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/rag_service.py

@@ -0,0 +1,164 @@
+from services.ollama_client import ollama_client
+from services.embedding_service import search_similar_by_text, build_document_text
+from services.db_client import get_issue_by_id
+
+
+def _load_prompt(path: str) -> str:
+    with open(path, "r", encoding="utf-8") as f:
+        return f.read()
+
+
+def _format_retrieved_issues(results: list[dict]) -> str:
+    if not results:
+        return "관련 과거 사례가 없습니다."
+    lines = []
+    for i, r in enumerate(results, 1):
+        meta = r.get("metadata", {})
+        similarity = round(r.get("similarity", 0) * 100)
+        doc = (r.get("document", ""))[:500]
+        cat = meta.get("category", "")
+        dept = meta.get("responsible_department", "")
+        status = meta.get("review_status", "")
+        has_sol = meta.get("has_solution", "false")
+        date = meta.get("report_date", "")
+        issue_id = meta.get("issue_id", r["id"])
+        lines.append(
+            f"[사례 {i}] No.{issue_id} (유사도 {similarity}%)\n"
+            f"  분류: {cat} | 부서: {dept} | 상태: {status} | 날짜: {date} | 해결여부: {'O' if has_sol == 'true' else 'X'}\n"
+            f"  내용: {doc}"
+        )
+    return "\n\n".join(lines)
+
+
+async def rag_suggest_solution(issue_id: int) -> dict:
+    """과거 유사 이슈의 해결 사례를 참고하여 해결방안을 제안"""
+    issue = get_issue_by_id(issue_id)
+    if not issue:
+        return {"available": False, "error": "이슈를 찾을 수 없습니다"}
+
+    doc_text = build_document_text(issue)
+    if not doc_text.strip():
+        return {"available": False, "error": "이슈 내용이 비어있습니다"}
+
+    # 해결 완료된 유사 이슈 검색
+    similar = await search_similar_by_text(
+        doc_text, n_results=5, filters={"has_solution": "true"}
+    )
+    # 해결 안 된 것도 포함 (참고용)
+    if len(similar) < 3:
+        all_similar = await search_similar_by_text(doc_text, n_results=5)
+        seen = {r["id"] for r in similar}
+        for r in all_similar:
+            if r["id"] not in seen:
+                similar.append(r)
+            if len(similar) >= 5:
+                break
+
+    context = _format_retrieved_issues(similar)
+    template = _load_prompt("prompts/rag_suggest_solution.txt")
+    prompt = template.format(
+        description=issue.get("description", ""),
+        detail_notes=issue.get("detail_notes", ""),
+        category=issue.get("category", ""),
+        retrieved_cases=context,
+    )
+
+    response = await ollama_client.generate_text(prompt)
+    return {
+        "available": True,
+        "issue_id": issue_id,
+        "suggestion": response,
+        "referenced_issues": [
+            {
+                "id": r.get("metadata", {}).get("issue_id", r["id"]),
+                "similarity": round(r.get("similarity", 0) * 100),
+                "has_solution": r.get("metadata", {}).get("has_solution", "false") == "true",
+            }
+            for r in similar
+        ],
+    }
+
+
+async def rag_ask(question: str, project_id: int = None) -> dict:
+    """부적합 데이터를 기반으로 자연어 질문에 답변"""
+    # 프로젝트 필터 없이 전체 데이터에서 검색 (과거 미지정 데이터 포함)
+    results = await search_similar_by_text(
+        question, n_results=15, filters=None
+    )
+    context = _format_retrieved_issues(results)
+
+    template = _load_prompt("prompts/rag_qa.txt")
+    prompt = template.format(
+        question=question,
+        retrieved_cases=context,
+    )
+
+    response = await ollama_client.generate_text(prompt)
+    return {
+        "available": True,
+        "answer": response,
+        "sources": [
+            {
+                "id": r.get("metadata", {}).get("issue_id", r["id"]),
+                "similarity": round(r.get("similarity", 0) * 100),
+                "snippet": (r.get("document", ""))[:100],
+            }
+            for r in results
+        ],
+    }
+
+
+async def rag_analyze_pattern(description: str, n_results: int = 10) -> dict:
+    """유사 부적합 패턴 분석 — 반복되는 문제인지, 근본 원인은 무엇인지"""
+    results = await search_similar_by_text(description, n_results=n_results)
+    context = _format_retrieved_issues(results)
+
+    template = _load_prompt("prompts/rag_pattern.txt")
+    prompt = template.format(
+        description=description,
+        retrieved_cases=context,
+        total_similar=len(results),
+    )
+
+    response = await ollama_client.generate_text(prompt)
+    return {
+        "available": True,
+        "analysis": response,
+        "similar_count": len(results),
+        "sources": [
+            {
+                "id": r.get("metadata", {}).get("issue_id", r["id"]),
+                "similarity": round(r.get("similarity", 0) * 100),
+                "category": r.get("metadata", {}).get("category", ""),
+            }
+            for r in results
+        ],
+    }
+
+
+async def rag_classify_with_context(description: str, detail_notes: str = "") -> dict:
+    """과거 사례를 참고하여 더 정확한 분류 수행 (기존 classify 강화)"""
+    query = f"{description} {detail_notes}".strip()
+    similar = await search_similar_by_text(query, n_results=5)
+    context = _format_retrieved_issues(similar)
+
+    template = _load_prompt("prompts/rag_classify.txt")
+    prompt = template.format(
+        description=description,
+        detail_notes=detail_notes,
+        retrieved_cases=context,
+    )
+
+    raw = await ollama_client.generate_text(prompt)
+    import json
+    try:
+        start = raw.find("{")
+        end = raw.rfind("}") + 1
+        if start >= 0 and end > start:
+            result = json.loads(raw[start:end])
+            result["rag_enhanced"] = True
+            result["referenced_count"] = len(similar)
+            return {"available": True, **result}
+    except json.JSONDecodeError:
+        pass
+    return {"available": True, "raw_response": raw, "rag_enhanced": True}