feat: 故障推送 / 公需课自动重学 / 插件解耦，发版 v0.0.8

体检修复：启动日志不再打印 AUTH_TOKEN、密钥可清空、推送失败不占整段冷却、目录名与文案统一成 plugins/。

src/study_tool/bank.py

@@ -0,0 +1,122 @@
+"""题库匹配 + 大模型兜底，与平台无关。
+
+插件负责两头：把平台的题目转成统一格式、把答案填回平台；中间「这题选什么」由这里决定：
+  1. 题库命中（题干归一化后：精确 -> 互相包含 -> 相似度）就直接用；
+  2. 没命中的题交给设置里配置的大模型；没配大模型就留空，由插件决定怎么处理。
+
+题目格式：{"no": 题号, "type": 题型名（单选/多选/判断），"title": 题干, "options": [{"key": "A", "text": "..."}]}
+题库格式（插件目录里的 *.json，可放多个）：[{"q": 题干, "a": "B"}]，多选写 "ABC"。
+返回：{题号: {"answer": ["A", "C"], "source": "bank" | "llm"}}，没答出的题不在结果里。
+"""
+import difflib
+import json
+import re
+from pathlib import Path
+
+from study_tool import runtime
+from study_tool.config.settings import load_settings
+from study_tool.llm import llm_chat
+
+BATCH = 5  # 大模型每批问几题，单批失败只影响这一批
+FUZZY = 0.9  # 相似度命中的下限
+MIN_CONTAIN = 8  # 互相包含匹配时，两边都至少要有这么多字，免得短题干乱命中
+SYSTEM = (
+    "你是考试答题助手。根据题干和选项判断正确答案。\n"
+    "单选题返回一个选项字母；多选题返回全部正确的选项字母连写（如 AC）；判断题返回 A（正确）或 B（错误）。\n"
+    '只输出 JSON：{"answers":{"题号":"答案"}}，不要输出解释或多余文字。'
+)
+
+
+def norm(s):
+    """去掉空白和中英文标点、统一大小写，题干才能跨页面格式比较。"""
+    return re.sub(r"[\W_]+", "", str(s or "")).lower()
+
+
+def letters(raw, options=None):
+    """把 "A#C" / "AC" / ["A", "C"] 收敛成选项字母列表；有选项时只留合法字母，不是字母答案返回空。"""
+    keys = [o.get("key") for o in options or [] if o.get("key")]
+    if isinstance(raw, (list, tuple)):
+        raw = "".join(map(str, raw))
+    s = re.sub(r"[#,，、/\s]+", "", str(raw or "")).upper()
+    if not re.fullmatch(r"[A-F]+", s):
+        return []
+    return [c for c in dict.fromkeys(s) if not keys or c in keys]
+
+
+class Bank:
+    def __init__(self, items):
+        self.items = [(norm(i["q"]), i["a"]) for i in items if i.get("q") and i.get("a")]
+        self.exact = dict(self.items)
+
+    def find(self, title):
+        """返回题库里的答案原文，没命中返回空串。"""
+        t = norm(title)
+        if not t:
+            return ""
+        if t in self.exact:
+            return self.exact[t]
+        for q, a in self.items:
+            if min(len(q), len(t)) >= MIN_CONTAIN and (q in t or t in q):
+                return a
+        q, a = max(self.items, key=lambda x: difflib.SequenceMatcher(None, t, x[0]).ratio(), default=("", ""))
+        return a if q and difflib.SequenceMatcher(None, t, q).ratio() >= FUZZY else ""
+
+
+def load(folder):
+    """读目录下全部 *.json 合并成一个题库；目录不存在就是空题库。"""
+    items = []
+    for f in sorted(Path(folder).glob("*.json")):
+        items += json.loads(f.read_text(encoding="utf-8"))
+    return Bank(items)
+
+
+def ask(part, cfg):
+    """问大模型一批题，返回 {题号: 原始答案}。"""
+    lines = []
+    for q in part:
+        opts = "；".join("%s. %s" % (o.get("key"), o.get("text")) for o in q.get("options") or [])
+        lines.append("第%s题（%s）%s\n选项：%s" % (q.get("no"), q.get("type") or "单选", q.get("title") or "", opts or "无"))
+    content = llm_chat(
+        [{"role": "system", "content": cfg.get("prompt") or SYSTEM}, {"role": "user", "content": "\n\n".join(lines)}],
+        cfg,
+        max_tokens=1600,
+    )
+    m = re.search(r"\{.*\}", content.replace("```json", "").replace("```", ""), re.S)
+    data = json.loads(m.group(0)) if m else {}
+    data = data.get("answers") if isinstance(data.get("answers"), dict) else data
+    return {str(k): v for k, v in data.items()}
+
+
+def llm_fill(key, miss, out):
+    cfg = load_settings()["llm"]
+    if not (cfg.get("api_key") and cfg.get("base_url")):
+        runtime.log(key, "题库没命中 %d 题，且还没配置大模型（右上角「设置」），这些题留空" % len(miss), level="warn")
+        return
+    before = len(out)
+    for i in range(0, len(miss), BATCH):
+        part = miss[i : i + BATCH]
+        try:
+            got = ask(part, cfg)
+        except Exception as e:
+            runtime.log(key, "AI 答题第 %d 批失败：%s" % (i // BATCH + 1, str(e)[:160]), level="warn")
+            continue
+        for q in part:
+            a = letters(got.get(str(q.get("no"))), q.get("options"))
+            if a:
+                out[str(q.get("no"))] = {"answer": a, "source": "llm"}
+    runtime.log(key, "AI 补答 %d/%d 题（答案仅供参考，未核对）" % (len(out) - before, len(miss)))
+
+
+def answer(key, questions, bank=None, use_llm=True):
+    """题库优先，没命中的走大模型。日志统一走 runtime.log(key, ...)。"""
+    out, miss = {}, []
+    for q in questions:
+        a = letters(bank.find(q.get("title")), q.get("options")) if bank else []
+        if a:
+            out[str(q.get("no"))] = {"answer": a, "source": "bank"}
+        else:
+            miss.append(q)
+    runtime.log(key, "题库命中 %d/%d 题" % (len(out), len(questions)))
+    if miss and use_llm:
+        llm_fill(key, miss, out)
+    return out