Add workflow support and enhance runtime configuration in models and services

- Introduce RuntimeModelResource and RuntimeKnowledgeBase classes to manage workflow resources.
- Update AssistantConfig to include workflow_model_resources and workflow_knowledge_bases for better integration.
- Refactor validation and processing logic in routes and services to accommodate workflow types.
- Implement dynamic variable support for workflow assistants and enhance graph normalization.
- Add ToolExecutor for reusable tool execution across different assistant types.
- Update various services to ensure compatibility with new workflow features and improve error handling.

backend/models.py

@@ -27,6 +27,21 @@ class RuntimeTool(BaseModel):
     secrets: dict = Field(default_factory=dict)
 
 
+class RuntimeModelResource(BaseModel):
+    id: str
+    name: str = ""
+    capability: str
+    interface_type: str
+    values: dict = Field(default_factory=dict)
+    secrets: dict = Field(default_factory=dict)
+
+
+class RuntimeKnowledgeBase(BaseModel):
+    id: str
+    name: str = ""
+    description: str = ""
+
+
 class AssistantConfig(BaseModel):
     """运行时配置:前端可见部分(name/prompt/...) + 服务端注入部分(*_api_key/*_base_url)。"""
 
@@ -93,6 +108,8 @@ class AssistantConfig(BaseModel):
 
     # workflow 类型:节点图(nodes/edges)。非 workflow 为空,引擎据此决定是否启用。
     graph: dict = {}
+    workflow_model_resources: dict[str, RuntimeModelResource] = Field(default_factory=dict)
+    workflow_knowledge_bases: dict[str, RuntimeKnowledgeBase] = Field(default_factory=dict)
 
     # 外部托管类型(fastgpt/dify/opencode)的连接信息:context/KB/tools 由对方服务端接管。
     dify_api_url: str = ""