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已完成注释修改
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didi
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4 changed files with 27 additions and 27 deletions
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@ -1,6 +1,6 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Desc : MemoryBasic,AgentMemory实现
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# @Desc : BasicMemory,AgentMemory实现
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from metagpt.memory.memory import Memory
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from metagpt.schema import Message
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@ -8,14 +8,14 @@ import json
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from datetime import datetime
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class MemoryBasic(Message):
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class BasicMemory(Message):
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def __init__(self, memory_id: str, memory_count: int, type_count: int, memory_type: str, depth: int, content: int,
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creaetd: datetime, expiration: datetime,
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subject: str, predicate: str, object: str,
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embedding_key: str, poignancy: int, keywords: list, filling: list):
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"""
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MemoryBasic继承于MG的Message类，其中content属性替代description属性
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BasicMemory继承于MG的Message类，其中content属性替代description属性
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Message类中对于Chat类型支持的非常好，对于Agent个体的Perceive,Reflection,Plan支持的并不多
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在Type设计上，我们延续GA的三个种类，但是对于Chat种类的对话进行特别设计（具体怎么设计还没想好）
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"""
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@ -27,23 +27,23 @@ class MemoryBasic(Message):
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cause_by 接受一个Action类，在此项目中，每个Agent需要有一个基础动作[Receive] 用于接受假对话Message；而每个Agent需要有独一无二的动作类，用以接受真对话Message
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"""
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self.memory_id: str = memory_id # 记忆ID
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self.memory_count: int = memory_count # 第几个记忆，实际数值与Memory相等，但是类型为整数
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self.memory_count: int = memory_count # 第几个记忆，实际数值与Memory相等
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self.type_count: int = type_count # 第几种记忆，类型为整数（具体不太理解如何生成的）
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self.memory_type: str = memory_type # 记忆类型，使用Field，包含 event,thought,chat三种类型
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self.memory_type: str = memory_type # 记忆类型，包含 event,thought,chat三种类型
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self.depth: str = depth # 记忆深度，类型为整数
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self.created: datetime = creaetd # 创建时间
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self.expiration: datetime = expiration # 记忆失效时间，默认为空（）
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self.last_accessed: datetime = creaetd # 上一次调用的时间，初始化时候与self.created一致
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self.subject: str = subject # 主语，str类型
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self.predicate: str = predicate # 谓语，str类型
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self.object: str = object # 宾语，str类型
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self.subject: str = subject # 主语
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self.predicate: str = predicate # 谓语
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self.object: str = object # 宾语
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self.embedding_key: str = embedding_key # 内容与self.content一致
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self.poignancy: int = poignancy # importance值，整数类型
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self.keywords: list = keywords # keywords，列表
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self.filling: list = filling # None或者列表
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self.poignancy: int = poignancy # importance值
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self.keywords: list = keywords # keywords
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self.filling: list = filling # None或者列表
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class AgentMemory(Memory):
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@ -60,7 +60,7 @@ class AgentMemory(Memory):
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@李嵩@张凯 这里的storage是List，你们需要写一个JSON转化器，将List修改为node.json一致的格式
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"""
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super.__init__()
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self.storage: list[MemoryBasic] = [] # 重写Stroage，存储MemoryBasic所有节点
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self.storage: list[BasicMemory] = [] # 重写Stroage，存储BasicMemory所有节点
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self.event_list = [] # 存储event记忆
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self.thought_list = [] # 存储thought记忆
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@ -71,27 +71,27 @@ class AgentMemory(Memory):
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self.strength_event_keywords = dict() # 不知道具体作用，所以没有删除
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self.strength_thought_keywords = dict()
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self.embeddings = json.load(open(memory_saved + "/embeddings.json")) # dict类型，load embedding.json
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self.memory_load()
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self.embeddings = json.load(open(memory_saved + "/embeddings.json"))
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self.load()
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def memory_save(self):
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def save(self):
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"""
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将MemormyBasic类存储为Nodes.json形式。复现GA中的Kw Strength.json形式
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@张凯补充一个可调用的函数
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"""
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pass
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def memory_load(self):
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def load(self):
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"""
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将GA的JSON解析，填充到AgentMemory类之中
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"""
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pass
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def add(self, memory_basic: MemoryBasic):
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def add(self, memory_basic: BasicMemory):
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"""
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Add a new message to storage, while updating the index
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重写add方法，修改原有的Message类为MemoryBasic类，并添加不同的记忆类型添加方式
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重写add方法，修改原有的Message类为BasicMemory类，并添加不同的记忆类型添加方式
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"""
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if memory_basic in self.storage:
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return
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@ -5,19 +5,19 @@
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import datetime
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from numpy import dot
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from numpy.linalg import norm
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from associative_memory import AgentMemory, MemoryBasic
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from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory
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from utils.utils import embedding_tools
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def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str, n: int = 30, topk: int = 4) -> list[MemoryBasic]:
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def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str, n: int = 30, topk: int = 4) -> list[BasicMemory]:
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"""
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Retrieve需要集合Role使用,原因在于Role才具有AgentMemory,scratch
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逻辑:Role调用该函数,self._rc.AgentMemory,self._rc.scratch.curr_time,self._rc.scratch.memory_forget
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输入希望查询的内容与希望回顾的条数,返回TopK条高分记忆，即List[MemoryBasic]
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输入希望查询的内容与希望回顾的条数,返回TopK条高分记忆，即List[BasicMemory]
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Score_lists示例
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{
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"memory": memories[i], MemoryBasic类
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"memory": memories[i], BasicMemory类
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"importance": memories[i].poignancy
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"recency": 衰减因子计算结果
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"relevance": 搜索结果
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@ -34,7 +34,7 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo
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score_list = normalize_score_floats(score_list, 0, 1)
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total_dict = {}
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gw = [1, 1, 1] # 三个因素的权重,重要性，近因性，相关性
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gw = [1, 1, 1] # 三个因素的权重,重要性,近因性,相关性
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for i in range(len(score_list)):
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total_score = (score_list[i]['importance'] * gw[0] +
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score_list[i]['recency'] * gw[1] +
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@ -50,7 +50,7 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo
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def top_highest_x_values(d, x):
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"""
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输入字典，Topx
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返回以字典值排序，字典键组成的List[MemoryBasic]
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返回以字典值排序，字典键组成的List[BasicMemory]
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"""
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top_v = [item[0] for item in sorted(d.items(), key=lambda item: item[1], reverse=True)[:x]]
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return top_v
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@ -4,11 +4,11 @@
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from wrapper_prompt import special_response_generate, prompt_generate
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from memory.scratch import Scratch
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from memory.associative_memory import MemoryBasic
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from examples.st_game.memory.agent_memory import BasicMemory
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import json
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def get_poignancy_action(scratch: Scratch, content: MemoryBasic.content) -> str:
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def get_poignancy_action(scratch: Scratch, content: BasicMemory.content) -> str:
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"""
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衡量事件心酸度
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"""
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@ -17,7 +17,7 @@ from pathlib import Path
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from metagpt.roles.role import Role, RoleContext
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from metagpt.schema import Message
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from ..memory.associative_memory import AgentMemory
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from ..memory.agent_memory import AgentMemory
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from ..actions.dummy_action import DummyAction
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from ..actions.user_requirement import UserRequirement
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from ..maze_environment import MazeEnvironment
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