feat: 回忆录证据血缘与内部评测可追溯，顺带对齐本地评测台与 CI

数据库与模型：新增多版迁移（章节证据快照、对话血缘、记忆事实/时间线 lineage 等），把「成稿 ↔ 对话/记忆」的溯源信息落到表结构里。
业务链路：会话与 WS、回忆录/故事流水线、记忆写入与 enrichment 等跟着接上线索与快照；新增章节证据快照与评测侧 EvalTraceService 等模块，方便组评审用的证据包。
内部评测：自动化 run 与手工 memoir 评审共用可追溯证据；rubric/ judge 相关脚本与文档有配套调整。
app-eval-web：Memoir/实验详情里能展开看证据摘要与 evidence_trace（含对话轮次 id）；Vite 代理与 development.sh 注入的 API 端口与当前默认内部评测端口一致，避免改端口后页面连错服务。
工程杂项：GitHub Actions / 仓库说明有更新；各适配器与支付/配额/plan 等多处为小改动或跟随主改动的收尾；新增/扩充了?

api/scripts/backfill_chapter_evidence_snapshots.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+"""回填 chapters.evidence_bundle_json（Phase 九：历史 chapter 无快照时可批量物化）。
+
+用法（需在已执行 Alembic 0009+ 的库上）::
+
+    cd api && uv run python scripts/backfill_chapter_evidence_snapshots.py
+    cd api && uv run python scripts/backfill_chapter_evidence_snapshots.py --user-id <uuid>
+"""
+
+from __future__ import annotations
+
+import argparse
+
+from sqlalchemy import select
+
+from app.features.auth import models as _auth_models  # noqa: F401
+from app.features.conversation import models as _conv_models  # noqa: F401
+from app.features.memory import models as _memory_models  # noqa: F401
+from app.features.memoir import models as _memoir_models  # noqa: F401
+from app.features.payment import models as _payment_models  # noqa: F401
+from app.features.story import models as _story_models  # noqa: F401
+from app.features.user import models as _user_models  # noqa: F401
+
+from app.core.db import SessionLocal
+from app.features.memoir.chapter_evidence_snapshot import (
+    refresh_chapter_evidence_snapshot_sync,
+)
+from app.features.memoir.models import Chapter
+
+
+def main() -> None:
+    p = argparse.ArgumentParser()
+    p.add_argument("--user-id", default="", help="仅刷新该用户的章节；默认全表")
+    p.add_argument("--limit", type=int, default=0, help="最多处理条数，0 表示不限制")
+    args = p.parse_args()
+
+    uid = (args.user_id or "").strip()
+    session = SessionLocal()
+    n_ok = 0
+    try:
+        stmt = select(Chapter.id)
+        if uid:
+            stmt = stmt.where(Chapter.user_id == uid)
+        if args.limit > 0:
+            stmt = stmt.limit(args.limit)
+        ids = list(session.execute(stmt).scalars().all())
+        for cid in ids:
+            if refresh_chapter_evidence_snapshot_sync(session, str(cid)):
+                n_ok += 1
+        session.commit()
+        print(f"refreshed_snapshots={n_ok} chapter_rows={len(ids)}")
+    finally:
+        session.close()
+
+
+if __name__ == "__main__":
+    main()

api/scripts/backfill_segment_dialogue_lineage.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+"""回填 segments.user_message_id / lineage_json（由既有 conversation_messages 配对）。
+
+用法::
+
+    cd api && uv run python scripts/backfill_segment_dialogue_lineage.py
+    cd api && uv run python scripts/backfill_segment_dialogue_lineage.py --limit 500
+"""
+
+from __future__ import annotations
+
+import argparse
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from app.features.auth import models as _auth_models  # noqa: F401
+from app.features.conversation import models as _conv_models  # noqa: F401
+from app.features.memory import models as _memory_models  # noqa: F401
+from app.features.memoir import models as _memoir_models  # noqa: F401
+from app.features.payment import models as _payment_models  # noqa: F401
+from app.features.story import models as _story_models  # noqa: F401
+from app.features.user import models as _user_models  # noqa: F401
+
+from app.core.db import SessionLocal
+from app.features.conversation.lineage_schemas import DialogueLineage
+from app.features.conversation.models import Conversation, ConversationMessage, Segment
+
+
+def _first_message_for_segment(
+    session: Session, *, segment_id: str, role: str
+) -> ConversationMessage | None:
+    stmt = (
+        select(ConversationMessage)
+        .where(
+            ConversationMessage.segment_id == segment_id,
+            ConversationMessage.role == role,
+        )
+        .order_by(ConversationMessage.created_at.asc(), ConversationMessage.id.asc())
+        .limit(1)
+    )
+    return session.execute(stmt).scalar_one_or_none()
+
+
+def main() -> None:
+    p = argparse.ArgumentParser()
+    p.add_argument("--limit", type=int, default=0, help="最多处理 segment 条数，0 不限制")
+    args = p.parse_args()
+
+    session = SessionLocal()
+    n = 0
+    try:
+        stmt = (
+            select(Segment)
+            .join(Conversation, Segment.conversation_id == Conversation.id)
+            .where(
+                Segment.lineage_json.is_(None),
+                Conversation.deleted_at.is_(None),
+            )
+            .order_by(Segment.created_at.asc())
+        )
+        if args.limit > 0:
+            stmt = stmt.limit(args.limit)
+        segments = list(session.execute(stmt).scalars().all())
+        for seg in segments:
+            hum = _first_message_for_segment(session, segment_id=str(seg.id), role="human")
+            if not hum:
+                continue
+            ai = _first_message_for_segment(session, segment_id=str(seg.id), role="ai")
+            ln = DialogueLineage.for_single_turn(
+                conversation_id=str(seg.conversation_id),
+                user_message_id=str(hum.id),
+                assistant_message_id=str(ai.id) if ai else None,
+                segment_ids=[str(seg.id)],
+            )
+            seg.user_message_id = str(hum.id)
+            seg.lineage_json = ln.model_dump(mode="json")
+            n += 1
+        session.commit()
+        print(f"updated_segments={n} scanned={len(segments)}")
+    finally:
+        session.close()
+
+
+if __name__ == "__main__":
+    main()