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

fix：优化激活值，优化logger显示

Browse Source
This commit is contained in:
SengokuCola
2025-03-28 09:09:30 +08:00
parent 4a72fe104a
commit de8d2aba68
8 changed files with 102 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
src/plugins/memory_system/Hippocampus.py
View File

@@ -817,8 +817,8 @@ class Hippocampus:
self.parahippocampal_gyrus = ParahippocampalGyrus(self)
# 从数据库加载记忆图
self.entorhinal_cortex.sync_memory_from_db()
self.llm_topic_judge = LLM_request(self.config.llm_topic_judge)
self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic)
self.llm_topic_judge = LLM_request(self.config.llm_topic_judge,request_type="memory")
self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic,request_type="memory")
def get_all_node_names(self) -> list:
"""获取记忆图中所有节点的名字列表"""
@@ -950,7 +950,7 @@ class Hippocampus:
# 提取关键词
keywords = re.findall(r'<([^>]+)>', topics_response[0])
if not keywords:
keywords = ['none']
keywords = []
else:
keywords = [
keyword.strip()
@@ -1025,7 +1025,7 @@ class Hippocampus:
# 基于激活值平方的独立概率选择
remember_map = {}
logger.info("基于激活值平方的归一化选择:")
# logger.info("基于激活值平方的归一化选择:")
# 计算所有激活值的平方和
total_squared_activation = sum(activation ** 2 for activation in activate_map.values())
@@ -1079,12 +1079,11 @@ class Hippocampus:
memory_similarities.sort(key=lambda x: x[1], reverse=True)
# 获取最匹配的记忆
top_memories = memory_similarities[:max_memory_length]
# 添加到结果中
for memory, similarity in top_memories:
all_memories.append((node, [memory], similarity))
logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
# logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
else:
logger.info("节点没有记忆")
@@ -1148,7 +1147,7 @@ class Hippocampus:
# 提取关键词
keywords = re.findall(r'<([^>]+)>', topics_response[0])
if not keywords:
keywords = ['none']
keywords = []
else:
keywords = [
keyword.strip()
@@ -1221,10 +1220,13 @@ class Hippocampus:
# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
# 计算激活节点数与总节点数的比值
total_activation = sum(activate_map.values())
logger.info(f"总激活值: {total_activation:.2f}")
total_nodes = len(self.memory_graph.G.nodes())
activated_nodes = len(activate_map)
activation_ratio = activated_nodes / total_nodes if total_nodes > 0 else 0
logger.info(f"激活节点数: {activated_nodes}, 总节点数: {total_nodes}, 激活比例: {activation_ratio}")
# activated_nodes = len(activate_map)
activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
activation_ratio = activation_ratio*40
logger.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
return activation_ratio