@@ -506,319 +506,6 @@ class EntorhinalCortex:
logger . success ( f " [数据库] 同步了 { len ( memory_nodes ) } 个节点和 { len ( memory_edges ) } 条边 " )
# 负责整合，遗忘，合并记忆
class ParahippocampalGyrus :
def __init__ ( self , hippocampus ) :
self . hippocampus = hippocampus
self . memory_graph = hippocampus . memory_graph
self . config = hippocampus . config
async def memory_compress ( self , messages : list , compress_rate = 0.1 ) :
""" 压缩和总结消息内容，生成记忆主题和摘要。
Args:
messages (list): 消息列表，每个消息是一个字典，包含以下字段：
- time: float, 消息的时间戳
- detailed_plain_text: str, 消息的详细文本内容
compress_rate (float, optional): 压缩率，
Returns:
tuple: (compressed_memory, similar_topics_dict)
- compressed_memory: set, 压缩后的记忆集合，每个元素是一个元组 (topic, summary)
- topic: str, 记忆主题
- summary: str, 主题的摘要描述
- similar_topics_dict: dict, 相似主题字典， ，
每个相似主题是一个元组 (similar_topic, similarity)
- similar_topic: str, 相似的主题
- similarity: float, 相似度分数（ ）
Process:
1. 合并消息文本并生成时间信息
2. 使用LLM提取关键主题
3. 过滤掉包含禁用关键词的主题
4. 为每个主题生成摘要
5. 查找与现有记忆中的相似主题
"""
if not messages :
return set ( ) , { }
# 合并消息文本，同时保留时间信息
input_text = " "
time_info = " "
# 计算最早和最晚时间
earliest_time = min ( msg [ " time " ] for msg in messages )
latest_time = max ( msg [ " time " ] for msg in messages )
earliest_dt = datetime . datetime . fromtimestamp ( earliest_time )
latest_dt = datetime . datetime . fromtimestamp ( latest_time )
# 如果是同一年
if earliest_dt . year == latest_dt . year :
earliest_str = earliest_dt . strftime ( " % m- %d % H: % M: % S " )
latest_str = latest_dt . strftime ( " % m- %d % H: % M: % S " )
time_info + = f " 是在 { earliest_dt . year } 年， { earliest_str } 到 { latest_str } 的对话: \n "
else :
earliest_str = earliest_dt . strftime ( " % Y- % m- %d % H: % M: % S " )
latest_str = latest_dt . strftime ( " % Y- % m- %d % H: % M: % S " )
time_info + = f " 是从 { earliest_str } 到 { latest_str } 的对话: \n "
for msg in messages :
input_text + = f " { msg [ ' detailed_plain_text ' ] } \n "
logger . debug ( input_text )
topic_num = self . hippocampus . calculate_topic_num ( input_text , compress_rate )
topics_response = await self . hippocampus . llm_topic_judge . generate_response (
self . hippocampus . find_topic_llm ( input_text , topic_num )
)
# 使用正则表达式提取<>中的内容
topics = re . findall ( r " <([^>]+)> " , topics_response [ 0 ] )
# 如果没有找到<>包裹的内容，返回['none']
if not topics :
topics = [ " none " ]
else :
# 处理提取出的话题
topics = [
topic . strip ( )
for topic in " , " . join ( topics ) . replace ( " ， " , " , " ) . replace ( " 、 " , " , " ) . replace ( " " , " , " ) . split ( " , " )
if topic . strip ( )
]
# 过滤掉包含禁用关键词的topic
filtered_topics = [
topic for topic in topics if not any ( keyword in topic for keyword in self . config . memory_ban_words )
]
logger . debug ( f " 过滤后话题: { filtered_topics } " )
# 创建所有话题的请求任务
tasks = [ ]
for topic in filtered_topics :
topic_what_prompt = self . hippocampus . topic_what ( input_text , topic , time_info )
task = self . hippocampus . llm_summary_by_topic . generate_response_async ( topic_what_prompt )
tasks . append ( ( topic . strip ( ) , task ) )
# 等待所有任务完成
compressed_memory = set ( )
similar_topics_dict = { }
for topic , task in tasks :
response = await task
if response :
compressed_memory . add ( ( topic , response [ 0 ] ) )
existing_topics = list ( self . memory_graph . G . nodes ( ) )
similar_topics = [ ]
for existing_topic in existing_topics :
topic_words = set ( jieba . cut ( topic ) )
existing_words = set ( jieba . cut ( existing_topic ) )
all_words = topic_words | existing_words
v1 = [ 1 if word in topic_words else 0 for word in all_words ]
v2 = [ 1 if word in existing_words else 0 for word in all_words ]
similarity = cosine_similarity ( v1 , v2 )
if similarity > = 0.7 :
similar_topics . append ( ( existing_topic , similarity ) )
similar_topics . sort ( key = lambda x : x [ 1 ] , reverse = True )
similar_topics = similar_topics [ : 3 ]
similar_topics_dict [ topic ] = similar_topics
return compressed_memory , similar_topics_dict
async def operation_build_memory ( self ) :
logger . debug ( " ------------------------------------开始构建记忆-------------------------------------- " )
start_time = time . time ( )
memory_samples = self . hippocampus . entorhinal_cortex . get_memory_sample ( )
all_added_nodes = [ ]
all_connected_nodes = [ ]
all_added_edges = [ ]
for i , messages in enumerate ( memory_samples , 1 ) :
all_topics = [ ]
progress = ( i / len ( memory_samples ) ) * 100
bar_length = 30
filled_length = int ( bar_length * i / / len ( memory_samples ) )
bar = " █ " * filled_length + " - " * ( bar_length - filled_length )
logger . debug ( f " 进度: [ { bar } ] { progress : .1f } % ( { i } / { len ( memory_samples ) } ) " )
compress_rate = self . config . memory_compress_rate
compressed_memory , similar_topics_dict = await self . memory_compress ( messages , compress_rate )
logger . debug ( f " 压缩后记忆数量: { compressed_memory } ，似曾相识的话题: { similar_topics_dict } " )
current_time = datetime . datetime . now ( ) . timestamp ( )
logger . debug ( f " 添加节点: { ' , ' . join ( topic for topic , _ in compressed_memory ) } " )
all_added_nodes . extend ( topic for topic , _ in compressed_memory )
for topic , memory in compressed_memory :
self . memory_graph . add_dot ( topic , memory )
all_topics . append ( topic )
if topic in similar_topics_dict :
similar_topics = similar_topics_dict [ topic ]
for similar_topic , similarity in similar_topics :
if topic != similar_topic :
strength = int ( similarity * 10 )
logger . debug ( f " 连接相似节点: { topic } 和 { similar_topic } (强度: { strength } ) " )
all_added_edges . append ( f " { topic } - { similar_topic } " )
all_connected_nodes . append ( topic )
all_connected_nodes . append ( similar_topic )
self . memory_graph . G . add_edge (
topic ,
similar_topic ,
strength = strength ,
created_time = current_time ,
last_modified = current_time ,
)
for i in range ( len ( all_topics ) ) :
for j in range ( i + 1 , len ( all_topics ) ) :
logger . debug ( f " 连接同批次节点: { all_topics [ i ] } 和 { all_topics [ j ] } " )
all_added_edges . append ( f " { all_topics [ i ] } - { all_topics [ j ] } " )
self . memory_graph . connect_dot ( all_topics [ i ] , all_topics [ j ] )
logger . success ( f " 更新记忆: { ' , ' . join ( all_added_nodes ) } " )
logger . debug ( f " 强化连接: { ' , ' . join ( all_added_edges ) } " )
logger . info ( f " 强化连接节点: { ' , ' . join ( all_connected_nodes ) } " )
await self . hippocampus . entorhinal_cortex . sync_memory_to_db ( )
end_time = time . time ( )
logger . success ( f " ---------------------记忆构建耗时: { end_time - start_time : .2f } 秒--------------------- " )
async def operation_forget_topic ( self , percentage = 0.005 ) :
start_time = time . time ( )
logger . info ( " [遗忘] 开始检查数据库... " )
# 验证百分比参数
if not 0 < = percentage < = 1 :
logger . warning ( f " [遗忘] 无效的遗忘百分比: { percentage } , 使用默认值 0.005 " )
percentage = 0.005
all_nodes = list ( self . memory_graph . G . nodes ( ) )
all_edges = list ( self . memory_graph . G . edges ( ) )
if not all_nodes and not all_edges :
logger . info ( " [遗忘] 记忆图为空,无需进行遗忘操作 " )
return
# 确保至少检查1个节点和边，
check_nodes_count = max ( 1 , min ( len ( all_nodes ) , int ( len ( all_nodes ) * percentage ) ) )
check_edges_count = max ( 1 , min ( len ( all_edges ) , int ( len ( all_edges ) * percentage ) ) )
# 只有在有足够的节点和边时才进行采样
if len ( all_nodes ) > = check_nodes_count and len ( all_edges ) > = check_edges_count :
try :
nodes_to_check = random . sample ( all_nodes , check_nodes_count )
edges_to_check = random . sample ( all_edges , check_edges_count )
except ValueError as e :
logger . error ( f " [遗忘] 采样错误: { str ( e ) } " )
return
else :
logger . info ( " [遗忘] 没有足够的节点或边进行遗忘操作 " )
return
# 使用列表存储变化信息
edge_changes = {
" weakened " : [ ] , # 存储减弱的边
" removed " : [ ] , # 存储移除的边
}
node_changes = {
" reduced " : [ ] , # 存储减少记忆的节点
" removed " : [ ] , # 存储移除的节点
}
current_time = datetime . datetime . now ( ) . timestamp ( )
logger . info ( " [遗忘] 开始检查连接... " )
edge_check_start = time . time ( )
for source , target in edges_to_check :
edge_data = self . memory_graph . G [ source ] [ target ]
last_modified = edge_data . get ( " last_modified " )
if current_time - last_modified > 3600 * self . config . memory_forget_time :
current_strength = edge_data . get ( " strength " , 1 )
new_strength = current_strength - 1
if new_strength < = 0 :
self . memory_graph . G . remove_edge ( source , target )
edge_changes [ " removed " ] . append ( f " { source } -> { target } " )
else :
edge_data [ " strength " ] = new_strength
edge_data [ " last_modified " ] = current_time
edge_changes [ " weakened " ] . append ( f " { source } - { target } (强度: { current_strength } -> { new_strength } ) " )
edge_check_end = time . time ( )
logger . info ( f " [遗忘] 连接检查耗时: { edge_check_end - edge_check_start : .2f } 秒 " )
logger . info ( " [遗忘] 开始检查节点... " )
node_check_start = time . time ( )
for node in nodes_to_check :
node_data = self . memory_graph . G . nodes [ node ]
last_modified = node_data . get ( " last_modified " , current_time )
if current_time - last_modified > 3600 * 24 :
memory_items = node_data . get ( " memory_items " , [ ] )
if not isinstance ( memory_items , list ) :
memory_items = [ memory_items ] if memory_items else [ ]
if memory_items :
current_count = len ( memory_items )
removed_item = random . choice ( memory_items )
memory_items . remove ( removed_item )
if memory_items :
self . memory_graph . G . nodes [ node ] [ " memory_items " ] = memory_items
self . memory_graph . G . nodes [ node ] [ " last_modified " ] = current_time
node_changes [ " reduced " ] . append ( f " { node } (数量: { current_count } -> { len ( memory_items ) } ) " )
else :
self . memory_graph . G . remove_node ( node )
node_changes [ " removed " ] . append ( node )
node_check_end = time . time ( )
logger . info ( f " [遗忘] 节点检查耗时: { node_check_end - node_check_start : .2f } 秒 " )
if any ( edge_changes . values ( ) ) or any ( node_changes . values ( ) ) :
sync_start = time . time ( )
await self . hippocampus . entorhinal_cortex . resync_memory_to_db ( )
sync_end = time . time ( )
logger . info ( f " [遗忘] 数据库同步耗时: { sync_end - sync_start : .2f } 秒 " )
# 汇总输出所有变化
logger . info ( " [遗忘] 遗忘操作统计: " )
if edge_changes [ " weakened " ] :
logger . info (
f " [遗忘] 减弱的连接 ( { len ( edge_changes [ ' weakened ' ] ) } 个): { ' , ' . join ( edge_changes [ ' weakened ' ] ) } "
)
if edge_changes [ " removed " ] :
logger . info (
f " [遗忘] 移除的连接 ( { len ( edge_changes [ ' removed ' ] ) } 个): { ' , ' . join ( edge_changes [ ' removed ' ] ) } "
)
if node_changes [ " reduced " ] :
logger . info (
f " [遗忘] 减少记忆的节点 ( { len ( node_changes [ ' reduced ' ] ) } 个): { ' , ' . join ( node_changes [ ' reduced ' ] ) } "
)
if node_changes [ " removed " ] :
logger . info (
f " [遗忘] 移除的节点 ( { len ( node_changes [ ' removed ' ] ) } 个): { ' , ' . join ( node_changes [ ' removed ' ] ) } "
)
else :
logger . info ( " [遗忘] 本次检查没有节点或连接满足遗忘条件 " )
end_time = time . time ( )
logger . info ( f " [遗忘] 总耗时: { end_time - start_time : .2f } 秒 " )
# 海马体
class Hippocampus :
def __init__ ( self ) :
@@ -1247,6 +934,327 @@ class Hippocampus:
return activation_ratio
# 负责整合，遗忘，合并记忆
class ParahippocampalGyrus :
def __init__ ( self , hippocampus : Hippocampus ) :
self . hippocampus = hippocampus
self . memory_graph = hippocampus . memory_graph
self . config = hippocampus . config
async def memory_compress ( self , messages : list , compress_rate = 0.1 ) :
""" 压缩和总结消息内容，生成记忆主题和摘要。
Args:
messages (list): 消息列表，每个消息是一个字典，包含以下字段：
- time: float, 消息的时间戳
- detailed_plain_text: str, 消息的详细文本内容
compress_rate (float, optional): 压缩率，
Returns:
tuple: (compressed_memory, similar_topics_dict)
- compressed_memory: set, 压缩后的记忆集合，每个元素是一个元组 (topic, summary)
- topic: str, 记忆主题
- summary: str, 主题的摘要描述
- similar_topics_dict: dict, 相似主题字典， ，
每个相似主题是一个元组 (similar_topic, similarity)
- similar_topic: str, 相似的主题
- similarity: float, 相似度分数（ ）
Process:
1. 合并消息文本并生成时间信息
2. 使用LLM提取关键主题
3. 过滤掉包含禁用关键词的主题
4. 为每个主题生成摘要
5. 查找与现有记忆中的相似主题
"""
if not messages :
return set ( ) , { }
# 合并消息文本，同时保留时间信息
input_text = " "
time_info = " "
# 计算最早和最晚时间
earliest_time = min ( msg [ " time " ] for msg in messages )
latest_time = max ( msg [ " time " ] for msg in messages )
earliest_dt = datetime . datetime . fromtimestamp ( earliest_time )
latest_dt = datetime . datetime . fromtimestamp ( latest_time )
# 如果是同一年
if earliest_dt . year == latest_dt . year :
earliest_str = earliest_dt . strftime ( " % m- %d % H: % M: % S " )
latest_str = latest_dt . strftime ( " % m- %d % H: % M: % S " )
time_info + = f " 是在 { earliest_dt . year } 年， { earliest_str } 到 { latest_str } 的对话: \n "
else :
earliest_str = earliest_dt . strftime ( " % Y- % m- %d % H: % M: % S " )
latest_str = latest_dt . strftime ( " % Y- % m- %d % H: % M: % S " )
time_info + = f " 是从 { earliest_str } 到 { latest_str } 的对话: \n "
for msg in messages :
input_text + = f " { msg [ ' detailed_plain_text ' ] } \n "
logger . debug ( input_text )
topic_num = self . hippocampus . calculate_topic_num ( input_text , compress_rate )
topics_response = await self . hippocampus . llm_topic_judge . generate_response (
self . hippocampus . find_topic_llm ( input_text , topic_num )
)
# 使用正则表达式提取<>中的内容
topics = re . findall ( r " <([^>]+)> " , topics_response [ 0 ] )
# 如果没有找到<>包裹的内容，返回['none']
if not topics :
topics = [ " none " ]
else :
# 处理提取出的话题
topics = [
topic . strip ( )
for topic in " , " . join ( topics ) . replace ( " ， " , " , " ) . replace ( " 、 " , " , " ) . replace ( " " , " , " ) . split ( " , " )
if topic . strip ( )
]
# 过滤掉包含禁用关键词的topic
filtered_topics = [
topic for topic in topics if not any ( keyword in topic for keyword in self . config . memory_ban_words )
]
logger . debug ( f " 过滤后话题: { filtered_topics } " )
# 创建所有话题的请求任务
tasks = [ ]
for topic in filtered_topics :
topic_what_prompt = self . hippocampus . topic_what ( input_text , topic , time_info )
try :
task = self . hippocampus . llm_summary_by_topic . generate_response_async ( topic_what_prompt )
tasks . append ( ( topic . strip ( ) , task ) )
except Exception as e :
logger . error ( f " 生成话题 ' { topic } ' 的摘要时发生错误: { e } " )
continue
# 等待所有任务完成
compressed_memory = set ( )
similar_topics_dict = { }
for topic , task in tasks :
response = await task
if response :
compressed_memory . add ( ( topic , response [ 0 ] ) )
existing_topics = list ( self . memory_graph . G . nodes ( ) )
similar_topics = [ ]
for existing_topic in existing_topics :
topic_words = set ( jieba . cut ( topic ) )
existing_words = set ( jieba . cut ( existing_topic ) )
all_words = topic_words | existing_words
v1 = [ 1 if word in topic_words else 0 for word in all_words ]
v2 = [ 1 if word in existing_words else 0 for word in all_words ]
similarity = cosine_similarity ( v1 , v2 )
if similarity > = 0.7 :
similar_topics . append ( ( existing_topic , similarity ) )
similar_topics . sort ( key = lambda x : x [ 1 ] , reverse = True )
similar_topics = similar_topics [ : 3 ]
similar_topics_dict [ topic ] = similar_topics
return compressed_memory , similar_topics_dict
async def operation_build_memory ( self ) :
logger . debug ( " ------------------------------------开始构建记忆-------------------------------------- " )
start_time = time . time ( )
memory_samples = self . hippocampus . entorhinal_cortex . get_memory_sample ( )
all_added_nodes = [ ]
all_connected_nodes = [ ]
all_added_edges = [ ]
for i , messages in enumerate ( memory_samples , 1 ) :
all_topics = [ ]
progress = ( i / len ( memory_samples ) ) * 100
bar_length = 30
filled_length = int ( bar_length * i / / len ( memory_samples ) )
bar = " █ " * filled_length + " - " * ( bar_length - filled_length )
logger . debug ( f " 进度: [ { bar } ] { progress : .1f } % ( { i } / { len ( memory_samples ) } ) " )
compress_rate = self . config . memory_compress_rate
try :
compressed_memory , similar_topics_dict = await self . memory_compress ( messages , compress_rate )
except Exception as e :
logger . error ( f " 压缩记忆时发生错误: { e } " )
continue
logger . debug ( f " 压缩后记忆数量: { compressed_memory } ，似曾相识的话题: { similar_topics_dict } " )
current_time = datetime . datetime . now ( ) . timestamp ( )
logger . debug ( f " 添加节点: { ' , ' . join ( topic for topic , _ in compressed_memory ) } " )
all_added_nodes . extend ( topic for topic , _ in compressed_memory )
for topic , memory in compressed_memory :
self . memory_graph . add_dot ( topic , memory )
all_topics . append ( topic )
if topic in similar_topics_dict :
similar_topics = similar_topics_dict [ topic ]
for similar_topic , similarity in similar_topics :
if topic != similar_topic :
strength = int ( similarity * 10 )
logger . debug ( f " 连接相似节点: { topic } 和 { similar_topic } (强度: { strength } ) " )
all_added_edges . append ( f " { topic } - { similar_topic } " )
all_connected_nodes . append ( topic )
all_connected_nodes . append ( similar_topic )
self . memory_graph . G . add_edge (
topic ,
similar_topic ,
strength = strength ,
created_time = current_time ,
last_modified = current_time ,
)
for i in range ( len ( all_topics ) ) :
for j in range ( i + 1 , len ( all_topics ) ) :
logger . debug ( f " 连接同批次节点: { all_topics [ i ] } 和 { all_topics [ j ] } " )
all_added_edges . append ( f " { all_topics [ i ] } - { all_topics [ j ] } " )
self . memory_graph . connect_dot ( all_topics [ i ] , all_topics [ j ] )
logger . success ( f " 更新记忆: { ' , ' . join ( all_added_nodes ) } " )
logger . debug ( f " 强化连接: { ' , ' . join ( all_added_edges ) } " )
logger . info ( f " 强化连接节点: { ' , ' . join ( all_connected_nodes ) } " )
await self . hippocampus . entorhinal_cortex . sync_memory_to_db ( )
end_time = time . time ( )
logger . success ( f " ---------------------记忆构建耗时: { end_time - start_time : .2f } 秒--------------------- " )
async def operation_forget_topic ( self , percentage = 0.005 ) :
start_time = time . time ( )
logger . info ( " [遗忘] 开始检查数据库... " )
# 验证百分比参数
if not 0 < = percentage < = 1 :
logger . warning ( f " [遗忘] 无效的遗忘百分比: { percentage } , 使用默认值 0.005 " )
percentage = 0.005
all_nodes = list ( self . memory_graph . G . nodes ( ) )
all_edges = list ( self . memory_graph . G . edges ( ) )
if not all_nodes and not all_edges :
logger . info ( " [遗忘] 记忆图为空,无需进行遗忘操作 " )
return
# 确保至少检查1个节点和边，
check_nodes_count = max ( 1 , min ( len ( all_nodes ) , int ( len ( all_nodes ) * percentage ) ) )
check_edges_count = max ( 1 , min ( len ( all_edges ) , int ( len ( all_edges ) * percentage ) ) )
# 只有在有足够的节点和边时才进行采样
if len ( all_nodes ) > = check_nodes_count and len ( all_edges ) > = check_edges_count :
try :
nodes_to_check = random . sample ( all_nodes , check_nodes_count )
edges_to_check = random . sample ( all_edges , check_edges_count )
except ValueError as e :
logger . error ( f " [遗忘] 采样错误: { str ( e ) } " )
return
else :
logger . info ( " [遗忘] 没有足够的节点或边进行遗忘操作 " )
return
# 使用列表存储变化信息
edge_changes = {
" weakened " : [ ] , # 存储减弱的边
" removed " : [ ] , # 存储移除的边
}
node_changes = {
" reduced " : [ ] , # 存储减少记忆的节点
" removed " : [ ] , # 存储移除的节点
}
current_time = datetime . datetime . now ( ) . timestamp ( )
logger . info ( " [遗忘] 开始检查连接... " )
edge_check_start = time . time ( )
for source , target in edges_to_check :
edge_data = self . memory_graph . G [ source ] [ target ]
last_modified = edge_data . get ( " last_modified " )
if current_time - last_modified > 3600 * self . config . memory_forget_time :
current_strength = edge_data . get ( " strength " , 1 )
new_strength = current_strength - 1
if new_strength < = 0 :
self . memory_graph . G . remove_edge ( source , target )
edge_changes [ " removed " ] . append ( f " { source } -> { target } " )
else :
edge_data [ " strength " ] = new_strength
edge_data [ " last_modified " ] = current_time
edge_changes [ " weakened " ] . append ( f " { source } - { target } (强度: { current_strength } -> { new_strength } ) " )
edge_check_end = time . time ( )
logger . info ( f " [遗忘] 连接检查耗时: { edge_check_end - edge_check_start : .2f } 秒 " )
logger . info ( " [遗忘] 开始检查节点... " )
node_check_start = time . time ( )
for node in nodes_to_check :
node_data = self . memory_graph . G . nodes [ node ]
last_modified = node_data . get ( " last_modified " , current_time )
if current_time - last_modified > 3600 * 24 :
memory_items = node_data . get ( " memory_items " , [ ] )
if not isinstance ( memory_items , list ) :
memory_items = [ memory_items ] if memory_items else [ ]
if memory_items :
current_count = len ( memory_items )
removed_item = random . choice ( memory_items )
memory_items . remove ( removed_item )
if memory_items :
self . memory_graph . G . nodes [ node ] [ " memory_items " ] = memory_items
self . memory_graph . G . nodes [ node ] [ " last_modified " ] = current_time
node_changes [ " reduced " ] . append ( f " { node } (数量: { current_count } -> { len ( memory_items ) } ) " )
else :
self . memory_graph . G . remove_node ( node )
node_changes [ " removed " ] . append ( node )
node_check_end = time . time ( )
logger . info ( f " [遗忘] 节点检查耗时: { node_check_end - node_check_start : .2f } 秒 " )
if any ( edge_changes . values ( ) ) or any ( node_changes . values ( ) ) :
sync_start = time . time ( )
await self . hippocampus . entorhinal_cortex . resync_memory_to_db ( )
sync_end = time . time ( )
logger . info ( f " [遗忘] 数据库同步耗时: { sync_end - sync_start : .2f } 秒 " )
# 汇总输出所有变化
logger . info ( " [遗忘] 遗忘操作统计: " )
if edge_changes [ " weakened " ] :
logger . info (
f " [遗忘] 减弱的连接 ( { len ( edge_changes [ ' weakened ' ] ) } 个): { ' , ' . join ( edge_changes [ ' weakened ' ] ) } "
)
if edge_changes [ " removed " ] :
logger . info (
f " [遗忘] 移除的连接 ( { len ( edge_changes [ ' removed ' ] ) } 个): { ' , ' . join ( edge_changes [ ' removed ' ] ) } "
)
if node_changes [ " reduced " ] :
logger . info (
f " [遗忘] 减少记忆的节点 ( { len ( node_changes [ ' reduced ' ] ) } 个): { ' , ' . join ( node_changes [ ' reduced ' ] ) } "
)
if node_changes [ " removed " ] :
logger . info (
f " [遗忘] 移除的节点 ( { len ( node_changes [ ' removed ' ] ) } 个): { ' , ' . join ( node_changes [ ' removed ' ] ) } "
)
else :
logger . info ( " [遗忘] 本次检查没有节点或连接满足遗忘条件 " )
end_time = time . time ( )
logger . info ( f " [遗忘] 总耗时: { end_time - start_time : .2f } 秒 " )
class HippocampusManager :
_instance = None
_hippocampus = None
@@ -1317,12 +1325,13 @@ class HippocampusManager:
if not self . _initialized :
raise RuntimeError ( " HippocampusManager 尚未初始化，请先调用 initialize 方法 " )
try :
response = await self . _hippocampus . get_memory_from_text ( text , max_memory_num , max_memory_length , max_depth , fast_retrieval )
response = await self . _hippocampus . get_memory_from_text (
text , max_memory_num , max_memory_length , max_depth , fast_retrieval
)
except Exception as e :
logger . error ( f " 文本激活记忆失败: { e } " )
response = [ ]
return response
async def get_activate_from_text ( self , text : str , max_depth : int = 3 , fast_retrieval : bool = False ) - > float :
""" 从文本中获取激活值的公共接口 """
UnCLAS-Prommer