1786 lines
77 KiB
Python
1786 lines
77 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 collections import Counter
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from ...common.database import db
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from ...plugins.models.utils_model import LLM_request
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from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG
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from src.plugins.memory_system.sample_distribution import MemoryBuildScheduler # 分布生成器
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from .memory_config import MemoryConfig
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def get_closest_chat_from_db(length: int, timestamp: str):
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# print(f"获取最接近指定时间戳的聊天记录，长度: {length}, 时间戳: {timestamp}")
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# print(f"当前时间: {timestamp},转换后时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))}")
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chat_records = []
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closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[("time", -1)])
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# print(f"最接近的记录: {closest_record}")
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if closest_record:
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closest_time = closest_record["time"]
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chat_id = closest_record["chat_id"] # 获取chat_id
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# 获取该时间戳之后的length条消息，保持相同的chat_id
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chat_records = list(
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db.messages.find(
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{
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"time": {"$gt": closest_time},
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"chat_id": chat_id, # 添加chat_id过滤
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}
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)
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.sort("time", 1)
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.limit(length)
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)
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# print(f"获取到的记录: {chat_records}")
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length = len(chat_records)
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# print(f"获取到的记录长度: {length}")
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# 转换记录格式
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formatted_records = []
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for record in chat_records:
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# 兼容行为，前向兼容老数据
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formatted_records.append(
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{
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"_id": record["_id"],
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"time": record["time"],
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"chat_id": record["chat_id"],
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"detailed_plain_text": record.get("detailed_plain_text", ""), # 添加文本内容
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"memorized_times": record.get("memorized_times", 0), # 添加记忆次数
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}
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)
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return formatted_records
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return []
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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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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):
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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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# 定义日志配置
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memory_config = LogConfig(
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# 使用海马体专用样式
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console_format=MEMORY_STYLE_CONFIG["console_format"],
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file_format=MEMORY_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("memory_system", config=memory_config)
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class Memory_graph:
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def __init__(self):
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self.G = nx.Graph() # 使用 networkx 的图结构
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def connect_dot(self, concept1, concept2):
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# 避免自连接
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if concept1 == concept2:
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return
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current_time = datetime.datetime.now().timestamp()
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# 如果边已存在,增加 strength
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if self.G.has_edge(concept1, concept2):
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self.G[concept1][concept2]["strength"] = self.G[concept1][concept2].get("strength", 1) + 1
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# 更新最后修改时间
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self.G[concept1][concept2]["last_modified"] = current_time
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else:
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# 如果是新边,初始化 strength 为 1
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self.G.add_edge(
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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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# 更新最后修改时间
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self.G.nodes[concept]["last_modified"] = current_time
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else:
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self.G.nodes[concept]["memory_items"] = [memory]
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# 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
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if "created_time" not in self.G.nodes[concept]:
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self.G.nodes[concept]["created_time"] = current_time
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self.G.nodes[concept]["last_modified"] = current_time
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else:
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# 如果是新节点,创建新的记忆列表
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self.G.add_node(
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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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if concept in self.G:
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# 从图中获取节点数据
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node_data = self.G.nodes[concept]
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return concept, node_data
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return None
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def get_related_item(self, topic, depth=1):
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if topic not in self.G:
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return [], []
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first_layer_items = []
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second_layer_items = []
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# 获取相邻节点
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neighbors = list(self.G.neighbors(topic))
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# 获取当前节点的记忆项
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node_data = self.get_dot(topic)
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if node_data:
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concept, data = node_data
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if "memory_items" in data:
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memory_items = data["memory_items"]
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if isinstance(memory_items, list):
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first_layer_items.extend(memory_items)
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else:
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first_layer_items.append(memory_items)
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# 只在depth=2时获取第二层记忆
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if depth >= 2:
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# 获取相邻节点的记忆项
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for neighbor in neighbors:
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node_data = self.get_dot(neighbor)
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if node_data:
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concept, data = node_data
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if "memory_items" in data:
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memory_items = data["memory_items"]
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if isinstance(memory_items, list):
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second_layer_items.extend(memory_items)
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else:
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second_layer_items.append(memory_items)
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return first_layer_items, second_layer_items
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@property
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def dots(self):
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# 返回所有节点对应的 Memory_dot 对象
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return [self.get_dot(node) for node in self.G.nodes()]
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def forget_topic(self, topic):
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"""随机删除指定话题中的一条记忆，如果话题没有记忆则移除该话题节点"""
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if topic not in self.G:
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return None
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# 获取话题节点数据
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node_data = self.G.nodes[topic]
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# 如果节点存在memory_items
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if "memory_items" in node_data:
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memory_items = node_data["memory_items"]
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# 确保memory_items是列表
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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# 如果有记忆项可以删除
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if memory_items:
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# 随机选择一个记忆项删除
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removed_item = random.choice(memory_items)
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memory_items.remove(removed_item)
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# 更新节点的记忆项
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if memory_items:
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self.G.nodes[topic]["memory_items"] = memory_items
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else:
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# 如果没有记忆项了，删除整个节点
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self.G.remove_node(topic)
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return removed_item
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return None
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# 海马体
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class Hippocampus:
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def __init__(self):
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self.memory_graph = Memory_graph()
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self.llm_topic_judge = None
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self.llm_summary_by_topic = None
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self.entorhinal_cortex = None
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self.parahippocampal_gyrus = None
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self.config = None
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def initialize(self, global_config):
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self.config = MemoryConfig.from_global_config(global_config)
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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.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
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self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
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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_node_hash(self, 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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sorted_items = sorted(memory_items)
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content = f"{concept}:{'|'.join(sorted_items)}"
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return hash(content)
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def calculate_edge_hash(self, source, target) -> int:
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"""计算边的特征值"""
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nodes = sorted([source, target])
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return hash(f"{nodes[0]}:{nodes[1]}")
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def find_topic_llm(self, text, topic_num):
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prompt = (
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f"这是一段文字：{text}。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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def topic_what(self, text, topic, time_info):
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prompt = (
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f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，'
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f"可以包含时间和人物，以及具体的观点。只输出这句话就好"
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)
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return prompt
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def calculate_topic_num(self, text, compress_rate):
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"""计算文本的话题数量"""
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information_content = calculate_information_content(text)
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topic_by_length = text.count("\n") * compress_rate
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topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
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topic_num = int((topic_by_length + topic_by_information_content) / 2)
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logger.debug(
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f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
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f"topic_num: {topic_num}"
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)
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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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num (int, optional): 需要返回的记忆数量。默认为5。
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max_depth (int, optional): 记忆检索深度。默认为2。
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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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keywords = keywords[:5]
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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 = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
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# 提取关键词
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||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
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||
if not keywords:
|
||
keywords = []
|
||
else:
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||
keywords = [
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keyword.strip()
|
||
for keyword in ",".join(keywords).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||
if keyword.strip()
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||
]
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||
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# logger.info(f"提取的关键词: {', '.join(keywords)}")
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||
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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.info("没有找到有效的关键词节点")
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return []
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||
logger.info(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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||
all_memories = []
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||
activate_map = {} # 存储每个词的累计激活值
|
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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)
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nodes_to_process = [(keyword, 1.0, 0)]
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||
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||
while nodes_to_process:
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||
current_node, current_activation, current_depth = nodes_to_process.pop(0)
|
||
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||
# 如果激活值小于0或超过最大深度，停止扩散
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||
if current_activation <= 0 or current_depth >= max_depth:
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||
continue
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||
|
||
# 获取当前节点的所有邻居
|
||
neighbors = list(self.memory_graph.G.neighbors(current_node))
|
||
|
||
for neighbor in neighbors:
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||
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)
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||
nodes_to_process.append((neighbor, new_activation, current_depth + 1))
|
||
logger.trace(
|
||
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
|
||
for memory, similarity in top_memories:
|
||
all_memories.append((node, [memory], similarity))
|
||
# logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
|
||
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.info(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
|
||
num (int, optional): 需要返回的记忆数量。默认为5。
|
||
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 = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||
|
||
# 提取关键词
|
||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||
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.info(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.info(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.info(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
|
||
self.config = hippocampus.config
|
||
|
||
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 = 3
|
||
|
||
# 创建双峰分布的记忆调度器
|
||
sample_scheduler = MemoryBuildScheduler(
|
||
n_hours1=self.config.memory_build_distribution[0],
|
||
std_hours1=self.config.memory_build_distribution[1],
|
||
weight1=self.config.memory_build_distribution[2],
|
||
n_hours2=self.config.memory_build_distribution[3],
|
||
std_hours2=self.config.memory_build_distribution[4],
|
||
weight2=self.config.memory_build_distribution[5],
|
||
total_samples=self.config.build_memory_sample_num,
|
||
)
|
||
|
||
timestamps = sample_scheduler.get_timestamp_array()
|
||
logger.info(f"回忆往事: {[time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ts)) for ts in timestamps]}")
|
||
chat_samples = []
|
||
for timestamp in timestamps:
|
||
messages = self.random_get_msg_snippet(
|
||
timestamp, self.config.build_memory_sample_length, max_memorized_time_per_msg
|
||
)
|
||
if messages:
|
||
time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600
|
||
logger.debug(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} 的消息样本抽取失败")
|
||
|
||
return chat_samples
|
||
|
||
def random_get_msg_snippet(self, target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list:
|
||
"""从数据库中随机获取指定时间戳附近的消息片段"""
|
||
try_count = 0
|
||
while try_count < 3:
|
||
messages = get_closest_chat_from_db(length=chat_size, timestamp=target_timestamp)
|
||
if messages:
|
||
for message in messages:
|
||
if message["memorized_times"] >= max_memorized_time_per_msg:
|
||
messages = None
|
||
break
|
||
if messages:
|
||
for message in messages:
|
||
db.messages.update_one(
|
||
{"_id": message["_id"]}, {"$set": {"memorized_times": message["memorized_times"] + 1}}
|
||
)
|
||
return messages
|
||
try_count += 1
|
||
return None
|
||
|
||
async def sync_memory_to_db(self):
|
||
"""将记忆图同步到数据库"""
|
||
# 获取数据库中所有节点和内存中所有节点
|
||
db_nodes = list(db.graph_data.nodes.find())
|
||
memory_nodes = list(self.memory_graph.G.nodes(data=True))
|
||
|
||
# 转换数据库节点为字典格式,方便查找
|
||
db_nodes_dict = {node["concept"]: node for node in db_nodes}
|
||
|
||
# 检查并更新节点
|
||
for concept, data in memory_nodes:
|
||
memory_items = data.get("memory_items", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
# 计算内存中节点的特征值
|
||
memory_hash = self.hippocampus.calculate_node_hash(concept, memory_items)
|
||
|
||
# 获取时间信息
|
||
created_time = data.get("created_time", datetime.datetime.now().timestamp())
|
||
last_modified = data.get("last_modified", datetime.datetime.now().timestamp())
|
||
|
||
if concept not in db_nodes_dict:
|
||
# 数据库中缺少的节点,添加
|
||
node_data = {
|
||
"concept": concept,
|
||
"memory_items": memory_items,
|
||
"hash": memory_hash,
|
||
"created_time": created_time,
|
||
"last_modified": last_modified,
|
||
}
|
||
db.graph_data.nodes.insert_one(node_data)
|
||
else:
|
||
# 获取数据库中节点的特征值
|
||
db_node = db_nodes_dict[concept]
|
||
db_hash = db_node.get("hash", None)
|
||
|
||
# 如果特征值不同,则更新节点
|
||
if db_hash != memory_hash:
|
||
db.graph_data.nodes.update_one(
|
||
{"concept": concept},
|
||
{
|
||
"$set": {
|
||
"memory_items": memory_items,
|
||
"hash": memory_hash,
|
||
"created_time": created_time,
|
||
"last_modified": last_modified,
|
||
}
|
||
},
|
||
)
|
||
|
||
# 处理边的信息
|
||
db_edges = list(db.graph_data.edges.find())
|
||
memory_edges = list(self.memory_graph.G.edges(data=True))
|
||
|
||
# 创建边的哈希值字典
|
||
db_edge_dict = {}
|
||
for edge in db_edges:
|
||
edge_hash = self.hippocampus.calculate_edge_hash(edge["source"], edge["target"])
|
||
db_edge_dict[(edge["source"], edge["target"])] = {"hash": edge_hash, "strength": edge.get("strength", 1)}
|
||
|
||
# 检查并更新边
|
||
for source, target, data in memory_edges:
|
||
edge_hash = self.hippocampus.calculate_edge_hash(source, target)
|
||
edge_key = (source, target)
|
||
strength = data.get("strength", 1)
|
||
|
||
# 获取边的时间信息
|
||
created_time = data.get("created_time", datetime.datetime.now().timestamp())
|
||
last_modified = data.get("last_modified", datetime.datetime.now().timestamp())
|
||
|
||
if edge_key not in db_edge_dict:
|
||
# 添加新边
|
||
edge_data = {
|
||
"source": source,
|
||
"target": target,
|
||
"strength": strength,
|
||
"hash": edge_hash,
|
||
"created_time": created_time,
|
||
"last_modified": last_modified,
|
||
}
|
||
db.graph_data.edges.insert_one(edge_data)
|
||
else:
|
||
# 检查边的特征值是否变化
|
||
if db_edge_dict[edge_key]["hash"] != edge_hash:
|
||
db.graph_data.edges.update_one(
|
||
{"source": source, "target": target},
|
||
{
|
||
"$set": {
|
||
"hash": edge_hash,
|
||
"strength": strength,
|
||
"created_time": created_time,
|
||
"last_modified": last_modified,
|
||
}
|
||
},
|
||
)
|
||
|
||
def sync_memory_from_db(self):
|
||
"""从数据库同步数据到内存中的图结构"""
|
||
current_time = datetime.datetime.now().timestamp()
|
||
need_update = False
|
||
|
||
# 清空当前图
|
||
self.memory_graph.G.clear()
|
||
|
||
# 从数据库加载所有节点
|
||
nodes = list(db.graph_data.nodes.find())
|
||
for node in nodes:
|
||
concept = node["concept"]
|
||
memory_items = node.get("memory_items", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
# 检查时间字段是否存在
|
||
if "created_time" not in node or "last_modified" not in node:
|
||
need_update = True
|
||
# 更新数据库中的节点
|
||
update_data = {}
|
||
if "created_time" not in node:
|
||
update_data["created_time"] = current_time
|
||
if "last_modified" not in node:
|
||
update_data["last_modified"] = current_time
|
||
|
||
db.graph_data.nodes.update_one({"concept": concept}, {"$set": update_data})
|
||
logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段")
|
||
|
||
# 获取时间信息(如果不存在则使用当前时间)
|
||
created_time = node.get("created_time", current_time)
|
||
last_modified = node.get("last_modified", current_time)
|
||
|
||
# 添加节点到图中
|
||
self.memory_graph.G.add_node(
|
||
concept, memory_items=memory_items, created_time=created_time, last_modified=last_modified
|
||
)
|
||
|
||
# 从数据库加载所有边
|
||
edges = list(db.graph_data.edges.find())
|
||
for edge in edges:
|
||
source = edge["source"]
|
||
target = edge["target"]
|
||
strength = edge.get("strength", 1)
|
||
|
||
# 检查时间字段是否存在
|
||
if "created_time" not in edge or "last_modified" not in edge:
|
||
need_update = True
|
||
# 更新数据库中的边
|
||
update_data = {}
|
||
if "created_time" not in edge:
|
||
update_data["created_time"] = current_time
|
||
if "last_modified" not in edge:
|
||
update_data["last_modified"] = current_time
|
||
|
||
db.graph_data.edges.update_one({"source": source, "target": target}, {"$set": update_data})
|
||
logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段")
|
||
|
||
# 获取时间信息(如果不存在则使用当前时间)
|
||
created_time = edge.get("created_time", current_time)
|
||
last_modified = edge.get("last_modified", current_time)
|
||
|
||
# 只有当源节点和目标节点都存在时才添加边
|
||
if source in self.memory_graph.G and target in self.memory_graph.G:
|
||
self.memory_graph.G.add_edge(
|
||
source, target, strength=strength, created_time=created_time, last_modified=last_modified
|
||
)
|
||
|
||
if need_update:
|
||
logger.success("[数据库] 已为缺失的时间字段进行补充")
|
||
|
||
async def 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()
|
||
db.graph_data.nodes.delete_many({})
|
||
db.graph_data.edges.delete_many({})
|
||
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))
|
||
|
||
# 重新写入节点
|
||
node_start = time.time()
|
||
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 []
|
||
|
||
node_data = {
|
||
"concept": concept,
|
||
"memory_items": memory_items,
|
||
"hash": self.hippocampus.calculate_node_hash(concept, memory_items),
|
||
"created_time": data.get("created_time", datetime.datetime.now().timestamp()),
|
||
"last_modified": data.get("last_modified", datetime.datetime.now().timestamp()),
|
||
}
|
||
db.graph_data.nodes.insert_one(node_data)
|
||
node_end = time.time()
|
||
logger.info(f"[数据库] 写入 {len(memory_nodes)} 个节点耗时: {node_end - node_start:.2f}秒")
|
||
|
||
# 重新写入边
|
||
edge_start = time.time()
|
||
for source, target, data in memory_edges:
|
||
edge_data = {
|
||
"source": source,
|
||
"target": target,
|
||
"strength": data.get("strength", 1),
|
||
"hash": self.hippocampus.calculate_edge_hash(source, target),
|
||
"created_time": data.get("created_time", datetime.datetime.now().timestamp()),
|
||
"last_modified": data.get("last_modified", datetime.datetime.now().timestamp()),
|
||
}
|
||
db.graph_data.edges.insert_one(edge_data)
|
||
edge_end = time.time()
|
||
logger.info(f"[数据库] 写入 {len(memory_edges)} 条边耗时: {edge_end - edge_start:.2f}秒")
|
||
|
||
end_time = time.time()
|
||
logger.success(f"[数据库] 重新同步完成，总耗时: {end_time - start_time:.2f}秒")
|
||
logger.success(f"[数据库] 同步了 {len(memory_nodes)} 个节点和 {len(memory_edges)} 条边")
|
||
|
||
|
||
# 海马体
|
||
class Hippocampus:
|
||
def __init__(self):
|
||
self.memory_graph = Memory_graph()
|
||
self.llm_topic_judge = None
|
||
self.llm_summary_by_topic = None
|
||
self.entorhinal_cortex = None
|
||
self.parahippocampal_gyrus = None
|
||
self.config = None
|
||
|
||
def initialize(self, global_config):
|
||
self.config = MemoryConfig.from_global_config(global_config)
|
||
# 初始化子组件
|
||
self.entorhinal_cortex = EntorhinalCortex(self)
|
||
self.parahippocampal_gyrus = ParahippocampalGyrus(self)
|
||
# 从数据库加载记忆图
|
||
self.entorhinal_cortex.sync_memory_from_db()
|
||
self.llm_topic_judge = LLM_request(self.config.llm_topic_judge, request_type="memory")
|
||
self.llm_summary_by_topic = LLM_request(self.config.llm_summary_by_topic, request_type="memory")
|
||
|
||
def get_all_node_names(self) -> list:
|
||
"""获取记忆图中所有节点的名字列表"""
|
||
return list(self.memory_graph.G.nodes())
|
||
|
||
def calculate_node_hash(self, concept, memory_items) -> int:
|
||
"""计算节点的特征值"""
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
sorted_items = sorted(memory_items)
|
||
content = f"{concept}:{'|'.join(sorted_items)}"
|
||
return hash(content)
|
||
|
||
def calculate_edge_hash(self, source, target) -> int:
|
||
"""计算边的特征值"""
|
||
nodes = sorted([source, target])
|
||
return hash(f"{nodes[0]}:{nodes[1]}")
|
||
|
||
def find_topic_llm(self, text, topic_num):
|
||
prompt = (
|
||
f"这是一段文字：{text}。请你从这段话中总结出最多{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，"
|
||
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。"
|
||
f"如果确定找不出主题或者没有明显主题，返回<none>。"
|
||
)
|
||
return prompt
|
||
|
||
def topic_what(self, text, topic, time_info):
|
||
prompt = (
|
||
f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，'
|
||
f"可以包含时间和人物，以及具体的观点。只输出这句话就好"
|
||
)
|
||
return prompt
|
||
|
||
def calculate_topic_num(self, text, compress_rate):
|
||
"""计算文本的话题数量"""
|
||
information_content = calculate_information_content(text)
|
||
topic_by_length = text.count("\n") * compress_rate
|
||
topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
|
||
topic_num = int((topic_by_length + topic_by_information_content) / 2)
|
||
logger.debug(
|
||
f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
|
||
f"topic_num: {topic_num}"
|
||
)
|
||
return topic_num
|
||
|
||
def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
|
||
"""从关键词获取相关记忆。
|
||
|
||
Args:
|
||
keyword (str): 关键词
|
||
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。
|
||
|
||
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, 与关键词的相似度
|
||
"""
|
||
if not keyword:
|
||
return []
|
||
|
||
# 获取所有节点
|
||
all_nodes = list(self.memory_graph.G.nodes())
|
||
memories = []
|
||
|
||
# 计算关键词的词集合
|
||
keyword_words = set(jieba.cut(keyword))
|
||
|
||
# 遍历所有节点，计算相似度
|
||
for node in all_nodes:
|
||
node_words = set(jieba.cut(node))
|
||
all_words = keyword_words | node_words
|
||
v1 = [1 if word in keyword_words else 0 for word in all_words]
|
||
v2 = [1 if word in node_words else 0 for word in all_words]
|
||
similarity = cosine_similarity(v1, v2)
|
||
|
||
# 如果相似度超过阈值，获取该节点的记忆
|
||
if similarity >= 0.3: # 可以调整这个阈值
|
||
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 []
|
||
|
||
memories.append((node, memory_items, similarity))
|
||
|
||
# 按相似度降序排序
|
||
memories.sort(key=lambda x: x[2], reverse=True)
|
||
return memories
|
||
|
||
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:
|
||
"""从文本中提取关键词并获取相关记忆。
|
||
|
||
Args:
|
||
text (str): 输入文本
|
||
num (int, optional): 需要返回的记忆数量。默认为5。
|
||
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:
|
||
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 text:
|
||
return []
|
||
|
||
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 = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||
|
||
# 提取关键词
|
||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||
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 []
|
||
|
||
logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
|
||
|
||
# 从每个关键词获取记忆
|
||
all_memories = []
|
||
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.trace(
|
||
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
|
||
for memory, similarity in top_memories:
|
||
all_memories.append((node, [memory], similarity))
|
||
# logger.info(f"选中记忆: {memory} (相似度: {similarity:.2f})")
|
||
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.info(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
|
||
num (int, optional): 需要返回的记忆数量。默认为5。
|
||
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 = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, topic_num))
|
||
|
||
# 提取关键词
|
||
keywords = re.findall(r"<([^>]+)>", topics_response[0])
|
||
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.info(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.trace(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.trace(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.info(f"总激活值: {total_activation:.2f}, 总节点数: {total_nodes}, 激活: {activation_ratio}")
|
||
|
||
return activation_ratio
|
||
|
||
|
||
# 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.config = hippocampus.config
|
||
|
||
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，包含以下字段：
|
||
- time: float, 消息的时间戳
|
||
- detailed_plain_text: str, 消息的详细文本内容
|
||
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)
|
||
- topic: str, 记忆主题
|
||
- summary: str, 主题的摘要描述
|
||
- similar_topics_dict: dict, 相似主题字典，key为主题，value为相似主题列表
|
||
每个相似主题是一个元组 (similar_topic, similarity)
|
||
- similar_topic: str, 相似的主题
|
||
- similarity: float, 相似度分数（0-1之间）
|
||
|
||
Process:
|
||
1. 合并消息文本并生成时间信息
|
||
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(), {}
|
||
|
||
# 合并消息文本，同时保留时间信息
|
||
input_text = ""
|
||
time_info = ""
|
||
# 计算最早和最晚时间
|
||
earliest_time = min(msg["time"] for msg in messages)
|
||
latest_time = max(msg["time"] for msg in messages)
|
||
|
||
earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
|
||
latest_dt = datetime.datetime.fromtimestamp(latest_time)
|
||
|
||
# 如果是同一年
|
||
if earliest_dt.year == latest_dt.year:
|
||
earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
|
||
latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
|
||
time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
|
||
else:
|
||
earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
||
latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
|
||
time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
|
||
|
||
for msg in messages:
|
||
input_text += f"{msg['detailed_plain_text']}\n"
|
||
|
||
logger.debug(input_text)
|
||
|
||
topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
|
||
topics_response = await self.hippocampus.llm_topic_judge.generate_response(
|
||
self.hippocampus.find_topic_llm(input_text, topic_num)
|
||
)
|
||
|
||
# 使用正则表达式提取<>中的内容
|
||
topics = re.findall(r"<([^>]+)>", topics_response[0])
|
||
|
||
# 如果没有找到<>包裹的内容，返回['none']
|
||
if not topics:
|
||
topics = ["none"]
|
||
else:
|
||
# 处理提取出的话题
|
||
topics = [
|
||
topic.strip()
|
||
for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||
if topic.strip()
|
||
]
|
||
|
||
# 过滤掉包含禁用关键词的topic
|
||
filtered_topics = [
|
||
topic for topic in topics if not any(keyword in topic for keyword in self.config.memory_ban_words)
|
||
]
|
||
|
||
logger.debug(f"过滤后话题: {filtered_topics}")
|
||
|
||
# 创建所有话题的请求任务
|
||
tasks = []
|
||
for topic in filtered_topics:
|
||
topic_what_prompt = self.hippocampus.topic_what(input_text, topic, time_info)
|
||
try:
|
||
task = self.hippocampus.llm_summary_by_topic.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.debug("------------------------------------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 = []
|
||
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)})")
|
||
|
||
compress_rate = self.config.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
|
||
logger.debug(f"压缩后记忆数量: {compressed_memory}，似曾相识的话题: {similar_topics_dict}")
|
||
|
||
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 i in range(len(all_topics)):
|
||
for j in range(i + 1, len(all_topics)):
|
||
logger.debug(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
|
||
all_added_edges.append(f"{all_topics[i]}-{all_topics[j]}")
|
||
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
|
||
|
||
logger.success(f"更新记忆: {', '.join(all_added_nodes)}")
|
||
logger.debug(f"强化连接: {', '.join(all_added_edges)}")
|
||
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.success(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 * self.config.memory_forget_time:
|
||
current_strength = edge_data.get("strength", 1)
|
||
new_strength = current_strength - 1
|
||
|
||
if new_strength <= 0:
|
||
self.memory_graph.G.remove_edge(source, target)
|
||
edge_changes["removed"].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:
|
||
node_data = self.memory_graph.G.nodes[node]
|
||
last_modified = node_data.get("last_modified", current_time)
|
||
|
||
if current_time - last_modified > 3600 * 24:
|
||
memory_items = node_data.get("memory_items", [])
|
||
if not isinstance(memory_items, list):
|
||
memory_items = [memory_items] if memory_items else []
|
||
|
||
if memory_items:
|
||
current_count = len(memory_items)
|
||
removed_item = random.choice(memory_items)
|
||
memory_items.remove(removed_item)
|
||
|
||
if memory_items:
|
||
self.memory_graph.G.nodes[node]["memory_items"] = memory_items
|
||
self.memory_graph.G.nodes[node]["last_modified"] = current_time
|
||
node_changes["reduced"].append(f"{node} (数量: {current_count} -> {len(memory_items)})")
|
||
else:
|
||
self.memory_graph.G.remove_node(node)
|
||
node_changes["removed"].append(node)
|
||
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:
|
||
_instance = None
|
||
_hippocampus = None
|
||
_global_config = None
|
||
_initialized = False
|
||
|
||
@classmethod
|
||
def get_instance(cls):
|
||
if cls._instance is None:
|
||
cls._instance = cls()
|
||
return cls._instance
|
||
|
||
@classmethod
|
||
def get_hippocampus(cls):
|
||
if not cls._initialized:
|
||
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
|
||
return cls._hippocampus
|
||
|
||
def initialize(self, global_config):
|
||
"""初始化海马体实例"""
|
||
if self._initialized:
|
||
return self._hippocampus
|
||
|
||
self._global_config = global_config
|
||
self._hippocampus = Hippocampus()
|
||
self._hippocampus.initialize(global_config)
|
||
self._initialized = True
|
||
|
||
# 输出记忆系统参数信息
|
||
config = self._hippocampus.config
|
||
|
||
# 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.success(f"""--------------------------------
|
||
记忆系统参数配置:
|
||
构建间隔: {global_config.build_memory_interval}秒|样本数: {config.build_memory_sample_num},长度: {config.build_memory_sample_length}|压缩率: {config.memory_compress_rate}
|
||
记忆构建分布: {config.memory_build_distribution}
|
||
遗忘间隔: {global_config.forget_memory_interval}秒|遗忘比例: {global_config.memory_forget_percentage}|遗忘: {config.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
|
||
|
||
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 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_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()
|