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app/schemas/ai.py

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+"""
+Pydantic schemas for AI generation endpoints.
+
+Request/response models for admin AI generation playground.
+"""
+
+from typing import Dict, Literal, Optional
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class AIGeneratePreviewRequest(BaseModel):
+    basis_item_id: int = Field(
+        ..., description="ID of the basis item (must be sedang level)"
+    )
+    target_level: Literal["mudah", "sulit"] = Field(
+        ..., description="Target difficulty level for generated question"
+    )
+    ai_model: str = Field(
+        default="qwen/qwen-2.5-coder-32b-instruct",
+        description="AI model to use for generation",
+    )
+
+
+class AIGeneratePreviewResponse(BaseModel):
+    success: bool = Field(..., description="Whether generation was successful")
+    stem: Optional[str] = None
+    options: Optional[Dict[str, str]] = None
+    correct: Optional[str] = None
+    explanation: Optional[str] = None
+    ai_model: Optional[str] = None
+    basis_item_id: Optional[int] = None
+    target_level: Optional[str] = None
+    error: Optional[str] = None
+    cached: bool = False
+
+
+class AISaveRequest(BaseModel):
+    stem: str = Field(..., description="Question stem")
+    options: Dict[str, str] = Field(
+        ..., description="Answer options (A, B, C, D)"
+    )
+    correct: str = Field(..., description="Correct answer (A/B/C/D)")
+    explanation: Optional[str] = None
+    tryout_id: str = Field(..., description="Tryout identifier")
+    website_id: int = Field(..., description="Website identifier")
+    basis_item_id: int = Field(..., description="Basis item ID")
+    slot: int = Field(..., description="Question slot position")
+    level: Literal["mudah", "sedang", "sulit"] = Field(
+        ..., description="Difficulty level"
+    )
+    ai_model: str = Field(
+        default="qwen/qwen-2.5-coder-32b-instruct",
+        description="AI model used for generation",
+    )
+
+    @field_validator("correct")
+    @classmethod
+    def validate_correct(cls, v: str) -> str:
+        if v.upper() not in ["A", "B", "C", "D"]:
+            raise ValueError("Correct answer must be A, B, C, or D")
+        return v.upper()
+
+    @field_validator("options")
+    @classmethod
+    def validate_options(cls, v: Dict[str, str]) -> Dict[str, str]:
+        required_keys = {"A", "B", "C", "D"}
+        if not required_keys.issubset(set(v.keys())):
+            raise ValueError("Options must contain keys A, B, C, D")
+        return v
+
+
+class AISaveResponse(BaseModel):
+    success: bool = Field(..., description="Whether save was successful")
+    item_id: Optional[int] = None
+    error: Optional[str] = None
+
+
+class AIStatsResponse(BaseModel):
+    total_ai_items: int = Field(..., description="Total AI-generated items")
+    items_by_model: Dict[str, int] = Field(
+        default_factory=dict, description="Items count by AI model"
+    )
+    cache_hit_rate: float = Field(
+        default=0.0, description="Cache hit rate (0.0 to 1.0)"
+    )
+    total_cache_hits: int = Field(default=0, description="Total cache hits")
+    total_requests: int = Field(default=0, description="Total generation requests")
+
+
+class GeneratedQuestion(BaseModel):
+    stem: str
+    options: Dict[str, str]
+    correct: str
+    explanation: Optional[str] = None
+
+    @field_validator("correct")
+    @classmethod
+    def validate_correct(cls, v: str) -> str:
+        if v.upper() not in ["A", "B", "C", "D"]:
+            raise ValueError("Correct answer must be A, B, C, or D")
+        return v.upper()