321 lines
12 KiB
Python
321 lines
12 KiB
Python
"""按教材章节调用大模型生成客观题，并做结构化校验后入库。"""
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from __future__ import annotations
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from models import Chapter, Knowledge, LlmSetting, Question, Textbook
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from services import llm_client
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SYSTEM_PROMPT = (
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"你是一位经验丰富的高中数学教师和命题人，擅长编写规范、严谨、"
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"区分度良好的单项选择题。所有题面必须自洽且答案唯一。"
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)
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def build_user_prompt(
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*,
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textbook_name: str,
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chapter_name: str,
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chapter_summary: str,
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count: int,
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difficulty: int,
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knowledge_name: str,
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instructions: str,
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available_knowledge: list[str],
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need_figure: bool = False,
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) -> str:
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difficulty_text = {1: "基础识记与直接计算", 2: "概念理解与中等计算", 3: "综合推理与易错辨析"}[
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difficulty
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]
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knowledge_hint = (
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f"知识点限定为“{knowledge_name}”。"
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if knowledge_name
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else "知识点由你依据章节内容选择，尽量使用下面给出的知识点名称；"
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"若下面列表没有合适名称，可自拟简洁的中文知识点名。"
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)
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extra = f"额外要求：{instructions}" if instructions else ""
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figure_requirement = (
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(
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"配图要求：每题必须在 image_svg 字段中给出与题意对应的原创 SVG 图像"
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"（只画该题需要的函数图像/几何示意图，含坐标轴与必要标注），"
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"SVG 用单行字符串输出（内部换行用 \\n），不要用 Markdown 图片或外链；"
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"题干中不要再重复插入任何图片标记。"
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)
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if need_figure
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else "配图要求：默认不配图，image_svg 字段填空字符串 \"\"。"
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)
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figure_hint = f"\n{figure_requirement}\n" if need_figure else f"\n{figure_requirement}"
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return f"""请围绕以下教材章节命制 {count} 道高中数学单选题。
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教材：{textbook_name}
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章节：{chapter_name}
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章节内容提示：{chapter_summary or "请结合章节标题自行判断"}
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难度：{difficulty_text}
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{knowledge_hint}
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本系统已有知识点：{", ".join(available_knowledge) or "（暂无）"}
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{extra}
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{figure_hint}
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硬性要求：
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1. 每题必须恰好 4 个选项，且只有一个正确选项；
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2. 不使用“以上都对”“以上都错”这类选项；
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3. 涉及数学符号、公式时必须用 LaTeX：行内公式写 $...$，独立公式写 $$...$$，
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不要使用 x²、x³、√、¼ 这类 Unicode 记号，例如 $x^2$、$\\sqrt{{x+1}}$、
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$\\frac{{1}}{{x^2-9}}$、$f(-x)=-f(x)$；
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4. 图像只通过 JSON 的 image_svg 字段提供：配图要求开启时，每题必须有与题目
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语义一致的原创 SVG 图（函数图或几何示意图，含坐标轴/关键标注），
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题干中不得再写任何 Markdown 图片；未开启配图时不要输出 image_svg；
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5. explanation 用 1-2 句中文解释关键思路，便于学生理解，含公式时同样用 LaTeX；
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6. 不要输出多余文字，只输出 JSON：
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{{"questions":[
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{{"stem":"题干","options":["A选项","B选项","C选项","D选项"],
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"correct_index":0,"explanation":"解析","knowledge_name":"知识点","difficulty":{difficulty},
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"image_svg":"<svg xmlns=\\"http://www.w3.org/2000/svg\\" ...></svg> 或空字符串"}}
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]}}"""
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def _extract_json(content: str) -> dict:
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from services.json_utils import extract_json_lax
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return extract_json_lax(content)
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def _normalize_item(raw: dict, fallback_knowledge: str, difficulty: int) -> dict:
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stem = str(raw.get("stem", "")).strip()
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options = raw.get("options") or []
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options = [str(option).strip() for option in options]
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explanation = str(raw.get("explanation", "")).strip()
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knowledge_name = str(raw.get("knowledge_name", "")).strip() or fallback_knowledge
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image_svg = str(raw.get("image_svg") or "").strip()
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# 部分兼容网关会对 JSON 内的 SVG 引号/换行做双重转义
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image_svg = image_svg.replace("\\\\", "\\")
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image_svg = (
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image_svg.replace('\\"', '"')
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.replace("\\n", "\n")
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.replace("\\t", "\t")
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.replace("\\r", "\r")
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)
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if not stem:
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raise ValueError("模型返回的题干为空")
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if len(options) != 4 or any(not option for option in options):
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raise ValueError(f"选项必须是 4 个非空选项，实际为 {len(options)} 个")
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if len(set(options)) != 4:
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raise ValueError("选项存在重复")
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try:
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correct_index = int(raw.get("correct_index", -1))
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except (TypeError, ValueError) as exc:
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raise ValueError("correct_index 必须是数字") from exc
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if correct_index < 0 or correct_index >= 4:
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raise ValueError("correct_index 超出选项范围")
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return {
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"stem": stem,
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"options": options,
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"correct_index": correct_index,
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"explanation": explanation or "请依据本章定义与性质重新判断。",
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"knowledge_name": knowledge_name,
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"difficulty": max(1, min(3, difficulty)),
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"image_svg": image_svg,
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}
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def generate_questions(
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*,
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db,
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setting: LlmSetting,
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chapter: Chapter,
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textbook: Textbook,
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count: int,
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difficulty: int,
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knowledge_name: str,
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instructions: str,
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need_figure: bool = False,
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) -> list[dict]:
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if not setting or not setting.api_key:
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raise ValueError("尚未配置大模型 API Key，请先到“模型配置”页面保存并测试")
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# 带图模式先按无图生成文本题目，再逐题补 SVG，避免长 JSON 超时/截断
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if need_figure:
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text_items = generate_questions(
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db=db,
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setting=setting,
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chapter=chapter,
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textbook=textbook,
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count=count,
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difficulty=difficulty,
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knowledge_name=knowledge_name,
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instructions=instructions,
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need_figure=False,
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)
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for item in text_items:
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item["image_svg"] = _request_figure_svg(setting, item["stem"])
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return text_items
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knowledge_names = {name for (name,) in db.query(Knowledge.name).all()}
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knowledge_names.update(
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name for (name,) in db.query(Question.knowledge_name).all()
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)
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available_knowledge = sorted(knowledge_names)
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prompt = build_user_prompt(
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textbook_name=textbook.name,
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chapter_name=chapter.name,
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chapter_summary=chapter.summary,
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count=count,
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difficulty=difficulty,
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knowledge_name=knowledge_name,
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instructions=instructions,
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available_knowledge=available_knowledge,
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need_figure=need_figure,
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)
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payload = {}
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last_error = ""
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for attempt in range(3):
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try:
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content = llm_client.chat_completion(
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base_url=setting.base_url,
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api_key=setting.api_key,
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model=setting.model,
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temperature=setting.temperature,
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max_tokens=_effective_max_tokens(setting, need_figure),
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timeout=200.0,
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messages=[
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{
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"role": "system",
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"content": (
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SYSTEM_PROMPT
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+ " JSON 必须完整且闭合：字符串中的双引号写 \\\"，"
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"反斜杠写 \\\\，不要输出多余字符。"
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),
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},
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{"role": "user", "content": prompt},
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],
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)
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except ValueError as exc:
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last_error = str(exc)
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continue
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try:
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payload = _extract_json(content)
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break
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except ValueError as exc:
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last_error = str(exc)
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else:
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raise ValueError(
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f"模型连续 3 次未返回合法 JSON，最后一次：{last_error}"
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)
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raw_items = payload.get("questions")
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if not isinstance(raw_items, list) or not raw_items:
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raise ValueError("模型返回内容中没有 questions 列表")
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generated: list[dict] = []
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for raw in raw_items[:count]:
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item = _normalize_item(raw, knowledge_name or "章节综合", difficulty)
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if need_figure and (
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"<svg" not in item["image_svg"].lower()
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or "</svg>" not in item["image_svg"].lower()
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):
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# 补图：只让模型针对该题生成一张 SVG，缩短输出降低截断概率
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for _ in range(3):
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svg_content = llm_client.chat_completion(
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base_url=setting.base_url,
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api_key=setting.api_key,
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model=setting.model,
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temperature=0.1,
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max_tokens=max(
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int(setting.max_tokens)
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if setting.max_tokens
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else 3000,
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1200,
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),
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timeout=120.0,
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messages=[
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{
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"role": "system",
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"content": "只输出 JSON：{\"image_svg\":\"<svg ...>\"}",
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},
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{
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"role": "user",
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"content": (
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f"请为下列题干生成与题意一致的原创 SVG 示意图：\n{item['stem']}"
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),
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},
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],
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)
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try:
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svg_payload = _extract_json(svg_content)
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svg_value = str(svg_payload.get("image_svg") or "")
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svg_value = svg_value.replace("\\\\", "\\")
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svg_value = (
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svg_value.replace('\\"', '"')
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.replace("\\n", "\n")
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.replace("\\t", "\t")
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.replace("\\r", "\r")
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)
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if (
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"<svg" in svg_value.lower()
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and "</svg>" in svg_value.lower()
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):
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item["image_svg"] = svg_value
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break
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except ValueError:
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continue
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if (
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"<svg" not in item["image_svg"].lower()
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or "</svg>" not in item["image_svg"].lower()
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):
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raise ValueError(
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"模型未能生成本题有效配图，请尝试减少题量或更换模型通道"
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)
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generated.append(item)
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return generated
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def _request_figure_svg(setting: LlmSetting, stem: str) -> str:
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"""针对单一题干请求一张尽量简单的 SVG 配图。"""
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max_tokens = int(setting.max_tokens) if setting.max_tokens else 1600
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for _ in range(3):
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try:
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content = llm_client.chat_completion(
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base_url=setting.base_url,
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api_key=setting.api_key,
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model=setting.model,
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temperature=0.1,
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max_tokens=max_tokens,
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timeout=300.0,
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messages=[
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{
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"role": "system",
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"content": (
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"你是数学示意图生成器。只输出 JSON："
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'{"image_svg":"<svg ...></svg>"}。SVG 必须非常简洁，'
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"建议坐标轴+图形+少量标注，控制在 100 行内。"
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),
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},
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{
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"role": "user",
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"content": f"请为以下题干生成与题意一致的示意图：\n{stem}",
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},
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],
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)
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except ValueError:
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continue
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try:
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payload = _extract_json(content)
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svg = str(payload.get("image_svg") or "")
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svg = svg.replace("\\\\", "\\")
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svg = (
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svg.replace('\\"', '"')
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.replace("\\n", "\n")
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.replace("\\t", "\t")
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.replace("\\r", "\r")
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)
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if "<svg" in svg.lower() and "</svg>" in svg.lower():
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return svg
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except ValueError:
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continue
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raise ValueError("模型未能为本题生成有效 SVG，请更换模型通道或关闭配图")
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def _effective_max_tokens(setting: LlmSetting, need_figure: bool) -> int:
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if getattr(setting, "max_tokens", None):
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return int(setting.max_tokens)
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return 3000 if need_figure else 1600
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