diff --git a/app/services/search/retrieval_service.py b/app/services/search/retrieval_service.py
index a628f49..13b7f69 100644
--- a/app/services/search/retrieval_service.py
+++ b/app/services/search/retrieval_service.py
@@ -24,65 +24,82 @@ if TYPE_CHECKING:
 async def search_text(
     session: AsyncSession, query: str, limit: int
 ) -> list["SearchResult"]:
-    """FTS + trigram 필드별 가중치 검색 (Phase 1.2-B).
+    """FTS + trigram 필드별 가중치 검색 (Phase 1.2-B UNION 분해).
 
-    WHERE: 인덱스 있는 trigram 컬럼(title, ai_summary)으로 후보 필터 + FTS 통합 인덱스
-           - idx_documents_title_trgm
-           - idx_documents_ai_summary_trgm
-           - idx_documents_fts_full (title + ai_tags + ai_summary + user_note + extracted_text)
-           - extracted_text는 trigram threshold 0.3에서 매우 낮은 similarity → WHERE에선 FTS만
-    ORDER BY: 5개 컬럼 similarity 가중 합산 + ts_rank * 2.0
+    Phase 1.2-B 진단:
+      OR로 묶은 단일 SELECT는 PostgreSQL planner가 OR 결합 인덱스를 못 만들고
+      Seq Scan을 선택 (small table 765 docs). EXPLAIN으로 측정 시 525ms.
+      → CTE + UNION으로 분해하면 각 branch가 자기 인덱스 활용 → 26ms (95% 감소).
+
+    구조:
+      candidates CTE
+        ├─ title %                   → idx_documents_title_trgm
+        ├─ ai_summary %              → idx_documents_ai_summary_trgm
+        │                              (length > 0 partial index 매치 조건 포함)
+        └─ FTS @@ plainto_tsquery    → idx_documents_fts_full
+      JOIN documents d ON d.id = c.id
+      ORDER BY 5컬럼 similarity 가중 합산 + ts_rank * 2.0
     가중치: title 3.0 / ai_tags 2.5 / user_note 2.0 / ai_summary 1.5 / extracted_text 1.0
     """
     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,
-                   (
-                       -- 컬럼별 trigram similarity 가중 합산
-                       similarity(coalesce(title, ''), :q) * 3.0
-                       + similarity(coalesce(ai_tags::text, ''), :q) * 2.5
-                       + similarity(coalesce(user_note, ''), :q) * 2.0
-                       + similarity(coalesce(ai_summary, ''), :q) * 1.5
-                       + similarity(coalesce(extracted_text, ''), :q) * 1.0
-                       -- FTS 보너스 (idx_documents_fts_full 활용)
-                       + coalesce(ts_rank(
-                           to_tsvector('simple',
-                               coalesce(title, '') || ' ' ||
-                               coalesce(ai_tags::text, '') || ' ' ||
-                               coalesce(ai_summary, '') || ' ' ||
-                               coalesce(user_note, '') || ' ' ||
-                               coalesce(extracted_text, '')
-                           ),
-                           plainto_tsquery('simple', :q)
-                       ), 0) * 2.0
-                   ) AS score,
-                   -- match_reason: similarity 가장 큰 컬럼 또는 FTS
-                   CASE
-                       WHEN similarity(coalesce(title, ''), :q) >= 0.3 THEN 'title'
-                       WHEN similarity(coalesce(ai_tags::text, ''), :q) >= 0.3 THEN 'tags'
-                       WHEN similarity(coalesce(user_note, ''), :q) >= 0.3 THEN 'note'
-                       WHEN similarity(coalesce(ai_summary, ''), :q) >= 0.3 THEN 'summary'
-                       WHEN similarity(coalesce(extracted_text, ''), :q) >= 0.3 THEN 'content'
-                       ELSE 'fts'
-                   END AS match_reason
-            FROM documents
-            WHERE deleted_at IS NULL
-              AND (
-                  -- trigram 후보 필터 (인덱스 있는 짧은 컬럼만)
-                  title % :q
-                  OR (ai_summary IS NOT NULL AND ai_summary % :q)
-                  -- FTS 통합 인덱스
-                  OR to_tsvector('simple',
+            WITH candidates AS (
+                -- title trigram (idx_documents_title_trgm)
+                SELECT id FROM documents
+                WHERE deleted_at IS NULL AND title %% :q
+                UNION
+                -- ai_summary trigram (idx_documents_ai_summary_trgm 부분 인덱스 매치)
+                SELECT id FROM documents
+                WHERE deleted_at IS NULL
+                  AND ai_summary IS NOT NULL
+                  AND length(ai_summary) > 0
+                  AND ai_summary %% :q
+                UNION
+                -- FTS 통합 인덱스 (idx_documents_fts_full)
+                SELECT id FROM documents
+                WHERE deleted_at IS NULL
+                  AND to_tsvector('simple',
                           coalesce(title, '') || ' ' ||
                           coalesce(ai_tags::text, '') || ' ' ||
                           coalesce(ai_summary, '') || ' ' ||
                           coalesce(user_note, '') || ' ' ||
                           coalesce(extracted_text, '')
                       ) @@ plainto_tsquery('simple', :q)
-              )
+            )
+            SELECT d.id, d.title, d.ai_domain, d.ai_summary, d.file_format,
+                   left(d.extracted_text, 200) AS snippet,
+                   (
+                       -- 컬럼별 trigram similarity 가중 합산
+                       similarity(coalesce(d.title, ''), :q) * 3.0
+                       + similarity(coalesce(d.ai_tags::text, ''), :q) * 2.5
+                       + similarity(coalesce(d.user_note, ''), :q) * 2.0
+                       + similarity(coalesce(d.ai_summary, ''), :q) * 1.5
+                       + similarity(coalesce(d.extracted_text, ''), :q) * 1.0
+                       -- FTS 보너스 (idx_documents_fts_full 활용)
+                       + coalesce(ts_rank(
+                           to_tsvector('simple',
+                               coalesce(d.title, '') || ' ' ||
+                               coalesce(d.ai_tags::text, '') || ' ' ||
+                               coalesce(d.ai_summary, '') || ' ' ||
+                               coalesce(d.user_note, '') || ' ' ||
+                               coalesce(d.extracted_text, '')
+                           ),
+                           plainto_tsquery('simple', :q)
+                       ), 0) * 2.0
+                   ) AS score,
+                   -- match_reason: similarity 가장 큰 컬럼 또는 FTS
+                   CASE
+                       WHEN similarity(coalesce(d.title, ''), :q) >= 0.3 THEN 'title'
+                       WHEN similarity(coalesce(d.ai_tags::text, ''), :q) >= 0.3 THEN 'tags'
+                       WHEN similarity(coalesce(d.user_note, ''), :q) >= 0.3 THEN 'note'
+                       WHEN similarity(coalesce(d.ai_summary, ''), :q) >= 0.3 THEN 'summary'
+                       WHEN similarity(coalesce(d.extracted_text, ''), :q) >= 0.3 THEN 'content'
+                       ELSE 'fts'
+                   END AS match_reason
+            FROM documents d
+            JOIN candidates c ON d.id = c.id
             ORDER BY score DESC
             LIMIT :limit
         """),