mirror of https://github.com/Mai-with-u/MaiBot.git
1538 lines
64 KiB
Python
1538 lines
64 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 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, Coroutine, Any, Dict
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from collections import Counter
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from itertools import combinations
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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 global_config, 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.chat_message_builder import (
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build_readable_messages,
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get_raw_msg_by_timestamp_with_chat_inclusive,
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) # 导入 build_readable_messages
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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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if norm1 == 0 or norm2 == 0:
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return 0
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return 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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# 如果现有记忆不为空，则使用LLM整合新旧记忆
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if existing_memory and hippocampus_instance and hippocampus_instance.model_small:
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try:
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integrated_memory = await self._integrate_memories_with_llm(
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existing_memory, str(memory), hippocampus_instance.model_small
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)
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self.G.nodes[concept]["memory_items"] = integrated_memory
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# 整合成功，增加权重
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current_weight = self.G.nodes[concept].get("weight", 0.0)
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self.G.nodes[concept]["weight"] = current_weight + 1.0
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logger.debug(f"节点 {concept} 记忆整合成功，权重增加到 {current_weight + 1.0}")
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except Exception as e:
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logger.error(f"LLM整合记忆失败: {e}")
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# 降级到简单连接
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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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else:
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new_memory_str = str(memory)
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self.G.nodes[concept]["memory_items"] = 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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# 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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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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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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# 直接使用完整的记忆内容
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if 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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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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# 直接使用完整的记忆内容
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if 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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async def _integrate_memories_with_llm(self, existing_memory: str, new_memory: str, llm_model: LLMRequest) -> str:
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"""
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使用LLM整合新旧记忆内容
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Args:
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existing_memory: 现有的记忆内容（字符串格式，可能包含多条记忆）
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new_memory: 新的记忆内容
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llm_model: LLM模型实例
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Returns:
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str: 整合后的记忆内容
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"""
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try:
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# 构建整合提示
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integration_prompt = f"""你是一个记忆整合专家。请将以下的旧记忆和新记忆整合成一条更完整、更准确的记忆内容。
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旧记忆内容：
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{existing_memory}
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新记忆内容：
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{new_memory}
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整合要求：
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1. 保留重要信息，去除重复内容
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2. 如果新旧记忆有冲突，合理整合矛盾的地方
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3. 将相关信息合并，形成更完整的描述
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4. 保持语言简洁、准确
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5. 只返回整合后的记忆内容，不要添加任何解释
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整合后的记忆："""
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# 调用LLM进行整合
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content, (reasoning_content, model_name, tool_calls) = await llm_model.generate_response_async(integration_prompt)
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if content and content.strip():
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integrated_content = content.strip()
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logger.debug(f"LLM记忆整合成功，模型: {model_name}")
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return integrated_content
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else:
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logger.warning("LLM返回的整合结果为空，使用默认连接方式")
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return f"{existing_memory} | {new_memory}"
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except Exception as e:
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logger.error(f"LLM记忆整合过程中出错: {e}")
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return f"{existing_memory} | {new_memory}"
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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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# 既然每个节点现在是一个完整的记忆内容，直接删除整个节点
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if memory_items:
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# 删除整个节点
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self.G.remove_node(topic)
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return f"删除了节点 {topic} 的完整记忆: {memory_items[:50]}..." if len(memory_items) > 50 else f"删除了节点 {topic} 的完整记忆: {memory_items}"
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else:
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# 如果没有记忆项，删除该节点
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self.G.remove_node(topic)
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return None
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else:
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# 如果没有memory_items字段，删除该节点
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self.G.remove_node(topic)
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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.model_small: LLMRequest = None # type: ignore
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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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self.model_small = LLMRequest(model_set=model_config.model_task_config.utils_small, request_type="memory.modify")
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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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def calculate_weighted_activation(self, current_activation: float, edge_strength: int, target_node: str) -> float:
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"""
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计算考虑节点权重的激活值
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Args:
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current_activation: 当前激活值
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edge_strength: 边的强度
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target_node: 目标节点名称
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Returns:
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float: 计算后的激活值
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"""
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# 基础激活值计算
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base_activation = current_activation - (1 / edge_strength)
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if base_activation <= 0:
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return 0.0
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# 获取目标节点的权重
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if target_node in self.memory_graph.G:
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node_data = self.memory_graph.G.nodes[target_node]
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node_weight = node_data.get("weight", 1.0)
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# 权重加成：每次整合增加10%激活值，最大加成200%
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weight_multiplier = 1.0 + min((node_weight - 1.0) * 0.1, 2.0)
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return base_activation * weight_multiplier
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else:
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return base_activation
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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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@staticmethod
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def find_topic_llm(text: str, topic_num: int | list[int]):
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# sourcery skip: inline-immediately-returned-variable
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topic_num_str = ""
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if isinstance(topic_num, list):
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topic_num_str = f"{topic_num[0]}-{topic_num[1]}"
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else:
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topic_num_str = topic_num
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prompt = (
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f"这是一段文字：\n{text}\n\n请你从这段话中总结出最多{topic_num_str}个关键的概念，必须是某种概念，比如人，事，物，概念，事件，地点 等等，帮我列出来，"
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f"将主题用逗号隔开，并加上<>,例如<主题1>,<主题2>......尽可能精简。只需要列举最多{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
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f"如果确定找不出主题或者没有明显主题，返回<none>。"
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)
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return prompt
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@staticmethod
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def topic_what(text, topic):
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# sourcery skip: inline-immediately-returned-variable
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# 不再需要 time_info 参数
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prompt = (
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f'这是一段文字：\n{text}\n\n我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成几句自然的话，'
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f"要求包含对这个概念的定义，内容，知识，时间和人物，这些信息必须来自这段文字，不能添加信息。\n只输出几句自然的话就好"
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)
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return prompt
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@staticmethod
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def calculate_topic_num(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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)
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return topic_num
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def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
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"""从关键词获取相关记忆。
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Args:
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keyword (str): 关键词
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max_depth (int, optional): 记忆检索深度，默认为2。1表示只获取直接相关的记忆，2表示获取间接相关的记忆。
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Returns:
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list: 记忆列表，每个元素是一个元组 (topic, memory_content, similarity)
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- topic: str, 记忆主题
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- memory_content: str, 该主题下的完整记忆内容
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- similarity: float, 与关键词的相似度
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"""
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if not keyword:
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return []
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# 获取所有节点
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all_nodes = list(self.memory_graph.G.nodes())
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memories = []
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# 计算关键词的词集合
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keyword_words = set(jieba.cut(keyword))
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# 遍历所有节点，计算相似度
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for node in all_nodes:
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node_words = set(jieba.cut(node))
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all_words = keyword_words | node_words
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v1 = [1 if word in keyword_words else 0 for word in all_words]
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v2 = [1 if word in node_words else 0 for word in all_words]
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similarity = cosine_similarity(v1, v2)
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# 如果相似度超过阈值，获取该节点的记忆
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if similarity >= 0.3: # 可以调整这个阈值
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node_data = self.memory_graph.G.nodes[node]
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memory_items = node_data.get("memory_items", "")
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# 直接使用完整的记忆内容
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if memory_items:
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memories.append((node, memory_items, similarity))
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# 按相似度降序排序
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memories.sort(key=lambda x: x[2], reverse=True)
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return memories
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async def get_keywords_from_text(self, text: str) -> list:
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"""从文本中提取关键词。
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Args:
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text (str): 输入文本
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fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
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如果为True，使用jieba分词提取关键词，速度更快但可能不够准确。
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如果为False，使用LLM提取关键词，速度较慢但更准确。
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"""
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if not text:
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return []
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# 使用LLM提取关键词 - 根据详细文本长度分布优化topic_num计算
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text_length = len(text)
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topic_num: int | list[int] = 0
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words = jieba.cut(text)
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keywords_lite = [word for word in words if len(word) > 1]
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keywords_lite = list(set(keywords_lite))
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if keywords_lite:
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logger.debug(f"提取关键词极简版: {keywords_lite}")
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if text_length <= 12:
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topic_num = [1, 3] # 6-10字符: 1个关键词 (27.18%的文本)
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elif text_length <= 20:
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topic_num = [2, 4] # 11-20字符: 2个关键词 (22.76%的文本)
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elif text_length <= 30:
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topic_num = [3, 5] # 21-30字符: 3个关键词 (10.33%的文本)
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elif text_length <= 50:
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||
topic_num = [4, 5] # 31-50字符: 4个关键词 (9.79%的文本)
|
||
else:
|
||
topic_num = 5 # 51+字符: 5个关键词 (其余长文本)
|
||
|
||
topics_response, _ = await self.model_small.generate_response_async(self.find_topic_llm(text, topic_num))
|
||
|
||
# 提取关键词
|
||
keywords = re.findall(r"<([^>]+)>", topics_response)
|
||
if not keywords:
|
||
keywords = []
|
||
else:
|
||
keywords = [
|
||
keyword.strip()
|
||
for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||
if keyword.strip()
|
||
]
|
||
|
||
if keywords:
|
||
logger.debug(f"提取关键词: {keywords}")
|
||
|
||
return keywords,keywords_lite
|
||
|
||
async def get_memory_from_topic(
|
||
self,
|
||
keywords: list[str],
|
||
max_memory_num: int = 3,
|
||
max_memory_length: int = 2,
|
||
max_depth: int = 3,
|
||
) -> list:
|
||
"""从文本中提取关键词并获取相关记忆。
|
||
|
||
Args:
|
||
keywords (list): 输入文本
|
||
max_memory_num (int, optional): 返回的记忆条目数量上限。默认为3，表示最多返回3条与输入文本相关度最高的记忆。
|
||
max_memory_length (int, optional): 每个主题最多返回的记忆条目数量。默认为2，表示每个主题最多返回2条相似度最高的记忆。
|
||
max_depth (int, optional): 记忆检索深度。默认为3。值越大，检索范围越广，可以获取更多间接相关的记忆，但速度会变慢。
|
||
|
||
Returns:
|
||
list: 记忆列表，每个元素是一个元组 (topic, memory_content)
|
||
- topic: str, 记忆主题
|
||
- memory_content: str, 该主题下的完整记忆内容
|
||
"""
|
||
if not keywords:
|
||
return []
|
||
|
||
logger.info(f"提取的关键词: {', '.join(keywords)}")
|
||
|
||
# 过滤掉不存在于记忆图中的关键词
|
||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||
if not valid_keywords:
|
||
logger.debug("没有找到有效的关键词节点")
|
||
return []
|
||
|
||
logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
|
||
|
||
# 从每个关键词获取记忆
|
||
activate_map = {} # 存储每个词的累计激活值
|
||
|
||
# 对每个关键词进行扩散式检索
|
||
for keyword in valid_keywords:
|
||
logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
|
||
# 初始化激活值
|
||
activation_values = {keyword: 1.0}
|
||
# 记录已访问的节点
|
||
visited_nodes = {keyword}
|
||
# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||
nodes_to_process = [(keyword, 1.0, 0)]
|
||
|
||
while nodes_to_process:
|
||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||
|
||
# 如果激活值小于0或超过最大深度，停止扩散
|
||
if current_activation <= 0 or current_depth >= max_depth:
|
||
continue
|
||
|
||
# 获取当前节点的所有邻居
|
||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||
|
||
for neighbor in neighbors:
|
||
if neighbor in visited_nodes:
|
||
continue
|
||
|
||
# 获取连接强度
|
||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||
strength = edge_data.get("strength", 1)
|
||
|
||
# 计算新的激活值
|
||
new_activation = current_activation - (1 / strength)
|
||
|
||
if new_activation > 0:
|
||
activation_values[neighbor] = new_activation
|
||
visited_nodes.add(neighbor)
|
||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||
# logger.debug(
|
||
# f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})"
|
||
# ) # noqa: E501
|
||
|
||
# 更新激活映射
|
||
for node, activation_value in activation_values.items():
|
||
if activation_value > 0:
|
||
if node in activate_map:
|
||
activate_map[node] += activation_value
|
||
else:
|
||
activate_map[node] = activation_value
|
||
|
||
# 基于激活值平方的独立概率选择
|
||
remember_map = {}
|
||
# logger.info("基于激活值平方的归一化选择:")
|
||
|
||
# 计算所有激活值的平方和
|
||
total_squared_activation = sum(activation**2 for activation in activate_map.values())
|
||
if total_squared_activation > 0:
|
||
# 计算归一化的激活值
|
||
normalized_activations = {
|
||
node: (activation**2) / total_squared_activation for node, activation in activate_map.items()
|
||
}
|
||
|
||
# 按归一化激活值排序并选择前max_memory_num个
|
||
sorted_nodes = sorted(normalized_activations.items(), key=lambda x: x[1], reverse=True)[:max_memory_num]
|
||
|
||
# 将选中的节点添加到remember_map
|
||
for node, normalized_activation in sorted_nodes:
|
||
remember_map[node] = activate_map[node] # 使用原始激活值
|
||
logger.debug(
|
||
f"节点 '{node}' (归一化激活值: {normalized_activation:.2f}, 激活值: {activate_map[node]:.2f})"
|
||
)
|
||
else:
|
||
logger.info("没有有效的激活值")
|
||
|
||
# 从选中的节点中提取记忆
|
||
all_memories = []
|
||
# logger.info("开始从选中的节点中提取记忆:")
|
||
for node, activation in remember_map.items():
|
||
logger.debug(f"处理节点 '{node}' (激活值: {activation:.2f}):")
|
||
node_data = self.memory_graph.G.nodes[node]
|
||
memory_items = node_data.get("memory_items", "")
|
||
# 直接使用完整的记忆内容
|
||
if memory_items:
|
||
logger.debug("节点包含完整记忆")
|
||
# 计算记忆与关键词的相似度
|
||
memory_words = set(jieba.cut(memory_items))
|
||
text_words = set(keywords)
|
||
all_words = memory_words | text_words
|
||
if all_words:
|
||
# 计算相似度（虽然这里没有使用，但保持逻辑一致性）
|
||
v1 = [1 if word in memory_words else 0 for word in all_words]
|
||
v2 = [1 if word in text_words else 0 for word in all_words]
|
||
_ = cosine_similarity(v1, v2) # 计算但不使用，用_表示
|
||
|
||
# 添加完整记忆到结果中
|
||
all_memories.append((node, memory_items, activation))
|
||
else:
|
||
logger.info("节点没有记忆")
|
||
|
||
# 去重（基于记忆内容）
|
||
logger.debug("开始记忆去重:")
|
||
seen_memories = set()
|
||
unique_memories = []
|
||
for topic, memory_items, activation_value in all_memories:
|
||
# memory_items现在是完整的字符串格式
|
||
memory = memory_items if memory_items else ""
|
||
if memory not in seen_memories:
|
||
seen_memories.add(memory)
|
||
unique_memories.append((topic, memory_items, activation_value))
|
||
logger.debug(f"保留记忆: {memory} (来自节点: {topic}, 激活值: {activation_value:.2f})")
|
||
else:
|
||
logger.debug(f"跳过重复记忆: {memory} (来自节点: {topic})")
|
||
|
||
# 转换为(关键词, 记忆)格式
|
||
result = []
|
||
for topic, memory_items, _ in unique_memories:
|
||
# memory_items现在是完整的字符串格式
|
||
memory = memory_items if memory_items else ""
|
||
result.append((topic, memory))
|
||
logger.debug(f"选中记忆: {memory} (来自节点: {topic})")
|
||
|
||
return result
|
||
|
||
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> tuple[float, list[str],list[str]]:
|
||
"""从文本中提取关键词并获取相关记忆。
|
||
|
||
Args:
|
||
text (str): 输入文本
|
||
max_depth (int, optional): 记忆检索深度。默认为2。
|
||
fast_retrieval (bool, optional): 是否使用快速检索。默认为False。
|
||
如果为True，使用jieba分词和TF-IDF提取关键词，速度更快但可能不够准确。
|
||
如果为False，使用LLM提取关键词，速度较慢但更准确。
|
||
|
||
Returns:
|
||
float: 激活节点数与总节点数的比值
|
||
list[str]: 有效的关键词
|
||
"""
|
||
keywords,keywords_lite = await self.get_keywords_from_text(text)
|
||
|
||
# 过滤掉不存在于记忆图中的关键词
|
||
valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
|
||
if not valid_keywords:
|
||
# logger.info("没有找到有效的关键词节点")
|
||
return 0, keywords,keywords_lite
|
||
|
||
logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
|
||
|
||
# 从每个关键词获取记忆
|
||
activate_map = {} # 存储每个词的累计激活值
|
||
|
||
# 对每个关键词进行扩散式检索
|
||
for keyword in valid_keywords:
|
||
logger.debug(f"开始以关键词 '{keyword}' 为中心进行扩散检索 (最大深度: {max_depth}):")
|
||
# 初始化激活值
|
||
activation_values = {keyword: 1.5}
|
||
# 记录已访问的节点
|
||
visited_nodes = {keyword}
|
||
# 待处理的节点队列，每个元素是(节点, 激活值, 当前深度)
|
||
nodes_to_process = [(keyword, 1.0, 0)]
|
||
|
||
while nodes_to_process:
|
||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||
|
||
# 如果激活值小于0或超过最大深度，停止扩散
|
||
if current_activation <= 0 or current_depth >= max_depth:
|
||
continue
|
||
|
||
# 获取当前节点的所有邻居
|
||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||
|
||
for neighbor in neighbors:
|
||
if neighbor in visited_nodes:
|
||
continue
|
||
|
||
# 获取连接强度
|
||
edge_data = self.memory_graph.G[current_node][neighbor]
|
||
strength = edge_data.get("strength", 1)
|
||
|
||
# 计算新的激活值
|
||
new_activation = current_activation - (1 / strength)
|
||
|
||
if new_activation > 0:
|
||
activation_values[neighbor] = new_activation
|
||
visited_nodes.add(neighbor)
|
||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||
# logger.debug(
|
||
# f"节点 '{neighbor}' 被激活，激活值: {new_activation:.2f} (通过 '{current_node}' 连接，强度: {strength}, 深度: {current_depth + 1})") # noqa: E501
|
||
|
||
# 更新激活映射
|
||
for node, activation_value in activation_values.items():
|
||
if activation_value > 0:
|
||
if node in activate_map:
|
||
activate_map[node] += activation_value
|
||
else:
|
||
activate_map[node] = activation_value
|
||
|
||
# 输出激活映射
|
||
# logger.info("激活映射统计:")
|
||
# for node, total_activation in sorted(activate_map.items(), key=lambda x: x[1], reverse=True):
|
||
# logger.info(f"节点 '{node}': 累计激活值 = {total_activation:.2f}")
|
||
|
||
# 计算激活节点数与总节点数的比值
|
||
total_activation = sum(activate_map.values())
|
||
# logger.debug(f"总激活值: {total_activation:.2f}")
|
||
total_nodes = len(self.memory_graph.G.nodes())
|
||
# activated_nodes = len(activate_map)
|
||
activation_ratio = total_activation / total_nodes if total_nodes > 0 else 0
|
||
activation_ratio = activation_ratio * 50
|
||
logger.debug(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
|
||
|
||
return activation_ratio, keywords,keywords_lite
|
||
|
||
|
||
# 负责海马体与其他部分的交互
|
||
class EntorhinalCortex:
|
||
def __init__(self, hippocampus: Hippocampus):
|
||
self.hippocampus = hippocampus
|
||
self.memory_graph = hippocampus.memory_graph
|
||
|
||
async def sync_memory_to_db(self):
|
||
"""将记忆图同步到数据库"""
|
||
start_time = time.time()
|
||
current_time = datetime.datetime.now().timestamp()
|
||
|
||
# 获取数据库中所有节点和内存中所有节点
|
||
db_nodes = {node.concept: node for node in GraphNodes.select()}
|
||
memory_nodes = list(self.memory_graph.G.nodes(data=True))
|
||
|
||
# 批量准备节点数据
|
||
nodes_to_create = []
|
||
nodes_to_update = []
|
||
nodes_to_delete = set()
|
||
|
||
# 处理节点
|
||
for concept, data in memory_nodes:
|
||
if not concept or not isinstance(concept, str):
|
||
self.memory_graph.G.remove_node(concept)
|
||
continue
|
||
|
||
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)
|
||
|
||
if concept not in db_nodes:
|
||
nodes_to_create.append(
|
||
{
|
||
"concept": concept,
|
||
"memory_items": memory_items,
|
||
"weight": weight,
|
||
"hash": memory_hash,
|
||
"created_time": created_time,
|
||
"last_modified": last_modified,
|
||
}
|
||
)
|
||
else:
|
||
db_node = db_nodes[concept]
|
||
if db_node.hash != memory_hash:
|
||
nodes_to_update.append(
|
||
{
|
||
"concept": concept,
|
||
"memory_items": memory_items,
|
||
"weight": weight,
|
||
"hash": memory_hash,
|
||
"last_modified": last_modified,
|
||
}
|
||
)
|
||
|
||
# 计算需要删除的节点
|
||
memory_concepts = {concept for concept, _ in memory_nodes}
|
||
nodes_to_delete = set(db_nodes.keys()) - memory_concepts
|
||
|
||
# 批量处理节点
|
||
if nodes_to_create:
|
||
batch_size = 100
|
||
for i in range(0, len(nodes_to_create), batch_size):
|
||
batch = nodes_to_create[i : i + batch_size]
|
||
GraphNodes.insert_many(batch).execute()
|
||
|
||
if nodes_to_update:
|
||
batch_size = 100
|
||
for i in range(0, len(nodes_to_update), batch_size):
|
||
batch = nodes_to_update[i : i + batch_size]
|
||
for node_data in batch:
|
||
GraphNodes.update(**{k: v for k, v in node_data.items() if k != "concept"}).where(
|
||
GraphNodes.concept == node_data["concept"]
|
||
).execute()
|
||
|
||
if nodes_to_delete:
|
||
GraphNodes.delete().where(GraphNodes.concept.in_(nodes_to_delete)).execute() # type: ignore
|
||
|
||
# 处理边的信息
|
||
db_edges = list(GraphEdges.select())
|
||
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.hippocampus.calculate_edge_hash(edge.source, edge.target)
|
||
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")
|
||
|
||
|
||
# 负责整合，遗忘，合并记忆
|
||
class ParahippocampalGyrus:
|
||
def __init__(self, hippocampus: Hippocampus):
|
||
self.hippocampus = hippocampus
|
||
self.memory_graph = hippocampus.memory_graph
|
||
|
||
self.memory_modify_model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="memory.modify")
|
||
|
||
async def memory_compress(self, messages: list, compress_rate=0.1):
|
||
"""压缩和总结消息内容，生成记忆主题和摘要。
|
||
|
||
Args:
|
||
messages (list): 消息列表，每个消息是一个字典，包含数据库消息结构。
|
||
compress_rate (float, optional): 压缩率，用于控制生成的主题数量。默认为0.1。
|
||
|
||
Returns:
|
||
tuple: (compressed_memory, similar_topics_dict)
|
||
- compressed_memory: set, 压缩后的记忆集合，每个元素是一个元组 (topic, summary)
|
||
- similar_topics_dict: dict, 相似主题字典
|
||
|
||
Process:
|
||
1. 使用 build_readable_messages 生成包含时间、人物信息的格式化文本。
|
||
2. 使用LLM提取关键主题。
|
||
3. 过滤掉包含禁用关键词的主题。
|
||
4. 为每个主题生成摘要。
|
||
5. 查找与现有记忆中的相似主题。
|
||
"""
|
||
if not messages:
|
||
return set(), {}
|
||
|
||
# 1. 使用 build_readable_messages 生成格式化文本
|
||
# build_readable_messages 只返回一个字符串，不需要解包
|
||
input_text = build_readable_messages(
|
||
messages,
|
||
merge_messages=True, # 合并连续消息
|
||
timestamp_mode="normal_no_YMD", # 使用 'YYYY-MM-DD HH:MM:SS' 格式
|
||
replace_bot_name=False, # 保留原始用户名
|
||
)
|
||
|
||
# 如果生成的可读文本为空（例如所有消息都无效），则直接返回
|
||
if not input_text:
|
||
logger.warning("无法从提供的消息生成可读文本，跳过记忆压缩。")
|
||
return set(), {}
|
||
|
||
current_date = f"当前日期: {datetime.datetime.now().isoformat()}"
|
||
input_text = f"{current_date}\n{input_text}"
|
||
|
||
logger.debug(f"记忆来源:\n{input_text}")
|
||
|
||
# 2. 使用LLM提取关键主题
|
||
topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
|
||
topics_response, _ = await self.memory_modify_model.generate_response_async(
|
||
self.hippocampus.find_topic_llm(input_text, topic_num)
|
||
)
|
||
|
||
# 提取<>中的内容
|
||
topics = re.findall(r"<([^>]+)>", topics_response)
|
||
|
||
if not topics:
|
||
topics = ["none"]
|
||
else:
|
||
topics = [
|
||
topic.strip()
|
||
for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||
if topic.strip()
|
||
]
|
||
|
||
# 3. 过滤掉包含禁用关键词的topic
|
||
filtered_topics = [
|
||
topic for topic in topics if all(keyword not in topic for keyword in global_config.memory.memory_ban_words)
|
||
]
|
||
|
||
logger.debug(f"过滤后话题: {filtered_topics}")
|
||
|
||
# 4. 创建所有话题的摘要生成任务
|
||
tasks: List[Tuple[str, Coroutine[Any, Any, Tuple[str, Tuple[str, str, List | None]]]]] = []
|
||
for topic in filtered_topics:
|
||
# 调用修改后的 topic_what，不再需要 time_info
|
||
topic_what_prompt = self.hippocampus.topic_what(input_text, topic)
|
||
try:
|
||
task = self.memory_modify_model.generate_response_async(topic_what_prompt)
|
||
tasks.append((topic.strip(), task))
|
||
except Exception as e:
|
||
logger.error(f"生成话题 '{topic}' 的摘要时发生错误: {e}")
|
||
continue
|
||
|
||
# 等待所有任务完成
|
||
compressed_memory: Set[Tuple[str, str]] = set()
|
||
similar_topics_dict = {}
|
||
|
||
for topic, task in tasks:
|
||
response = await task
|
||
if response:
|
||
compressed_memory.add((topic, response[0]))
|
||
|
||
existing_topics = list(self.memory_graph.G.nodes())
|
||
similar_topics = []
|
||
|
||
for existing_topic in existing_topics:
|
||
topic_words = set(jieba.cut(topic))
|
||
existing_words = set(jieba.cut(existing_topic))
|
||
|
||
all_words = topic_words | existing_words
|
||
v1 = [1 if word in topic_words else 0 for word in all_words]
|
||
v2 = [1 if word in existing_words else 0 for word in all_words]
|
||
|
||
similarity = cosine_similarity(v1, v2)
|
||
|
||
if similarity >= 0.7:
|
||
similar_topics.append((existing_topic, similarity))
|
||
|
||
similar_topics.sort(key=lambda x: x[1], reverse=True)
|
||
similar_topics = similar_topics[:3]
|
||
similar_topics_dict[topic] = similar_topics
|
||
|
||
if global_config.debug.show_prompt:
|
||
logger.info(f"prompt: {topic_what_prompt}")
|
||
logger.info(f"压缩后的记忆: {compressed_memory}")
|
||
logger.info(f"相似主题: {similar_topics_dict}")
|
||
|
||
return compressed_memory, similar_topics_dict
|
||
|
||
async def operation_forget_topic(self, percentage=0.005):
|
||
start_time = time.time()
|
||
logger.info("[遗忘] 开始检查数据库...")
|
||
|
||
# 验证百分比参数
|
||
if not 0 <= percentage <= 1:
|
||
logger.warning(f"[遗忘] 无效的遗忘百分比: {percentage}, 使用默认值 0.005")
|
||
percentage = 0.005
|
||
|
||
all_nodes = list(self.memory_graph.G.nodes())
|
||
all_edges = list(self.memory_graph.G.edges())
|
||
|
||
if not all_nodes and not all_edges:
|
||
logger.info("[遗忘] 记忆图为空,无需进行遗忘操作")
|
||
return
|
||
|
||
# 确保至少检查1个节点和边，且不超过总数
|
||
check_nodes_count = max(1, min(len(all_nodes), int(len(all_nodes) * percentage)))
|
||
check_edges_count = max(1, min(len(all_edges), int(len(all_edges) * percentage)))
|
||
|
||
# 只有在有足够的节点和边时才进行采样
|
||
if len(all_nodes) >= check_nodes_count and len(all_edges) >= check_edges_count:
|
||
try:
|
||
nodes_to_check = random.sample(all_nodes, check_nodes_count)
|
||
edges_to_check = random.sample(all_edges, check_edges_count)
|
||
except ValueError as e:
|
||
logger.error(f"[遗忘] 采样错误: {str(e)}")
|
||
return
|
||
else:
|
||
logger.info("[遗忘] 没有足够的节点或边进行遗忘操作")
|
||
return
|
||
|
||
# 使用列表存储变化信息
|
||
edge_changes = {
|
||
"weakened": [], # 存储减弱的边
|
||
"removed": [], # 存储移除的边
|
||
}
|
||
node_changes = {
|
||
"reduced": [], # 存储减少记忆的节点
|
||
"removed": [], # 存储移除的节点
|
||
}
|
||
|
||
current_time = datetime.datetime.now().timestamp()
|
||
|
||
logger.info("[遗忘] 开始检查连接...")
|
||
edge_check_start = time.time()
|
||
for source, target in edges_to_check:
|
||
edge_data = self.memory_graph.G[source][target]
|
||
last_modified = edge_data.get("last_modified")
|
||
|
||
if current_time - last_modified > 3600 * global_config.memory.memory_forget_time:
|
||
current_strength = edge_data.get("strength", 1)
|
||
new_strength = current_strength - 1
|
||
|
||
if new_strength <= 0:
|
||
self.memory_graph.G.remove_edge(source, target)
|
||
edge_changes["removed"].append(f"{source} -> {target}")
|
||
else:
|
||
edge_data["strength"] = new_strength
|
||
edge_data["last_modified"] = current_time
|
||
edge_changes["weakened"].append(f"{source}-{target} (强度: {current_strength} -> {new_strength})")
|
||
edge_check_end = time.time()
|
||
logger.info(f"[遗忘] 连接检查耗时: {edge_check_end - edge_check_start:.2f}秒")
|
||
|
||
logger.info("[遗忘] 开始检查节点...")
|
||
node_check_start = time.time()
|
||
for node in nodes_to_check:
|
||
# 检查节点是否存在，以防在迭代中被移除（例如边移除导致）
|
||
if node not in self.memory_graph.G:
|
||
continue
|
||
|
||
node_data = self.memory_graph.G.nodes[node]
|
||
|
||
# 首先获取记忆项
|
||
memory_items = node_data.get("memory_items", "")
|
||
# 直接检查记忆内容是否为空
|
||
if not memory_items or memory_items.strip() == "":
|
||
try:
|
||
self.memory_graph.G.remove_node(node)
|
||
node_changes["removed"].append(f"{node}(空节点)") # 标记为空节点移除
|
||
logger.debug(f"[遗忘] 移除了空的节点: {node}")
|
||
except nx.NetworkXError as e:
|
||
logger.warning(f"[遗忘] 移除空节点 {node} 时发生错误（可能已被移除）: {e}")
|
||
continue # 处理下一个节点
|
||
|
||
# --- 如果节点不为空，则执行原来的不活跃检查和随机移除逻辑 ---
|
||
last_modified = node_data.get("last_modified", current_time)
|
||
node_weight = node_data.get("weight", 1.0)
|
||
|
||
# 条件1：检查是否长时间未修改 (使用配置的遗忘时间)
|
||
time_threshold = 3600 * global_config.memory.memory_forget_time
|
||
|
||
# 基于权重调整遗忘阈值：权重越高，需要更长时间才能被遗忘
|
||
# 权重为1时使用默认阈值，权重越高阈值越大（越难遗忘）
|
||
adjusted_threshold = time_threshold * node_weight
|
||
|
||
if current_time - last_modified > adjusted_threshold and memory_items:
|
||
# 既然每个节点现在是完整记忆，直接删除整个节点
|
||
try:
|
||
self.memory_graph.G.remove_node(node)
|
||
node_changes["removed"].append(f"{node}(长时间未修改,权重{node_weight:.1f})")
|
||
logger.debug(f"[遗忘] 移除了长时间未修改的节点: {node} (权重: {node_weight:.1f})")
|
||
except nx.NetworkXError as e:
|
||
logger.warning(f"[遗忘] 移除节点 {node} 时发生错误（可能已被移除）: {e}")
|
||
continue
|
||
node_check_end = time.time()
|
||
logger.info(f"[遗忘] 节点检查耗时: {node_check_end - node_check_start:.2f}秒")
|
||
|
||
if any(edge_changes.values()) or any(node_changes.values()):
|
||
sync_start = time.time()
|
||
|
||
await self.hippocampus.entorhinal_cortex.resync_memory_to_db()
|
||
|
||
sync_end = time.time()
|
||
logger.info(f"[遗忘] 数据库同步耗时: {sync_end - sync_start:.2f}秒")
|
||
|
||
# 汇总输出所有变化
|
||
logger.info("[遗忘] 遗忘操作统计:")
|
||
if edge_changes["weakened"]:
|
||
logger.info(
|
||
f"[遗忘] 减弱的连接 ({len(edge_changes['weakened'])}个): {', '.join(edge_changes['weakened'])}"
|
||
)
|
||
|
||
if edge_changes["removed"]:
|
||
logger.info(
|
||
f"[遗忘] 移除的连接 ({len(edge_changes['removed'])}个): {', '.join(edge_changes['removed'])}"
|
||
)
|
||
|
||
if node_changes["reduced"]:
|
||
logger.info(
|
||
f"[遗忘] 减少记忆的节点 ({len(node_changes['reduced'])}个): {', '.join(node_changes['reduced'])}"
|
||
)
|
||
|
||
if node_changes["removed"]:
|
||
logger.info(
|
||
f"[遗忘] 移除的节点 ({len(node_changes['removed'])}个): {', '.join(node_changes['removed'])}"
|
||
)
|
||
else:
|
||
logger.info("[遗忘] 本次检查没有节点或连接满足遗忘条件")
|
||
|
||
end_time = time.time()
|
||
logger.info(f"[遗忘] 总耗时: {end_time - start_time:.2f}秒")
|
||
|
||
|
||
|
||
|
||
class HippocampusManager:
|
||
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:
|
||
构建频率: {global_config.memory.memory_build_frequency}秒|压缩率: {global_config.memory.memory_compress_rate}
|
||
遗忘间隔: {global_config.memory.forget_memory_interval}秒|遗忘比例: {global_config.memory.memory_forget_percentage}|遗忘: {global_config.memory.memory_forget_time}小时之后
|
||
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
|
||
|
||
async def forget_memory(self, percentage: float = 0.005):
|
||
"""遗忘记忆的公共接口"""
|
||
if not self._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
return await self._hippocampus.parahippocampal_gyrus.operation_forget_topic(percentage)
|
||
|
||
async def build_memory_for_chat(self, chat_id: str):
|
||
"""为指定chat_id构建记忆（在heartFC_chat.py中调用）"""
|
||
if not self._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
|
||
try:
|
||
# 检查是否需要构建记忆
|
||
logger.info(f"为 {chat_id} 构建记忆")
|
||
if memory_segment_manager.check_and_build_memory_for_chat(chat_id):
|
||
logger.info(f"为 {chat_id} 构建记忆，需要构建记忆")
|
||
messages = memory_segment_manager.get_messages_for_memory_build(chat_id, 30 / global_config.memory.memory_build_frequency)
|
||
if messages:
|
||
logger.info(f"为 {chat_id} 构建记忆，消息数量: {len(messages)}")
|
||
|
||
# 调用记忆压缩和构建
|
||
compressed_memory, similar_topics_dict = await self._hippocampus.parahippocampal_gyrus.memory_compress(
|
||
messages, global_config.memory.memory_compress_rate
|
||
)
|
||
|
||
# 添加记忆节点
|
||
current_time = time.time()
|
||
for topic, memory in compressed_memory:
|
||
await self._hippocampus.memory_graph.add_dot(topic, memory, self._hippocampus)
|
||
|
||
# 连接相似主题
|
||
if topic in similar_topics_dict:
|
||
similar_topics = similar_topics_dict[topic]
|
||
for similar_topic, similarity in similar_topics:
|
||
if topic != similar_topic:
|
||
strength = int(similarity * 10)
|
||
self._hippocampus.memory_graph.G.add_edge(
|
||
topic, similar_topic,
|
||
strength=strength,
|
||
created_time=current_time,
|
||
last_modified=current_time
|
||
)
|
||
|
||
# 同步到数据库
|
||
await self._hippocampus.entorhinal_cortex.sync_memory_to_db()
|
||
logger.info(f"为 {chat_id} 构建记忆完成")
|
||
return True
|
||
|
||
except Exception as e:
|
||
logger.error(f"为 {chat_id} 构建记忆失败: {e}")
|
||
return False
|
||
|
||
return False
|
||
|
||
|
||
async def get_memory_from_topic(
|
||
self, valid_keywords: list[str], max_memory_num: int = 3, max_memory_length: int = 2, max_depth: int = 3
|
||
) -> list:
|
||
"""从文本中获取相关记忆的公共接口"""
|
||
if not self._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
try:
|
||
response = await self._hippocampus.get_memory_from_topic(
|
||
valid_keywords, max_memory_num, max_memory_length, max_depth
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"文本激活记忆失败: {e}")
|
||
response = []
|
||
return response
|
||
|
||
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> tuple[float, list[str]]:
|
||
"""从文本中获取激活值的公共接口"""
|
||
if not self._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
try:
|
||
response, keywords,keywords_lite = await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
|
||
except Exception as e:
|
||
logger.error(f"文本产生激活值失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
return 0.0, [],[]
|
||
|
||
def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
|
||
"""从关键词获取相关记忆的公共接口"""
|
||
if not self._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
return self._hippocampus.get_memory_from_keyword(keyword, max_depth)
|
||
|
||
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()
|
||
|
||
|
||
# 在Hippocampus类中添加新的记忆构建管理器
|
||
class MemoryBuilder:
|
||
"""记忆构建器
|
||
|
||
为每个chat_id维护消息缓存和触发机制，类似ExpressionLearner
|
||
"""
|
||
|
||
def __init__(self, chat_id: str):
|
||
self.chat_id = chat_id
|
||
self.last_update_time: float = time.time()
|
||
self.last_processed_time: float = 0.0
|
||
|
||
def should_trigger_memory_build(self) -> bool:
|
||
"""检查是否应该触发记忆构建"""
|
||
current_time = time.time()
|
||
|
||
# 检查时间间隔
|
||
time_diff = current_time - self.last_update_time
|
||
if time_diff < 600 /global_config.memory.memory_build_frequency:
|
||
return False
|
||
|
||
# 检查消息数量
|
||
|
||
recent_messages = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||
chat_id=self.chat_id,
|
||
timestamp_start=self.last_update_time,
|
||
timestamp_end=current_time,
|
||
)
|
||
|
||
logger.info(f"最近消息数量: {len(recent_messages)}，间隔时间: {time_diff}")
|
||
|
||
if not recent_messages or len(recent_messages) < 30/global_config.memory.memory_build_frequency :
|
||
return False
|
||
|
||
return True
|
||
|
||
def get_messages_for_memory_build(self, threshold: int = 25) -> List[Dict[str, Any]]:
|
||
"""获取用于记忆构建的消息"""
|
||
current_time = time.time()
|
||
|
||
|
||
messages = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||
chat_id=self.chat_id,
|
||
timestamp_start=self.last_update_time,
|
||
timestamp_end=current_time,
|
||
limit=threshold,
|
||
)
|
||
|
||
if messages:
|
||
# 更新最后处理时间
|
||
self.last_processed_time = current_time
|
||
self.last_update_time = current_time
|
||
|
||
return messages or []
|
||
|
||
|
||
|
||
class MemorySegmentManager:
|
||
"""记忆段管理器
|
||
|
||
管理所有chat_id的MemoryBuilder实例，自动检查和触发记忆构建
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.builders: Dict[str, MemoryBuilder] = {}
|
||
|
||
def get_or_create_builder(self, chat_id: str) -> MemoryBuilder:
|
||
"""获取或创建指定chat_id的MemoryBuilder"""
|
||
if chat_id not in self.builders:
|
||
self.builders[chat_id] = MemoryBuilder(chat_id)
|
||
return self.builders[chat_id]
|
||
|
||
def check_and_build_memory_for_chat(self, chat_id: str) -> bool:
|
||
"""检查指定chat_id是否需要构建记忆，如果需要则返回True"""
|
||
builder = self.get_or_create_builder(chat_id)
|
||
return builder.should_trigger_memory_build()
|
||
|
||
def get_messages_for_memory_build(self, chat_id: str, threshold: int = 25) -> List[Dict[str, Any]]:
|
||
"""获取指定chat_id用于记忆构建的消息"""
|
||
if chat_id not in self.builders:
|
||
return []
|
||
return self.builders[chat_id].get_messages_for_memory_build(threshold)
|
||
|
||
|
||
# 创建全局实例
|
||
memory_segment_manager = MemorySegmentManager()
|
||
|