"""하이브리드 검색 API — FTS + 트리그램 + 벡터"""

from typing import Annotated

from fastapi import APIRouter, Depends, Query
from pydantic import BaseModel
from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession

from ai.client import AIClient
from core.auth import get_current_user
from core.database import get_session
from models.user import User

router = APIRouter()

# 가중치 (초기값, 튜닝 가능)
W_FTS = 0.4
W_TRGM = 0.2
W_VECTOR = 0.4


class SearchResult(BaseModel):
    id: int
    title: str | None
    ai_domain: str | None
    ai_summary: str | None
    file_format: str
    score: float
    snippet: str | None


class SearchResponse(BaseModel):
    results: list[SearchResult]
    total: int
    query: str
    mode: str


@router.get("/", response_model=SearchResponse)
async def search(
    q: str,
    user: Annotated[User, Depends(get_current_user)],
    session: Annotated[AsyncSession, Depends(get_session)],
    mode: str = Query("hybrid", pattern="^(fts|trgm|vector|hybrid)$"),
    limit: int = Query(20, ge=1, le=100),
):
    """문서 검색

    mode:
    - fts: PostgreSQL 전문검색 (GIN 인덱스)
    - trgm: 트리그램 부분매칭 (한국어 지원)
    - vector: 벡터 유사도 검색 (의미검색)
    - hybrid: FTS + 트리그램 + 벡터 결합 (기본)
    """
    if mode == "fts":
        results = await _search_fts(session, q, limit)
    elif mode == "trgm":
        results = await _search_trgm(session, q, limit)
    elif mode == "vector":
        results = await _search_vector(session, q, limit)
    else:
        results = await _search_hybrid(session, q, limit)

    return SearchResponse(
        results=results,
        total=len(results),
        query=q,
        mode=mode,
    )


async def _search_fts(session: AsyncSession, query: str, limit: int) -> list[SearchResult]:
    """PostgreSQL 전문검색 (GIN 인덱스)"""
    # simple 설정으로 한국어 토큰화 없이 공백 기반 분리
    result = await session.execute(
        text("""
            SELECT id, title, ai_domain, ai_summary, file_format,
                   ts_rank(
                       to_tsvector('simple', coalesce(title, '') || ' ' || coalesce(extracted_text, '')),
                       plainto_tsquery('simple', :query)
                   ) AS score,
                   left(extracted_text, 200) AS snippet
            FROM documents
            WHERE to_tsvector('simple', coalesce(title, '') || ' ' || coalesce(extracted_text, ''))
                  @@ plainto_tsquery('simple', :query)
            ORDER BY score DESC
            LIMIT :limit
        """),
        {"query": query, "limit": limit},
    )
    return [SearchResult(**row._mapping) for row in result]


async def _search_trgm(session: AsyncSession, query: str, limit: int) -> list[SearchResult]:
    """트리그램 부분매칭 (한국어 지원)"""
    result = await session.execute(
        text("""
            SELECT id, title, ai_domain, ai_summary, file_format,
                   similarity(
                       coalesce(title, '') || ' ' || coalesce(extracted_text, ''),
                       :query
                   ) AS score,
                   left(extracted_text, 200) AS snippet
            FROM documents
            WHERE (coalesce(title, '') || ' ' || coalesce(extracted_text, '')) %% :query
            ORDER BY score DESC
            LIMIT :limit
        """),
        {"query": query, "limit": limit},
    )
    return [SearchResult(**row._mapping) for row in result]


async def _search_vector(session: AsyncSession, query: str, limit: int) -> list[SearchResult]:
    """벡터 유사도 검색 (코사인 거리)"""
    client = AIClient()
    try:
        query_embedding = await client.embed(query)
    except Exception:
        return []  # GPU 서버 불가 시 빈 결과
    finally:
        await client.close()

    # pgvector 코사인 거리 (0=동일, 2=반대)
    result = await session.execute(
        text("""
            SELECT id, title, ai_domain, ai_summary, file_format,
                   (1 - (embedding <=> :embedding::vector)) AS score,
                   left(extracted_text, 200) AS snippet
            FROM documents
            WHERE embedding IS NOT NULL
            ORDER BY embedding <=> :embedding::vector
            LIMIT :limit
        """),
        {"embedding": str(query_embedding), "limit": limit},
    )
    return [SearchResult(**row._mapping) for row in result]


async def _search_hybrid(session: AsyncSession, query: str, limit: int) -> list[SearchResult]:
    """하이브리드 검색 — FTS + 트리그램 + 벡터 가중 합산"""
    # 벡터 임베딩 생성 (실패 시 FTS+트리그램만)
    query_embedding = None
    try:
        client = AIClient()
        query_embedding = await client.embed(query)
        await client.close()
    except Exception:
        pass

    vector_clause = ""
    vector_score = "0"
    params = {"query": query, "limit": limit, "w_fts": W_FTS, "w_trgm": W_TRGM, "w_vector": W_VECTOR}

    if query_embedding:
        vector_clause = "LEFT JOIN LATERAL (SELECT 1 - (d.embedding <=> :embedding::vector) AS vscore) v ON true"
        vector_score = "coalesce(v.vscore, 0)"
        params["embedding"] = str(query_embedding)
    else:
        # 벡터 없으면 FTS+트리그램만 사용
        params["w_fts"] = 0.6
        params["w_trgm"] = 0.4
        params["w_vector"] = 0.0

    result = await session.execute(
        text(f"""
            SELECT d.id, d.title, d.ai_domain, d.ai_summary, d.file_format,
                   (
                       :w_fts * coalesce(ts_rank(
                           to_tsvector('simple', coalesce(d.title, '') || ' ' || coalesce(d.extracted_text, '')),
                           plainto_tsquery('simple', :query)
                       ), 0)
                       + :w_trgm * coalesce(similarity(
                           coalesce(d.title, '') || ' ' || coalesce(d.extracted_text, ''),
                           :query
                       ), 0)
                       + :w_vector * {vector_score}
                   ) AS score,
                   left(d.extracted_text, 200) AS snippet
            FROM documents d
            {vector_clause}
            WHERE coalesce(d.extracted_text, '') != ''
            ORDER BY score DESC
            LIMIT :limit
        """),
        params,
    )
    return [SearchResult(**row._mapping) for row in result]