# -*- coding: utf-8 -*-
"""智能交互融合数字人答疑模块：问题解析 -> 图谱检索 -> 大模型扩充"""
from fastapi import APIRouter, Depends
from pydantic import BaseModel
from sqlalchemy.orm import Session

from backend.database import get_db
from backend.models import QASession
from backend.services.auth import get_current_user_id
from backend.services.knowledge_graph import search_knowledge_graph
from backend.services.llm_service import expand_with_llm
from backend.services.question_parser import analyze_question

router = APIRouter(prefix="/qa", tags=["答疑"])


class AskRequest(BaseModel):
    question: str


@router.post("/ask")
def ask(
    req: AskRequest,
    db: Session = Depends(get_db),
    user_id: int = Depends(get_current_user_id),
):
    """智能答疑：问题解析 -> 图谱检索 -> 大模型扩充 -> 返回文本（供 Live2D 数字人+语音合成展示）"""
    raw_q = (req.question or "").strip()
    if not raw_q:
        return {"ok": False, "error": "请输入问题"}

    # 1. 问题解析与意图识别
    analysis = analyze_question(raw_q)
    kg_terms = analysis.get("kg_terms") or [analysis.get("clean_text") or raw_q]

    # 2. 基于核心关键词+扩展词进行知识图谱检索，合并去重
    triples = []
    seen = set()
    for term in kg_terms:
        results = search_knowledge_graph(term, limit=10)
        for r in results:
            key = (r.get("subject"), r.get("predicate"), r.get("obj"))
            if key in seen:
                continue
            seen.add(key)
            triples.append(r)

    # 3. 知识图谱约束 + 大模型扩展回答
    answer = expand_with_llm(raw_q, triples, meta=analysis)

    # 记录答疑会话
    session = QASession(user_id=user_id, question=raw_q, answer=answer)
    db.add(session)
    db.commit()

    return {"ok": True, "answer": answer}