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Dwindi Ramadhana
292
app/routers/ai.py
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292
app/routers/ai.py
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"""
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AI Generation Router.
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Admin endpoints for AI question generation playground.
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"""
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import logging
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from typing import Annotated
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from fastapi import APIRouter, Depends, HTTPException, status
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from sqlalchemy import and_, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.models.item import Item
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from app.schemas.ai import (
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AIGeneratePreviewRequest,
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AIGeneratePreviewResponse,
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AISaveRequest,
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AISaveResponse,
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AIStatsResponse,
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)
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from app.services.ai_generation import (
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generate_question,
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get_ai_stats,
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save_ai_question,
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validate_ai_model,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/admin/ai", tags=["admin", "ai-generation"])
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@router.post(
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"/generate-preview",
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response_model=AIGeneratePreviewResponse,
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summary="Preview AI-generated question",
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description="""
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Generate a question preview using AI without saving to database.
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This is an admin playground endpoint for testing AI generation quality.
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Admins can retry unlimited times until satisfied with the result.
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Requirements:
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- basis_item_id must reference an existing item at 'sedang' level
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- target_level must be 'mudah' or 'sulit'
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- ai_model must be a supported OpenRouter model
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""",
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responses={
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200: {"description": "Question generated successfully (preview mode)"},
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400: {"description": "Invalid request (wrong level, unsupported model)"},
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404: {"description": "Basis item not found"},
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500: {"description": "AI generation failed"},
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},
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)
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async def generate_preview(
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request: AIGeneratePreviewRequest,
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db: Annotated[AsyncSession, Depends(get_db)],
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) -> AIGeneratePreviewResponse:
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"""
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Generate AI question preview (no database save).
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- **basis_item_id**: ID of the sedang-level question to base generation on
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- **target_level**: Target difficulty (mudah/sulit)
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- **ai_model**: OpenRouter model to use (default: qwen/qwen-2.5-coder-32b-instruct)
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"""
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# Validate AI model
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if not validate_ai_model(request.ai_model):
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=f"Unsupported AI model: {request.ai_model}. "
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f"Supported models: qwen/qwen-2.5-coder-32b-instruct, meta-llama/llama-3.3-70b-instruct",
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)
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# Fetch basis item
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result = await db.execute(
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select(Item).where(Item.id == request.basis_item_id)
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)
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basis_item = result.scalar_one_or_none()
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if not basis_item:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"Basis item not found: {request.basis_item_id}",
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)
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# Validate basis item is sedang level
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if basis_item.level != "sedang":
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=f"Basis item must be 'sedang' level, got: {basis_item.level}",
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)
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# Generate question
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try:
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generated = await generate_question(
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basis_item=basis_item,
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target_level=request.target_level,
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ai_model=request.ai_model,
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)
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if not generated:
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return AIGeneratePreviewResponse(
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success=False,
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error="AI generation failed. Please check logs or try again.",
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ai_model=request.ai_model,
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basis_item_id=request.basis_item_id,
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target_level=request.target_level,
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)
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return AIGeneratePreviewResponse(
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success=True,
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stem=generated.stem,
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options=generated.options,
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correct=generated.correct,
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explanation=generated.explanation,
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ai_model=request.ai_model,
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basis_item_id=request.basis_item_id,
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target_level=request.target_level,
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cached=False,
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)
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except Exception as e:
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logger.error(f"AI preview generation failed: {e}")
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return AIGeneratePreviewResponse(
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success=False,
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error=f"AI generation error: {str(e)}",
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ai_model=request.ai_model,
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basis_item_id=request.basis_item_id,
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target_level=request.target_level,
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)
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@router.post(
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"/generate-save",
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response_model=AISaveResponse,
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summary="Save AI-generated question",
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description="""
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Save an AI-generated question to the database.
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This endpoint creates a new Item record with:
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- generated_by='ai'
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- ai_model from request
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- basis_item_id linking to original question
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- calibrated=False (will be calculated later)
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""",
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responses={
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200: {"description": "Question saved successfully"},
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400: {"description": "Invalid request data"},
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404: {"description": "Basis item or tryout not found"},
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409: {"description": "Item already exists at this slot/level"},
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500: {"description": "Database save failed"},
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},
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)
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async def generate_save(
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request: AISaveRequest,
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db: Annotated[AsyncSession, Depends(get_db)],
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) -> AISaveResponse:
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"""
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Save AI-generated question to database.
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- **stem**: Question text
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- **options**: Dict with A, B, C, D options
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- **correct**: Correct answer (A/B/C/D)
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- **explanation**: Answer explanation (optional)
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- **tryout_id**: Tryout identifier
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- **website_id**: Website identifier
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- **basis_item_id**: Original item ID this was generated from
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- **slot**: Question slot position
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- **level**: Difficulty level
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- **ai_model**: AI model used for generation
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"""
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# Verify basis item exists
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basis_result = await db.execute(
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select(Item).where(Item.id == request.basis_item_id)
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)
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basis_item = basis_result.scalar_one_or_none()
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if not basis_item:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"Basis item not found: {request.basis_item_id}",
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)
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# Check for duplicate (same tryout, website, slot, level)
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existing_result = await db.execute(
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select(Item).where(
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and_(
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Item.tryout_id == request.tryout_id,
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Item.website_id == request.website_id,
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Item.slot == request.slot,
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Item.level == request.level,
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)
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)
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)
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existing = existing_result.scalar_one_or_none()
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if existing:
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raise HTTPException(
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status_code=status.HTTP_409_CONFLICT,
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detail=f"Item already exists at slot={request.slot}, level={request.level} "
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f"for tryout={request.tryout_id}",
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)
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# Create GeneratedQuestion from request
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from app.schemas.ai import GeneratedQuestion
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generated_data = GeneratedQuestion(
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stem=request.stem,
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options=request.options,
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correct=request.correct,
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explanation=request.explanation,
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)
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# Save to database
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item_id = await save_ai_question(
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generated_data=generated_data,
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tryout_id=request.tryout_id,
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website_id=request.website_id,
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basis_item_id=request.basis_item_id,
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slot=request.slot,
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level=request.level,
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ai_model=request.ai_model,
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db=db,
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)
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if not item_id:
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Failed to save AI-generated question",
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)
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return AISaveResponse(
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success=True,
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item_id=item_id,
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)
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@router.get(
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"/stats",
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response_model=AIStatsResponse,
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summary="Get AI generation statistics",
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description="""
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Get statistics about AI-generated questions.
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Returns:
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- Total AI-generated items count
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- Items count by model
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- Cache hit rate (placeholder)
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""",
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)
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async def get_stats(
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db: Annotated[AsyncSession, Depends(get_db)],
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) -> AIStatsResponse:
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"""
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Get AI generation statistics.
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"""
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stats = await get_ai_stats(db)
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return AIStatsResponse(
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total_ai_items=stats["total_ai_items"],
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items_by_model=stats["items_by_model"],
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cache_hit_rate=stats["cache_hit_rate"],
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total_cache_hits=stats["total_cache_hits"],
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total_requests=stats["total_requests"],
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)
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@router.get(
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"/models",
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summary="List supported AI models",
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description="Returns list of supported AI models for question generation.",
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)
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async def list_models() -> dict:
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"""
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List supported AI models.
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"""
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return {
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"models": [
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{
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"id": "qwen/qwen-2.5-coder-32b-instruct",
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"name": "Qwen 2.5 Coder 32B",
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"description": "Fast and efficient model for question generation",
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},
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{
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"id": "meta-llama/llama-3.3-70b-instruct",
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"name": "Llama 3.3 70B",
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"description": "High-quality model with better reasoning",
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},
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]
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}
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