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/services/classification_service.py

@@ -0,0 +1,60 @@
+import json
+from services.ollama_client import ollama_client
+from config import settings
+
+
+CLASSIFY_PROMPT_PATH = "prompts/classify_issue.txt"
+SUMMARIZE_PROMPT_PATH = "prompts/summarize_issue.txt"
+
+
+def _load_prompt(path: str) -> str:
+    with open(path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+async def classify_issue(description: str, detail_notes: str = "") -> dict:
+    template = _load_prompt(CLASSIFY_PROMPT_PATH)
+    prompt = template.format(
+        description=description or "",
+        detail_notes=detail_notes or "",
+    )
+    raw = await ollama_client.generate_text(prompt)
+    try:
+        start = raw.find("{")
+        end = raw.rfind("}") + 1
+        if start >= 0 and end > start:
+            return json.loads(raw[start:end])
+    except json.JSONDecodeError:
+        pass
+    return {"raw_response": raw, "parse_error": True}
+
+
+async def summarize_issue(
+    description: str, detail_notes: str = "", solution: str = ""
+) -> dict:
+    template = _load_prompt(SUMMARIZE_PROMPT_PATH)
+    prompt = template.format(
+        description=description or "",
+        detail_notes=detail_notes or "",
+        solution=solution or "",
+    )
+    raw = await ollama_client.generate_text(prompt)
+    try:
+        start = raw.find("{")
+        end = raw.rfind("}") + 1
+        if start >= 0 and end > start:
+            return json.loads(raw[start:end])
+    except json.JSONDecodeError:
+        pass
+    return {"summary": raw.strip()}
+
+
+async def classify_and_summarize(
+    description: str, detail_notes: str = ""
+) -> dict:
+    classification = await classify_issue(description, detail_notes)
+    summary_result = await summarize_issue(description, detail_notes)
+    return {
+        "classification": classification,
+        "summary": summary_result.get("summary", ""),
+    }