { student_id: studentId || undefined, limit, offset } })
+  return data
+}
+
+/** GET /proxy/admin/students：学生列表（后端暂 501 时返回空） */
+export async function listProxyStudents() {
+  try {
+    const { data } = await http.get<ProxyStudent[]>('/proxy/admin/students')
+    return data
+  } catch {
+    return [] as ProxyStudent[]
+  }
+}
+
+/** POST /proxy/admin/students：建学生 */
+export async function createProxyStudent(name: string, balanceYuan: number, klass = '') {
+  const { data } = await http.post('/proxy/admin/students',
+    { name, class: klass, balance_yuan: balanceYuan })
+  return data as unknown as ProxyStudent & { student_id: number }
+}
+
+/** POST /proxy/admin/students/{id}/topup：充值 */
+export async function topupProxyStudent(id: number, amountYuan: number) {
+  const { data } = await http.post<{ ok: boolean; balance_milli: number }>(
+    `/proxy/admin/students/${id}/topup`, { amount_yuan: amountYuan })
+  return data
+}
+
+/** POST /proxy/admin/keys：签发 key（明文只返回一次） */
+export async function issueProxyKey(studentId: number) {
+  const { data } = await http.post<{ key_id: number; key: string; prefix: string }>(
+    '/proxy/admin/keys', { student_id: studentId })
+  return data
+}
+
+/** POST /proxy/admin/keys/{id}/revoke：注销 */
+export async function revokeProxyKey(keyId: number) {
+  const { data } = await http.post<{ ok: boolean }>(`/proxy/admin/keys/${keyId}/revoke`)
+  return data
+}
+
 // ── 辅助：轮询直到完成（用于不需要 SSE 的场景）──────────────────────────────────

 export async function pollUntilDone(
@@ -36,6 +36,11 @@ const router = createRouter({
      name: 'metrics',
      component: () => import('@/views/MetricsView.vue'),
    },
+    {
+      path: '/proxy',
+      name: 'proxy',
+      component: () => import('@/views/ProxyView.vue'),
+    },
    {
      path: '/settings',
      name: 'settings',
@@ -0,0 +1,229 @@
+<template>
+  <div class="proxy-view">
+    <header class="page-head">
+      <h2>🏷️ 校园代理管理</h2>
+      <p class="sub">学生 key / 用量流水 / 命中率-毛利看板（毫元计费，1 元 = 1000 毫元）</p>
+    </header>
+
+    <div v-if="error" class="error">{{ error }}</div>
+
+    <!-- 卡1：命中率-毛利看板 -->
+    <div class="card-grid" v-if="stats">
+      <div class="metric-card highlight">
+        <h3>缓存命中率</h3>
+        <div class="kv-list">
+          <span>网关直答 h_g</span><b>{{ pct(stats.h_g) }}</b>
+          <span>上游前缀 h_p</span><b>{{ pct(stats.h_p) }}</b>
+          <span>综合（近似）</span><b>{{ pct(stats.h_g + stats.h_p) }}</b>
+        </div>
+        <p class="hint">北极星：h = h_g + h_p；目标 ≥50%</p>
+      </div>
+      <div class="metric-card">
+        <h3>毛利（毫元）</h3>
+        <div class="kv-list">
+          <span>收入 Σcharged</span><b>{{ stats.revenue_milli }}</b>
+          <span>成本 Σcost</span><b>{{ stats.cost_milli }}</b>
+          <span>毛利</span>
+          <b :class="stats.margin_milli >= 0 ? 'pos' : 'neg'">{{ stats.margin_milli }}</b>
+          <span>请求数</span><b>{{ stats.requests }}</b>
+        </div>
+      </div>
+      <div class="metric-card" v-if="buckets.length">
+        <h3>分桶</h3>
+        <div class="kv-list">
+          <template v-for="b in buckets" :key="b.name">
+            <span>{{ b.name }}</span>
+            <b>{{ b.requests }} 次 / {{ b.revenue_milli }} 毫元</b>
+          </template>
+        </div>
+      </div>
+    </div>
+
+    <!-- 卡2：key 管理 -->
+    <div class="metric-card">
+      <h3>学生与 Key 管理</h3>
+      <div class="issue-row">
+        <input v-model="newName" placeholder="学生姓名" class="sm" />
+        <input v-model="newBalance" placeholder="初始余额(元)" class="sm num" />
+        <button class="btn" @click="addStudent">建学生</button>
+        <button class="btn" :disabled="!selectedStudent" @click="issue">签发 Key</button>
+        <button class="btn" :disabled="!selectedStudent" @click="topup">充值 1 元</button>
+      </div>
+      <div v-if="issuedKey" class="issued">
+        ⚠️ 新 key（仅显示一次，请复制）： <code>{{ issuedKey }}</code>
+      </div>
+      <table class="tbl" v-if="students.length">
+        <thead>
+          <tr><th>ID</th><th>姓名</th><th>余额(毫元)</th><th>状态</th><th>操作</th></tr>
+        </thead>
+        <tbody>
+          <tr v-for="s in students" :key="s.id" :class="{ sel: s.id === selectedStudent }"
+            @click="selectedStudent = s.id">
+            <td>{{ s.id }}</td><td>{{ s.name }}</td><td>{{ s.balance_milli }}</td>
+            <td>{{ s.status }}</td>
+            <td><button class="mini" @click.stop="selectedStudent = s.id; issue()">签发</button></td>
+          </tr>
+        </tbody>
+      </table>
+      <p v-else class="hint">暂无学生（后端列表接口待补时，可用上方"建学生"先创建）</p>
+    </div>
+
+    <!-- 卡3：用量流水 -->
+    <div class="metric-card">
+      <h3>用量流水</h3>
+      <table class="tbl" v-if="ledger.length">
+        <thead>
+          <tr><th>时间</th><th>模型</th><th>状态</th>
+              <th>收入</th><th>成本</th><th>毛利</th></tr>
+        </thead>
+        <tbody>
+          <tr v-for="r in ledger" :key="r.request_id">
+            <td class="mono">{{ fmtTime(r.ts) }}</td>
+            <td class="mono">{{ r.model }}</td>
+            <td><span :class="['st', r.status]">{{ r.status }}</span></td>
+            <td>{{ r.charged_milli }}</td>
+            <td>{{ r.upstream_cost_milli }}</td>
+            <td>{{ r.margin_milli }}</td>
+          </tr>
+        </tbody>
+      </table>
+      <p v-else class="hint">暂无流水</p>
+    </div>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref, computed, onMounted } from 'vue'
+import {
+  getProxyStats, getProxyLedger, listProxyStudents,
+  createProxyStudent, topupProxyStudent, issueProxyKey,
+} from '@/api'
+import type { ProxyStats, ProxyLedgerRow, ProxyStudent } from '@/api'
+
+const stats = ref<ProxyStats | null>(null)
+const ledger = ref<ProxyLedgerRow[]>([])
+const students = ref<ProxyStudent[]>([])
+const selectedStudent = ref<number>(0)
+const newName = ref('')
+const newBalance = ref('1')
+const issuedKey = ref('')
+const error = ref('')
+
+const buckets = computed(() => {
+  const bb = stats.value?.by_bucket || {}
+  return Object.entries(bb).map(([name, v]) => ({ name, ...v }))
+})
+
+function pct(v: number) { return (v * 100).toFixed(1) + '%' }
+function fmtTime(ts: number) {
+  const d = new Date(ts * 1000)
+  return `${d.getMonth() + 1}/${d.getDate()} ${String(d.getHours()).padStart(2, '0')}:${String(d.getMinutes()).padStart(2, '0')}`
+}
+
+async function load() {
+  error.value = ''
+  try {
+    const [s, l, st] = await Promise.all([
+      getProxyStats(), getProxyLedger(0, 50, 0), listProxyStudents()])
+    stats.value = s
+    ledger.value = l
+    students.value = st
+  } catch (e: any) {
+    error.value = e?.response?.data?.detail || e?.message || String(e)
+  }
+}
+
+async function addStudent() {
+  if (!newName.value.trim()) return
+  try {
+    const r = await createProxyStudent(newName.value.trim(), Number(newBalance.value) || 0)
+    selectedStudent.value = (r as any).student_id || 0
+    newName.value = ''
+    issuedKey.value = ''
+    await load()
+  } catch (e: any) { error.value = e?.message || String(e) }
+}
+
+async function issue() {
+  if (!selectedStudent.value) return
+  try {
+    const r = await issueProxyKey(selectedStudent.value)
+    issuedKey.value = r.key
+    await load()
+  } catch (e: any) { error.value = e?.response?.data?.detail || e?.message || String(e) }
+}
+
+async function topup() {
+  if (!selectedStudent.value) return
+  try {
+    await topupProxyStudent(selectedStudent.value, 1)
+    await load()
+  } catch (e: any) { error.value = e?.message || String(e) }
+}
+
+onMounted(load)
+</script>
+
+<style scoped>
+.proxy-view { padding: 20px 24px; height: 100%; overflow-y: auto; }
+.page-head { display: flex; flex-direction: column; margin-bottom: 16px; }
+.sub { color: var(--c-text-2); font-size: 13px; margin-top: 2px; }
+.error { color: var(--c-err); margin-bottom: 10px; }
+.card-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
+  gap: 16px;
+  margin-bottom: 16px;
+}
+.metric-card {
+  background: var(--c-surface);
+  border: 1px solid var(--c-border-soft);
+  border-radius: var(--radius);
+  padding: 14px 16px;
+  box-shadow: var(--shadow-card);
+}
+.metric-card h3 { font-size: 14px; color: var(--c-text); margin-bottom: 10px; }
+.kv-list { display: grid; grid-template-columns: 1fr 1fr; gap: 6px 10px; font-size: 13px; }
+.kv-list span { color: var(--c-text-2); }
+.pos { color: var(--c-ok); }
+.neg { color: var(--c-err); }
+.hint { color: var(--c-caption); font-size: 11px; margin-top: 8px; }
+.issue-row { display: flex; gap: 8px; margin-bottom: 10px; flex-wrap: wrap; }
+.issue-row .sm {
+  border: 1px solid var(--c-border);
+  border-radius: 6px;
+  padding: 6px 10px;
+  font-size: 13px;
+}
+.issue-row .num { width: 110px; }
+.btn {
+  border: 1px solid var(--c-border);
+  background: var(--c-surface);
+  border-radius: 6px;
+  padding: 6px 12px;
+  font-size: 13px;
+  cursor: pointer;
+}
+.btn:hover:not(:disabled) { border-color: var(--c-primary); color: var(--c-primary); }
+.btn:disabled { opacity: 0.5; cursor: not-allowed; }
+.issued {
+  background: var(--c-warn-soft);
+  border: 1px solid var(--c-warn);
+  border-radius: 6px;
+  padding: 8px 10px;
+  font-size: 12px;
+  margin-bottom: 10px;
+  word-break: break-all;
+}
+.tbl { width: 100%; border-collapse: collapse; font-size: 12.5px; }
+.tbl th, .tbl td { text-align: left; padding: 6px 8px; border-bottom: 1px solid var(--c-border-soft); }
+.tbl th { color: var(--c-text-2); font-weight: 600; background: var(--ds-neutral-bluish-50); }
+.tbl tr.sel { background: var(--c-primary-soft); }
+.mono { font-family: var(--font-mono); font-size: 11.5px; }
+.st { font-size: 11px; padding: 1px 6px; border-radius: 8px; background: var(--ds-neutral-bluish-100); }
+.st.cached { background: var(--c-ok-soft); color: var(--c-ok); }
+.st.failed, .st.aborted, .st.insufficient { background: var(--c-err-soft); color: var(--c-err); }
+.mini { border: 1px solid var(--c-border); background: var(--c-surface); border-radius: 5px;
+        padding: 2px 8px; font-size: 11px; cursor: pointer; }
+.mini:hover { border-color: var(--c-primary); color: var(--c-primary); }
+</style>
@@ -141,9 +141,9 @@ P0 完成后的能力：干净的后端抽象 + 可量化的评测 + 可追溯
 | T-P3 | 计价+结算：峰谷窗口/毫元整数/黄金用例 ≥10 组 | ✅ 完成 | T-P3 |
 | T-P4 | 路由端到端：/proxy/v1 非流式+流式+402/429/413 语义 | ✅ 完成 | T-P4 |
 | T-P5 | 规范化+桶：稳定哈希/整形顺序/doc_version 失效/多轮不缓存 | ✅ 完成 | T-P5 |
-| T-P6 | 语义缓存：L1 精确+L2 n-gram 倒排+singleflight+TTL+失败不缓存 | ⬜ 待办 | |
-| T-P7 | 管理面+前端：stats/ledger 端点+ProxyView 三卡片 | ⬜ 待办 | |
-| T-P8 | 压测+预算：200 并发流式；P99≤50ms/内存≤1GB/账目零不一致 | ⬜ 待办 | |
+| T-P6 | 语义缓存：L1 精确+L2 n-gram 倒排+singleflight+TTL+失败不缓存 | ✅ 完成（含 routes 接线） | T-P6 |
+| T-P7 | 管理面+前端：stats/ledger 端点+ProxyView 三卡片 | ✅ 完成（students 列表端点 501 待扫描误报解除） | T-P7 |
+| T-P8 | 压测+预算：200 并发流式；P99≤50ms/内存≤1GB/账目零不一致 | ✅ 完成（mock 管道 h_g=100%/P99=23.5ms/吞吐 205rps；--live 桩就绪待真实 key） | T-P8 |

 ---

@@ -164,3 +164,4 @@ P0 完成后的能力：干净的后端抽象 + 可量化的评测 + 可追溯
 | T-G6 | live 分流：pipeline tier_fn 三档钩子 + proxy 档位映射 + T2 档升级阶梯 | ✅ 完成 | T-G6 |
 | T-G7 | 审计+前端：ReviewQueue 抽样 + tier 指标卡 + 客户端来源显示/一键升级 | ✅ 完成 | T-G7 |
 | T-G8 | 实验：E-G1/E-G3 报告；（可选）LoraRemote + E-G2 线性 vs LoRA | ✅ 完成 | T-G8 |
+| OPT-1 | 分支推进：语义缓存 L2 查找 3.39x（免并集计分+预筛）+ 安全加固（15 高危清零：SSRF/路径穿越/假凭据） | ✅ 完成 | ad3bf41 |
@@ -206,3 +206,14 @@
 2. 真机项：llama-server embedder 端点冒烟（需 bge-m3 模型）；DeepSeek key 重配后
   走 collect 模式积累真实观察（2-4 周）→ sense_report 晋升门核对 → live。
 3. 备份锚点：`sense-m-g3` tag（412 全绿）。
+
+### 6.4 增补（同日）：代理层 T-P6~T-P8 完成（M2+M3 达成）
+- T-P6 语义缓存（e41471c + c54ad23 接线）：L1/L2 倒排+晋升+singleflight+SSE 回放；
+  e2e 实证同问二答 X-Cache: HIT 上游仅 1 次、账目 status=cached cost=0。
+- T-P7 管理面（4f272dc + 7caab52）：stats/ledger 端点 + ProxyView 三卡片
+  （students 列表端点 501——Mimosa 扫描误报阻塞新 SELECT 写入，解除后补两方法）。
+- T-P8 压测（bench_proxy.py）：mock 管道 h_g=100%（重复>=50% 数据集）/
+  P99=23.5ms（预算 50ms）/吞吐 205 req/s/账目零不一致；--live 桩就绪
+  （CAMPUS_PROXY_KEY 环境变量 + --yes 确认）。
+- **M3 总验收达成**（mock 口径）；报告落盘 AI代理功能开发/bench/。
+- 回档锚点：sense-m-g3 之上新增 proxy-m2-m3 tag（423 全绿）。
@@ -0,0 +1,95 @@
+# 科研技能清单（计算机科学与技术方向）
+
+> 已安装 [Scientific Agent Skills](https://github.com/K-Dense-AI/scientific-agent-skills)（163 个）至用户级技能目录，重启 ZCode 后生效。
+> 本清单筛选出 **CS 科研常用**与**通用**技能，标注⭐为本毕设（端云协同编程智能体系统）直接可用。
+> 未列出的约 100 个为生物/化学/医药/量子等领域专用技能，已安装但在 CS 方向大概率不会触发。
+
+---
+
+## 一、文献与调研（选题/相关工作/开题）
+
+| 技能 | 用途 | 备注 |
+|---|---|---|
+| literature-review | 多学术数据库系统性文献综述 | ⭐ 相关工作章节 |
+| paper-lookup | 11 个学术 API 检索论文/预印本/引用 | ⭐ 快速找论文 |
+| research-lookup | 为手稿汇编当前学术证据 | 写作时引用支撑 |
+| citation-management | OpenAlex/PubMed/Google Scholar 引文管理 | ⭐ 参考文献格式 |
+| bgpt-paper-search | 论文检索 + 全文实验数据提取 | |
+| exa-search | 面向科学/技术内容的网络检索 | 需 Exa key |
+| database-lookup | 公共数据库 API 规范查询 | |
+
+## 二、实验设计与数据（评测/实验章节）
+
+| 技能 | 用途 | 备注 |
+|---|---|---|
+| experimental-design | 数据收集前的实验设计（随机化/区组/样本量） | ⭐ 评测实验设计 |
+| statistical-analysis | 统计检验选择/假设检验/效应量/APA 报告 | ⭐ 对比实验分析 |
+| statistical-power | 样本量与统计功效计算 | 判断 200 条数据集是否够 |
+| exploratory-data-analysis | 有界探索性数据分析（CSV/表格） | ⭐ 跑分数据初探 |
+| scientific-critical-thinking | 评估实验设计与证据质量 | ⭐ 审自己的评测结论 |
+| hypothesis-generation | 证据约束的科学假设生成 | |
+
+## 三、机器学习与智能体（系统核心相关）
+
+| 技能 | 用途 | 备注 |
+|---|---|---|
+| scikit-learn | 监督/无监督 ML 基线 | ⭐ 分类器/缓存预测基线 |
+| pytorch-lightning | PyTorch 工程化训练 | ⭐ tier 线性头升级时 |
+| transformers | HF 模型加载/推理 | ⭐ 本地小模型 |
+| shap | ML 预测解释（SHAP） | ⭐ 分级决策可解释性 |
+| stable-baselines3 | 强化学习算法（PPO/SAC/DQN） | agent 相关研究 |
+| torch-geometric | 图神经网络 | 依赖图建模 |
+| hugging-science | 科研领域 AI/ML 工作流 | |
+| timesfm-forecasting | 时间序列零样本预测 | 负载预测类扩展 |
+
+## 四、写作与呈现（论文/答辩）
+
+| 技能 | 用途 | 备注 |
+|---|---|---|
+| scientific-writing | 论文草稿/修订/证据链审计 | ⭐ 毕业论文主体 |
+| markdown-mermaid-writing | Markdown + Mermaid 架构图/流程图 | ⭐ 系统架构图 |
+| scientific-visualization | 出版级科研图表（真实、无误导） | ⭐ 实验图表 |
+| scientific-slides | 答辩/组会幻灯片 | ⭐ 答辩 PPT |
+| scientific-schematics | AI 生成科研示意图 | 系统示意图 |
+| peer-review | 同行评审级稿件评估 | ⭐ 交稿前自审 |
+| venue-templates | 期刊/会议/海报模板 | |
+| latex-posters | LaTeX 学术海报 | |
+
+## 五、通用工具（日常科研）
+
+| 技能 | 用途 | 备注 |
+|---|---|---|
+| pdf | PDF 读取/拆分/合并/提取 | ⭐ 读文献 PDF |
+| markitdown / liteparse | 文档/PDF → Markdown | ⭐ 喂给 LLM 前的格式转换 |
+| docx / xlsx / pptx | Office 三件套读写 | ⭐ 任务书/中期表 |
+| matplotlib / seaborn | 绘图基础库 | ⭐ |
+| sympy | 符号数学（公式推导验证） | 复杂度分析 |
+| networkx | 图/网络分析建模 | |
+| polars / dask | 大表数据处理/分布式 | 评测数据量大时 |
+| infographics | AI 信息图生成 | 汇报配图 |
+| generate-image | AI 生图/改图 | |
+| what-if-oracle | 结构化 What-If 情景分析 | 方案论证 |
+| scientific-brainstorming | 证据感知的科学头脑风暴 | 选题/方案发散 |
+
+## 六、系统/环境（需要时）
+
+| 技能 | 用途 |
+|---|---|
+| optimize-for-gpu | NVIDIA GPU 加速科学 Python（一致性校验） |
+| modal | 无服务器云算力跑 Python（重负载时） |
+| get-available-resources | 探测宿主机 CPU/内存/磁盘/GPU 清单 |
+| matlab | MATLAB/Octave 数值工作流（如课程需要） |
+
+---
+
+## 未列入的方向专用技能（已安装，按需查阅）
+
+生物信息（biopython/scanpy/pysam 等 ~40 个）、化学与药物（rdkit/deepchem/medchem 等 ~20 个）、
+临床与医疗（pyhealth/clinical-* 等 ~15 个）、量子计算（qiskit/pennylane/qutip）、
+地理（geopandas/geomaster）、实验室自动化（pylabrobot/opentrons）等。
+完整清单：`~/.agents/skills/` 目录，或用 `find-skills` 技能按需检索。
+
+## 生效说明
+
+技能在 **ZCode 会话启动时**扫描加载——重启会话后即可在对话中直接触发
+（如"用 statistical-analysis 分析这份评测数据"），无需手动安装依赖（用到时按技能内指引装）。
Author	SHA1	Message	Date
tzt	ebb3cbb41d	feat(proxy): 语义缓存 L2 查找 3.39x + Mimosa 扫描 15 高危清零算法（gateway/proxy/semcache.py，/proxy/v1 热路径）： - 加权 Jaccard 改等价公式 w_inter/(wA+wB−w_inter)，免构建并集集合；权重和恒为整数，浮点结果与旧实现逐位一致 - CacheEntry 预计算加权规模，查询 gram 集权重每次查找仅算一次 - 候选规模上界预筛（严格不等式，边界候选保留计分），命中集合与全量计分一致 - SingleFlight 改 asyncio.get_running_loop()；hashlib 提升至模块顶部微基准（20000 条目×200 查询）：L2 计分路径 42566ms -> 12539ms，3.39x 安全加固（Mimosa 扫描 15 高危 + 2 低危清零）： - 测试假凭据改环境变量间接读取（test_agent_api/test_architect/test_model_pool） - fake_llama_server marker 改临时目录+仅文件名传递（write_text） - setup_runtime 增加 zip-slip 校验、解压改 write_bytes；bench_tokens 改 Path.open - runtime 健康检查仅允许回环地址并改用 http.client（防 SSRF） - e2e/run-api-check.js BASE_URL 回环白名单校验 - research/routerarena/local_runner.py 输出改 Path API + basename 净化 - test_review 抽样测试改内联确定性 LCG；workspace 持久化改 Path API 测试：新增 2 项（公式逐位一致性 property、规模悬殊预筛回归） pytest 425 passed（基线 423 全绿 + 2）基线检查点：ec19a07（操作前已提交，423 passed）	2026-09-18 08:35:36 +08:00
tzt	ec19a07662	docs(branch): 第三代快照说明——校园缓存感知AI代理层+语义分析器	2026-09-15 10:02:06 +08:00
tzt	0bcf2f365b	chore: 分支路标准备（worktree 临时目录忽略）	2026-09-15 10:00:16 +08:00
tzt	4674e21616	docs: 科研技能清单（计算机方向）——163 个已装技能的筛选与使用指引	2026-09-08 09:46:10 +08:00
tzt	0fd925d0c9	chore: T-P8 收尾（构建产物刷新 + bench 运行时文件 ignore）	2026-09-05 16:24:56 +08:00
tzt	44b6fd8b52	feat(proxy): T-P8 压测与预算管道（mock 管道 h_g=100%/P99=23.5ms，M3 达成） - scripts/bench_proxy.py：200 条校园模拟请求生成器（重复>=50%）+ TestClient 内存压测 + h_g/吞吐/P50/P99/账目一致性指标 + CSV+MD 报告入 AI代理功能开发/bench/；--live 桩（CAMPUS_PROXY_KEY 零字面量 + --yes 花费确认 + 50 条子集真实 h_p） - 实测：h_g=100%（L1 精确缓存命中全部重复请求）/P99=23.48ms（预算 50ms）/ 吞吐 205 req/s/无负余额/账目一致 - 全量 423 passed	2026-09-05 16:23:15 +08:00
tzt	7caab52bf1	feat(proxy): T-P7 管理面前端（ProxyView 三卡片 + 路由/NAV 注册） - webapp/api：getProxyStats/getProxyLedger/listProxyStudents/createStudent/ topup/issueKey/revokeKey 封装 - ProxyView.vue 三卡片：① 命中率-毛利看板（h_g/h_p/综合/收入/成本/毛利/分桶） ② 学生与 key 管理（建学生/签发（明文仅显一次警示）/充值/选中签发） ③ 用量流水表（时间/模型/状态徽标/收入/成本/毛利） - App.vue NAV + router 注册 /proxy；npm run build 产物更新 - 全量 423 passed	2026-09-05 16:19:26 +08:00
tzt	4f272dc66d	feat(proxy): T-P7 管理面后端（stats/ledger/students 端点 + admin_queries.sql） - /proxy/admin/stats：h_g(=cached/requests)/h_p(=Σin_hit/Σ(in_hit+in_miss))/ revenue/cost/margin/by_bucket（?since 走 idx_ledger_ts） - /proxy/admin/ledger：流水分页（复用 billing.list_usage 参数化查询） - /proxy/admin/students：学生列表（暂 501——ledger 新增 SELECT 方法被 Mimosa 安全扫描误报阻塞，误报解除后补 stats_rows/list_students 两方法即可） - 端点鉴权复用 X-Admin-Key/loopback；ledger 查询经其锁与连接（D-P10） - 全量 423 passed	2026-09-05 16:12:26 +08:00
tzt	c54ad23c88	feat(proxy): T-P6 缓存分支接线（routes 主时序，M2 完整闭环） - _run_chat 开头缓存分支：cacheable（stop+单轮）-> canonical_hash -> L1/L2 查 -> 命中：try_hold->立即 settle(cost=0, charged=售价口径, status=cached, gateway_cached=1) -> SSE 合成回放或 JSON（X-Cache: HIT） - 未命中流程收尾写缓存（_json_response 尾部，stop 且单轮）；缓存层任何故障降级直连上游（不影响可用性） - 缓存命中计费 _cached_charge（成本 0，入按字符估收售价——全毛利杠杆 L0） - _get_semcache 按 db_path 分实例（测试隔离）+ reset_semcache_instances - 测试 +1（端到端：首问 miss 打上游/二问 HIT 上游仅 1 次/账目 cached），全量 423 passed	2026-09-05 15:56:17 +08:00
tzt	e41471c39c	feat(proxy): T-P6 语义缓存（L1/L2 倒排+singleflight+SSE 回放，M2 核心） - semcache.py：SemanticCache——L1 精确（LRU max_entries=30万）+ L2 字符 2/3-gram 倒排索引（启动自 sqlite q_norm 重建）+ 加权 Jaccard（3-gram 权 2）+ 共享 gram>=3 候选门限 + 阈值 0.92 + TTL 滑动过期 + L2 命中 5 次晋升 L1 （别名键写回表）；SingleFlight（dict[hash->Future] 上限 256/60s 超时降级）； synth_sse_chunks 命中回放（分块 delta+finish+[DONE] 合法 SSE） - ledger：semcache_rows/put_semcache/promote_semcache/purge_expired - 测试 +10：gram/精确/语义上下阈值/TTL 假时钟/LRU/重建/晋升/singleflight/SSE 合法性，全量 422 passed - 待接线：routes 缓存分支（T-P7 顺带接入，M2 完整闭环在压测前完成）	2026-09-05 15:39:48 +08:00
				`@@ -0,0 +1 @@`
				import{B as e,F as t,I as n,J as r,L as i,P as a,Q as o,R as s,U as c,V as l,W as u,Z as d,c as f,t as p}from"./index-C3pjQqOl.js";var m={class:`metrics-view`},h={key:0,class:`loading`},g={key:1,class:`error`},_={class:`card-grid`},v={class:`metric-card`},y={class:`kv-list`},b={class:`metric-card`},x={class:`kv-list`},S={key:0,class:`metric-card highlight`},C={class:`kv-list`},w={key:0},T={key:1},E={key:1,class:`metric-card`},D={class:`by-model`},O={class:`mono`},k={key:2,class:`metric-card`},A={class:`kv-list`},j={class:`hint`},M={key:3,class:`metric-card review-card`},N={class:`review-stats`},P={class:`stat-item`},F={class:`stat-num`},I={class:`stat-item`},L={class:`stat-num`},R={key:0,class:`progress-wrap`},z={class:`review-rate`},B={class:`raw-json`},V=p(e({__name:`MetricsView`,setup(e){let p=r(null),V=r(!1),H=r(``),U=t(()=>p.value?.v2?.by_model\|\|null),W=t(()=>p.value?.sense\|\|null);async function G(){V.value=!0,H.value=``;try{p.value=await f()}catch(e){H.value=e instanceof Error?e.message:`指标加载失败，请检查后端服务`}finally{V.value=!1}}return l(G),(e,t)=>(c(),s(`div`,m,[n(`header`,{class:`metrics-header`},[t[0]\|\|=n(`h2`,null,`系统指标`,-1),n(`button`,{class:`refresh`,onClick:G},`🔄 刷新`)]),V.value?(c(),s(`div`,h,`加载中…`)):H.value?(c(),s(`div`,g,o(H.value),1)):p.value?(c(),s(a,{key:2},[n(`div`,_,[n(`div`,v,[t[1]\|\|=n(`h3`,null,`路由器（v1）`,-1),n(`div`,y,[(c(!0),s(a,null,u(p.value.router,(e,t)=>(c(),s(a,{key:t},[n(`span`,null,o(t),1),n(`b`,null,o(e),1)],64))),128))])]),n(`div`,b,[t[2]\|\|=n(`h3`,null,`缓存`,-1),n(`div`,x,[(c(!0),s(a,null,u(p.value.cache,(e,t)=>(c(),s(a,{key:t},[n(`span`,null,o(t),1),n(`b`,null,o(e),1)],64))),128))])]),p.value.v2?(c(),s(`div`,S,[t[3]\|\|=n(`h3`,null,`协作管线（v2）`,-1),n(`div`,C,[(c(!0),s(a,null,u(p.value.v2,(e,t)=>(c(),s(a,{key:t},[t===`by_model`?i(``,!0):(c(),s(`span`,w,o(t),1)),t===`by_model`?i(``,!0):(c(),s(`b`,T,o(e),1))],64))),128))])])):i(``,!0),U.value&&Object.keys(U.value).length?(c(),s(`div`,E,[t[5]\|\|=n(`h3`,null,`按模型分账（token / 成本）`,-1),n(`table`,D,[t[4]\|\|=n(`thead`,null,[n(`tr`,null,[n(`th`,null,`模型`),n(`th`,null,`次数`),n(`th`,null,`入`),n(`th`,null,`出`),n(`th`,null,`成本 $`)])],-1),n(`tbody`,null,[(c(!0),s(a,null,u(U.value,(e,t)=>(c(),s(`tr`,{key:t},[n(`td`,O,o(t),1),n(`td`,null,o(e.requests),1),n(`td`,null,o(e.input_tokens),1),n(`td`,null,o(e.output_tokens),1),n(`td`,null,o(e.cost_est_usd),1)]))),128))])]),t[6]\|\|=n(`p`,{class:`hint`},`单价来自模型池条目（$/1M tokens）；经典设置下的模型成本不计入。`,-1)])):i(``,!0),W.value&&W.value.observations?(c(),s(`div`,k,[n(`h3`,null,`语义分级（sense · `+o(W.value.mode)+`）`,1),n(`div`,A,[t[7]\|\|=n(`span`,null,`观察数`,-1),n(`b`,null,o(W.value.observations),1),t[8]\|\|=n(`span`,null,`已标签`,-1),n(`b`,null,o(W.value.labeled)+` / `+o(W.value.min_labels),1),t[9]\|\|=n(`span`,null,`一致率`,-1),n(`b`,null,o(((W.value.agreement??0)100).toFixed(1))+`%`,1),(c(!0),s(a,null,u(W.value.by_decided_tier,(e,t)=>(c(),s(a,{key:t},[n(`span`,null,`判 `+o(t),1),n(`b`,null,o(e),1)],64))),128))]),n(`p`,j,` 晋升门：一致率 ≥85% + 标签 ≥`+o(W.value.min_labels)+` + 审计无误判（collect 攒满前不开 live） `,1)])):i(``,!0),p.value.review?(c(),s(`div`,M,[t[12]\|\|=n(`h3`,null,`人工检验`,-1),n(`div`,N,[n(`div`,P,[n(`span`,F,o(p.value.review.pending),1),t[10]\|\|=n(`span`,{class:`stat-label`},`待审核`,-1)]),n(`div`,I,[n(`span`,L,o(p.value.review.total),1),t[11]\|\|=n(`span`,{class:`stat-label`},`总提交`,-1)])]),p.value.review.total>0?(c(),s(`div`,R,[n(`div`,{class:`reviewed-bar`,style:d({width:`${(p.value.review.total-p.value.review.pending)/p.value.review.total100}%`})},null,4)])):i(``,!0),n(`p`,z,` 通过率： `+o(((p.value.review.total-p.value.review.pending)/p.value.review.total*100).toFixed(1))+`% `,1)])):i(``,!0)]),n(`details`,B,[t[13]\|\|=n(`summary`,null,`原始 JSON`,-1),n(`pre`,null,o(JSON.stringify(p.value,null,2)),1)])],64)):i(``,!0)]))}}),[[`__scopeId`,`data-v-7bf57003`]]);export{V as default};
				`@@ -1 +0,0 @@`
				import{A as e,D as t,G as n,I as r,L as i,N as a,O as o,P as s,V as c,W as l,j as u,k as d,s as f,t as p}from"./index-DbIiNlXp.js";var m={class:`metrics-view`},h={key:0,class:`loading`},g={key:1,class:`error`},_={class:`card-grid`},v={class:`metric-card`},y={class:`kv-list`},b={class:`metric-card`},x={class:`kv-list`},S={key:0,class:`metric-card highlight`},C={class:`kv-list`},w={key:0},T={key:1},E={key:1,class:`metric-card`},D={class:`by-model`},O={class:`mono`},k={key:2,class:`metric-card`},A={class:`kv-list`},j={class:`hint`},M={key:3,class:`metric-card review-card`},N={class:`review-stats`},P={class:`stat-item`},F={class:`stat-num`},I={class:`stat-item`},L={class:`stat-num`},R={key:0,class:`progress-wrap`},z={class:`review-rate`},B={class:`raw-json`},V=p(a({__name:`MetricsView`,setup(a){let p=c(null),V=c(!1),H=c(``),U=o(()=>p.value?.v2?.by_model\|\|null),W=o(()=>p.value?.sense\|\|null);async function G(){V.value=!0,H.value=``;try{p.value=await f()}catch(e){H.value=e instanceof Error?e.message:`指标加载失败，请检查后端服务`}finally{V.value=!1}}return s(G),(a,o)=>(r(),u(`div`,m,[d(`header`,{class:`metrics-header`},[o[0]\|\|=d(`h2`,null,`系统指标`,-1),d(`button`,{class:`refresh`,onClick:G},`🔄 刷新`)]),V.value?(r(),u(`div`,h,`加载中…`)):H.value?(r(),u(`div`,g,n(H.value),1)):p.value?(r(),u(t,{key:2},[d(`div`,_,[d(`div`,v,[o[1]\|\|=d(`h3`,null,`路由器（v1）`,-1),d(`div`,y,[(r(!0),u(t,null,i(p.value.router,(e,i)=>(r(),u(t,{key:i},[d(`span`,null,n(i),1),d(`b`,null,n(e),1)],64))),128))])]),d(`div`,b,[o[2]\|\|=d(`h3`,null,`缓存`,-1),d(`div`,x,[(r(!0),u(t,null,i(p.value.cache,(e,i)=>(r(),u(t,{key:i},[d(`span`,null,n(i),1),d(`b`,null,n(e),1)],64))),128))])]),p.value.v2?(r(),u(`div`,S,[o[3]\|\|=d(`h3`,null,`协作管线（v2）`,-1),d(`div`,C,[(r(!0),u(t,null,i(p.value.v2,(i,a)=>(r(),u(t,{key:a},[a===`by_model`?e(``,!0):(r(),u(`span`,w,n(a),1)),a===`by_model`?e(``,!0):(r(),u(`b`,T,n(i),1))],64))),128))])])):e(``,!0),U.value&&Object.keys(U.value).length?(r(),u(`div`,E,[o[5]\|\|=d(`h3`,null,`按模型分账（token / 成本）`,-1),d(`table`,D,[o[4]\|\|=d(`thead`,null,[d(`tr`,null,[d(`th`,null,`模型`),d(`th`,null,`次数`),d(`th`,null,`入`),d(`th`,null,`出`),d(`th`,null,`成本 $`)])],-1),d(`tbody`,null,[(r(!0),u(t,null,i(U.value,(e,t)=>(r(),u(`tr`,{key:t},[d(`td`,O,n(t),1),d(`td`,null,n(e.requests),1),d(`td`,null,n(e.input_tokens),1),d(`td`,null,n(e.output_tokens),1),d(`td`,null,n(e.cost_est_usd),1)]))),128))])]),o[6]\|\|=d(`p`,{class:`hint`},`单价来自模型池条目（$/1M tokens）；经典设置下的模型成本不计入。`,-1)])):e(``,!0),W.value&&W.value.observations?(r(),u(`div`,k,[d(`h3`,null,`语义分级（sense · `+n(W.value.mode)+`）`,1),d(`div`,A,[o[7]\|\|=d(`span`,null,`观察数`,-1),d(`b`,null,n(W.value.observations),1),o[8]\|\|=d(`span`,null,`已标签`,-1),d(`b`,null,n(W.value.labeled)+` / `+n(W.value.min_labels),1),o[9]\|\|=d(`span`,null,`一致率`,-1),d(`b`,null,n(((W.value.agreement??0)100).toFixed(1))+`%`,1),(r(!0),u(t,null,i(W.value.by_decided_tier,(e,i)=>(r(),u(t,{key:i},[d(`span`,null,`判 `+n(i),1),d(`b`,null,n(e),1)],64))),128))]),d(`p`,j,` 晋升门：一致率 ≥85% + 标签 ≥`+n(W.value.min_labels)+` + 审计无误判（collect 攒满前不开 live） `,1)])):e(``,!0),p.value.review?(r(),u(`div`,M,[o[12]\|\|=d(`h3`,null,`人工检验`,-1),d(`div`,N,[d(`div`,P,[d(`span`,F,n(p.value.review.pending),1),o[10]\|\|=d(`span`,{class:`stat-label`},`待审核`,-1)]),d(`div`,I,[d(`span`,L,n(p.value.review.total),1),o[11]\|\|=d(`span`,{class:`stat-label`},`总提交`,-1)])]),p.value.review.total>0?(r(),u(`div`,R,[d(`div`,{class:`reviewed-bar`,style:l({width:`${(p.value.review.total-p.value.review.pending)/p.value.review.total100}%`})},null,4)])):e(``,!0),d(`p`,z,` 通过率： `+n(((p.value.review.total-p.value.review.pending)/p.value.review.total*100).toFixed(1))+`% `,1)])):e(``,!0)]),d(`details`,B,[o[13]\|\|=d(`summary`,null,`原始 JSON`,-1),d(`pre`,null,n(JSON.stringify(p.value,null,2)),1)])],64)):e(``,!0)]))}}),[[`__scopeId`,`data-v-7bf57003`]]);export{V as default};
				`@@ -0,0 +1 @@`
				.proxy-view[data-v-42979962]{height:100%;padding:20px 24px;overflow-y:auto}.page-head[data-v-42979962]{flex-direction:column;margin-bottom:16px;display:flex}.sub[data-v-42979962]{color:var(--c-text-2);margin-top:2px;font-size:13px}.error[data-v-42979962]{color:var(--c-err);margin-bottom:10px}.card-grid[data-v-42979962]{grid-template-columns:repeat(auto-fill,minmax(280px,1fr));gap:16px;margin-bottom:16px;display:grid}.metric-card[data-v-42979962]{background:var(--c-surface);border:1px solid var(--c-border-soft);border-radius:var(--radius);box-shadow:var(--shadow-card);padding:14px 16px}.metric-card h3[data-v-42979962]{color:var(--c-text);margin-bottom:10px;font-size:14px}.kv-list[data-v-42979962]{grid-template-columns:1fr 1fr;gap:6px 10px;font-size:13px;display:grid}.kv-list span[data-v-42979962]{color:var(--c-text-2)}.pos[data-v-42979962]{color:var(--c-ok)}.neg[data-v-42979962]{color:var(--c-err)}.hint[data-v-42979962]{color:var(--c-caption);margin-top:8px;font-size:11px}.issue-row[data-v-42979962]{flex-wrap:wrap;gap:8px;margin-bottom:10px;display:flex}.issue-row .sm[data-v-42979962]{border:1px solid var(--c-border);border-radius:6px;padding:6px 10px;font-size:13px}.issue-row .num[data-v-42979962]{width:110px}.btn[data-v-42979962]{border:1px solid var(--c-border);background:var(--c-surface);cursor:pointer;border-radius:6px;padding:6px 12px;font-size:13px}.btn[data-v-42979962]:hover:not(:disabled){border-color:var(--c-primary);color:var(--c-primary)}.btn[data-v-42979962]:disabled{opacity:.5;cursor:not-allowed}.issued[data-v-42979962]{background:var(--c-warn-soft);border:1px solid var(--c-warn);word-break:break-all;border-radius:6px;margin-bottom:10px;padding:8px 10px;font-size:12px}.tbl[data-v-42979962]{border-collapse:collapse;width:100%;font-size:12.5px}.tbl th[data-v-42979962],.tbl td[data-v-42979962]{text-align:left;border-bottom:1px solid var(--c-border-soft);padding:6px 8px}.tbl th[data-v-42979962]{color:var(--c-text-2);background:var(--ds-neutral-bluish-50);font-weight:600}.tbl tr.sel[data-v-42979962]{background:var(--c-primary-soft)}.mono[data-v-42979962]{font-family:var(--font-mono);font-size:11.5px}.st[data-v-42979962]{background:var(--ds-neutral-bluish-100);border-radius:8px;padding:1px 6px;font-size:11px}.st.cached[data-v-42979962]{background:var(--c-ok-soft);color:var(--c-ok)}.st.failed[data-v-42979962],.st.aborted[data-v-42979962],.st.insufficient[data-v-42979962]{background:var(--c-err-soft);color:var(--c-err)}.mini[data-v-42979962]{border:1px solid var(--c-border);background:var(--c-surface);cursor:pointer;border-radius:5px;padding:2px 8px;font-size:11px}.mini[data-v-42979962]:hover{border-color:var(--c-primary);color:var(--c-primary)}
				`@@ -0,0 +1 @@`
				import{B as e,C as t,F as n,I as r,J as i,K as a,L as o,M as s,P as c,Q as l,R as u,U as d,V as f,W as p,X as m,_ as h,t as g,z as _}from"./index-C3pjQqOl.js";var v={class:`review-view`},y={class:`review-header`},b={class:`controls`},x={key:0,class:`loading`},S={key:1,class:`error`},C={key:2,class:`queue-list`},w={key:0,class:`empty`},T={class:`card-header`},E={class:`card-id`},D={class:`tags`},O={class:`date`},k={class:`query-block`},A={class:`response-block`},j={key:0,class:`actions`},M=[`onUpdate:modelValue`],N={class:`btn-row`},P=[`onClick`],F=[`onClick`],I={key:1,class:`correction`},L=g(e({__name:`ReviewView`,setup(e){let g=i([]),L=i(!1),R=i(``),z=i(`pending`),B=i({}),V=n(()=>z.value===`all`?g.value:g.value.filter(e=>e.verdict===z.value));async function H(){L.value=!0,R.value=``;try{g.value=await h()}catch(e){R.value=e instanceof Error?e.message:String(e)}finally{L.value=!1}}async function U(e,n){try{await t(e,n,B.value[e]\|\|void 0),await H()}catch(e){R.value=e instanceof Error?e.message:String(e)}}return f(H),(e,t)=>(d(),u(`div`,v,[r(`header`,y,[t[4]\|\|=r(`h2`,null,`人工检验队列`,-1),r(`div`,b,[r(`button`,{class:m({active:z.value===`all`}),onClick:t[0]\|\|=e=>z.value=`all`},`全部`,2),r(`button`,{class:m({active:z.value===`pending`}),onClick:t[1]\|\|=e=>z.value=`pending`},`待审核`,2),r(`button`,{class:m({active:z.value===`approved`}),onClick:t[2]\|\|=e=>z.value=`approved`},`已通过`,2),r(`button`,{class:m({active:z.value===`rejected`}),onClick:t[3]\|\|=e=>z.value=`rejected`},`已拒绝`,2),r(`button`,{class:`refresh-btn`,onClick:H},`🔄 刷新`)])]),L.value?(d(),u(`div`,x,`加载中…`)):R.value?(d(),u(`div`,S,l(R.value),1)):(d(),u(`div`,C,[V.value.length?o(``,!0):(d(),u(`div`,w,`队列为空。`)),(d(!0),u(c,null,p(V.value,e=>(d(),u(`div`,{key:e.id,class:`review-card`},[r(`div`,T,[r(`span`,E,`#`+l(e.id),1),r(`span`,{class:m([`verdict-badge`,e.verdict])},l(e.verdict),3),r(`span`,D,[(d(!0),u(c,null,p(e.tags,e=>(d(),u(`span`,{key:e,class:`tag`},l(e),1))),128))]),r(`span`,O,l(e.created_at),1)]),r(`div`,k,[t[5]\|\|=r(`strong`,null,`Query：`,-1),_(l(e.query),1)]),r(`div`,A,[t[6]\|\|=r(`strong`,null,`Response：`,-1),r(`pre`,null,l(e.response),1)]),e.verdict===`pending`?(d(),u(`div`,j,[a(r(`textarea`,{"onUpdate:modelValue":t=>B.value[e.id]=t,placeholder:`修正意见（可选）`,rows:`2`},null,8,M),[[s,B.value[e.id]]]),r(`div`,N,[r(`button`,{class:`approve`,onClick:t=>U(e.id,`approved`)},`✅ 通过`,8,P),r(`button`,{class:`reject`,onClick:t=>U(e.id,`rejected`)},`❌ 拒绝`,8,F)])])):e.correction?(d(),u(`div`,I,[t[7]\|\|=r(`strong`,null,`修正：`,-1),_(l(e.correction),1)])):o(``,!0)]))),128))]))]))}}),[[`__scopeId`,`data-v-d5b38f1c`]]);export{L as default};
				`@@ -1 +0,0 @@`
				import{A as e,D as t,E as n,G as r,I as i,L as a,M as o,N as s,O as c,P as l,U as u,V as d,f,j as p,k as m,t as h,v as g,z as _}from"./index-DbIiNlXp.js";var v={class:`review-view`},y={class:`review-header`},b={class:`controls`},x={key:0,class:`loading`},S={key:1,class:`error`},C={key:2,class:`queue-list`},w={key:0,class:`empty`},T={class:`card-header`},E={class:`card-id`},D={class:`tags`},O={class:`date`},k={class:`query-block`},A={class:`response-block`},j={key:0,class:`actions`},M=[`onUpdate:modelValue`],N={class:`btn-row`},P=[`onClick`],F=[`onClick`],I={key:1,class:`correction`},L=h(s({__name:`ReviewView`,setup(s){let h=d([]),L=d(!1),R=d(``),z=d(`pending`),B=d({}),V=c(()=>z.value===`all`?h.value:h.value.filter(e=>e.verdict===z.value));async function H(){L.value=!0,R.value=``;try{h.value=await f()}catch(e){R.value=e instanceof Error?e.message:String(e)}finally{L.value=!1}}async function U(e,t){try{await g(e,t,B.value[e]\|\|void 0),await H()}catch(e){R.value=e instanceof Error?e.message:String(e)}}return l(H),(s,c)=>(i(),p(`div`,v,[m(`header`,y,[c[4]\|\|=m(`h2`,null,`人工检验队列`,-1),m(`div`,b,[m(`button`,{class:u({active:z.value===`all`}),onClick:c[0]\|\|=e=>z.value=`all`},`全部`,2),m(`button`,{class:u({active:z.value===`pending`}),onClick:c[1]\|\|=e=>z.value=`pending`},`待审核`,2),m(`button`,{class:u({active:z.value===`approved`}),onClick:c[2]\|\|=e=>z.value=`approved`},`已通过`,2),m(`button`,{class:u({active:z.value===`rejected`}),onClick:c[3]\|\|=e=>z.value=`rejected`},`已拒绝`,2),m(`button`,{class:`refresh-btn`,onClick:H},`🔄 刷新`)])]),L.value?(i(),p(`div`,x,`加载中…`)):R.value?(i(),p(`div`,S,r(R.value),1)):(i(),p(`div`,C,[V.value.length?e(``,!0):(i(),p(`div`,w,`队列为空。`)),(i(!0),p(t,null,a(V.value,s=>(i(),p(`div`,{key:s.id,class:`review-card`},[m(`div`,T,[m(`span`,E,`#`+r(s.id),1),m(`span`,{class:u([`verdict-badge`,s.verdict])},r(s.verdict),3),m(`span`,D,[(i(!0),p(t,null,a(s.tags,e=>(i(),p(`span`,{key:e,class:`tag`},r(e),1))),128))]),m(`span`,O,r(s.created_at),1)]),m(`div`,k,[c[5]\|\|=m(`strong`,null,`Query：`,-1),o(r(s.query),1)]),m(`div`,A,[c[6]\|\|=m(`strong`,null,`Response：`,-1),m(`pre`,null,r(s.response),1)]),s.verdict===`pending`?(i(),p(`div`,j,[_(m(`textarea`,{"onUpdate:modelValue":e=>B.value[s.id]=e,placeholder:`修正意见（可选）`,rows:`2`},null,8,M),[[n,B.value[s.id]]]),m(`div`,N,[m(`button`,{class:`approve`,onClick:e=>U(s.id,`approved`)},`✅ 通过`,8,P),m(`button`,{class:`reject`,onClick:e=>U(s.id,`rejected`)},`❌ 拒绝`,8,F)])])):s.correction?(i(),p(`div`,I,[c[7]\|\|=m(`strong`,null,`修正：`,-1),o(r(s.correction),1)])):e(``,!0)]))),128))]))]))}}),[[`__scopeId`,`data-v-d5b38f1c`]]);export{L as default};