saas-backend
01afc2cf69
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- 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
34 lines
885 B
Python
34 lines
885 B
Python
"""asset_atom_clips 新增 caption/embedding 字段（#2035 语义标签增强）
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Revision ID: 085_atom_clip_caption_embedding
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Revises: 084_lipsync_jobs_style
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Create Date: 2026-09-25
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"""
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import sqlalchemy as sa
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from alembic import op
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revision = "085_atom_clip_caption_embedding"
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down_revision = "084_lipsync_jobs_style"
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branch_labels = None
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depends_on = None
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def upgrade() -> None:
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# caption: 中文画面描述（10-30字）
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op.add_column(
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"asset_atom_clips",
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sa.Column("caption", sa.Text(), nullable=True),
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)
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# embedding: caption 对应的向量（豆包 embedding 接口返回，JSON 存 float 数组）
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op.add_column(
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"asset_atom_clips",
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sa.Column("embedding", sa.JSON(), nullable=True),
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)
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def downgrade() -> None:
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op.drop_column("asset_atom_clips", "embedding")
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op.drop_column("asset_atom_clips", "caption")
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