Add spell practice, memory analytics, auth persistence, and word library UX.

Includes per-word training stats and curves, quiz session auto-save, remember-login,
paginated word list with floating page arrows, and Obsidian-style relationship graph baseline.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/schemas.py

@@ -1,5 +1,5 @@
 from typing import Optional
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, computed_field
 
 
 # Auth
@@ -11,6 +11,7 @@ class UserRegister(BaseModel):
 class UserLogin(BaseModel):
     username: str
     password: str
+    remember: bool = True
 
 
 class TokenResponse(BaseModel):
@@ -72,14 +73,104 @@ class WordOut(BaseModel):
     wrong_count: int
     consecutive_correct_count: int
     mastery_score: int
+    train_count: int = 0
+    total_train_seconds: int = 0
     review_due_date: Optional[str] = None
     last_reviewed_at: Optional[str] = None
     created_at: str
 
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def entered_at(self) -> str:
+        """词库进入时间（与 created_at 一致）。"""
+        return self.created_at
+
     class Config:
         from_attributes = True
 
 
+class MemoryCurvePoint(BaseModel):
+    day_index: int
+    date: str
+    forgetting: float
+    mastery: float
+    risk: float
+
+
+class MemoryFutureRiskPoint(BaseModel):
+    day_offset: int
+    date: str
+    risk: float
+
+
+class MemoryGraphNode(BaseModel):
+    id: str
+    label: str
+    zh: str
+    status: str
+    mastery: int
+    entered_at: str
+    size: int
+
+
+class MemoryGraphLink(BaseModel):
+    source: str
+    target: str
+    kind: str
+    strength: float
+
+
+class MemoryGraph(BaseModel):
+    nodes: list[MemoryGraphNode]
+    links: list[MemoryGraphLink]
+
+
+class MemoryWordSummary(BaseModel):
+    id: int
+    en: str
+    zh: str
+    status: str
+    mastery_score: int
+    correct_count: int = 0
+    wrong_count: int = 0
+    train_count: int = 0
+    total_train_seconds: int = 0
+    entered_at: str
+    retention_now: float
+    risk_7d: float
+
+
+class WordMemoryCurvePoint(BaseModel):
+    date: str
+    datetime: str
+    forgetting: float
+    mastery: float
+    risk: float
+    wrong_count: int
+    train_count: int
+    train_seconds: int
+    is_correct: Optional[bool] = None
+
+
+class WordMemoryDetailResponse(BaseModel):
+    word_id: int
+    en: str
+    zh: str
+    correct_count: int
+    wrong_count: int
+    train_count: int
+    total_train_seconds: int
+    curve_points: list[WordMemoryCurvePoint]
+    future_risk: list[MemoryFutureRiskPoint]
+
+
+class MemoryVisualizationResponse(BaseModel):
+    curve_points: list[MemoryCurvePoint]
+    future_risk: list[MemoryFutureRiskPoint]
+    words: list[MemoryWordSummary]
+    graph: MemoryGraph
+
+
 # Quiz
 class QuizOption(BaseModel):
     label: str
@@ -90,8 +181,9 @@ class QuizQuestion(BaseModel):
     word_id: int
     question_type: str
     prompt: str
-    options: list[QuizOption]
+    options: list[QuizOption] = []
     correct_answer: str
+    phonetic: Optional[str] = None
 
 
 class DailyQuizResponse(BaseModel):
@@ -104,6 +196,7 @@ class QuizAnswerRequest(BaseModel):
     question_type: str
     user_answer: str
     correct_answer: str
+    duration_seconds: Optional[int] = Field(None, ge=0, le=3600)
 
 
 class QuizAnswerResponse(BaseModel):