feat: AI 서비스 MLX 듀얼 백엔드 및 모델 최적화

- MLX(맥미니 27B) 우선 → Ollama(조립컴 9B) fallback 구조
- pydantic-settings 기반 config 전환
- health check에 MLX 상태 추가
- 텍스트 모델 qwen3:8b → qwen3.5:9b-q8_0 변경

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

ai-service/routers/classification.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
 from services.classification_service import (
     classify_issue,
@@ -26,7 +26,7 @@ async def classify(req: ClassifyRequest):
         result = await classify_issue(req.description, req.detail_notes)
         return {"available": True, **result}
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/summarize")
@@ -35,7 +35,7 @@ async def summarize(req: SummarizeRequest):
         result = await summarize_issue(req.description, req.detail_notes, req.solution)
         return {"available": True, **result}
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/classify-and-summarize")
@@ -44,4 +44,4 @@ async def classify_and_summarize_endpoint(req: ClassifyRequest):
         result = await classify_and_summarize(req.description, req.detail_notes)
         return {"available": True, **result}
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")

ai-service/routers/daily_report.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter, Request
+from fastapi import APIRouter, HTTPException, Request
 from pydantic import BaseModel
 from services.report_service import generate_daily_report
 from datetime import date
@@ -19,7 +19,7 @@ async def daily_report(req: DailyReportRequest, request: Request):
         result = await generate_daily_report(report_date, req.project_id, token)
         return {"available": True, **result}
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/report/preview")
@@ -30,4 +30,4 @@ async def report_preview(req: DailyReportRequest, request: Request):
         result = await generate_daily_report(report_date, req.project_id, token)
         return {"available": True, "preview": True, **result}
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")

ai-service/routers/embeddings.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter, BackgroundTasks, Query
+from fastapi import APIRouter, BackgroundTasks, HTTPException, Query
 from pydantic import BaseModel
 from services.embedding_service import (
     sync_all_issues,
@@ -53,7 +53,7 @@ async def get_similar(issue_id: int, n_results: int = Query(default=5, le=20)):
         results = await search_similar_by_id(issue_id, n_results)
         return {"available": True, "results": results, "query_issue_id": issue_id}
     except Exception as e:
-        return {"available": False, "results": [], "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/similar/search")
@@ -69,7 +69,7 @@ async def search_similar(req: SearchRequest):
         )
         return {"available": True, "results": results}
     except Exception as e:
-        return {"available": False, "results": [], "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.get("/embeddings/stats")

ai-service/routers/rag.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
 from services.rag_service import (
     rag_suggest_solution,
@@ -30,7 +30,7 @@ async def suggest_solution(issue_id: int):
     try:
         return await rag_suggest_solution(issue_id)
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/rag/ask")
@@ -38,7 +38,7 @@ async def ask_question(req: AskRequest):
     try:
         return await rag_ask(req.question, req.project_id)
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/rag/pattern")
@@ -46,7 +46,7 @@ async def analyze_pattern(req: PatternRequest):
     try:
         return await rag_analyze_pattern(req.description, req.n_results)
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")
 
 
 @router.post("/rag/classify")
@@ -54,4 +54,4 @@ async def classify_with_rag(req: ClassifyRequest):
     try:
         return await rag_classify_with_context(req.description, req.detail_notes)
     except Exception as e:
-        return {"available": False, "error": str(e)}
+        raise HTTPException(status_code=500, detail="AI 서비스 처리 중 오류가 발생했습니다")