feat(ask): Phase 3.5a guardrails (classifier + refusal gate + grounding + partial)

신규 파일:
- classifier_service.py: exaone binary classifier (sufficient/insufficient)
  parallel with evidence, circuit breaker, timeout 5s
- refusal_gate.py: multi-signal fusion (score + classifier)
  AND 조건, conservative fallback 3-tier (classifier 부재 시)
- grounding_check.py: strong/weak flag 분리
  strong: fabricated_number + intent_misalignment(important keywords)
  weak: uncited_claim + low_overlap + intent_misalignment(generic)
  re-gate: 2+ strong → refuse, 1 strong → partial
- sentence_splitter.py: regex 기반 (Phase 3.5b KSS 업그레이드)
- classifier.txt: exaone Y+ prompt (calibration examples 포함)
- search_synthesis_partial.txt: partial answer 전용 프롬프트
- 102_ask_events.sql: /ask 관측 테이블 (completeness 3-분리 지표)
- queries.yaml: Phase 3.5 smoke test 평가셋 10개

수정 파일:
- search.py /ask: classifier parallel + refusal gate + grounding re-gate
  + defense_layers 로깅 + AskResponse completeness/aspects/confirmed_items
- config.yaml: classifier model 섹션 (exaone3.5:7.8b GPU Ollama)
- config.py: classifier optional 파싱
- AskAnswer.svelte: 4분기 렌더 (full/partial/insufficient/loading)
- ask.ts: Completeness + ConfirmedItem 타입

P1 실측: exaone ternary 불안정 → binary gate 축소. partial은 grounding이 담당.
토론 9라운드 확정. plan: quiet-meandering-nova.md

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

app/core/config.py

@@ -24,6 +24,8 @@ class AIConfig(BaseModel):
     embedding: AIModelConfig
     vision: AIModelConfig
     rerank: AIModelConfig
+    # Phase 3.5a: exaone classifier (optional — 없으면 score-only gate)
+    classifier: AIModelConfig | None = None
 
 
 class Settings(BaseModel):
@@ -79,6 +81,11 @@ def load_settings() -> Settings:
                 embedding=AIModelConfig(**ai_raw["models"]["embedding"]),
                 vision=AIModelConfig(**ai_raw["models"]["vision"]),
                 rerank=AIModelConfig(**ai_raw["models"]["rerank"]),
+                classifier=(
+                    AIModelConfig(**ai_raw["models"]["classifier"])
+                    if "classifier" in ai_raw.get("models", {})
+                    else None
+                ),
             )
 
         if "nas" in raw: