diff --git a/examples/st_game/memory/associative_memory.py b/examples/st_game/memory/agent_memory.py similarity index 78% rename from examples/st_game/memory/associative_memory.py rename to examples/st_game/memory/agent_memory.py index c771906ec..adb8a5f1f 100644 --- a/examples/st_game/memory/associative_memory.py +++ b/examples/st_game/memory/agent_memory.py @@ -1,6 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -# @Desc : MemoryBasic,AgentMemory实现 +# @Desc : BasicMemory,AgentMemory实现 from metagpt.memory.memory import Memory from metagpt.schema import Message @@ -8,14 +8,14 @@ import json from datetime import datetime -class MemoryBasic(Message): +class BasicMemory(Message): def __init__(self, memory_id: str, memory_count: int, type_count: int, memory_type: str, depth: int, content: int, creaetd: datetime, expiration: datetime, subject: str, predicate: str, object: str, embedding_key: str, poignancy: int, keywords: list, filling: list): """ - MemoryBasic继承于MG的Message类，其中content属性替代description属性 + BasicMemory继承于MG的Message类，其中content属性替代description属性 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 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） """ @@ -27,23 +27,23 @@ class MemoryBasic(Message): 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 """ self.memory_id: str = memory_id # 记忆ID - 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 + self.memory_count: int = memory_count # 第几个记忆，实际数值与Memory相等 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） - self.memory_type: str = memory_type # 记忆类型，使用Field，包含 event,thought,chat三种类型 + self.memory_type: str = memory_type # 记忆类型，包含 event,thought,chat三种类型 self.depth: str = depth # 记忆深度，类型为整数 self.created: datetime = creaetd # 创建时间 self.expiration: datetime = expiration # 记忆失效时间，默认为空（） self.last_accessed: datetime = creaetd # 上一次调用的时间，初始化时候与self.created一致 - self.subject: str = subject # 主语，str类型 - self.predicate: str = predicate # 谓语，str类型 - self.object: str = object # 宾语，str类型 + self.subject: str = subject # 主语 + self.predicate: str = predicate # 谓语 + self.object: str = object # 宾语 self.embedding_key: str = embedding_key # 内容与self.content一致 - self.poignancy: int = poignancy # importance值，整数类型 - self.keywords: list = keywords # keywords，列表 - self.filling: list = filling # None或者列表 + self.poignancy: int = poignancy # importance值 + self.keywords: list = keywords # keywords + self.filling: list = filling # None或者列表 class AgentMemory(Memory): @@ -60,7 +60,7 @@ class AgentMemory(Memory): @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 """ super.__init__() - self.storage: list[MemoryBasic] = [] # 重写Stroage，存储MemoryBasic所有节点 + self.storage: list[BasicMemory] = [] # 重写Stroage，存储BasicMemory所有节点 self.event_list = [] # 存储event记忆 self.thought_list = [] # 存储thought记忆 @@ -71,27 +71,27 @@ class AgentMemory(Memory): self.strength_event_keywords = dict() # 不知道具体作用，所以没有删除 self.strength_thought_keywords = dict() - self.embeddings = json.load(open(memory_saved + "/embeddings.json")) # dict类型，load embedding.json - self.memory_load() + self.embeddings = json.load(open(memory_saved + "/embeddings.json")) + self.load() - def memory_save(self): + def save(self): """ 将MemormyBasic类存储为Nodes.json形式。复现GA中的Kw Strength.json形式 @张凯补充一个可调用的函数 """ pass - def memory_load(self): + def load(self): """ 将GA的JSON解析，填充到AgentMemory类之中 """ pass - def add(self, memory_basic: MemoryBasic): + def add(self, memory_basic: BasicMemory): """ Add a new message to storage, while updating the index - 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 + 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 """ if memory_basic in self.storage: return diff --git a/examples/st_game/memory/retrieve.py b/examples/st_game/memory/retrieve.py index 5ac4a9b29..97eb3b6f0 100644 --- a/examples/st_game/memory/retrieve.py +++ b/examples/st_game/memory/retrieve.py @@ -5,19 +5,19 @@ import datetime from numpy import dot from numpy.linalg import norm -from associative_memory import AgentMemory, MemoryBasic +from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory from utils.utils import embedding_tools -def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str, n: int = 30, topk: int = 4) -> list[MemoryBasic]: +def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str, n: int = 30, topk: int = 4) -> list[BasicMemory]: """ Retrieve需要集合Role使用,原因在于Role才具有AgentMemory,scratch 逻辑:Role调用该函数,self._rc.AgentMemory,self._rc.scratch.curr_time,self._rc.scratch.memory_forget - 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] + 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] Score_lists示例 { - "memory": memories[i], MemoryBasic类 + "memory": memories[i], BasicMemory类 "importance": memories[i].poignancy "recency": 衰减因子计算结果 "relevance": 搜索结果 @@ -34,7 +34,7 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo score_list = normalize_score_floats(score_list, 0, 1) total_dict = {} - gw = [1, 1, 1] # 三个因素的权重,重要性，近因性，相关性 + gw = [1, 1, 1] # 三个因素的权重,重要性,近因性,相关性 for i in range(len(score_list)): total_score = (score_list[i]['importance'] * gw[0] + score_list[i]['recency'] * gw[1] + @@ -50,7 +50,7 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo def top_highest_x_values(d, x): """ 输入字典，Topx - 返回以字典值排序，字典键组成的List[MemoryBasic] + 返回以字典值排序，字典键组成的List[BasicMemory] """ top_v = [item[0] for item in sorted(d.items(), key=lambda item: item[1], reverse=True)[:x]] return top_v diff --git a/examples/st_game/prompts/run_gpt_prompts.py b/examples/st_game/prompts/run_gpt_prompts.py index 16ccbc29c..14b699c15 100644 --- a/examples/st_game/prompts/run_gpt_prompts.py +++ b/examples/st_game/prompts/run_gpt_prompts.py @@ -4,11 +4,11 @@ from wrapper_prompt import special_response_generate, prompt_generate from memory.scratch import Scratch -from memory.associative_memory import MemoryBasic +from examples.st_game.memory.agent_memory import BasicMemory import json -def get_poignancy_action(scratch: Scratch, content: MemoryBasic.content) -> str: +def get_poignancy_action(scratch: Scratch, content: BasicMemory.content) -> str: """ 衡量事件心酸度 """ diff --git a/examples/st_game/roles/st_role.py b/examples/st_game/roles/st_role.py index bc6988e28..047f9545b 100644 --- a/examples/st_game/roles/st_role.py +++ b/examples/st_game/roles/st_role.py @@ -17,7 +17,7 @@ from pathlib import Path from metagpt.roles.role import Role, RoleContext from metagpt.schema import Message -from ..memory.associative_memory import AgentMemory +from ..memory.agent_memory import AgentMemory from ..actions.dummy_action import DummyAction from ..actions.user_requirement import UserRequirement from ..maze_environment import MazeEnvironment