"""Schemas powering the knowledge tracing mini-model.
中文：知识追踪接口的请求/响应模型，包含技能交互、掌握度与预测输出。
"""
from __future__ import annotations
from typing import Dict, List, Optional
from pydantic import BaseModel, Field
class SkillInteraction(BaseModel):
skill: str = Field(..., description="知识技能名称")
correct: bool = Field(..., description="是否答对")
time_spent_seconds: Optional[int] = Field(
default=None, description="作答耗时（秒）"
)
confidence: Optional[float] = Field(
default=None, ge=0.0, le=1.0, description="学生自评信心"
)
class KnowledgeTracingRequest(BaseModel):
student_id: str
interactions: List[SkillInteraction]
prior_mastery: Dict[str, float] = Field(
default_factory=dict, description="先验掌握度，键为技能名，值为 0-1"
)
class SkillProgress(BaseModel):
skill: str
probability_mastery: float
trend: str
next_action: str
class KnowledgeTracingResponse(BaseModel):
student_id: str
skills: List[SkillProgress]
recommended_sequence: List[str]
# --- Advanced knowledge tracing (MRTKT / DKVMN-style) ---
class StudentInteraction(BaseModel):
item_id: int = Field(..., description="题目 ID")
correct: int = Field(..., ge=0, le=1, description="是否答对，1/0")
timestamp: float = Field(..., description="时间戳或相对时间间隔")
class KTResponse(BaseModel):
next_question_correct_prob: float = Field(
..., description="下一题答对的概率预测"
)
mastery: Dict[str, float] = Field(
..., description="当前各知识点掌握程度"
)
