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/db/metadata_store.py

@@ -0,0 +1,39 @@
+import sqlite3
+from config import settings
+
+
+class MetadataStore:
+    def __init__(self):
+        self.db_path = settings.METADATA_DB_PATH
+
+    def initialize(self):
+        conn = sqlite3.connect(self.db_path)
+        conn.execute(
+            "CREATE TABLE IF NOT EXISTS sync_state ("
+            "  key TEXT PRIMARY KEY,"
+            "  value TEXT"
+            ")"
+        )
+        conn.commit()
+        conn.close()
+
+    def get_last_synced_id(self) -> int:
+        conn = sqlite3.connect(self.db_path)
+        cur = conn.execute(
+            "SELECT value FROM sync_state WHERE key = 'last_synced_id'"
+        )
+        row = cur.fetchone()
+        conn.close()
+        return int(row[0]) if row else 0
+
+    def set_last_synced_id(self, issue_id: int):
+        conn = sqlite3.connect(self.db_path)
+        conn.execute(
+            "INSERT OR REPLACE INTO sync_state (key, value) VALUES ('last_synced_id', ?)",
+            (str(issue_id),),
+        )
+        conn.commit()
+        conn.close()
+
+
+metadata_store = MetadataStore()

ai-service/db/vector_store.py

@@ -0,0 +1,76 @@
+import chromadb
+from config import settings
+
+
+class VectorStore:
+    def __init__(self):
+        self.client = None
+        self.collection = None
+
+    def initialize(self):
+        self.client = chromadb.PersistentClient(path=settings.CHROMA_PERSIST_DIR)
+        self.collection = self.client.get_or_create_collection(
+            name="qc_issues",
+            metadata={"hnsw:space": "cosine"},
+        )
+
+    def upsert(
+        self,
+        doc_id: str,
+        document: str,
+        embedding: list[float],
+        metadata: dict = None,
+    ):
+        self.collection.upsert(
+            ids=[doc_id],
+            documents=[document],
+            embeddings=[embedding],
+            metadatas=[metadata] if metadata else None,
+        )
+
+    def query(
+        self,
+        embedding: list[float],
+        n_results: int = 5,
+        where: dict = None,
+    ) -> list[dict]:
+        kwargs = {
+            "query_embeddings": [embedding],
+            "n_results": n_results,
+            "include": ["documents", "metadatas", "distances"],
+        }
+        if where:
+            kwargs["where"] = where
+        try:
+            results = self.collection.query(**kwargs)
+        except Exception:
+            return []
+
+        items = []
+        if results and results["ids"] and results["ids"][0]:
+            for i, doc_id in enumerate(results["ids"][0]):
+                item = {
+                    "id": doc_id,
+                    "document": results["documents"][0][i] if results["documents"] else "",
+                    "distance": results["distances"][0][i] if results["distances"] else 0,
+                    "metadata": results["metadatas"][0][i] if results["metadatas"] else {},
+                }
+                # cosine distance → similarity
+                item["similarity"] = round(1 - item["distance"], 4)
+                items.append(item)
+        return items
+
+    def delete(self, doc_id: str):
+        self.collection.delete(ids=[doc_id])
+
+    def count(self) -> int:
+        return self.collection.count()
+
+    def stats(self) -> dict:
+        return {
+            "total_documents": self.count(),
+            "collection_name": "qc_issues",
+        }
+
+
+vector_store = VectorStore()