1771 lines
78 KiB
Python
1771 lines
78 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 json
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from itertools import combinations
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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 collections import Counter
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from ...llm_models.utils_model import LLMRequest
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from src.common.logger import get_logger
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from src.chat.memory_system.sample_distribution import MemoryBuildScheduler # 分布生成器
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from ..utils.chat_message_builder import (
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get_raw_msg_by_timestamp,
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build_readable_messages,
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get_raw_msg_by_timestamp_with_chat,
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) # 导入 build_readable_messages
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from ..utils.utils import translate_timestamp_to_human_readable
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from rich.traceback import install
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from ...config.config import global_config
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from src.common.database.database_model import Messages, GraphNodes, GraphEdges # Peewee Models导入
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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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def cosine_similarity(v1, v2): # sourcery skip: assign-if-exp, reintroduce-else
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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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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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def add_dot(self, concept, memory):
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current_time = datetime.datetime.now().timestamp()
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if concept in self.G:
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if "memory_items" in self.G.nodes[concept]:
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if not isinstance(self.G.nodes[concept]["memory_items"], list):
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self.G.nodes[concept]["memory_items"] = [self.G.nodes[concept]["memory_items"]]
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self.G.nodes[concept]["memory_items"].append(memory)
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else:
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self.G.nodes[concept]["memory_items"] = [memory]
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# 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
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if "created_time" not in self.G.nodes[concept]:
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self.G.nodes[concept]["created_time"] = current_time
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# 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,创建新的记忆列表
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self.G.add_node(
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concept,
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memory_items=[memory],
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created_time=current_time, # 添加创建时间
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last_modified=current_time,
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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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if isinstance(memory_items, list):
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first_layer_items.extend(memory_items)
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else:
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first_layer_items.append(memory_items)
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# 只在depth=2时获取第二层记忆
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if depth >= 2:
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# 获取相邻节点的记忆项
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for neighbor in neighbors:
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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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if isinstance(memory_items, list):
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second_layer_items.extend(memory_items)
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else:
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second_layer_items.append(memory_items)
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return first_layer_items, second_layer_items
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@property
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def dots(self):
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# 返回所有节点对应的 Memory_dot 对象
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return [self.get_dot(node) for node in self.G.nodes()]
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def forget_topic(self, topic):
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"""随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点"""
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if topic not in self.G:
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return None
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# 获取话题节点数据
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node_data = self.G.nodes[topic]
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# 如果节点存在memory_items
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if "memory_items" in node_data:
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memory_items = node_data["memory_items"]
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# 确保memory_items是列表
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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# 如果有记忆项可以删除
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if memory_items:
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# 随机选择一个记忆项删除
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removed_item = random.choice(memory_items)
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memory_items.remove(removed_item)
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# 更新节点的记忆项
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if memory_items:
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self.G.nodes[topic]["memory_items"] = memory_items
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else:
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# 如果没有记忆项了，删除整个节点
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self.G.remove_node(topic)
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return removed_item
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return None
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# 海马体
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class Hippocampus:
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def __init__(self):
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self.memory_graph = MemoryGraph()
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self.model_summary: 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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# TODO: API-Adapter修改标记
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self.model_summary = LLMRequest(global_config.model.memory, request_type="memory.builder")
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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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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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# 使用集合来去重，避免排序
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unique_items = {str(item) for item in memory_items}
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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, topic_num):
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# sourcery skip: inline-immediately-returned-variable
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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}个关键的概念，可以是名词，动词，或者特定人物，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，帮我总结成一句自然的话，'
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f"要求包含对这个概念的定义，内容，知识，但是这些信息必须来自这段文字，不能添加信息。\n，请包含时间和人物。只输出这句话就好"
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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_items, similarity)
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- topic: str, 记忆主题
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- memory_items: list, 该主题下的记忆项列表
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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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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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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_memory_from_text(
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self,
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text: str,
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max_memory_num: int = 3,
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max_memory_length: int = 2,
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max_depth: int = 3,
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fast_retrieval: bool = False,
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) -> list:
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"""从文本中提取关键词并获取相关记忆。
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Args:
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text (str): 输入文本
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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。
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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。
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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。
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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分词和TF-IDF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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Returns:
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list: 记忆列表，每个元素是一个元组 (topic, memory_items, similarity)
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- topic: str, 记忆主题
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- memory_items: list, 该主题下的记忆项列表
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- similarity: float, 与文本的相似度
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"""
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if not text:
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return []
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if fast_retrieval:
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# 使用jieba分词提取关键词
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words = jieba.cut(text)
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# 过滤掉停用词和单字词
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keywords = [word for word in words if len(word) > 1]
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# 去重
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keywords = list(set(keywords))
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# 限制关键词数量
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logger.debug(f"提取关键词: {keywords}")
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else:
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# 使用LLM提取关键词
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topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
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# logger.info(f"提取关键词数量: {topic_num}")
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topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(
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self.find_topic_llm(text, topic_num)
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)
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# 提取关键词
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keywords = re.findall(r"<([^>]+)>", topics_response)
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if not keywords:
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keywords = []
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else:
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keywords = [
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keyword.strip()
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for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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if keyword.strip()
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]
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# logger.info(f"提取的关键词: {', '.join(keywords)}")
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# 过滤掉不存在于记忆图中的关键词
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valid_keywords = [keyword for keyword in keywords if keyword in self.memory_graph.G]
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if not valid_keywords:
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logger.debug("没有找到有效的关键词节点")
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return []
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logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
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# 从每个关键词获取记忆
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activate_map = {} # 存储每个词的累计激活值
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||
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# 对每个关键词进行扩散式检索
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||
for keyword in valid_keywords:
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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}):")
|
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# 初始化激活值
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activation_values = {keyword: 1.0}
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# 记录已访问的节点
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||
visited_nodes = {keyword}
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# 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:
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||
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:
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||
activation_values[neighbor] = new_activation
|
||
visited_nodes.add(neighbor)
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||
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}")
|
||
|
||
# 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", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
if memory_items:
|
||
logger.debug(f"节点包含 {len(memory_items)} 条记忆")
|
||
# 计算每条记忆与输入文本的相似度
|
||
memory_similarities = []
|
||
for memory in memory_items:
|
||
# 计算与输入文本的相似度
|
||
memory_words = set(jieba.cut(memory))
|
||
text_words = set(jieba.cut(text))
|
||
all_words = memory_words | text_words
|
||
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]
|
||
similarity = cosine_similarity(v1, v2)
|
||
memory_similarities.append((memory, similarity))
|
||
|
||
# 按相似度排序
|
||
memory_similarities.sort(key=lambda x: x[1], reverse=True)
|
||
# 获取最匹配的记忆
|
||
top_memories = memory_similarities[:max_memory_length]
|
||
|
||
# 添加到结果中
|
||
all_memories.extend((node, [memory], similarity) for memory, similarity in top_memories)
|
||
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 = memory_items[0] # 因为每个topic只有一条记忆
|
||
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 = memory_items[0] # 因为每个topic只有一条记忆
|
||
result.append((topic, memory))
|
||
logger.debug(f"选中记忆: {memory} (来自节点: {topic})")
|
||
|
||
return result
|
||
|
||
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_items, similarity)
|
||
- topic: str, 记忆主题
|
||
- memory_items: list, 该主题下的记忆项列表
|
||
- similarity: float, 与文本的相似度
|
||
"""
|
||
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", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
if memory_items:
|
||
logger.debug(f"节点包含 {len(memory_items)} 条记忆")
|
||
# 计算每条记忆与输入文本的相似度
|
||
memory_similarities = []
|
||
for memory in memory_items:
|
||
# 计算与输入文本的相似度
|
||
memory_words = set(jieba.cut(memory))
|
||
text_words = set(keywords)
|
||
all_words = memory_words | text_words
|
||
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]
|
||
similarity = cosine_similarity(v1, v2)
|
||
memory_similarities.append((memory, similarity))
|
||
|
||
# 按相似度排序
|
||
memory_similarities.sort(key=lambda x: x[1], reverse=True)
|
||
# 获取最匹配的记忆
|
||
top_memories = memory_similarities[:max_memory_length]
|
||
|
||
# 添加到结果中
|
||
all_memories.extend((node, [memory], similarity) for memory, similarity in top_memories)
|
||
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 = memory_items[0] # 因为每个topic只有一条记忆
|
||
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 = memory_items[0] # 因为每个topic只有一条记忆
|
||
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) -> float:
|
||
"""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
|
||
"""
|
||
if not text:
|
||
return 0
|
||
|
||
if fast_retrieval:
|
||
# 使用jieba分词提取关键词
|
||
words = jieba.cut(text)
|
||
# 过滤掉停用词和单字词
|
||
keywords = [word for word in words if len(word) > 1]
|
||
# 去重
|
||
keywords = list(set(keywords))
|
||
# 限制关键词数量
|
||
keywords = keywords[:5]
|
||
else:
|
||
# 使用LLM提取关键词
|
||
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
|
||
# logger.info(f"提取关键词数量: {topic_num}")
|
||
topics_response, (reasoning_content, model_name) = await self.model_summary.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()
|
||
]
|
||
|
||
# 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.info("没有找到有效的关键词节点")
|
||
return 0
|
||
|
||
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
|
||
|
||
# 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 * 60
|
||
logger.debug(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
|
||
|
||
return activation_ratio
|
||
|
||
|
||
# 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
|
||
|
||
def get_memory_sample(self):
|
||
"""从数据库获取记忆样本"""
|
||
# 硬编码：每条消息最大记忆次数
|
||
max_memorized_time_per_msg = 2
|
||
|
||
# 创建双峰分布的记忆调度器
|
||
sample_scheduler = MemoryBuildScheduler(
|
||
n_hours1=global_config.memory.memory_build_distribution[0],
|
||
std_hours1=global_config.memory.memory_build_distribution[1],
|
||
weight1=global_config.memory.memory_build_distribution[2],
|
||
n_hours2=global_config.memory.memory_build_distribution[3],
|
||
std_hours2=global_config.memory.memory_build_distribution[4],
|
||
weight2=global_config.memory.memory_build_distribution[5],
|
||
total_samples=global_config.memory.memory_build_sample_num,
|
||
)
|
||
|
||
timestamps = sample_scheduler.get_timestamp_array()
|
||
# 使用 translate_timestamp_to_human_readable 并指定 mode="normal"
|
||
readable_timestamps = [translate_timestamp_to_human_readable(ts, mode="normal") for ts in timestamps]
|
||
for _, readable_timestamp in zip(timestamps, readable_timestamps, strict=False):
|
||
logger.debug(f"回忆往事: {readable_timestamp}")
|
||
chat_samples = []
|
||
for timestamp in timestamps:
|
||
if messages := self.random_get_msg_snippet(
|
||
timestamp,
|
||
global_config.memory.memory_build_sample_length,
|
||
max_memorized_time_per_msg,
|
||
):
|
||
time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600
|
||
logger.info(f"成功抽取 {time_diff:.1f} 小时前的消息样本，共{len(messages)}条")
|
||
chat_samples.append(messages)
|
||
else:
|
||
logger.debug(f"时间戳 {timestamp} 的消息无需记忆")
|
||
|
||
return chat_samples
|
||
|
||
@staticmethod
|
||
def random_get_msg_snippet(target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list | None:
|
||
# sourcery skip: invert-any-all, use-any, use-named-expression, use-next
|
||
"""从数据库中随机获取指定时间戳附近的消息片段 (使用 chat_message_builder)"""
|
||
time_window_seconds = random.randint(300, 1800) # 随机时间窗口，5到30分钟
|
||
|
||
for _ in range(3):
|
||
# 定义时间范围：从目标时间戳开始，向后推移 time_window_seconds
|
||
timestamp_start = target_timestamp
|
||
timestamp_end = target_timestamp + time_window_seconds
|
||
|
||
if chosen_message := get_raw_msg_by_timestamp(
|
||
timestamp_start=timestamp_start,
|
||
timestamp_end=timestamp_end,
|
||
limit=1,
|
||
limit_mode="earliest",
|
||
):
|
||
chat_id: str = chosen_message[0].get("chat_id") # type: ignore
|
||
|
||
if messages := get_raw_msg_by_timestamp_with_chat(
|
||
timestamp_start=timestamp_start,
|
||
timestamp_end=timestamp_end,
|
||
limit=chat_size,
|
||
limit_mode="earliest",
|
||
chat_id=chat_id,
|
||
):
|
||
# 检查获取到的所有消息是否都未达到最大记忆次数
|
||
all_valid = True
|
||
for message in messages:
|
||
if message.get("memorized_times", 0) >= max_memorized_time_per_msg:
|
||
all_valid = False
|
||
break
|
||
|
||
# 如果所有消息都有效
|
||
if all_valid:
|
||
# 更新数据库中的记忆次数
|
||
for message in messages:
|
||
# 确保在更新前获取最新的 memorized_times
|
||
current_memorized_times = message.get("memorized_times", 0)
|
||
# 使用 Peewee 更新记录
|
||
Messages.update(memorized_times=current_memorized_times + 1).where(
|
||
Messages.message_id == message["message_id"]
|
||
).execute()
|
||
return messages # 直接返回原始的消息列表
|
||
|
||
target_timestamp -= 120 # 如果第一次尝试失败，稍微向前调整时间戳再试
|
||
|
||
# 三次尝试都失败，返回 None
|
||
return None
|
||
|
||
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", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
if not memory_items:
|
||
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)
|
||
|
||
# 将memory_items转换为JSON字符串
|
||
try:
|
||
memory_items = [str(item) for item in memory_items]
|
||
memory_items_json = json.dumps(memory_items, ensure_ascii=False)
|
||
if not memory_items_json:
|
||
continue
|
||
except Exception:
|
||
self.memory_graph.G.remove_node(concept)
|
||
continue
|
||
|
||
if concept not in db_nodes:
|
||
nodes_to_create.append(
|
||
{
|
||
"concept": concept,
|
||
"memory_items": memory_items_json,
|
||
"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_json,
|
||
"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"[同步] 总耗时: {end_time - start_time:.2f}秒")
|
||
logger.info(f"[同步] 同步了 {len(memory_nodes)} 个节点和 {len(memory_edges)} 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", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
try:
|
||
memory_items = [str(item) for item in memory_items]
|
||
if memory_items_json := json.dumps(memory_items, ensure_ascii=False):
|
||
nodes_data.append(
|
||
{
|
||
"concept": concept,
|
||
"memory_items": memory_items_json,
|
||
"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),
|
||
}
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"准备节点 {concept} 数据时发生错误: {e}")
|
||
continue
|
||
|
||
# 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
|
||
|
||
# 使用事务批量写入节点
|
||
node_start = time.time()
|
||
if nodes_data:
|
||
batch_size = 500 # 增加批量大小
|
||
with GraphNodes._meta.database.atomic(): # type: ignore
|
||
for i in range(0, len(nodes_data), batch_size):
|
||
batch = nodes_data[i : i + batch_size]
|
||
GraphNodes.insert_many(batch).execute()
|
||
node_end = time.time()
|
||
logger.info(f"[数据库] 写入 {len(nodes_data)} 个节点耗时: {node_end - node_start:.2f}秒")
|
||
|
||
# 使用事务批量写入边
|
||
edge_start = time.time()
|
||
if edges_data:
|
||
batch_size = 500 # 增加批量大小
|
||
with GraphEdges._meta.database.atomic(): # type: ignore
|
||
for i in range(0, len(edges_data), batch_size):
|
||
batch = edges_data[i : i + batch_size]
|
||
GraphEdges.insert_many(batch).execute()
|
||
edge_end = time.time()
|
||
logger.info(f"[数据库] 写入 {len(edges_data)} 条边耗时: {edge_end - edge_start:.2f}秒")
|
||
|
||
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()
|
||
|
||
# 从数据库加载所有节点
|
||
nodes = list(GraphNodes.select())
|
||
for node in nodes:
|
||
concept = node.concept
|
||
try:
|
||
memory_items = json.loads(node.memory_items)
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
# 检查时间字段是否存在
|
||
if not node.created_time or not node.last_modified:
|
||
need_update = True
|
||
# 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
|
||
|
||
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
|
||
|
||
# 添加节点到图中
|
||
self.memory_graph.G.add_node(
|
||
concept, memory_items=memory_items, created_time=created_time, last_modified=last_modified
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"加载节点 {concept} 时发生错误: {e}")
|
||
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")
|
||
|
||
|
||
# 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
|
||
|
||
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, (reasoning_content, model_name) = await self.hippocampus.model_summary.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 = []
|
||
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.hippocampus.model_summary.generate_response_async(topic_what_prompt)
|
||
tasks.append((topic.strip(), task))
|
||
except Exception as e:
|
||
logger.error(f"生成话题 '{topic}' 的摘要时发生错误: {e}")
|
||
continue
|
||
|
||
# 等待所有任务完成
|
||
compressed_memory = set()
|
||
similar_topics_dict = {}
|
||
|
||
for topic, task in tasks:
|
||
response = await task
|
||
if response:
|
||
compressed_memory.add((topic, response[0]))
|
||
|
||
existing_topics = list(self.memory_graph.G.nodes())
|
||
similar_topics = []
|
||
|
||
for existing_topic in existing_topics:
|
||
topic_words = set(jieba.cut(topic))
|
||
existing_words = set(jieba.cut(existing_topic))
|
||
|
||
all_words = topic_words | existing_words
|
||
v1 = [1 if word in topic_words else 0 for word in all_words]
|
||
v2 = [1 if word in existing_words else 0 for word in all_words]
|
||
|
||
similarity = cosine_similarity(v1, v2)
|
||
|
||
if similarity >= 0.7:
|
||
similar_topics.append((existing_topic, similarity))
|
||
|
||
similar_topics.sort(key=lambda x: x[1], reverse=True)
|
||
similar_topics = similar_topics[:3]
|
||
similar_topics_dict[topic] = similar_topics
|
||
|
||
return compressed_memory, similar_topics_dict
|
||
|
||
async def operation_build_memory(self):
|
||
logger.info("------------------------------------开始构建记忆--------------------------------------")
|
||
start_time = time.time()
|
||
memory_samples = self.hippocampus.entorhinal_cortex.get_memory_sample()
|
||
all_added_nodes = []
|
||
all_connected_nodes = []
|
||
all_added_edges = []
|
||
for i, messages in enumerate(memory_samples, 1):
|
||
all_topics = []
|
||
compress_rate = global_config.memory.memory_compress_rate
|
||
try:
|
||
compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
|
||
except Exception as e:
|
||
logger.error(f"压缩记忆时发生错误: {e}")
|
||
continue
|
||
for topic, memory in compressed_memory:
|
||
logger.info(f"取得记忆: {topic} - {memory}")
|
||
for topic, similar_topics in similar_topics_dict.items():
|
||
logger.debug(f"相似话题: {topic} - {similar_topics}")
|
||
|
||
current_time = datetime.datetime.now().timestamp()
|
||
logger.debug(f"添加节点: {', '.join(topic for topic, _ in compressed_memory)}")
|
||
all_added_nodes.extend(topic for topic, _ in compressed_memory)
|
||
|
||
for topic, memory in compressed_memory:
|
||
self.memory_graph.add_dot(topic, memory)
|
||
all_topics.append(topic)
|
||
|
||
if topic in similar_topics_dict:
|
||
similar_topics = similar_topics_dict[topic]
|
||
for similar_topic, similarity in similar_topics:
|
||
if topic != similar_topic:
|
||
strength = int(similarity * 10)
|
||
|
||
logger.debug(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
|
||
all_added_edges.append(f"{topic}-{similar_topic}")
|
||
|
||
all_connected_nodes.append(topic)
|
||
all_connected_nodes.append(similar_topic)
|
||
|
||
self.memory_graph.G.add_edge(
|
||
topic,
|
||
similar_topic,
|
||
strength=strength,
|
||
created_time=current_time,
|
||
last_modified=current_time,
|
||
)
|
||
|
||
for topic1, topic2 in combinations(all_topics, 2):
|
||
logger.debug(f"连接同批次节点: {topic1} 和 {topic2}")
|
||
all_added_edges.append(f"{topic1}-{topic2}")
|
||
self.memory_graph.connect_dot(topic1, topic2)
|
||
|
||
progress = (i / len(memory_samples)) * 100
|
||
bar_length = 30
|
||
filled_length = int(bar_length * i // len(memory_samples))
|
||
bar = "█" * filled_length + "-" * (bar_length - filled_length)
|
||
logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
|
||
|
||
if all_added_nodes:
|
||
logger.info(f"更新记忆: {', '.join(all_added_nodes)}")
|
||
if all_added_edges:
|
||
logger.debug(f"强化连接: {', '.join(all_added_edges)}")
|
||
if all_connected_nodes:
|
||
logger.info(f"强化连接节点: {', '.join(all_connected_nodes)}")
|
||
|
||
await self.hippocampus.entorhinal_cortex.sync_memory_to_db()
|
||
|
||
end_time = time.time()
|
||
logger.info(f"---------------------记忆构建耗时: {end_time - start_time:.2f} 秒---------------------")
|
||
|
||
async def operation_forget_topic(self, percentage=0.005):
|
||
start_time = time.time()
|
||
logger.info("[遗忘] 开始检查数据库...")
|
||
|
||
# 验证百分比参数
|
||
if not 0 <= percentage <= 1:
|
||
logger.warning(f"[遗忘] 无效的遗忘百分比: {percentage}, 使用默认值 0.005")
|
||
percentage = 0.005
|
||
|
||
all_nodes = list(self.memory_graph.G.nodes())
|
||
all_edges = list(self.memory_graph.G.edges())
|
||
|
||
if not all_nodes and not all_edges:
|
||
logger.info("[遗忘] 记忆图为空,无需进行遗忘操作")
|
||
return
|
||
|
||
# 确保至少检查1个节点和边，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", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
# 新增：检查节点是否为空
|
||
if not memory_items:
|
||
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)
|
||
# 条件1：检查是否长时间未修改 (超过24小时)
|
||
if current_time - last_modified > 3600 * 24 and memory_items:
|
||
current_count = len(memory_items)
|
||
# 如果列表非空，才进行随机选择
|
||
if current_count > 0:
|
||
removed_item = random.choice(memory_items)
|
||
try:
|
||
memory_items.remove(removed_item)
|
||
|
||
# 条件3：检查移除后 memory_items 是否变空
|
||
if memory_items: # 如果移除后列表不为空
|
||
# self.memory_graph.G.nodes[node]["memory_items"] = memory_items # 直接修改列表即可
|
||
self.memory_graph.G.nodes[node]["last_modified"] = current_time # 更新修改时间
|
||
node_changes["reduced"].append(f"{node} (数量: {current_count} -> {len(memory_items)})")
|
||
else: # 如果移除后列表为空
|
||
# 尝试移除节点，处理可能的错误
|
||
try:
|
||
self.memory_graph.G.remove_node(node)
|
||
node_changes["removed"].append(f"{node}(遗忘清空)") # 标记为遗忘清空
|
||
logger.debug(f"[遗忘] 节点 {node} 因移除最后一项而被清空。")
|
||
except nx.NetworkXError as e:
|
||
logger.warning(f"[遗忘] 尝试移除节点 {node} 时发生错误（可能已被移除）：{e}")
|
||
except ValueError:
|
||
# 这个错误理论上不应发生，因为 removed_item 来自 memory_items
|
||
logger.warning(f"[遗忘] 尝试从节点 '{node}' 移除不存在的项目 '{removed_item[:30]}...'")
|
||
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")
|
||
|
||
async def operation_consolidate_memory(self):
|
||
"""整合记忆：合并节点内相似的记忆项"""
|
||
start_time = time.time()
|
||
percentage = global_config.memory.consolidate_memory_percentage
|
||
similarity_threshold = global_config.memory.consolidation_similarity_threshold
|
||
logger.info(f"[整合] 开始检查记忆节点... 检查比例: {percentage:.2%}, 合并阈值: {similarity_threshold}")
|
||
|
||
# 获取所有至少有2条记忆项的节点
|
||
eligible_nodes = []
|
||
for node, data in self.memory_graph.G.nodes(data=True):
|
||
memory_items = data.get("memory_items", [])
|
||
if isinstance(memory_items, list) and len(memory_items) >= 2:
|
||
eligible_nodes.append(node)
|
||
|
||
if not eligible_nodes:
|
||
logger.info("[整合] 没有找到包含多个记忆项的节点，无需整合。")
|
||
return
|
||
|
||
# 计算需要检查的节点数量
|
||
check_nodes_count = max(1, min(len(eligible_nodes), int(len(eligible_nodes) * percentage)))
|
||
|
||
# 随机抽取节点进行检查
|
||
try:
|
||
nodes_to_check = random.sample(eligible_nodes, check_nodes_count)
|
||
except ValueError as e:
|
||
logger.error(f"[整合] 抽样节点时出错: {e}")
|
||
return
|
||
|
||
logger.info(f"[整合] 将检查 {len(nodes_to_check)} / {len(eligible_nodes)} 个符合条件的节点。")
|
||
|
||
merged_count = 0
|
||
nodes_modified = set()
|
||
current_timestamp = datetime.datetime.now().timestamp()
|
||
|
||
for node in nodes_to_check:
|
||
node_data = self.memory_graph.G.nodes[node]
|
||
memory_items = node_data.get("memory_items", [])
|
||
if not isinstance(memory_items, list) or len(memory_items) < 2:
|
||
continue # 双重检查，理论上不会进入
|
||
|
||
items_copy = list(memory_items) # 创建副本以安全迭代和修改
|
||
|
||
# 遍历所有记忆项组合
|
||
for item1, item2 in combinations(items_copy, 2):
|
||
# 确保 item1 和 item2 仍然存在于原始列表中（可能已被之前的合并移除）
|
||
if item1 not in memory_items or item2 not in memory_items:
|
||
continue
|
||
|
||
similarity = self._calculate_item_similarity(item1, item2)
|
||
|
||
if similarity >= similarity_threshold:
|
||
logger.debug(f"[整合] 节点 '{node}' 中发现相似项 (相似度: {similarity:.2f}):")
|
||
logger.debug(f" - '{item1}'")
|
||
logger.debug(f" - '{item2}'")
|
||
|
||
# 比较信息量
|
||
info1 = calculate_information_content(item1)
|
||
info2 = calculate_information_content(item2)
|
||
|
||
if info1 >= info2:
|
||
item_to_keep = item1
|
||
item_to_remove = item2
|
||
else:
|
||
item_to_keep = item2
|
||
item_to_remove = item1
|
||
|
||
# 从原始列表中移除信息量较低的项
|
||
try:
|
||
memory_items.remove(item_to_remove)
|
||
logger.info(
|
||
f"[整合] 已合并节点 '{node}' 中的记忆，保留: '{item_to_keep[:60]}...', 移除: '{item_to_remove[:60]}...'"
|
||
)
|
||
merged_count += 1
|
||
nodes_modified.add(node)
|
||
node_data["last_modified"] = current_timestamp # 更新修改时间
|
||
_merged_in_this_node = True
|
||
break # 每个节点每次检查只合并一对
|
||
except ValueError:
|
||
# 如果项已经被移除（例如，在之前的迭代中作为 item_to_keep），则跳过
|
||
logger.warning(
|
||
f"[整合] 尝试移除节点 '{node}' 中不存在的项 '{item_to_remove[:30]}...'，可能已被合并。"
|
||
)
|
||
continue
|
||
# # 如果节点内发生了合并，更新节点数据 (这种方式不安全，会丢失其他属性)
|
||
# if merged_in_this_node:
|
||
# self.memory_graph.G.nodes[node]["memory_items"] = memory_items
|
||
|
||
if merged_count > 0:
|
||
logger.info(f"[整合] 共合并了 {merged_count} 对相似记忆项，分布在 {len(nodes_modified)} 个节点中。")
|
||
sync_start = time.time()
|
||
logger.info("[整合] 开始将变更同步到数据库...")
|
||
# 使用 resync 更安全地处理删除和添加
|
||
await self.hippocampus.entorhinal_cortex.resync_memory_to_db()
|
||
sync_end = time.time()
|
||
logger.info(f"[整合] 数据库同步耗时: {sync_end - sync_start:.2f}秒")
|
||
else:
|
||
logger.info("[整合] 本次检查未发现需要合并的记忆项。")
|
||
|
||
end_time = time.time()
|
||
logger.info(f"[整合] 整合检查完成，总耗时: {end_time - start_time:.2f}秒")
|
||
|
||
@staticmethod
|
||
def _calculate_item_similarity(item1: str, item2: str) -> float:
|
||
"""计算两条记忆项文本的余弦相似度"""
|
||
words1 = set(jieba.cut(item1))
|
||
words2 = set(jieba.cut(item2))
|
||
all_words = words1 | words2
|
||
if not all_words:
|
||
return 0.0
|
||
v1 = [1 if word in words1 else 0 for word in all_words]
|
||
v2 = [1 if word in words2 else 0 for word in all_words]
|
||
return cosine_similarity(v1, v2)
|
||
|
||
|
||
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:
|
||
构建间隔: {global_config.memory.memory_build_interval}秒|样本数: {global_config.memory.memory_build_sample_num},长度: {global_config.memory.memory_build_sample_length}|压缩率: {global_config.memory.memory_compress_rate}
|
||
记忆构建分布: {global_config.memory.memory_build_distribution}
|
||
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 build_memory(self):
|
||
"""构建记忆的公共接口"""
|
||
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_build_memory()
|
||
|
||
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 consolidate_memory(self):
|
||
"""整合记忆的公共接口"""
|
||
if not self._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
# 注意：目前 operation_consolidate_memory 内部直接读取配置，percentage 参数暂时无效
|
||
# 如果需要外部控制比例，需要修改 operation_consolidate_memory
|
||
return await self._hippocampus.parahippocampal_gyrus.operation_consolidate_memory()
|
||
|
||
async def get_memory_from_text(
|
||
self,
|
||
text: str,
|
||
max_memory_num: int = 3,
|
||
max_memory_length: int = 2,
|
||
max_depth: int = 3,
|
||
fast_retrieval: bool = False,
|
||
) -> 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_text(
|
||
text, max_memory_num, max_memory_length, max_depth, fast_retrieval
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"文本激活记忆失败: {e}")
|
||
response = []
|
||
return response
|
||
|
||
async def get_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) -> float:
|
||
"""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 = 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}")
|
||
response = 0.0
|
||
return response
|
||
|
||
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()
|