mirror of https://github.com/Mai-with-u/MaiBot.git
ref:彻底移除海马体，优化log
SengokuCola
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feb40cac70
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6a0a07582e
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# 海马体到记忆仓库转换任务
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## 功能描述
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这个功能实现了每60秒自动将海马体的节点转换为记忆仓库格式的功能。系统会随机选择5个海马体节点，将它们的记忆内容拼接成一个完整的文本，然后使用LLM生成标题并存储到记忆仓库中。
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## 实现细节
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### 核心组件
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1. **HippocampusToMemoryChestTask** (`src/chat/memory_system/hippocampus_to_memory_chest_task.py`)
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- 继承自 `AsyncTask` 基类
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- 每60秒执行一次转换任务
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- 启动后等待60秒再开始第一次执行
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### 工作流程
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1. **节点选择**：从海马体的所有节点中随机选择5个节点
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2. **内容拼接**：将选中节点的记忆内容按格式拼接：
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```
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【节点名称1】记忆内容1
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【节点名称2】记忆内容2
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...
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```
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3. **标题生成**：使用Memory_chest的LLM模型为拼接的内容生成描述性标题
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4. **数据存储**：将标题和内容保存到MemoryChest数据库表中
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5. **节点删除**：如果保存成功，删除已转换的海马体节点，防止重复构建
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### 集成方式
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任务已集成到主系统 (`src/main.py`) 中：
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```python
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# 初始化记忆系统
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hippocampus_manager.initialize()
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logger.info("记忆系统初始化成功")
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# 添加海马体到记忆仓库的转换任务
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await async_task_manager.add_task(HippocampusToMemoryChestTask())
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logger.info("海马体到记忆仓库转换任务已启动")
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```
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## 配置参数
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- **等待时间**：60秒（启动后等待时间）
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- **执行间隔**：60秒（每次执行间隔）
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- **节点数量**：5个（每次随机选择的节点数）
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## 日志输出
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任务执行过程中会输出详细的日志信息：
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- `[海马体转换] 开始执行海马体到记忆仓库的转换任务`
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- `[海马体转换] 随机选择了 X 个节点: [节点列表]`
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- `[海马体转换] 拼接完成，内容长度: X 字符`
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- `[海马体转换] 已保存到记忆仓库，标题: [生成的标题]`
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- `[海马体转换] 已删除节点: [节点名称]`
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- `[海马体转换] 已删除 X 个节点并同步到数据库`
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- `[海马体转换] 转换任务完成`
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## 错误处理
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- 如果海马体管理器未初始化，会跳过本次转换
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- 如果节点数量少于5个，会跳过本次转换
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- 如果没有有效的记忆内容，会跳过本次转换
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- 如果标题生成失败，会跳过保存操作
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- 所有错误都会记录到日志中
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## 测试
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可以使用提供的测试脚本进行功能验证：
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```bash
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python test_hippocampus_task.py
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```
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## 注意事项
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1. 确保海马体管理器已正确初始化
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2. 确保Memory_chest的LLM模型可用
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3. 确保数据库连接正常
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4. 任务会在系统启动后60秒开始第一次执行
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5. **重要**：转换后的海马体节点会被永久删除，确保不会重复构建
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6. 删除操作会自动同步到数据库，保持数据一致性
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@ -13,7 +13,6 @@ from src.common.logger import get_logger
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from src.common.server import get_global_server, Server
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from src.mood.mood_manager import mood_manager
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from src.chat.knowledge import lpmm_start_up
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from src.memory_system.Hippocampus import hippocampus_manager
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from src.memory_system.memory_management_task import MemoryManagementTask
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from rich.traceback import install
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from src.migrate_helper.migrate import check_and_run_migrations
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@ -94,10 +93,6 @@ class MainSystem:
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asyncio.create_task(get_chat_manager()._auto_save_task())
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logger.info("聊天管理器初始化成功")
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# 初始化记忆系统
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hippocampus_manager.initialize()
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logger.info("记忆系统初始化成功")
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# 添加记忆管理任务
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await async_task_manager.add_task(MemoryManagementTask())
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@ -1,611 +0,0 @@
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# -*- 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 re
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import jieba
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import networkx as nx
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import numpy as np
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from typing import List, Tuple, Set
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from collections import Counter
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import traceback
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from rich.traceback import install
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config
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from src.common.database.database_model import GraphNodes, GraphEdges # Peewee Models导入
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from src.common.logger import get_logger
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from src.chat.utils.utils import cut_key_words
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# 添加cosine_similarity函数
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def cosine_similarity(v1, v2):
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"""计算余弦相似度"""
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dot_product = np.dot(v1, v2)
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norm1 = np.linalg.norm(v1)
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norm2 = np.linalg.norm(v2)
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return 0 if norm1 == 0 or norm2 == 0 else dot_product / (norm1 * norm2)
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install(extra_lines=3)
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def calculate_information_content(text):
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"""计算文本的信息量（熵）"""
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char_count = Counter(text)
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total_chars = len(text)
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if total_chars == 0:
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return 0
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entropy = 0
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for count in char_count.values():
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probability = count / total_chars
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entropy -= probability * math.log2(probability)
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return entropy
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logger = get_logger("memory")
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class MemoryGraph:
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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(
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concept1,
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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,
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) # 添加最后修改时间
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async def add_dot(self, concept, memory, hippocampus_instance=None):
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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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# 获取现有的记忆项（已经是str格式）
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existing_memory = self.G.nodes[concept]["memory_items"]
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# 简单连接新旧记忆
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new_memory_str = f"{existing_memory} | {memory}"
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self.G.nodes[concept]["memory_items"] = new_memory_str
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logger.info(f"节点 {concept} 记忆内容已简单拼接并更新：{new_memory_str}")
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else:
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self.G.nodes[concept]["memory_items"] = str(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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logger.info(f"节点 {concept} 创建新记忆：{str(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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# 如果是新节点,创建新的记忆字符串
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self.G.add_node(
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concept,
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memory_items=str(memory),
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weight=1.0, # 新节点初始权重为1.0
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created_time=current_time, # 添加创建时间
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last_modified=current_time,
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) # 添加最后修改时间
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logger.info(f"新节点 {concept} 已添加，记忆内容已写入：{str(memory)}")
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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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return (concept, self.G.nodes[concept]) if concept in self.G else 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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_, data = node_data
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if "memory_items" in data:
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# 直接使用完整的记忆内容
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if memory_items := data["memory_items"]:
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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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if node_data := self.get_dot(neighbor):
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_, data = node_data
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if "memory_items" in data:
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# 直接使用完整的记忆内容
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if memory_items := data["memory_items"]:
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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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# 删除整个节点
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self.G.remove_node(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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if memory_items := node_data["memory_items"]:
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return (
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f"删除了节点 {topic} 的完整记忆: {memory_items[:50]}..."
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if len(memory_items) > 50
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else f"删除了节点 {topic} 的完整记忆: {memory_items}"
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)
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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):
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self.memory_graph = MemoryGraph()
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self.entorhinal_cortex: EntorhinalCortex = None # type: ignore
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self.parahippocampal_gyrus: ParahippocampalGyrus = None # type: ignore
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def initialize(self):
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# 初始化子组件
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self.entorhinal_cortex = EntorhinalCortex(self)
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self.parahippocampal_gyrus = ParahippocampalGyrus(self)
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# 从数据库加载记忆图
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self.entorhinal_cortex.sync_memory_from_db()
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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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return list(self.memory_graph.G.nodes())
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@staticmethod
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def calculate_node_hash(concept, memory_items) -> int:
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"""计算节点的特征值"""
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# memory_items已经是str格式，直接按分隔符分割
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if memory_items:
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unique_items = {item.strip() for item in memory_items.split(" | ") if item.strip()}
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else:
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unique_items = set()
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# 使用frozenset来保证顺序一致性
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content = f"{concept}:{frozenset(unique_items)}"
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return hash(content)
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@staticmethod
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def calculate_edge_hash(source, target) -> int:
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"""计算边的特征值"""
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# 直接使用元组，保证顺序一致性
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return hash((source, target))
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# 负责海马体与其他部分的交互
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class EntorhinalCortex:
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def __init__(self, hippocampus: Hippocampus):
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self.hippocampus = hippocampus
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self.memory_graph = hippocampus.memory_graph
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async def sync_memory_to_db(self):
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"""将记忆图同步到数据库"""
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start_time = time.time()
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current_time = datetime.datetime.now().timestamp()
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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 = {node.concept: node for node in GraphNodes.select()}
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memory_nodes = list(self.memory_graph.G.nodes(data=True))
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# 批量准备节点数据
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nodes_to_create = []
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nodes_to_update = []
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nodes_to_delete = set()
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# 处理节点
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for concept, data in memory_nodes:
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if not concept or not isinstance(concept, str):
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self.memory_graph.G.remove_node(concept)
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continue
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memory_items = data.get("memory_items", "")
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# 直接检查字符串是否为空，不需要分割成列表
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if not memory_items or memory_items.strip() == "":
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self.memory_graph.G.remove_node(concept)
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continue
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# 计算内存中节点的特征值
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memory_hash = self.hippocampus.calculate_node_hash(concept, memory_items)
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created_time = data.get("created_time", current_time)
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last_modified = data.get("last_modified", current_time)
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# memory_items直接作为字符串存储，不需要JSON序列化
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if not memory_items:
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continue
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# 获取权重属性
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weight = data.get("weight", 1.0)
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if concept not in db_nodes:
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nodes_to_create.append(
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{
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"concept": concept,
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"memory_items": memory_items,
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"weight": weight,
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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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)
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else:
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db_node = db_nodes[concept]
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if db_node.hash != memory_hash:
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nodes_to_update.append(
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{
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"concept": concept,
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"memory_items": memory_items,
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"weight": weight,
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"hash": memory_hash,
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"last_modified": last_modified,
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}
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)
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# 计算需要删除的节点
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memory_concepts = {concept for concept, _ in memory_nodes}
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nodes_to_delete = set(db_nodes.keys()) - memory_concepts
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# 批量处理节点
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if nodes_to_create:
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batch_size = 100
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for i in range(0, len(nodes_to_create), batch_size):
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batch = nodes_to_create[i : i + batch_size]
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GraphNodes.insert_many(batch).execute()
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if nodes_to_update:
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batch_size = 100
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for i in range(0, len(nodes_to_update), batch_size):
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batch = nodes_to_update[i : i + batch_size]
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for node_data in batch:
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GraphNodes.update(**{k: v for k, v in node_data.items() if k != "concept"}).where(
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GraphNodes.concept == node_data["concept"]
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).execute()
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if nodes_to_delete:
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GraphNodes.delete().where(GraphNodes.concept.in_(nodes_to_delete)).execute() # type: ignore
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# 处理边的信息
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db_edges = list(GraphEdges.select())
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memory_edges = list(self.memory_graph.G.edges(data=True))
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# 创建边的哈希值字典
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db_edge_dict = {}
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for edge in db_edges:
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edge_hash = self.hippocampus.calculate_edge_hash(edge.source, edge.target)
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||||
db_edge_dict[(edge.source, edge.target)] = {"hash": edge_hash, "strength": edge.strength}
|
||||
|
||||
# 批量准备边数据
|
||||
edges_to_create = []
|
||||
edges_to_update = []
|
||||
|
||||
# 处理边
|
||||
for source, target, data in memory_edges:
|
||||
edge_hash = self.hippocampus.calculate_edge_hash(source, target)
|
||||
edge_key = (source, target)
|
||||
strength = data.get("strength", 1)
|
||||
created_time = data.get("created_time", current_time)
|
||||
last_modified = data.get("last_modified", current_time)
|
||||
|
||||
if edge_key not in db_edge_dict:
|
||||
edges_to_create.append(
|
||||
{
|
||||
"source": source,
|
||||
"target": target,
|
||||
"strength": strength,
|
||||
"hash": edge_hash,
|
||||
"created_time": created_time,
|
||||
"last_modified": last_modified,
|
||||
}
|
||||
)
|
||||
elif db_edge_dict[edge_key]["hash"] != edge_hash:
|
||||
edges_to_update.append(
|
||||
{
|
||||
"source": source,
|
||||
"target": target,
|
||||
"strength": strength,
|
||||
"hash": edge_hash,
|
||||
"last_modified": last_modified,
|
||||
}
|
||||
)
|
||||
|
||||
# 计算需要删除的边
|
||||
memory_edge_keys = {(source, target) for source, target, _ in memory_edges}
|
||||
edges_to_delete = set(db_edge_dict.keys()) - memory_edge_keys
|
||||
|
||||
# 批量处理边
|
||||
if edges_to_create:
|
||||
batch_size = 100
|
||||
for i in range(0, len(edges_to_create), batch_size):
|
||||
batch = edges_to_create[i : i + batch_size]
|
||||
GraphEdges.insert_many(batch).execute()
|
||||
|
||||
if edges_to_update:
|
||||
batch_size = 100
|
||||
for i in range(0, len(edges_to_update), batch_size):
|
||||
batch = edges_to_update[i : i + batch_size]
|
||||
for edge_data in batch:
|
||||
GraphEdges.update(**{k: v for k, v in edge_data.items() if k not in ["source", "target"]}).where(
|
||||
(GraphEdges.source == edge_data["source"]) & (GraphEdges.target == edge_data["target"])
|
||||
).execute()
|
||||
|
||||
if edges_to_delete:
|
||||
for source, target in edges_to_delete:
|
||||
GraphEdges.delete().where((GraphEdges.source == source) & (GraphEdges.target == target)).execute()
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"[数据库] 同步完成，总耗时: {end_time - start_time:.2f}秒")
|
||||
logger.info(
|
||||
f"[数据库] 同步了 {len(nodes_to_create) + len(nodes_to_update)} 个节点和 {len(edges_to_create) + len(edges_to_update)} 条边"
|
||||
)
|
||||
|
||||
async def resync_memory_to_db(self):
|
||||
"""清空数据库并重新同步所有记忆数据"""
|
||||
start_time = time.time()
|
||||
logger.info("[数据库] 开始重新同步所有记忆数据...")
|
||||
|
||||
# 清空数据库
|
||||
clear_start = time.time()
|
||||
GraphNodes.delete().execute()
|
||||
GraphEdges.delete().execute()
|
||||
clear_end = time.time()
|
||||
logger.info(f"[数据库] 清空数据库耗时: {clear_end - clear_start:.2f}秒")
|
||||
|
||||
# 获取所有节点和边
|
||||
memory_nodes = list(self.memory_graph.G.nodes(data=True))
|
||||
memory_edges = list(self.memory_graph.G.edges(data=True))
|
||||
current_time = datetime.datetime.now().timestamp()
|
||||
|
||||
# 批量准备节点数据
|
||||
nodes_data = []
|
||||
for concept, data in memory_nodes:
|
||||
memory_items = data.get("memory_items", "")
|
||||
|
||||
# 直接检查字符串是否为空，不需要分割成列表
|
||||
if not memory_items or memory_items.strip() == "":
|
||||
self.memory_graph.G.remove_node(concept)
|
||||
continue
|
||||
|
||||
# 计算内存中节点的特征值
|
||||
memory_hash = self.hippocampus.calculate_node_hash(concept, memory_items)
|
||||
created_time = data.get("created_time", current_time)
|
||||
last_modified = data.get("last_modified", current_time)
|
||||
|
||||
# memory_items直接作为字符串存储，不需要JSON序列化
|
||||
if not memory_items:
|
||||
continue
|
||||
|
||||
# 获取权重属性
|
||||
weight = data.get("weight", 1.0)
|
||||
|
||||
nodes_data.append(
|
||||
{
|
||||
"concept": concept,
|
||||
"memory_items": memory_items,
|
||||
"weight": weight,
|
||||
"hash": memory_hash,
|
||||
"created_time": created_time,
|
||||
"last_modified": last_modified,
|
||||
}
|
||||
)
|
||||
|
||||
# 批量插入节点
|
||||
if nodes_data:
|
||||
batch_size = 100
|
||||
for i in range(0, len(nodes_data), batch_size):
|
||||
batch = nodes_data[i : i + batch_size]
|
||||
GraphNodes.insert_many(batch).execute()
|
||||
|
||||
# 批量准备边数据
|
||||
edges_data = []
|
||||
for source, target, data in memory_edges:
|
||||
try:
|
||||
edges_data.append(
|
||||
{
|
||||
"source": source,
|
||||
"target": target,
|
||||
"strength": data.get("strength", 1),
|
||||
"hash": self.hippocampus.calculate_edge_hash(source, target),
|
||||
"created_time": data.get("created_time", current_time),
|
||||
"last_modified": data.get("last_modified", current_time),
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"准备边 {source}-{target} 数据时发生错误: {e}")
|
||||
continue
|
||||
|
||||
# 批量插入边
|
||||
if edges_data:
|
||||
batch_size = 100
|
||||
for i in range(0, len(edges_data), batch_size):
|
||||
batch = edges_data[i : i + batch_size]
|
||||
GraphEdges.insert_many(batch).execute()
|
||||
|
||||
end_time = time.time()
|
||||
logger.info(f"[数据库] 重新同步完成，总耗时: {end_time - start_time:.2f}秒")
|
||||
logger.info(f"[数据库] 同步了 {len(nodes_data)} 个节点和 {len(edges_data)} 条边")
|
||||
|
||||
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()
|
||||
|
||||
# 统计加载情况
|
||||
total_nodes = 0
|
||||
loaded_nodes = 0
|
||||
skipped_nodes = 0
|
||||
|
||||
# 从数据库加载所有节点
|
||||
nodes = list(GraphNodes.select())
|
||||
total_nodes = len(nodes)
|
||||
|
||||
for node in nodes:
|
||||
concept = node.concept
|
||||
try:
|
||||
# 处理空字符串或None的情况
|
||||
if not node.memory_items or node.memory_items.strip() == "":
|
||||
logger.warning(f"节点 {concept} 的memory_items为空，跳过")
|
||||
skipped_nodes += 1
|
||||
continue
|
||||
|
||||
# 直接使用memory_items
|
||||
memory_items = node.memory_items.strip()
|
||||
|
||||
# 检查时间字段是否存在
|
||||
if not node.created_time or not node.last_modified:
|
||||
# 更新数据库中的节点
|
||||
update_data = {}
|
||||
if not node.created_time:
|
||||
update_data["created_time"] = current_time
|
||||
if not node.last_modified:
|
||||
update_data["last_modified"] = current_time
|
||||
|
||||
if update_data:
|
||||
GraphNodes.update(**update_data).where(GraphNodes.concept == concept).execute()
|
||||
|
||||
# 获取时间信息(如果不存在则使用当前时间)
|
||||
created_time = node.created_time or current_time
|
||||
last_modified = node.last_modified or current_time
|
||||
|
||||
# 获取权重属性
|
||||
weight = node.weight if hasattr(node, "weight") and node.weight is not None else 1.0
|
||||
|
||||
# 添加节点到图中
|
||||
self.memory_graph.G.add_node(
|
||||
concept,
|
||||
memory_items=memory_items,
|
||||
weight=weight,
|
||||
created_time=created_time,
|
||||
last_modified=last_modified,
|
||||
)
|
||||
loaded_nodes += 1
|
||||
except Exception as e:
|
||||
logger.error(f"加载节点 {concept} 时发生错误: {e}")
|
||||
skipped_nodes += 1
|
||||
continue
|
||||
|
||||
# 从数据库加载所有边
|
||||
edges = list(GraphEdges.select())
|
||||
for edge in edges:
|
||||
source = edge.source
|
||||
target = edge.target
|
||||
strength = edge.strength
|
||||
|
||||
# 检查时间字段是否存在
|
||||
if not edge.created_time or not edge.last_modified:
|
||||
need_update = True
|
||||
# 更新数据库中的边
|
||||
update_data = {}
|
||||
if not edge.created_time:
|
||||
update_data["created_time"] = current_time
|
||||
if not edge.last_modified:
|
||||
update_data["last_modified"] = current_time
|
||||
|
||||
GraphEdges.update(**update_data).where(
|
||||
(GraphEdges.source == source) & (GraphEdges.target == target)
|
||||
).execute()
|
||||
|
||||
# 获取时间信息(如果不存在则使用当前时间)
|
||||
created_time = edge.created_time or current_time
|
||||
last_modified = edge.last_modified or 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.info("[数据库] 已为缺失的时间字段进行补充")
|
||||
|
||||
# 输出加载统计信息
|
||||
logger.info(
|
||||
f"[数据库] 记忆加载完成: 总计 {total_nodes} 个节点, 成功加载 {loaded_nodes} 个, 跳过 {skipped_nodes} 个"
|
||||
)
|
||||
|
||||
|
||||
# 负责记忆管理
|
||||
class ParahippocampalGyrus:
|
||||
def __init__(self, hippocampus: Hippocampus):
|
||||
self.hippocampus = hippocampus
|
||||
self.memory_graph = hippocampus.memory_graph
|
||||
|
||||
|
||||
class HippocampusManager:
|
||||
def __init__(self):
|
||||
self._hippocampus: Hippocampus = None # type: ignore
|
||||
self._initialized = False
|
||||
|
||||
def initialize(self):
|
||||
"""初始化海马体实例"""
|
||||
if self._initialized:
|
||||
return self._hippocampus
|
||||
|
||||
self._hippocampus = Hippocampus()
|
||||
self._hippocampus.initialize()
|
||||
self._initialized = True
|
||||
|
||||
# 输出记忆图统计信息
|
||||
memory_graph = self._hippocampus.memory_graph.G
|
||||
node_count = len(memory_graph.nodes())
|
||||
edge_count = len(memory_graph.edges())
|
||||
|
||||
logger.info(f"""
|
||||
--------------------------------
|
||||
记忆系统参数配置:
|
||||
记忆图统计信息: 节点数量: {node_count}, 连接数量: {edge_count}
|
||||
--------------------------------""") # noqa: E501
|
||||
|
||||
return self._hippocampus
|
||||
|
||||
def get_hippocampus(self):
|
||||
if not self._initialized:
|
||||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
return self._hippocampus
|
||||
|
||||
def get_all_node_names(self) -> list:
|
||||
"""获取所有节点名称的公共接口"""
|
||||
if not self._initialized:
|
||||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||||
return self._hippocampus.get_all_node_names()
|
||||
|
||||
|
||||
# 创建全局实例
|
||||
hippocampus_manager = HippocampusManager()
|
||||
|
|
@ -17,7 +17,7 @@ from .memory_utils import (
|
|||
check_title_exists_fuzzy
|
||||
)
|
||||
|
||||
logger = get_logger("memory_chest")
|
||||
logger = get_logger("memory")
|
||||
|
||||
class MemoryChest:
|
||||
def __init__(self):
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from src.common.logger import get_logger
|
|||
from src.common.database.database_model import MemoryChest as MemoryChestModel
|
||||
from src.config.config import global_config
|
||||
|
||||
logger = get_logger("memory_management")
|
||||
logger = get_logger("memory")
|
||||
|
||||
|
||||
class MemoryManagementTask(AsyncTask):
|
||||
|
|
@ -86,12 +86,11 @@ class MemoryManagementTask(AsyncTask):
|
|||
async def run(self):
|
||||
"""执行记忆管理任务"""
|
||||
try:
|
||||
logger.info("[记忆管理] 开始执行记忆管理任务")
|
||||
|
||||
|
||||
# 获取当前记忆数量
|
||||
current_count = self._get_memory_count()
|
||||
percentage = current_count / self.max_memory_number
|
||||
logger.info(f"[记忆管理] 当前记忆数量: {current_count}/{self.max_memory_number} ({percentage:.1%})")
|
||||
logger.info(f"当前记忆数量: {current_count}/{self.max_memory_number} ({percentage:.1%})")
|
||||
|
||||
# 如果记忆数量为0，跳过执行
|
||||
if current_count < 10:
|
||||
|
|
@ -100,7 +99,7 @@ class MemoryManagementTask(AsyncTask):
|
|||
# 随机选择一个记忆标题
|
||||
selected_title = self._get_random_memory_title()
|
||||
if not selected_title:
|
||||
logger.warning("[记忆管理] 无法获取随机记忆标题，跳过执行")
|
||||
logger.warning("无法获取随机记忆标题，跳过执行")
|
||||
return
|
||||
|
||||
# 执行choose_merge_target获取相关记忆（标题与内容）
|
||||
|
|
@ -117,15 +116,13 @@ class MemoryManagementTask(AsyncTask):
|
|||
logger.warning("[记忆管理] 记忆合并失败，跳过删除")
|
||||
return
|
||||
|
||||
logger.info(f"[记忆管理] 记忆合并成功，新标题: {merged_title}")
|
||||
logger.info(f"记忆合并成功，新标题: {merged_title}")
|
||||
|
||||
# 删除原始记忆（包括选中的标题和相关的记忆标题）
|
||||
titles_to_delete = [selected_title] + related_titles
|
||||
deleted_count = self._delete_original_memories(titles_to_delete)
|
||||
logger.info(f"[记忆管理] 已删除 {deleted_count} 条原始记忆")
|
||||
|
||||
logger.info("[记忆管理] 记忆管理任务完成")
|
||||
|
||||
logger.info(f"已删除 {deleted_count} 条原始记忆")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[记忆管理] 执行记忆管理任务时发生错误: {e}", exc_info=True)
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ from src.config.config import global_config
|
|||
from src.chat.utils.prompt_builder import Prompt
|
||||
from src.llm_models.payload_content.tool_option import ToolParamType
|
||||
from src.plugin_system import BaseAction, ActionActivationType
|
||||
from src.memory_system.Hippocampus import hippocampus_manager
|
||||
from src.chat.utils.utils import cut_key_words
|
||||
from src.memory_system.Memory_chest import global_memory_chest
|
||||
from src.plugin_system.base.base_tool import BaseTool
|
||||
|
|
|
|||
Loading…
Reference in New Issue