feat: Add Expert Transformation Agent with multi-expert perspective system

- Backend: Add expert transformation router with 3-step SSE pipeline
  - Step 0: Generate diverse expert team (random domains)
  - Step 1: Each expert generates keywords for attributes
  - Step 2: Batch generate descriptions for expert keywords
- Backend: Add simplified prompts for reliable JSON output
- Frontend: Add TransformationPanel with React Flow visualization
- Frontend: Add TransformationInputPanel for expert configuration
  - Expert count (2-8), keywords per expert (1-3)
  - Custom expert domains support
- Frontend: Add expert keyword nodes with expert badges
- Frontend: Improve description card layout (wider cards, more spacing)
- Frontend: Add fallback for missing descriptions with visual indicators

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

backend/app/models/schemas.py

@@ -131,3 +131,92 @@ class DAGRelationship(BaseModel):
     source: str  # source attribute name
     target_category: str
     target: str  # target attribute name
+
+
+# ===== Transformation Agent schemas =====
+
+class TransformationRequest(BaseModel):
+    """Transformation Agent 請求"""
+    query: str                        # 原始查詢 (e.g., "腳踏車")
+    category: str                     # 類別名稱 (e.g., "功能")
+    attributes: List[str]             # 該類別的屬性列表
+    model: Optional[str] = None
+    temperature: Optional[float] = 0.7
+    keyword_count: int = 3            # 要生成的新關鍵字數量
+
+
+class TransformationDescription(BaseModel):
+    """單一轉換描述"""
+    keyword: str                      # 新關鍵字
+    description: str                  # 與 query 結合的描述
+
+
+class TransformationCategoryResult(BaseModel):
+    """單一類別的轉換結果"""
+    category: str
+    original_attributes: List[str]    # 原始屬性
+    new_keywords: List[str]           # 新生成的關鍵字
+    descriptions: List[TransformationDescription]
+
+
+class TransformationDAGResult(BaseModel):
+    """完整 Transformation 結果"""
+    query: str
+    results: List[TransformationCategoryResult]
+
+
+# ===== Expert Transformation Agent schemas =====
+
+class ExpertProfile(BaseModel):
+    """專家檔案"""
+    id: str                           # e.g., "expert-0"
+    name: str                         # e.g., "藥師"
+    domain: str                       # e.g., "醫療與健康"
+    perspective: Optional[str] = None # e.g., "從藥物與健康管理角度思考"
+
+
+class ExpertKeyword(BaseModel):
+    """專家視角生成的關鍵字"""
+    keyword: str                      # 關鍵字本身
+    expert_id: str                    # 哪個專家生成的
+    expert_name: str                  # 專家名稱（冗餘，方便前端）
+    source_attribute: str             # 來自哪個原始屬性
+
+
+class ExpertTransformationDescription(BaseModel):
+    """專家關鍵字的描述"""
+    keyword: str
+    expert_id: str
+    expert_name: str
+    description: str
+
+
+class ExpertTransformationCategoryResult(BaseModel):
+    """單一類別的轉換結果（專家版）"""
+    category: str
+    original_attributes: List[str]
+    expert_keywords: List[ExpertKeyword]  # 所有專家生成的關鍵字
+    descriptions: List[ExpertTransformationDescription]
+
+
+class ExpertTransformationDAGResult(BaseModel):
+    """完整轉換結果（專家版）"""
+    query: str
+    experts: List[ExpertProfile]      # 使用的專家列表
+    results: List[ExpertTransformationCategoryResult]
+
+
+class ExpertTransformationRequest(BaseModel):
+    """Expert Transformation Agent 請求"""
+    query: str
+    category: str
+    attributes: List[str]
+
+    # Expert parameters
+    expert_count: int = 3                        # 專家數量 (2-8)
+    keywords_per_expert: int = 1                 # 每個專家為每個屬性生成幾個關鍵字 (1-3)
+    custom_experts: Optional[List[str]] = None   # 用戶指定專家 ["藥師", "工程師"]
+
+    # LLM parameters
+    model: Optional[str] = None
+    temperature: Optional[float] = 0.7