-- ============================================================================
-- Phase V2-Q: pgvector 활성화 + knowledge 시맨틱 검색
-- embedding 컬럼 + cosine 유사도 검색 RPC
-- ============================================================================

CREATE EXTENSION IF NOT EXISTS vector;

-- 기존 knowledge_chunks에 embedding 컬럼 추가 (1536 차원 — OpenAI text-embedding-3-small)
ALTER TABLE public.knowledge_chunks
  ADD COLUMN embedding vector(1536);

-- IVFFlat 인덱스 (빠른 근사 최근접)
-- 데이터 삽입 후 `REINDEX TABLE knowledge_chunks;` 권장
CREATE INDEX idx_knowledge_chunks_embedding
  ON public.knowledge_chunks
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

-- ============================================================================
-- match_knowledge_chunks: 쿼리 임베딩과 가장 유사한 청크 반환
-- ============================================================================
CREATE OR REPLACE FUNCTION public.match_knowledge_chunks(
  query_embedding vector(1536),
  match_count integer DEFAULT 5,
  similarity_threshold double precision DEFAULT 0.5
)
RETURNS TABLE (
  id uuid,
  document_id uuid,
  chunk_index integer,
  content text,
  similarity double precision
)
LANGUAGE plpgsql
STABLE
AS $$
BEGIN
  RETURN QUERY
  SELECT
    kc.id,
    kc.document_id,
    kc.chunk_index,
    kc.content,
    (1 - (kc.embedding <=> query_embedding))::double precision AS similarity
  FROM public.knowledge_chunks kc
  INNER JOIN public.knowledge_documents kd ON kd.id = kc.document_id
  WHERE
    kc.embedding IS NOT NULL
    AND (
      kd.user_id = auth.uid()
      OR (
        kd.team_id IS NOT NULL
        AND kd.team_id IN (
          SELECT team_id FROM public.team_members WHERE user_id = auth.uid()
        )
      )
    )
    AND (1 - (kc.embedding <=> query_embedding)) > similarity_threshold
  ORDER BY kc.embedding <=> query_embedding
  LIMIT match_count;
END;
$$;

GRANT EXECUTE ON FUNCTION public.match_knowledge_chunks TO authenticated;