cfc/cfc-langgraph/app/api/question_generate.py

"""五维自检题目生成 API

提供基于用户画路径的智能题目生成服务，包含：
- AI生成 20 道情境化问卷题目
- 支持自定义维度分配
- 支持历史记题目去重
- 与现有 15 题固定题库并存"""

import logging
from fastapi import APIRouter
from pydantic import BaseModel
from typing import Any, Dict, List, Optional
from app.graphs.question_generation_graph import run_question_gen

logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1", tags=["question-generate"])


class QuestionGenRequest(BaseModel):
    """题目生成请求"""
    user_id: int
    count: int = 20  # 题目总数，默认20（满足系统≥10题门槛）
    difficulty: str = "balanced"  # balanced/easy/hard/contextual
    dimensions: Optional[List[str]] = None  # 可选指定维度，如 ["body", "wisdom"]
    exclude_ids: Optional[List[int]] = None  # 历史题目ID列表，用于避免重复


class QuestionGenResponse(BaseModel):
    """题目生成响应"""
    questions: List[Dict[str, Any]]
    total_generated: int
    generation_time: float
    rationale: str
    dimension_distribution: Dict[str, int]
    fallback_used: bool


@router.post("/question-generate", response_model=QuestionGenResponse)
async def question_generate(req: QuestionGenRequest):
    """AI生成五维自检题目
    
    根据用户画像生成 20 道情境化问卷题目，满足系统最低门槛：
    - 总题数≥10
    - 每个维度至少 1 题
    
    请求体:
    - user_id: 必填，用户 ID
    - count: 可选，题目总数，默认 20
    - difficulty: 可选，难度提示，默认 "balanced"
    - dimensions: 可选，指定维度列表，如 ["body", "wisdom"]
    - exclude_ids: 可选，历史题目 ID 列表，用于避免重复提问
    
    响应体:
    - questions: 生成的题目列表，每题包含 id, dimension, text, options
    - total_generated: 实际生成数量
    - generation_time: 生成耗时（秒）
    - rationale: 生成 rationale，解释维度选择逻辑
    - dimension_distribution: 各维度题目数量分布
    - fallback_used: 是否使用了降级方案（LLM失败回退到本地题库）
    """
    try:
        result = await run_question_gen(
            user_id=user_id,
            count=req.count,
            difficulty=req.difficulty,
            dimensions=req.dimensions,
            exclude_ids=req.exclude_ids,
        )
        
        return QuestionGenResponse(
            questions=result.get("questions", []),
            total_generated=result.get("total_generated", 0),
            generation_time=result.get("generation_time", 0.0),
            rationale=result.get("rationale", ""),
            dimension_distribution=result.get("dimension_distribution", {}),
            fallback_used=result.get("fallback_used", False),
        )
        
    except Exception as e:
        logger.error(f"AI 生成题目失败: {e}", exc_info=True)
        # 降级：返回空结果，前端可据此提示用户或使用固定题库
        return QuestionGenResponse(
            questions=[],
            total_generated=0,
            generation_time=0.0,
            rationale=f"生成失败: {str(e)}，将使用固定题库",
            dimension_distribution={},
            fallback_used=True,
        )