feat: AI 서비스 및 AI 어시스턴트 전용 페이지 추가

- ai-service: Ollama 기반 AI 서비스 (분류, 시맨틱 검색, RAG Q&A, 패턴 분석)
- AI 어시스턴트 페이지: 채팅형 Q&A, 시맨틱 검색, 패턴 분석, 분류 테스트
- 권한 시스템에 ai_assistant 페이지 등록 (기본 비활성)
- 기존 페이지에 AI 기능 통합 (대시보드, 수신함, 관리함)
- docker-compose, gateway, nginx 설정 업데이트

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

ai-service/routers/classification.py

@@ -0,0 +1,47 @@
+from fastapi import APIRouter
+from pydantic import BaseModel
+from services.classification_service import (
+    classify_issue,
+    summarize_issue,
+    classify_and_summarize,
+)
+
+router = APIRouter(tags=["classification"])
+
+
+class ClassifyRequest(BaseModel):
+    description: str
+    detail_notes: str = ""
+
+
+class SummarizeRequest(BaseModel):
+    description: str
+    detail_notes: str = ""
+    solution: str = ""
+
+
+@router.post("/classify")
+async def classify(req: ClassifyRequest):
+    try:
+        result = await classify_issue(req.description, req.detail_notes)
+        return {"available": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}
+
+
+@router.post("/summarize")
+async def summarize(req: SummarizeRequest):
+    try:
+        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)}
+
+
+@router.post("/classify-and-summarize")
+async def classify_and_summarize_endpoint(req: ClassifyRequest):
+    try:
+        result = await classify_and_summarize(req.description, req.detail_notes)
+        return {"available": True, **result}
+    except Exception as e:
+        return {"available": False, "error": str(e)}

ai-service/routers/daily_report.py

@@ -0,0 +1,33 @@
+from fastapi import APIRouter, Request
+from pydantic import BaseModel
+from services.report_service import generate_daily_report
+from datetime import date
+
+router = APIRouter(tags=["daily_report"])
+
+
+class DailyReportRequest(BaseModel):
+    date: str | None = None
+    project_id: int | None = None
+
+
+@router.post("/report/daily")
+async def daily_report(req: DailyReportRequest, request: Request):
+    report_date = req.date or date.today().isoformat()
+    token = request.headers.get("authorization", "").replace("Bearer ", "")
+    try:
+        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)}
+
+
+@router.post("/report/preview")
+async def report_preview(req: DailyReportRequest, request: Request):
+    report_date = req.date or date.today().isoformat()
+    token = request.headers.get("authorization", "").replace("Bearer ", "")
+    try:
+        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)}

ai-service/routers/embeddings.py

@@ -0,0 +1,77 @@
+from fastapi import APIRouter, BackgroundTasks, Query
+from pydantic import BaseModel
+from services.embedding_service import (
+    sync_all_issues,
+    sync_single_issue,
+    sync_incremental,
+    search_similar_by_id,
+    search_similar_by_text,
+)
+from db.vector_store import vector_store
+
+router = APIRouter(tags=["embeddings"])
+
+
+class SyncSingleRequest(BaseModel):
+    issue_id: int
+
+
+class SearchRequest(BaseModel):
+    query: str
+    n_results: int = 5
+    project_id: int | None = None
+    category: str | None = None
+
+
+@router.post("/embeddings/sync")
+async def sync_embeddings(background_tasks: BackgroundTasks):
+    background_tasks.add_task(sync_all_issues)
+    return {"status": "sync_started", "message": "전체 임베딩 동기화가 시작되었습니다"}
+
+
+@router.post("/embeddings/sync-full")
+async def sync_embeddings_full():
+    result = await sync_all_issues()
+    return {"status": "completed", **result}
+
+
+@router.post("/embeddings/sync-single")
+async def sync_single(req: SyncSingleRequest):
+    result = await sync_single_issue(req.issue_id)
+    return result
+
+
+@router.post("/embeddings/sync-incremental")
+async def sync_incr():
+    result = await sync_incremental()
+    return result
+
+
+@router.get("/similar/{issue_id}")
+async def get_similar(issue_id: int, n_results: int = Query(default=5, le=20)):
+    try:
+        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)}
+
+
+@router.post("/similar/search")
+async def search_similar(req: SearchRequest):
+    filters = {}
+    if req.project_id is not None:
+        filters["project_id"] = str(req.project_id)
+    if req.category:
+        filters["category"] = req.category
+    try:
+        results = await search_similar_by_text(
+            req.query, req.n_results, filters or None
+        )
+        return {"available": True, "results": results}
+    except Exception as e:
+        return {"available": False, "results": [], "error": str(e)}
+
+
+@router.get("/embeddings/stats")
+async def embedding_stats():
+    return vector_store.stats()

ai-service/routers/health.py

@@ -0,0 +1,21 @@
+from fastapi import APIRouter
+from services.ollama_client import ollama_client
+from db.vector_store import vector_store
+
+router = APIRouter(tags=["health"])
+
+
+@router.get("/health")
+async def health_check():
+    ollama_status = await ollama_client.check_health()
+    return {
+        "status": "ok",
+        "service": "tk-ai-service",
+        "ollama": ollama_status,
+        "embeddings": vector_store.stats(),
+    }
+
+
+@router.get("/models")
+async def list_models():
+    return await ollama_client.check_health()

ai-service/routers/rag.py

@@ -0,0 +1,57 @@
+from fastapi import APIRouter
+from pydantic import BaseModel
+from services.rag_service import (
+    rag_suggest_solution,
+    rag_ask,
+    rag_analyze_pattern,
+    rag_classify_with_context,
+)
+
+router = APIRouter(tags=["rag"])
+
+
+class AskRequest(BaseModel):
+    question: str
+    project_id: int | None = None
+
+
+class PatternRequest(BaseModel):
+    description: str
+    n_results: int = 10
+
+
+class ClassifyRequest(BaseModel):
+    description: str
+    detail_notes: str = ""
+
+
+@router.post("/rag/suggest-solution/{issue_id}")
+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)}
+
+
+@router.post("/rag/ask")
+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)}
+
+
+@router.post("/rag/pattern")
+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)}
+
+
+@router.post("/rag/classify")
+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)}