Losita/mai-bot
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

v0.5.2 记忆系统更新

Browse Source
This commit is contained in:
SengokuCola
2025-03-05 16:48:53 +08:00
parent 543504858d
commit 3fec29d045
13 changed files with 1235 additions and 1346 deletions
Download Patch File Download Diff File Expand all files Collapse all files

374
src/plugins/memory_system/memory.py
View File

@@ -17,7 +17,12 @@ class Memory_graph:
self.db = Database.get_instance()
def connect_dot(self, concept1, concept2):
self.G.add_edge(concept1, concept2)
# 如果边已存在，增加 strength
if self.G.has_edge(concept1, concept2):
self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
else:
# 如果是新边，初始化 strength 为 1
self.G.add_edge(concept1, concept2, strength=1)
def add_dot(self, concept, memory):
if concept in self.G:
@@ -38,9 +43,7 @@ class Memory_graph:
if concept in self.G:
# 从图中获取节点数据
node_data = self.G.nodes[concept]
# print(node_data)
# 创建新的Memory_dot对象
return concept,node_data
return concept, node_data
return None
def get_related_item(self, topic, depth=1):
@@ -52,7 +55,6 @@ class Memory_graph:
# 获取相邻节点
neighbors = list(self.G.neighbors(topic))
# print(f"第一层: {topic}")
# 获取当前节点的记忆项
node_data = self.get_dot(topic)
@@ -69,7 +71,6 @@ class Memory_graph:
if depth >= 2:
# 获取相邻节点的记忆项
for neighbor in neighbors:
# print(f"第二层: {neighbor}")
node_data = self.get_dot(neighbor)
if node_data:
concept, data = node_data
@@ -87,79 +88,38 @@ class Memory_graph:
# 返回所有节点对应的 Memory_dot 对象
return [self.get_dot(node) for node in self.G.nodes()]
def save_graph_to_db(self):
# 保存节点
for node in self.G.nodes(data=True):
concept = node[0]
memory_items = node[1].get('memory_items', [])
def forget_topic(self, topic):
"""随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点"""
if topic not in self.G:
return None
# 查找是否存在同名节点
existing_node = self.db.db.graph_data.nodes.find_one({'concept': concept})
if existing_node:
# 如果存在,合并memory_items并去重
existing_items = existing_node.get('memory_items', [])
if not isinstance(existing_items, list):
existing_items = [existing_items] if existing_items else []
# 合并并去重
all_items = list(set(existing_items + memory_items))
# 更新节点
self.db.db.graph_data.nodes.update_one(
{'concept': concept},
{'$set': {'memory_items': all_items}}
)
else:
# 如果不存在,创建新节点
node_data = {
'concept': concept,
'memory_items': memory_items
}
self.db.db.graph_data.nodes.insert_one(node_data)
# 获取话题节点数据
node_data = self.G.nodes[topic]
# 保存边
for edge in self.G.edges():
source, target = edge
# 如果节点存在memory_items
if 'memory_items' in node_data:
memory_items = node_data['memory_items']
# 查找是否存在同样的边
existing_edge = self.db.db.graph_data.edges.find_one({
'source': source,
'target': target
})
if existing_edge:
# 如果存在,增加num属性
num = existing_edge.get('num', 1) + 1
self.db.db.graph_data.edges.update_one(
{'source': source, 'target': target},
{'$set': {'num': num}}
)
else:
# 如果不存在,创建新边
edge_data = {
'source': source,
'target': target,
'num': 1
}
self.db.db.graph_data.edges.insert_one(edge_data)
def load_graph_from_db(self):
# 清空当前图
self.G.clear()
# 加载节点
nodes = self.db.db.graph_data.nodes.find()
for node in nodes:
memory_items = node.get('memory_items', [])
# 确保memory_items是列表
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
self.G.add_node(node['concept'], memory_items=memory_items)
# 加载边
edges = self.db.db.graph_data.edges.find()
for edge in edges:
self.G.add_edge(edge['source'], edge['target'], num=edge.get('num', 1))
# 如果有记忆项可以删除
if memory_items:
# 随机选择一个记忆项删除
removed_item = random.choice(memory_items)
memory_items.remove(removed_item)
# 更新节点的记忆项
if memory_items:
self.G.nodes[topic]['memory_items'] = memory_items
else:
# 如果没有记忆项了，删除整个节点
self.G.remove_node(topic)
return removed_item
return None
# 海马体
@@ -169,23 +129,33 @@ class Hippocampus:
self.llm_model = LLM_request(model = global_config.llm_normal,temperature=0.5)
self.llm_model_small = LLM_request(model = global_config.llm_normal_minor,temperature=0.5)
def calculate_node_hash(self, concept, memory_items):
"""计算节点的特征值"""
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
sorted_items = sorted(memory_items)
content = f"{concept}:{'|'.join(sorted_items)}"
return hash(content)
def calculate_edge_hash(self, source, target):
"""计算边的特征值"""
nodes = sorted([source, target])
return hash(f"{nodes[0]}:{nodes[1]}")
def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}):
current_timestamp = datetime.datetime.now().timestamp()
chat_text = []
#短期：1h 中期：4h 长期：24h
for _ in range(time_frequency.get('near')): # 循环10次
random_time = current_timestamp - random.randint(1, 3600) # 随机时间
# print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
chat_text.append(chat_)
for _ in range(time_frequency.get('mid')): # 循环10次
random_time = current_timestamp - random.randint(3600, 3600*4) # 随机时间
# print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
chat_text.append(chat_)
for _ in range(time_frequency.get('far')): # 循环10次
random_time = current_timestamp - random.randint(3600*4, 3600*24) # 随机时间
# print(f"获得 最近 随机时间戳对应的时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(random_time))}")
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
chat_text.append(chat_)
return chat_text
@@ -207,8 +177,8 @@ class Hippocampus:
topic_what_response = await self.llm_model_small.generate_response(topic_what_prompt)
compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储
return compressed_memory
async def build_memory(self,chat_size=12):
async def operation_build_memory(self,chat_size=12):
#最近消息获取频率
time_frequency = {'near':1,'mid':2,'far':2}
memory_sample = self.get_memory_sample(chat_size,time_frequency)
@@ -236,7 +206,247 @@ class Hippocampus:
self.memory_graph.connect_dot(split_topic, other_split_topic)
else:
print(f"空消息 跳过")
self.memory_graph.save_graph_to_db()
self.sync_memory_to_db()
def sync_memory_to_db(self):
"""检查并同步内存中的图结构与数据库"""
# 获取数据库中所有节点和内存中所有节点
db_nodes = list(self.memory_graph.db.db.graph_data.nodes.find())
memory_nodes = list(self.memory_graph.G.nodes(data=True))
# 转换数据库节点为字典格式，方便查找
db_nodes_dict = {node['concept']: node for node in db_nodes}
# 检查并更新节点
for concept, data in memory_nodes:
memory_items = data.get('memory_items', [])
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
# 计算内存中节点的特征值
memory_hash = self.calculate_node_hash(concept, memory_items)
if concept not in db_nodes_dict:
# 数据库中缺少的节点，添加
node_data = {
'concept': concept,
'memory_items': memory_items,
'hash': memory_hash
}
self.memory_graph.db.db.graph_data.nodes.insert_one(node_data)
else:
# 获取数据库中节点的特征值
db_node = db_nodes_dict[concept]
db_hash = db_node.get('hash', None)
# 如果特征值不同，则更新节点
if db_hash != memory_hash:
self.memory_graph.db.db.graph_data.nodes.update_one(
{'concept': concept},
{'$set': {
'memory_items': memory_items,
'hash': memory_hash
}}
)
# 检查并删除数据库中多余的节点
memory_concepts = set(node[0] for node in memory_nodes)
for db_node in db_nodes:
if db_node['concept'] not in memory_concepts:
self.memory_graph.db.db.graph_data.nodes.delete_one({'concept': db_node['concept']})
# 处理边的信息
db_edges = list(self.memory_graph.db.db.graph_data.edges.find())
memory_edges = list(self.memory_graph.G.edges())
# 创建边的哈希值字典
db_edge_dict = {}
for edge in db_edges:
edge_hash = self.calculate_edge_hash(edge['source'], edge['target'])
db_edge_dict[(edge['source'], edge['target'])] = {
'hash': edge_hash,
'strength': edge.get('strength', 1)
}
# 检查并更新边
for source, target in memory_edges:
edge_hash = self.calculate_edge_hash(source, target)
edge_key = (source, target)
strength = self.memory_graph.G[source][target].get('strength', 1)
if edge_key not in db_edge_dict:
# 添加新边
edge_data = {
'source': source,
'target': target,
'strength': strength,
'hash': edge_hash
}
self.memory_graph.db.db.graph_data.edges.insert_one(edge_data)
else:
# 检查边的特征值是否变化
if db_edge_dict[edge_key]['hash'] != edge_hash:
self.memory_graph.db.db.graph_data.edges.update_one(
{'source': source, 'target': target},
{'$set': {
'hash': edge_hash,
'strength': strength
}}
)
# 删除多余的边
memory_edge_set = set(memory_edges)
for edge_key in db_edge_dict:
if edge_key not in memory_edge_set:
source, target = edge_key
self.memory_graph.db.db.graph_data.edges.delete_one({
'source': source,
'target': target
})
def sync_memory_from_db(self):
"""从数据库同步数据到内存中的图结构"""
# 清空当前图
self.memory_graph.G.clear()
# 从数据库加载所有节点
nodes = self.memory_graph.db.db.graph_data.nodes.find()
for node in nodes:
concept = node['concept']
memory_items = node.get('memory_items', [])
# 确保memory_items是列表
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
# 添加节点到图中
self.memory_graph.G.add_node(concept, memory_items=memory_items)
# 从数据库加载所有边
edges = self.memory_graph.db.db.graph_data.edges.find()
for edge in edges:
source = edge['source']
target = edge['target']
strength = edge.get('strength', 1) # 获取 strength，默认为 1
# 只有当源节点和目标节点都存在时才添加边
if source in self.memory_graph.G and target in self.memory_graph.G:
self.memory_graph.G.add_edge(source, target, strength=strength)
async def operation_forget_topic(self, percentage=0.1):
"""随机选择图中一定比例的节点进行检查，根据条件决定是否遗忘"""
# 获取所有节点
all_nodes = list(self.memory_graph.G.nodes())
# 计算要检查的节点数量
check_count = max(1, int(len(all_nodes) * percentage))
# 随机选择节点
nodes_to_check = random.sample(all_nodes, check_count)
forgotten_nodes = []
for node in nodes_to_check:
# 获取节点的连接数
connections = self.memory_graph.G.degree(node)
# 获取节点的内容条数
memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
content_count = len(memory_items)
# 检查连接强度
weak_connections = True
if connections > 1: # 只有当连接数大于1时才检查强度
for neighbor in self.memory_graph.G.neighbors(node):
strength = self.memory_graph.G[node][neighbor].get('strength', 1)
if strength > 2:
weak_connections = False
break
# 如果满足遗忘条件
if (connections <= 1 and weak_connections) or content_count <= 2:
removed_item = self.memory_graph.forget_topic(node)
if removed_item:
forgotten_nodes.append((node, removed_item))
print(f"遗忘节点 {node} 的记忆: {removed_item}")
# 同步到数据库
if forgotten_nodes:
self.sync_memory_to_db()
print(f"完成遗忘操作，共遗忘 {len(forgotten_nodes)} 个节点的记忆")
else:
print("本次检查没有节点满足遗忘条件")
async def merge_memory(self, topic):
"""
对指定话题的记忆进行合并压缩
Args:
topic: 要合并的话题节点
"""
# 获取节点的记忆项
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
# 如果记忆项不足，直接返回
if len(memory_items) < 10:
return
# 随机选择10条记忆
selected_memories = random.sample(memory_items, 10)
# 拼接成文本
merged_text = "\n".join(selected_memories)
print(f"\n[合并记忆] 话题: {topic}")
print(f"选择的记忆:\n{merged_text}")
# 使用memory_compress生成新的压缩记忆
compressed_memories = await self.memory_compress(merged_text, 0.1)
# 从原记忆列表中移除被选中的记忆
for memory in selected_memories:
memory_items.remove(memory)
# 添加新的压缩记忆
for _, compressed_memory in compressed_memories:
memory_items.append(compressed_memory)
print(f"添加压缩记忆: {compressed_memory}")
# 更新节点的记忆项
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
print(f"完成记忆合并，当前记忆数量: {len(memory_items)}")
async def operation_merge_memory(self, percentage=0.1):
"""
随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
Args:
percentage: 要检查的节点比例，默认为0.1（10%）
"""
# 获取所有节点
all_nodes = list(self.memory_graph.G.nodes())
# 计算要检查的节点数量
check_count = max(1, int(len(all_nodes) * percentage))
# 随机选择节点
nodes_to_check = random.sample(all_nodes, check_count)
merged_nodes = []
for node in nodes_to_check:
# 获取节点的内容条数
memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
if not isinstance(memory_items, list):
memory_items = [memory_items] if memory_items else []
content_count = len(memory_items)
# 如果内容数量超过100，进行合并
if content_count > 100:
print(f"\n检查节点: {node}, 当前记忆数量: {content_count}")
await self.merge_memory(node)
merged_nodes.append(node)
# 同步到数据库
if merged_nodes:
self.sync_memory_to_db()
print(f"\n完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
else:
print("\n本次检查没有需要合并的节点")
def segment_text(text):
@@ -268,10 +478,10 @@ Database.initialize(
)
#创建记忆图
memory_graph = Memory_graph()
#加载数据库中存储的记忆图
memory_graph.load_graph_from_db()
#创建海马体
hippocampus = Hippocampus(memory_graph)
#从数据库加载记忆图
hippocampus.sync_memory_from_db()
end_time = time.time()
print(f"\033[32m[加载海马体耗时: {end_time - start_time:.2f} 秒]\033[0m")