feat(search): Phase 0.4 debug 응답 옵션 + timing 로그

?debug=true로 호출 시 단계별 candidates + timing을 응답에 포함.
디버그 옵션과 별개로 모든 검색에 timing 라인을 구조화 로그로 출력
(사용자 feedback: 운영 관찰엔 debug 응답만으론 부족).

신규 응답 필드 (debug=true 시):
- timing_ms: text_ms / vector_ms / merge_ms / total_ms
- text_candidates / vector_candidates / fused_candidates (top 20)
- confidence (telemetry와 동일 휴리스틱)
- notes (예: vector 검색 실패 시 fallback 표시)
- query_analysis / reranker_scores: Phase 1/2용 placeholder

기본 응답(debug=false)은 변화 없음 (results, total, query, mode).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

app/api/search.py

@@ -1,5 +1,7 @@
 """하이브리드 검색 API — FTS + ILIKE + 벡터 (필드별 가중치)"""
 
+import logging
+import time
 from typing import Annotated
 
 from fastapi import APIRouter, BackgroundTasks, Depends, Query
@@ -11,7 +13,9 @@ from ai.client import AIClient
 from core.auth import get_current_user
 from core.database import get_session
 from models.user import User
-from services.search_telemetry import record_search_event
+from services.search_telemetry import compute_confidence, record_search_event
+
+logger = logging.getLogger("search")
 
 router = APIRouter()
 
@@ -27,11 +31,44 @@ class SearchResult(BaseModel):
     match_reason: str | None = None
 
 
+# ─── Phase 0.4: 디버그 응답 스키마 ─────────────────────────
+
+
+class DebugCandidate(BaseModel):
+    """단계별 후보 (debug=true 응답에서만 노출)."""
+    id: int
+    rank: int
+    score: float
+    match_reason: str | None = None
+
+
+class SearchDebug(BaseModel):
+    timing_ms: dict[str, float]
+    text_candidates: list[DebugCandidate] | None = None
+    vector_candidates: list[DebugCandidate] | None = None
+    fused_candidates: list[DebugCandidate] | None = None
+    confidence: float
+    notes: list[str] = []
+    # Phase 1/2 도입 후 채워질 placeholder
+    query_analysis: dict | None = None
+    reranker_scores: list[DebugCandidate] | None = None
+
+
 class SearchResponse(BaseModel):
     results: list[SearchResult]
     total: int
     query: str
     mode: str
+    debug: SearchDebug | None = None
+
+
+def _to_debug_candidates(rows: list[SearchResult], n: int = 20) -> list[DebugCandidate]:
+    return [
+        DebugCandidate(
+            id=r.id, rank=i + 1, score=r.score, match_reason=r.match_reason
+        )
+        for i, r in enumerate(rows[:n])
+    ]
 
 
 @router.get("/", response_model=SearchResponse)
@@ -42,26 +79,70 @@ async def search(
     background_tasks: BackgroundTasks,
     mode: str = Query("hybrid", pattern="^(fts|trgm|vector|hybrid)$"),
     limit: int = Query(20, ge=1, le=100),
+    debug: bool = Query(False, description="단계별 candidates + timing 응답에 포함"),
 ):
     """문서 검색 — FTS + ILIKE + 벡터 결합"""
-    if mode == "vector":
+    timing: dict[str, float] = {}
-        results = await _search_vector(session, q, limit)
+    notes: list[str] = []
-    else:
+    text_results: list[SearchResult] = []
-        results = await _search_text(session, q, limit)
+    vector_results: list[SearchResult] = []
 
-        # hybrid: 벡터 결과도 합산
+    t_total = time.perf_counter()
-        if mode == "hybrid":
+
+    if mode == "vector":
+        t0 = time.perf_counter()
         vector_results = await _search_vector(session, q, limit)
-            results = _merge_results(results, vector_results, limit)
+        timing["vector_ms"] = (time.perf_counter() - t0) * 1000
+        if not vector_results:
+            notes.append("vector_search_returned_empty (AI client error or no embeddings)")
+        results = vector_results
+    else:
+        t0 = time.perf_counter()
+        text_results = await _search_text(session, q, limit)
+        timing["text_ms"] = (time.perf_counter() - t0) * 1000
+
+        if mode == "hybrid":
+            t1 = time.perf_counter()
+            vector_results = await _search_vector(session, q, limit)
+            timing["vector_ms"] = (time.perf_counter() - t1) * 1000
+            if not vector_results:
+                notes.append("vector_search_returned_empty — text-only fallback")
+
+            t2 = time.perf_counter()
+            results = _merge_results(text_results, vector_results, limit)
+            timing["merge_ms"] = (time.perf_counter() - t2) * 1000
+        else:
+            results = text_results
+
+    timing["total_ms"] = (time.perf_counter() - t_total) * 1000
+
+    # 사용자 feedback: 모든 단계 timing은 debug 응답과 별도로 항상 로그로 남긴다
+    timing_str = " ".join(f"{k}={v:.0f}" for k, v in timing.items())
+    logger.info(
+        "search query=%r mode=%s results=%d %s",
+        q[:80], mode, len(results), timing_str,
+    )
 
     # Phase 0.3: 실패 자동 로깅 (응답 latency에 영향 X — background task)
     background_tasks.add_task(record_search_event, q, user.id, results, mode)
 
+    debug_obj: SearchDebug | None = None
+    if debug:
+        debug_obj = SearchDebug(
+            timing_ms=timing,
+            text_candidates=_to_debug_candidates(text_results) if text_results or mode != "vector" else None,
+            vector_candidates=_to_debug_candidates(vector_results) if vector_results or mode in ("vector", "hybrid") else None,
+            fused_candidates=_to_debug_candidates(results) if mode == "hybrid" else None,
+            confidence=compute_confidence(results, mode),
+            notes=notes,
+        )
+
     return SearchResponse(
         results=results,
         total=len(results),
         query=q,
         mode=mode,
+        debug=debug_obj,
     )