mirror of https://github.com/Mai-with-u/MaiBot.git
957 lines
40 KiB
Python
957 lines
40 KiB
Python
# -*- coding: utf-8 -*-
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import datetime
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import math
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import random
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import time
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import jieba
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import networkx as nx
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from nonebot import get_driver
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from ...common.database import db
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from ..chat.config import global_config
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from ..chat.utils import (
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calculate_information_content,
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cosine_similarity,
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get_closest_chat_from_db,
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text_to_vector,
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)
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from ..models.utils_model import LLM_request
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from src.common.logger import get_module_logger
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logger = get_module_logger("memory_sys")
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class Memory_graph:
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def __init__(self):
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self.G = nx.Graph() # 使用 networkx 的图结构
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def connect_dot(self, concept1, concept2):
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# 避免自连接
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if concept1 == concept2:
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return
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current_time = datetime.datetime.now().timestamp()
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# 如果边已存在,增加 strength
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if self.G.has_edge(concept1, concept2):
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self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
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# 更新最后修改时间
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self.G[concept1][concept2]['last_modified'] = current_time
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else:
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# 如果是新边,初始化 strength 为 1
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self.G.add_edge(concept1, concept2,
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strength=1,
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created_time=current_time, # 添加创建时间
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last_modified=current_time) # 添加最后修改时间
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def add_dot(self, concept, memory):
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current_time = datetime.datetime.now().timestamp()
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if concept in self.G:
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if 'memory_items' in self.G.nodes[concept]:
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if not isinstance(self.G.nodes[concept]['memory_items'], list):
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self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
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self.G.nodes[concept]['memory_items'].append(memory)
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# 更新最后修改时间
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self.G.nodes[concept]['last_modified'] = current_time
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else:
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self.G.nodes[concept]['memory_items'] = [memory]
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# 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
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if 'created_time' not in self.G.nodes[concept]:
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self.G.nodes[concept]['created_time'] = current_time
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self.G.nodes[concept]['last_modified'] = current_time
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else:
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# 如果是新节点,创建新的记忆列表
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self.G.add_node(concept,
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memory_items=[memory],
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created_time=current_time, # 添加创建时间
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last_modified=current_time) # 添加最后修改时间
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def get_dot(self, concept):
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# 检查节点是否存在于图中
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if concept in self.G:
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# 从图中获取节点数据
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node_data = self.G.nodes[concept]
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return concept, node_data
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return None
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def get_related_item(self, topic, depth=1):
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if topic not in self.G:
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return [], []
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first_layer_items = []
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second_layer_items = []
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# 获取相邻节点
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neighbors = list(self.G.neighbors(topic))
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# 获取当前节点的记忆项
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node_data = self.get_dot(topic)
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if node_data:
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concept, data = node_data
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if 'memory_items' in data:
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memory_items = data['memory_items']
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if isinstance(memory_items, list):
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first_layer_items.extend(memory_items)
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else:
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first_layer_items.append(memory_items)
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# 只在depth=2时获取第二层记忆
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if depth >= 2:
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# 获取相邻节点的记忆项
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for neighbor in neighbors:
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node_data = self.get_dot(neighbor)
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if node_data:
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concept, data = node_data
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if 'memory_items' in data:
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memory_items = data['memory_items']
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if isinstance(memory_items, list):
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second_layer_items.extend(memory_items)
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else:
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second_layer_items.append(memory_items)
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return first_layer_items, second_layer_items
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@property
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def dots(self):
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# 返回所有节点对应的 Memory_dot 对象
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return [self.get_dot(node) for node in self.G.nodes()]
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def forget_topic(self, topic):
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"""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"""
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if topic not in self.G:
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return None
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# 获取话题节点数据
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node_data = self.G.nodes[topic]
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# 如果节点存在memory_items
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if 'memory_items' in node_data:
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memory_items = node_data['memory_items']
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# 确保memory_items是列表
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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# 如果有记忆项可以删除
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if memory_items:
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# 随机选择一个记忆项删除
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removed_item = random.choice(memory_items)
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memory_items.remove(removed_item)
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# 更新节点的记忆项
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if memory_items:
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self.G.nodes[topic]['memory_items'] = memory_items
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else:
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# 如果没有记忆项了，删除整个节点
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self.G.remove_node(topic)
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return removed_item
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return None
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# 海马体
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class Hippocampus:
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def __init__(self, memory_graph: Memory_graph):
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self.memory_graph = memory_graph
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self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, temperature=0.5)
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self.llm_summary_by_topic = LLM_request(model=global_config.llm_summary_by_topic, temperature=0.5)
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def get_all_node_names(self) -> list:
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"""获取记忆图中所有节点的名字列表
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Returns:
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list: 包含所有节点名字的列表
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"""
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return list(self.memory_graph.G.nodes())
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def calculate_node_hash(self, concept, memory_items):
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"""计算节点的特征值"""
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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sorted_items = sorted(memory_items)
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content = f"{concept}:{'|'.join(sorted_items)}"
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return hash(content)
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def calculate_edge_hash(self, source, target):
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"""计算边的特征值"""
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nodes = sorted([source, target])
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return hash(f"{nodes[0]}:{nodes[1]}")
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def random_get_msg_snippet(self, target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list:
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"""随机抽取一段时间内的消息片段
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Args:
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- target_timestamp: 目标时间戳
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- chat_size: 抽取的消息数量
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- max_memorized_time_per_msg: 每条消息的最大记忆次数
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Returns:
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- list: 抽取出的消息记录列表
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"""
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try_count = 0
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# 最多尝试三次抽取
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while try_count < 3:
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messages = get_closest_chat_from_db(length=chat_size, timestamp=target_timestamp)
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if messages:
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# 检查messages是否均没有达到记忆次数限制
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for message in messages:
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if message["memorized_times"] >= max_memorized_time_per_msg:
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messages = None
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break
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if messages:
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# 成功抽取短期消息样本
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# 数据写回：增加记忆次数
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for message in messages:
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db.messages.update_one({"_id": message["_id"]},
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{"$set": {"memorized_times": message["memorized_times"] + 1}})
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return messages
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try_count += 1
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# 三次尝试均失败
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return None
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def get_memory_sample(self, chat_size=20, time_frequency: dict = {'near': 2, 'mid': 4, 'far': 3}):
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"""获取记忆样本
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Returns:
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list: 消息记录列表，每个元素是一个消息记录字典列表
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"""
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# 硬编码：每条消息最大记忆次数
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# 如有需求可写入global_config
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max_memorized_time_per_msg = 3
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current_timestamp = datetime.datetime.now().timestamp()
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chat_samples = []
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# 短期：1h 中期：4h 长期：24h
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logger.debug(f"正在抽取短期消息样本")
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for i in range(time_frequency.get('near')):
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random_time = current_timestamp - random.randint(1, 3600)
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messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
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if messages:
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logger.debug(f"成功抽取短期消息样本{len(messages)}条")
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chat_samples.append(messages)
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else:
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logger.warning(f"第{i}次短期消息样本抽取失败")
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logger.debug(f"正在抽取中期消息样本")
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for i in range(time_frequency.get('mid')):
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random_time = current_timestamp - random.randint(3600, 3600 * 4)
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messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
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if messages:
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logger.debug(f"成功抽取中期消息样本{len(messages)}条")
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chat_samples.append(messages)
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else:
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logger.warning(f"第{i}次中期消息样本抽取失败")
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logger.debug(f"正在抽取长期消息样本")
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for i in range(time_frequency.get('far')):
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random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24)
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messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
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if messages:
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logger.debug(f"成功抽取长期消息样本{len(messages)}条")
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chat_samples.append(messages)
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else:
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logger.warning(f"第{i}次长期消息样本抽取失败")
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return chat_samples
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async def memory_compress(self, messages: list, compress_rate=0.1):
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"""压缩消息记录为记忆
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Returns:
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tuple: (压缩记忆集合, 相似主题字典)
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"""
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if not messages:
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return set(), {}
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# 合并消息文本，同时保留时间信息
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input_text = ""
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time_info = ""
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# 计算最早和最晚时间
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earliest_time = min(msg['time'] for msg in messages)
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latest_time = max(msg['time'] for msg in messages)
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earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
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latest_dt = datetime.datetime.fromtimestamp(latest_time)
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# 如果是同一年
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if earliest_dt.year == latest_dt.year:
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earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
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latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
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time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
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else:
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earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
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latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
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time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
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for msg in messages:
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input_text += f"{msg['detailed_plain_text']}\n"
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logger.debug(input_text)
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topic_num = self.calculate_topic_num(input_text, compress_rate)
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topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
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# 过滤topics
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filter_keywords = global_config.memory_ban_words
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topics = [topic.strip() for topic in
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topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
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filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
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logger.info(f"过滤后话题: {filtered_topics}")
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# 创建所有话题的请求任务
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tasks = []
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for topic in filtered_topics:
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topic_what_prompt = self.topic_what(input_text, topic, time_info)
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task = self.llm_summary_by_topic.generate_response_async(topic_what_prompt)
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tasks.append((topic.strip(), task))
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# 等待所有任务完成
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compressed_memory = set()
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similar_topics_dict = {} # 存储每个话题的相似主题列表
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for topic, task in tasks:
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response = await task
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if response:
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compressed_memory.add((topic, response[0]))
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# 为每个话题查找相似的已存在主题
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existing_topics = list(self.memory_graph.G.nodes())
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similar_topics = []
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for existing_topic in existing_topics:
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topic_words = set(jieba.cut(topic))
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existing_words = set(jieba.cut(existing_topic))
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all_words = topic_words | existing_words
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v1 = [1 if word in topic_words else 0 for word in all_words]
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v2 = [1 if word in existing_words else 0 for word in all_words]
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similarity = cosine_similarity(v1, v2)
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if similarity >= 0.6:
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similar_topics.append((existing_topic, similarity))
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similar_topics.sort(key=lambda x: x[1], reverse=True)
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similar_topics = similar_topics[:5]
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similar_topics_dict[topic] = similar_topics
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return compressed_memory, similar_topics_dict
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def calculate_topic_num(self, text, compress_rate):
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"""计算文本的话题数量"""
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information_content = calculate_information_content(text)
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topic_by_length = text.count('\n') * compress_rate
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topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
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topic_num = int((topic_by_length + topic_by_information_content) / 2)
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logger.debug(
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f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
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f"topic_num: {topic_num}")
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return topic_num
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async def operation_build_memory(self, chat_size=20):
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time_frequency = {'near': 1, 'mid': 4, 'far': 4}
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memory_samples = self.get_memory_sample(chat_size, time_frequency)
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for i, messages in enumerate(memory_samples, 1):
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all_topics = []
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# 加载进度可视化
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progress = (i / len(memory_samples)) * 100
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bar_length = 30
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filled_length = int(bar_length * i // len(memory_samples))
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bar = '█' * filled_length + '-' * (bar_length - filled_length)
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logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
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compress_rate = global_config.memory_compress_rate
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compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
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logger.info(f"压缩后记忆数量: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}")
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current_time = datetime.datetime.now().timestamp()
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for topic, memory in compressed_memory:
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logger.info(f"添加节点: {topic}")
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self.memory_graph.add_dot(topic, memory)
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all_topics.append(topic)
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# 连接相似的已存在主题
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if topic in similar_topics_dict:
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similar_topics = similar_topics_dict[topic]
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for similar_topic, similarity in similar_topics:
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if topic != similar_topic:
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strength = int(similarity * 10)
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logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
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self.memory_graph.G.add_edge(topic, similar_topic,
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strength=strength,
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created_time=current_time,
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last_modified=current_time)
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# 连接同批次的相关话题
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for i in range(len(all_topics)):
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for j in range(i + 1, len(all_topics)):
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logger.info(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
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self.memory_graph.connect_dot(all_topics[i], all_topics[j])
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self.sync_memory_to_db()
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def sync_memory_to_db(self):
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"""检查并同步内存中的图结构与数据库"""
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# 获取数据库中所有节点和内存中所有节点
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db_nodes = list(db.graph_data.nodes.find())
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memory_nodes = list(self.memory_graph.G.nodes(data=True))
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# 转换数据库节点为字典格式,方便查找
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db_nodes_dict = {node['concept']: node for node in db_nodes}
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# 检查并更新节点
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for concept, data in memory_nodes:
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memory_items = data.get('memory_items', [])
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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# 计算内存中节点的特征值
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memory_hash = self.calculate_node_hash(concept, memory_items)
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# 获取时间信息
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created_time = data.get('created_time', datetime.datetime.now().timestamp())
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last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
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if concept not in db_nodes_dict:
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# 数据库中缺少的节点,添加
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node_data = {
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'concept': concept,
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'memory_items': memory_items,
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'hash': memory_hash,
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'created_time': created_time,
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'last_modified': last_modified
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}
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db.graph_data.nodes.insert_one(node_data)
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else:
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# 获取数据库中节点的特征值
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db_node = db_nodes_dict[concept]
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db_hash = db_node.get('hash', None)
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# 如果特征值不同,则更新节点
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if db_hash != memory_hash:
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db.graph_data.nodes.update_one(
|
||
{'concept': concept},
|
||
{'$set': {
|
||
'memory_items': memory_items,
|
||
'hash': memory_hash,
|
||
'created_time': created_time,
|
||
'last_modified': last_modified
|
||
}}
|
||
)
|
||
|
||
# 处理边的信息
|
||
db_edges = list(db.graph_data.edges.find())
|
||
memory_edges = list(self.memory_graph.G.edges(data=True))
|
||
|
||
# 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, data in memory_edges:
|
||
edge_hash = self.calculate_edge_hash(source, target)
|
||
edge_key = (source, target)
|
||
strength = data.get('strength', 1)
|
||
|
||
# 获取边的时间信息
|
||
created_time = data.get('created_time', datetime.datetime.now().timestamp())
|
||
last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
|
||
|
||
if edge_key not in db_edge_dict:
|
||
# 添加新边
|
||
edge_data = {
|
||
'source': source,
|
||
'target': target,
|
||
'strength': strength,
|
||
'hash': edge_hash,
|
||
'created_time': created_time,
|
||
'last_modified': last_modified
|
||
}
|
||
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:
|
||
db.graph_data.edges.update_one(
|
||
{'source': source, 'target': target},
|
||
{'$set': {
|
||
'hash': edge_hash,
|
||
'strength': strength,
|
||
'created_time': created_time,
|
||
'last_modified': last_modified
|
||
}}
|
||
)
|
||
|
||
def sync_memory_from_db(self):
|
||
"""从数据库同步数据到内存中的图结构"""
|
||
current_time = datetime.datetime.now().timestamp()
|
||
need_update = False
|
||
|
||
# 清空当前图
|
||
self.memory_graph.G.clear()
|
||
|
||
# 从数据库加载所有节点
|
||
nodes = list(db.graph_data.nodes.find())
|
||
for node in nodes:
|
||
concept = node['concept']
|
||
memory_items = node.get('memory_items', [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
# 检查时间字段是否存在
|
||
if 'created_time' not in node or 'last_modified' not in node:
|
||
need_update = True
|
||
# 更新数据库中的节点
|
||
update_data = {}
|
||
if 'created_time' not in node:
|
||
update_data['created_time'] = current_time
|
||
if 'last_modified' not in node:
|
||
update_data['last_modified'] = current_time
|
||
|
||
db.graph_data.nodes.update_one(
|
||
{'concept': concept},
|
||
{'$set': update_data}
|
||
)
|
||
logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段")
|
||
|
||
# 获取时间信息(如果不存在则使用当前时间)
|
||
created_time = node.get('created_time', current_time)
|
||
last_modified = node.get('last_modified', current_time)
|
||
|
||
# 添加节点到图中
|
||
self.memory_graph.G.add_node(concept,
|
||
memory_items=memory_items,
|
||
created_time=created_time,
|
||
last_modified=last_modified)
|
||
|
||
# 从数据库加载所有边
|
||
edges = list(db.graph_data.edges.find())
|
||
for edge in edges:
|
||
source = edge['source']
|
||
target = edge['target']
|
||
strength = edge.get('strength', 1)
|
||
|
||
# 检查时间字段是否存在
|
||
if 'created_time' not in edge or 'last_modified' not in edge:
|
||
need_update = True
|
||
# 更新数据库中的边
|
||
update_data = {}
|
||
if 'created_time' not in edge:
|
||
update_data['created_time'] = current_time
|
||
if 'last_modified' not in edge:
|
||
update_data['last_modified'] = current_time
|
||
|
||
db.graph_data.edges.update_one(
|
||
{'source': source, 'target': target},
|
||
{'$set': update_data}
|
||
)
|
||
logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段")
|
||
|
||
# 获取时间信息(如果不存在则使用当前时间)
|
||
created_time = edge.get('created_time', current_time)
|
||
last_modified = edge.get('last_modified', current_time)
|
||
|
||
# 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,
|
||
created_time=created_time,
|
||
last_modified=last_modified)
|
||
|
||
if need_update:
|
||
logger.success("[数据库] 已为缺失的时间字段进行补充")
|
||
|
||
async def operation_forget_topic(self, percentage=0.1):
|
||
"""随机选择图中一定比例的节点和边进行检查,根据时间条件决定是否遗忘"""
|
||
# 检查数据库是否为空
|
||
# logger.remove()
|
||
|
||
logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
||
# logger.info(f"- Logger名称: {logger.name}")
|
||
logger.info(f"- Logger等级: {logger.level}")
|
||
# logger.info(f"- Logger处理器: {[handler.__class__.__name__ for handler in logger.handlers]}")
|
||
|
||
# logger2 = setup_logger(LogModule.MEMORY)
|
||
# logger2.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
||
# logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
||
|
||
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
|
||
|
||
check_nodes_count = max(1, int(len(all_nodes) * percentage))
|
||
check_edges_count = max(1, int(len(all_edges) * percentage))
|
||
|
||
nodes_to_check = random.sample(all_nodes, check_nodes_count)
|
||
edges_to_check = random.sample(all_edges, check_edges_count)
|
||
|
||
edge_changes = {'weakened': 0, 'removed': 0}
|
||
node_changes = {'reduced': 0, 'removed': 0}
|
||
|
||
current_time = datetime.datetime.now().timestamp()
|
||
|
||
# 检查并遗忘连接
|
||
logger.info("[遗忘] 开始检查连接...")
|
||
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 * global_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'] += 1
|
||
logger.info(f"[遗忘] 连接移除: {source} -> {target}")
|
||
else:
|
||
edge_data['strength'] = new_strength
|
||
edge_data['last_modified'] = current_time
|
||
edge_changes['weakened'] += 1
|
||
logger.info(f"[遗忘] 连接减弱: {source} -> {target} (强度: {current_strength} -> {new_strength})")
|
||
|
||
# 检查并遗忘话题
|
||
logger.info("[遗忘] 开始检查节点...")
|
||
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'] += 1
|
||
logger.info(f"[遗忘] 记忆减少: {node} (数量: {current_count} -> {len(memory_items)})")
|
||
else:
|
||
self.memory_graph.G.remove_node(node)
|
||
node_changes['removed'] += 1
|
||
logger.info(f"[遗忘] 节点移除: {node}")
|
||
|
||
if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
|
||
self.sync_memory_to_db()
|
||
logger.info("[遗忘] 统计信息:")
|
||
logger.info(f"[遗忘] 连接变化: {edge_changes['weakened']} 个减弱, {edge_changes['removed']} 个移除")
|
||
logger.info(f"[遗忘] 节点变化: {node_changes['reduced']} 个减少记忆, {node_changes['removed']} 个移除")
|
||
else:
|
||
logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件")
|
||
|
||
async def merge_memory(self, topic):
|
||
"""对指定话题的记忆进行合并压缩"""
|
||
# 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)
|
||
logger.debug(f"[合并] 话题: {topic}")
|
||
logger.debug(f"[合并] 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(selected_memories, 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)
|
||
logger.info(f"[合并] 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
|
||
logger.debug(f"[合并] 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:
|
||
logger.debug(f"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()
|
||
logger.debug(f"完成记忆合并操作，共处理 {len(merged_nodes)} 个节点")
|
||
else:
|
||
logger.debug("本次检查没有需要合并的节点")
|
||
|
||
def find_topic_llm(self, text, topic_num):
|
||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
|
||
return prompt
|
||
|
||
def topic_what(self, text, topic, time_info):
|
||
prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
|
||
return prompt
|
||
|
||
async def _identify_topics(self, text: str) -> list:
|
||
"""从文本中识别可能的主题
|
||
|
||
Args:
|
||
text: 输入文本
|
||
|
||
Returns:
|
||
list: 识别出的主题列表
|
||
"""
|
||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
|
||
# print(f"话题: {topics_response[0]}")
|
||
topics = [topic.strip() for topic in
|
||
topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
|
||
# print(f"话题: {topics}")
|
||
|
||
return topics
|
||
|
||
def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
|
||
"""查找与给定主题相似的记忆主题
|
||
|
||
Args:
|
||
topics: 主题列表
|
||
similarity_threshold: 相似度阈值
|
||
debug_info: 调试信息前缀
|
||
|
||
Returns:
|
||
list: (主题, 相似度) 元组列表
|
||
"""
|
||
all_memory_topics = self.get_all_node_names()
|
||
all_similar_topics = []
|
||
|
||
# 计算每个识别出的主题与记忆主题的相似度
|
||
for topic in topics:
|
||
if debug_info:
|
||
# print(f"\033[1;32m[{debug_info}]\033[0m 正在思考有没有见过: {topic}")
|
||
pass
|
||
|
||
topic_vector = text_to_vector(topic)
|
||
has_similar_topic = False
|
||
|
||
for memory_topic in all_memory_topics:
|
||
memory_vector = text_to_vector(memory_topic)
|
||
# 获取所有唯一词
|
||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
||
# 构建向量
|
||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
||
# 计算相似度
|
||
similarity = cosine_similarity(v1, v2)
|
||
|
||
if similarity >= similarity_threshold:
|
||
has_similar_topic = True
|
||
if debug_info:
|
||
# print(f"\033[1;32m[{debug_info}]\033[0m 找到相似主题: {topic} -> {memory_topic} (相似度: {similarity:.2f})")
|
||
pass
|
||
all_similar_topics.append((memory_topic, similarity))
|
||
|
||
if not has_similar_topic and debug_info:
|
||
# print(f"\033[1;31m[{debug_info}]\033[0m 没有见过: {topic} ，呃呃")
|
||
pass
|
||
|
||
return all_similar_topics
|
||
|
||
def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
|
||
"""获取相似度最高的主题
|
||
|
||
Args:
|
||
similar_topics: (主题, 相似度) 元组列表
|
||
max_topics: 最大主题数量
|
||
|
||
Returns:
|
||
list: (主题, 相似度) 元组列表
|
||
"""
|
||
seen_topics = set()
|
||
top_topics = []
|
||
|
||
for topic, score in sorted(similar_topics, key=lambda x: x[1], reverse=True):
|
||
if topic not in seen_topics and len(top_topics) < max_topics:
|
||
seen_topics.add(topic)
|
||
top_topics.append((topic, score))
|
||
|
||
return top_topics
|
||
|
||
async def memory_activate_value(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.3) -> int:
|
||
"""计算输入文本对记忆的激活程度"""
|
||
logger.info(f"[激活] 识别主题: {await self._identify_topics(text)}")
|
||
|
||
# 识别主题
|
||
identified_topics = await self._identify_topics(text)
|
||
if not identified_topics:
|
||
return 0
|
||
|
||
# 查找相似主题
|
||
all_similar_topics = self._find_similar_topics(
|
||
identified_topics,
|
||
similarity_threshold=similarity_threshold,
|
||
debug_info="激活"
|
||
)
|
||
|
||
if not all_similar_topics:
|
||
return 0
|
||
|
||
# 获取最相关的主题
|
||
top_topics = self._get_top_topics(all_similar_topics, max_topics)
|
||
|
||
# 如果只找到一个主题，进行惩罚
|
||
if len(top_topics) == 1:
|
||
topic, score = top_topics[0]
|
||
# 获取主题内容数量并计算惩罚系数
|
||
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 []
|
||
content_count = len(memory_items)
|
||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
||
|
||
activation = int(score * 50 * penalty)
|
||
logger.info(
|
||
f"[激活] 单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
|
||
return activation
|
||
|
||
# 计算关键词匹配率，同时考虑内容数量
|
||
matched_topics = set()
|
||
topic_similarities = {}
|
||
|
||
for memory_topic, similarity in top_topics:
|
||
# 计算内容数量惩罚
|
||
memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
content_count = len(memory_items)
|
||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
||
|
||
# 对每个记忆主题，检查它与哪些输入主题相似
|
||
for input_topic in identified_topics:
|
||
topic_vector = text_to_vector(input_topic)
|
||
memory_vector = text_to_vector(memory_topic)
|
||
all_words = set(topic_vector.keys()) | set(memory_vector.keys())
|
||
v1 = [topic_vector.get(word, 0) for word in all_words]
|
||
v2 = [memory_vector.get(word, 0) for word in all_words]
|
||
sim = cosine_similarity(v1, v2)
|
||
if sim >= similarity_threshold:
|
||
matched_topics.add(input_topic)
|
||
adjusted_sim = sim * penalty
|
||
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
|
||
# logger.debug(
|
||
# f"[激活] 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
|
||
|
||
# 计算主题匹配率和平均相似度
|
||
topic_match = len(matched_topics) / len(identified_topics)
|
||
average_similarities = sum(topic_similarities.values()) / len(topic_similarities) if topic_similarities else 0
|
||
|
||
# 计算最终激活值
|
||
activation = int((topic_match + average_similarities) / 2 * 100)
|
||
logger.info(
|
||
f"[激活] 匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
|
||
|
||
return activation
|
||
|
||
async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4,
|
||
max_memory_num: int = 5) -> list:
|
||
"""根据输入文本获取相关的记忆内容"""
|
||
# 识别主题
|
||
identified_topics = await self._identify_topics(text)
|
||
|
||
# 查找相似主题
|
||
all_similar_topics = self._find_similar_topics(
|
||
identified_topics,
|
||
similarity_threshold=similarity_threshold,
|
||
debug_info="记忆检索"
|
||
)
|
||
|
||
# 获取最相关的主题
|
||
relevant_topics = self._get_top_topics(all_similar_topics, max_topics)
|
||
|
||
# 获取相关记忆内容
|
||
relevant_memories = []
|
||
for topic, score in relevant_topics:
|
||
# 获取该主题的记忆内容
|
||
first_layer, _ = self.memory_graph.get_related_item(topic, depth=1)
|
||
if first_layer:
|
||
# 如果记忆条数超过限制，随机选择指定数量的记忆
|
||
if len(first_layer) > max_memory_num / 2:
|
||
first_layer = random.sample(first_layer, max_memory_num // 2)
|
||
# 为每条记忆添加来源主题和相似度信息
|
||
for memory in first_layer:
|
||
relevant_memories.append({
|
||
'topic': topic,
|
||
'similarity': score,
|
||
'content': memory
|
||
})
|
||
|
||
# 如果记忆数量超过5个,随机选择5个
|
||
# 按相似度排序
|
||
relevant_memories.sort(key=lambda x: x['similarity'], reverse=True)
|
||
|
||
if len(relevant_memories) > max_memory_num:
|
||
relevant_memories = random.sample(relevant_memories, max_memory_num)
|
||
|
||
return relevant_memories
|
||
|
||
|
||
def segment_text(text):
|
||
seg_text = list(jieba.cut(text))
|
||
return seg_text
|
||
|
||
|
||
driver = get_driver()
|
||
config = driver.config
|
||
|
||
start_time = time.time()
|
||
|
||
# 创建记忆图
|
||
memory_graph = Memory_graph()
|
||
# 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()
|
||
logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")
|