From 545ac141fff3715ac13c3acb012d74a27edd99f8 Mon Sep 17 00:00:00 2001
From: Jan' s <jans@JandeMac-mini-2.local>
Date: Wed, 6 May 2026 20:54:11 +0800
Subject: [PATCH] feat: add AIRecognizerEnhancer plugin

---
 icons/airecognizerenhancer.png              |  Bin 0 -> 221789 bytes
 package.json                                |   27 +-
 package.v2.json                             |   25 +
 plugins.v2/airecognizerenhancer/README.md   |   99 +
 plugins.v2/airecognizerenhancer/__init__.py | 2039 +++++++++++++++++++
 plugins/airecognizerenhancer/README.md      |   99 +
 plugins/airecognizerenhancer/__init__.py    | 2039 +++++++++++++++++++
 7 files changed, 4327 insertions(+), 1 deletion(-)
 create mode 100644 icons/airecognizerenhancer.png
 create mode 100644 plugins.v2/airecognizerenhancer/README.md
 create mode 100644 plugins.v2/airecognizerenhancer/__init__.py
 create mode 100644 plugins/airecognizerenhancer/README.md
 create mode 100644 plugins/airecognizerenhancer/__init__.py

diff --git a/icons/airecognizerenhancer.png b/icons/airecognizerenhancer.png
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/package.json b/package.json
index 8af8f18..52c2343 100644
--- a/package.json
+++ b/package.json
@@ -1073,5 +1073,30 @@
     "history": {
       "v1.0": "初始版本，支持 IPv4/IPv6、泛域名、多记录配置、更新历史详情页"
     }
+  },
+  "AIRecognizerEnhancer": {
+    "name": "AI识别增强",
+    "description": "直接复用 MoviePilot 当前 LLM 配置，在原生识别失败后做本地结构化识别兜底，并交回原生链路继续二次识别。",
+    "labels": "AI,识别,LLM,本地兜底,MoviePilot,TMDB",
+    "version": "0.1.12",
+    "icon": "airecognizerenhancer.png",
+    "author": "liuyuexi1987",
+    "level": 1,
+    "v2": true,
+    "history": {
+      "0.1.12": "兼容 MoviePilot 最新 LLM 路径与异步 get_llm 接口，修复最新版 MP 下插件加载失败问题。",
+      "0.1.11": "同步运行态版本，保持本地结构化识别、失败样本闭环和识别词建议能力一致。",
+      "0.1.10": "新增识别词建议模型退化时的精确规则兜底，保证批量建议/批量写入在上游异常时仍能尽量落地。",
+      "0.1.9": "新增失败样本精简摘要接口，并让批量建议/批量写入附带低 token 文本摘要，便于智能体直接消费。",
+      "0.1.8": "新增失败样本批量建议与批量写入接口，可一次处理一批失败样本，进一步减少人工逐条操作。",
+      "0.1.7": "新增失败样本批量复查接口，可批量重跑样本并在确认修复后批量出队。",
+      "0.1.6": "新增失败样本复查接口，可按当前识别词与当前识别器重跑样本，并在确认修复后自动出队。",
+      "0.1.5": "新增失败样本出队动作，支持按索引移除单条样本，并在写入识别词后自动移除已处理样本。",
+      "0.1.4": "新增失败样本洞察接口，自动归纳重复问题、失败原因和优先处理样本，帮助更快挑出值得写识别词的样本。",
+      "0.1.3": "新增失败样本摘要、样本清理、样本去重和保留上限控制，让样本工作流更适合长期运行与智能体使用。",
+      "0.1.2": "新增按失败样本直接生成建议和直接写入规则的快捷 API，进一步缩短从失败样本到 CustomIdentifiers 的闭环。",
+      "0.1.1": "新增失败样本查看、自定义识别词建议和一键追加写入能力，让 AI 识别增强开始和 MoviePilot 原生 CustomIdentifiers 闭环联动。",
+      "0.1.0": "首个可用版本，复用 MoviePilot 当前 LLM 配置，在原生识别失败后通过 Chain NameRecognize 做本地结构化兜底。"
+    }
   }
-}
\ No newline at end of file
+}
diff --git a/package.v2.json b/package.v2.json
index 2059ee5..da0a17e 100644
--- a/package.v2.json
+++ b/package.v2.json
@@ -663,4 +663,29 @@
       "v0.1.0": "初始版本"
     }
   }
+,
+  "AIRecognizerEnhancer": {
+    "name": "AI识别增强",
+    "description": "直接复用 MoviePilot 当前 LLM 配置，在原生识别失败后做本地结构化识别兜底，并交回原生链路继续二次识别。",
+    "labels": "AI,识别,LLM,本地兜底,MoviePilot,TMDB",
+    "version": "0.1.12",
+    "icon": "airecognizerenhancer.png",
+    "author": "liuyuexi1987",
+    "level": 1,
+    "history": {
+      "0.1.12": "兼容 MoviePilot 最新 LLM 路径与异步 get_llm 接口，修复最新版 MP 下插件加载失败问题。",
+      "0.1.11": "同步运行态版本，保持本地结构化识别、失败样本闭环和识别词建议能力一致。",
+      "0.1.10": "新增识别词建议模型退化时的精确规则兜底，保证批量建议/批量写入在上游异常时仍能尽量落地。",
+      "0.1.9": "新增失败样本精简摘要接口，并让批量建议/批量写入附带低 token 文本摘要，便于智能体直接消费。",
+      "0.1.8": "新增失败样本批量建议与批量写入接口，可一次处理一批失败样本，进一步减少人工逐条操作。",
+      "0.1.7": "新增失败样本批量复查接口，可批量重跑样本并在确认修复后批量出队。",
+      "0.1.6": "新增失败样本复查接口，可按当前识别词与当前识别器重跑样本，并在确认修复后自动出队。",
+      "0.1.5": "新增失败样本出队动作，支持按索引移除单条样本，并在写入识别词后自动移除已处理样本。",
+      "0.1.4": "新增失败样本洞察接口，自动归纳重复问题、失败原因和优先处理样本，帮助更快挑出值得写识别词的样本。",
+      "0.1.3": "新增失败样本摘要、样本清理、样本去重和保留上限控制，让样本工作流更适合长期运行与智能体使用。",
+      "0.1.2": "新增按失败样本直接生成建议和直接写入规则的快捷 API，进一步缩短从失败样本到 CustomIdentifiers 的闭环。",
+      "0.1.1": "新增失败样本查看、自定义识别词建议和一键追加写入能力，让 AI 识别增强开始和 MoviePilot 原生 CustomIdentifiers 闭环联动。",
+      "0.1.0": "首个可用版本，复用 MoviePilot 当前 LLM 配置，在原生识别失败后通过 Chain NameRecognize 做本地结构化兜底。"
+    }
+  }
 }
diff --git a/plugins.v2/airecognizerenhancer/README.md b/plugins.v2/airecognizerenhancer/README.md
new file mode 100644
index 0000000..ea48943
--- /dev/null
+++ b/plugins.v2/airecognizerenhancer/README.md
@@ -0,0 +1,99 @@
+# AI识别增强
+
+`AI识别增强` 用来补强 MoviePilot 原生整理链里的识别阶段。
+
+它的核心思路很简单：
+
+- 复用 MoviePilot 当前已经启用的 LLM 配置
+- 在原生识别失败或置信度不足时，做一次本地结构化识别兜底
+- 把结果回写给 MoviePilot，继续走原生二次识别和后续整理链
+
+## 适合什么场景
+
+- 文件名比较脏，混有压制组、分辨率、语言、站点标记
+- 同一部剧经常出现英文名、别名、原名、翻译名混用
+- 网盘挂载、手动整理、历史资源补录时，原生识别偶尔不稳定
+- 你想把失败样本沉淀下来，后面持续优化 `CustomIdentifiers`
+
+## 和 MoviePilot 原版智能体的区别
+
+MoviePilot 原版智能体已经提供“整理失败后自动接管再试一次”的能力。
+
+这和 `AI识别增强` 有重叠，但定位不同：
+
+- **MP 原版智能体**
+  - 更偏“一次性补救”
+  - 适合偶发失败、想省事的场景
+
+- **AI识别增强**
+  - 更偏“识别失败治理层”
+  - 除了补救当前这次，还能：
+    - 保存失败样本
+    - 汇总样本洞察
+    - 生成 `CustomIdentifiers` 建议
+    - 写入识别词
+    - 重放 / 复查 / 批量出队
+
+一句话区分：
+
+- 原版智能体：自动接管一次
+- `AI识别增强`：把失败样本沉淀下来，长期减少同类失败
+
+## 当前能力
+
+- 监听 `ChainEventType.NameRecognize`
+- 用当前 LLM 结构化判断标题、年份、类型、季集
+- 回写 `name / year / season / episode`
+- 交回 MoviePilot 原生链路继续二次识别
+- 保存低置信度失败样本
+- 提供失败样本工作清单、洞察、重放、删除和清空能力
+- 生成并应用 `CustomIdentifiers` 建议
+
+## 主要接口
+
+- `GET /api/v1/plugin/AIRecognizerEnhancer/health`
+  - 查看插件状态、LLM 提供方、模型、阈值和超时配置
+- `POST /api/v1/plugin/AIRecognizerEnhancer/recognize`
+  - 对单个标题做一次本地结构化识别测试
+- `GET /api/v1/plugin/AIRecognizerEnhancer/failed_samples`
+  - 查看最近保存的失败样本
+- `GET /api/v1/plugin/AIRecognizerEnhancer/sample_worklist`
+  - 返回适合继续处理的失败样本摘要列表
+- `GET /api/v1/plugin/AIRecognizerEnhancer/sample_insights`
+  - 汇总失败原因、重复问题和优先处理样本
+- `POST /api/v1/plugin/AIRecognizerEnhancer/replay_failed_sample`
+  - 用当前识别词和当前识别器重放复查某条失败样本
+- `POST /api/v1/plugin/AIRecognizerEnhancer/suggest_identifiers_from_sample`
+  - 直接基于失败样本生成识别词建议
+- `POST /api/v1/plugin/AIRecognizerEnhancer/apply_suggested_identifier`
+  - 把建议规则写入系统 `CustomIdentifiers`
+
+其余批量接口和清理接口可以按需要继续使用，详细路径以插件 `get_api()` 暴露结果为准。
+
+## 配置建议
+
+- 先确认 MoviePilot 本身已经配置好可用的 LLM
+- 建议保持“保存失败样本”开启
+- 如果你经常处理历史资源或网盘资源，建议定期查看：
+  - `failed_samples`
+  - `sample_worklist`
+  - `sample_insights`
+
+## 已验证情况
+
+当前版本：`0.1.12`
+
+这版已经验证过：
+
+- 最新版 MoviePilot 下可以正常加载
+- 正常中文标题识别可用
+- 英文别名、韩文原名、中文别名可识别回标准媒体信息
+- 低置信度标题会落失败样本
+- `replay_failed_sample` 复查链可用
+
+## 说明
+
+- 这个插件不依赖外部 AI Gateway 回调链
+- 重点是增强识别，不负责替代 MoviePilot 全部整理流程
+- 如果你只是偶发整理失败，原版智能体可能已经够用
+- 如果你长期受命名混乱困扰，这个插件更有价值
diff --git a/plugins.v2/airecognizerenhancer/__init__.py b/plugins.v2/airecognizerenhancer/__init__.py
new file mode 100644
index 0000000..4471184
--- /dev/null
+++ b/plugins.v2/airecognizerenhancer/__init__.py
@@ -0,0 +1,2039 @@
+import hmac
+import asyncio
+import inspect
+import json
+import re
+import threading
+from collections import Counter
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from fastapi import Request
+from langchain_core.prompts import ChatPromptTemplate
+from pydantic import BaseModel, Field
+
+from app.chain.media import MediaChain
+from app.core.config import settings
+from app.core.event import eventmanager
+from app.core.meta.words import WordsMatcher
+from app.core.metainfo import MetaInfo
+from app.db.systemconfig_oper import SystemConfigOper
+try:
+    from app.helper.llm import LLMHelper
+except ImportError:  # MoviePilot 新版已迁移到 app.agent.llm
+    from app.agent.llm import LLMHelper
+from app.log import logger
+from app.plugins import _PluginBase
+from app.schemas.types import ChainEventType, MediaType, SystemConfigKey
+
+
+class AIRecognitionGuess(BaseModel):
+    name: str = Field(default="", description="标准化后的影视标题；无法判断时返回空字符串")
+    year: str = Field(default="", description="四位年份；无法判断时返回空字符串")
+    media_type: str = Field(default="unknown", description="movie、tv 或 unknown")
+    season: int = Field(default=0, description="剧集季号，电影填 0")
+    episode: int = Field(default=0, description="剧集集号，电影或未知填 0")
+    confidence: float = Field(default=0.0, description="0 到 1 之间的置信度")
+    reason: str = Field(default="", description="简短说明为什么这样判断")
+
+
+class IdentifierSuggestion(BaseModel):
+    comment: str = Field(default="", description="可选注释，不带 #")
+    rule: str = Field(default="", description="一条 MoviePilot 自定义识别词规则")
+    confidence: float = Field(default=0.0, description="0 到 1 之间的置信度")
+    reason: str = Field(default="", description="为什么建议这条规则")
+
+
+class IdentifierSuggestionBundle(BaseModel):
+    summary: str = Field(default="", description="整体建议摘要")
+    suggestions: List[IdentifierSuggestion] = Field(default_factory=list, description="建议规则列表")
+
+
+class AIRecognizerEnhancer(_PluginBase):
+    plugin_name = "AI识别增强"
+    plugin_desc = "直接复用 MoviePilot 当前 LLM 配置，在原生识别失败后做本地结构化识别兜底，并交回原生链路继续二次识别。"
+    plugin_icon = "https://raw.githubusercontent.com/liuyuexi1987/MoviePilot-Plugins/main/icons/airecognizerenhancer.png"
+    plugin_version = "0.1.12"
+    plugin_author = "liuyuexi1987"
+    plugin_level = 1
+    author_url = "https://github.com/liuyuexi1987"
+    plugin_config_prefix = "arrecognizerenhancer_"
+    plugin_order = 41
+    auth_level = 1
+
+    _enabled = False
+    _debug = False
+    _confidence_threshold = 0.65
+    _request_timeout = 25
+    _max_retries = 2
+    _save_failed_samples = True
+    _max_failed_samples = 200
+    _auto_remove_applied_sample = True
+    _systemconfig: Optional[SystemConfigOper] = None
+
+    def init_plugin(self, config: Optional[Dict[str, Any]] = None):
+        config = config or {}
+        self._enabled = bool(config.get("enabled", False))
+        self._debug = bool(config.get("debug", False))
+        self._confidence_threshold = self._safe_float(config.get("confidence_threshold"), 0.65)
+        self._request_timeout = self._safe_int(config.get("request_timeout"), 25)
+        self._max_retries = max(1, min(5, self._safe_int(config.get("max_retries"), 2)))
+        self._save_failed_samples = bool(config.get("save_failed_samples", True))
+        self._max_failed_samples = max(20, min(1000, self._safe_int(config.get("max_failed_samples"), 200)))
+        self._auto_remove_applied_sample = bool(config.get("auto_remove_applied_sample", True))
+        self._systemconfig = SystemConfigOper()
+        self._register_events()
+
+    def get_state(self) -> bool:
+        return self._enabled
+
+    @staticmethod
+    def get_command() -> List[Dict[str, Any]]:
+        return []
+
+    def stop_service(self):
+        try:
+            eventmanager.disable_event_handler(self.on_chain_name_recognize)
+        except Exception:
+            pass
+
+    @staticmethod
+    def _safe_int(value: Any, default: int) -> int:
+        try:
+            return int(value)
+        except Exception:
+            return default
+
+    @staticmethod
+    def _safe_float(value: Any, default: float) -> float:
+        try:
+            return float(value)
+        except Exception:
+            return default
+
+    @staticmethod
+    def _extract_apikey(request: Request, body: Optional[Dict[str, Any]] = None) -> str:
+        header = str(request.headers.get("Authorization") or "").strip()
+        if header.lower().startswith("bearer "):
+            return header.split(" ", 1)[1].strip()
+        if body:
+            for key in ("apikey", "api_key"):
+                token = str(body.get(key) or "").strip()
+                if token:
+                    return token
+        return str(request.query_params.get("apikey") or "").strip()
+
+    def _check_api_access(self, request: Request, body: Optional[Dict[str, Any]] = None) -> Tuple[bool, str]:
+        expected = str(getattr(settings, "API_TOKEN", "") or "").strip()
+        if not expected:
+            return False, "服务端未配置 API Token"
+        actual = self._extract_apikey(request, body)
+        if not hmac.compare_digest(actual, expected):
+            return False, "API Token 无效"
+        return True, ""
+
+    def _register_events(self) -> None:
+        try:
+            eventmanager.register(ChainEventType.NameRecognize)(self.on_chain_name_recognize)
+            if self._enabled:
+                eventmanager.enable_event_handler(self.on_chain_name_recognize)
+            else:
+                eventmanager.disable_event_handler(self.on_chain_name_recognize)
+        except Exception as exc:
+            logger.warning(f"[AI识别增强] 注册链式识别事件失败: {exc}")
+
+    @staticmethod
+    def _extract_title_path(event_data: Any) -> Tuple[str, str]:
+        title = ""
+        path = ""
+        if isinstance(event_data, dict):
+            title = (
+                event_data.get("title")
+                or event_data.get("name")
+                or event_data.get("org_string")
+                or ""
+            )
+            path = (
+                event_data.get("path")
+                or event_data.get("file_path")
+                or event_data.get("org_string")
+                or ""
+            )
+        else:
+            title = (
+                getattr(event_data, "title", "")
+                or getattr(event_data, "name", "")
+                or getattr(event_data, "org_string", "")
+                or ""
+            )
+            path = (
+                getattr(event_data, "path", "")
+                or getattr(event_data, "file_path", "")
+                or getattr(event_data, "org_string", "")
+                or ""
+            )
+        return str(title or "").strip(), str(path or "").strip()
+
+    def _build_meta_hint(self, raw_text: str) -> Dict[str, Any]:
+        try:
+            meta = MetaInfo(raw_text)
+        except Exception:
+            return {}
+        return {
+            "name": getattr(meta, "name", "") or "",
+            "year": getattr(meta, "year", "") or "",
+            "type": getattr(getattr(meta, "type", None), "to_agent", lambda: None)() or "",
+            "season": getattr(meta, "begin_season", None) or 0,
+            "episode": getattr(meta, "begin_episode", None) or 0,
+            "org_string": getattr(meta, "org_string", "") or "",
+        }
+
+    @staticmethod
+    def _clean_guess_name(name: str) -> str:
+        text = str(name or "").strip()
+        if not text:
+            return ""
+        text = text.split("/")[0].strip().replace(".", " ")
+        return " ".join(text.split())
+
+    def _normalize_guess(self, guess: AIRecognitionGuess) -> AIRecognitionGuess:
+        name = self._clean_guess_name(guess.name)
+        year = str(guess.year or "").strip()
+        if not (len(year) == 4 and year.isdigit()):
+            year = ""
+        media_type = str(guess.media_type or "unknown").strip().lower()
+        if media_type not in {"movie", "tv"}:
+            media_type = "unknown"
+        season = max(0, self._safe_int(guess.season, 0))
+        episode = max(0, self._safe_int(guess.episode, 0))
+        confidence = min(1.0, max(0.0, self._safe_float(guess.confidence, 0.0)))
+        reason = str(guess.reason or "").strip()
+        return AIRecognitionGuess(
+            name=name,
+            year=year,
+            media_type=media_type,
+            season=season,
+            episode=episode,
+            confidence=confidence,
+            reason=reason,
+        )
+
+    def _sample_path(self) -> Path:
+        return self.get_data_path() / "failed_samples.jsonl"
+
+    @staticmethod
+    def _sample_identity(payload: Dict[str, Any]) -> str:
+        return json.dumps(
+            {
+                "title": str(payload.get("title") or "").strip(),
+                "path": str(payload.get("path") or "").strip(),
+                "reason": str(payload.get("reason") or "").strip(),
+            },
+            ensure_ascii=False,
+            sort_keys=True,
+        )
+
+    def _write_failed_samples(self, rows: List[Dict[str, Any]]) -> None:
+        sample_path = self._sample_path()
+        sample_path.parent.mkdir(parents=True, exist_ok=True)
+        trimmed = rows[-self._max_failed_samples:]
+        with sample_path.open("w", encoding="utf-8") as f:
+            for row in trimmed:
+                f.write(json.dumps(row, ensure_ascii=False) + "\n")
+
+    def _record_failed_sample(self, payload: Dict[str, Any]) -> None:
+        if not self._save_failed_samples:
+            return
+        try:
+            rows = self._read_failed_samples(limit=1000)
+            rows.reverse()
+            identity = self._sample_identity(payload)
+            filtered = [row for row in rows if self._sample_identity(row) != identity]
+            filtered.append(payload)
+            self._write_failed_samples(filtered)
+        except Exception as exc:
+            logger.warning(f"[AI识别增强] 写入失败样本失败: {exc}")
+
+    def _read_failed_samples(self, limit: int = 20) -> List[Dict[str, Any]]:
+        sample_path = self._sample_path()
+        if not sample_path.exists():
+            return []
+        rows: List[Dict[str, Any]] = []
+        try:
+            with sample_path.open("r", encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        rows.append(json.loads(line))
+                    except Exception:
+                        continue
+        except Exception as exc:
+            logger.warning(f"[AI识别增强] 读取失败样本失败: {exc}")
+            return []
+        if limit > 0:
+            rows = rows[-limit:]
+        rows.reverse()
+        return rows
+
+    def _clear_failed_samples(self) -> int:
+        rows = self._read_failed_samples(limit=1000)
+        sample_path = self._sample_path()
+        if sample_path.exists():
+            sample_path.unlink()
+        return len(rows)
+
+    def _remove_failed_sample(self, sample_index: Optional[Any], limit: int = 1000) -> Dict[str, Any]:
+        rows = self._read_failed_samples(limit=max(1, min(limit, 1000)))
+        if not rows:
+            return {"removed": False, "message": "暂无失败样本", "removed_count": 0}
+        index = self._safe_int(sample_index, 0)
+        if index < 0:
+            index = 0
+        if index >= len(rows):
+            return {
+                "removed": False,
+                "message": f"失败样本索引超出范围，当前共有 {len(rows)} 条",
+                "removed_count": 0,
+            }
+        removed_sample = dict(rows[index] or {})
+        del rows[index]
+        if rows:
+            rows.reverse()
+            self._write_failed_samples(rows)
+        else:
+            self._clear_failed_samples()
+        return {
+            "removed": True,
+            "message": "success",
+            "removed_count": 1,
+            "remaining_count": len(rows),
+            "removed_sample": removed_sample,
+            "removed_sample_index": index,
+        }
+
+    def _remove_failed_samples(self, sample_indexes: List[Any], limit: int = 1000) -> Dict[str, Any]:
+        rows = self._read_failed_samples(limit=max(1, min(limit, 1000)))
+        if not rows:
+            return {"removed": False, "message": "暂无失败样本", "removed_count": 0, "remaining_count": 0}
+        normalized_indexes = sorted(
+            {self._safe_int(index, -1) for index in (sample_indexes or []) if self._safe_int(index, -1) >= 0},
+            reverse=True,
+        )
+        valid_indexes = [index for index in normalized_indexes if index < len(rows)]
+        if not valid_indexes:
+            return {
+                "removed": False,
+                "message": "没有可移除的有效样本索引",
+                "removed_count": 0,
+                "remaining_count": len(rows),
+            }
+        removed_samples: List[Dict[str, Any]] = []
+        for index in valid_indexes:
+            removed_samples.append(dict(rows[index] or {}))
+            del rows[index]
+        if rows:
+            rows.reverse()
+            self._write_failed_samples(rows)
+        else:
+            self._clear_failed_samples()
+        removed_samples.reverse()
+        return {
+            "removed": True,
+            "message": "success",
+            "removed_count": len(valid_indexes),
+            "remaining_count": len(rows),
+            "removed_sample_indexes": sorted(valid_indexes),
+            "removed_samples": removed_samples,
+        }
+
+    def _resolve_failed_sample(
+        self,
+        sample_index: Optional[Any] = None,
+        limit: int = 100,
+    ) -> Tuple[Optional[int], Optional[Dict[str, Any]], str]:
+        samples = self._read_failed_samples(limit=max(1, min(limit, 200)))
+        if not samples:
+            return None, None, "暂无失败样本"
+        index = self._safe_int(sample_index, 0)
+        if index < 0:
+            index = 0
+        if index >= len(samples):
+            return None, None, f"失败样本索引超出范围，当前共有 {len(samples)} 条"
+        row = dict(samples[index] or {})
+        row["sample_index"] = index
+        return index, row, ""
+
+    def _select_failed_sample_indexes(
+        self,
+        sample_indexes: Optional[List[Any]] = None,
+        limit: int = 10,
+        pool_limit: int = 200,
+    ) -> Tuple[List[int], List[Dict[str, Any]], str]:
+        current_samples = self._inject_sample_indices(self._read_failed_samples(limit=max(1, min(pool_limit, 1000))))
+        if not current_samples:
+            return [], [], "暂无失败样本"
+        if isinstance(sample_indexes, list) and sample_indexes:
+            selected_indexes: List[int] = []
+            seen = set()
+            for raw in sample_indexes:
+                idx = self._safe_int(raw, -1)
+                if idx < 0 or idx >= len(current_samples) or idx in seen:
+                    continue
+                seen.add(idx)
+                selected_indexes.append(idx)
+        else:
+            selected_indexes = [int(sample.get("sample_index", 0)) for sample in current_samples[: max(1, min(limit, 50))]]
+        if not selected_indexes:
+            return [], current_samples, "没有可处理的有效样本索引"
+        return selected_indexes, current_samples, ""
+
+    def _inject_sample_indices(self, samples: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        indexed: List[Dict[str, Any]] = []
+        for idx, sample in enumerate(samples):
+            row = dict(sample or {})
+            row["sample_index"] = idx
+            indexed.append(row)
+        return indexed
+
+    def _summarize_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        sample = dict(sample or {})
+        guess = sample.get("guess") or {}
+        verified = sample.get("verified_media_info") or {}
+        inferred_target = {
+            "name": verified.get("title") or guess.get("name") or "",
+            "year": verified.get("year") or guess.get("year") or "",
+            "media_type": self._normalize_media_type(verified.get("type") or guess.get("media_type")),
+            "season": self._safe_int(guess.get("season"), 0),
+            "episode": self._safe_int(guess.get("episode"), 0),
+            "tmdb_id": self._safe_int(verified.get("tmdb_id"), 0),
+        }
+        return {
+            "sample_index": sample.get("sample_index"),
+            "title": sample.get("title"),
+            "path": sample.get("path"),
+            "reason": sample.get("reason"),
+            "guess_name": guess.get("name"),
+            "guess_confidence": self._safe_float(guess.get("confidence"), 0.0),
+            "verified_title": verified.get("title"),
+            "verified_year": verified.get("year"),
+            "verified_tmdb_id": verified.get("tmdb_id"),
+            "inferred_target": inferred_target,
+            "can_auto_suggest": bool(inferred_target["name"]),
+        }
+
+    def _target_from_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        summary = self._summarize_sample(sample)
+        return summary.get("inferred_target") or {}
+
+    @staticmethod
+    def _normalize_reason_tag(reason: Any) -> str:
+        text = str(reason or "").strip()
+        if not text:
+            return "unknown"
+        if ":" in text:
+            return text.split(":", 1)[0].strip() or "unknown"
+        return text
+
+    @staticmethod
+    def _sample_group_key(summary: Dict[str, Any]) -> str:
+        target = summary.get("inferred_target") or {}
+        title = (
+            str(target.get("name") or "").strip()
+            or str(summary.get("verified_title") or "").strip()
+            or str(summary.get("guess_name") or "").strip()
+            or str(summary.get("title") or "").strip()
+        )
+        media_type = str(target.get("media_type") or "unknown").strip().lower()
+        season = int(target.get("season") or 0)
+        episode = int(target.get("episode") or 0)
+        return json.dumps(
+            {
+                "title": title.lower(),
+                "media_type": media_type,
+                "season": season,
+                "episode": episode,
+            },
+            ensure_ascii=False,
+            sort_keys=True,
+        )
+
+    @staticmethod
+    def _sample_display_name(summary: Dict[str, Any]) -> str:
+        target = summary.get("inferred_target") or {}
+        title = (
+            str(target.get("name") or "").strip()
+            or str(summary.get("verified_title") or "").strip()
+            or str(summary.get("guess_name") or "").strip()
+            or str(summary.get("title") or "").strip()
+        )
+        if not title:
+            return "未命名样本"
+        media_type = str(target.get("media_type") or "").strip().lower()
+        season = int(target.get("season") or 0)
+        episode = int(target.get("episode") or 0)
+        suffix = ""
+        if media_type == "tv" and (season or episode):
+            suffix = f" S{season:02d}E{episode:02d}"
+        return f"{title}{suffix}"
+
+    def _build_sample_insights(self, samples: List[Dict[str, Any]], top: int = 10) -> Dict[str, Any]:
+        summaries = [self._summarize_sample(sample) for sample in samples]
+        reason_counter = Counter()
+        title_counter = Counter()
+        group_counter = Counter()
+        for summary in summaries:
+            reason_counter[self._normalize_reason_tag(summary.get("reason"))] += 1
+            title_counter[self._sample_display_name(summary)] += 1
+            group_counter[self._sample_group_key(summary)] += 1
+
+        actionable: List[Dict[str, Any]] = []
+        for summary in summaries:
+            duplicate_count = group_counter[self._sample_group_key(summary)]
+            priority_reasons: List[str] = []
+            score = 0
+            if duplicate_count >= 2:
+                score += min(duplicate_count, 5)
+                priority_reasons.append(f"同类样本重复出现 {duplicate_count} 次")
+            if summary.get("verified_tmdb_id"):
+                score += 3
+                priority_reasons.append("已有 TMDB 命中")
+            if summary.get("can_auto_suggest"):
+                score += 2
+                priority_reasons.append("可直接生成识别词")
+            confidence = self._safe_float(summary.get("guess_confidence"), 0.0)
+            if 0 < confidence < self._confidence_threshold:
+                gap = round(self._confidence_threshold - confidence, 2)
+                score += 1
+                priority_reasons.append(f"距注入阈值还差 {gap}")
+            row = dict(summary)
+            row["duplicate_count"] = duplicate_count
+            row["priority_score"] = score
+            row["priority_reasons"] = priority_reasons
+            actionable.append(row)
+
+        actionable.sort(
+            key=lambda item: (
+                -int(item.get("priority_score") or 0),
+                -int(item.get("duplicate_count") or 0),
+                -self._safe_float(item.get("guess_confidence"), 0.0),
+                int(item.get("sample_index") or 0),
+            )
+        )
+
+        repeated_groups = [
+            {"title": name, "count": count}
+            for name, count in title_counter.most_common(top)
+            if count >= 2
+        ]
+
+        return {
+            "total_count": len(summaries),
+            "reason_counts": [
+                {"reason": reason, "count": count}
+                for reason, count in reason_counter.most_common(top)
+            ],
+            "top_titles": [
+                {"title": title, "count": count}
+                for title, count in title_counter.most_common(top)
+            ],
+            "repeated_groups": repeated_groups,
+            "priority_samples": actionable[:top],
+        }
+
+    def _render_sample_brief(self, samples: List[Dict[str, Any]], top: int = 5) -> str:
+        summaries = [self._summarize_sample(sample) for sample in samples[: max(1, min(top, 20))]]
+        if not summaries:
+            return "当前没有失败样本。"
+        lines = [f"失败样本 {len(samples)} 条，展示前 {len(summaries)} 条："]
+        for summary in summaries:
+            label = self._sample_display_name(summary)
+            confidence = round(self._safe_float(summary.get("guess_confidence"), 0.0), 2)
+            can_suggest = "可建议" if summary.get("can_auto_suggest") else "需人工"
+            lines.append(f"{summary.get('sample_index')}. {label} | 置信度 {confidence} | {can_suggest}")
+        lines.append("下一步：可直接调用批量建议或批量复查接口。")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _render_batch_results_brief(
+        action_name: str,
+        requested_count: int,
+        success_count: int,
+        failed_count: int,
+        results: List[Dict[str, Any]],
+    ) -> str:
+        lines = [f"{action_name}：共处理 {requested_count} 条，成功 {success_count}，失败 {failed_count}。"]
+        for item in results[:10]:
+            idx = item.get("sample_index")
+            if item.get("success"):
+                label = (
+                    ((item.get("source_sample") or {}).get("title"))
+                    or ((item.get("target") or {}).get("name"))
+                    or "样本"
+                )
+                lines.append(f"{idx}. 成功 | {label}")
+            else:
+                lines.append(f"{idx}. 失败 | {item.get('message', '未知错误')}")
+        return "\n".join(lines)
+
+    def _build_body_from_sample(self, body: Dict[str, Any]) -> Tuple[Dict[str, Any], Optional[Dict[str, Any]], str]:
+        body = dict(body or {})
+        title = str(body.get("title") or "").strip()
+        path = str(body.get("path") or "").strip()
+        sample_requested = body.get("use_latest_sample") or body.get("sample_index") is not None
+        if title or path:
+            return body, None, ""
+        if not sample_requested:
+            return body, None, ""
+
+        sample_index, sample, message = self._resolve_failed_sample(body.get("sample_index"), limit=100)
+        if not sample:
+            return body, None, message
+        body["title"] = str(sample.get("title") or "").strip()
+        body["path"] = str(sample.get("path") or "").strip()
+        verified = sample.get("verified_media_info") or {}
+        guess = sample.get("guess") or {}
+        if not body.get("desired_name"):
+            body["desired_name"] = verified.get("title") or guess.get("name") or ""
+        if not body.get("desired_year"):
+            body["desired_year"] = verified.get("year") or guess.get("year") or ""
+        if not body.get("desired_media_type"):
+            body["desired_media_type"] = self._normalize_media_type(
+                verified.get("type") or guess.get("media_type")
+            )
+        if body.get("desired_season") is None:
+            body["desired_season"] = guess.get("season") or 0
+        if body.get("desired_episode") is None:
+            body["desired_episode"] = guess.get("episode") or 0
+        if body.get("desired_tmdb_id") is None:
+            body["desired_tmdb_id"] = verified.get("tmdb_id") or 0
+        body["sample_index"] = sample_index
+        return body, sample, ""
+
+    def _build_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """你是 MoviePilot 的影视文件名识别增强助手。
+
+你的任务不是搜索 TMDB，也不是编造结果，而是根据文件名、路径和已有解析提示，尽量提炼出更适合 MoviePilot 二次识别的结构化信息。
+
+规则：
+1. 只依据输入内容推断，不要臆造不存在的信息。
+2. 如果不确定，请返回空标题，并把 media_type 设为 unknown，confidence 降低。
+3. title/name 只保留作品名，不要包含分辨率、制作组、音频编码、网盘标记等噪音。
+4. year 只有在比较确定时才给四位年份。
+5. 电影 season/episode 必须为 0。
+6. 剧集如果能确定季集就填写，否则保持 0。
+7. media_type 只能是 movie、tv、unknown。
+8. confidence 范围为 0 到 1。
+""",
+                ),
+                (
+                    "human",
+                    """原始标题：
+{title}
+
+原始路径：
+{path}
+
+MoviePilot 当前基础解析提示：
+{meta_hint}
+""",
+                ),
+            ]
+        )
+
+    def _build_identifier_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """你是 MoviePilot 自定义识别词规则助手。
+
+你的任务是根据错误标题、当前解析结果和目标结果，生成尽量窄作用域、可直接用于 MoviePilot CustomIdentifiers 的规则。
+
+支持格式只有四种：
+1. 屏蔽词
+2. 替换词：被替换词 => 替换词
+3. 集偏移：前定位词 <> 后定位词 >> EP±N
+4. 组合规则：被替换词 => 替换词 && 前定位词 <> 后定位词 >> EP±N
+
+硬性要求：
+1. 运算符两侧必须保留空格： => 、 <> 、 >> 、 &&
+2. 优先生成窄作用域规则，尽量带发布组、年份、季集、分辨率等锚点
+3. 不要生成过宽的裸屏蔽词，比如 1080p、WEB-DL、字幕
+4. 如果需要强制绑 TMDB，可使用 {{[tmdbid=xxx;type=tv/movies;s=1;e=14]}} 这种替换词
+5. comment 不带 #，rule 里不要再包 markdown 或代码块
+6. 如果没有把握，请返回空 suggestions
+""",
+                ),
+                (
+                    "human",
+                    """原始标题：
+{title}
+
+原始路径：
+{path}
+
+MoviePilot 当前基础解析：
+{meta_hint}
+
+AI 识别增强结果：
+{guess}
+
+二次校验到的媒体信息摘要：
+{verified_summary}
+
+希望修正成的目标结果：
+{target}
+""",
+                ),
+            ]
+        )
+
+    @staticmethod
+    def _run_async_compatible(value: Any) -> Any:
+        """
+        兼容 MoviePilot 新版 `LLMHelper.get_llm()` 的异步返回。
+        在同步上下文直接 asyncio.run；如果当前线程已有事件循环，则开一个短线程执行。
+        """
+        if not inspect.isawaitable(value):
+            return value
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            return asyncio.run(value)
+
+        result: Dict[str, Any] = {}
+        error: Dict[str, BaseException] = {}
+
+        def _worker() -> None:
+            try:
+                result["value"] = asyncio.run(value)
+            except BaseException as exc:  # noqa: BLE001
+                error["exc"] = exc
+
+        thread = threading.Thread(target=_worker, daemon=True)
+        thread.start()
+        thread.join()
+        if "exc" in error:
+            raise error["exc"]
+        return result.get("value")
+
+    def _get_llm(self):
+        llm = LLMHelper.get_llm(streaming=False)
+        return self._run_async_compatible(llm)
+
+    def _invoke_llm(self, title: str, path: str) -> AIRecognitionGuess:
+        raw_text = path or title
+        meta_hint = self._build_meta_hint(raw_text)
+        llm = self._get_llm()
+        prompt = self._build_prompt()
+        chain = (
+            prompt
+            | llm.with_structured_output(AIRecognitionGuess).with_retry(stop_after_attempt=self._max_retries)
+        )
+        result: AIRecognitionGuess = chain.invoke(
+            {
+                "title": title,
+                "path": path,
+                "meta_hint": meta_hint,
+            },
+            config={"configurable": {"timeout": self._request_timeout}},
+        )
+        return self._normalize_guess(result)
+
+    @staticmethod
+    def _normalize_media_type(value: Any) -> str:
+        if value == MediaType.MOVIE:
+            return "movie"
+        if value == MediaType.TV:
+            return "tv"
+        text = str(value or "").strip().lower()
+        if text in {"movie", "movies", "电影"}:
+            return "movie"
+        if text in {"tv", "电视剧", "剧集"}:
+            return "tv"
+        return "unknown"
+
+    def _build_target(self, body: Dict[str, Any], result: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        body = body or {}
+        result = result or {}
+        guess = result.get("guess") or {}
+        verified = result.get("verified_media_info") or {}
+        verified_type = self._normalize_media_type(verified.get("type"))
+        target = {
+            "name": str(body.get("desired_name") or verified.get("title") or guess.get("name") or "").strip(),
+            "year": str(body.get("desired_year") or verified.get("year") or guess.get("year") or "").strip(),
+            "media_type": self._normalize_media_type(
+                body.get("desired_media_type") or verified_type or guess.get("media_type")
+            ),
+            "season": self._safe_int(
+                body.get("desired_season"),
+                self._safe_int(guess.get("season"), 0),
+            ),
+            "episode": self._safe_int(
+                body.get("desired_episode"),
+                self._safe_int(guess.get("episode"), 0),
+            ),
+            "tmdb_id": self._safe_int(body.get("desired_tmdb_id") or verified.get("tmdb_id"), 0),
+        }
+        if len(target["year"]) != 4 or not target["year"].isdigit():
+            target["year"] = ""
+        return target
+
+    @staticmethod
+    def _compact_verified_summary(verified: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+        verified = verified or {}
+        return {
+            "title": verified.get("title"),
+            "year": verified.get("year"),
+            "type": verified.get("type"),
+            "tmdb_id": verified.get("tmdb_id"),
+            "title_year": verified.get("title_year"),
+            "season_years": verified.get("season_years"),
+            "seasons": verified.get("seasons"),
+            "names": (verified.get("names") or [])[:8],
+        }
+
+    @staticmethod
+    def _normalize_identifier_line(value: Any) -> str:
+        return " ".join(str(value or "").strip().split())
+
+    def _validate_identifier_rule(self, rule: str) -> bool:
+        rule = self._normalize_identifier_line(rule)
+        if not rule or rule.startswith("#"):
+            return False
+        if " => " in rule and " && " in rule and " >> " in rule and " <> " in rule:
+            return True
+        if " => " in rule:
+            return True
+        if " >> " in rule and " <> " in rule:
+            return True
+        return len(rule) >= 4
+
+    def _enrich_identifier_rule(self, rule: str, target: Dict[str, Any]) -> str:
+        rule = self._normalize_identifier_line(rule)
+        target_name = str((target or {}).get("name") or "").strip()
+        if not target_name or " => " not in rule:
+            return rule
+        left, right = rule.split(" => ", 1)
+        suffix = ""
+        replace_part = right
+        if " && " in right:
+            replace_part, extra = right.split(" && ", 1)
+            suffix = f" && {extra}"
+        if replace_part.startswith("{["):
+            replace_part = f"{target_name}{replace_part}"
+        return f"{left} => {replace_part}{suffix}"
+
+    @staticmethod
+    def _clean_comment_line(comment: str) -> str:
+        text = str(comment or "").strip()
+        if not text:
+            return ""
+        return f"#{text.lstrip('#').strip()}"
+
+    def _preview_custom_words(self, title: str, custom_words: List[str], target: Dict[str, Any]) -> Dict[str, Any]:
+        prepared_title, apply_words = WordsMatcher().prepare(title, custom_words=custom_words)
+        meta = MetaInfo(title=title, custom_words=custom_words)
+        preview = {
+            "prepared_title": prepared_title,
+            "applied_words": apply_words or [],
+            "applied": bool(apply_words),
+            "name": getattr(meta, "name", "") or "",
+            "year": getattr(meta, "year", "") or "",
+            "media_type": self._normalize_media_type(getattr(meta, "type", None)),
+            "season": getattr(meta, "begin_season", None) or 0,
+            "episode": getattr(meta, "begin_episode", None) or 0,
+        }
+        if target:
+            matched = True
+            if target.get("name"):
+                matched = matched and (preview["name"].strip().lower() == str(target["name"]).strip().lower())
+            if target.get("year"):
+                matched = matched and (preview["year"] == target["year"])
+            if target.get("media_type") and target.get("media_type") != "unknown":
+                matched = matched and (preview["media_type"] == target["media_type"])
+            if target.get("season"):
+                matched = matched and (preview["season"] == target["season"])
+            if target.get("episode"):
+                matched = matched and (preview["episode"] == target["episode"])
+            preview["matched_target"] = matched
+        return preview
+
+    def _preview_identifier_rule(self, title: str, rule: str, target: Dict[str, Any]) -> Dict[str, Any]:
+        preview = self._preview_custom_words(title=title, custom_words=[rule], target=target)
+        preview["applied"] = rule in (preview.get("applied_words") or [])
+        return preview
+
+    def _preview_current_identifiers(self, title: str, target: Dict[str, Any]) -> Dict[str, Any]:
+        custom_words = self._get_custom_identifiers()
+        preview = self._preview_custom_words(title=title, custom_words=custom_words, target=target)
+        preview["custom_identifier_count"] = len(custom_words)
+        preview["applied_count"] = len(preview.get("applied_words") or [])
+        return preview
+
+    @staticmethod
+    def _match_recognize_result_to_target(result: Dict[str, Any], target: Dict[str, Any]) -> bool:
+        if not target:
+            return bool(result.get("success"))
+        guess = result.get("guess") or {}
+        matched = True
+        if target.get("name"):
+            matched = matched and (str(guess.get("name") or "").strip().lower() == str(target.get("name") or "").strip().lower())
+        if target.get("year"):
+            matched = matched and (str(guess.get("year") or "") == str(target.get("year") or ""))
+        if target.get("media_type") and target.get("media_type") != "unknown":
+            matched = matched and (str(guess.get("media_type") or "unknown") == str(target.get("media_type") or "unknown"))
+        if target.get("season"):
+            matched = matched and (int(guess.get("season") or 0) == int(target.get("season") or 0))
+        if target.get("episode"):
+            matched = matched and (int(guess.get("episode") or 0) == int(target.get("episode") or 0))
+        return bool(result.get("success")) and matched
+
+    def _replay_failed_sample(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        sample_index, sample, message = self._resolve_failed_sample(
+            body.get("sample_index"),
+            limit=1000,
+        )
+        if not sample:
+            return {"success": False, "message": message}
+        title = str(sample.get("title") or "").strip()
+        path = str(sample.get("path") or "").strip()
+        target = self._target_from_sample(sample)
+        identifier_preview = self._preview_current_identifiers(title=title, target=target)
+        recognize_result = self._recognize(title=title, path=path, record_failed_sample=False)
+        resolved_by_identifiers = bool(identifier_preview.get("applied")) and bool(identifier_preview.get("matched_target"))
+        resolved_by_recognizer = self._match_recognize_result_to_target(recognize_result, target)
+        resolved = resolved_by_identifiers or resolved_by_recognizer
+        removal_result = None
+        if resolved and bool(body.get("remove_if_resolved")):
+            removal_result = self._remove_failed_sample(sample_index, limit=1000)
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "source_sample_index": sample_index,
+                "source_sample": sample,
+                "target": target,
+                "identifier_preview": identifier_preview,
+                "recognize_result": recognize_result,
+                "resolved_by_identifiers": resolved_by_identifiers,
+                "resolved_by_recognizer": resolved_by_recognizer,
+                "resolved": resolved,
+                "sample_removed": bool(removal_result and removal_result.get("removed")),
+                "sample_removal_result": removal_result,
+            },
+        }
+
+    def _replay_failed_samples(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        limit = max(1, min(self._safe_int(body.get("limit"), 10), 50))
+        selected_indexes, _, message = self._select_failed_sample_indexes(
+            sample_indexes=body.get("sample_indexes"),
+            limit=limit,
+            pool_limit=200,
+        )
+        if not selected_indexes:
+            return {"success": False, "message": message}
+
+        replay_results: List[Dict[str, Any]] = []
+        resolved_indexes: List[int] = []
+        for sample_index in selected_indexes:
+            replay = self._replay_failed_sample(
+                {
+                    "sample_index": sample_index,
+                    "remove_if_resolved": False,
+                }
+            )
+            if not replay.get("success"):
+                replay_results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": False,
+                        "message": replay.get("message", "复查失败"),
+                    }
+                )
+                continue
+            data = replay.get("data") or {}
+            replay_results.append(
+                {
+                    "sample_index": sample_index,
+                    "success": True,
+                    "resolved": bool(data.get("resolved")),
+                    "resolved_by_identifiers": bool(data.get("resolved_by_identifiers")),
+                    "resolved_by_recognizer": bool(data.get("resolved_by_recognizer")),
+                    "source_sample": data.get("source_sample"),
+                    "target": data.get("target"),
+                    "identifier_preview": data.get("identifier_preview"),
+                    "recognize_result": data.get("recognize_result"),
+                }
+            )
+            if data.get("resolved"):
+                resolved_indexes.append(sample_index)
+
+        removal_result = None
+        if body.get("remove_if_resolved") and resolved_indexes:
+            removal_result = self._remove_failed_samples(resolved_indexes, limit=1000)
+
+        success_count = sum(1 for item in replay_results if item.get("success"))
+        resolved_count = sum(1 for item in replay_results if item.get("resolved"))
+        unresolved_count = success_count - resolved_count
+        failed_count = len(replay_results) - success_count
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "requested_count": len(selected_indexes),
+                "success_count": success_count,
+                "resolved_count": resolved_count,
+                "unresolved_count": unresolved_count,
+                "failed_count": failed_count,
+                "sample_removed_count": int((removal_result or {}).get("removed_count") or 0),
+                "sample_removal_result": removal_result,
+                "results": replay_results,
+            },
+        }
+
+    def _suggest_identifiers_for_failed_samples(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        limit = max(1, min(self._safe_int(body.get("limit"), 5), 20))
+        selected_indexes, _, message = self._select_failed_sample_indexes(
+            sample_indexes=body.get("sample_indexes"),
+            limit=limit,
+            pool_limit=200,
+        )
+        if not selected_indexes:
+            return {"success": False, "message": message}
+
+        results: List[Dict[str, Any]] = []
+        success_count = 0
+        for sample_index in selected_indexes:
+            suggest_body = dict(body)
+            suggest_body.pop("sample_indexes", None)
+            suggest_body["sample_index"] = sample_index
+            suggest_body["use_latest_sample"] = False
+            suggested = self._suggest_identifiers(suggest_body)
+            if suggested.get("success"):
+                success_count += 1
+                data = suggested.get("data") or {}
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": True,
+                        "summary": data.get("summary"),
+                        "source_sample": data.get("source_sample"),
+                        "target": data.get("target"),
+                        "suggestions": data.get("suggestions") or [],
+                    }
+                )
+            else:
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": False,
+                        "message": suggested.get("message", "建议生成失败"),
+                        "data": suggested.get("data"),
+                    }
+                )
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "requested_count": len(selected_indexes),
+                "success_count": success_count,
+                "failed_count": len(selected_indexes) - success_count,
+                "brief": self._render_batch_results_brief(
+                    action_name="批量建议",
+                    requested_count=len(selected_indexes),
+                    success_count=success_count,
+                    failed_count=len(selected_indexes) - success_count,
+                    results=results,
+                ),
+                "results": results,
+            },
+        }
+
+    def _apply_suggested_identifier_internal(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        if body.get("title") is None and body.get("path") is None:
+            body["use_latest_sample"] = True if body.get("use_latest_sample") is None else body.get("use_latest_sample")
+        suggested = self._suggest_identifiers(body)
+        if not suggested.get("success"):
+            return suggested
+        data = suggested.get("data") or {}
+        suggestions = data.get("suggestions") or []
+        suggestion_index = self._safe_int(body.get("suggestion_index"), 0)
+        if suggestion_index < 0:
+            suggestion_index = 0
+        if suggestion_index >= len(suggestions):
+            return {"success": False, "message": f"建议索引超出范围，当前共有 {len(suggestions)} 条"}
+        chosen = suggestions[suggestion_index]
+        applied = self._append_custom_identifiers(chosen.get("lines") or [])
+        should_remove_sample = bool(
+            self._auto_remove_applied_sample if body.get("remove_sample") is None else body.get("remove_sample")
+        )
+        removal_result = None
+        source_sample = data.get("source_sample") or {}
+        if should_remove_sample and source_sample.get("sample_index") is not None:
+            removal_result = self._remove_failed_sample(source_sample.get("sample_index"), limit=1000)
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "chosen_suggestion": chosen,
+                "apply_result": applied,
+                "source_sample_index": source_sample.get("sample_index"),
+                "source_sample": source_sample,
+                "sample_removed": bool(removal_result and removal_result.get("removed")),
+                "sample_removal_result": removal_result,
+                "target": data.get("target"),
+            },
+        }
+
+    def _apply_suggested_identifiers_for_failed_samples(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        limit = max(1, min(self._safe_int(body.get("limit"), 5), 20))
+        selected_indexes, _, message = self._select_failed_sample_indexes(
+            sample_indexes=body.get("sample_indexes"),
+            limit=limit,
+            pool_limit=200,
+        )
+        if not selected_indexes:
+            return {"success": False, "message": message}
+
+        results: List[Dict[str, Any]] = []
+        success_count = 0
+        removable_indexes: List[int] = []
+        should_remove_samples = bool(
+            self._auto_remove_applied_sample if body.get("remove_sample") is None else body.get("remove_sample")
+        )
+        for sample_index in selected_indexes:
+            apply_body = dict(body)
+            apply_body.pop("sample_indexes", None)
+            apply_body["sample_index"] = sample_index
+            apply_body["use_latest_sample"] = False
+            apply_body["remove_sample"] = False
+            applied = self._apply_suggested_identifier_internal(apply_body)
+            if applied.get("success"):
+                success_count += 1
+                data = applied.get("data") or {}
+                if should_remove_samples:
+                    removable_indexes.append(sample_index)
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": True,
+                        "source_sample": data.get("source_sample"),
+                        "target": data.get("target"),
+                        "chosen_suggestion": data.get("chosen_suggestion"),
+                        "apply_result": data.get("apply_result"),
+                        "sample_removed": False,
+                    }
+                )
+            else:
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": False,
+                        "message": applied.get("message", "写入失败"),
+                        "data": applied.get("data"),
+                    }
+                )
+        removal_result = None
+        if should_remove_samples and removable_indexes:
+            removal_result = self._remove_failed_samples(removable_indexes, limit=1000)
+            removed_index_set = set((removal_result or {}).get("removed_sample_indexes") or [])
+            for item in results:
+                if item.get("success"):
+                    item["sample_removed"] = item.get("sample_index") in removed_index_set
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "requested_count": len(selected_indexes),
+                "success_count": success_count,
+                "failed_count": len(selected_indexes) - success_count,
+                "sample_removed_count": int((removal_result or {}).get("removed_count") or 0),
+                "sample_removal_result": removal_result,
+                "brief": self._render_batch_results_brief(
+                    action_name="批量写入",
+                    requested_count=len(selected_indexes),
+                    success_count=success_count,
+                    failed_count=len(selected_indexes) - success_count,
+                    results=results,
+                ),
+                "results": results,
+            },
+        }
+
+    def _build_exact_identifier_fallback(self, title: str, target: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        target_name = str((target or {}).get("name") or "").strip()
+        tmdb_id = self._safe_int((target or {}).get("tmdb_id"), 0)
+        media_type = self._normalize_media_type((target or {}).get("media_type"))
+        if not title or not target_name or not tmdb_id or media_type == "unknown":
+            return None
+        replace = target_name
+        target_year = str((target or {}).get("year") or "").strip()
+        if len(target_year) == 4 and target_year.isdigit():
+            replace += f".{target_year}"
+        replace += f"{{[tmdbid={tmdb_id};type={'tv' if media_type == 'tv' else 'movie'}"
+        if media_type == "tv" and self._safe_int(target.get("season"), 0):
+            replace += f";s={self._safe_int(target.get('season'), 0)}"
+        if media_type == "tv" and self._safe_int(target.get("episode"), 0):
+            replace += f";e={self._safe_int(target.get('episode'), 0)}"
+        replace += "]}"
+        rule = f"{re.escape(title)} => {replace}"
+        preview = self._preview_identifier_rule(title=title, rule=rule, target=target)
+        if not preview.get("applied"):
+            return None
+        return {
+            "comment": "当 AI 建议无法稳定通过本地预演时，使用精确标题绑定规则直接固定到目标 TMDB 与季集",
+            "comment_line": "#当 AI 建议无法稳定通过本地预演时，使用精确标题绑定规则直接固定到目标 TMDB 与季集",
+            "rule": rule,
+            "confidence": 0.95,
+            "reason": "精确匹配当前标题并强制绑定目标 TMDB / 季集，作用域最窄，稳定性最高。",
+            "preview": preview,
+            "lines": [
+                "#当 AI 建议无法稳定通过本地预演时，使用精确标题绑定规则直接固定到目标 TMDB 与季集",
+                rule,
+            ],
+        }
+
+    def _invoke_identifier_llm(
+        self,
+        title: str,
+        path: str,
+        result: Dict[str, Any],
+        target: Dict[str, Any],
+    ) -> IdentifierSuggestionBundle:
+        llm = self._get_llm()
+        prompt = self._build_identifier_prompt()
+        chain = (
+            prompt
+            | llm.with_structured_output(IdentifierSuggestionBundle).with_retry(
+                stop_after_attempt=self._max_retries
+            )
+        )
+        bundle: IdentifierSuggestionBundle = chain.invoke(
+            {
+                "title": title,
+                "path": path,
+                "meta_hint": self._build_meta_hint(path or title),
+                "guess": result.get("guess") or {},
+                "verified_summary": self._compact_verified_summary(result.get("verified_media_info")),
+                "target": target,
+            },
+            config={"configurable": {"timeout": self._request_timeout}},
+        )
+        return bundle
+
+    def _suggest_identifiers(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body, source_sample, sample_message = self._build_body_from_sample(body)
+        if sample_message:
+            return {"success": False, "message": sample_message}
+        title = str(body.get("title") or "").strip()
+        path = str(body.get("path") or "").strip()
+        if not title and path:
+            title = Path(path).name
+        if not title:
+            return {"success": False, "message": "标题为空"}
+
+        result = self._recognize(title=title, path=path, record_failed_sample=False)
+        target = self._build_target(body, result=result)
+        invoke_error = ""
+        try:
+            bundle = self._invoke_identifier_llm(title=title, path=path, result=result, target=target)
+        except Exception as exc:
+            bundle = IdentifierSuggestionBundle(
+                summary="识别词建议模型暂不可用，已自动回退到精确规则兜底。",
+                suggestions=[],
+            )
+            invoke_error = str(exc)
+
+        cleaned: List[Dict[str, Any]] = []
+        for item in bundle.suggestions:
+            rule = self._enrich_identifier_rule(item.rule, target=target)
+            if not self._validate_identifier_rule(rule):
+                continue
+            comment_line = self._clean_comment_line(item.comment)
+            preview = self._preview_identifier_rule(title=title, rule=rule, target=target)
+            if not preview.get("applied"):
+                continue
+            if target and any(target.values()) and preview.get("matched_target") is False:
+                continue
+            cleaned.append(
+                {
+                    "comment": item.comment.strip(),
+                    "comment_line": comment_line,
+                    "rule": rule,
+                    "confidence": min(1.0, max(0.0, self._safe_float(item.confidence, 0.0))),
+                    "reason": str(item.reason or "").strip(),
+                    "preview": preview,
+                    "lines": [line for line in [comment_line, rule] if line],
+                }
+            )
+
+        if not cleaned:
+            fallback = self._build_exact_identifier_fallback(title=title, target=target)
+            if fallback:
+                if invoke_error:
+                    fallback["reason"] = f"{fallback.get('reason', '')} 当前识别词建议模型不可用，已自动切到精确规则兜底。".strip()
+                cleaned.append(fallback)
+
+        if not cleaned:
+            return {
+                "success": False,
+                "message": f"识别词建议生成失败: {invoke_error}" if invoke_error else "没有生成可直接使用的识别词规则",
+                "data": {
+                    "summary": bundle.summary,
+                    "target": target,
+                    "recognize_result": result,
+                },
+            }
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "summary": bundle.summary,
+                "source_sample_index": (source_sample or {}).get("sample_index"),
+                "source_sample": source_sample,
+                "target": target,
+                "recognize_result": result,
+                "suggestions": cleaned,
+            },
+        }
+
+    def _get_custom_identifiers(self) -> List[str]:
+        if not self._systemconfig:
+            self._systemconfig = SystemConfigOper()
+        return self._systemconfig.get(SystemConfigKey.CustomIdentifiers) or []
+
+    def _append_custom_identifiers(self, lines: List[str]) -> Dict[str, Any]:
+        existing = self._get_custom_identifiers()
+        added: List[str] = []
+        for line in lines:
+            normalized = str(line or "").rstrip()
+            if not normalized:
+                continue
+            if normalized in existing or normalized in added:
+                continue
+            added.append(normalized)
+        if added:
+            merged = existing + added
+            self._systemconfig.set(SystemConfigKey.CustomIdentifiers, merged)
+        return {
+            "added": added,
+            "added_count": len(added),
+            "total_count": len(self._get_custom_identifiers()),
+        }
+
+    def _verify_guess(self, title: str, path: str, guess: AIRecognitionGuess) -> Optional[Dict[str, Any]]:
+        if not guess.name:
+            return None
+        try:
+            raw_text = path or title or guess.name
+            meta = MetaInfo(raw_text)
+            meta.name = guess.name
+            meta.year = guess.year or None
+            meta.begin_season = guess.season or None
+            meta.begin_episode = guess.episode or None
+            if guess.media_type == "tv" or meta.begin_season or meta.begin_episode:
+                meta.type = MediaType.TV
+            elif guess.media_type == "movie":
+                meta.type = MediaType.MOVIE
+            mediainfo = MediaChain().recognize_media(meta=meta, cache=False)
+            if not mediainfo:
+                return None
+            return mediainfo.to_dict()
+        except Exception as exc:
+            if self._debug:
+                logger.warning(f"[AI识别增强] 二次校验失败: {exc}")
+            return None
+
+    def _recognize(self, title: str, path: str = "", record_failed_sample: bool = True) -> Dict[str, Any]:
+        title = str(title or "").strip()
+        path = str(path or "").strip()
+        if not title and path:
+            title = Path(path).name
+        if not title:
+            return {"success": False, "message": "标题为空"}
+        try:
+            guess = self._invoke_llm(title, path)
+        except Exception as exc:
+            if record_failed_sample:
+                self._record_failed_sample(
+                    {
+                        "title": title,
+                        "path": path,
+                        "meta_hint": self._build_meta_hint(path or title),
+                        "reason": f"llm_error:{exc}",
+                    }
+                )
+            return {"success": False, "message": f"LLM 调用失败: {exc}"}
+
+        verified = self._verify_guess(title, path, guess)
+        passed = bool(guess.name and guess.confidence >= self._confidence_threshold)
+        if not passed and record_failed_sample:
+            self._record_failed_sample(
+                {
+                    "title": title,
+                    "path": path,
+                    "meta_hint": self._build_meta_hint(path or title),
+                    "guess": guess.model_dump(),
+                    "verified_media_info": self._compact_verified_summary(verified),
+                    "reason": "low_confidence_or_empty_name",
+                }
+            )
+        return {
+            "success": passed,
+            "message": "success" if passed else "识别结果置信度不足，已放弃注入",
+            "guess": guess.model_dump(),
+            "verified_media_info": verified,
+        }
+
+    def on_chain_name_recognize(self, event) -> None:
+        if not self._enabled:
+            return
+        event_data = getattr(event, "event_data", None) or {}
+        title, path = self._extract_title_path(event_data)
+        if not title and not path:
+            return
+        result = self._recognize(title=title, path=path)
+        if not result.get("success"):
+            if self._debug:
+                logger.info(f"[AI识别增强] 跳过注入: {title or path} - {result.get('message')}")
+            return
+        guess = result.get("guess") or {}
+        if isinstance(event_data, dict):
+            event_data["name"] = guess.get("name", "")
+            event_data["year"] = guess.get("year", "")
+            event_data["season"] = guess.get("season", 0)
+            event_data["episode"] = guess.get("episode", 0)
+            event_data["source_plugin"] = "AIRecognizerEnhancer"
+            event_data["confidence"] = guess.get("confidence", 0)
+            event_data["reason"] = guess.get("reason", "")
+
+    async def api_health(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        llm_ready = bool(getattr(settings, "LLM_API_KEY", None))
+        return {
+            "success": True,
+            "data": {
+                "plugin_version": self.plugin_version,
+                "enabled": self._enabled,
+                "llm_ready": llm_ready,
+                "llm_provider": getattr(settings, "LLM_PROVIDER", ""),
+                "llm_model": getattr(settings, "LLM_MODEL", ""),
+                "confidence_threshold": self._confidence_threshold,
+                "request_timeout": self._request_timeout,
+            },
+        }
+
+    async def api_recognize(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        title = str(body.get("title") or "").strip()
+        path = str(body.get("path") or "").strip()
+        result = self._recognize(title=title, path=path)
+        return {
+            "success": result.get("success", False),
+            "message": result.get("message", ""),
+            "data": {
+                "guess": result.get("guess"),
+                "verified_media_info": result.get("verified_media_info"),
+            },
+        }
+
+    async def api_failed_samples(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 20)
+        limit = max(1, min(limit, 100))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=limit))
+        return {
+            "success": True,
+            "data": {
+                "count": len(samples),
+                "samples": samples,
+            },
+        }
+
+    async def api_sample_worklist(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 20)
+        limit = max(1, min(limit, 100))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=limit))
+        worklist = [self._summarize_sample(sample) for sample in samples]
+        return {
+            "success": True,
+            "data": {
+                "count": len(worklist),
+                "samples": worklist,
+            },
+        }
+
+    async def api_sample_insights(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 50)
+        limit = max(1, min(limit, 200))
+        top = self._safe_int(request.query_params.get("top"), 10)
+        top = max(1, min(top, 20))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=limit))
+        insights = self._build_sample_insights(samples, top=top)
+        return {
+            "success": True,
+            "data": insights,
+        }
+
+    async def api_sample_brief(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 5)
+        limit = max(1, min(limit, 20))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=100))
+        return {
+            "success": True,
+            "data": {
+                "count": len(samples),
+                "text": self._render_sample_brief(samples, top=limit),
+            },
+        }
+
+    async def api_suggest_identifiers(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._suggest_identifiers(body)
+
+    async def api_apply_identifiers(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        identifiers = body.get("identifiers") or []
+        if not isinstance(identifiers, list):
+            return {"success": False, "message": "identifiers 必须是数组"}
+        result = self._append_custom_identifiers([str(line or "") for line in identifiers])
+        return {
+            "success": True,
+            "message": "success",
+            "data": result,
+        }
+
+    async def api_clear_failed_samples(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        cleared = self._clear_failed_samples()
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "cleared_count": cleared,
+            },
+        }
+
+    async def api_remove_failed_sample(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        result = self._remove_failed_sample(body.get("sample_index"), limit=1000)
+        if not result.get("removed"):
+            return {"success": False, "message": result.get("message", "移除失败"), "data": result}
+        return {
+            "success": True,
+            "message": "success",
+            "data": result,
+        }
+
+    async def api_replay_failed_sample(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._replay_failed_sample(body)
+
+    async def api_replay_failed_samples(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._replay_failed_samples(body)
+
+    async def api_suggest_identifiers_from_sample(self, request: Request):
+        body = await request.json()
+        body["use_latest_sample"] = True if body.get("use_latest_sample") is None else body.get("use_latest_sample")
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        if body.get("sample_index") is None and body.get("use_latest_sample") is False:
+            body["use_latest_sample"] = True
+        return self._suggest_identifiers(body)
+
+    async def api_suggest_identifiers_for_failed_samples(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._suggest_identifiers_for_failed_samples(body)
+
+    async def api_apply_suggested_identifier(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._apply_suggested_identifier_internal(body)
+
+    async def api_apply_suggested_identifiers_for_failed_samples(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._apply_suggested_identifiers_for_failed_samples(body)
+
+    def get_api(self) -> List[Dict[str, Any]]:
+        return [
+            {
+                "path": "/health",
+                "endpoint": self.api_health,
+                "methods": ["GET"],
+                "summary": "检查 AI识别增强 的运行状态",
+            },
+            {
+                "path": "/recognize",
+                "endpoint": self.api_recognize,
+                "methods": ["POST"],
+                "summary": "用当前 LLM 对失败标题做一次本地结构化识别测试",
+            },
+            {
+                "path": "/failed_samples",
+                "endpoint": self.api_failed_samples,
+                "methods": ["GET"],
+                "summary": "查看最近保存的低置信度失败样本",
+            },
+            {
+                "path": "/sample_worklist",
+                "endpoint": self.api_sample_worklist,
+                "methods": ["GET"],
+                "summary": "返回适合智能体使用的失败样本摘要列表",
+            },
+            {
+                "path": "/sample_insights",
+                "endpoint": self.api_sample_insights,
+                "methods": ["GET"],
+                "summary": "汇总失败样本原因、重复问题和优先处理样本",
+            },
+            {
+                "path": "/sample_brief",
+                "endpoint": self.api_sample_brief,
+                "methods": ["GET"],
+                "summary": "返回适合智能体低 token 消费的失败样本精简摘要",
+            },
+            {
+                "path": "/suggest_identifiers",
+                "endpoint": self.api_suggest_identifiers,
+                "methods": ["POST"],
+                "summary": "根据标题和目标结果生成 MoviePilot 自定义识别词建议",
+            },
+            {
+                "path": "/suggest_identifiers_from_sample",
+                "endpoint": self.api_suggest_identifiers_from_sample,
+                "methods": ["POST"],
+                "summary": "直接基于最近失败样本或指定样本生成自定义识别词建议",
+            },
+            {
+                "path": "/suggest_identifiers_for_failed_samples",
+                "endpoint": self.api_suggest_identifiers_for_failed_samples,
+                "methods": ["POST"],
+                "summary": "批量为失败样本生成自定义识别词建议",
+            },
+            {
+                "path": "/apply_identifiers",
+                "endpoint": self.api_apply_identifiers,
+                "methods": ["POST"],
+                "summary": "将确认后的自定义识别词追加写入系统 CustomIdentifiers",
+            },
+            {
+                "path": "/clear_failed_samples",
+                "endpoint": self.api_clear_failed_samples,
+                "methods": ["POST"],
+                "summary": "清空失败样本文件",
+            },
+            {
+                "path": "/remove_failed_sample",
+                "endpoint": self.api_remove_failed_sample,
+                "methods": ["POST"],
+                "summary": "按索引移除单条失败样本",
+            },
+            {
+                "path": "/replay_failed_sample",
+                "endpoint": self.api_replay_failed_sample,
+                "methods": ["POST"],
+                "summary": "按当前识别词和当前识别器复查某条失败样本，并可在确认修复后自动出队",
+            },
+            {
+                "path": "/replay_failed_samples",
+                "endpoint": self.api_replay_failed_samples,
+                "methods": ["POST"],
+                "summary": "批量复查失败样本，并可在确认修复后批量出队",
+            },
+            {
+                "path": "/apply_suggested_identifier",
+                "endpoint": self.api_apply_suggested_identifier,
+                "methods": ["POST"],
+                "summary": "直接把最近失败样本或指定样本生成的建议规则写入 CustomIdentifiers，并按需移除该样本",
+            },
+            {
+                "path": "/apply_suggested_identifiers_for_failed_samples",
+                "endpoint": self.api_apply_suggested_identifiers_for_failed_samples,
+                "methods": ["POST"],
+                "summary": "批量把失败样本生成的建议规则写入 CustomIdentifiers，并按需移除对应样本",
+            },
+        ]
+
+    def get_page(self) -> List[dict]:
+        llm_ready = bool(getattr(settings, "LLM_API_KEY", None))
+        failed_samples_count = len(self._read_failed_samples(limit=200))
+        custom_identifiers_count = len(self._get_custom_identifiers())
+        llm_provider = getattr(settings, "LLM_PROVIDER", "—")
+        llm_model = getattr(settings, "LLM_MODEL", "—")
+
+        def stat_card(title: str, value: Any, subtitle: str = "") -> dict:
+            content = [
+                {
+                    "component": "div",
+                    "props": {"class": "text-caption text-medium-emphasis mb-1"},
+                    "text": title,
+                },
+                {
+                    "component": "div",
+                    "props": {"class": "text-h6 font-weight-bold"},
+                    "text": str(value),
+                },
+            ]
+            if subtitle:
+                content.append(
+                    {
+                        "component": "div",
+                        "props": {"class": "text-caption text-medium-emphasis mt-1"},
+                        "text": subtitle,
+                    }
+                )
+            return {
+                "component": "VCard",
+                "props": {"variant": "tonal", "class": "pa-4 h-100"},
+                "content": content,
+            }
+
+        return [
+            {
+                "component": "VContainer",
+                "props": {"fluid": True, "class": "pa-0"},
+                "content": [
+                    {
+                        "component": "VAlert",
+                        "props": {
+                            "type": "info",
+                            "variant": "tonal",
+                            "class": "mb-4",
+                            "title": "本地 LLM 识别兜底",
+                            "text": "复用 MoviePilot 当前 LLM 配置，在原生识别失败时做结构化兜底，并把结果交回 MoviePilot 继续二次识别。",
+                        },
+                    },
+                    {
+                        "component": "VRow",
+                        "props": {"dense": True, "class": "mb-2"},
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("当前状态", "已启用" if self._enabled else "未启用")],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("LLM 可用", "是" if llm_ready else "否", f"{llm_provider} / {llm_model}")],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("失败样本", f"{failed_samples_count} 条", f"上限 {self._max_failed_samples} 条")],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("自定义识别词", f"{custom_identifiers_count} 条", "系统 CustomIdentifiers")],
+                            },
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "props": {"dense": True},
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VCard",
+                                        "props": {"variant": "outlined", "class": "pa-4 h-100"},
+                                        "content": [
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-subtitle-1 font-weight-bold mb-2"},
+                                                "text": "识别兜底",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-body-2 text-medium-emphasis"},
+                                                "text": "在 Chain NameRecognize 阶段回写 name / year / season / episode，供 MoviePilot 继续原生二次识别。",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-caption text-medium-emphasis mt-3"},
+                                                "text": f"置信度阈值：{self._confidence_threshold}；请求超时：{self._request_timeout} 秒",
+                                            },
+                                        ],
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VCard",
+                                        "props": {"variant": "outlined", "class": "pa-4 h-100"},
+                                        "content": [
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-subtitle-1 font-weight-bold mb-2"},
+                                                "text": "识别词闭环",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-body-2 text-medium-emphasis"},
+                                                "text": "失败样本可生成 CustomIdentifiers 建议，并按需追加写入系统配置。",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-caption text-medium-emphasis mt-3"},
+                                                "text": f"写入后自动移除样本：{'是' if self._auto_remove_applied_sample else '否'}",
+                                            },
+                                        ],
+                                    }
+                                ],
+                            },
+                        ],
+                    },
+                ],
+            }
+        ]
+
+    @staticmethod
+    def get_render_mode() -> Tuple[str, Optional[str]]:
+        return "vuetify", None
+
+    def get_form(self) -> Tuple[List[dict], Dict[str, Any]]:
+        form = [
+            {
+                "component": "VForm",
+                "content": [
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VAlert",
+                                        "props": {
+                                            "type": "info",
+                                            "variant": "tonal",
+                                            "text": "当前版本已改为直接复用 MoviePilot 当前启用的 LLM 配置，在原生识别失败后做本地结构化兜底。",
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 4},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {"model": "enabled", "label": "启用 AI识别增强"},
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 4},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {"model": "debug", "label": "调试模式"},
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 4},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {"model": "save_failed_samples", "label": "保存低置信度样本"},
+                                    }
+                                ],
+                            },
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "confidence_threshold",
+                                            "label": "置信度阈值",
+                                            "type": "number",
+                                            "hint": "低于该值的结果不注入 MoviePilot，默认 0.65",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "request_timeout",
+                                            "label": "LLM 请求超时（秒）",
+                                            "type": "number",
+                                            "hint": "默认 25 秒",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            },
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "max_retries",
+                                            "label": "结构化输出重试次数",
+                                            "type": "number",
+                                            "hint": "默认 2 次",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "max_failed_samples",
+                                            "label": "失败样本保留上限",
+                                            "type": "number",
+                                            "hint": "默认保留最近 200 条，并对重复样本自动去重",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {
+                                            "model": "auto_remove_applied_sample",
+                                            "label": "写入识别词后自动移除对应失败样本",
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                ],
+            }
+        ]
+        return form, {
+            "enabled": False,
+            "debug": False,
+            "confidence_threshold": 0.65,
+            "request_timeout": 25,
+            "max_retries": 2,
+            "save_failed_samples": True,
+            "max_failed_samples": 200,
+            "auto_remove_applied_sample": True,
+        }
diff --git a/plugins/airecognizerenhancer/README.md b/plugins/airecognizerenhancer/README.md
new file mode 100644
index 0000000..ea48943
--- /dev/null
+++ b/plugins/airecognizerenhancer/README.md
@@ -0,0 +1,99 @@
+# AI识别增强
+
+`AI识别增强` 用来补强 MoviePilot 原生整理链里的识别阶段。
+
+它的核心思路很简单：
+
+- 复用 MoviePilot 当前已经启用的 LLM 配置
+- 在原生识别失败或置信度不足时，做一次本地结构化识别兜底
+- 把结果回写给 MoviePilot，继续走原生二次识别和后续整理链
+
+## 适合什么场景
+
+- 文件名比较脏，混有压制组、分辨率、语言、站点标记
+- 同一部剧经常出现英文名、别名、原名、翻译名混用
+- 网盘挂载、手动整理、历史资源补录时，原生识别偶尔不稳定
+- 你想把失败样本沉淀下来，后面持续优化 `CustomIdentifiers`
+
+## 和 MoviePilot 原版智能体的区别
+
+MoviePilot 原版智能体已经提供“整理失败后自动接管再试一次”的能力。
+
+这和 `AI识别增强` 有重叠，但定位不同：
+
+- **MP 原版智能体**
+  - 更偏“一次性补救”
+  - 适合偶发失败、想省事的场景
+
+- **AI识别增强**
+  - 更偏“识别失败治理层”
+  - 除了补救当前这次，还能：
+    - 保存失败样本
+    - 汇总样本洞察
+    - 生成 `CustomIdentifiers` 建议
+    - 写入识别词
+    - 重放 / 复查 / 批量出队
+
+一句话区分：
+
+- 原版智能体：自动接管一次
+- `AI识别增强`：把失败样本沉淀下来，长期减少同类失败
+
+## 当前能力
+
+- 监听 `ChainEventType.NameRecognize`
+- 用当前 LLM 结构化判断标题、年份、类型、季集
+- 回写 `name / year / season / episode`
+- 交回 MoviePilot 原生链路继续二次识别
+- 保存低置信度失败样本
+- 提供失败样本工作清单、洞察、重放、删除和清空能力
+- 生成并应用 `CustomIdentifiers` 建议
+
+## 主要接口
+
+- `GET /api/v1/plugin/AIRecognizerEnhancer/health`
+  - 查看插件状态、LLM 提供方、模型、阈值和超时配置
+- `POST /api/v1/plugin/AIRecognizerEnhancer/recognize`
+  - 对单个标题做一次本地结构化识别测试
+- `GET /api/v1/plugin/AIRecognizerEnhancer/failed_samples`
+  - 查看最近保存的失败样本
+- `GET /api/v1/plugin/AIRecognizerEnhancer/sample_worklist`
+  - 返回适合继续处理的失败样本摘要列表
+- `GET /api/v1/plugin/AIRecognizerEnhancer/sample_insights`
+  - 汇总失败原因、重复问题和优先处理样本
+- `POST /api/v1/plugin/AIRecognizerEnhancer/replay_failed_sample`
+  - 用当前识别词和当前识别器重放复查某条失败样本
+- `POST /api/v1/plugin/AIRecognizerEnhancer/suggest_identifiers_from_sample`
+  - 直接基于失败样本生成识别词建议
+- `POST /api/v1/plugin/AIRecognizerEnhancer/apply_suggested_identifier`
+  - 把建议规则写入系统 `CustomIdentifiers`
+
+其余批量接口和清理接口可以按需要继续使用，详细路径以插件 `get_api()` 暴露结果为准。
+
+## 配置建议
+
+- 先确认 MoviePilot 本身已经配置好可用的 LLM
+- 建议保持“保存失败样本”开启
+- 如果你经常处理历史资源或网盘资源，建议定期查看：
+  - `failed_samples`
+  - `sample_worklist`
+  - `sample_insights`
+
+## 已验证情况
+
+当前版本：`0.1.12`
+
+这版已经验证过：
+
+- 最新版 MoviePilot 下可以正常加载
+- 正常中文标题识别可用
+- 英文别名、韩文原名、中文别名可识别回标准媒体信息
+- 低置信度标题会落失败样本
+- `replay_failed_sample` 复查链可用
+
+## 说明
+
+- 这个插件不依赖外部 AI Gateway 回调链
+- 重点是增强识别，不负责替代 MoviePilot 全部整理流程
+- 如果你只是偶发整理失败，原版智能体可能已经够用
+- 如果你长期受命名混乱困扰，这个插件更有价值
diff --git a/plugins/airecognizerenhancer/__init__.py b/plugins/airecognizerenhancer/__init__.py
new file mode 100644
index 0000000..4471184
--- /dev/null
+++ b/plugins/airecognizerenhancer/__init__.py
@@ -0,0 +1,2039 @@
+import hmac
+import asyncio
+import inspect
+import json
+import re
+import threading
+from collections import Counter
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+from fastapi import Request
+from langchain_core.prompts import ChatPromptTemplate
+from pydantic import BaseModel, Field
+
+from app.chain.media import MediaChain
+from app.core.config import settings
+from app.core.event import eventmanager
+from app.core.meta.words import WordsMatcher
+from app.core.metainfo import MetaInfo
+from app.db.systemconfig_oper import SystemConfigOper
+try:
+    from app.helper.llm import LLMHelper
+except ImportError:  # MoviePilot 新版已迁移到 app.agent.llm
+    from app.agent.llm import LLMHelper
+from app.log import logger
+from app.plugins import _PluginBase
+from app.schemas.types import ChainEventType, MediaType, SystemConfigKey
+
+
+class AIRecognitionGuess(BaseModel):
+    name: str = Field(default="", description="标准化后的影视标题；无法判断时返回空字符串")
+    year: str = Field(default="", description="四位年份；无法判断时返回空字符串")
+    media_type: str = Field(default="unknown", description="movie、tv 或 unknown")
+    season: int = Field(default=0, description="剧集季号，电影填 0")
+    episode: int = Field(default=0, description="剧集集号，电影或未知填 0")
+    confidence: float = Field(default=0.0, description="0 到 1 之间的置信度")
+    reason: str = Field(default="", description="简短说明为什么这样判断")
+
+
+class IdentifierSuggestion(BaseModel):
+    comment: str = Field(default="", description="可选注释，不带 #")
+    rule: str = Field(default="", description="一条 MoviePilot 自定义识别词规则")
+    confidence: float = Field(default=0.0, description="0 到 1 之间的置信度")
+    reason: str = Field(default="", description="为什么建议这条规则")
+
+
+class IdentifierSuggestionBundle(BaseModel):
+    summary: str = Field(default="", description="整体建议摘要")
+    suggestions: List[IdentifierSuggestion] = Field(default_factory=list, description="建议规则列表")
+
+
+class AIRecognizerEnhancer(_PluginBase):
+    plugin_name = "AI识别增强"
+    plugin_desc = "直接复用 MoviePilot 当前 LLM 配置，在原生识别失败后做本地结构化识别兜底，并交回原生链路继续二次识别。"
+    plugin_icon = "https://raw.githubusercontent.com/liuyuexi1987/MoviePilot-Plugins/main/icons/airecognizerenhancer.png"
+    plugin_version = "0.1.12"
+    plugin_author = "liuyuexi1987"
+    plugin_level = 1
+    author_url = "https://github.com/liuyuexi1987"
+    plugin_config_prefix = "arrecognizerenhancer_"
+    plugin_order = 41
+    auth_level = 1
+
+    _enabled = False
+    _debug = False
+    _confidence_threshold = 0.65
+    _request_timeout = 25
+    _max_retries = 2
+    _save_failed_samples = True
+    _max_failed_samples = 200
+    _auto_remove_applied_sample = True
+    _systemconfig: Optional[SystemConfigOper] = None
+
+    def init_plugin(self, config: Optional[Dict[str, Any]] = None):
+        config = config or {}
+        self._enabled = bool(config.get("enabled", False))
+        self._debug = bool(config.get("debug", False))
+        self._confidence_threshold = self._safe_float(config.get("confidence_threshold"), 0.65)
+        self._request_timeout = self._safe_int(config.get("request_timeout"), 25)
+        self._max_retries = max(1, min(5, self._safe_int(config.get("max_retries"), 2)))
+        self._save_failed_samples = bool(config.get("save_failed_samples", True))
+        self._max_failed_samples = max(20, min(1000, self._safe_int(config.get("max_failed_samples"), 200)))
+        self._auto_remove_applied_sample = bool(config.get("auto_remove_applied_sample", True))
+        self._systemconfig = SystemConfigOper()
+        self._register_events()
+
+    def get_state(self) -> bool:
+        return self._enabled
+
+    @staticmethod
+    def get_command() -> List[Dict[str, Any]]:
+        return []
+
+    def stop_service(self):
+        try:
+            eventmanager.disable_event_handler(self.on_chain_name_recognize)
+        except Exception:
+            pass
+
+    @staticmethod
+    def _safe_int(value: Any, default: int) -> int:
+        try:
+            return int(value)
+        except Exception:
+            return default
+
+    @staticmethod
+    def _safe_float(value: Any, default: float) -> float:
+        try:
+            return float(value)
+        except Exception:
+            return default
+
+    @staticmethod
+    def _extract_apikey(request: Request, body: Optional[Dict[str, Any]] = None) -> str:
+        header = str(request.headers.get("Authorization") or "").strip()
+        if header.lower().startswith("bearer "):
+            return header.split(" ", 1)[1].strip()
+        if body:
+            for key in ("apikey", "api_key"):
+                token = str(body.get(key) or "").strip()
+                if token:
+                    return token
+        return str(request.query_params.get("apikey") or "").strip()
+
+    def _check_api_access(self, request: Request, body: Optional[Dict[str, Any]] = None) -> Tuple[bool, str]:
+        expected = str(getattr(settings, "API_TOKEN", "") or "").strip()
+        if not expected:
+            return False, "服务端未配置 API Token"
+        actual = self._extract_apikey(request, body)
+        if not hmac.compare_digest(actual, expected):
+            return False, "API Token 无效"
+        return True, ""
+
+    def _register_events(self) -> None:
+        try:
+            eventmanager.register(ChainEventType.NameRecognize)(self.on_chain_name_recognize)
+            if self._enabled:
+                eventmanager.enable_event_handler(self.on_chain_name_recognize)
+            else:
+                eventmanager.disable_event_handler(self.on_chain_name_recognize)
+        except Exception as exc:
+            logger.warning(f"[AI识别增强] 注册链式识别事件失败: {exc}")
+
+    @staticmethod
+    def _extract_title_path(event_data: Any) -> Tuple[str, str]:
+        title = ""
+        path = ""
+        if isinstance(event_data, dict):
+            title = (
+                event_data.get("title")
+                or event_data.get("name")
+                or event_data.get("org_string")
+                or ""
+            )
+            path = (
+                event_data.get("path")
+                or event_data.get("file_path")
+                or event_data.get("org_string")
+                or ""
+            )
+        else:
+            title = (
+                getattr(event_data, "title", "")
+                or getattr(event_data, "name", "")
+                or getattr(event_data, "org_string", "")
+                or ""
+            )
+            path = (
+                getattr(event_data, "path", "")
+                or getattr(event_data, "file_path", "")
+                or getattr(event_data, "org_string", "")
+                or ""
+            )
+        return str(title or "").strip(), str(path or "").strip()
+
+    def _build_meta_hint(self, raw_text: str) -> Dict[str, Any]:
+        try:
+            meta = MetaInfo(raw_text)
+        except Exception:
+            return {}
+        return {
+            "name": getattr(meta, "name", "") or "",
+            "year": getattr(meta, "year", "") or "",
+            "type": getattr(getattr(meta, "type", None), "to_agent", lambda: None)() or "",
+            "season": getattr(meta, "begin_season", None) or 0,
+            "episode": getattr(meta, "begin_episode", None) or 0,
+            "org_string": getattr(meta, "org_string", "") or "",
+        }
+
+    @staticmethod
+    def _clean_guess_name(name: str) -> str:
+        text = str(name or "").strip()
+        if not text:
+            return ""
+        text = text.split("/")[0].strip().replace(".", " ")
+        return " ".join(text.split())
+
+    def _normalize_guess(self, guess: AIRecognitionGuess) -> AIRecognitionGuess:
+        name = self._clean_guess_name(guess.name)
+        year = str(guess.year or "").strip()
+        if not (len(year) == 4 and year.isdigit()):
+            year = ""
+        media_type = str(guess.media_type or "unknown").strip().lower()
+        if media_type not in {"movie", "tv"}:
+            media_type = "unknown"
+        season = max(0, self._safe_int(guess.season, 0))
+        episode = max(0, self._safe_int(guess.episode, 0))
+        confidence = min(1.0, max(0.0, self._safe_float(guess.confidence, 0.0)))
+        reason = str(guess.reason or "").strip()
+        return AIRecognitionGuess(
+            name=name,
+            year=year,
+            media_type=media_type,
+            season=season,
+            episode=episode,
+            confidence=confidence,
+            reason=reason,
+        )
+
+    def _sample_path(self) -> Path:
+        return self.get_data_path() / "failed_samples.jsonl"
+
+    @staticmethod
+    def _sample_identity(payload: Dict[str, Any]) -> str:
+        return json.dumps(
+            {
+                "title": str(payload.get("title") or "").strip(),
+                "path": str(payload.get("path") or "").strip(),
+                "reason": str(payload.get("reason") or "").strip(),
+            },
+            ensure_ascii=False,
+            sort_keys=True,
+        )
+
+    def _write_failed_samples(self, rows: List[Dict[str, Any]]) -> None:
+        sample_path = self._sample_path()
+        sample_path.parent.mkdir(parents=True, exist_ok=True)
+        trimmed = rows[-self._max_failed_samples:]
+        with sample_path.open("w", encoding="utf-8") as f:
+            for row in trimmed:
+                f.write(json.dumps(row, ensure_ascii=False) + "\n")
+
+    def _record_failed_sample(self, payload: Dict[str, Any]) -> None:
+        if not self._save_failed_samples:
+            return
+        try:
+            rows = self._read_failed_samples(limit=1000)
+            rows.reverse()
+            identity = self._sample_identity(payload)
+            filtered = [row for row in rows if self._sample_identity(row) != identity]
+            filtered.append(payload)
+            self._write_failed_samples(filtered)
+        except Exception as exc:
+            logger.warning(f"[AI识别增强] 写入失败样本失败: {exc}")
+
+    def _read_failed_samples(self, limit: int = 20) -> List[Dict[str, Any]]:
+        sample_path = self._sample_path()
+        if not sample_path.exists():
+            return []
+        rows: List[Dict[str, Any]] = []
+        try:
+            with sample_path.open("r", encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    if not line:
+                        continue
+                    try:
+                        rows.append(json.loads(line))
+                    except Exception:
+                        continue
+        except Exception as exc:
+            logger.warning(f"[AI识别增强] 读取失败样本失败: {exc}")
+            return []
+        if limit > 0:
+            rows = rows[-limit:]
+        rows.reverse()
+        return rows
+
+    def _clear_failed_samples(self) -> int:
+        rows = self._read_failed_samples(limit=1000)
+        sample_path = self._sample_path()
+        if sample_path.exists():
+            sample_path.unlink()
+        return len(rows)
+
+    def _remove_failed_sample(self, sample_index: Optional[Any], limit: int = 1000) -> Dict[str, Any]:
+        rows = self._read_failed_samples(limit=max(1, min(limit, 1000)))
+        if not rows:
+            return {"removed": False, "message": "暂无失败样本", "removed_count": 0}
+        index = self._safe_int(sample_index, 0)
+        if index < 0:
+            index = 0
+        if index >= len(rows):
+            return {
+                "removed": False,
+                "message": f"失败样本索引超出范围，当前共有 {len(rows)} 条",
+                "removed_count": 0,
+            }
+        removed_sample = dict(rows[index] or {})
+        del rows[index]
+        if rows:
+            rows.reverse()
+            self._write_failed_samples(rows)
+        else:
+            self._clear_failed_samples()
+        return {
+            "removed": True,
+            "message": "success",
+            "removed_count": 1,
+            "remaining_count": len(rows),
+            "removed_sample": removed_sample,
+            "removed_sample_index": index,
+        }
+
+    def _remove_failed_samples(self, sample_indexes: List[Any], limit: int = 1000) -> Dict[str, Any]:
+        rows = self._read_failed_samples(limit=max(1, min(limit, 1000)))
+        if not rows:
+            return {"removed": False, "message": "暂无失败样本", "removed_count": 0, "remaining_count": 0}
+        normalized_indexes = sorted(
+            {self._safe_int(index, -1) for index in (sample_indexes or []) if self._safe_int(index, -1) >= 0},
+            reverse=True,
+        )
+        valid_indexes = [index for index in normalized_indexes if index < len(rows)]
+        if not valid_indexes:
+            return {
+                "removed": False,
+                "message": "没有可移除的有效样本索引",
+                "removed_count": 0,
+                "remaining_count": len(rows),
+            }
+        removed_samples: List[Dict[str, Any]] = []
+        for index in valid_indexes:
+            removed_samples.append(dict(rows[index] or {}))
+            del rows[index]
+        if rows:
+            rows.reverse()
+            self._write_failed_samples(rows)
+        else:
+            self._clear_failed_samples()
+        removed_samples.reverse()
+        return {
+            "removed": True,
+            "message": "success",
+            "removed_count": len(valid_indexes),
+            "remaining_count": len(rows),
+            "removed_sample_indexes": sorted(valid_indexes),
+            "removed_samples": removed_samples,
+        }
+
+    def _resolve_failed_sample(
+        self,
+        sample_index: Optional[Any] = None,
+        limit: int = 100,
+    ) -> Tuple[Optional[int], Optional[Dict[str, Any]], str]:
+        samples = self._read_failed_samples(limit=max(1, min(limit, 200)))
+        if not samples:
+            return None, None, "暂无失败样本"
+        index = self._safe_int(sample_index, 0)
+        if index < 0:
+            index = 0
+        if index >= len(samples):
+            return None, None, f"失败样本索引超出范围，当前共有 {len(samples)} 条"
+        row = dict(samples[index] or {})
+        row["sample_index"] = index
+        return index, row, ""
+
+    def _select_failed_sample_indexes(
+        self,
+        sample_indexes: Optional[List[Any]] = None,
+        limit: int = 10,
+        pool_limit: int = 200,
+    ) -> Tuple[List[int], List[Dict[str, Any]], str]:
+        current_samples = self._inject_sample_indices(self._read_failed_samples(limit=max(1, min(pool_limit, 1000))))
+        if not current_samples:
+            return [], [], "暂无失败样本"
+        if isinstance(sample_indexes, list) and sample_indexes:
+            selected_indexes: List[int] = []
+            seen = set()
+            for raw in sample_indexes:
+                idx = self._safe_int(raw, -1)
+                if idx < 0 or idx >= len(current_samples) or idx in seen:
+                    continue
+                seen.add(idx)
+                selected_indexes.append(idx)
+        else:
+            selected_indexes = [int(sample.get("sample_index", 0)) for sample in current_samples[: max(1, min(limit, 50))]]
+        if not selected_indexes:
+            return [], current_samples, "没有可处理的有效样本索引"
+        return selected_indexes, current_samples, ""
+
+    def _inject_sample_indices(self, samples: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        indexed: List[Dict[str, Any]] = []
+        for idx, sample in enumerate(samples):
+            row = dict(sample or {})
+            row["sample_index"] = idx
+            indexed.append(row)
+        return indexed
+
+    def _summarize_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        sample = dict(sample or {})
+        guess = sample.get("guess") or {}
+        verified = sample.get("verified_media_info") or {}
+        inferred_target = {
+            "name": verified.get("title") or guess.get("name") or "",
+            "year": verified.get("year") or guess.get("year") or "",
+            "media_type": self._normalize_media_type(verified.get("type") or guess.get("media_type")),
+            "season": self._safe_int(guess.get("season"), 0),
+            "episode": self._safe_int(guess.get("episode"), 0),
+            "tmdb_id": self._safe_int(verified.get("tmdb_id"), 0),
+        }
+        return {
+            "sample_index": sample.get("sample_index"),
+            "title": sample.get("title"),
+            "path": sample.get("path"),
+            "reason": sample.get("reason"),
+            "guess_name": guess.get("name"),
+            "guess_confidence": self._safe_float(guess.get("confidence"), 0.0),
+            "verified_title": verified.get("title"),
+            "verified_year": verified.get("year"),
+            "verified_tmdb_id": verified.get("tmdb_id"),
+            "inferred_target": inferred_target,
+            "can_auto_suggest": bool(inferred_target["name"]),
+        }
+
+    def _target_from_sample(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        summary = self._summarize_sample(sample)
+        return summary.get("inferred_target") or {}
+
+    @staticmethod
+    def _normalize_reason_tag(reason: Any) -> str:
+        text = str(reason or "").strip()
+        if not text:
+            return "unknown"
+        if ":" in text:
+            return text.split(":", 1)[0].strip() or "unknown"
+        return text
+
+    @staticmethod
+    def _sample_group_key(summary: Dict[str, Any]) -> str:
+        target = summary.get("inferred_target") or {}
+        title = (
+            str(target.get("name") or "").strip()
+            or str(summary.get("verified_title") or "").strip()
+            or str(summary.get("guess_name") or "").strip()
+            or str(summary.get("title") or "").strip()
+        )
+        media_type = str(target.get("media_type") or "unknown").strip().lower()
+        season = int(target.get("season") or 0)
+        episode = int(target.get("episode") or 0)
+        return json.dumps(
+            {
+                "title": title.lower(),
+                "media_type": media_type,
+                "season": season,
+                "episode": episode,
+            },
+            ensure_ascii=False,
+            sort_keys=True,
+        )
+
+    @staticmethod
+    def _sample_display_name(summary: Dict[str, Any]) -> str:
+        target = summary.get("inferred_target") or {}
+        title = (
+            str(target.get("name") or "").strip()
+            or str(summary.get("verified_title") or "").strip()
+            or str(summary.get("guess_name") or "").strip()
+            or str(summary.get("title") or "").strip()
+        )
+        if not title:
+            return "未命名样本"
+        media_type = str(target.get("media_type") or "").strip().lower()
+        season = int(target.get("season") or 0)
+        episode = int(target.get("episode") or 0)
+        suffix = ""
+        if media_type == "tv" and (season or episode):
+            suffix = f" S{season:02d}E{episode:02d}"
+        return f"{title}{suffix}"
+
+    def _build_sample_insights(self, samples: List[Dict[str, Any]], top: int = 10) -> Dict[str, Any]:
+        summaries = [self._summarize_sample(sample) for sample in samples]
+        reason_counter = Counter()
+        title_counter = Counter()
+        group_counter = Counter()
+        for summary in summaries:
+            reason_counter[self._normalize_reason_tag(summary.get("reason"))] += 1
+            title_counter[self._sample_display_name(summary)] += 1
+            group_counter[self._sample_group_key(summary)] += 1
+
+        actionable: List[Dict[str, Any]] = []
+        for summary in summaries:
+            duplicate_count = group_counter[self._sample_group_key(summary)]
+            priority_reasons: List[str] = []
+            score = 0
+            if duplicate_count >= 2:
+                score += min(duplicate_count, 5)
+                priority_reasons.append(f"同类样本重复出现 {duplicate_count} 次")
+            if summary.get("verified_tmdb_id"):
+                score += 3
+                priority_reasons.append("已有 TMDB 命中")
+            if summary.get("can_auto_suggest"):
+                score += 2
+                priority_reasons.append("可直接生成识别词")
+            confidence = self._safe_float(summary.get("guess_confidence"), 0.0)
+            if 0 < confidence < self._confidence_threshold:
+                gap = round(self._confidence_threshold - confidence, 2)
+                score += 1
+                priority_reasons.append(f"距注入阈值还差 {gap}")
+            row = dict(summary)
+            row["duplicate_count"] = duplicate_count
+            row["priority_score"] = score
+            row["priority_reasons"] = priority_reasons
+            actionable.append(row)
+
+        actionable.sort(
+            key=lambda item: (
+                -int(item.get("priority_score") or 0),
+                -int(item.get("duplicate_count") or 0),
+                -self._safe_float(item.get("guess_confidence"), 0.0),
+                int(item.get("sample_index") or 0),
+            )
+        )
+
+        repeated_groups = [
+            {"title": name, "count": count}
+            for name, count in title_counter.most_common(top)
+            if count >= 2
+        ]
+
+        return {
+            "total_count": len(summaries),
+            "reason_counts": [
+                {"reason": reason, "count": count}
+                for reason, count in reason_counter.most_common(top)
+            ],
+            "top_titles": [
+                {"title": title, "count": count}
+                for title, count in title_counter.most_common(top)
+            ],
+            "repeated_groups": repeated_groups,
+            "priority_samples": actionable[:top],
+        }
+
+    def _render_sample_brief(self, samples: List[Dict[str, Any]], top: int = 5) -> str:
+        summaries = [self._summarize_sample(sample) for sample in samples[: max(1, min(top, 20))]]
+        if not summaries:
+            return "当前没有失败样本。"
+        lines = [f"失败样本 {len(samples)} 条，展示前 {len(summaries)} 条："]
+        for summary in summaries:
+            label = self._sample_display_name(summary)
+            confidence = round(self._safe_float(summary.get("guess_confidence"), 0.0), 2)
+            can_suggest = "可建议" if summary.get("can_auto_suggest") else "需人工"
+            lines.append(f"{summary.get('sample_index')}. {label} | 置信度 {confidence} | {can_suggest}")
+        lines.append("下一步：可直接调用批量建议或批量复查接口。")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _render_batch_results_brief(
+        action_name: str,
+        requested_count: int,
+        success_count: int,
+        failed_count: int,
+        results: List[Dict[str, Any]],
+    ) -> str:
+        lines = [f"{action_name}：共处理 {requested_count} 条，成功 {success_count}，失败 {failed_count}。"]
+        for item in results[:10]:
+            idx = item.get("sample_index")
+            if item.get("success"):
+                label = (
+                    ((item.get("source_sample") or {}).get("title"))
+                    or ((item.get("target") or {}).get("name"))
+                    or "样本"
+                )
+                lines.append(f"{idx}. 成功 | {label}")
+            else:
+                lines.append(f"{idx}. 失败 | {item.get('message', '未知错误')}")
+        return "\n".join(lines)
+
+    def _build_body_from_sample(self, body: Dict[str, Any]) -> Tuple[Dict[str, Any], Optional[Dict[str, Any]], str]:
+        body = dict(body or {})
+        title = str(body.get("title") or "").strip()
+        path = str(body.get("path") or "").strip()
+        sample_requested = body.get("use_latest_sample") or body.get("sample_index") is not None
+        if title or path:
+            return body, None, ""
+        if not sample_requested:
+            return body, None, ""
+
+        sample_index, sample, message = self._resolve_failed_sample(body.get("sample_index"), limit=100)
+        if not sample:
+            return body, None, message
+        body["title"] = str(sample.get("title") or "").strip()
+        body["path"] = str(sample.get("path") or "").strip()
+        verified = sample.get("verified_media_info") or {}
+        guess = sample.get("guess") or {}
+        if not body.get("desired_name"):
+            body["desired_name"] = verified.get("title") or guess.get("name") or ""
+        if not body.get("desired_year"):
+            body["desired_year"] = verified.get("year") or guess.get("year") or ""
+        if not body.get("desired_media_type"):
+            body["desired_media_type"] = self._normalize_media_type(
+                verified.get("type") or guess.get("media_type")
+            )
+        if body.get("desired_season") is None:
+            body["desired_season"] = guess.get("season") or 0
+        if body.get("desired_episode") is None:
+            body["desired_episode"] = guess.get("episode") or 0
+        if body.get("desired_tmdb_id") is None:
+            body["desired_tmdb_id"] = verified.get("tmdb_id") or 0
+        body["sample_index"] = sample_index
+        return body, sample, ""
+
+    def _build_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """你是 MoviePilot 的影视文件名识别增强助手。
+
+你的任务不是搜索 TMDB，也不是编造结果，而是根据文件名、路径和已有解析提示，尽量提炼出更适合 MoviePilot 二次识别的结构化信息。
+
+规则：
+1. 只依据输入内容推断，不要臆造不存在的信息。
+2. 如果不确定，请返回空标题，并把 media_type 设为 unknown，confidence 降低。
+3. title/name 只保留作品名，不要包含分辨率、制作组、音频编码、网盘标记等噪音。
+4. year 只有在比较确定时才给四位年份。
+5. 电影 season/episode 必须为 0。
+6. 剧集如果能确定季集就填写，否则保持 0。
+7. media_type 只能是 movie、tv、unknown。
+8. confidence 范围为 0 到 1。
+""",
+                ),
+                (
+                    "human",
+                    """原始标题：
+{title}
+
+原始路径：
+{path}
+
+MoviePilot 当前基础解析提示：
+{meta_hint}
+""",
+                ),
+            ]
+        )
+
+    def _build_identifier_prompt(self) -> ChatPromptTemplate:
+        return ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """你是 MoviePilot 自定义识别词规则助手。
+
+你的任务是根据错误标题、当前解析结果和目标结果，生成尽量窄作用域、可直接用于 MoviePilot CustomIdentifiers 的规则。
+
+支持格式只有四种：
+1. 屏蔽词
+2. 替换词：被替换词 => 替换词
+3. 集偏移：前定位词 <> 后定位词 >> EP±N
+4. 组合规则：被替换词 => 替换词 && 前定位词 <> 后定位词 >> EP±N
+
+硬性要求：
+1. 运算符两侧必须保留空格： => 、 <> 、 >> 、 &&
+2. 优先生成窄作用域规则，尽量带发布组、年份、季集、分辨率等锚点
+3. 不要生成过宽的裸屏蔽词，比如 1080p、WEB-DL、字幕
+4. 如果需要强制绑 TMDB，可使用 {{[tmdbid=xxx;type=tv/movies;s=1;e=14]}} 这种替换词
+5. comment 不带 #，rule 里不要再包 markdown 或代码块
+6. 如果没有把握，请返回空 suggestions
+""",
+                ),
+                (
+                    "human",
+                    """原始标题：
+{title}
+
+原始路径：
+{path}
+
+MoviePilot 当前基础解析：
+{meta_hint}
+
+AI 识别增强结果：
+{guess}
+
+二次校验到的媒体信息摘要：
+{verified_summary}
+
+希望修正成的目标结果：
+{target}
+""",
+                ),
+            ]
+        )
+
+    @staticmethod
+    def _run_async_compatible(value: Any) -> Any:
+        """
+        兼容 MoviePilot 新版 `LLMHelper.get_llm()` 的异步返回。
+        在同步上下文直接 asyncio.run；如果当前线程已有事件循环，则开一个短线程执行。
+        """
+        if not inspect.isawaitable(value):
+            return value
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            return asyncio.run(value)
+
+        result: Dict[str, Any] = {}
+        error: Dict[str, BaseException] = {}
+
+        def _worker() -> None:
+            try:
+                result["value"] = asyncio.run(value)
+            except BaseException as exc:  # noqa: BLE001
+                error["exc"] = exc
+
+        thread = threading.Thread(target=_worker, daemon=True)
+        thread.start()
+        thread.join()
+        if "exc" in error:
+            raise error["exc"]
+        return result.get("value")
+
+    def _get_llm(self):
+        llm = LLMHelper.get_llm(streaming=False)
+        return self._run_async_compatible(llm)
+
+    def _invoke_llm(self, title: str, path: str) -> AIRecognitionGuess:
+        raw_text = path or title
+        meta_hint = self._build_meta_hint(raw_text)
+        llm = self._get_llm()
+        prompt = self._build_prompt()
+        chain = (
+            prompt
+            | llm.with_structured_output(AIRecognitionGuess).with_retry(stop_after_attempt=self._max_retries)
+        )
+        result: AIRecognitionGuess = chain.invoke(
+            {
+                "title": title,
+                "path": path,
+                "meta_hint": meta_hint,
+            },
+            config={"configurable": {"timeout": self._request_timeout}},
+        )
+        return self._normalize_guess(result)
+
+    @staticmethod
+    def _normalize_media_type(value: Any) -> str:
+        if value == MediaType.MOVIE:
+            return "movie"
+        if value == MediaType.TV:
+            return "tv"
+        text = str(value or "").strip().lower()
+        if text in {"movie", "movies", "电影"}:
+            return "movie"
+        if text in {"tv", "电视剧", "剧集"}:
+            return "tv"
+        return "unknown"
+
+    def _build_target(self, body: Dict[str, Any], result: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        body = body or {}
+        result = result or {}
+        guess = result.get("guess") or {}
+        verified = result.get("verified_media_info") or {}
+        verified_type = self._normalize_media_type(verified.get("type"))
+        target = {
+            "name": str(body.get("desired_name") or verified.get("title") or guess.get("name") or "").strip(),
+            "year": str(body.get("desired_year") or verified.get("year") or guess.get("year") or "").strip(),
+            "media_type": self._normalize_media_type(
+                body.get("desired_media_type") or verified_type or guess.get("media_type")
+            ),
+            "season": self._safe_int(
+                body.get("desired_season"),
+                self._safe_int(guess.get("season"), 0),
+            ),
+            "episode": self._safe_int(
+                body.get("desired_episode"),
+                self._safe_int(guess.get("episode"), 0),
+            ),
+            "tmdb_id": self._safe_int(body.get("desired_tmdb_id") or verified.get("tmdb_id"), 0),
+        }
+        if len(target["year"]) != 4 or not target["year"].isdigit():
+            target["year"] = ""
+        return target
+
+    @staticmethod
+    def _compact_verified_summary(verified: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+        verified = verified or {}
+        return {
+            "title": verified.get("title"),
+            "year": verified.get("year"),
+            "type": verified.get("type"),
+            "tmdb_id": verified.get("tmdb_id"),
+            "title_year": verified.get("title_year"),
+            "season_years": verified.get("season_years"),
+            "seasons": verified.get("seasons"),
+            "names": (verified.get("names") or [])[:8],
+        }
+
+    @staticmethod
+    def _normalize_identifier_line(value: Any) -> str:
+        return " ".join(str(value or "").strip().split())
+
+    def _validate_identifier_rule(self, rule: str) -> bool:
+        rule = self._normalize_identifier_line(rule)
+        if not rule or rule.startswith("#"):
+            return False
+        if " => " in rule and " && " in rule and " >> " in rule and " <> " in rule:
+            return True
+        if " => " in rule:
+            return True
+        if " >> " in rule and " <> " in rule:
+            return True
+        return len(rule) >= 4
+
+    def _enrich_identifier_rule(self, rule: str, target: Dict[str, Any]) -> str:
+        rule = self._normalize_identifier_line(rule)
+        target_name = str((target or {}).get("name") or "").strip()
+        if not target_name or " => " not in rule:
+            return rule
+        left, right = rule.split(" => ", 1)
+        suffix = ""
+        replace_part = right
+        if " && " in right:
+            replace_part, extra = right.split(" && ", 1)
+            suffix = f" && {extra}"
+        if replace_part.startswith("{["):
+            replace_part = f"{target_name}{replace_part}"
+        return f"{left} => {replace_part}{suffix}"
+
+    @staticmethod
+    def _clean_comment_line(comment: str) -> str:
+        text = str(comment or "").strip()
+        if not text:
+            return ""
+        return f"#{text.lstrip('#').strip()}"
+
+    def _preview_custom_words(self, title: str, custom_words: List[str], target: Dict[str, Any]) -> Dict[str, Any]:
+        prepared_title, apply_words = WordsMatcher().prepare(title, custom_words=custom_words)
+        meta = MetaInfo(title=title, custom_words=custom_words)
+        preview = {
+            "prepared_title": prepared_title,
+            "applied_words": apply_words or [],
+            "applied": bool(apply_words),
+            "name": getattr(meta, "name", "") or "",
+            "year": getattr(meta, "year", "") or "",
+            "media_type": self._normalize_media_type(getattr(meta, "type", None)),
+            "season": getattr(meta, "begin_season", None) or 0,
+            "episode": getattr(meta, "begin_episode", None) or 0,
+        }
+        if target:
+            matched = True
+            if target.get("name"):
+                matched = matched and (preview["name"].strip().lower() == str(target["name"]).strip().lower())
+            if target.get("year"):
+                matched = matched and (preview["year"] == target["year"])
+            if target.get("media_type") and target.get("media_type") != "unknown":
+                matched = matched and (preview["media_type"] == target["media_type"])
+            if target.get("season"):
+                matched = matched and (preview["season"] == target["season"])
+            if target.get("episode"):
+                matched = matched and (preview["episode"] == target["episode"])
+            preview["matched_target"] = matched
+        return preview
+
+    def _preview_identifier_rule(self, title: str, rule: str, target: Dict[str, Any]) -> Dict[str, Any]:
+        preview = self._preview_custom_words(title=title, custom_words=[rule], target=target)
+        preview["applied"] = rule in (preview.get("applied_words") or [])
+        return preview
+
+    def _preview_current_identifiers(self, title: str, target: Dict[str, Any]) -> Dict[str, Any]:
+        custom_words = self._get_custom_identifiers()
+        preview = self._preview_custom_words(title=title, custom_words=custom_words, target=target)
+        preview["custom_identifier_count"] = len(custom_words)
+        preview["applied_count"] = len(preview.get("applied_words") or [])
+        return preview
+
+    @staticmethod
+    def _match_recognize_result_to_target(result: Dict[str, Any], target: Dict[str, Any]) -> bool:
+        if not target:
+            return bool(result.get("success"))
+        guess = result.get("guess") or {}
+        matched = True
+        if target.get("name"):
+            matched = matched and (str(guess.get("name") or "").strip().lower() == str(target.get("name") or "").strip().lower())
+        if target.get("year"):
+            matched = matched and (str(guess.get("year") or "") == str(target.get("year") or ""))
+        if target.get("media_type") and target.get("media_type") != "unknown":
+            matched = matched and (str(guess.get("media_type") or "unknown") == str(target.get("media_type") or "unknown"))
+        if target.get("season"):
+            matched = matched and (int(guess.get("season") or 0) == int(target.get("season") or 0))
+        if target.get("episode"):
+            matched = matched and (int(guess.get("episode") or 0) == int(target.get("episode") or 0))
+        return bool(result.get("success")) and matched
+
+    def _replay_failed_sample(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        sample_index, sample, message = self._resolve_failed_sample(
+            body.get("sample_index"),
+            limit=1000,
+        )
+        if not sample:
+            return {"success": False, "message": message}
+        title = str(sample.get("title") or "").strip()
+        path = str(sample.get("path") or "").strip()
+        target = self._target_from_sample(sample)
+        identifier_preview = self._preview_current_identifiers(title=title, target=target)
+        recognize_result = self._recognize(title=title, path=path, record_failed_sample=False)
+        resolved_by_identifiers = bool(identifier_preview.get("applied")) and bool(identifier_preview.get("matched_target"))
+        resolved_by_recognizer = self._match_recognize_result_to_target(recognize_result, target)
+        resolved = resolved_by_identifiers or resolved_by_recognizer
+        removal_result = None
+        if resolved and bool(body.get("remove_if_resolved")):
+            removal_result = self._remove_failed_sample(sample_index, limit=1000)
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "source_sample_index": sample_index,
+                "source_sample": sample,
+                "target": target,
+                "identifier_preview": identifier_preview,
+                "recognize_result": recognize_result,
+                "resolved_by_identifiers": resolved_by_identifiers,
+                "resolved_by_recognizer": resolved_by_recognizer,
+                "resolved": resolved,
+                "sample_removed": bool(removal_result and removal_result.get("removed")),
+                "sample_removal_result": removal_result,
+            },
+        }
+
+    def _replay_failed_samples(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        limit = max(1, min(self._safe_int(body.get("limit"), 10), 50))
+        selected_indexes, _, message = self._select_failed_sample_indexes(
+            sample_indexes=body.get("sample_indexes"),
+            limit=limit,
+            pool_limit=200,
+        )
+        if not selected_indexes:
+            return {"success": False, "message": message}
+
+        replay_results: List[Dict[str, Any]] = []
+        resolved_indexes: List[int] = []
+        for sample_index in selected_indexes:
+            replay = self._replay_failed_sample(
+                {
+                    "sample_index": sample_index,
+                    "remove_if_resolved": False,
+                }
+            )
+            if not replay.get("success"):
+                replay_results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": False,
+                        "message": replay.get("message", "复查失败"),
+                    }
+                )
+                continue
+            data = replay.get("data") or {}
+            replay_results.append(
+                {
+                    "sample_index": sample_index,
+                    "success": True,
+                    "resolved": bool(data.get("resolved")),
+                    "resolved_by_identifiers": bool(data.get("resolved_by_identifiers")),
+                    "resolved_by_recognizer": bool(data.get("resolved_by_recognizer")),
+                    "source_sample": data.get("source_sample"),
+                    "target": data.get("target"),
+                    "identifier_preview": data.get("identifier_preview"),
+                    "recognize_result": data.get("recognize_result"),
+                }
+            )
+            if data.get("resolved"):
+                resolved_indexes.append(sample_index)
+
+        removal_result = None
+        if body.get("remove_if_resolved") and resolved_indexes:
+            removal_result = self._remove_failed_samples(resolved_indexes, limit=1000)
+
+        success_count = sum(1 for item in replay_results if item.get("success"))
+        resolved_count = sum(1 for item in replay_results if item.get("resolved"))
+        unresolved_count = success_count - resolved_count
+        failed_count = len(replay_results) - success_count
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "requested_count": len(selected_indexes),
+                "success_count": success_count,
+                "resolved_count": resolved_count,
+                "unresolved_count": unresolved_count,
+                "failed_count": failed_count,
+                "sample_removed_count": int((removal_result or {}).get("removed_count") or 0),
+                "sample_removal_result": removal_result,
+                "results": replay_results,
+            },
+        }
+
+    def _suggest_identifiers_for_failed_samples(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        limit = max(1, min(self._safe_int(body.get("limit"), 5), 20))
+        selected_indexes, _, message = self._select_failed_sample_indexes(
+            sample_indexes=body.get("sample_indexes"),
+            limit=limit,
+            pool_limit=200,
+        )
+        if not selected_indexes:
+            return {"success": False, "message": message}
+
+        results: List[Dict[str, Any]] = []
+        success_count = 0
+        for sample_index in selected_indexes:
+            suggest_body = dict(body)
+            suggest_body.pop("sample_indexes", None)
+            suggest_body["sample_index"] = sample_index
+            suggest_body["use_latest_sample"] = False
+            suggested = self._suggest_identifiers(suggest_body)
+            if suggested.get("success"):
+                success_count += 1
+                data = suggested.get("data") or {}
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": True,
+                        "summary": data.get("summary"),
+                        "source_sample": data.get("source_sample"),
+                        "target": data.get("target"),
+                        "suggestions": data.get("suggestions") or [],
+                    }
+                )
+            else:
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": False,
+                        "message": suggested.get("message", "建议生成失败"),
+                        "data": suggested.get("data"),
+                    }
+                )
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "requested_count": len(selected_indexes),
+                "success_count": success_count,
+                "failed_count": len(selected_indexes) - success_count,
+                "brief": self._render_batch_results_brief(
+                    action_name="批量建议",
+                    requested_count=len(selected_indexes),
+                    success_count=success_count,
+                    failed_count=len(selected_indexes) - success_count,
+                    results=results,
+                ),
+                "results": results,
+            },
+        }
+
+    def _apply_suggested_identifier_internal(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        if body.get("title") is None and body.get("path") is None:
+            body["use_latest_sample"] = True if body.get("use_latest_sample") is None else body.get("use_latest_sample")
+        suggested = self._suggest_identifiers(body)
+        if not suggested.get("success"):
+            return suggested
+        data = suggested.get("data") or {}
+        suggestions = data.get("suggestions") or []
+        suggestion_index = self._safe_int(body.get("suggestion_index"), 0)
+        if suggestion_index < 0:
+            suggestion_index = 0
+        if suggestion_index >= len(suggestions):
+            return {"success": False, "message": f"建议索引超出范围，当前共有 {len(suggestions)} 条"}
+        chosen = suggestions[suggestion_index]
+        applied = self._append_custom_identifiers(chosen.get("lines") or [])
+        should_remove_sample = bool(
+            self._auto_remove_applied_sample if body.get("remove_sample") is None else body.get("remove_sample")
+        )
+        removal_result = None
+        source_sample = data.get("source_sample") or {}
+        if should_remove_sample and source_sample.get("sample_index") is not None:
+            removal_result = self._remove_failed_sample(source_sample.get("sample_index"), limit=1000)
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "chosen_suggestion": chosen,
+                "apply_result": applied,
+                "source_sample_index": source_sample.get("sample_index"),
+                "source_sample": source_sample,
+                "sample_removed": bool(removal_result and removal_result.get("removed")),
+                "sample_removal_result": removal_result,
+                "target": data.get("target"),
+            },
+        }
+
+    def _apply_suggested_identifiers_for_failed_samples(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body = dict(body or {})
+        limit = max(1, min(self._safe_int(body.get("limit"), 5), 20))
+        selected_indexes, _, message = self._select_failed_sample_indexes(
+            sample_indexes=body.get("sample_indexes"),
+            limit=limit,
+            pool_limit=200,
+        )
+        if not selected_indexes:
+            return {"success": False, "message": message}
+
+        results: List[Dict[str, Any]] = []
+        success_count = 0
+        removable_indexes: List[int] = []
+        should_remove_samples = bool(
+            self._auto_remove_applied_sample if body.get("remove_sample") is None else body.get("remove_sample")
+        )
+        for sample_index in selected_indexes:
+            apply_body = dict(body)
+            apply_body.pop("sample_indexes", None)
+            apply_body["sample_index"] = sample_index
+            apply_body["use_latest_sample"] = False
+            apply_body["remove_sample"] = False
+            applied = self._apply_suggested_identifier_internal(apply_body)
+            if applied.get("success"):
+                success_count += 1
+                data = applied.get("data") or {}
+                if should_remove_samples:
+                    removable_indexes.append(sample_index)
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": True,
+                        "source_sample": data.get("source_sample"),
+                        "target": data.get("target"),
+                        "chosen_suggestion": data.get("chosen_suggestion"),
+                        "apply_result": data.get("apply_result"),
+                        "sample_removed": False,
+                    }
+                )
+            else:
+                results.append(
+                    {
+                        "sample_index": sample_index,
+                        "success": False,
+                        "message": applied.get("message", "写入失败"),
+                        "data": applied.get("data"),
+                    }
+                )
+        removal_result = None
+        if should_remove_samples and removable_indexes:
+            removal_result = self._remove_failed_samples(removable_indexes, limit=1000)
+            removed_index_set = set((removal_result or {}).get("removed_sample_indexes") or [])
+            for item in results:
+                if item.get("success"):
+                    item["sample_removed"] = item.get("sample_index") in removed_index_set
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "requested_count": len(selected_indexes),
+                "success_count": success_count,
+                "failed_count": len(selected_indexes) - success_count,
+                "sample_removed_count": int((removal_result or {}).get("removed_count") or 0),
+                "sample_removal_result": removal_result,
+                "brief": self._render_batch_results_brief(
+                    action_name="批量写入",
+                    requested_count=len(selected_indexes),
+                    success_count=success_count,
+                    failed_count=len(selected_indexes) - success_count,
+                    results=results,
+                ),
+                "results": results,
+            },
+        }
+
+    def _build_exact_identifier_fallback(self, title: str, target: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        target_name = str((target or {}).get("name") or "").strip()
+        tmdb_id = self._safe_int((target or {}).get("tmdb_id"), 0)
+        media_type = self._normalize_media_type((target or {}).get("media_type"))
+        if not title or not target_name or not tmdb_id or media_type == "unknown":
+            return None
+        replace = target_name
+        target_year = str((target or {}).get("year") or "").strip()
+        if len(target_year) == 4 and target_year.isdigit():
+            replace += f".{target_year}"
+        replace += f"{{[tmdbid={tmdb_id};type={'tv' if media_type == 'tv' else 'movie'}"
+        if media_type == "tv" and self._safe_int(target.get("season"), 0):
+            replace += f";s={self._safe_int(target.get('season'), 0)}"
+        if media_type == "tv" and self._safe_int(target.get("episode"), 0):
+            replace += f";e={self._safe_int(target.get('episode'), 0)}"
+        replace += "]}"
+        rule = f"{re.escape(title)} => {replace}"
+        preview = self._preview_identifier_rule(title=title, rule=rule, target=target)
+        if not preview.get("applied"):
+            return None
+        return {
+            "comment": "当 AI 建议无法稳定通过本地预演时，使用精确标题绑定规则直接固定到目标 TMDB 与季集",
+            "comment_line": "#当 AI 建议无法稳定通过本地预演时，使用精确标题绑定规则直接固定到目标 TMDB 与季集",
+            "rule": rule,
+            "confidence": 0.95,
+            "reason": "精确匹配当前标题并强制绑定目标 TMDB / 季集，作用域最窄，稳定性最高。",
+            "preview": preview,
+            "lines": [
+                "#当 AI 建议无法稳定通过本地预演时，使用精确标题绑定规则直接固定到目标 TMDB 与季集",
+                rule,
+            ],
+        }
+
+    def _invoke_identifier_llm(
+        self,
+        title: str,
+        path: str,
+        result: Dict[str, Any],
+        target: Dict[str, Any],
+    ) -> IdentifierSuggestionBundle:
+        llm = self._get_llm()
+        prompt = self._build_identifier_prompt()
+        chain = (
+            prompt
+            | llm.with_structured_output(IdentifierSuggestionBundle).with_retry(
+                stop_after_attempt=self._max_retries
+            )
+        )
+        bundle: IdentifierSuggestionBundle = chain.invoke(
+            {
+                "title": title,
+                "path": path,
+                "meta_hint": self._build_meta_hint(path or title),
+                "guess": result.get("guess") or {},
+                "verified_summary": self._compact_verified_summary(result.get("verified_media_info")),
+                "target": target,
+            },
+            config={"configurable": {"timeout": self._request_timeout}},
+        )
+        return bundle
+
+    def _suggest_identifiers(self, body: Dict[str, Any]) -> Dict[str, Any]:
+        body, source_sample, sample_message = self._build_body_from_sample(body)
+        if sample_message:
+            return {"success": False, "message": sample_message}
+        title = str(body.get("title") or "").strip()
+        path = str(body.get("path") or "").strip()
+        if not title and path:
+            title = Path(path).name
+        if not title:
+            return {"success": False, "message": "标题为空"}
+
+        result = self._recognize(title=title, path=path, record_failed_sample=False)
+        target = self._build_target(body, result=result)
+        invoke_error = ""
+        try:
+            bundle = self._invoke_identifier_llm(title=title, path=path, result=result, target=target)
+        except Exception as exc:
+            bundle = IdentifierSuggestionBundle(
+                summary="识别词建议模型暂不可用，已自动回退到精确规则兜底。",
+                suggestions=[],
+            )
+            invoke_error = str(exc)
+
+        cleaned: List[Dict[str, Any]] = []
+        for item in bundle.suggestions:
+            rule = self._enrich_identifier_rule(item.rule, target=target)
+            if not self._validate_identifier_rule(rule):
+                continue
+            comment_line = self._clean_comment_line(item.comment)
+            preview = self._preview_identifier_rule(title=title, rule=rule, target=target)
+            if not preview.get("applied"):
+                continue
+            if target and any(target.values()) and preview.get("matched_target") is False:
+                continue
+            cleaned.append(
+                {
+                    "comment": item.comment.strip(),
+                    "comment_line": comment_line,
+                    "rule": rule,
+                    "confidence": min(1.0, max(0.0, self._safe_float(item.confidence, 0.0))),
+                    "reason": str(item.reason or "").strip(),
+                    "preview": preview,
+                    "lines": [line for line in [comment_line, rule] if line],
+                }
+            )
+
+        if not cleaned:
+            fallback = self._build_exact_identifier_fallback(title=title, target=target)
+            if fallback:
+                if invoke_error:
+                    fallback["reason"] = f"{fallback.get('reason', '')} 当前识别词建议模型不可用，已自动切到精确规则兜底。".strip()
+                cleaned.append(fallback)
+
+        if not cleaned:
+            return {
+                "success": False,
+                "message": f"识别词建议生成失败: {invoke_error}" if invoke_error else "没有生成可直接使用的识别词规则",
+                "data": {
+                    "summary": bundle.summary,
+                    "target": target,
+                    "recognize_result": result,
+                },
+            }
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "summary": bundle.summary,
+                "source_sample_index": (source_sample or {}).get("sample_index"),
+                "source_sample": source_sample,
+                "target": target,
+                "recognize_result": result,
+                "suggestions": cleaned,
+            },
+        }
+
+    def _get_custom_identifiers(self) -> List[str]:
+        if not self._systemconfig:
+            self._systemconfig = SystemConfigOper()
+        return self._systemconfig.get(SystemConfigKey.CustomIdentifiers) or []
+
+    def _append_custom_identifiers(self, lines: List[str]) -> Dict[str, Any]:
+        existing = self._get_custom_identifiers()
+        added: List[str] = []
+        for line in lines:
+            normalized = str(line or "").rstrip()
+            if not normalized:
+                continue
+            if normalized in existing or normalized in added:
+                continue
+            added.append(normalized)
+        if added:
+            merged = existing + added
+            self._systemconfig.set(SystemConfigKey.CustomIdentifiers, merged)
+        return {
+            "added": added,
+            "added_count": len(added),
+            "total_count": len(self._get_custom_identifiers()),
+        }
+
+    def _verify_guess(self, title: str, path: str, guess: AIRecognitionGuess) -> Optional[Dict[str, Any]]:
+        if not guess.name:
+            return None
+        try:
+            raw_text = path or title or guess.name
+            meta = MetaInfo(raw_text)
+            meta.name = guess.name
+            meta.year = guess.year or None
+            meta.begin_season = guess.season or None
+            meta.begin_episode = guess.episode or None
+            if guess.media_type == "tv" or meta.begin_season or meta.begin_episode:
+                meta.type = MediaType.TV
+            elif guess.media_type == "movie":
+                meta.type = MediaType.MOVIE
+            mediainfo = MediaChain().recognize_media(meta=meta, cache=False)
+            if not mediainfo:
+                return None
+            return mediainfo.to_dict()
+        except Exception as exc:
+            if self._debug:
+                logger.warning(f"[AI识别增强] 二次校验失败: {exc}")
+            return None
+
+    def _recognize(self, title: str, path: str = "", record_failed_sample: bool = True) -> Dict[str, Any]:
+        title = str(title or "").strip()
+        path = str(path or "").strip()
+        if not title and path:
+            title = Path(path).name
+        if not title:
+            return {"success": False, "message": "标题为空"}
+        try:
+            guess = self._invoke_llm(title, path)
+        except Exception as exc:
+            if record_failed_sample:
+                self._record_failed_sample(
+                    {
+                        "title": title,
+                        "path": path,
+                        "meta_hint": self._build_meta_hint(path or title),
+                        "reason": f"llm_error:{exc}",
+                    }
+                )
+            return {"success": False, "message": f"LLM 调用失败: {exc}"}
+
+        verified = self._verify_guess(title, path, guess)
+        passed = bool(guess.name and guess.confidence >= self._confidence_threshold)
+        if not passed and record_failed_sample:
+            self._record_failed_sample(
+                {
+                    "title": title,
+                    "path": path,
+                    "meta_hint": self._build_meta_hint(path or title),
+                    "guess": guess.model_dump(),
+                    "verified_media_info": self._compact_verified_summary(verified),
+                    "reason": "low_confidence_or_empty_name",
+                }
+            )
+        return {
+            "success": passed,
+            "message": "success" if passed else "识别结果置信度不足，已放弃注入",
+            "guess": guess.model_dump(),
+            "verified_media_info": verified,
+        }
+
+    def on_chain_name_recognize(self, event) -> None:
+        if not self._enabled:
+            return
+        event_data = getattr(event, "event_data", None) or {}
+        title, path = self._extract_title_path(event_data)
+        if not title and not path:
+            return
+        result = self._recognize(title=title, path=path)
+        if not result.get("success"):
+            if self._debug:
+                logger.info(f"[AI识别增强] 跳过注入: {title or path} - {result.get('message')}")
+            return
+        guess = result.get("guess") or {}
+        if isinstance(event_data, dict):
+            event_data["name"] = guess.get("name", "")
+            event_data["year"] = guess.get("year", "")
+            event_data["season"] = guess.get("season", 0)
+            event_data["episode"] = guess.get("episode", 0)
+            event_data["source_plugin"] = "AIRecognizerEnhancer"
+            event_data["confidence"] = guess.get("confidence", 0)
+            event_data["reason"] = guess.get("reason", "")
+
+    async def api_health(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        llm_ready = bool(getattr(settings, "LLM_API_KEY", None))
+        return {
+            "success": True,
+            "data": {
+                "plugin_version": self.plugin_version,
+                "enabled": self._enabled,
+                "llm_ready": llm_ready,
+                "llm_provider": getattr(settings, "LLM_PROVIDER", ""),
+                "llm_model": getattr(settings, "LLM_MODEL", ""),
+                "confidence_threshold": self._confidence_threshold,
+                "request_timeout": self._request_timeout,
+            },
+        }
+
+    async def api_recognize(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        title = str(body.get("title") or "").strip()
+        path = str(body.get("path") or "").strip()
+        result = self._recognize(title=title, path=path)
+        return {
+            "success": result.get("success", False),
+            "message": result.get("message", ""),
+            "data": {
+                "guess": result.get("guess"),
+                "verified_media_info": result.get("verified_media_info"),
+            },
+        }
+
+    async def api_failed_samples(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 20)
+        limit = max(1, min(limit, 100))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=limit))
+        return {
+            "success": True,
+            "data": {
+                "count": len(samples),
+                "samples": samples,
+            },
+        }
+
+    async def api_sample_worklist(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 20)
+        limit = max(1, min(limit, 100))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=limit))
+        worklist = [self._summarize_sample(sample) for sample in samples]
+        return {
+            "success": True,
+            "data": {
+                "count": len(worklist),
+                "samples": worklist,
+            },
+        }
+
+    async def api_sample_insights(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 50)
+        limit = max(1, min(limit, 200))
+        top = self._safe_int(request.query_params.get("top"), 10)
+        top = max(1, min(top, 20))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=limit))
+        insights = self._build_sample_insights(samples, top=top)
+        return {
+            "success": True,
+            "data": insights,
+        }
+
+    async def api_sample_brief(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        limit = self._safe_int(request.query_params.get("limit"), 5)
+        limit = max(1, min(limit, 20))
+        samples = self._inject_sample_indices(self._read_failed_samples(limit=100))
+        return {
+            "success": True,
+            "data": {
+                "count": len(samples),
+                "text": self._render_sample_brief(samples, top=limit),
+            },
+        }
+
+    async def api_suggest_identifiers(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._suggest_identifiers(body)
+
+    async def api_apply_identifiers(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        identifiers = body.get("identifiers") or []
+        if not isinstance(identifiers, list):
+            return {"success": False, "message": "identifiers 必须是数组"}
+        result = self._append_custom_identifiers([str(line or "") for line in identifiers])
+        return {
+            "success": True,
+            "message": "success",
+            "data": result,
+        }
+
+    async def api_clear_failed_samples(self, request: Request):
+        ok, message = self._check_api_access(request)
+        if not ok:
+            return {"success": False, "message": message}
+        cleared = self._clear_failed_samples()
+        return {
+            "success": True,
+            "message": "success",
+            "data": {
+                "cleared_count": cleared,
+            },
+        }
+
+    async def api_remove_failed_sample(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        result = self._remove_failed_sample(body.get("sample_index"), limit=1000)
+        if not result.get("removed"):
+            return {"success": False, "message": result.get("message", "移除失败"), "data": result}
+        return {
+            "success": True,
+            "message": "success",
+            "data": result,
+        }
+
+    async def api_replay_failed_sample(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._replay_failed_sample(body)
+
+    async def api_replay_failed_samples(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._replay_failed_samples(body)
+
+    async def api_suggest_identifiers_from_sample(self, request: Request):
+        body = await request.json()
+        body["use_latest_sample"] = True if body.get("use_latest_sample") is None else body.get("use_latest_sample")
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        if body.get("sample_index") is None and body.get("use_latest_sample") is False:
+            body["use_latest_sample"] = True
+        return self._suggest_identifiers(body)
+
+    async def api_suggest_identifiers_for_failed_samples(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._suggest_identifiers_for_failed_samples(body)
+
+    async def api_apply_suggested_identifier(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._apply_suggested_identifier_internal(body)
+
+    async def api_apply_suggested_identifiers_for_failed_samples(self, request: Request):
+        body = await request.json()
+        ok, message = self._check_api_access(request, body)
+        if not ok:
+            return {"success": False, "message": message}
+        if not self._enabled:
+            return {"success": False, "message": "插件未启用"}
+        return self._apply_suggested_identifiers_for_failed_samples(body)
+
+    def get_api(self) -> List[Dict[str, Any]]:
+        return [
+            {
+                "path": "/health",
+                "endpoint": self.api_health,
+                "methods": ["GET"],
+                "summary": "检查 AI识别增强 的运行状态",
+            },
+            {
+                "path": "/recognize",
+                "endpoint": self.api_recognize,
+                "methods": ["POST"],
+                "summary": "用当前 LLM 对失败标题做一次本地结构化识别测试",
+            },
+            {
+                "path": "/failed_samples",
+                "endpoint": self.api_failed_samples,
+                "methods": ["GET"],
+                "summary": "查看最近保存的低置信度失败样本",
+            },
+            {
+                "path": "/sample_worklist",
+                "endpoint": self.api_sample_worklist,
+                "methods": ["GET"],
+                "summary": "返回适合智能体使用的失败样本摘要列表",
+            },
+            {
+                "path": "/sample_insights",
+                "endpoint": self.api_sample_insights,
+                "methods": ["GET"],
+                "summary": "汇总失败样本原因、重复问题和优先处理样本",
+            },
+            {
+                "path": "/sample_brief",
+                "endpoint": self.api_sample_brief,
+                "methods": ["GET"],
+                "summary": "返回适合智能体低 token 消费的失败样本精简摘要",
+            },
+            {
+                "path": "/suggest_identifiers",
+                "endpoint": self.api_suggest_identifiers,
+                "methods": ["POST"],
+                "summary": "根据标题和目标结果生成 MoviePilot 自定义识别词建议",
+            },
+            {
+                "path": "/suggest_identifiers_from_sample",
+                "endpoint": self.api_suggest_identifiers_from_sample,
+                "methods": ["POST"],
+                "summary": "直接基于最近失败样本或指定样本生成自定义识别词建议",
+            },
+            {
+                "path": "/suggest_identifiers_for_failed_samples",
+                "endpoint": self.api_suggest_identifiers_for_failed_samples,
+                "methods": ["POST"],
+                "summary": "批量为失败样本生成自定义识别词建议",
+            },
+            {
+                "path": "/apply_identifiers",
+                "endpoint": self.api_apply_identifiers,
+                "methods": ["POST"],
+                "summary": "将确认后的自定义识别词追加写入系统 CustomIdentifiers",
+            },
+            {
+                "path": "/clear_failed_samples",
+                "endpoint": self.api_clear_failed_samples,
+                "methods": ["POST"],
+                "summary": "清空失败样本文件",
+            },
+            {
+                "path": "/remove_failed_sample",
+                "endpoint": self.api_remove_failed_sample,
+                "methods": ["POST"],
+                "summary": "按索引移除单条失败样本",
+            },
+            {
+                "path": "/replay_failed_sample",
+                "endpoint": self.api_replay_failed_sample,
+                "methods": ["POST"],
+                "summary": "按当前识别词和当前识别器复查某条失败样本，并可在确认修复后自动出队",
+            },
+            {
+                "path": "/replay_failed_samples",
+                "endpoint": self.api_replay_failed_samples,
+                "methods": ["POST"],
+                "summary": "批量复查失败样本，并可在确认修复后批量出队",
+            },
+            {
+                "path": "/apply_suggested_identifier",
+                "endpoint": self.api_apply_suggested_identifier,
+                "methods": ["POST"],
+                "summary": "直接把最近失败样本或指定样本生成的建议规则写入 CustomIdentifiers，并按需移除该样本",
+            },
+            {
+                "path": "/apply_suggested_identifiers_for_failed_samples",
+                "endpoint": self.api_apply_suggested_identifiers_for_failed_samples,
+                "methods": ["POST"],
+                "summary": "批量把失败样本生成的建议规则写入 CustomIdentifiers，并按需移除对应样本",
+            },
+        ]
+
+    def get_page(self) -> List[dict]:
+        llm_ready = bool(getattr(settings, "LLM_API_KEY", None))
+        failed_samples_count = len(self._read_failed_samples(limit=200))
+        custom_identifiers_count = len(self._get_custom_identifiers())
+        llm_provider = getattr(settings, "LLM_PROVIDER", "—")
+        llm_model = getattr(settings, "LLM_MODEL", "—")
+
+        def stat_card(title: str, value: Any, subtitle: str = "") -> dict:
+            content = [
+                {
+                    "component": "div",
+                    "props": {"class": "text-caption text-medium-emphasis mb-1"},
+                    "text": title,
+                },
+                {
+                    "component": "div",
+                    "props": {"class": "text-h6 font-weight-bold"},
+                    "text": str(value),
+                },
+            ]
+            if subtitle:
+                content.append(
+                    {
+                        "component": "div",
+                        "props": {"class": "text-caption text-medium-emphasis mt-1"},
+                        "text": subtitle,
+                    }
+                )
+            return {
+                "component": "VCard",
+                "props": {"variant": "tonal", "class": "pa-4 h-100"},
+                "content": content,
+            }
+
+        return [
+            {
+                "component": "VContainer",
+                "props": {"fluid": True, "class": "pa-0"},
+                "content": [
+                    {
+                        "component": "VAlert",
+                        "props": {
+                            "type": "info",
+                            "variant": "tonal",
+                            "class": "mb-4",
+                            "title": "本地 LLM 识别兜底",
+                            "text": "复用 MoviePilot 当前 LLM 配置，在原生识别失败时做结构化兜底，并把结果交回 MoviePilot 继续二次识别。",
+                        },
+                    },
+                    {
+                        "component": "VRow",
+                        "props": {"dense": True, "class": "mb-2"},
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("当前状态", "已启用" if self._enabled else "未启用")],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("LLM 可用", "是" if llm_ready else "否", f"{llm_provider} / {llm_model}")],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("失败样本", f"{failed_samples_count} 条", f"上限 {self._max_failed_samples} 条")],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 3},
+                                "content": [stat_card("自定义识别词", f"{custom_identifiers_count} 条", "系统 CustomIdentifiers")],
+                            },
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "props": {"dense": True},
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VCard",
+                                        "props": {"variant": "outlined", "class": "pa-4 h-100"},
+                                        "content": [
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-subtitle-1 font-weight-bold mb-2"},
+                                                "text": "识别兜底",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-body-2 text-medium-emphasis"},
+                                                "text": "在 Chain NameRecognize 阶段回写 name / year / season / episode，供 MoviePilot 继续原生二次识别。",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-caption text-medium-emphasis mt-3"},
+                                                "text": f"置信度阈值：{self._confidence_threshold}；请求超时：{self._request_timeout} 秒",
+                                            },
+                                        ],
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VCard",
+                                        "props": {"variant": "outlined", "class": "pa-4 h-100"},
+                                        "content": [
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-subtitle-1 font-weight-bold mb-2"},
+                                                "text": "识别词闭环",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-body-2 text-medium-emphasis"},
+                                                "text": "失败样本可生成 CustomIdentifiers 建议，并按需追加写入系统配置。",
+                                            },
+                                            {
+                                                "component": "div",
+                                                "props": {"class": "text-caption text-medium-emphasis mt-3"},
+                                                "text": f"写入后自动移除样本：{'是' if self._auto_remove_applied_sample else '否'}",
+                                            },
+                                        ],
+                                    }
+                                ],
+                            },
+                        ],
+                    },
+                ],
+            }
+        ]
+
+    @staticmethod
+    def get_render_mode() -> Tuple[str, Optional[str]]:
+        return "vuetify", None
+
+    def get_form(self) -> Tuple[List[dict], Dict[str, Any]]:
+        form = [
+            {
+                "component": "VForm",
+                "content": [
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VAlert",
+                                        "props": {
+                                            "type": "info",
+                                            "variant": "tonal",
+                                            "text": "当前版本已改为直接复用 MoviePilot 当前启用的 LLM 配置，在原生识别失败后做本地结构化兜底。",
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 4},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {"model": "enabled", "label": "启用 AI识别增强"},
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 4},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {"model": "debug", "label": "调试模式"},
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 4},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {"model": "save_failed_samples", "label": "保存低置信度样本"},
+                                    }
+                                ],
+                            },
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "confidence_threshold",
+                                            "label": "置信度阈值",
+                                            "type": "number",
+                                            "hint": "低于该值的结果不注入 MoviePilot，默认 0.65",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            },
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12, "md": 6},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "request_timeout",
+                                            "label": "LLM 请求超时（秒）",
+                                            "type": "number",
+                                            "hint": "默认 25 秒",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            },
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "max_retries",
+                                            "label": "结构化输出重试次数",
+                                            "type": "number",
+                                            "hint": "默认 2 次",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VTextField",
+                                        "props": {
+                                            "model": "max_failed_samples",
+                                            "label": "失败样本保留上限",
+                                            "type": "number",
+                                            "hint": "默认保留最近 200 条，并对重复样本自动去重",
+                                            "persistent-hint": True,
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                    {
+                        "component": "VRow",
+                        "content": [
+                            {
+                                "component": "VCol",
+                                "props": {"cols": 12},
+                                "content": [
+                                    {
+                                        "component": "VSwitch",
+                                        "props": {
+                                            "model": "auto_remove_applied_sample",
+                                            "label": "写入识别词后自动移除对应失败样本",
+                                        },
+                                    }
+                                ],
+                            }
+                        ],
+                    },
+                ],
+            }
+        ]
+        return form, {
+            "enabled": False,
+            "debug": False,
+            "confidence_threshold": 0.65,
+            "request_timeout": 25,
+            "max_retries": 2,
+            "save_failed_samples": True,
+            "max_failed_samples": 200,
+            "auto_remove_applied_sample": True,
+        }