iomgaa
9eb9b86954
- AnchorContext frozen dataclass: 锚节点生成上下文（node_id, card_text, frame_paths, subtitle, distractor_texts） - TaskTypeSpec frozen dataclass: 题型生成规格（level, needs_frames, frame_count, context_fields） - TASK_TYPE_LEVEL_MAP: 12 种 Video-MME 题型 → 树层级 + 生成规格映射 - 11 项单元测试覆盖：映射完整性、值类型、层级合法性、frozen 不变性 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
"""赛题合成核心逻辑 — 节点采样、prompt 构造、去重。
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纯函数为主，异步编排仅 generate_one。
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通过 DI 接收 VLMProvider / EmbeddingProvider，不 import adapters/。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class AnchorContext:
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"""锚节点上下文——生成单道题所需的全部素材。
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属性:
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node_id: 锚节点 ID。
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card_text: 锚节点 card 序列化文本。
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frame_paths: 帧图片路径列表。
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subtitle: 对应字幕（可空）。
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distractor_texts: 同视频其他节点摘要（供 VLM 生成干扰项）。
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"""
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node_id: str
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card_text: str
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frame_paths: list[str]
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subtitle: str
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distractor_texts: list[str]
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@dataclass(frozen=True)
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class TaskTypeSpec:
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"""题型的生成规格。
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属性:
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level: 锚定层级（"L3" / "L2" / "L1" / "L1-L2"）。
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needs_frames: 是否必须提供帧图。
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frame_count: 帧数范围描述（如 "1", "2-3", "0-1"）。
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context_fields: 需要提取的 card 字段元组。
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"""
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level: str
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needs_frames: bool
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frame_count: str
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context_fields: tuple[str, ...]
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# ---------------------------------------------------------------------------
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# 12 种 Video-MME 题型 → 树层级 + 生成规格映射
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# ---------------------------------------------------------------------------
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TASK_TYPE_LEVEL_MAP: dict[str, TaskTypeSpec] = {
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# --- L3 单帧题型 ---
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"Object Recognition": TaskTypeSpec("L3", True, "1", ("frame_summary",)),
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"Attribute Perception": TaskTypeSpec("L3", True, "1", ("frame_summary",)),
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"OCR Problems": TaskTypeSpec("L3", True, "1", ("frame_summary",)),
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"Spatial Reasoning": TaskTypeSpec("L3", True, "1", ("frame_summary", "spatial_layout")),
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"Spatial Perception": TaskTypeSpec("L3", True, "1", ("frame_summary",)),
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# --- L2 多帧 / 事件级题型 ---
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"Action Recognition": TaskTypeSpec("L2", True, "2-3", ("event_description",)),
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"Action Reasoning": TaskTypeSpec("L2", True, "2-3", ("event_description",)),
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"Counting Problem": TaskTypeSpec("L2", True, "2-3", ("event_description",)),
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"Temporal Perception": TaskTypeSpec("L2", False, "0-1", ("event_description", "time_range")),
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# --- L1 / 跨层级题型 ---
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"Temporal Reasoning": TaskTypeSpec("L1", True, "per-L2", ("scene_summary",)),
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"Information Synopsis": TaskTypeSpec("L1", True, "per-L2", ("scene_summary",)),
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"Object Reasoning": TaskTypeSpec("L1-L2", True, "per-L2", ("event_description",)),
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}
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