第一次提交

2026-08-01 16:50:55 +08:00
commit c8f9e39039
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+"""AI 任务：判分、讲解、知识点总结。统一记录到 ai_generations。"""
+import json
+import logging
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.practice import AiGeneration
+from app.models.question import Question
+from app.models.user import UserSettings
+from app.services.llm.client import LLMClient
+from app.services.llm.prompts import (
+    EXPLAIN_SYSTEM,
+    EXPLAIN_USER_TEMPLATE,
+    JUDGE_SYSTEM,
+    JUDGE_USER_TEMPLATE,
+    TAGS_SYSTEM,
+    TAGS_USER_TEMPLATE,
+)
+
+logger = logging.getLogger(__name__)
+
+
+async def load_llm(db: AsyncSession, user_id: str) -> LLMClient:
+    """加载用户的 LLMClient（未配置会抛 LLMConfigError）。"""
+    row = await db.scalar(select(UserSettings).where(UserSettings.user_id == user_id))
+    return LLMClient(row)
+
+
+def _parse_json(raw: str) -> dict:
+    text = raw.strip()
+    if text.startswith("```"):
+        text = text.split("```", 2)[1]
+        if text.startswith("json"):
+            text = text[4:]
+    return json.loads(text.strip())
+
+
+async def _record(
+    db: AsyncSession, user_id: str, question_id: str | None, task: str, result, content: str
+):
+    db.add(
+        AiGeneration(
+            user_id=user_id,
+            question_id=question_id,
+            task=task,
+            model=result.model,
+            request_tokens=result.prompt_tokens,
+            response_tokens=result.completion_tokens,
+            content_markdown=content,
+        )
+    )
+
+
+async def judge_answer(
+    db: AsyncSession, llm: LLMClient, user_id: str, q: Question, user_answer: list[str]
+) -> dict:
+    """主观题判分。返回 {is_correct, feedback_markdown, key_points_missed}。"""
+    answer = "、".join(q.correct_answer) if q.correct_answer else "（未提供标准答案）"
+    result = await llm.chat_text(
+        JUDGE_SYSTEM,
+        JUDGE_USER_TEMPLATE.format(
+            stem=q.stem_markdown, answer=answer, user_answer="\n".join(user_answer)
+        ),
+        json_mode=True,
+    )
+    await _record(db, user_id, q.id, "judge", result, result.content)
+    try:
+        data = _parse_json(result.content)
+        return {
+            "is_correct": bool(data.get("is_correct")),
+            "feedback_markdown": data.get("feedback_markdown", ""),
+            "key_points_missed": data.get("key_points_missed", []),
+        }
+    except (json.JSONDecodeError, KeyError) as e:
+        logger.warning("judge 解析失败：%s", e)
+        return {
+            "is_correct": False,
+            "feedback_markdown": result.content,
+            "key_points_missed": [],
+        }
+
+
+async def explain_question(
+    db: AsyncSession, llm: LLMClient, user_id: str, q: Question
+) -> str:
+    answer_hint = ""
+    if q.correct_answer:
+        answer_hint = f"参考答案：{'、'.join(q.correct_answer)}\n\n"
+    result = await llm.chat_text(
+        EXPLAIN_SYSTEM,
+        EXPLAIN_USER_TEMPLATE.format(stem=q.stem_markdown, answer_hint=answer_hint),
+    )
+    await _record(db, user_id, q.id, "explain", result, result.content)
+    return result.content
+
+
+async def summarize_tags(
+    db: AsyncSession, llm: LLMClient, user_id: str, q: Question
+) -> list[str]:
+    result = await llm.chat_text(
+        TAGS_SYSTEM, TAGS_USER_TEMPLATE.format(stem=q.stem_markdown), json_mode=True
+    )
+    await _record(db, user_id, q.id, "summarize", result, result.content)
+    try:
+        data = _parse_json(result.content)
+        tags = data.get("tags", [])
+        return [str(t).strip() for t in tags if str(t).strip()][:4]
+    except (json.JSONDecodeError, KeyError) as e:
+        logger.warning("tags 解析失败：%s", e)
+        return []