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/embedding_service.py

@@ -0,0 +1,144 @@
+from services.ollama_client import ollama_client
+from db.vector_store import vector_store
+from db.metadata_store import metadata_store
+from services.db_client import get_all_issues, get_issue_by_id, get_issues_since
+
+
+def build_document_text(issue: dict) -> str:
+    parts = []
+    if issue.get("description"):
+        parts.append(issue["description"])
+    if issue.get("final_description"):
+        parts.append(issue["final_description"])
+    if issue.get("detail_notes"):
+        parts.append(issue["detail_notes"])
+    if issue.get("solution"):
+        parts.append(f"해결: {issue['solution']}")
+    if issue.get("management_comment"):
+        parts.append(f"의견: {issue['management_comment']}")
+    if issue.get("cause_detail"):
+        parts.append(f"원인: {issue['cause_detail']}")
+    return " ".join(parts)
+
+
+def build_metadata(issue: dict) -> dict:
+    meta = {"issue_id": issue["id"]}
+    for key in [
+        "category", "project_id", "review_status",
+        "responsible_department", "location_info",
+    ]:
+        val = issue.get(key)
+        if val is not None:
+            meta[key] = str(val)
+    rd = issue.get("report_date")
+    if rd:
+        meta["report_date"] = str(rd)[:10]
+    meta["has_solution"] = "true" if issue.get("solution") else "false"
+    return meta
+
+
+async def sync_all_issues() -> dict:
+    issues = get_all_issues()
+    synced = 0
+    skipped = 0
+    for issue in issues:
+        doc_text = build_document_text(issue)
+        if not doc_text.strip():
+            skipped += 1
+            continue
+        try:
+            embedding = await ollama_client.generate_embedding(doc_text)
+            vector_store.upsert(
+                doc_id=f"issue_{issue['id']}",
+                document=doc_text,
+                embedding=embedding,
+                metadata=build_metadata(issue),
+            )
+            synced += 1
+        except Exception as e:
+            skipped += 1
+    if issues:
+        max_id = max(i["id"] for i in issues)
+        metadata_store.set_last_synced_id(max_id)
+    return {"synced": synced, "skipped": skipped, "total": len(issues)}
+
+
+async def sync_single_issue(issue_id: int) -> dict:
+    issue = get_issue_by_id(issue_id)
+    if not issue:
+        return {"status": "not_found"}
+    doc_text = build_document_text(issue)
+    if not doc_text.strip():
+        return {"status": "empty_text"}
+    embedding = await ollama_client.generate_embedding(doc_text)
+    vector_store.upsert(
+        doc_id=f"issue_{issue['id']}",
+        document=doc_text,
+        embedding=embedding,
+        metadata=build_metadata(issue),
+    )
+    return {"status": "synced", "issue_id": issue_id}
+
+
+async def sync_incremental() -> dict:
+    last_id = metadata_store.get_last_synced_id()
+    issues = get_issues_since(last_id)
+    synced = 0
+    for issue in issues:
+        doc_text = build_document_text(issue)
+        if not doc_text.strip():
+            continue
+        try:
+            embedding = await ollama_client.generate_embedding(doc_text)
+            vector_store.upsert(
+                doc_id=f"issue_{issue['id']}",
+                document=doc_text,
+                embedding=embedding,
+                metadata=build_metadata(issue),
+            )
+            synced += 1
+        except Exception:
+            pass
+    if issues:
+        max_id = max(i["id"] for i in issues)
+        metadata_store.set_last_synced_id(max_id)
+    return {"synced": synced, "new_issues": len(issues)}
+
+
+async def search_similar_by_id(issue_id: int, n_results: int = 5) -> list[dict]:
+    issue = get_issue_by_id(issue_id)
+    if not issue:
+        return []
+    doc_text = build_document_text(issue)
+    if not doc_text.strip():
+        return []
+    embedding = await ollama_client.generate_embedding(doc_text)
+    results = vector_store.query(
+        embedding=embedding,
+        n_results=n_results + 1,
+    )
+    # exclude self
+    filtered = []
+    for r in results:
+        if r["id"] != f"issue_{issue_id}":
+            filtered.append(r)
+    return filtered[:n_results]
+
+
+async def search_similar_by_text(query: str, n_results: int = 5, filters: dict = None) -> list[dict]:
+    embedding = await ollama_client.generate_embedding(query)
+    where = None
+    if filters:
+        conditions = []
+        for k, v in filters.items():
+            if v is not None:
+                conditions.append({k: str(v)})
+        if len(conditions) == 1:
+            where = conditions[0]
+        elif len(conditions) > 1:
+            where = {"$and": conditions}
+    return vector_store.query(
+        embedding=embedding,
+        n_results=n_results,
+        where=where,
+    )