Add memory coach, transformer recall model, and training FAB.
Introduce Q/K/V memory dialogue with coach APIs, a lightweight NumPy transformer for per-word forgetting prediction, and a floating training menu linking daily quiz, spell, and coach flows. Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
John
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import re
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from datetime import datetime, timezone
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from typing import Optional
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from sqlalchemy.orm import Session
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from models import QuizRecord, User, Word
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from schemas import CoachSessionResponse, CoachTurnResponse, CoachWordBrief, MemoryToken
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from services.memory_visual_service import (
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memory_visual_service,
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parse_iso,
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retention_percent,
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stability_hours,
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word_en,
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word_zh,
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)
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from services.quiz_service import quiz_service
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from services.word_service import word_service
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def _mask_zh(zh: str) -> str:
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zh = zh.strip()
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if len(zh) <= 1:
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return zh
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return zh[0] + "※" * (len(zh) - 1)
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def _blank_example(example: str, en: str) -> str:
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if not example:
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return ""
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pattern = re.compile(re.escape(en), re.IGNORECASE)
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return pattern.sub("______", example, count=1)
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def _retention_now(word: Word) -> float:
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now = datetime.now(timezone.utc)
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last_at = parse_iso(word.last_reviewed_at or word.created_at)
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hours = (now - last_at).total_seconds() / 3600
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return round(retention_percent(hours, stability_hours(word)), 1)
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def _last_quiz_hint(db: Session, word_id: int) -> Optional[str]:
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r = (
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db.query(QuizRecord)
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.filter(QuizRecord.word_id == word_id)
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.order_by(QuizRecord.created_at.desc())
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.first()
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)
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if not r:
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return None
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return "上次答对" if r.is_correct else "上次答错"
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class MemoryCoachService:
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def build_tokens(self, db: Session, word: Word, reveal_extra: int = 0) -> list[MemoryToken]:
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en = word_en(word).strip()
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zh = word_zh(word).strip()
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retention = _retention_now(word)
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hint = _last_quiz_hint(db, word.id)
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tokens: list[MemoryToken] = [
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MemoryToken(
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role="K",
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key="retention",
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label="记忆保持",
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value=f"{retention}%",
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revealed=True,
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),
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MemoryToken(
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role="K",
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key="mastery",
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label="掌握率",
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value=f"{word.mastery_score}%",
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revealed=True,
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),
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MemoryToken(
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role="K",
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key="status",
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label="词库状态",
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value=word.status,
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revealed=True,
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),
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]
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if hint:
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tokens.append(
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MemoryToken(
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role="K",
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key="last_quiz",
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label="练习记录",
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value=hint,
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revealed=True,
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)
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)
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q_tokens: list[MemoryToken] = [
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MemoryToken(
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role="Q",
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key="zh_full",
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label="中文释义",
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value=zh,
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revealed=True,
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),
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MemoryToken(
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role="Q",
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key="length",
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label="字母数",
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value=str(len(en)),
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revealed=True,
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),
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]
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if zh:
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q_tokens.append(
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MemoryToken(
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role="Q",
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key="zh_hint",
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label="释义提示",
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value=_mask_zh(zh),
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revealed=reveal_extra > 0,
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)
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)
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if word.phonetic:
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q_tokens.append(
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MemoryToken(
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role="Q",
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key="phonetic",
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label="音标",
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value=word.phonetic,
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revealed=reveal_extra > 0,
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)
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)
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if len(en) >= 2:
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q_tokens.append(
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MemoryToken(
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role="Q",
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key="prefix",
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label="英文前缀",
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value=en[:2] + "…",
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revealed=reveal_extra > 1,
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)
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)
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if len(en) >= 4:
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q_tokens.append(
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MemoryToken(
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role="Q",
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key="suffix",
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label="英文尾缀",
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value="…" + en[-2:],
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revealed=reveal_extra > 2,
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)
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)
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if word.example_en:
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blanked = _blank_example(word.example_en, en)
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if blanked and blanked != word.example_en:
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q_tokens.append(
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MemoryToken(
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role="Q",
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key="example",
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label="例句挖空",
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value=blanked,
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revealed=reveal_extra > 3,
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)
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)
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tokens.extend(q_tokens)
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tokens.extend(
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[
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MemoryToken(
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role="V",
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key="en",
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label="英文",
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value=en,
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revealed=False,
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),
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MemoryToken(
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role="V",
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key="zh",
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label="中文",
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value=zh,
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revealed=False,
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),
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]
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)
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if word.example_en:
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tokens.append(
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MemoryToken(
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role="V",
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key="example_en",
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label="例句",
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value=word.example_en,
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revealed=False,
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)
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)
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if word.example_cn:
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tokens.append(
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MemoryToken(
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role="V",
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key="example_cn",
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label="例句译文",
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value=word.example_cn,
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revealed=False,
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)
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)
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return tokens
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def _reveal_q_tokens(self, tokens: list[MemoryToken], count: int) -> list[MemoryToken]:
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hidden_q = [t for t in tokens if t.role == "Q" and not t.revealed]
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for t in hidden_q[:count]:
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t.revealed = True
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return tokens
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def _reveal_all_v(self, tokens: list[MemoryToken]) -> list[MemoryToken]:
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for t in tokens:
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if t.role == "V":
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t.revealed = True
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if t.role == "Q":
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t.revealed = True
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return tokens
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def start_session(self, db: Session, user: User) -> CoachSessionResponse:
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settings = quiz_service.get_settings(db, user)
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words = quiz_service.select_daily_words(db, user, settings.daily_target)
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if not words:
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return CoachSessionResponse(words=[], total=0)
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summaries = memory_visual_service.get_visualization(db, user)["words"]
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risk_map = {w["id"]: w.get("retention_now", 0) for w in summaries}
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items = [
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CoachWordBrief(
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word_id=w.id,
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zh=word_zh(w),
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phonetic=w.phonetic,
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retention_now=risk_map.get(w.id, _retention_now(w)),
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mastery_score=w.mastery_score,
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status=w.status,
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)
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for w in words
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]
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return CoachSessionResponse(words=items, total=len(items))
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def handle_turn(
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self,
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db: Session,
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user: User,
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word_id: int,
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stage: str,
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user_message: str = "",
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hints_used: int = 0,
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duration_seconds: int = 0,
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) -> CoachTurnResponse:
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word = word_service.get_word(db, user, word_id)
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en = word_en(word).strip()
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zh = word_zh(word).strip()
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phonetic = word.phonetic
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tokens = self.build_tokens(db, word, reveal_extra=hints_used)
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messages: list[str] = []
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expect_input = False
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is_correct: Optional[bool] = None
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quiz_recorded = False
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word_complete = False
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next_stage = stage
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input_hint = f"请输入「{zh}」的英文"
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if stage == "intro":
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next_stage = "derive"
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expect_input = True
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elif stage == "derive":
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answer = user_message.strip()
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if not answer:
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expect_input = True
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next_stage = "derive"
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else:
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is_correct = answer.lower() == en.lower()
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if is_correct:
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tokens = self._reveal_all_v(tokens)
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messages.append(f"正确：{en}")
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quiz_service.submit_answer(
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db,
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user,
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word.id,
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"memory_coach",
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answer,
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en,
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duration_seconds,
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)
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quiz_recorded = True
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word_complete = True
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next_stage = "done"
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else:
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hints_used += 1
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tokens = self.build_tokens(db, word, reveal_extra=hints_used)
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tokens = self._reveal_q_tokens(tokens, 1)
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hidden_left = sum(1 for t in tokens if t.role == "Q" and not t.revealed)
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messages.append("不对，再试一次。")
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if hidden_left == 0:
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tokens = self._reveal_all_v(tokens)
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messages.append(f"答案：{en}")
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quiz_service.submit_answer(
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db,
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user,
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word.id,
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"memory_coach",
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answer,
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en,
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duration_seconds,
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)
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quiz_recorded = True
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word_complete = True
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next_stage = "done"
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else:
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expect_input = True
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next_stage = "derive"
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elif stage == "done":
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word_complete = True
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next_stage = "done"
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else:
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next_stage = "intro"
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expect_input = True
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return CoachTurnResponse(
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assistant_messages=messages,
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tokens=tokens,
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stage=next_stage,
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expect_input=expect_input,
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prompt_zh=zh,
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prompt_phonetic=phonetic,
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input_hint=input_hint,
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is_correct=is_correct,
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quiz_recorded=quiz_recorded,
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word_complete=word_complete,
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hints_used=hints_used,
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target_en=en if word_complete else None,
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target_zh=zh if word_complete else None,
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)
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memory_coach_service = MemoryCoachService()
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