feat: 章节软删除、对话左滑删除，移除已读状态

- 章节：详情页增加删除按钮，软删除（is_active=False），AI 不再修改但保留供参考
- 章节：get_chapter 增加 is_active 校验，已删除章节返回 404
- 章节：AI 生成时参考同类别已删除章节摘要
- 对话：左滑显示删除，调用 hard delete API，删除前二次确认
- 对话：根布局包裹 GestureHandlerRootView 以支持 Swipeable
- 对话：移除已读/未读状态展示及相关 i18n

api/app/agents/prompts/memory_prompts.py

@@ -302,6 +302,7 @@ def get_narrative_prompt(
     existing_content: str = "",
     user_profile: str = "",
     birth_year: Optional[int] = None,
+    archived_summaries: str = "",
 ) -> str:
     """将新对话改写为叙述（只输出新内容的改写，不重复已有内容）"""
     context_tail = ""
@@ -309,6 +310,7 @@ def get_narrative_prompt(
         context_tail = existing_content[-300:] if len(existing_content) > 300 else existing_content
 
     context_section = f"\n\n【衔接上下文（已有内容的末尾，仅供参考衔接，不要重复）】：\n{context_tail}" if context_tail else ""
+    archived_section = f"\n\n【已删除的该类别历史章节（仅供参考，请勿直接使用或重复）】：\n{archived_summaries}" if archived_summaries else ""
 
     profile_section = f"\n\n用户基本信息：\n{user_profile}" if user_profile else ""
     age_hint = _build_age_hint(stage, birth_year)
@@ -323,6 +325,7 @@ def get_narrative_prompt(
 新的对话内容：
 {new_content}
 {context_section}
+{archived_section}
 
 ## 第一步：提炼核心内容
 在改写之前，请先从对话内容中提炼出与人生经历相关的核心信息：

api/app/features/memoir/repo.py

@@ -2,7 +2,7 @@
 
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy.orm import joinedload
+from sqlalchemy.orm import Session, joinedload
 
 from app.features.memoir.models import Book, Chapter, ChapterSection, MemoirState
 
@@ -56,3 +56,33 @@ async def get_memoir_state(user_id: str, db: AsyncSession) -> MemoirState | None
     stmt = select(MemoirState).where(MemoirState.user_id == user_id)
     result = await db.execute(stmt)
     return result.scalar_one_or_none()
+
+
+def get_archived_chapter_summaries_sync(
+    session: Session, user_id: str, category: str
+) -> list[tuple[str, str]]:
+    """获取已删除（is_active=False）的同类别章节的标题与内容摘要，供 AI 参考。"""
+    stmt = (
+        select(Chapter)
+        .where(
+            Chapter.user_id == user_id,
+            Chapter.category == category,
+            Chapter.is_active == False,  # noqa: E712
+        )
+        .options(joinedload(Chapter.sections))
+        .order_by(Chapter.updated_at.desc())
+    )
+    result = session.execute(stmt)
+    chapters = list(result.unique().scalars().all())
+    summaries: list[tuple[str, str]] = []
+    for ch in chapters:
+        sections = getattr(ch, "sections", None) or []
+        parts = [
+            (s.content or "").strip()
+            for s in sorted(sections, key=lambda x: getattr(x, "order_index", 0))
+        ]
+        combined = "".join(parts)
+        preview = (combined[:200] + "...") if len(combined) > 200 else combined
+        if preview.strip():
+            summaries.append((ch.title or "", preview))
+    return summaries

api/app/features/memoir/service.py

@@ -170,6 +170,8 @@ class MemoirService:
             raise HTTPException(status_code=404, detail="Chapter not found")
         if chapter.user_id != user_id:
             raise HTTPException(status_code=403, detail="无权访问此章节")
+        if not chapter.is_active:
+            raise HTTPException(status_code=404, detail="Chapter not found")
         await self._cleanup_unavailable_images(chapter)
         return chapter_to_dict(chapter)
 

api/app/tasks/memoir_tasks.py

@@ -23,6 +23,7 @@ from app.features.memoir.models import (
     MemoirImage,
     MemoirState,
 )
+from app.features.memoir import repo as memoir_repo
 from app.features.user.models import User
 from app.core.dependencies import get_llm_provider
 from app.agents.state_schema import MemoirStateSchema, SlotData, default_state
@@ -713,6 +714,12 @@ def process_memoir_segments(self, user_id: str, segment_ids: List[str]):
                         )
                     narrative = combined_text
 
+                    # 已删除章节摘要供 AI 参考
+                    archived = memoir_repo.get_archived_chapter_summaries_sync(db, user_id, chapter_category)
+                    archived_summaries = "\n".join(
+                        f"- 《{title_text}》：{preview}" for title_text, preview in archived
+                    ) if archived else ""
+
                     if llm:
                         try:
                             if not chapter:
@@ -733,6 +740,7 @@ def process_memoir_segments(self, user_id: str, segment_ids: List[str]):
                                 existing_content=existing_content,
                                 user_profile=user_profile,
                                 birth_year=user_birth_year,
+                                archived_summaries=archived_summaries,
                             )
                             narrative_response = llm.invoke(narrative_prompt)
                             new_narrative = narrative_response.content.strip()
@@ -861,12 +869,18 @@ def generate_chapter_content(self, user_id: str, stage: str, new_content: str):
                     s.content for s in sorted(chapter.sections, key=lambda x: x.order_index) if (s.content or "").strip()
                 )
 
+            archived = memoir_repo.get_archived_chapter_summaries_sync(db, user_id, stage)
+            archived_summaries = "\n".join(
+                f"- 《{title_text}》：{preview}" for title_text, preview in archived
+            ) if archived else ""
+
             if llm:
                 prompt = get_narrative_prompt(
                     stage=stage,
                     slots={},
                     new_content=new_content,
                     existing_content=existing_content,
+                    archived_summaries=archived_summaries,
                 )
                 response = llm.invoke(prompt)
                 new_narrative = response.content.strip()