112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
"""AI 任务：判分、讲解、知识点总结。统一记录到 ai_generations。"""
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import json
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import logging
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.practice import AiGeneration
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from app.models.question import Question
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from app.models.user import UserSettings
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from app.services.llm.client import LLMClient
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from app.services.llm.prompts import (
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EXPLAIN_SYSTEM,
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EXPLAIN_USER_TEMPLATE,
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JUDGE_SYSTEM,
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JUDGE_USER_TEMPLATE,
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TAGS_SYSTEM,
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TAGS_USER_TEMPLATE,
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)
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logger = logging.getLogger(__name__)
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async def load_llm(db: AsyncSession, user_id: str) -> LLMClient:
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"""加载用户的 LLMClient（未配置会抛 LLMConfigError）。"""
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row = await db.scalar(select(UserSettings).where(UserSettings.user_id == user_id))
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return LLMClient(row)
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def _parse_json(raw: str) -> dict:
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text = raw.strip()
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if text.startswith("```"):
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text = text.split("```", 2)[1]
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if text.startswith("json"):
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text = text[4:]
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return json.loads(text.strip())
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async def _record(
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db: AsyncSession, user_id: str, question_id: str | None, task: str, result, content: str
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):
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db.add(
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AiGeneration(
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user_id=user_id,
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question_id=question_id,
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task=task,
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model=result.model,
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request_tokens=result.prompt_tokens,
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response_tokens=result.completion_tokens,
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content_markdown=content,
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)
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)
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async def judge_answer(
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db: AsyncSession, llm: LLMClient, user_id: str, q: Question, user_answer: list[str]
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) -> dict:
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"""主观题判分。返回 {is_correct, feedback_markdown, key_points_missed}。"""
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answer = "、".join(q.correct_answer) if q.correct_answer else "（未提供标准答案）"
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result = await llm.chat_text(
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JUDGE_SYSTEM,
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JUDGE_USER_TEMPLATE.format(
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stem=q.stem_markdown, answer=answer, user_answer="\n".join(user_answer)
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),
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json_mode=True,
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)
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await _record(db, user_id, q.id, "judge", result, result.content)
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try:
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data = _parse_json(result.content)
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return {
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"is_correct": bool(data.get("is_correct")),
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"feedback_markdown": data.get("feedback_markdown", ""),
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"key_points_missed": data.get("key_points_missed", []),
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}
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except (json.JSONDecodeError, KeyError) as e:
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logger.warning("judge 解析失败：%s", e)
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return {
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"is_correct": False,
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"feedback_markdown": result.content,
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"key_points_missed": [],
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}
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async def explain_question(
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db: AsyncSession, llm: LLMClient, user_id: str, q: Question
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) -> str:
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answer_hint = ""
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if q.correct_answer:
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answer_hint = f"参考答案：{'、'.join(q.correct_answer)}\n\n"
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result = await llm.chat_text(
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EXPLAIN_SYSTEM,
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EXPLAIN_USER_TEMPLATE.format(stem=q.stem_markdown, answer_hint=answer_hint),
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)
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await _record(db, user_id, q.id, "explain", result, result.content)
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return result.content
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async def summarize_tags(
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db: AsyncSession, llm: LLMClient, user_id: str, q: Question
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) -> list[str]:
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result = await llm.chat_text(
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TAGS_SYSTEM, TAGS_USER_TEMPLATE.format(stem=q.stem_markdown), json_mode=True
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)
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await _record(db, user_id, q.id, "summarize", result, result.content)
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try:
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data = _parse_json(result.content)
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tags = data.get("tags", [])
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return [str(t).strip() for t in tags if str(t).strip()][:4]
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except (json.JSONDecodeError, KeyError) as e:
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logger.warning("tags 解析失败：%s", e)
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return []
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