style: 统一代码风格并采用现代化类型注解

对整个代码库进行了一次全面的代码风格清理和现代化改造，主要包括：

- 移除了所有文件中多余的行尾空格。
- 将类型提示更新为 PEP 585 和 PEP 604 引入的现代语法（例如，使用 `list` 代替 `List`，使用 `|` 代替 `Optional`）。
- 清理了多个模块中未被使用的导入语句。
- 移除了不含插值变量的冗余 f-string。
- 调整了部分 `__init__.py` 文件中的 `__all__` 导出顺序，以保持一致性。

这些改动旨在提升代码的可读性和可维护性，使其与现代 Python 最佳实践保持一致，但未修改任何核心逻辑。

src/api/memory_visualizer_router.py

@@ -4,13 +4,13 @@
 提供 Web API 用于可视化记忆图数据
 """
 
+from collections import defaultdict
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, List, Optional
-from collections import defaultdict
+from typing import Any
 
 import orjson
-from fastapi import APIRouter, HTTPException, Request, Query
+from fastapi import APIRouter, HTTPException, Query, Request
 from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.templating import Jinja2Templates
 
@@ -29,7 +29,7 @@ router = APIRouter()
 templates = Jinja2Templates(directory=str(Path(__file__).parent / "templates"))
 
 
-def find_available_data_files() -> List[Path]:
+def find_available_data_files() -> list[Path]:
     """查找所有可用的记忆图数据文件"""
     files = []
     if not data_dir.exists():
@@ -62,7 +62,7 @@ def find_available_data_files() -> List[Path]:
     return sorted(files, key=lambda f: f.stat().st_mtime, reverse=True)
 
 
-def load_graph_data_from_file(file_path: Optional[Path] = None) -> Dict[str, Any]:
+def load_graph_data_from_file(file_path: Path | None = None) -> dict[str, Any]:
     """从磁盘加载图数据"""
     global graph_data_cache, current_data_file
 
@@ -85,7 +85,7 @@ def load_graph_data_from_file(file_path: Optional[Path] = None) -> Dict[str, Any
         if not graph_file.exists():
             return {"error": f"文件不存在: {graph_file}", "nodes": [], "edges": [], "stats": {}}
 
-        with open(graph_file, "r", encoding="utf-8") as f:
+        with open(graph_file, encoding="utf-8") as f:
             data = orjson.loads(f.read())
 
         nodes = data.get("nodes", [])
@@ -150,7 +150,7 @@ async def index(request: Request):
     return templates.TemplateResponse("visualizer.html", {"request": request})
 
 
-def _format_graph_data_from_manager(memory_manager) -> Dict[str, Any]:
+def _format_graph_data_from_manager(memory_manager) -> dict[str, Any]:
     """从 MemoryManager 提取并格式化图数据"""
     if not memory_manager.graph_store:
         return {"nodes": [], "edges": [], "memories": [], "stats": {}}
@@ -188,7 +188,7 @@ def _format_graph_data_from_manager(memory_manager) -> Dict[str, Any]:
                     "arrows": "to",
                     "memory_id": memory.id,
                 }
-    
+
     edges_list = list(edges_dict.values())
 
     stats = memory_manager.get_statistics()
@@ -261,7 +261,7 @@ async def get_paginated_graph(
     page: int = Query(1, ge=1, description="页码"),
     page_size: int = Query(500, ge=100, le=2000, description="每页节点数"),
     min_importance: float = Query(0.0, ge=0.0, le=1.0, description="最小重要性阈值"),
-    node_types: Optional[str] = Query(None, description="节点类型过滤，逗号分隔"),
+    node_types: str | None = Query(None, description="节点类型过滤，逗号分隔"),
 ):
     """分页获取图数据，支持重要性过滤"""
     try:
@@ -301,13 +301,13 @@ async def get_paginated_graph(
         total_pages = (total_nodes + page_size - 1) // page_size
         start_idx = (page - 1) * page_size
         end_idx = min(start_idx + page_size, total_nodes)
-        
+
         paginated_nodes = nodes_with_importance[start_idx:end_idx]
         node_ids = set(n["id"] for n in paginated_nodes)
 
         # 只保留连接分页节点的边
         paginated_edges = [
-            e for e in edges 
+            e for e in edges
             if e.get("from") in node_ids and e.get("to") in node_ids
         ]
 
@@ -383,7 +383,7 @@ async def get_clustered_graph(
         return JSONResponse(content={"success": False, "error": str(e)}, status_code=500)
 
 
-def _cluster_graph_data(nodes: List[Dict], edges: List[Dict], max_nodes: int, cluster_threshold: int) -> Dict:
+def _cluster_graph_data(nodes: list[dict], edges: list[dict], max_nodes: int, cluster_threshold: int) -> dict:
     """简单的图聚类算法：按类型和连接度聚类"""
     # 构建邻接表
     adjacency = defaultdict(set)
@@ -412,21 +412,21 @@ def _cluster_graph_data(nodes: List[Dict], edges: List[Dict], max_nodes: int, cl
             for node in type_nodes:
                 importance = len(adjacency[node["id"]])
                 node_importance.append((node, importance))
-            
+
             node_importance.sort(key=lambda x: x[1], reverse=True)
-            
+
             # 保留前N个重要节点
             keep_count = min(len(type_nodes), max_nodes // len(type_groups))
             for node, importance in node_importance[:keep_count]:
                 clustered_nodes.append(node)
                 node_mapping[node["id"]] = node["id"]
-            
+
             # 其余节点聚合为一个超级节点
             if len(node_importance) > keep_count:
                 clustered_node_ids = [n["id"] for n, _ in node_importance[keep_count:]]
                 cluster_id = f"cluster_{node_type}_{len(clustered_nodes)}"
                 cluster_label = f"{node_type} 集群 ({len(clustered_node_ids)}个节点)"
-                
+
                 clustered_nodes.append({
                     "id": cluster_id,
                     "label": cluster_label,
@@ -436,7 +436,7 @@ def _cluster_graph_data(nodes: List[Dict], edges: List[Dict], max_nodes: int, cl
                     "cluster_size": len(clustered_node_ids),
                     "clustered_nodes": clustered_node_ids[:10],  # 只保留前10个用于展示
                 })
-                
+
                 for node_id in clustered_node_ids:
                     node_mapping[node_id] = cluster_id
 
@@ -445,7 +445,7 @@ def _cluster_graph_data(nodes: List[Dict], edges: List[Dict], max_nodes: int, cl
     for edge in edges:
         from_id = node_mapping.get(edge["from"])
         to_id = node_mapping.get(edge["to"])
-        
+
         if from_id and to_id and from_id != to_id:
             edge_key = tuple(sorted([from_id, to_id]))
             if edge_key not in edge_set:

src/api/statistic_router.py

@@ -1,6 +1,5 @@
-from collections import defaultdict
 from datetime import datetime, timedelta
-from typing import Any, Literal
+from typing import Literal
 
 from fastapi import APIRouter, HTTPException, Query