feat(api): 拆分章节物化与 Story 后处理，并加固 Redis 锁与腾讯 ASR

回忆录 Story 流水线（同步）
- 同步路径仅写入 Story 与章节关联，改为 mark_chapter_dirty_sync，不再内联 compose
- 物化由 Celery recompose_chapter 异步完成；compose 不变量与异常时保留 dirty 的语义在 repo 中补充说明
- Evidence：大批次时降低 top_k；路由候选 story 携带 char_count/version_count；append 超长/版本过多时强制新开 story
- 叙事 prompt：relevant_chunks 去重，减少重复证据噪声
- 叙事回退与忠实度 gate：返回 fallback 类型并记录结构化日志（含耗时、JSON 有效性等）

Post-commit 与任务编排
- 新增 post_commit.enqueue_story_post_commit_effects：统一派发 generate_story_image（Redis 去重）、延迟 recompose_chapter、可选 memory compaction
- memoir_tasks / story_service / story_image_tasks 改为调用 post-commit 入口；主图回填后按关联章节重算并调度物化与 compacs（锁委托、Redis 单例、ASR to_thread）
- 更新 test_narrative_pipeline 以适配 _apply_narrative_fallbacks 返回值

api/app/agents/memoir/prompts.py

@@ -673,18 +673,58 @@ def format_narrative_user_content(oral_text: str, evidence_text: str = "") -> st
     )
 
 
+def _normalize_evidence_line(s: str) -> str:
+    return re.sub(r"\s+", " ", (s or "").strip().lower())
+
+
+def dedupe_evidence_chunk_rows(chunks: list) -> list:
+    """
+    对 relevant_chunks 做稳定去重：按归一化后长度降序 + 原下标，单遍包含判定；
+    复杂度 O(n log n)；输出按原顺序中保留条目的相对顺序稳定。
+    """
+    extracted: list[tuple[int, str, object]] = []
+    for i, c in enumerate(chunks):
+        content = (
+            c.get("content", "") if isinstance(c, dict) else getattr(c, "content", "")
+        )
+        t = (content or "").strip()
+        if not t:
+            continue
+        extracted.append((i, t, c))
+    if len(extracted) <= 1:
+        return [x[2] for x in extracted]
+    extracted.sort(
+        key=lambda x: (-len(_normalize_evidence_line(x[1])), x[0]),
+    )
+    kept_norms: list[str] = []
+    kept: list[tuple[int, object]] = []
+    for orig_idx, text, c in extracted:
+        n = _normalize_evidence_line(text)
+        dup = False
+        for kn in kept_norms:
+            if len(n) <= len(kn) and n in kn:
+                dup = True
+                break
+        if not dup:
+            kept_norms.append(n)
+            kept.append((orig_idx, c))
+    kept.sort(key=lambda x: x[0])
+    return [x[1] for x in kept]
+
+
 def format_evidence_chunks_for_prompt(evidence: dict) -> str:
     """将 retrieve_evidence / retrieve_evidence_sync 结果格式化为简短文本，供叙事 prompt 使用。
 
     包含 chunks、摘要（若有）、confirmed facts、timeline、故事摘要（若有）。
     """
     chunks = evidence.get("relevant_chunks") or []
+    chunks = dedupe_evidence_chunk_rows(chunks[:10])
     summaries = evidence.get("relevant_summaries") or []
     facts = evidence.get("relevant_facts") or []
     timeline = evidence.get("timeline_hints") or []
     stories = evidence.get("relevant_stories") or []
     parts: list[str] = []
-    for c in chunks[:10]:
+    for c in chunks:
         content = (
             c.get("content", "") if isinstance(c, dict) else getattr(c, "content", "")
         )

api/app/agents/memoir/story_route_agent.py

@@ -63,8 +63,15 @@ class StoryRouteDecision(BaseModel):
         return str(v)
 
 
-def _build_candidate_json(stories: list[Story], *, preview_chars: int = 220) -> str:
+def _build_candidate_json(
+    stories: list[Story],
+    *,
+    preview_chars: int = 220,
+    story_meta: dict[str, dict[str, int]] | None = None,
+) -> str:
+    """story_meta: story_id -> { char_count, version_count }，供路由感知篇幅与版本数。"""
     rows: list[dict[str, Any]] = []
+    meta = story_meta or {}
     for s in stories:
         md = (s.canonical_markdown or "").strip().replace("\n", " ")
         preview = md[:preview_chars] + ("…" if len(md) > preview_chars else "")
@@ -76,14 +83,17 @@ def _build_candidate_json(stories: list[Story], *, preview_chars: int = 220) ->
             cat = getattr(ch, "category", None) or ""
             tit = getattr(ch, "title", None) or ""
             links.append(f"{tit}({cat})")
-        rows.append(
-            {
-                "id": s.id,
-                "title": s.title,
-                "preview": preview,
-                "linked_chapters": links,
-            }
-        )
+        row: dict[str, Any] = {
+            "id": s.id,
+            "title": s.title,
+            "preview": preview,
+            "linked_chapters": links,
+        }
+        m = meta.get(str(s.id))
+        if m:
+            row["char_count"] = int(m.get("char_count", 0))
+            row["version_count"] = int(m.get("version_count", 0))
+        rows.append(row)
     return json.dumps(rows, ensure_ascii=False, indent=2)
 
 
@@ -142,6 +152,7 @@ class StoryRouteAgent:
         candidate_stories: list[Story],
         llm: Any,
         valid_story_ids: set[str],
+        story_meta: dict[str, dict[str, int]] | None = None,
     ) -> StoryRouteDecision:
         if not llm:
             return StoryRouteDecision(
@@ -149,7 +160,7 @@ class StoryRouteAgent:
                 new_story_title=None,
                 reason="no_llm",
             )
-        payload = _build_candidate_json(candidate_stories)
+        payload = _build_candidate_json(candidate_stories, story_meta=story_meta)
         prompt = get_story_route_prompt(
             chapter_category=chapter_category,
             chapter_title=chapter_title,
@@ -200,13 +211,14 @@ class StoryRouteAgent:
         candidate_stories: list[Story],
         llm: Any,
         valid_story_ids: set[str],
+        story_meta: dict[str, dict[str, int]] | None = None,
     ) -> StoryBatchPlan | None:
         """
         将本批 segment 划分为多个写入单元。解析失败返回 None，由调用方回退 decide()。
         """
         if not llm or len(segments) < 2:
             return None
-        payload = _build_candidate_json(candidate_stories)
+        payload = _build_candidate_json(candidate_stories, story_meta=story_meta)
         segments_json = _build_segments_json_for_plan(segments)
         prompt = get_story_batch_plan_prompt(
             chapter_category=chapter_category,