iomgaa
afa77173e3
- generator_v2: _load_prompt_template takes template_name str instead of QuestionFamilySpec; _build_v2_prompt takes prompt_template + strategy_name + sub_pattern_instruction; generate_one_v2 takes discrete params (prompt_template, strategy_name, skill_target, sub_pattern_instruction) - gates: _gate_leak_test and run_gates take leak_probe_template str instead of QuestionFamilySpec - run_store: add sub_pattern column to DDL + idempotent migration; record_item accepts optional sub_pattern param - Remove QuestionFamilySpec imports from generator_v2 and gates modules - Update test call sites accordingly Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
462 lines
14 KiB
Python
462 lines
14 KiB
Python
"""v2 生成器 — 基于策略特化 prompt 模板的单题 VLM 出题模块。
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使用 VLMProvider 接口调用视觉语言模型，结合 per-strategy prompt 模板
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和 MaterialContext 素材上下文，生成一道四选一候选题。
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典型调用路径::
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candidate = await generate_one_v2(
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vlm=vlm_client,
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tree=tree_index,
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material=material_ctx,
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task_type="Action Reasoning",
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seq=1,
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video_id="vid_001",
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prompt_template="retrieval.md",
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strategy_name="RETRIEVAL",
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skill_target="M1",
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session_id="sess_001",
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)
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import TYPE_CHECKING
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from json_repair import repair_json
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from loguru import logger
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if TYPE_CHECKING:
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from app.question_gen.sampler_v2 import MaterialContext
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from app.tree.index import TreeIndex
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from core.protocols import VLMProvider
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# ---------------------------------------------------------------------------
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# 常量
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# ---------------------------------------------------------------------------
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_PROMPTS_DIR = Path(__file__).resolve().parent.parent.parent / "store" / "prompts" / "question_gen"
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_VALID_ANSWERS = frozenset({"A", "B", "C", "D"})
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_VALID_DIFFICULTIES = frozenset({"easy", "medium", "hard"})
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# ---------------------------------------------------------------------------
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# CandidateQuestion（规范定义位置）
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# ---------------------------------------------------------------------------
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@dataclass(frozen=True)
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class CandidateQuestion:
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"""候选题目 — 出题管线生成、待门控审核的题目数据。
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属性:
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question_id: 题目唯一标识（格式 "{video_id}_{task_type}_{seq:04d}"）。
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video_id: 所属视频标识。
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task_type: 题型（如 "Action Reasoning"）。
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skill_target: 目标失败机制编号（M1-M5）。
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question: 题目文本。
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options: 选项元组（如 ("A. ...", "B. ...", "C. ...", "D. ...")）。
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answer: 正确答案字母（如 "A"）。
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source_nodes: 来源节点 ID 元组。
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difficulty: 难度等级（easy/medium/hard）。
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subtitle_sentences: 验证材料 — 字幕句子元组。
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frame_paths: 验证材料 — 帧图片路径元组。
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"""
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question_id: str
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video_id: str
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task_type: str
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skill_target: str
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question: str
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options: tuple[str, ...]
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answer: str
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source_nodes: tuple[str, ...]
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difficulty: str
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subtitle_sentences: tuple[str, ...] = field(default_factory=tuple)
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frame_paths: tuple[str, ...] = field(default_factory=tuple)
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# ---------------------------------------------------------------------------
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# Prompt 模板加载
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# ---------------------------------------------------------------------------
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def _load_prompt_template(template_name: str) -> str:
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"""加载 prompt 模板文件。
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参数:
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template_name: store/prompts/question_gen/ 下的模板文件名。
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返回:
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模板内容字符串。
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异常:
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FileNotFoundError: 模板文件不存在。
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"""
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path = _PROMPTS_DIR / template_name
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if not path.exists():
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msg = f"Prompt 模板文件不存在: {path}"
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raise FileNotFoundError(msg)
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return path.read_text(encoding="utf-8")
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# ---------------------------------------------------------------------------
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# Prompt 构建
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# ---------------------------------------------------------------------------
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def _build_v2_prompt(
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prompt_template: str,
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strategy_name: str,
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material: MaterialContext,
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task_type: str,
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seq: int,
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*,
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reject_reason: str | None = None,
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sub_pattern_instruction: str | None = None,
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) -> tuple[list[dict[str, str]], list[str]]:
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"""构建 VLM 出题调用的 messages 和帧路径列表。
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参数:
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prompt_template: prompt 模板文件名（store/prompts/question_gen/ 下）。
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strategy_name: 策略名称（如 "RETRIEVAL"）。
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material: 采样素材上下文。
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task_type: 任务类型字符串。
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seq: 当前序号。
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reject_reason: 上一次被门控拒绝的原因（用于引导 VLM 避免相同错误）。
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sub_pattern_instruction: 子模式特殊指令（如有）。
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返回:
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二元组:
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- messages: 适配 VLMProvider 的 message 列表（system + user）。
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- frame_paths: 需发送给 VLM 的帧路径列表。
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"""
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# Phase 1: 加载策略模板作为 system prompt
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template_content = _load_prompt_template(prompt_template)
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system_message = template_content
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# Phase 2: 构建 user prompt — 聚合素材信息
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user_parts: list[str] = []
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user_parts.append(f"## Task Type: {task_type}")
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user_parts.append(f"## Question Family: {strategy_name}")
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user_parts.append(f"## Sequence: #{seq}")
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# 字幕素材
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if material.subtitle_sentences:
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user_parts.append("\n## Subtitle Content:")
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for i, sent in enumerate(material.subtitle_sentences, 1):
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user_parts.append(f" {i}. {sent}")
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# 跨 L2 上下文
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if material.cross_l2_texts:
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user_parts.append("\n## Cross-Segment Context:")
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for text in material.cross_l2_texts:
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user_parts.append(f" - {text}")
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# 帧路径提示（VLM 会接收实际图像，此处仅作文本参考）
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if material.frame_paths:
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user_parts.append(f"\n## Visual Frames: {len(material.frame_paths)} frames attached.")
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# 拒绝原因注入
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if reject_reason is not None:
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user_parts.append(
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f"\n## IMPORTANT - Previous Attempt Rejected:\n"
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f"Your previous question was rejected for the following reason:\n"
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f'"{reject_reason}"\n'
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f"Please generate a NEW question that avoids this issue."
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)
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# 子模式特殊指令注入
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if sub_pattern_instruction is not None:
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user_parts.append(f"\n## Special Focus:\n{sub_pattern_instruction}")
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# 输出格式指令
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user_parts.append(
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"\n## Output Format:\n"
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"Respond with ONLY a JSON object in this exact format:\n"
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"```json\n"
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"{\n"
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' "question": "Your question text here",\n'
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' "options": ["A. ...", "B. ...", "C. ...", "D. ..."],\n'
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' "answer": "A",\n'
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' "difficulty": "easy|medium|hard"\n'
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"}\n"
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"```"
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)
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user_content = "\n".join(user_parts)
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messages = [
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_content},
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]
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# Phase 3: 帧路径
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frame_paths = list(material.frame_paths)
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return messages, frame_paths
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# ---------------------------------------------------------------------------
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# 响应解析
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# ---------------------------------------------------------------------------
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def _extract_json_from_text(raw: str) -> str:
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"""从可能被 markdown 代码块包裹的文本中提取 JSON 部分。
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参数:
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raw: VLM 原始返回文本。
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返回:
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清理后的 JSON 字符串。
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"""
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content = raw.strip()
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if "```" in content:
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parts = content.split("```")
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for part in parts:
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stripped = part.strip()
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if stripped.startswith("json"):
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stripped = stripped[4:].strip()
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if stripped.startswith("{"):
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return stripped
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return content
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@dataclass(frozen=True)
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class _ValidatedFields:
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"""字段校验通过后的中间结构。"""
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question: str
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options: tuple[str, ...]
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answer: str
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difficulty: str
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def _validate_parsed_fields(data: dict) -> _ValidatedFields:
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"""校验 VLM 响应 JSON 的必填字段并规范化。
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参数:
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data: 已解析的 JSON 字典。
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返回:
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_ValidatedFields 实例（字段已规范化）。
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异常:
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ValueError: 缺少必填字段或字段值非法。
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"""
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missing = [f for f in ("question", "options", "answer", "difficulty") if f not in data]
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if missing:
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msg = f"VLM 响应缺少必填字段: {', '.join(missing)}"
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raise ValueError(msg)
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question_text = str(data["question"])
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options_raw = data["options"]
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answer = str(data["answer"]).strip().upper()
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difficulty = str(data["difficulty"]).strip().lower()
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# 校验 options（answer 约束为 A-D，因此必须恰好 4 个选项）
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if not isinstance(options_raw, list) or len(options_raw) != 4:
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msg = f"options 字段必须恰好包含 4 个选项，实际数量: {len(options_raw) if isinstance(options_raw, list) else type(options_raw).__name__}"
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raise ValueError(msg)
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# 校验 answer
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if answer not in _VALID_ANSWERS:
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msg = f"answer 字段值 '{answer}' 非法，必须为 A/B/C/D 之一"
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raise ValueError(msg)
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# 校验 difficulty（宽容处理：非法值回退为 medium）
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if difficulty not in _VALID_DIFFICULTIES:
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logger.warning(
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"difficulty '{}' 不在预设范围 {}，回退为 'medium'",
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difficulty,
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_VALID_DIFFICULTIES,
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)
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difficulty = "medium"
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return _ValidatedFields(
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question=question_text,
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options=tuple(str(o) for o in options_raw),
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answer=answer,
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difficulty=difficulty,
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)
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def _parse_v2_response(
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raw: str,
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video_id: str,
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task_type: str,
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skill_target: str,
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seq: int,
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source_nodes: tuple[str, ...],
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) -> CandidateQuestion:
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"""解析 VLM 返回的 JSON 响应，构造 CandidateQuestion。
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流程：
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1. 提取 JSON（处理 markdown 包裹）。
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2. json_repair 修复常见格式错误。
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3. 校验必填字段（委托 _validate_parsed_fields）。
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4. 构造 CandidateQuestion 实例。
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参数:
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raw: VLM 原始返回文本。
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video_id: 视频标识。
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task_type: 任务类型。
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skill_target: 目标技能编号。
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seq: 当前序号。
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source_nodes: 来源节点 ID 元组。
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返回:
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CandidateQuestion 实例。
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异常:
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ValueError: JSON 解析失败或缺少必填字段或字段值非法。
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"""
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# Phase 1: 提取 + 修复 JSON
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json_text = _extract_json_from_text(raw)
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repaired = repair_json(json_text, return_objects=False)
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# Phase 2: 解析为字典
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try:
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data = json.loads(repaired)
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except json.JSONDecodeError as e:
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msg = f"VLM 响应 JSON 解析失败: {e}. 原始文本: {raw[:200]}"
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raise ValueError(msg) from e
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if not isinstance(data, dict):
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msg = f"VLM 响应顶层不是 JSON 对象: type={type(data).__name__}"
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raise ValueError(msg)
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# Phase 3: 字段校验
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fields = _validate_parsed_fields(data)
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# Phase 4: 构造 CandidateQuestion
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question_id = f"{video_id}_{task_type}_{seq:04d}"
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return CandidateQuestion(
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question_id=question_id,
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video_id=video_id,
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task_type=task_type,
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skill_target=skill_target,
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question=fields.question,
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options=fields.options,
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answer=fields.answer,
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source_nodes=source_nodes,
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difficulty=fields.difficulty,
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)
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# ---------------------------------------------------------------------------
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# 主入口
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# ---------------------------------------------------------------------------
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async def generate_one_v2(
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vlm: VLMProvider,
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tree: TreeIndex,
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material: MaterialContext,
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task_type: str,
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seq: int,
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*,
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video_id: str,
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prompt_template: str,
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strategy_name: str,
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skill_target: str,
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reject_reason: str | None = None,
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sub_pattern_instruction: str | None = None,
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session_id: str,
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) -> CandidateQuestion:
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"""调用 VLM 生成一道候选题目。
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流程：
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1. 构建 per-strategy prompt + 帧路径。
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2. 调用 VLMProvider.chat_with_images。
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3. 解析响应为 CandidateQuestion。
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4. 附加素材验证信息（subtitle_sentences、frame_paths）。
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参数:
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vlm: VLM 调用端口。
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tree: 视频树索引（当前未直接使用，预留后续扩展）。
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material: 采样素材上下文。
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task_type: 任务类型字符串。
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seq: 当前序号。
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video_id: 视频标识。
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prompt_template: prompt 模板文件名。
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strategy_name: 策略名称（如 "RETRIEVAL"）。
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skill_target: 目标失败机制编号（M1-M5）。
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reject_reason: 上一次被门控拒绝的原因。
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sub_pattern_instruction: 子模式特殊指令（如有）。
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session_id: 会话 ID（遥测关联）。
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返回:
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CandidateQuestion 实例（包含验证材料）。
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异常:
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ValueError: VLM 响应解析失败。
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FileNotFoundError: prompt 模板不存在。
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"""
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# Phase 1: 构建 prompt
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messages, frame_paths = _build_v2_prompt(
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prompt_template=prompt_template,
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strategy_name=strategy_name,
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material=material,
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task_type=task_type,
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seq=seq,
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reject_reason=reject_reason,
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sub_pattern_instruction=sub_pattern_instruction,
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)
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# Phase 2: 调用 VLM
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logger.debug(
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"generate_one_v2: strategy={}, task_type={}, seq={}, frames={}",
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strategy_name,
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task_type,
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seq,
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len(frame_paths),
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)
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response = await vlm.chat_with_images(
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messages,
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frame_paths,
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session_id=session_id,
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)
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# Phase 3: 解析响应
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candidate = _parse_v2_response(
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raw=response.content,
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video_id=video_id,
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task_type=task_type,
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skill_target=skill_target,
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seq=seq,
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source_nodes=material.source_nodes,
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)
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# Phase 4: 附加验证材料（构造新实例，因 frozen=True）
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candidate = CandidateQuestion(
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question_id=candidate.question_id,
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video_id=candidate.video_id,
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task_type=candidate.task_type,
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skill_target=candidate.skill_target,
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question=candidate.question,
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options=candidate.options,
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answer=candidate.answer,
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source_nodes=candidate.source_nodes,
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difficulty=candidate.difficulty,
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subtitle_sentences=tuple(material.subtitle_sentences),
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frame_paths=tuple(material.frame_paths),
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)
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logger.debug(
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"generate_one_v2 完成: question_id={}, difficulty={}",
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candidate.question_id,
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candidate.difficulty,
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)
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return candidate
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