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/services/brains/base.py

@@ -9,7 +9,7 @@ without coupling brains to Pipecat internals more than necessary.
 from __future__ import annotations
 
 from collections.abc import Awaitable, Callable
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Protocol, runtime_checkable
 
 from models import AssistantConfig
@@ -52,6 +52,13 @@ class BrainRuntime:
     set_system_prompt: Callable[[str], None]
     set_tools: Callable[[list[FunctionSchema] | None], None]
     call_end: CallEndPort
+    worker: Any = None
+    context_aggregator: Any = None
+    transport: Any = None
+    switch_services: Callable[[str | None, str | None], Awaitable[None]] | None = None
+    set_knowledge_scope: Callable[[dict[str, Any]], None] | None = None
+    set_input_enabled: Callable[[bool], None] | None = None
+    flow_global_functions: list[Any] = field(default_factory=list)
 
 
 class BaseBrain: