feat(dedup): 优化歌词去重逻辑并引入作者字段增量合并

- 新增 L2 候选加载缓存机制，避免重复下载歌词内容 - L2 去重返回 top-K 召回候选信息，丰富判定依据 - 分类函数新增跳过导入的规则，如空歌词且作者不详、原声类歌曲名等 - 实现作者字段（作词、作曲）增量合并功能，避免重复覆盖 - 对合并冲突添加异常处理并记录日志，确保稳定执行 - 引入后台线程异步写入数据库，提升批量处理性能 - 优化 L1 索引更新，避免去重误差，提高去重准确率 - 扩展统计字段，新增作者合并和跳过导入计数 - 前端增加决策过滤选项，支持根据决策筛选展示记录 - 修改界面显示逻辑，支持删除已审核样本，完善用户操作 - 调整页面显示候选数目为 Top5，优化视觉体验 - 参数新增 L2 精确哈希自动合并的最小歌词长度配置 - 添加数据库表结构自动迁移，新增 recalled_candidates 字段支持新功能 - 增强程序中断时数据库写入的安全关闭与日志提示

feat(dedup): 优化歌词去重逻辑并引入作者字段增量合并
- 新增 L2 候选加载缓存机制，避免重复下载歌词内容 - L2 去重返回 top-K 召回候选信息，丰富判定依据 - 分类函数新增跳过导入的规则，如空歌词且作者不详、原声类歌曲名等 - 实现作者字段（作词、作曲）增量合并功能，避免重复覆盖 - 对合并冲突添加异常处理并记录日志，确保稳定执行 - 引入后台线程异步写入数据库，提升批量处理性能 - 优化 L1 索引更新，避免去重误差，提高去重准确率 - 扩展统计字段，新增作者合并和跳过导入计数 - 前端增加决策过滤选项，支持根据决策筛选展示记录 - 修改界面显示逻辑，支持删除已审核样本，完善用户操作 - 调整页面显示候选数目为 Top5，优化视觉体验 - 参数新增 L2 精确哈希自动合并的最小歌词长度配置 - 添加数据库表结构自动迁移，新增 recalled_candidates 字段支持新功能 - 增强程序中断时数据库写入的安全关闭与日志提示
沈秋雨
Commit 93b03ea9 ... 93b03ea91557e1bebbe201d31f48271e2d68830e authored 2026-07-06 21:26:13 +0800 by 沈秋雨
Showing 5 changed files with 844 additions and 130 deletions
import_hk_songs.py
l2_review_dashboard.html
lyric_dedup/checker.py
serve_l2_dashboard.py
test_dedup.py
--- a/import_hk_songs.py
View file @93b03ea
+++ b/import_hk_songs.py
View file @93b03ea
@@ -6,8 +6,10 @@ hk_music_record 聚合数据导入脚本
 import argparse
 import csv
+import json
 import logging
 import os
+import queue
 import re
 import sys
 import time
@@ -40,8 +42,10 @@ load_dotenv()
 OUTPUT_DIR = Path(__file__).resolve().parent / 'output'
 LOG_DIR = OUTPUT_DIR / 'logs'
 REPORT_DIR = OUTPUT_DIR / 'reports'
+CACHE_DIR = OUTPUT_DIR / 'cache'
 LOG_DIR.mkdir(parents=True, exist_ok=True)
 REPORT_DIR.mkdir(parents=True, exist_ok=True)
+CACHE_DIR.mkdir(parents=True, exist_ok=True)
 # 日志配置
 logging.basicConfig(
@@ -245,7 +249,8 @@ INSERT INTO {table} (
  lyric_archive_element_id, melody_archive_element_id, audio_fingerprint,
  record_count,
  dedup_action, dedup_decision, dedup_confidence, matched_song_id, l1_matched_id,
-  merge_authors, dedup_reason, biz_review_status, staging_status, imported_song_id
+  merge_authors, dedup_reason, biz_review_status, staging_status, imported_song_id,
+  recalled_candidates
 ) VALUES (
  %s,%s,
  %s,%s,%s,%s,%s,%s,
@@ -261,7 +266,8 @@ INSERT INTO {table} (
  %s,%s,%s,
  %s,%s,%s,%s,%s,
  %s,
-  %s,%s,%s,%s,%s
+  %s,%s,%s,%s,%s,
+  %s
 )
 ON DUPLICATE KEY UPDATE
  dedup_action = VALUES(dedup_action),
@@ -274,6 +280,7 @@ ON DUPLICATE KEY UPDATE
  biz_review_status = VALUES(biz_review_status),
  staging_status = VALUES(staging_status),
  imported_song_id = VALUES(imported_song_id),
+  recalled_candidates = VALUES(recalled_candidates),
  error_message = NULL
 """
@@ -566,11 +573,18 @@ def build_staging_tuple(tuple_row: tuple, action: dict, import_batch_id: str, re
        biz_review_status = 'not_required'
        staging_status = 'skipped'
        imported_song_id = None
+    elif dedup_action == 'skip':
+        biz_review_status = 'not_required'
+        staging_status = 'skipped'
+        imported_song_id = None
    else:
        biz_review_status = 'pending'
        staging_status = 'staged'
        imported_song_id = None
+    recalled_candidates = action.get('recalled_candidates')
+    recalled_json = json.dumps(recalled_candidates, ensure_ascii=False) if recalled_candidates else None
    return (
        ID_GENERATOR.next_id(),
        import_batch_id,
@@ -586,6 +600,7 @@ def build_staging_tuple(tuple_row: tuple, action: dict, import_batch_id: str, re
        biz_review_status,
        staging_status,
        imported_song_id,
+        recalled_json,
    )
@@ -605,6 +620,8 @@ def process_row_with_oss(args_tuple):
 _T2S = opencc.OpenCC('t2s')
 _METADATA_PUNCT = set(' \t\n\r，。！？；：、"“”‘’·…—～！￥（）【】《》〈〉「」『』﹏,.:;!?()[]{}<>|/\\_-')
 _INSTRUMENTAL_LYRIC_RE = re.compile(r'(?:纯音乐|純音樂|无歌词|無歌詞|没有歌词|沒有歌詞|instrumental)', re.IGNORECASE)
+_ORIGINAL_SOUND_RE = re.compile(r'^@\S+的原声$')
+_UNKNOWN_AUTHORS = {'不详', '未知', '无', '佚名', 'unknown', 'none', 'n/a', ''}
 def _normalize_meta(text: str | None) -> str:
@@ -616,6 +633,26 @@ def _normalize_meta(text: str | None) -> str:
    return ''.join(c for c in text if c not in _METADATA_PUNCT)
+def mark_rows_in_l1_index(rows: list[dict], l1_index: dict[tuple[str, str, str], int | str]) -> None:
+    """把已接受处理的行预先写入内存 L1 索引，供后续批次去重使用。"""
+    for row in rows:
+        key = (
+            _normalize_meta(row.get('name')),
+            _normalize_meta(row.get('lyricist')),
+            _normalize_meta(row.get('composer')),
+        )
+        if key[0] and key not in l1_index:
+            l1_index[key] = row['source_id']
+def stop_db_writer(write_queue: queue.Queue, writer_thread: threading.Thread) -> None:
+    """等待已入队 DB 写入完成，再停止后台写入线程。"""
+    write_queue.join()
+    if writer_thread.is_alive():
+        write_queue.put(None)
+        writer_thread.join()
 def is_instrumental_lyrics(text: str | None) -> bool:
    """识别纯音乐/无歌词提示文本，这类内容不参与 L2 歌词去重。"""
    if not text:
@@ -678,6 +715,86 @@ def load_source_record_counts(source_conn, source_song_ids: set[str]) -> dict[st
    return counts
+def _decode_lyric_content(content: bytes) -> str:
+    try:
+        return content.decode('utf-8')
+    except UnicodeDecodeError:
+        return content.decode('utf-8', errors='replace')
+def load_existing_l2_candidates(
+    rows: list[dict],
+    cache_path: Path,
+    downloader=download_file,
+) -> tuple[list[LyricRecord], dict[str, int]]:
+    """从目标库 lyrics_url 行构建 L2 候选；本地缓存已下载歌词，避免重启全量拉 OSS。"""
+    cached_by_key: dict[tuple[str, str], dict] = {}
+    if cache_path.exists():
+        with cache_path.open('r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    item = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+                record_id = str(item.get('id') or '')
+                url = str(item.get('url') or '')
+                lyrics = item.get('lyrics')
+                if record_id and url and isinstance(lyrics, str) and lyrics.strip():
+                    cached_by_key[(record_id, url)] = item
+    records: list[LyricRecord] = []
+    next_cache: dict[tuple[str, str], dict] = {}
+    stats = {'cached': 0, 'downloaded': 0, 'failed': 0}
+    for row in rows:
+        url = row.get('lyrics_url')
+        if not url or not str(url).startswith(('http://', 'https://')):
+            continue
+        record_id = str(row['id'])
+        key = (record_id, str(url))
+        item = cached_by_key.get(key)
+        if item:
+            stats['cached'] += 1
+        else:
+            content, _ = downloader(str(url), timeout=10)
+            if not content:
+                stats['failed'] += 1
+                continue
+            item = {
+                'id': record_id,
+                'url': str(url),
+                'lyrics': _decode_lyric_content(content),
+                'name': row.get('name'),
+                'singer': row.get('singer'),
+                'lyricist': row.get('lyricist'),
+                'composer': row.get('composer'),
+            }
+            stats['downloaded'] += 1
+        next_cache[key] = item
+        records.append(LyricRecord(
+            record_id=record_id,
+            lyrics=item['lyrics'],
+            title=row.get('name') or item.get('name'),
+            artist=row.get('singer') or item.get('singer'),
+            lyricist=row.get('lyricist') or item.get('lyricist'),
+            composer=row.get('composer') or item.get('composer'),
+        ))
+    cache_path.parent.mkdir(parents=True, exist_ok=True)
+    tmp_path = cache_path.with_suffix(cache_path.suffix + '.tmp')
+    with tmp_path.open('w', encoding='utf-8') as f:
+        for item in next_cache.values():
+            f.write(json.dumps(item, ensure_ascii=False) + '\n')
+    tmp_path.replace(cache_path)
+    return records, stats
 def check_l1(row: dict, l1_index: dict) -> tuple[bool, int | None]:
    """L1 元数据去重，返回 (is_duplicate, matched_id)"""
    key = (
@@ -760,13 +877,16 @@ def check_l2(
    row: dict,
    checker: DuplicateChecker,
    candidates: list[LyricRecord] | L2CandidateIndex,
-) -> tuple[str, float, str | None, str]:
+) -> tuple[str, float, str | None, str, list[dict]]:
-    """L2 歌词内容去重，返回 (decision, confidence, matched_id, reason)"""
+    """L2 歌词内容去重，返回 (decision, confidence, matched_id, reason, recalled_candidates)。
+    recalled_candidates: top-K 召回候选信息列表，即使最终判定为 new 也返回。
+    """
    lyrics_text = row.get('lyrics_txt_content')
    if not lyrics_text or not lyrics_text.strip():
-        return 'new', 1.0, None, '无歌词内容，跳过 L2'
+        return 'new', 1.0, None, '无歌词内容，跳过 L2', []
    if is_instrumental_lyrics(lyrics_text):
-        return 'new', 1.0, None, '歌词内容包含纯音乐/无歌词提示，跳过 L2'
+        return 'new', 1.0, None, '歌词内容包含纯音乐/无歌词提示，跳过 L2', []
    record_id = row.get('source_id', row.get('id'))
    record = LyricRecord(
@@ -778,18 +898,37 @@ def check_l2(
        composer=row.get('composer'),
    )
    if isinstance(candidates, L2CandidateIndex):
-        candidates = candidates.recall(record)
+        recalled_records = candidates.recall(record)
-    if not candidates:
+    else:
-        return 'new', 1.0, None, '无候选集'
+        recalled_records = candidates
+    if not recalled_records:
+        return 'new', 1.0, None, '无候选集', []
+    result = checker.check_record_against_candidates(record, recalled_records, max_candidates=5)
+    # 构建召回候选信息（保留 top 10，即使判定为 new）
+    recalled_candidates: list[dict] = []
+    candidate_record_map: dict[str, LyricRecord] = {r.record_id: r for r in recalled_records}
+    for cm in result.candidates[:10]:
+        cr = candidate_record_map.get(cm.record_id)
+        recalled_candidates.append({
+            'id': cm.record_id,
+            'name': cr.title if cr else None,
+            'lyricist': cr.lyricist if cr else None,
+            'composer': cr.composer if cr else None,
+            'decision': cm.decision.value,
+            'confidence': round(cm.confidence, 4),
+            'jaccard': round(cm.jaccard, 4),
+            'line_coverage': round(cm.line_coverage, 4),
+        })
-    result = checker.check_record_against_candidates(record, candidates, max_candidates=5)
    # new 记录不返回 matched_id，避免前端误认为有命中
    if result.decision == DuplicateDecision.NEW:
        matched_id = None
    else:
        matched_id = result.candidates[0].record_id if result.candidates else None
-    return result.decision.value, result.confidence, matched_id, result.reason
+    return result.decision.value, result.confidence, matched_id, result.reason, recalled_candidates
 def _candidate_by_id(
@@ -817,6 +956,61 @@ def _writers_differ(row: dict, candidate: LyricRecord | None) -> bool:
    )
+_AUTHOR_SEP_RE = re.compile(r'[,，/;；、]')
+_AUTHOR_MERGE_SQL = 'UPDATE `{table}` SET {{col}} = %s WHERE id = %s'.format(table=TARGET_TABLE_NAME)
+def _merge_author_field(existing: str | None, new: str | None) -> str:
+    """增量合并作者字段：保留已有作者，追加新作者（去重）。"""
+    if not new:
+        return existing or ''
+    if not existing:
+        return new
+    def _split_authors(text: str) -> list[str]:
+        return [a.strip() for a in _AUTHOR_SEP_RE.split(text) if a.strip()]
+    existing_list = _split_authors(existing)
+    new_list = _split_authors(new)
+    existing_norm = {_normalize_meta(a) for a in existing_list}
+    result = list(existing_list)
+    for author in new_list:
+        if _normalize_meta(author) not in existing_norm:
+            result.append(author)
+            existing_norm.add(_normalize_meta(author))
+    merged = '、'.join(result)
+    return merged if merged != existing else ''
+def execute_author_merges(cursor, author_merge_tasks: list[dict]) -> int:
+    """执行作者字段增量合并 UPDATE，返回成功合并的记录数。"""
+    merged_count = 0
+    for task in author_merge_tasks:
+        try:
+            if task['lyricist']:
+                cursor.execute(
+                    _AUTHOR_MERGE_SQL.format(col='lyricist'),
+                    (task['lyricist'], task['matched_id']),
+                )
+            if task['composer']:
+                cursor.execute(
+                    _AUTHOR_MERGE_SQL.format(col='composer'),
+                    (task['composer'], task['matched_id']),
+                )
+            merged_count += 1
+        except Exception as e:
+            logger.error(
+                f"作者字段合并失败 matched_id={task['matched_id']}: {e}"
+            )
+    return merged_count
+def _is_short_exact_l2_review(decision: str, reason: str) -> bool:
+    return decision == 'review' and '规范化后的原文哈希一致' in reason and '有效歌词过短' in reason
 def classify_dedup_action(
    row: dict,
    l1_index: dict,
@@ -830,8 +1024,48 @@ def classify_dedup_action(
    当判定为 duplicate 时，根据源库关联录音数量决定合并方向：
    录音少的合并到录音多的。
    """
-    l1_matched, l1_matched_id = check_l1(row, l1_index)
+    # 规则1：歌词为空 + 词曲作者不详 → 不导
    lyrics_text = row.get('lyrics_txt_content') or ''
+    no_lyrics = not lyrics_text or not lyrics_text.strip()
+    lyricist_raw = (row.get('lyricist') or '').strip()
+    composer_raw = (row.get('composer') or '').strip()
+    if no_lyrics and lyricist_raw.lower() in _UNKNOWN_AUTHORS and composer_raw.lower() in _UNKNOWN_AUTHORS:
+        return {
+            'action': 'skip',
+            'decision': 'skip',
+            'confidence': 1.0,
+            'matched_id': None,
+            'reason': '歌词为空且词曲作者不详，不导入',
+            'l1_matched': False,
+            'l1_matched_id': None,
+            'merge_authors': False,
+            'matched_candidate': None,
+            'merge_direction': None,
+            'new_record_count': row.get('record_count'),
+            'existing_record_count': None,
+            'recalled_candidates': [],
+        }
+    # 规则2：歌名形如 "@XXX的原声" → 不导
+    song_name = (row.get('name') or '').strip()
+    if _ORIGINAL_SOUND_RE.match(song_name):
+        return {
+            'action': 'skip',
+            'decision': 'skip',
+            'confidence': 1.0,
+            'matched_id': None,
+            'reason': f'歌名「{song_name}」为原声类内容，不导入',
+            'l1_matched': False,
+            'l1_matched_id': None,
+            'merge_authors': False,
+            'matched_candidate': None,
+            'merge_direction': None,
+            'new_record_count': row.get('record_count'),
+            'existing_record_count': None,
+            'recalled_candidates': [],
+        }
+    l1_matched, l1_matched_id = check_l1(row, l1_index)
    l2_candidates_for_check: list[LyricRecord] | L2CandidateIndex = candidates
    if isinstance(candidates, L2CandidateIndex):
        record = LyricRecord(
@@ -849,7 +1083,57 @@ def classify_dedup_action(
                recalled.append(l1_candidate)
        l2_candidates_for_check = recalled
-    decision, confidence, matched_id, reason = check_l2(row, checker, l2_candidates_for_check)
+    decision, confidence, matched_id, reason, recalled_candidates = check_l2(row, checker, l2_candidates_for_check)
+    if _is_short_exact_l2_review(decision, reason):
+        if not l1_matched:
+            return {
+                'action': 'new',
+                'decision': 'new',
+                'confidence': 1.0,
+                'matched_id': None,
+                'reason': f'{reason}；L1 元数据未命中，按新歌处理',
+                'l1_matched': l1_matched,
+                'l1_matched_id': l1_matched_id,
+                'merge_authors': False,
+                'matched_candidate': None,
+                'merge_direction': None,
+                'new_record_count': row.get('record_count'),
+                'existing_record_count': None,
+                'recalled_candidates': recalled_candidates,
+            }
+        matched_id = str(l1_matched_id)
+        matched_candidate = _candidate_by_id(l2_candidates_for_check, matched_id)
+        new_count = (row.get('record_count') or 0) if source_record_counts is not None else None
+        existing_count = None
+        if source_record_counts is not None and l1_matched_id is not None:
+            try:
+                existing_sid = _target_id_to_source_sid.get(int(l1_matched_id))
+                if existing_sid:
+                    existing_count = source_record_counts.get(existing_sid, 0)
+            except (ValueError, TypeError):
+                pass
+        merge_direction = 'new_into_existing'
+        if new_count is not None and existing_count is not None and new_count > existing_count:
+            merge_direction = 'existing_into_new'
+        return {
+            'action': 'merge',
+            'decision': 'duplicate',
+            'confidence': 1.0,
+            'matched_id': matched_id,
+            'reason': f'{reason}；L1 元数据命中，按 L1 判定重复',
+            'l1_matched': l1_matched,
+            'l1_matched_id': l1_matched_id,
+            'merge_authors': _writers_differ(row, matched_candidate),
+            'matched_candidate': matched_candidate,
+            'merge_direction': merge_direction,
+            'new_record_count': new_count,
+            'existing_record_count': existing_count,
+            'recalled_candidates': recalled_candidates,
+        }
    if decision == 'duplicate':
        matched_candidate = _candidate_by_id(l2_candidates_for_check, matched_id)
@@ -866,6 +1150,27 @@ def classify_dedup_action(
            except (ValueError, TypeError):
                pass
+        # 规则3：歌词相似但歌名与词曲作者均不一致 → 洗盗蹭嫌疑，强制 review
+        if matched_candidate:
+            _name_match = _normalize_meta(row.get('name')) == _normalize_meta(matched_candidate.title)
+            _authors_match = not _writers_differ(row, matched_candidate)
+            if not _name_match and not _authors_match:
+                return {
+                    'action': 'review',
+                    'decision': 'review',
+                    'confidence': confidence,
+                    'matched_id': matched_id,
+                    'reason': f'歌词相似但歌名与词曲作者均不一致，有洗盗蹭嫌疑（{reason}）',
+                    'l1_matched': l1_matched,
+                    'l1_matched_id': l1_matched_id,
+                    'merge_authors': True,
+                    'matched_candidate': matched_candidate,
+                    'merge_direction': None,
+                    'new_record_count': new_count,
+                    'existing_record_count': existing_count,
+                    'recalled_candidates': recalled_candidates,
+                }
        # 歌词质量兜底：现有记录无歌词但新记录有歌词 → 强制人工复核
        existing_has_lyrics = matched_candidate and bool(matched_candidate.lyrics and matched_candidate.lyrics.strip())
        new_has_lyrics = bool(lyrics_text and lyrics_text.strip())
@@ -883,6 +1188,7 @@ def classify_dedup_action(
                'merge_direction': 'existing_into_new',
                'new_record_count': new_count,
                'existing_record_count': existing_count,
+                'recalled_candidates': recalled_candidates,
            }
        merge_direction = 'new_into_existing'
@@ -903,6 +1209,7 @@ def classify_dedup_action(
            'merge_direction': merge_direction,
            'new_record_count': new_count,
            'existing_record_count': existing_count,
+            'recalled_candidates': recalled_candidates,
        }
    # 空歌词 / 歌词获取失败：L2 无法比对，必须人工复核，不能自动入库
@@ -921,6 +1228,7 @@ def classify_dedup_action(
            'merge_direction': None,
            'new_record_count': row.get('record_count'),
            'existing_record_count': None,
+            'recalled_candidates': recalled_candidates,
        }
    if decision == 'review' or (l1_matched and reason == '无候选集'):
@@ -939,6 +1247,7 @@ def classify_dedup_action(
            'merge_direction': None,
            'new_record_count': row.get('record_count'),
            'existing_record_count': None,
+            'recalled_candidates': recalled_candidates,
        }
    return {
@@ -954,6 +1263,7 @@ def classify_dedup_action(
        'merge_direction': None,
        'new_record_count': row.get('record_count'),
        'existing_record_count': None,
+        'recalled_candidates': recalled_candidates,
    }
@@ -1096,6 +1406,8 @@ def main():
                        help='启动时从目标库下载已有歌词文本构建 L2 候选集（默认关闭）')
    parser.add_argument('--dedup-threshold', type=float, default=0.78,
                        help='L2 去重 Jaccard 阈值（默认 0.78）')
+    parser.add_argument('--dedup-min-primary-chars', type=int, default=40,
+                        help='L2 精确哈希自动合并所需的最小规范化原文歌词长度（默认 40，过短转人工复核）')
    parser.add_argument('--l2-recall', choices=['topk', 'full'], default='topk',
                        help='L2 候选召回模式：topk=倒排索引召回，full=全量候选精排（默认 topk）')
    parser.add_argument('--l2-recall-top-k', type=int, default=200,
@@ -1112,6 +1424,7 @@ def main():
    logger.info(f"参数: limit={args.limit}, offset={args.offset}, skip_oss={args.skip_oss}, "
                f"batch_size={args.batch_size}, workers={args.workers}, "
                f"skip_dedup={args.skip_dedup}, dedup_threshold={args.dedup_threshold}, "
+                f"dedup_min_primary_chars={args.dedup_min_primary_chars}, "
                f"l2_recall={args.l2_recall}, l2_recall_top_k={args.l2_recall_top_k}, "
                f"dedup_only={args.dedup_only}, review_result_csv={args.review_result_csv}, "
                f"dry_run={args.dry_run}")
@@ -1137,6 +1450,25 @@ def main():
    staging_insert_sql = STAGING_INSERT_SQL_TEMPLATE.format(table=TARGET_TABLE_NAME_TMP)
    import_batch_id = datetime.now().strftime("%Y%m%d_%H%M%S")
+    # 自动迁移：确保暂存表存在 recalled_candidates 列
+    try:
+        with target_conn.cursor() as cursor:
+            cursor.execute(
+                f"SELECT COLUMN_NAME FROM INFORMATION_SCHEMA.COLUMNS "
+                f"WHERE TABLE_SCHEMA = %s AND TABLE_NAME = %s AND COLUMN_NAME = 'recalled_candidates'",
+                (TARGET_DB_CONFIG['database'], TARGET_TABLE_NAME_TMP),
+            )
+            if not cursor.fetchone():
+                cursor.execute(
+                    f"ALTER TABLE `{TARGET_TABLE_NAME_TMP}` "
+                    f"ADD COLUMN `recalled_candidates` text DEFAULT NULL COMMENT 'L2 召回候选 JSON' "
+                    f"AFTER `imported_song_id`"
+                )
+                target_conn.commit()
+                logger.info(f"暂存表 {TARGET_TABLE_NAME_TMP} 新增 recalled_candidates 列")
+    except Exception as e:
+        logger.warning(f"暂存表 recalled_candidates 列迁移跳过: {e}")
    # ===== 加载已处理的 source_song_id 集合（增量去重）=====
    # 包括：1) 已写入目标表的 2) 已在暂存表中标记过 dedup_action 的（review/merge/new）
    imported_ids: set[str] = set()
@@ -1244,6 +1576,7 @@ def main():
        logger.info(f"初始化 L2 歌词去重检查器 (阈值={args.dedup_threshold})")
        l2_checker = DuplicateChecker(
            duplicate_jaccard_threshold=args.dedup_threshold,
+            exact_duplicate_min_primary_chars=args.dedup_min_primary_chars,
        )
        l2_candidates = L2CandidateIndex(
            l2_checker,
@@ -1259,24 +1592,16 @@ def main():
                lyric_sql = f"SELECT id, name, singer, lyricist, composer, lyrics_url FROM {TARGET_TABLE_NAME} WHERE deleted = '0' AND lyrics_url IS NOT NULL"
                with target_conn.cursor(pymysql.cursors.DictCursor) as cursor:
                    cursor.execute(lyric_sql)
-                    for r in cursor.fetchall():
+                    existing_lyric_rows = cursor.fetchall()
-                        url = r.get('lyrics_url')
+                cache_path = CACHE_DIR / f'{TARGET_TABLE_NAME}_existing_lyrics.jsonl'
-                        if url and url.startswith(('http://', 'https://')):
+                existing_records, cache_stats = load_existing_l2_candidates(existing_lyric_rows, cache_path)
-                            content, _ = download_file(url, timeout=10)
+                for record in existing_records:
-                            if content:
+                    l2_candidates.add(record)
-                                try:
+                logger.info(
-                                    text = content.decode('utf-8')
+                    f"L2 候选集加载完成: {len(l2_candidates)} 条 | "
-                                except UnicodeDecodeError:
+                    f"缓存命中={cache_stats['cached']} 下载={cache_stats['downloaded']} 失败={cache_stats['failed']} | "
-                                    text = content.decode('utf-8', errors='replace')
+                    f"缓存文件={cache_path}"
-                                l2_candidates.add(LyricRecord(
+                )
-                                    record_id=str(r['id']),
-                                    lyrics=text,
-                                    title=r.get('name'),
-                                    artist=r.get('singer'),
-                                    lyricist=r.get('lyricist'),
-                                    composer=r.get('composer'),
-                                ))
-                logger.info(f"L2 候选集加载完成: {len(l2_candidates)} 条")
            except Exception as e:
                logger.error(f"加载已有歌词失败: {e}")
@@ -1336,7 +1661,8 @@ def main():
                dedup_report.close()
        logger.info(
            f"去重审核清单完成: {review_report.filepath} | "
-            f"new={decision_counts['new']} review={decision_counts['review']} merge={decision_counts['merge']}"
+            f"new={decision_counts['new']} review={decision_counts['review']} "
+            f"merge={decision_counts['merge']} skip={decision_counts['skip']}"
        )
        return
@@ -1345,6 +1671,7 @@ def main():
    stats = {
        'inserted': 0, 'errors': 0,
        'l1_dup': 0, 'l2_dup': 0, 'l2_review': 0, 'l2_new': 0,
+        'author_merged': 0, 'skipped': 0,
    }
    start_time = time.time()
@@ -1355,16 +1682,84 @@ def main():
        pbar_db = tqdm(total=total, desc="DB 写入", unit="条", position=1, leave=True,
                       bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]')
+        # ===== 后台 DB 写入线程：与下一批次的 OSS 处理并行 =====
+        db_write_queue: queue.Queue = queue.Queue(maxsize=2)
+        db_write_errors: list[str] = []
+        def _background_db_writer():
+            """后台线程：顺序写入 DB，与主线程 OSS 处理并行。"""
+            while True:
+                item = db_write_queue.get()
+                if item is None:
+                    db_write_queue.task_done()
+                    break
+                batch_data, staging_data, batch_rows, batch_size_actual, batch_start, author_merge_tasks = item
+                try:
+                    with target_conn.cursor() as cursor:
+                        if batch_data:
+                            cursor.executemany(insert_sql, batch_data)
+                        if staging_data:
+                            cursor.executemany(staging_insert_sql, staging_data)
+                        if author_merge_tasks:
+                            merged_cnt = execute_author_merges(cursor, author_merge_tasks)
+                            with stats_lock:
+                                stats['author_merged'] += merged_cnt
+                        target_conn.commit()
+                        batch_inserted = len(batch_data)
+                        with stats_lock:
+                            stats['inserted'] += batch_inserted
+                except Exception as e:
+                    logger.error(f"批量写入失败 (offset {batch_start}): {e}")
+                    target_conn.rollback()
+                    db_write_errors.append(f"batch@{batch_start}: {e}")
+                    # 降级逐条写入
+                    for row_i, tuple_row in enumerate(batch_data):
+                        try:
+                            with target_conn.cursor() as cursor:
+                                cursor.execute(insert_sql, tuple_row)
+                                target_conn.commit()
+                                with stats_lock:
+                                    stats['inserted'] += 1
+                        except Exception as e2:
+                            with stats_lock:
+                                stats['errors'] += 1
+                            logger.error(f"单条写入失败 (batch offset {batch_start + row_i}): {e2}")
+                    # 降级模式下也尝试执行作者合并
+                    if author_merge_tasks:
+                        try:
+                            with target_conn.cursor() as cursor:
+                                merged_cnt = execute_author_merges(cursor, author_merge_tasks)
+                                target_conn.commit()
+                                with stats_lock:
+                                    stats['author_merged'] += merged_cnt
+                        except Exception as e_merge:
+                            logger.error(f"降级作者合并失败 (offset {batch_start}): {e_merge}")
+                pbar_db.update(batch_size_actual)
+                with stats_lock:
+                    pbar_db.set_postfix(
+                        ins=stats['inserted'],
+                        err=stats['errors'],
+                        l1=stats['l1_dup'],
+                        l2=stats['l2_dup'],
+                        rev=stats['l2_review'],
+                        auth=stats['author_merged'],
+                        skip=stats['skipped'],
+                        refresh=False
+                    )
+                db_write_queue.task_done()
+        db_writer_thread = threading.Thread(target=_background_db_writer, daemon=True)
+        db_writer_thread.start()
        for batch_start in range(0, total, args.batch_size):
            batch_rows = rows[batch_start:batch_start + args.batch_size]
            batch_size_actual = len(batch_rows)
-            # ===== 步骤1：去重分类（纯内存，先于 OSS）=====
+            # ===== 步骤1：去重分类（纯内存）=====
-            # L1/L2 去重只依赖源库查询结果（name/lyricist/composer/lyrics_txt_content），
-            # 不需要任何 URL 下载，因此可以在 OSS 之前完成，避免对 merge/review 记录做无效上传。
            staging_data = []
            new_rows: list = []
            row_actions: dict = {}
+            author_merge_tasks: list[dict] = []
            if not args.skip_dedup and not args.review_result_csv:
                for row in batch_rows:
@@ -1393,6 +1788,29 @@ def main():
                        if review_report:
                            review_report.write(row, action)
+                        # 收集作者增量合并任务
+                        if action.get('merge_authors') and action.get('matched_id'):
+                            matched_cand = action.get('matched_candidate')
+                            merged_lyricist = _merge_author_field(
+                                getattr(matched_cand, 'lyricist', None) if matched_cand else None,
+                                row.get('lyricist'),
+                            )
+                            merged_composer = _merge_author_field(
+                                getattr(matched_cand, 'composer', None) if matched_cand else None,
+                                row.get('composer'),
+                            )
+                            if merged_lyricist or merged_composer:
+                                author_merge_tasks.append({
+                                    'matched_id': int(action['matched_id']),
+                                    'lyricist': merged_lyricist,
+                                    'composer': merged_composer,
+                                    'source_id': record_id,
+                                })
+                                logger.debug(
+                                    f"作者合并任务: matched_id={action['matched_id']}, "
+                                    f"lyricist→{merged_lyricist!r}, composer→{merged_composer!r}"
+                                )
                    elif action['action'] == 'review':
                        with stats_lock:
                            stats['l2_review'] += 1
@@ -1402,6 +1820,13 @@ def main():
                        if review_report:
                            review_report.write(row, action)
+                    elif action['action'] == 'skip':
+                        with stats_lock:
+                            stats['skipped'] += 1
+                        dedup_report.write(record_id, record_name, 'PRE', 'skip',
+                                           confidence='1.0000',
+                                           reason=reason)
                    else:  # new
                        with stats_lock:
                            stats['l2_new'] += 1
@@ -1413,7 +1838,6 @@ def main():
                            review_report.write(row, action)
                        new_rows.append(row)
-                        # 加入 L2 候选集供后续批次比对
                        lyrics_text = row.get('lyrics_txt_content')
                        if lyrics_text and lyrics_text.strip():
                            l2_candidates.add(LyricRecord(
@@ -1425,7 +1849,6 @@ def main():
                                composer=row.get('composer'),
                            ))
            else:
-                # skip_dedup 或 review_result_csv 模式：全部视为 new
                new_rows = list(batch_rows)
            # ===== 步骤2：OSS 并发处理（只对 new 记录）=====
@@ -1445,15 +1868,13 @@ def main():
                            results_map[idx] = tuple_row
                        pbar_oss.update(1)
-            # merge/review 记录跳过 OSS，直接推进进度条
            pbar_oss.update(batch_size_actual - len(new_rows))
-            # 按顺序组装 batch_data（写入 hk_songs 的 new 记录）
+            # 按顺序组装 batch_data
            batch_data = [results_map[i] for i in range(len(new_rows)) if i in results_map]
            # ===== 步骤3：构建 staging tuples =====
            if not args.skip_dedup and not args.review_result_csv:
-                # new 记录：使用 OSS 处理后的 tuple_row
                for i, row in enumerate(new_rows):
                    if i not in results_map:
                        continue
@@ -1465,10 +1886,9 @@ def main():
                    staging_data.append(build_staging_tuple(results_map[i], action, import_batch_id,
                                                            record_count=row.get('record_count')))
-                # merge/review 记录：透传源 URL，跳过 OSS
                for row in batch_rows:
                    action = row_actions.get(row['source_id'])
-                    if action and action['action'] in ('merge', 'review'):
+                    if action and action['action'] in ('merge', 'review', 'skip'):
                        tuple_row = build_row_tuple(row, None, skip_oss=True)
                        staging_data.append(build_staging_tuple(tuple_row, action, import_batch_id,
                                                                record_count=row.get('record_count')))
@@ -1477,56 +1897,13 @@ def main():
                pbar_db.update(batch_size_actual)
                continue
-            # ===== 步骤4：顺序写入数据库（单连接，保证不出错）=====
+            # ===== 步骤4：交给后台线程写入 DB（主线程继续下一批次 OSS）=====
-            try:
-                with target_conn.cursor() as cursor:
-                    if batch_data:
-                        cursor.executemany(insert_sql, batch_data)
-                    if staging_data:
-                        cursor.executemany(staging_insert_sql, staging_data)
-                    target_conn.commit()
-                    batch_inserted = len(batch_data)
-                    with stats_lock:
-                        stats['inserted'] += batch_inserted
-                    # 更新 L1 索引（防止批次内重复）
            if not args.skip_dedup:
-                        for i, row in enumerate(batch_rows):
+                mark_rows_in_l1_index(batch_rows, l1_index)
-                            key = (
+            db_write_queue.put((batch_data, staging_data, batch_rows, batch_size_actual, batch_start, author_merge_tasks))
-                                _normalize_meta(row.get('name')),
-                                _normalize_meta(row.get('lyricist')),
-                                _normalize_meta(row.get('composer')),
-                            )
-                            if key[0] and key not in l1_index:
-                                l1_index[key] = row['source_id']
-            except Exception as e:
-                logger.error(f"批量写入失败 (offset {batch_start}): {e}")
-                target_conn.rollback()
-                # 降级逐条写入，保证其他行不受影响
-                for row_i, tuple_row in enumerate(batch_data):
-                    try:
-                        with target_conn.cursor() as cursor:
-                            cursor.execute(insert_sql, tuple_row)
-                            target_conn.commit()
-                            with stats_lock:
-                                stats['inserted'] += 1
-                    except Exception as e2:
-                        with stats_lock:
-                            stats['errors'] += 1
-                        logger.error(f"单条写入失败 (batch offset {batch_start + row_i}): {e2}")
-            pbar_db.update(batch_size_actual)
+        # 所有批次已入队；等待后台 DB 写入收尾后停止线程
+        stop_db_writer(db_write_queue, db_writer_thread)
-            # 更新进度条描述（附加统计）
-            with stats_lock:
-                pbar_db.set_postfix(
-                    ins=stats['inserted'],
-                    err=stats['errors'],
-                    l1=stats['l1_dup'],
-                    l2=stats['l2_dup'],
-                    rev=stats['l2_review'],
-                    refresh=False
-                )
        pbar_oss.close()
        pbar_db.close()
@@ -1540,7 +1917,8 @@ def main():
            if not args.skip_dedup:
                logger.info(f"去重统计: L1跳过={stats['l1_dup']} | "
                            f"L2合并命中={stats['l2_dup']} | L2待审={stats['l2_review']} | "
-                            f"L2新词={stats['l2_new']}")
+                            f"L2新词={stats['l2_new']} | 作者合并={stats['author_merged']} | "
+                            f"跳过={stats['skipped']}")
                if dedup_report:
                    logger.info(f"去重报告: {dedup_report.filepath}")
                if review_report:
@@ -1548,6 +1926,17 @@ def main():
            logger.info(f"耗时: {elapsed_total:.1f}s | 平均速度: {total/elapsed_total:.1f} 条/秒")
            logger.info("=" * 60)
+    except KeyboardInterrupt:
+        logger.warning("收到中断信号，正在等待已入队 DB 写入完成...")
+        if 'db_write_queue' in locals() and 'db_writer_thread' in locals():
+            stop_db_writer(db_write_queue, db_writer_thread)
+        if 'pbar_oss' in locals():
+            pbar_oss.close()
+        if 'pbar_db' in locals():
+            pbar_db.close()
+        logger.warning("已停止导入；未入队的 OSS 结果不会写入 DB，可安全重跑继续。")
+        raise
    finally:
        # 显式关闭 OSS session，避免 __del__ 中的 Bad file descriptor / ConnectionPool 警告
        if bucket is not None:
--- a/l2_review_dashboard.html
View file @93b03ea
+++ b/l2_review_dashboard.html
View file @93b03ea
@@ -460,6 +460,13 @@
      <select id="runSelect"></select>
      <label id="topKLabel" for="topKSelect">topK</label>
      <select id="topKSelect"></select>
+      <label for="decisionFilter">决策</label>
+      <select id="decisionFilter">
+        <option value="">全部</option>
+        <option value="new">new</option>
+        <option value="merge">merge</option>
+        <option value="review">review</option>
+      </select>
      <input id="searchInput" type="search" placeholder="搜索歌名 / ID">
      <label for="pageSizeSelect">每页</label>
      <select id="pageSizeSelect">
@@ -520,6 +527,7 @@
      pageSize: 50,
      mode: 'retrieval',
      topK: '',
+      decisionFilter: '',
      queryId: '',
      candidateId: '',
      lyricsCache: new Map()
@@ -529,6 +537,7 @@
      runSelect: document.getElementById('runSelect'),
      topKSelect: document.getElementById('topKSelect'),
      topKLabel: document.getElementById('topKLabel'),
+      decisionFilter: document.getElementById('decisionFilter'),
      searchInput: document.getElementById('searchInput'),
      pageSizeSelect: document.getElementById('pageSizeSelect'),
      exportBtn: document.getElementById('exportBtn'),
@@ -576,7 +585,7 @@
    }
    function rowDecision(row) {
-      return row.decision || row.candidate_decision || '';
+      return row.candidate_decision || row.decision || row.action || '';
    }
    function isConflict(row) {
@@ -706,7 +715,8 @@
    function selectedCandidate(rows) {
      if (!rows.length) return null;
      if (!state.candidateId || !rows.some(row => row.candidate_id === state.candidateId)) {
-        state.candidateId = rows[0].candidate_id || '';
+        const firstReal = rows.find(row => row.candidate_id);
+        state.candidateId = firstReal ? firstReal.candidate_id : (rows[0].candidate_id || '');
      }
      return rows.find(row => row.candidate_id === state.candidateId) || rows[0];
    }
@@ -726,6 +736,7 @@
      const conflict = isConflict(row);
      const l1 = l1Match(row);
      const extraClass = `${conflict ? ' conflict' : ''}${l1 ? ' l1hint' : ''}`;
+      const l2Decision = row.candidate_decision || '';
      return `<div class="candidate ${esc(decision)}${active}${extraClass}" data-candidate-id="${esc(row.candidate_id)}">
        <div class="candidate-head">
          <span class="rank">${esc(row.rank || '')}</span>
@@ -737,6 +748,7 @@
        <div class="query-meta">词 ${esc(row.candidate_lyricist || '-')} · 曲 ${esc(row.candidate_composer || '-')}</div>
        <div style="margin-top:7px;display:flex;gap:5px;flex-wrap:wrap">
          ${l1 ? '<span class="pill l1">L1 元数据命中</span>' : '<span class="pill">L1 未命中</span>'}
+          ${l2Decision ? `<span class="pill ${esc(l2Decision)}">L2 ${esc(l2Decision)}</span>` : ''}
          ${conflict ? '<span class="pill conflict">冲突</span>' : ''}
        </div>
        <div class="score-row">
@@ -764,6 +776,7 @@
      approved_import: '确认不重复（已入库）',
      rejected_duplicate: '确认重复',
      unsure: '待确认',
+      deleted: '已删除',
    };
    async function renderDetail() {
@@ -824,7 +837,7 @@
          <div class="section-head">
            <h2 class="section-title">人工标注</h2>
            ${query.review_status && query.review_status !== 'pending' && query.review_status !== 'not_required'
-              ? `<span class="pill ${query.review_status === 'approved_import' ? 'new' : query.review_status === 'rejected_duplicate' ? 'duplicate' : ''}">已审核</span>`
+              ? `<span class="pill ${query.review_status === 'approved_import' ? 'new' : query.review_status === 'rejected_duplicate' ? 'duplicate' : query.review_status === 'deleted' ? 'duplicate' : ''}">${query.review_status === 'deleted' ? '已删除' : '已审核'}</span>`
              : ''}
          </div>
          <div class="section-body">
@@ -846,14 +859,15 @@
                  </button>`).join('')}
                <input id="reviewNote" value="${esc(selectedReview.note || '')}" placeholder="人工备注">
                <button id="saveReviewBtn" class="secondary">保存</button>
+                <button id="deleteReviewBtn" class="secondary" style="color:#c0392b;border-color:#c0392b">删除</button>
              </div>
            `}
          </div>
        </div>
        <div class="section">
-          <div class="section-head"><h2 class="section-title">Top10 候选</h2></div>
+          <div class="section-head"><h2 class="section-title">Top5 候选</h2></div>
-          <div class="section-body"><div class="candidates">${rows.map(candidateCard).join('') || '<div class="empty">无召回候选</div>'}</div></div>
+          <div class="section-body"><div class="candidates">${rows.filter(r => r.candidate_id).map(candidateCard).join('') || '<div class="empty">无召回候选</div>'}</div></div>
        </div>
        <div class="section">
@@ -900,6 +914,21 @@
          renderDetail();
        });
      }
+      const deleteBtn = document.getElementById('deleteReviewBtn');
+      if (deleteBtn) {
+        deleteBtn.addEventListener('click', async () => {
+          if (!confirm('确认删除此样本？删除后将不再导入。')) return;
+          deleteBtn.disabled = true;
+          try {
+            await updateStagingReview(query.query_source_id, 'deleted',
+              document.getElementById('reviewNote')?.value || '');
+            await loadQueryRows(query.query_source_id);
+          } catch (e) {
+            alert(`删除失败: ${e.message}`);
+          }
+          renderDetail();
+        });
+      }
      const undoBtn = document.getElementById('undoReviewBtn');
      if (undoBtn) {
        undoBtn.addEventListener('click', async () => {
@@ -1024,6 +1053,7 @@
        path: dataPath(),
        top_k: state.topK,
        mode: state.mode,
+        decision: state.decisionFilter,
        q: els.searchInput.value.trim(),
        page: String(state.page),
        page_size: String(state.pageSize)
@@ -1100,6 +1130,14 @@
      await loadGroups();
      renderAll();
    });
+    els.decisionFilter.addEventListener('change', async () => {
+      state.decisionFilter = els.decisionFilter.value;
+      state.page = 1;
+      state.queryId = '';
+      state.candidateId = '';
+      await loadGroups();
+      renderAll();
+    });
    els.reloadBtn.addEventListener('click', loadRuns);
    els.exportBtn.addEventListener('click', exportAnnotations);
    els.importReviewedBtn.addEventListener('click', importReviewed);
--- a/lyric_dedup/checker.py
View file @93b03ea
+++ b/lyric_dedup/checker.py
View file @93b03ea
@@ -91,6 +91,7 @@ class DuplicateChecker:
        metadata_duplicate_line_coverage_threshold: float = 0.65,
        metadata_duplicate_min_primary_lines: int = 8,
        auto_duplicate_min_primary_lines: int = 4,
+        exact_duplicate_min_primary_chars: int = 40,
    ) -> None:
        self.duplicate_jaccard_threshold = duplicate_jaccard_threshold
        self.duplicate_line_coverage_threshold = duplicate_line_coverage_threshold
@@ -111,6 +112,7 @@ class DuplicateChecker:
        self.metadata_duplicate_line_coverage_threshold = metadata_duplicate_line_coverage_threshold
        self.metadata_duplicate_min_primary_lines = metadata_duplicate_min_primary_lines
        self.auto_duplicate_min_primary_lines = auto_duplicate_min_primary_lines
+        self.exact_duplicate_min_primary_chars = exact_duplicate_min_primary_chars
    def check_record_against_candidates(
        self,
@@ -209,6 +211,14 @@ class DuplicateChecker:
            decision = DuplicateDecision.REVIEW
            confidence = 0.95
            reason = "原文哈希一致，但疑似整段翻译结构拆分置信度较低，需要人工复核"
+        elif not _has_enough_primary_text_chars(
+            query.normalized,
+            candidate.normalized,
+            min_chars=self.exact_duplicate_min_primary_chars,
+        ):
+            decision = DuplicateDecision.REVIEW
+            confidence = 0.95
+            reason = "规范化后的原文哈希一致，但有效歌词过短，需要人工复核"
        elif _is_generic_primary_lyrics(query.normalized) or _is_generic_primary_lyrics(candidate.normalized):
            decision = DuplicateDecision.REVIEW
            confidence = 0.95
@@ -510,6 +520,21 @@ def _has_enough_primary_lyrics(
    return len(meaningful) >= min_lines
+def _has_enough_primary_text_chars(
+    left: NormalizedLyrics,
+    right: NormalizedLyrics,
+    *,
+    min_chars: int,
+) -> bool:
+    left_chars = _normalized_primary_text_length(left)
+    right_chars = _normalized_primary_text_length(right)
+    return min(left_chars, right_chars) >= min_chars
+def _normalized_primary_text_length(normalized: NormalizedLyrics) -> int:
+    return len(_normalize_meta("".join(normalized.primary_lines)))
 def _is_metadata_like_line(line: str) -> bool:
    normalized = _normalize_meta(line)
    metadata_prefixes = (
--- a/serve_l2_dashboard.py
View file @93b03ea
+++ b/serve_l2_dashboard.py
View file @93b03ea
@@ -164,7 +164,7 @@ def _staging_dashboard_row(row: dict[str, object]) -> dict[str, str]:
    }
-def _staging_groups_response(mode: str, term: str, page: int, page_size: int) -> dict[str, object]:
+def _staging_groups_response(mode: str, term: str, page: int, page_size: int, decision: str = '') -> dict[str, object]:
    where_clauses = []
    params: list[object] = []
    if term:
@@ -174,6 +174,10 @@ def _staging_groups_response(mode: str, term: str, page: int, page_size: int) ->
    # hits 模式下只展示有命中的记录（merge/review）
    if mode == "hits":
        where_clauses.append("dedup_action IN ('merge', 'review')")
+    # 决策筛选：new / merge / review
+    if decision and decision in ('new', 'merge', 'review'):
+        where_clauses.append("dedup_action = %s")
+        params.append(decision)
    where = "WHERE " + " AND ".join(where_clauses) if where_clauses else ""
    # 同一首歌可能因多次导入批次产生多行，按 source_song_id 去重取最新一行
    count_sql = f"SELECT COUNT(DISTINCT source_song_id) AS n FROM {TARGET_TABLE_NAME_TMP} {where}"
@@ -216,6 +220,7 @@ def _staging_groups_response(mode: str, term: str, page: int, page_size: int) ->
            "has_hit": row.get("dedup_action") in {"merge", "review"},
            "has_conflict": False,
            "has_l1_hint": bool(row.get("l1_matched_id")),
+            "decision": row.get("dedup_action") or "",
        }
        for row in rows
    ]
@@ -248,6 +253,9 @@ def _staging_query_rows(query_id: str) -> dict[str, object]:
                )
                matched_row = cursor.fetchone()
    dashboard_row = _staging_dashboard_row(row)
+    has_main_match = bool(matched_song_id)
+    # 主行 rank：仅 merge/review 有主匹配时显示 "1"，new 样本置空以便召回候选从 1 开始编号
+    dashboard_row["rank"] = "1" if has_main_match else ""
    if matched_row:
        dashboard_row["candidate_name"] = str(matched_row.get("name") or "")
        dashboard_row["candidate_lyricist"] = str(matched_row.get("lyricist") or "")
@@ -256,13 +264,98 @@ def _staging_query_rows(query_id: str) -> dict[str, object]:
        if cand_lyrics and not cand_lyrics.startswith(("http://", "https://", "/")):
            cand_lyrics = f"_raw:{cand_lyrics}"
        dashboard_row["candidate_lyrics_path"] = cand_lyrics
-    return {"rows": [dashboard_row]}
+    rows = [dashboard_row]
+    # 解析 recalled_candidates JSON，为每个召回候选生成一张卡片
+    recalled_json = row.get("recalled_candidates")
+    if recalled_json:
+        try:
+            recalled_list = json.loads(recalled_json) if isinstance(recalled_json, str) else recalled_json
+            main_candidate_id = str(matched_song_id or "")
+            # 批量查找召回候选的歌词 URL（目标表 + 暂存表 fallback）
+            recalled_ids_str = [
+                str(c.get("id") or "")
+                for c in recalled_list
+                if c.get("id") and str(c.get("id")) != main_candidate_id
+            ]
+            # 转 int 避免 bigint/string 类型不匹配导致 IN 查询返空
+            recalled_ids_int: list[int] = []
+            for rid in recalled_ids_str:
+                try:
+                    recalled_ids_int.append(int(rid))
+                except (ValueError, TypeError):
+                    pass
+            lyrics_map: dict[str, dict] = {}
+            if recalled_ids_int:
+                placeholders = ",".join(["%s"] * len(recalled_ids_int))
+                with _target_conn() as tconn, tconn.cursor() as tcursor:
+                    # 目标表查找
+                    tcursor.execute(
+                        f"SELECT id, name, lyricist, composer, lyrics_url"
+                        f" FROM {TARGET_TABLE_NAME} WHERE id IN ({placeholders})",
+                        recalled_ids_int,
+                    )
+                    for mrow in tcursor.fetchall():
+                        lyrics_map[str(mrow.get("id"))] = mrow
+                    # 暂存表 fallback：目标表找不到的 ID 从暂存表补查
+                    missing_ids = [rid for rid in recalled_ids_int if str(rid) not in lyrics_map]
+                    if missing_ids:
+                        ph2 = ",".join(["%s"] * len(missing_ids))
+                        tcursor.execute(
+                            f"SELECT source_song_id, name, lyricist, composer, lyrics_url"
+                            f" FROM {TARGET_TABLE_NAME_TMP}"
+                            f" WHERE source_song_id IN ({ph2})"
+                            f" ORDER BY staging_id DESC",
+                            [str(mid) for mid in missing_ids],
+                        )
+                        for mrow in tcursor.fetchall():
+                            sid = str(mrow.get("source_song_id"))
+                            if sid not in lyrics_map:
+                                lyrics_map[sid] = mrow
+            rank = 2 if has_main_match else 1
+            for cand in recalled_list:
+                cand_id = str(cand.get("id") or "")
+                # 跳过已经是主匹配候选的（避免重复显示）
+                if cand_id and cand_id == main_candidate_id:
+                    continue
+                cand_row = dict(dashboard_row)
+                cand_row["rank"] = str(rank)
+                cand_row["candidate_id"] = cand_id
+                # 优先用目标表/暂存表查到的元数据，回退到 JSON 中的信息
+                target_info = lyrics_map.get(cand_id)
+                if target_info:
+                    cand_row["candidate_name"] = str(target_info.get("name") or cand.get("name") or "")
+                    cand_row["candidate_lyricist"] = str(target_info.get("lyricist") or cand.get("lyricist") or "")
+                    cand_row["candidate_composer"] = str(target_info.get("composer") or cand.get("composer") or "")
+                    cand_lyrics = str(target_info.get("lyrics_url") or "")
+                    if cand_lyrics and not cand_lyrics.startswith(("http://", "https://", "/")):
+                        cand_lyrics = f"_raw:{cand_lyrics}"
+                    cand_row["candidate_lyrics_path"] = cand_lyrics
+                else:
+                    cand_row["candidate_name"] = str(cand.get("name") or "")
+                    cand_row["candidate_lyricist"] = str(cand.get("lyricist") or "")
+                    cand_row["candidate_composer"] = str(cand.get("composer") or "")
+                    cand_row["candidate_lyrics_path"] = ""
+                cand_row["candidate_decision"] = str(cand.get("decision") or "new")
+                cand_row["candidate_confidence"] = str(cand.get("confidence") or "")
+                cand_row["candidate_jaccard"] = str(cand.get("jaccard") or "")
+                cand_row["candidate_line_coverage"] = str(cand.get("line_coverage") or "")
+                cand_row["candidate_reason"] = ""
+                rows.append(cand_row)
+                rank += 1
+        except (json.JSONDecodeError, TypeError):
+            pass
+    return {"rows": rows}
 def _update_staging_review(source_song_id: str, decision: str, note: str, reviewer: str = "dashboard") -> dict[str, object]:
    """更新暂存表的人工审核状态。
-    decision: 'approved_import' | 'rejected_duplicate' | 'unsure' | 'pending' (撤销)
+    decision: 'approved_import' | 'rejected_duplicate' | 'unsure' | 'pending' (撤销) | 'deleted'
    """
    if decision == "pending":
        # 撤销审核
@@ -275,6 +368,18 @@ def _update_staging_review(source_song_id: str, decision: str, note: str, review
        WHERE source_song_id = %s AND dedup_action = 'review'
        """
        params = [note, source_song_id]
+    elif decision == "deleted":
+        # 删除：不限定 dedup_action，对 review/skip 等各种 action 均可执行
+        sql = f"""
+        UPDATE {TARGET_TABLE_NAME_TMP}
+        SET staging_status = 'deleted',
+            biz_review_status = 'deleted',
+            reviewed_by = %s,
+            reviewed_at = NOW(),
+            biz_review_note = NULLIF(%s, '')
+        WHERE source_song_id = %s
+        """
+        params = [reviewer, note, source_song_id]
    else:
        sql = f"""
        UPDATE {TARGET_TABLE_NAME_TMP}
@@ -502,6 +607,7 @@ def _group_index(path: Path, top_k: str) -> list[dict[str, object]]:
                "has_hit": False,
                "has_conflict": False,
                "has_l1_hint": False,
+                "decision": _row_decision(row),
            }
            groups_by_id[query_id] = group
            order += 1
@@ -528,11 +634,14 @@ def _group_index(path: Path, top_k: str) -> list[dict[str, object]]:
    return groups
-def _groups_response(path: Path, top_k: str, mode: str, term: str, page: int, page_size: int) -> dict[str, object]:
+def _groups_response(path: Path, top_k: str, mode: str, term: str, page: int, page_size: int, decision: str = '') -> dict[str, object]:
    groups = _group_index(path, top_k)
    # hits 模式下只展示有命中的记录（merge/review），排除纯 new
    if mode == "hits":
        groups = [g for g in groups if g.get("has_hit")]
+    # 决策筛选
+    if decision and decision in ('new', 'merge', 'review', 'duplicate'):
+        groups = [g for g in groups if g.get("decision") == decision or (decision == 'merge' and g.get("decision") == 'merge')]
    filtered = [group for group in groups if _matches_term(group, term)]
    start = max(0, (page - 1) * page_size)
    end = start + page_size
@@ -638,13 +747,14 @@ class Handler(BaseHTTPRequestHandler):
                top_k = params.get("top_k", [""])[0]
                mode = params.get("mode", ["retrieval"])[0]
                term = _norm(params.get("q", [""])[0])
+                decision = _norm(params.get("decision", [""])[0])
                page = max(1, int(params.get("page", ["1"])[0]))
                page_size = min(200, max(10, int(params.get("page_size", ["50"])[0])))
                if raw_path == STAGING_DB_PATH:
-                    _json_response(self, _staging_groups_response(mode, term, page, page_size))
+                    _json_response(self, _staging_groups_response(mode, term, page, page_size, decision))
                    return
                path = _safe_path(raw_path)
-                _json_response(self, _groups_response(path, top_k, mode, term, page, page_size))
+                _json_response(self, _groups_response(path, top_k, mode, term, page, page_size, decision))
            except Exception as exc:  # noqa: BLE001 - local diagnostic API
                _error(self, str(exc), status=404)
            return
@@ -694,7 +804,7 @@ class Handler(BaseHTTPRequestHandler):
                reviewer = str(payload.get("reviewer", "dashboard")).strip() or "dashboard"
                if not source_id:
                    raise ValueError("source_id is required")
-                if decision not in ("approved_import", "rejected_duplicate", "unsure", "pending"):
+                if decision not in ("approved_import", "rejected_duplicate", "unsure", "pending", "deleted"):
                    raise ValueError(f"invalid decision: {decision}")
                result = _update_staging_review(source_id, decision, note, reviewer)
                _json_response(self, result)
--- a/test_dedup.py
View file @93b03ea
+++ b/test_dedup.py
View file @93b03ea
@@ -5,9 +5,12 @@
 """
 import csv
+import json
 import os
+import queue
 import sys
 import tempfile
+import threading
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
@@ -28,6 +31,8 @@ from import_hk_songs import (
    TARGET_TABLE_NAME,
    OSS_CONFIG,
    _is_lrc_format,
+    mark_rows_in_l1_index,
+    stop_db_writer,
    _normalize_meta,
    build_row_tuple,
    classify_dedup_action,
@@ -39,6 +44,7 @@ from import_hk_songs import (
    DedupReport,
    L2CandidateIndex,
    load_approved_import_ids,
+    load_existing_l2_candidates,
 )
 from lyric_dedup import DuplicateChecker, DuplicateDecision, LyricRecord
 from lyric_dedup.checker import CandidateMatch
@@ -117,6 +123,97 @@ class TestNormalizeMeta:
        assert _normalize_meta('你好！？世界') == '你好世界'
+class TestPendingL1Index:
+    """测试异步 DB 写入时的内存 L1 索引维护"""
+    def test_marks_rows_before_background_db_writer_finishes(self):
+        l1_index = {}
+        rows = [
+            {'source_id': 101, 'name': '晴天', 'lyricist': '周杰伦', 'composer': '周杰伦'},
+            {'source_id': 102, 'name': '', 'lyricist': '无标题', 'composer': '匿名'},
+        ]
+        mark_rows_in_l1_index(rows, l1_index)
+        key = (_normalize_meta('晴天'), _normalize_meta('周杰伦'), _normalize_meta('周杰伦'))
+        assert l1_index[key] == 101
+        assert len(l1_index) == 1
+class TestDbWriterShutdown:
+    """测试后台 DB 写入线程的优雅停止"""
+    def test_stop_db_writer_drains_queued_items_before_stopping(self):
+        write_queue = queue.Queue()
+        processed = []
+        def writer():
+            while True:
+                item = write_queue.get()
+                if item is None:
+                    write_queue.task_done()
+                    break
+                processed.append(item)
+                write_queue.task_done()
+        thread = threading.Thread(target=writer)
+        thread.start()
+        write_queue.put('batch-1')
+        write_queue.put('batch-2')
+        stop_db_writer(write_queue, thread)
+        assert processed == ['batch-1', 'batch-2']
+        assert not thread.is_alive()
+class TestExistingLyricsCache:
+    """测试目标库已有歌词加载缓存"""
+    def test_load_existing_l2_candidates_reuses_cached_lyrics(self, tmp_path):
+        cache_path = tmp_path / 'lyrics_cache.jsonl'
+        cached = {
+            'id': '100',
+            'url': 'https://oss.example.test/100.txt',
+            'lyrics': '缓存里的歌词\n第二行',
+            'name': '旧歌',
+            'singer': '旧歌手',
+            'lyricist': '旧词',
+            'composer': '旧曲',
+        }
+        cache_path.write_text(json.dumps(cached, ensure_ascii=False) + '\n', encoding='utf-8')
+        rows = [
+            {
+                'id': 100,
+                'lyrics_url': 'https://oss.example.test/100.txt',
+                'name': '旧歌',
+                'singer': '旧歌手',
+                'lyricist': '旧词',
+                'composer': '旧曲',
+            },
+            {
+                'id': 101,
+                'lyrics_url': 'https://oss.example.test/101.txt',
+                'name': '新歌',
+                'singer': '新歌手',
+                'lyricist': '新词',
+                'composer': '新曲',
+            },
+        ]
+        downloaded_urls = []
+        def downloader(url, timeout=10):
+            downloaded_urls.append(url)
+            return '下载的新歌词\n第二行'.encode('utf-8'), 'text/plain'
+        records, stats = load_existing_l2_candidates(rows, cache_path, downloader=downloader)
+        assert downloaded_urls == ['https://oss.example.test/101.txt']
+        assert [r.record_id for r in records] == ['100', '101']
+        assert records[0].lyrics == '缓存里的歌词\n第二行'
+        assert stats == {'cached': 1, 'downloaded': 1, 'failed': 0}
 # ====================================================================
 # L1 元数据去重 check_l1
 # ====================================================================
@@ -192,13 +289,13 @@ class TestCheckL2:
    def test_empty_lyrics(self):
        """空歌词应返回 new"""
        row = {'id': 1, 'lyrics_txt_content': '', 'name': 'test', 'singer': 'test'}
-        decision, confidence, matched_id, reason = check_l2(row, self.checker, [])
+        decision, confidence, matched_id, reason, _ = check_l2(row, self.checker, [])
        assert decision == 'new'
        assert '无歌词内容' in reason
    def test_none_lyrics(self):
        row = {'id': 1, 'lyrics_txt_content': None, 'name': 'test', 'singer': 'test'}
-        decision, _, _, _ = check_l2(row, self.checker, [])
+        decision, _, _, _, _ = check_l2(row, self.checker, [])
        assert decision == 'new'
    def test_no_candidates(self):
@@ -209,7 +306,7 @@ class TestCheckL2:
            'name': '你是我的眼',
            'singer': '萧煌奇',
        }
-        decision, confidence, matched_id, reason = check_l2(row, self.checker, [])
+        decision, confidence, matched_id, reason, _ = check_l2(row, self.checker, [])
        assert decision == 'new'
        assert '无候选集' in reason
@@ -227,7 +324,7 @@ class TestCheckL2:
            'singer': '测试歌手',
        }
-        decision, confidence, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, confidence, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'new'
        assert matched_id is None
@@ -247,7 +344,7 @@ class TestCheckL2:
            'singer': '测试歌手',
        }
-        decision, confidence, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, confidence, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'new'
        assert matched_id is None
@@ -267,7 +364,7 @@ class TestCheckL2:
            'singer': '测试歌手',
        }
-        decision, confidence, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, confidence, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'new'
        assert matched_id is None
@@ -292,13 +389,30 @@ class TestCheckL2:
            'name': '你是我的眼',
            'singer': '萧煌奇',
        }
-        decision, confidence, matched_id, reason = check_l2(
+        decision, confidence, matched_id, reason, _ = check_l2(
            row, self.checker, [candidate]
        )
        assert decision == 'duplicate'
        assert confidence >= 0.9
        assert matched_id == 'existing_1'
+    def test_short_exact_hash_match_is_review_not_duplicate(self):
+        """规范化后歌词过短时，即使哈希完全一致也不能自动合并。"""
+        short_lyrics = '[00:00.730]春风吹过我的心'
+        candidate = LyricRecord(record_id='existing_short', lyrics=short_lyrics, title='旧歌')
+        row = {
+            'id': 22,
+            'lyrics_txt_content': '[00:01.120]春风吹过我的心',
+            'name': '另一首歌',
+            'singer': '不同歌手',
+        }
+        decision, _, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
+        assert decision == 'review'
+        assert matched_id == 'existing_short'
+        assert '过短' in reason
    def test_similar_lyrics_high_overlap(self):
        """高度相似的歌词应判为 duplicate 或 review"""
        base_lyrics = '\n'.join([
@@ -335,7 +449,7 @@ class TestCheckL2:
            'name': '如果有一天(另一版)',
            'singer': '未知',
        }
-        decision, confidence, matched_id, reason = check_l2(
+        decision, confidence, matched_id, reason, _ = check_l2(
            row, self.checker, [candidate]
        )
        # 高相似度 -> 应该不是 new（duplicate 或 review）
@@ -362,7 +476,7 @@ class TestCheckL2:
            'name': '夜晚',
            'singer': '未知',
        }
-        decision, confidence, matched_id, reason = check_l2(
+        decision, confidence, matched_id, reason, _ = check_l2(
            row, self.checker, [candidate]
        )
        assert decision == 'new'
@@ -392,7 +506,7 @@ class TestCheckL2:
            'name': '片段歌曲',
            'singer': '未知',
        }
-        decision, _, _, _ = check_l2(row, self.checker, [candidate])
+        decision, _, _, _, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'review'
    def test_lrc_with_timestamps_still_detects(self):
@@ -424,7 +538,7 @@ class TestCheckL2:
            'name': '小苹果(LRC版)',
            'singer': '筷子兄弟',
        }
-        decision, confidence, matched_id, reason = check_l2(
+        decision, confidence, matched_id, reason, _ = check_l2(
            row, self.checker, [candidate]
        )
        assert decision == 'duplicate', f"Expected duplicate, got {decision}: {reason}"
@@ -453,7 +567,7 @@ class TestCheckL2:
            'name': '小小鸟',
            'singer': '赵传',
        }
-        decision, confidence, matched_id, _ = check_l2(
+        decision, confidence, matched_id, _, _ = check_l2(
            row, self.checker, [candidate_different, candidate_exact]
        )
        assert decision == 'duplicate'
@@ -477,7 +591,7 @@ class TestCheckL2:
        # 极低阈值 -> 更容易判为 duplicate
        strict_checker = DuplicateChecker(duplicate_jaccard_threshold=0.3)
-        decision_strict, _, _, _ = check_l2(row, strict_checker, [candidate])
+        decision_strict, _, _, _, _ = check_l2(row, strict_checker, [candidate])
        assert decision_strict == 'duplicate'
    def test_strong_review_same_writers_promotes_to_duplicate(self):
@@ -527,7 +641,7 @@ class TestCheckL2:
            'composer': '王琪',
        }
-        decision, _, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, _, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'duplicate', reason
        assert matched_id == 'c1'
@@ -580,7 +694,7 @@ class TestCheckL2:
            'composer': '陈粒',
        }
-        decision, _, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, _, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'review', reason
        assert matched_id == 'c1'
@@ -603,7 +717,7 @@ class TestCheckL2:
            'composer': '安智英/Vanilla Man',
        }
-        decision, _, _, reason = check_l2(row, self.checker, [candidate])
+        decision, _, _, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision != 'duplicate', reason
@@ -643,7 +757,7 @@ class TestCheckL2:
            'composer': '共同作者',
        }
-        decision, _, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, _, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision != 'duplicate', reason
@@ -661,7 +775,7 @@ class TestCheckL2:
            'singer': '测试歌手',
        }
-        decision, _, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, _, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision != 'duplicate', reason
@@ -693,7 +807,7 @@ class TestCheckL2:
            'composer': '新曲作者',
        }
-        decision, _, matched_id, reason = check_l2(row, self.checker, [candidate])
+        decision, _, matched_id, reason, _ = check_l2(row, self.checker, [candidate])
        assert decision == 'duplicate', reason
        assert matched_id == 'c1'
@@ -841,7 +955,7 @@ class TestL2CandidateIndex:
        ])
        index.add(LyricRecord(record_id='existing', lyrics=lyrics, title='你是我的眼'))
-        decision, confidence, matched_id, reason = check_l2(
+        decision, confidence, matched_id, reason, _ = check_l2(
            {
                'source_id': 999,
                'lyrics_txt_content': lyrics,
@@ -979,7 +1093,7 @@ class TestDedupIntegration:
        assert is_dup is False
        # L2 应命中
-        decision, confidence, matched_id, reason = check_l2(
+        decision, confidence, matched_id, reason, _ = check_l2(
            row, self.checker, self.l2_candidates
        )
        assert decision == 'duplicate'
@@ -1009,7 +1123,7 @@ class TestDedupIntegration:
        assert is_dup is False
        # L2 也不命中
-        decision, _, _, _ = check_l2(row, self.checker, self.l2_candidates)
+        decision, _, _, _, _ = check_l2(row, self.checker, self.l2_candidates)
        assert decision == 'new'
    def test_l1_match_with_different_lyrics_is_not_skipped(self):
@@ -1056,6 +1170,44 @@ class TestDedupIntegration:
        assert action['merge_authors'] is True
        assert action['matched_id'] == '100'
+    def test_short_l2_exact_match_without_l1_is_new(self):
+        """L2 过短 exact-hash 命中但 L1 未命中时，直接作为新歌。"""
+        candidate = LyricRecord(record_id='102', lyrics='春风吹过我的心', title='旧歌')
+        row = {
+            'id': 205,
+            'name': '另一首歌',
+            'lyricist': '不同词作者',
+            'composer': '不同曲作者',
+            'lyrics_txt_content': '[00:00.730]春风吹过我的心',
+            'singer': '不同歌手',
+        }
+        action = classify_dedup_action(row, self.l1_index, self.checker, [candidate])
+        assert action['action'] == 'new'
+        assert action['decision'] == 'new'
+        assert action['matched_id'] is None
+        assert action['l1_matched'] is False
+    def test_short_l2_exact_match_with_l1_is_merge(self):
+        """L2 过短 exact-hash 命中且 L1 命中时，按 L1 结果自动合并。"""
+        candidate = LyricRecord(record_id='100', lyrics='春风吹过我的心', title='晴天')
+        row = {
+            'id': 206,
+            'name': '晴天',
+            'lyricist': '周杰伦',
+            'composer': '周杰伦',
+            'lyrics_txt_content': '[00:00.730]春风吹过我的心',
+            'singer': '周杰伦',
+        }
+        action = classify_dedup_action(row, self.l1_index, self.checker, [candidate])
+        assert action['action'] == 'merge'
+        assert action['decision'] == 'duplicate'
+        assert action['matched_id'] == '100'
+        assert action['l1_matched'] is True
    def test_full_pipeline_simulation(self):
        """模拟完整 L1 -> L2 -> 写入 的管线流程"""
        batch = [
@@ -1100,7 +1252,7 @@ class TestDedupIntegration:
                continue
            # L2
-            decision, confidence, l2_matched_id, reason = check_l2(
+            decision, confidence, l2_matched_id, reason, _ = check_l2(
                row, self.checker, self.l2_candidates
            )
            if decision == 'duplicate':
@@ -1138,7 +1290,7 @@ class TestEdgeCases:
        checker = DuplicateChecker()
        candidate = LyricRecord(record_id='c1', lyrics='爱', title='短歌')
        row = {'id': 1, 'lyrics_txt_content': '爱', 'name': '短歌', 'singer': 'test'}
-        decision, _, _, _ = check_l2(row, checker, [candidate])
+        decision, _, _, _, _ = check_l2(row, checker, [candidate])
        # 极短歌词可能是 duplicate（哈希匹配）或 new，但不应崩溃
        assert decision in ('duplicate', 'review', 'new')
@@ -1146,7 +1298,7 @@ class TestEdgeCases:
        """纯空白歌词"""
        checker = DuplicateChecker()
        row = {'id': 1, 'lyrics_txt_content': '   \n\n  ', 'name': 'test', 'singer': 'test'}
-        decision, _, _, reason = check_l2(row, checker, [])
+        decision, _, _, reason, _ = check_l2(row, checker, [])
        assert decision == 'new'
    def test_l1_index_with_duplicate_names(self):
@@ -1171,7 +1323,7 @@ class TestEdgeCases:
        candidate = LyricRecord(record_id='c1', lyrics=plain, title='我爱你中国')
        row = {'id': 2, 'lyrics_txt_content': with_ts, 'name': '我爱你中国', 'singer': '汪峰'}
-        decision, _, _, _ = check_l2(row, checker, [candidate])
+        decision, _, _, _, _ = check_l2(row, checker, [candidate])
        # normalization 应该去掉时间戳后识别为相同歌词
        assert decision == 'duplicate'
@@ -1780,7 +1932,7 @@ def _run_l2_retrieval_benchmark(
            'name': record.title,
            'singer': record.artist,
        }
-        decision, confidence, matched_id, reason = check_l2(row, checker, index)
+        decision, confidence, matched_id, reason, _ = check_l2(row, checker, index)
        decision_counts[decision] += 1
        if show_progress:
            source_iterable.set_postfix(