fix(search): semaphore로 LLM concurrency=1 강제 + run_eval analyze 파라미터 추가

## 배경
1차 Phase 2.2 eval에서 발견: 23개 쿼리가 순차 호출되지만 각 request의
background analyzer task는 모두 동시에 MLX에 요청 날림 → MLX single-inference
서버 queue 폭발 → 22개가 15초 timeout. cache 채워지지 않음.

## 수정

### query_analyzer.py
 - LLM_CONCURRENCY = 1 상수 추가
 - _LLM_SEMAPHORE: lazy init asyncio.Semaphore (event loop 바인딩)
 - analyze() 내부: semaphore → timeout(실제 LLM 호출만) 이중 래핑
   semaphore 대기 시간이 timeout에 포함되지 않도록 주의

### run_eval.py
 - --analyze true|false 파라미터 추가 (Phase 2.1+ 측정용)
 - call_search / evaluate 시그니처에 analyze 전달

## 기대 효과
 - prewarm/background/동기 호출 모두 1개씩 순차 MLX 호출
 - 23개 대기 시 최악 230초 소요, 단 모두 성공해서 cache 채움
 - MLX 서버 부하 안정

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

tests/search_eval/run_eval.py

@@ -134,6 +134,7 @@ async def call_search(
     limit: int = 20,
     fusion: str | None = None,
     rerank: str | None = None,
+    analyze: str | None = None,
 ) -> tuple[list[int], float]:
     """검색 API 호출 → (doc_ids, latency_ms)."""
     url = f"{base_url.rstrip('/')}/api/search/"
@@ -143,6 +144,8 @@ async def call_search(
         params["fusion"] = fusion
     if rerank is not None:
         params["rerank"] = rerank
+    if analyze is not None:
+        params["analyze"] = analyze
 
     import time
 
@@ -169,6 +172,7 @@ async def evaluate(
     mode: str = "hybrid",
     fusion: str | None = None,
     rerank: str | None = None,
+    analyze: str | None = None,
 ) -> list[QueryResult]:
     """전체 쿼리셋 평가."""
     results: list[QueryResult] = []
@@ -177,7 +181,7 @@ async def evaluate(
         for q in queries:
             try:
                 returned_ids, latency_ms = await call_search(
-                    client, base_url, token, q.query, mode=mode, fusion=fusion, rerank=rerank
+                    client, base_url, token, q.query, mode=mode, fusion=fusion, rerank=rerank, analyze=analyze
                 )
                 results.append(
                     QueryResult(
@@ -415,6 +419,13 @@ def main() -> int:
         choices=["true", "false"],
         help="bge-reranker-v2-m3 활성화 (Phase 1.3+, 미지정 시 서버 기본값=true)",
     )
+    parser.add_argument(
+        "--analyze",
+        type=str,
+        default=None,
+        choices=["true", "false"],
+        help="QueryAnalyzer 활성화 (Phase 2.1+, cache hit 시 multilingual 적용)",
+    )
     parser.add_argument(
         "--token",
         type=str,
@@ -454,21 +465,21 @@ def main() -> int:
     if args.base_url:
         print(f"\n>>> evaluating: {args.base_url}")
         results = asyncio.run(
-            evaluate(queries, args.base_url, args.token, "single", mode=args.mode, fusion=args.fusion, rerank=args.rerank)
+            evaluate(queries, args.base_url, args.token, "single", mode=args.mode, fusion=args.fusion, rerank=args.rerank, analyze=args.analyze)
         )
         print_summary("single", results)
         all_results.extend(results)
     else:
         print(f"\n>>> baseline: {args.baseline_url}")
         baseline_results = asyncio.run(
-            evaluate(queries, args.baseline_url, args.token, "baseline", mode=args.mode, fusion=args.fusion, rerank=args.rerank)
+            evaluate(queries, args.baseline_url, args.token, "baseline", mode=args.mode, fusion=args.fusion, rerank=args.rerank, analyze=args.analyze)
         )
         baseline_summary = print_summary("baseline", baseline_results)
 
         print(f"\n>>> candidate: {args.candidate_url}")
         candidate_results = asyncio.run(
             evaluate(
-                queries, args.candidate_url, args.token, "candidate", mode=args.mode, fusion=args.fusion, rerank=args.rerank
+                queries, args.candidate_url, args.token, "candidate", mode=args.mode, fusion=args.fusion, rerank=args.rerank, analyze=args.analyze
             )
         )
         candidate_summary = print_summary("candidate", candidate_results)