Fix: use Container class (not container instance) in eval script

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

eval/run_ragas.py

@@ -60,7 +60,11 @@ ROOT = Path(__file__).resolve().parent.parent
 sys.path.insert(0, str(ROOT))
 os.chdir(ROOT)  # .env 읽기 위해 프로젝트 루트로 이동
 
-from container import container  # noqa: E402 (after sys.path setup)
+from container import Container  # noqa: E402 (after sys.path setup)
+
+_container = Container()
+_container.db_service().connect()
+_container.db_service().init_schema()
 
 
 # ── Answer collection via API ────────────────────────────────────────────────
@@ -111,11 +115,11 @@ def _build_evaluator_llm():
         )
 
     print("[RAGAS] 평가 LLM: 로컬 Qwen3 (신뢰도 제한적)")
-    return LangchainLLMWrapper(container.chat_model())
+    return LangchainLLMWrapper(_container.chat_model())
 
 
 def _build_evaluator_embeddings():
-    return LangchainEmbeddingsWrapper(container.embeddings())
+    return LangchainEmbeddingsWrapper(_container.embeddings())
 
 
 # ── Main ──────────────────────────────────────────────────────────────────────
@@ -136,7 +140,7 @@ def run(dataset_path: str, api_url: str, api_token: str) -> None:
     print(f"[RAGAS] 평가 시작 — {len(samples)}개 질문, API: {api_url}")
 
     # 2. RetrieverService 초기화
-    retriever = container.retriever_service()
+    retriever = _container.retriever_service()
 
     # 3. 질문별 context + answer 수집
     questions: list[str] = []