dekun
309 lines
11 KiB
Python
309 lines
11 KiB
Python
import httpx
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from sqlalchemy.orm import Session
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from app.core.config import settings as app_settings
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from app.models.user import SchoolLevel, SystemSettings
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from app.services.school_level import school_level_label
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from app.services.url_sanitize import sanitize_http_url, sanitize_model_name
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CURRICULUM_JUNIOR = """初中课程标准：代数、几何（全等/相似/勾股）、一次函数与简单二次函数、基础概率统计。
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严禁使用：高中导数、向量、解析几何、排列组合进阶、复数、微积分、大学线性代数等。"""
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CURRICULUM_SENIOR = """高中课程标准：课内函数、三角、向量、解析几何、概率统计、导数（课内范围）等。
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严禁使用：大学数学分析、抽象代数、高等几何、超出课内的竞赛高阶技巧。"""
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CURRICULUM_JUNIOR_OLYMPIAD = """初中奥数培优范围：整数/整除、因数分解、简单数论、代数恒等变形、几何辅助线与全等相似、简单组合计数。
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严禁使用：高中及以上方法（导数、向量、解析几何、微积分、复数运算等）。"""
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CURRICULUM_SENIOR_OLYMPIAD = """高中奥数/竞赛入门范围：课内知识+常规竞赛技巧（不等式、构造、归纳、简单数论等）。
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严禁使用：大学数学、超出高中奥数培优体系的 IMO 高阶理论。"""
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def _curriculum_block(level: SchoolLevel | str | None, olympiad: bool) -> str:
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label = school_level_label(level)
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is_senior = level == SchoolLevel.senior_high or level == "senior_high"
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if olympiad:
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return CURRICULUM_SENIOR_OLYMPIAD if is_senior else CURRICULUM_JUNIOR_OLYMPIAD
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return CURRICULUM_SENIOR if is_senior else CURRICULUM_JUNIOR
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QUESTION_PROMPT = """你是一位{stage}老师。以下是从试卷 OCR 识别出的文字，可能含有噪声。
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科目：{subject}
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请整理出清晰的题目内容（保留题号、选项、公式），只输出题目正文，不要解释。
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OCR 原文：
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{ocr_text}
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"""
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SOLUTION_PROMPT = """你是一位耐心的{stage}{subject}老师。请像「作业帮」一样，先讲清楚解题思路，再给出完整解答。
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【学段要求 — 严禁超纲】
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{curriculum}
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题目：
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{question_text}
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请严格按以下 Markdown 结构输出：
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## 解题思路
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（2-5 句话：这题考什么、从哪里入手、关键一步是什么，让学生先懂「怎么想」）
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## 详细解答
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（分步骤完整推导，每步说明依据）
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## 易错点
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（指出常见错误及正确做法）
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严禁使用超纲方法；若原题超纲，请给出{stage}课内可理解的解法。
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"""
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OLYMPIAD_SOLUTION_PROMPT = """你是一位{stage}奥数教练。请像优秀辅导老师一样，先讲解题思路，再完整解答。
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【奥数学段要求 — 严禁超纲】
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{curriculum}
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题目：
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{question_text}
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请严格按以下 Markdown 结构输出：
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## 解题思路
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（点明题型、突破口、{stage}奥数常用技巧）
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## 详细解答
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（完整步骤）
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## 关键技巧
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（总结，仅限{stage}奥数范围）
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严禁超纲；过难题给出{stage}可接受的培优思路。
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"""
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ERROR_DETECT_PROMPT = """你是{stage}{subject}老师。以下是试卷/作业 OCR 识别结果，每行前有编号。
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请找出「学生答错的部分」：错误答案、被打叉的作答、明显不正确的计算结果等。
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{numbered_lines}
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只输出 JSON，不要其他文字：
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{{"wrong_line_ids": [行编号整数列表]}}
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若整张图就是一道错题，请标注含有错误答案或作答的行；找不到则标注最后作答行。
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"""
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REVIEW_INSIGHT_PROMPT = """你是一位{stage}{subject}学习顾问。请仅根据下方「复盘数据」做分析，不得编造未出现的考试或状态。
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【学段】{stage}
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【科目】{subject}（所有建议必须贴合本科目，禁止套用其他科目的说法）
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【复盘数据】
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{review_records}
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【状态含义（结合本科目理解）】
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- 粗心：{careless_hint}
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- 不会：该科知识点/题型尚未掌握
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- 紧张：心态影响，发挥低于平时水平
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- 正常发挥：状态稳定
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【科目建议方向】
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{subject_hints}
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【必须遵守】
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1. 解读时必须写清具体考试日期（如 2026-06-21），按时间从早到晚分析，不得把「第1次」说成最近一场
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2. 得分率 = 得分÷总分；95% 以上才可称「接近满分」，85% 左右应如实描述为「良好但仍有失分空间」，禁止夸大
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3. 改进建议必须针对 {subject}，禁止出现与本科目无关的表述（如英语科禁止写「计算验算」）
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4. 只分析数据中列出的复盘状态，不要臆测未勾选的原因
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请用 Markdown 输出：
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## 情况解读
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（2-4 句：按时间顺序说明每次考试得分率、失分与复盘状态的关系，以及是否有改善或反复）
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## 改进建议
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（3-5 条，针对出现最多的问题状态，具体可操作，仅限 {stage}{subject} 范围）
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## 近期重点
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（1-2 条本周可落实的小目标）
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语气务实，不要空泛鸡汤。
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"""
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SUBJECT_REVIEW_HINTS: dict[str, dict[str, str]] = {
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"语文": {
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"careless": "看错题干、漏读要求、作文偏题或漏写要点",
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"hints": "阅读审题、文言文/语言运用、作文结构与素材积累",
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},
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"数学": {
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"careless": "审题不清、计算或抄错、步骤跳步",
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"hints": "错题归类、计算验算、典型题型归纳与限时练习",
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},
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"英语": {
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"careless": "看错词义/时态、漏读题干、拼写与语法笔误",
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"hints": "词汇语法、阅读完形、听力与写作模板，禁止建议计算类训练",
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},
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"物理": {
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"careless": "审题漏条件、公式代错、单位换算失误",
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"hints": "概念理解、建模分析、实验题与计算规范",
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},
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"化学": {
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"careless": "方程式配平/条件遗漏、计算失误",
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"hints": "方程式、物质性质、实验与推断题",
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},
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"生物": {
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"careless": "概念混淆、漏答得分点",
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"hints": "教材概念、图表分析、实验设计表述",
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},
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"历史": {
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"careless": "材料题漏读、时间/人物混淆",
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"hints": "时间线、材料分析、论述题答题模板",
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},
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"地理": {
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"careless": "读图漏信息、术语使用不当",
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"hints": "地图判读、区域分析、综合题答题条理",
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},
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"政治": {
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"careless": "漏答采分点、概念表述不准",
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"hints": "时政结合、材料分析、观点表述规范",
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},
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}
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def _subject_review_hints(subject: str) -> tuple[str, str]:
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block = SUBJECT_REVIEW_HINTS.get(subject)
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if block:
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return block["careless"], block["hints"]
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return "审题或作答细节失误", f"针对{subject}常见失分点制定练习与错题巩固"
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class AIConfig:
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def __init__(
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self,
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provider: str,
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ollama_base_url: str,
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ollama_model: str,
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openai_base_url: str,
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openai_model: str,
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openai_api_key: str | None,
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):
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self.provider = provider
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self.ollama_base_url = ollama_base_url
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self.ollama_model = ollama_model
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self.openai_base_url = openai_base_url
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self.openai_model = openai_model
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self.openai_api_key = openai_api_key
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def load_ai_config(db: Session) -> AIConfig:
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row = db.get(SystemSettings, 1)
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if row is None:
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return AIConfig(
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provider="ollama",
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ollama_base_url=sanitize_http_url(app_settings.OLLAMA_BASE_URL),
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ollama_model=sanitize_model_name(app_settings.OLLAMA_MODEL),
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openai_base_url=sanitize_http_url(app_settings.OPENAI_BASE_URL),
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openai_model=sanitize_model_name(app_settings.OPENAI_MODEL),
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openai_api_key=None,
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)
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return AIConfig(
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provider=row.ai_provider or "ollama",
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ollama_base_url=sanitize_http_url(row.ollama_base_url or app_settings.OLLAMA_BASE_URL),
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ollama_model=sanitize_model_name(row.ollama_model or app_settings.OLLAMA_MODEL),
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openai_base_url=sanitize_http_url(row.openai_base_url or app_settings.OPENAI_BASE_URL),
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openai_model=sanitize_model_name(row.openai_model or app_settings.OPENAI_MODEL),
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openai_api_key=row.openai_api_key,
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)
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async def _ollama_generate(prompt: str, cfg: AIConfig, *, temperature: float = 0.3) -> str:
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url = f"{cfg.ollama_base_url.rstrip('/')}/api/generate"
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payload = {
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"model": cfg.ollama_model,
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": temperature},
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}
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async with httpx.AsyncClient(timeout=180.0) as client:
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response = await client.post(url, json=payload)
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response.raise_for_status()
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return (response.json().get("response") or "").strip()
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async def _openai_generate(prompt: str, cfg: AIConfig, *, temperature: float = 0.3) -> str:
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if not cfg.openai_api_key:
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raise ValueError("未配置 OpenAI API Key")
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url = f"{cfg.openai_base_url.rstrip('/')}/chat/completions"
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headers = {"Authorization": f"Bearer {cfg.openai_api_key}"}
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payload = {
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"model": cfg.openai_model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": temperature,
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}
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async with httpx.AsyncClient(timeout=180.0) as client:
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response = await client.post(url, json=payload, headers=headers)
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response.raise_for_status()
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data = response.json()
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return (data["choices"][0]["message"]["content"] or "").strip()
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async def generate_text(prompt: str, cfg: AIConfig, *, temperature: float = 0.3) -> str:
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if cfg.provider == "openai":
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return await _openai_generate(prompt, cfg, temperature=temperature)
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return await _ollama_generate(prompt, cfg, temperature=temperature)
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async def format_question(
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cfg: AIConfig,
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subject: str,
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ocr_text: str,
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school_level=None,
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) -> str:
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stage = school_level_label(school_level)
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prompt = QUESTION_PROMPT.format(stage=stage, subject=subject, ocr_text=ocr_text)
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return await generate_text(prompt, cfg)
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async def generate_solution(
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cfg: AIConfig,
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subject: str,
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question_text: str,
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school_level=None,
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*,
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olympiad: bool = False,
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) -> str:
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stage = school_level_label(school_level)
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curriculum = _curriculum_block(school_level, olympiad)
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template = OLYMPIAD_SOLUTION_PROMPT if olympiad else SOLUTION_PROMPT
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prompt = template.format(
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stage=stage,
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subject=subject,
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curriculum=curriculum,
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question_text=question_text,
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)
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return await generate_text(prompt, cfg)
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async def detect_wrong_line_ids(
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cfg: AIConfig,
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subject: str,
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ocr_lines: list[dict],
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school_level=None,
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) -> str:
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stage = school_level_label(school_level)
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numbered = "\n".join(f"[{i}] {line.get('text', '')}" for i, line in enumerate(ocr_lines))
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prompt = ERROR_DETECT_PROMPT.format(stage=stage, subject=subject, numbered_lines=numbered)
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return await generate_text(prompt, cfg)
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async def generate_review_insight(
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cfg: AIConfig,
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subject: str,
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review_records: str,
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school_level=None,
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) -> str:
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stage = school_level_label(school_level)
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careless_hint, subject_hints = _subject_review_hints(subject)
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prompt = REVIEW_INSIGHT_PROMPT.format(
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stage=stage,
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subject=subject,
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review_records=review_records,
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careless_hint=careless_hint,
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subject_hints=subject_hints,
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)
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return await generate_text(prompt, cfg, temperature=0.2) |