refactor(core): decouple actors and remove workflow templates (#67)

Removes the deprecated `workflow_template` concept entirely across both backend API routers, internal logic handling within the `supervisory_node` and `consciousness_node`, and front-end components. Enables `consciousness_node` to work autonomously.

Also refactors core package structure to enforce the "one python package, one Ray Actor" architectural rule. `GlobalWorkflowManager`, `WorkflowRunningEngine`, `PostgresDatabase`, and `WorkerCluster` have been moved to their own top-level decoupled package directories with properly exported `__init__.py` modules. Test suites have been relocated and import paths updated across the system.

Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: zhaoxi826 <198742034+zhaoxi826@users.noreply.github.com>

pretor/utils/get_tool.py

@@ -18,7 +18,8 @@ import sys
 from typing import Callable, Dict, List
 
 from pretor.utils.logger import get_logger
-logger = get_logger('get_tool')
+
+logger = get_logger("get_tool")
 _tool_cache: Dict[str, Callable] = {}
 
 
@@ -26,13 +27,15 @@ def _get_tool_func(tool_name: str) -> Callable | None:
     """检索并获取特定的  tool func 数据集合或实例对象。
     根据提供的查询条件或上下文凭证，从数据库、缓存或第三方服务中读取对应的资源状态。
     Args:        tool_name (str): 赋予该实体的人类可读名称或标题字符串，主要用于前端 UI 展示、日志记录或模糊检索。
-    Returns:         (Callable | None): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。    """
+    Returns:         (Callable | None): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
     func = _tool_cache.get(tool_name, None)
     if func:
         return func
 
     app_root = "/app"
-    tool_plugin_dir = os.path.join(app_root, "pretor", "plugin", "tool_plugin", tool_name)
+    tool_plugin_dir = os.path.join(
+        app_root, "pretor", "plugin", "tool_plugin", tool_name
+    )
 
     if not os.path.exists(tool_plugin_dir) or not os.path.isdir(tool_plugin_dir):
         logger.error(f"Tool directory not found: {tool_plugin_dir}")
@@ -57,7 +60,9 @@ def _get_tool_func(tool_name: str) -> Callable | None:
         func = getattr(module, tool_name, None)
 
         if not callable(func):
-            logger.error(f"Tool function '{tool_name}' not found or not callable in {module_name}")
+            logger.error(
+                f"Tool function '{tool_name}' not found or not callable in {module_name}"
+            )
             return None
         _tool_cache[tool_name] = func
         return func
@@ -65,19 +70,21 @@ def _get_tool_func(tool_name: str) -> Callable | None:
         logger.error(f"Failed to load module {module_name}: {e}")
         return None
 
+
 def del_tool_cache(tool_name: str) -> None:
     """执行与 del tool cache 相关的核心业务流转操作。
     该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
     Args:        tool_name (str): 赋予该实体的人类可读名称或标题字符串，主要用于前端 UI 展示、日志记录或模糊检索。
-    Returns:         (None): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。    """
+    Returns:         (None): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
     if tool_name in _tool_cache:
         del _tool_cache[tool_name]
 
+
 def load_tools_from_list(tool_names: List[str] | None) -> List[Callable]:
     """执行与 load tools from list 相关的核心业务流转操作。
     该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
     Args:        tool_names (List[str] | None): 赋予该实体的人类可读名称或标题字符串，主要用于前端 UI 展示、日志记录或模糊检索。
-    Returns:         (List[Callable]): 经过筛选、排序或分页处理后的实体对象列表集合。    """
+    Returns:         (List[Callable]): 经过筛选、排序或分页处理后的实体对象列表集合。"""
     if not tool_names:
         return []
 
@@ -87,4 +94,4 @@ def load_tools_from_list(tool_names: List[str] | None) -> List[Callable]:
         if tool_func:
             tool_list.append(tool_func)
 
-    return tool_list
+    return tool_list