feat(#2035): semantic tags + quality score + AI caption+embedding

- Fix: generation_tasks.py passes clip_ai_tags_by_asset to pick_narrative_assets
  so AI tags (weight 2.0) actually participate in narrative mode selection
- Feat: quality_score auto-computation via AssetAnalyzer on ingest
  (worker.calculate_asset_price celery task; fallback 50.0 on failure)
- Feat: smart_match adds ai_semantic dimension (20% weight) using Jaccard
  similarity between asset AI tags (scene/objects/action) and script tags
- Feat: atom_clip caption (10-30 Chinese chars) via Doubao Vision, saved to
  asset_atom_clips.caption (Text column, migration 085)
- Feat: atom_clip embedding vector via Doubao embeddings API, saved to
  asset_atom_clips.embedding (JSON column)
- Chore: remove dead calculate_quality_score_real wrapper
- Tests: 20 new unit tests covering caption parsing, ai_semantic scoring,
  narrative AI tag propagation, score weight changes; update existing tests
  for new fallback dict shape and reweighted dimensions
- Fail-open: tagging/embedding/quality failures never block main flow

apps/api/app/api/routes/generation_tasks.py

@@ -134,10 +134,11 @@ def _ensure_library_has_ready_video_assets(assets) -> None:
 def _select_assets_from_library(
     assets: list,
     mode: str,
-    count: int,
+    count: int = 0,
     rng=None,
     script_tags: list | None = None,
     tag_names_by_id: dict | None = None,
+    db=None,
 ) -> list[str]:
     """根据选取模式从素材库中选取 ready 状态的视频素材 ID。
 
@@ -158,6 +159,42 @@ def _select_assets_from_library(
     if not ready_video_assets:
         return []
 
+    # #2035：加载片段级 AI 标签，供叙事模式 AI 加权和 smart 模式语义匹配使用。
+    # 失败降级为空（不影响选片主流程）。
+    clip_ai_tags_by_asset: dict[str, list[dict]] = {}
+    ai_tags_by_asset: dict[str, dict] = {}  # asset_id → 聚合后的 ai_tags dict（取首个有 has_text 的片段；合并 scene/objects/action 去重）
+    try:
+        if db is not None:
+            from packages.adapters.sqlalchemy_impl.models import AssetAtomClipModel
+            ready_ids = [a.id for a in ready_video_assets]
+            clip_rows = (
+                db.query(AssetAtomClipModel.asset_id, AssetAtomClipModel.ai_tags)
+                .filter(AssetAtomClipModel.asset_id.in_(ready_ids))
+                .filter(AssetAtomClipModel.ai_tags.isnot(None))
+                .all()
+            )
+            agg: dict[str, dict] = {}
+            for asset_id, ai_tags in clip_rows:
+                if not isinstance(ai_tags, dict):
+                    continue
+                clip_ai_tags_by_asset.setdefault(asset_id, []).append(ai_tags)
+                # 聚合：合并 scene/objects/action 去重
+                agg.setdefault(asset_id, {"scene": [], "objects": [], "action": [], "shot": "", "has_text": False})
+                for key in ("scene", "objects", "action"):
+                    for v in ai_tags.get(key) or []:
+                        v = str(v).strip()
+                        if v and v not in agg[asset_id][key]:
+                            agg[asset_id][key].append(v)
+                if ai_tags.get("has_text") is True:
+                    agg[asset_id]["has_text"] = True
+                if not agg[asset_id]["shot"] and ai_tags.get("shot"):
+                    agg[asset_id]["shot"] = ai_tags["shot"]
+            ai_tags_by_asset = agg
+    except Exception:  # noqa: BLE001
+        logger.warning("[选片] 加载片段 AI 标签失败，降级不使用语义匹配", exc_info=True)
+        clip_ai_tags_by_asset = {}
+        ai_tags_by_asset = {}
+
     # 叙事模式（#1970 PR3）：文案标签命中池优先；无任何命中时完全降级为现有随机逻辑。
     if script_tags:
         from packages.domain.narrative_match import pick_narrative_assets
@@ -167,6 +204,7 @@ def _select_assets_from_library(
             ready_video_assets,
             script_tags=script_tags,
             tag_names_by_id=tag_names_by_id,
+            clip_ai_tags_by_asset=clip_ai_tags_by_asset,
             limit=limit,
             rng=rng,
         )
@@ -177,7 +215,16 @@ def _select_assets_from_library(
         # 评分维度：质量分(40%) + 时长适配(30%) + 新鲜度(20%) + 未使用加分(10%)
         # 排序注入随机噪声（#1743）：同分素材每次选出不同组合，从素材组合层面降重
         limit = count if count > 0 else None
-        results = smart_select_assets(ready_video_assets, limit=limit, kind="video", rng=rng)
+        # #2035：给 smart_select_assets 传入文案标签和 AI 标签映射，启用语义维度
+        norm_script = {t.strip().lower() for t in (script_tags or []) if t and t.strip()}
+        results = smart_select_assets(
+            ready_video_assets,
+            limit=limit,
+            kind="video",
+            rng=rng,
+            script_tags=norm_script if norm_script else None,
+            ai_tags_by_asset=ai_tags_by_asset or None,
+        )
         return [r.asset.id for r in results]
 
     # 默认 all 模式：返回全部 ready 视频素材
@@ -396,6 +443,7 @@ def create_generation_task(
                 count=request.asset_select_count,
                 script_tags=narrative_script_tags or None,
                 tag_names_by_id=_tag_index,
+                db=db,
             )
     elif project_id and not resolved_asset_ids and (request.asset_select_mode in ("smart",) or narrative_script_tags):
         # 项目级模式：未指定 asset_ids 且选择了 smart 模式（或叙事模式按标签匹配）时自动选取
@@ -410,6 +458,7 @@ def create_generation_task(
                 count=request.asset_select_count,
                 script_tags=narrative_script_tags or None,
                 tag_names_by_id=_tag_index,
+                db=db,
             )
             if not resolved_asset_ids:
                 raise HTTPException(