gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

style: 统一代码风格并采用现代化类型注解

Browse Source 
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。
This commit is contained in:
minecraft1024a
2025-11-12 12:49:40 +08:00
parent daf8ea7e6a
commit 0e1e9935b2
33 changed files with 227 additions and 229 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
src/memory_graph/manager.py
View File

@@ -149,7 +149,7 @@ class MemoryManager:
# 读取阈值过滤配置
search_min_importance = self.config.search_min_importance
search_similarity_threshold = self.config.search_similarity_threshold
logger.info(
f"📊 配置检查: search_max_expand_depth={expand_depth}, "
f"search_expand_semantic_threshold={expand_semantic_threshold}, "
@@ -415,7 +415,7 @@ class MemoryManager:
# 使用配置的默认值
if top_k is None:
top_k = getattr(self.config, "search_top_k", 10)
# 准备搜索参数
params = {
"query": query,
@@ -948,7 +948,7 @@ class MemoryManager:
)
else:
logger.debug(f"记忆已删除: {memory_id} (删除了 {deleted_vectors} 个向量)")
# 4. 保存更新
await self.persistence.save_graph_store(self.graph_store)
return True
@@ -981,7 +981,7 @@ class MemoryManager:
try:
forgotten_count = 0
all_memories = self.graph_store.get_all_memories()
# 获取配置参数
min_importance = getattr(self.config, "forgetting_min_importance", 0.8)
decay_rate = getattr(self.config, "activation_decay_rate", 0.9)
@@ -1007,10 +1007,10 @@ class MemoryManager:
try:
last_access_dt = datetime.fromisoformat(last_access)
days_passed = (datetime.now() - last_access_dt).days
# 应用指数衰减：activation = base * (decay_rate ^ days)
current_activation = base_activation * (decay_rate ** days_passed)
logger.debug(
f"记忆 {memory.id[:8]}: 基础激活度={base_activation:.3f}, "
f"经过{days_passed}天衰减后={current_activation:.3f}"
@@ -1032,20 +1032,20 @@ class MemoryManager:
# 批量遗忘记忆（不立即清理孤立节点）
if memories_to_forget:
logger.info(f"开始批量遗忘 {len(memories_to_forget)} 条记忆...")
for memory_id, activation in memories_to_forget:
# cleanup_orphans=False：暂不清理孤立节点
success = await self.forget_memory(memory_id, cleanup_orphans=False)
if success:
forgotten_count += 1
# 统一清理孤立节点和边
logger.info("批量遗忘完成，开始统一清理孤立节点和边...")
orphan_nodes, orphan_edges = await self._cleanup_orphan_nodes_and_edges()
# 保存最终更新
await self.persistence.save_graph_store(self.graph_store)
logger.info(
f"✅ 自动遗忘完成: 遗忘了 {forgotten_count} 条记忆, "
f"清理了 {orphan_nodes} 个孤立节点, {orphan_edges} 条孤立边"
@@ -1076,31 +1076,31 @@ class MemoryManager:
# 1. 清理孤立节点
# graph_store.node_to_memories 记录了每个节点属于哪些记忆
nodes_to_remove = []
for node_id, memory_ids in list(self.graph_store.node_to_memories.items()):
# 如果节点不再属于任何记忆，标记为删除
if not memory_ids:
nodes_to_remove.append(node_id)
# 从图中删除孤立节点
for node_id in nodes_to_remove:
if self.graph_store.graph.has_node(node_id):
self.graph_store.graph.remove_node(node_id)
orphan_nodes_count += 1
# 从映射中删除
if node_id in self.graph_store.node_to_memories:
del self.graph_store.node_to_memories[node_id]
# 2. 清理孤立边（指向已删除节点的边）
edges_to_remove = []
for source, target, edge_id in self.graph_store.graph.edges(data='edge_id'):
for source, target, edge_id in self.graph_store.graph.edges(data="edge_id"):
# 检查边的源节点和目标节点是否还存在于node_to_memories中
if source not in self.graph_store.node_to_memories or \
target not in self.graph_store.node_to_memories:
edges_to_remove.append((source, target))
# 删除孤立边
for source, target in edges_to_remove:
try:
@@ -1108,12 +1108,12 @@ class MemoryManager:
orphan_edges_count += 1
except Exception as e:
logger.debug(f"删除边失败 {source} -> {target}: {e}")
if orphan_nodes_count > 0 or orphan_edges_count > 0:
logger.info(
f"清理完成: {orphan_nodes_count} 个孤立节点, {orphan_edges_count} 条孤立边"
)
return orphan_nodes_count, orphan_edges_count
except Exception as e:
@@ -1255,7 +1255,7 @@ class MemoryManager:
mem for mem in recent_memories
if mem.importance >= min_importance_for_consolidation
]
result["importance_filtered"] = len(recent_memories) - len(important_memories)
logger.info(
f"📊 步骤2: 重要性过滤 (阈值={min_importance_for_consolidation:.2f}): "
@@ -1379,26 +1379,26 @@ class MemoryManager:
# ===== 步骤4: 向量检索关联记忆 + LLM分析关系 =====
# 过滤掉已删除的记忆
remaining_memories = [m for m in important_memories if m.id not in deleted_ids]
if not remaining_memories:
logger.info("✅ 记忆整理完成: 去重后无剩余记忆")
return
logger.info(f"📍 步骤4: 开始关联分析 ({len(remaining_memories)} 条记忆)...")
# 分批处理记忆关联
llm_batch_size = getattr(self.config, "consolidation_llm_batch_size", 10)
max_candidates_per_memory = getattr(self.config, "consolidation_max_candidates", 5)
min_confidence = getattr(self.config, "consolidation_min_confidence", 0.6)
all_new_edges = [] # 收集所有新建的边
for batch_start in range(0, len(remaining_memories), llm_batch_size):
batch_end = min(batch_start + llm_batch_size, len(remaining_memories))
batch = remaining_memories[batch_start:batch_end]
logger.debug(f"处理批次 {batch_start//llm_batch_size + 1}/{(len(remaining_memories)-1)//llm_batch_size + 1}")
for memory in batch:
# 跳过已经有很多连接的记忆
existing_edges = len([
@@ -1451,14 +1451,14 @@ class MemoryManager:
except Exception as e:
logger.warning(f"创建关联边失败: {e}")
continue
# 每个批次后让出控制权
await asyncio.sleep(0.01)
# ===== 步骤5: 统一更新记忆数据 =====
if all_new_edges:
logger.info(f"📍 步骤5: 统一更新 {len(all_new_edges)} 条新关联边...")
for memory, edge, relation in all_new_edges:
try:
# 添加到图
@@ -2298,7 +2298,7 @@ class MemoryManager:
# 使用 asyncio.wait_for 来支持取消
await asyncio.wait_for(
asyncio.sleep(initial_delay),
timeout=float('inf') # 允许随时取消
timeout=float("inf") # 允许随时取消
)
# 检查是否仍然需要运行