"""검색 후보 수집 서비스 (Phase 1.1).

text(documents FTS + 키워드) + vector(documents.embedding) 후보를
SearchResult 리스트로 반환.

Phase 1.1: search.py의 _search_text/_search_vector를 이전.
Phase 1.1 후속 substep: ILIKE → trigram `similarity()` + `gin_trgm_ops`.
Phase 1.2: vector retrieval을 document_chunks 테이블 기반으로 전환.
"""

from __future__ import annotations

from typing import TYPE_CHECKING

from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession

from ai.client import AIClient

if TYPE_CHECKING:
    from api.search import SearchResult


async def search_text(
    session: AsyncSession, query: str, limit: int
) -> list["SearchResult"]:
    """FTS + ILIKE 필드별 가중치 검색.

    가중치: title 3.0 / ai_tags 2.5 / user_note 2.0 / ai_summary 1.5 / extracted_text 1.0
    + ts_rank * 2.0 보너스.
    """
    from api.search import SearchResult  # 순환 import 회피

    result = await session.execute(
        text("""
            SELECT id, title, ai_domain, ai_summary, file_format,
                   left(extracted_text, 200) AS snippet,
                   (
                       -- title 매칭 (가중치 최고)
                       CASE WHEN coalesce(title, '') ILIKE '%%' || :q || '%%' THEN 3.0 ELSE 0 END
                       -- ai_tags 매칭 (가중치 높음)
                       + CASE WHEN coalesce(ai_tags::text, '') ILIKE '%%' || :q || '%%' THEN 2.5 ELSE 0 END
                       -- user_note 매칭 (가중치 높음)
                       + CASE WHEN coalesce(user_note, '') ILIKE '%%' || :q || '%%' THEN 2.0 ELSE 0 END
                       -- ai_summary 매칭 (가중치 중상)
                       + CASE WHEN coalesce(ai_summary, '') ILIKE '%%' || :q || '%%' THEN 1.5 ELSE 0 END
                       -- extracted_text 매칭 (가중치 중간)
                       + CASE WHEN coalesce(extracted_text, '') ILIKE '%%' || :q || '%%' THEN 1.0 ELSE 0 END
                       -- FTS 점수 (보너스)
                       + coalesce(ts_rank(
                           to_tsvector('simple', coalesce(title, '') || ' ' || coalesce(extracted_text, '')),
                           plainto_tsquery('simple', :q)
                       ), 0) * 2.0
                   ) AS score,
                   -- match reason
                   CASE
                       WHEN coalesce(title, '') ILIKE '%%' || :q || '%%' THEN 'title'
                       WHEN coalesce(ai_tags::text, '') ILIKE '%%' || :q || '%%' THEN 'tags'
                       WHEN coalesce(user_note, '') ILIKE '%%' || :q || '%%' THEN 'note'
                       WHEN coalesce(ai_summary, '') ILIKE '%%' || :q || '%%' THEN 'summary'
                       WHEN coalesce(extracted_text, '') ILIKE '%%' || :q || '%%' THEN 'content'
                       ELSE 'fts'
                   END AS match_reason
            FROM documents
            WHERE deleted_at IS NULL
              AND (coalesce(title, '') ILIKE '%%' || :q || '%%'
               OR coalesce(ai_tags::text, '') ILIKE '%%' || :q || '%%'
               OR coalesce(user_note, '') ILIKE '%%' || :q || '%%'
               OR coalesce(ai_summary, '') ILIKE '%%' || :q || '%%'
               OR coalesce(extracted_text, '') ILIKE '%%' || :q || '%%'
               OR to_tsvector('simple', coalesce(title, '') || ' ' || coalesce(extracted_text, ''))
                  @@ plainto_tsquery('simple', :q))
            ORDER BY score DESC
            LIMIT :limit
        """),
        {"q": 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).

    Phase 1.2에서 document_chunks 테이블 기반으로 전환 예정.
    현재는 documents.embedding 사용.
    """
    from api.search import SearchResult  # 순환 import 회피

    try:
        client = AIClient()
        query_embedding = await client.embed(query)
        await client.close()
    except Exception:
        return []

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