update code and fix reference

examples/st_game/.gitignore

@@ -0,0 +1 @@
+storage/test*

examples/st_game/README.md

@@ -1,5 +1,9 @@
 ## Stanford Town Game
 
-执行入口为：`python3 run_st_game.py "Host a activity"`   
+### 后端服务启动
+执行入口为：`python3 run_st_game.py "Host a open lunch party at 13:00 pm" "base_the_ville_isabella_maria_klaus" "test_sim" 10`   
 
 `idea`为用户给第一个Agent的用户心声，并通过这个心声进行传播，看最后多智能体是否达到举办、参加活动的目标。  
+
+### 前端服务启动
+进入`generative_agents/environment/frontend_server`，使用`~~python manage.py runserver~~`启动前端服务。  

examples/st_game/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

examples/st_game/actions/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

examples/st_game/actions/agent_chat_sum_rel.py

@@ -5,8 +5,8 @@
 from metagpt.logs import logger
 from metagpt.schema import Message
 
-from ..roles.st_role import STRole
-from ..actions.st_action import STAction
+from examples.st_game.roles.st_role import STRole
+from examples.st_game.actions.st_action import STAction
 
 
 class AgentChatSumRel(STAction):

examples/st_game/actions/decide_to_talk.py

@@ -5,8 +5,8 @@
 from metagpt.logs import logger
 from metagpt.schema import Message
 
-from ..roles.st_role import STRole
-from ..actions.st_action import STAction
+from examples.st_game.roles.st_role import STRole
+from examples.st_game.actions.st_action import STAction
 
 
 class DecideToTalk(STAction):

examples/st_game/actions/gen_iter_chat_utt.py

@@ -5,10 +5,10 @@
 from metagpt.logs import logger
 from metagpt.schema import Message
 
-from ..roles.st_role import STRole
-from ..actions.st_action import STAction
-from ..utils.utils import extract_first_json_dict
-from ..maze import Maze
+from examples.st_game.roles.st_role import STRole
+from examples.st_game.actions.st_action import STAction
+from examples.st_game.utils.utils import extract_first_json_dict
+from examples.st_game.maze import Maze
 
 
 class GenIterChatUTT(STAction):

examples/st_game/actions/new_decomp_schedule.py

@@ -7,8 +7,8 @@ import datetime
 from metagpt.logs import logger
 from metagpt.schema import Message
 
-from ..roles.st_role import STRole
-from ..actions.st_action import STAction
+from examples.st_game.roles.st_role import STRole
+from examples.st_game.actions.st_action import STAction
 
 
 class NewDecompSchedule(STAction):

examples/st_game/actions/run_reflect_action.py

@@ -3,11 +3,12 @@
 # @Desc   : Integration Reflect Action
 
 import re
-from ..roles.st_role import STRole
-from ..actions.st_action import STAction
-from ..memory.agent_memory import BasicMemory
 from metagpt.logs import logger
 
+from examples.st_game.actions.st_action import STAction
+from examples.st_game.memory.agent_memory import BasicMemory
+
+
 # Run GPT Prompt Focal Point method
 class AgentFocusPt(STAction):
 
@@ -31,8 +32,8 @@ class AgentFocusPt(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, role: STRole, statements: str, n: int, test_input=None) -> str:
-        def create_prompt_input(role: STRole, statements, n, test_input=None):
+    async def run(self, role: "STRole", statements: str, n: int, test_input=None) -> str:
+        def create_prompt_input(role: "STRole", statements, n, test_input=None):
             prompt_input = [statements, str(n)]
             return prompt_input
 
@@ -77,8 +78,8 @@ class AgentInsightAndGuidance(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, role: STRole, statements: str, n: int, test_input=None) -> str:
-        def create_prompt_input(role: STRole, statements, n, test_input=None):
+    async def run(self, role: "STRole", statements: str, n: int, test_input=None) -> str:
+        def create_prompt_input(role: "STRole", statements, n, test_input=None):
             prompt_input = [statements, str(n)]
             return prompt_input
 
@@ -113,7 +114,7 @@ class AgentEventTriple(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, statements: str, role: STRole, verbose=False) -> str:
+    async def run(self, statements: str, role: "STRole", verbose=False) -> str:
         def create_prompt_input(statements, role):
             if "(" in statements:
                 statements = statements.split("(")[-1].split(")")[0]
@@ -151,8 +152,8 @@ class AgentEventPoignancy(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, role: STRole, statements: str, test_input=None, verbose=False) -> str:
-        def create_prompt_input(role: STRole, statements: str, test_input=None):
+    async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
+        def create_prompt_input(role: "STRole", statements: str, test_input=None):
             prompt_input = [role._rc.scratch.name,
                             role._rc.scratch.get_str_iss(),
                             role._rc.scratch.name,
@@ -192,8 +193,8 @@ class AgentChatPoignancy(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, role: STRole, statements: str, test_input=None, verbose=False) -> str:
-        def create_prompt_input(role: STRole, statements, test_input=None):
+    async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
+        def create_prompt_input(role: "STRole", statements, test_input=None):
             prompt_input = [role._rc.scratch.name,
                             role._rc.scratch.get_str_iss(),
                             role._rc.scratch.name,
@@ -232,7 +233,7 @@ class AgentPlanThoughtOnConvo(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, role: STRole, statements: str, test_input=None, verbose=False) -> str:
+    async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
         def create_prompt_input(role, statements, test_input=None):
             prompt_input = [statements,
                             role._rc.scratch.name,
@@ -268,7 +269,7 @@ class AgentMemoryOnConvo(STAction):
     def _func_fail_default_resp(self) -> str:
         pass
 
-    async def run(self, role: STRole, statements: str, test_input=None, verbose=False) -> str:
+    async def run(self, role: "STRole", statements: str, test_input=None, verbose=False) -> str:
         def create_prompt_input(role, statements, test_input=None):
             prompt_input = [statements,
                             role._rc.scratch.name,

examples/st_game/actions/st_action.py

@@ -9,7 +9,7 @@ import json
 from metagpt.actions.action import Action
 from metagpt.schema import Message
 
-from ..utils.const import PROMPTS_DIR
+from examples.st_game.utils.const import PROMPTS_DIR
 
 
 class STAction(Action):

examples/st_game/actions/summarize_conv.py

@@ -4,7 +4,7 @@
 
 from metagpt.schema import Message
 
-from ..actions.st_action import STAction
+from examples.st_game.actions.st_action import STAction
 
 
 class SummarizeConv(STAction):

examples/st_game/maze.py

@@ -14,8 +14,8 @@ import json
 import math
 from pathlib import Path
 import networkx as nx
-from .utils.const import MAZE_ASSET_PATH
-from .utils.utils import read_csv_to_list
+from utils.const import MAZE_ASSET_PATH
+from utils.utils import read_csv_to_list
 
 
 class Maze:
@@ -212,13 +212,13 @@ class Maze:
                     else:
                         self.address_tiles[add] = set([(j, i)])
 
-        # Build an nx.Graph.
-        grid_graph = nx.grid_2d_graph(m=self.maze_width, n=self.maze_height)
-        for i in range(self.maze_height):
-            for j in range(self.maze_width):
-                if self.collision_maze[i][j] != 0:
-                    grid_graph.remove_node((i, j))
-        self.nx_graph = grid_graph
+        # # Build an nx.Graph.
+        # grid_graph = nx.grid_2d_graph(m=self.maze_width, n=self.maze_height)
+        # for i in range(self.maze_height):
+        #     for j in range(self.maze_width):
+        #         if self.collision_maze[i][j] != 0:
+        #             grid_graph.remove_node((i, j))
+        # self.nx_graph = grid_graph
 
     def turn_coordinate_to_tile(self, px_coordinate: tuple[int, int]) -> tuple[int, int]:
         """
@@ -389,24 +389,24 @@ class Maze:
             if event[0] == subject:
                 self.tiles[tile[1]][tile[0]]["events"].remove(event)
 
-    def _find_closest_node(self, coords: tuple[int, int]) -> tuple[int, int]:
-        target_coords = self.nx_graph.nodes
-        min_dist = None
-        closest_coordinate = None
-        for target in target_coords:
-            dist = math.dist(coords, target)
-            if not closest_coordinate:
-                min_dist = dist
-                closest_coordinate = target
-            else:
-                if min_dist > dist:
-                    min_dist = dist
-                    closest_coordinate = target
-        return closest_coordinate
-
-    def find_path(self, start: tuple[int, int], end: tuple[int, int]) -> list[tuple[int, int]]:
-        if start not in self.nx_graph.nodes:
-            start = self._find_closest_node(start)
-        if end not in self.nx_graph.nodes:
-            end = self._find_closest_node(end)
-        return nx.shortest_path(self.nx_graph, start, end)
+    # def _find_closest_node(self, coords: tuple[int, int]) -> tuple[int, int]:
+    #     target_coords = self.nx_graph.nodes
+    #     min_dist = None
+    #     closest_coordinate = None
+    #     for target in target_coords:
+    #         dist = math.dist(coords, target)
+    #         if not closest_coordinate:
+    #             min_dist = dist
+    #             closest_coordinate = target
+    #         else:
+    #             if min_dist > dist:
+    #                 min_dist = dist
+    #                 closest_coordinate = target
+    #     return closest_coordinate
+    #
+    # def find_path(self, start: tuple[int, int], end: tuple[int, int]) -> list[tuple[int, int]]:
+    #     if start not in self.nx_graph.nodes:
+    #         start = self._find_closest_node(start)
+    #     if end not in self.nx_graph.nodes:
+    #         end = self._find_closest_node(end)
+    #     return nx.shortest_path(self.nx_graph, start, end)

examples/st_game/maze_environment.py

@@ -7,12 +7,12 @@ from pydantic import Field
 from metagpt.environment import Environment
 from metagpt.roles.role import Role
 
-from .maze import Maze
+from maze import Maze
 
 
 class MazeEnvironment(Environment):
 
-    maze: Maze = Field(default=Maze)
+    maze: Maze = Field(default_factory=Maze)
 
     def add_role(self, role: Role):
         role.set_env(self)

examples/st_game/memory/agent_memory.py

@@ -2,11 +2,12 @@
 # -*- coding: utf-8 -*-
 # @Desc   : BasicMemory,AgentMemory实现
 
-from metagpt.memory.memory import Memory
-from metagpt.schema import Message
 import json
 from datetime import datetime
 
+from metagpt.memory.memory import Memory
+from metagpt.schema import Message
+
 
 class BasicMemory(Message):
 
@@ -93,13 +94,13 @@ class AgentMemory(Memory):
     3. kw_strength.json
     """
 
-    def __init__(self, memory_saved: str):
+    def __init__(self):
         """
         AgentMemory类继承自Memory类，重写storage替代GA中id_to_node，一方面存储所有信息，一方面作为JSON转化
         index存储与不同Agent的chat信息
         @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__()
+        super(AgentMemory, self).__init__()
         self.id_to_node = dict()  # TODO jiayi add
         self.storage: list[BasicMemory] = []  # 重写Stroage，存储BasicMemory所有节点
         self.event_list = []  # 存储event记忆
@@ -113,6 +114,10 @@ class AgentMemory(Memory):
         self.kw_strength_event = dict()  # 关键词影响存储
         self.kw_strength_thought = dict()
 
+        # self.load(memory_saved)
+
+    def set_mem_path(self, memory_saved: str):
+        self.memory_saved = memory_saved
         self.load(memory_saved)
 
     def save(self, memory_saved: str):

examples/st_game/memory/retrieve.py

@@ -8,11 +8,12 @@ from typing import Union
 from numpy import dot
 from numpy.linalg import norm
 
-from ..memory.agent_memory import AgentMemory, BasicMemory
-from ..utils.utils import get_embedding
-from ..roles.st_role import STRole
+from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory
+from examples.st_game.utils.utils import get_embedding
 
-def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str, topk: int = 4) -> list[BasicMemory]:
+
+def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str,
+                   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
@@ -46,25 +47,27 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo
 
     result = top_highest_x_values(total_dict, topk)
 
-    return result # 返回的是一个BasicMemory列表
+    return result  # 返回的是一个BasicMemory列表
 
-def new_agent_retrieve(strole: STRole, focus_points: list, n_count = 30):
+
+def new_agent_retrieve(strole: "STRole", focus_points: list, n_count=30):
     """
     输入为Strole，关注点列表,返回记忆数量
     输出为字典，键为focus_point，值为对应的记忆列表
     """
-    retrieved = dict() 
-    for focal_pt in focus_points: 
+    retrieved = dict()
+    for focal_pt in focus_points:
         nodes = [[i.last_accessed, i]
-                    for i in strole._rc.memory.event_list + strole._rc.memory.thought_list
-                    if "idle" not in i.embedding_key]
+                 for i in strole._rc.memory.event_list + strole._rc.memory.thought_list
+                 if "idle" not in i.embedding_key]
         nodes = sorted(nodes, key=lambda x: x[0])
         nodes = [i for created, i in nodes]
-        results =  agent_retrieve(strole._rc.memory, strole._rc.scratch.curr_time, strole._rc.scratch.recency_decay, focal_pt, n_count)
-        for n in results: 
+        results = agent_retrieve(strole._rc.memory, strole._rc.scratch.curr_time, strole._rc.scratch.recency_decay,
+                                 focal_pt, n_count)
+        for n in results:
             n.last_accessed = strole._rc.scratch.curr_time
 
-        retrieved[focal_pt] = results 
+        retrieved[focal_pt] = results
 
 
 def top_highest_x_values(d, x):
@@ -159,4 +162,4 @@ def normalize_score_floats(score_list, target_min, target_max):
         score_list[i]['relevance'] = relevance_list[i]
         score_list[i]['recency'] = recency_list[i]
 
-    return score_list
+    return score_list

examples/st_game/memory/scratch.py

@@ -5,11 +5,11 @@
 import datetime
 import json
 
-from ..utils.utils import check_if_file_exists
+from examples.st_game.utils.utils import check_if_file_exists
 
 
 class Scratch:
-    def __init__(self, f_saved):
+    def __init__(self):
         # 类别1:人物超参
         self.vision_r = 4
         self.att_bandwidth = 3
@@ -77,6 +77,7 @@ class Scratch:
         self.act_path_set = False
         self.planned_path = []
 
+    def set_scratch_path(self, f_saved: str):
         if check_if_file_exists(f_saved):
             # If we have a bootstrap file, load that here.
             scratch_load = json.load(open(f_saved))
@@ -510,4 +511,3 @@ class Scratch:
             minute = curr_min_sum % 60
             ret += f"{hour:02}:{minute:02} || {row[0]}\n"
         return ret
-    

examples/st_game/memory/spatial_memory.py

@@ -10,8 +10,10 @@ import os
 
 
 class MemoryTree:
-    def __init__(self, f_saved: str) -> None:
+    def __init__(self) -> None:
         self.tree = {}
+
+    def set_mem_path(self, f_saved: str):
         if os.path.isfile(f_saved) and os.path.exists(f_saved):
             with open(f_saved) as f:
                 self.tree = json.load(f)

examples/st_game/plan/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

examples/st_game/plan/converse.py

@@ -6,11 +6,11 @@ from typing import Union, Tuple
 
 from metagpt.logs import logger
 
-from ..maze import Maze
-from ..roles.st_role import STRole
-from ..memory.retrieve import new_retrieve
-from ..actions.agent_chat_sum_rel import AgentChatSumRel
-from ..actions.gen_iter_chat_utt import GenIterChatUTT
+from examples.st_game.maze import Maze
+from examples.st_game.roles.st_role import STRole
+from examples.st_game.memory.retrieve import new_agent_retrieve
+from examples.st_game.actions.agent_chat_sum_rel import AgentChatSumRel
+from examples.st_game.actions.gen_iter_chat_utt import GenIterChatUTT
 
 
 def agent_conversation(maze: Maze, init_role: STRole, target_role: STRole) -> list[str]:
@@ -23,7 +23,7 @@ def agent_conversation(maze: Maze, init_role: STRole, target_role: STRole) -> li
         target_scratch = target_role._rc.scratch
 
         focal_points = [f"{target_scratch.name}"]
-        retrieved = new_retrieve(init_role, focal_points, 50)
+        retrieved = new_agent_retrieve(init_role, focal_points, 50)
         relationship = generate_summarize_agent_relationship(init_role, target_role, retrieved)
         print("-------- relationship: ", relationship)
         last_chat = ""
@@ -36,7 +36,7 @@ def agent_conversation(maze: Maze, init_role: STRole, target_role: STRole) -> li
         else:
             focal_points = [f"{relationship}",
                             f"{target_scratch.name} is {target_scratch.act_description}"]
-        retrieved = new_retrieve(init_role, focal_points, 15)
+        retrieved = new_agent_retrieve(init_role, focal_points, 15)
         utt, end = generate_one_utterance(maze, init_role, target_role, retrieved, curr_chat)
 
         curr_chat += [[scratch.name, utt]]
@@ -44,7 +44,7 @@ def agent_conversation(maze: Maze, init_role: STRole, target_role: STRole) -> li
             break
 
         focal_points = [f"{scratch.name}"]
-        retrieved = new_retrieve(target_role, focal_points, 50)
+        retrieved = new_agent_retrieve(target_role, focal_points, 50)
         relationship = generate_summarize_agent_relationship(target_role, init_role, retrieved)
         print("-------- relationship: ", relationship)
         last_chat = ""
@@ -57,7 +57,7 @@ def agent_conversation(maze: Maze, init_role: STRole, target_role: STRole) -> li
         else:
             focal_points = [f"{relationship}",
                             f"{scratch.name} is {scratch.act_description}"]
-        retrieved = new_retrieve(target_role, focal_points, 15)
+        retrieved = new_agent_retrieve(target_role, focal_points, 15)
         utt, end = generate_one_utterance(maze, target_role, init_role, retrieved, curr_chat)
 
         curr_chat += [[target_scratch.name, utt]]

examples/st_game/plan/st_plan.py

@@ -7,12 +7,12 @@ from typing import Union, Tuple
 from datetime import datetime
 import math
 
-from ..maze import Maze
-from ..plan.converse import agent_conversation
-from ..roles.st_role import STRole
-from ..actions.decide_to_talk import DecideToTalk
-from ..actions.summarize_conv import SummarizeConv
-from ..actions.new_decomp_schedule import NewDecompSchedule
+from examples.st_game.maze import Maze
+from examples.st_game.plan.converse import agent_conversation
+from examples.st_game.roles.st_role import STRole
+from examples.st_game.actions.decide_to_talk import DecideToTalk
+from examples.st_game.actions.summarize_conv import SummarizeConv
+from examples.st_game.actions.new_decomp_schedule import NewDecompSchedule
 
 
 def plan(role: STRole, maze: Maze, roles: list[STRole], new_day: bool, retrieved: dict):

examples/st_game/prompts/run_gpt_prompts.py

@@ -2,11 +2,12 @@
 # -*- coding: utf-8 -*-
 # @Desc   : 调用Prompts中模板，实现相关Action
 
-from wrapper_prompt import special_response_generate, prompt_generate
-from memory.scratch import Scratch
-from examples.st_game.memory.agent_memory import BasicMemory
 import json
 
+from examples.st_game.prompts.wrapper_prompt import special_response_generate, prompt_generate
+from examples.st_game.memory.scratch import Scratch
+from examples.st_game.memory.agent_memory import BasicMemory
+
 
 def get_poignancy_action(scratch: Scratch, content: BasicMemory.content) -> str:
     """
@@ -31,7 +32,8 @@ def get_poignancy_action(scratch: Scratch, content: BasicMemory.content) -> str:
         return str(poi_dict['poignancy'])  # 将返回值强制转换为字符串
     except json.JSONDecodeError as e:
         return poignancy
-    
+
+
 def get_poignancy_chat(scratch: Scratch, content: BasicMemory.content) -> str:
     """
     衡量会话心酸度

examples/st_game/reflect/__init__.py

@@ -1,12 +1,3 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # @Desc   : reflection module
-
-from metagpt.reflect import agent_reflect
-from metagpt.reflect import ga_prompt_generator
-__all__ = [
-    "agent_reflect",
-    "LongTermMemory",
-    "ga_po"
-    "ga_prompt_generator"
-]

examples/st_game/reflect/ga_prompt_generator.py

@@ -37,19 +37,19 @@ async def final_response(prompt, special_instruction, example_output=None):
 
 def prompt_generate(curr_input, prompt_lib_file):
     """
-    Takes in the current input (e.g. comment that you want to classifiy) and 
+    Takes in the current input (e.g. comment that you want to classifiy) and
     the path to a prompt file. The prompt file contains the raw str prompt that
-    will be used, which contains the following substr: !<INPUT>! -- this 
-    function replaces this substr with the actual curr_input to produce the 
-    final promopt that will be sent to the GPT3 server. 
+    will be used, which contains the following substr: !<INPUT>! -- this
+    function replaces this substr with the actual curr_input to produce the
+    final promopt that will be sent to the GPT3 server.
     ARGS:
     curr_input: the input we want to feed in (IF THERE ARE MORE THAN ONE
                 INPUT, THIS CAN BE A LIST.)
-    prompt_lib_file: the path to the promopt file. 
-    RETURNS: 
-    a str prompt that will be sent to OpenAI's GPT server.  
+    prompt_lib_file: the path to the promopt file.
+    RETURNS:
+    a str prompt that will be sent to OpenAI's GPT server.
     """
-    if type(curr_input) is type("string"):
+    if isinstance(curr_input, str):
         curr_input = [curr_input]
         curr_input = [str(i) for i in curr_input]
 
@@ -62,23 +62,3 @@ def prompt_generate(curr_input, prompt_lib_file):
         prompt = prompt.split(
             "<commentblockmarker>###</commentblockmarker>")[1]
     return prompt.strip()
-
-# 使用OpenAI embedding库进行存储
-
-
-def embedding(query):
-    """
-    Generates an embedding for the given query.
-
-    Args:
-        query (str): The text query to be embedded.
-
-    Returns:
-        str: The embedding key generated for the query.
-    """
-    embedding_result = openai.Embedding.create(
-        model="text-embedding-ada-002",
-        input=query
-    )
-    embedding_key = embedding_result['data'][0]["embedding"]
-    return embedding_key

examples/st_game/reflect/reflect.py

@@ -1,23 +1,20 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # @Desc   : Reflect function
-import datetime
-import random
-import sys
 
-from numpy import dot
-from numpy.linalg import norm
-from ..roles.st_role import STRole
-from ..utils.utils import get_embedding
+import datetime
+
 from metagpt.logs import logger
-from ..actions.run_reflect_action import (
+
+from examples.st_game.utils.utils import get_embedding
+from examples.st_game.actions.run_reflect_action import (
     AgentFocusPt, AgentInsightAndGuidance, AgentEventTriple,
     AgentEventPoignancy, AgentChatPoignancy, AgentPlanThoughtOnConvo,
     AgentMemoryOnConvo
 )
 
 
-def generate_focal_points(role: STRole, n=3):
+def generate_focal_points(role: "STRole", n=3):
     nodes = [
         [i.last_accessed, i] for i in
         role._rc.memory.event_list + role._rc.memory.thought_list
@@ -65,7 +62,7 @@ def generate_action_event_triple(act_desp, role):
     return AgentEventTriple(act_desp, role)
 
 
-def generate_poig_score(role: STRole, event_type, description):
+def generate_poig_score(role: "STRole", event_type, description):
     if "is idle" in description:
         return 1
 
@@ -89,7 +86,7 @@ def generate_memo_on_convo(role, all_utt):
 
 
 # Done
-def run_reflect(role: STRole):
+def run_reflect(role: "STRole"):
     """
     Run the actual reflection. We generate the focal points, retrieve any
     relevant nodes, and generate thoughts and insights.
@@ -128,7 +125,7 @@ def run_reflect(role: STRole):
 
 
 # Done
-def reflection_trigger(role: STRole):
+def reflection_trigger(role: "STRole"):
     """
     Given the current role, determine whether the role should run a
     reflection.
@@ -155,7 +152,7 @@ def reflection_trigger(role: STRole):
 
 
 # Done
-def reset_reflection_counter(role: STRole):
+def reset_reflection_counter(role: "STRole"):
     """
     We reset the counters used for the reflection trigger.
 
@@ -170,7 +167,7 @@ def reset_reflection_counter(role: STRole):
 
 
 # Question 1 chat函数
-def reflect(role: STRole):
+def reflect(role: "STRole"):
     """
     The main reflection module for the role. We first check if the trigger
     conditions are met, and if so, run the reflection and reset any of the

examples/st_game/reflect/st_reflect.py

@@ -6,8 +6,9 @@ import asyncio
 import json
 import time
 from metagpt.logs import logger
-from ..prompts.wrapper_prompt import special_response_generate
-from ..memory.agent_memory import BasicMemory
+
+from examples.st_game.prompts.wrapper_prompt import special_response_generate
+from examples.st_game.memory.agent_memory import BasicMemory
 
 
 async def agent_reflect(memories_list):
@@ -21,7 +22,7 @@ async def agent_reflect(memories_list):
         B = await generate_insights_and_evidence(memories_list, question=i)
 
 
-async def generate_focus_point(memories_list: list[MemoryBasic], n=3):
+async def generate_focus_point(memories_list: list[BasicMemory], n=3):
     """
     生成关注点函数：根据记忆列表生成关注点
     """
@@ -47,7 +48,7 @@ async def generate_focus_point(memories_list: list[MemoryBasic], n=3):
         return out
 
 
-async def generate_insights_and_evidence(memories_list: list[MemoryBasic], question: str, n=5):
+async def generate_insights_and_evidence(memories_list: list[BasicMemory], question: str, n=5):
     """
     生成洞察和证据函数：根据问题生成洞察和证据
     """
@@ -68,7 +69,7 @@ async def generate_insights_and_evidence(memories_list: list[MemoryBasic], quest
     try:
         insight_list = json.loads(ret)
         for insight, index in insight_list:
-            agent.memory_list.append(MemoryBasic(
+            agent.memory_list.append(BasicMemory(
                 time.time(), None, insight, None, None))
         return insight_list
     except:

examples/st_game/roles/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

examples/st_game/roles/st_role.py

@@ -23,24 +23,24 @@ from metagpt.roles.role import Role, RoleContext
 from metagpt.schema import Message
 from metagpt.logs import logger
 
-from ..memory.agent_memory import AgentMemory, BasicMemory
-from ..memory.spatial_memory import MemoryTree
-from ..actions.dummy_action import DummyAction
-from ..actions.user_requirement import UserRequirement
-from ..maze_environment import MazeEnvironment
-from ..memory.retrieve import new_agent_retrieve
-from ..memory.scratch import Scratch
-from ..utils.utils import get_embedding, generate_poig_score, path_finder
-from ..utils.const import collision_block_id
-from ..reflect.st_reflect import agent_reflect
-from ..utils.mg_ga_transform import save_movement, get_role_environment
+from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory
+from examples.st_game.memory.spatial_memory import MemoryTree
+from examples.st_game.actions.dummy_action import DummyAction, DummyMessage
+from examples.st_game.actions.user_requirement import UserRequirement
+from examples.st_game.maze_environment import MazeEnvironment
+from examples.st_game.memory.retrieve import new_agent_retrieve
+from examples.st_game.memory.scratch import Scratch
+from examples.st_game.utils.utils import get_embedding, path_finder
+from examples.st_game.utils.const import collision_block_id, STORAGE_PATH
+from examples.st_game.reflect.reflect import generate_poig_score
+from examples.st_game.utils.mg_ga_transform import save_movement, get_role_environment
 
 
 class STRoleContext(RoleContext):
-    env: 'MazeEnvironment' = Field(default=MazeEnvironment)
-    memory: AgentMemory = Field(default=AgentMemory)
-    scratch: Scratch = Field(default=Scratch)
-    spatial_memory: MemoryTree = Field(default=MemoryTree)
+    env: 'MazeEnvironment' = Field(default_factory=MazeEnvironment)
+    memory: AgentMemory = Field(default_factory=AgentMemory)
+    scratch: Scratch = Field(default_factory=Scratch)
+    spatial_memory: MemoryTree = Field(default_factory=MemoryTree)
 
 
 class STRole(Role):
@@ -65,9 +65,18 @@ class STRole(Role):
         self.role_tile = (0, 0)
         self.game_obj_cleanup = dict()
 
-        self._rc = STRoleContext()
         super(STRole, self).__init__(name=name,
                                      profile=profile)
+        self._rc = STRoleContext()
+        memory_saved = str(STORAGE_PATH.joinpath(f"{sim_code}/personas/{self.name}/"
+                                                 f"bootstrap_memory/associative_memory"))
+        self._rc.memory.set_mem_path(memory_saved)
+        sp_mem_saved = str(STORAGE_PATH.joinpath(f"{sim_code}/personas/{self.name}/"
+                                                 f"bootstrap_memory/spatial_memory.json"))
+        self._rc.spatial_memory.set_mem_path(f_saved=sp_mem_saved)
+        scratch_f_saved = str(STORAGE_PATH.joinpath(f"{sim_code}/personas/{self.name}/"
+                                                    f"bootstrap_memory/scratch.json"))
+        self._rc.scratch.set_scratch_path(f_saved=scratch_f_saved)
 
         self._init_actions([])
 
@@ -104,6 +113,19 @@ class STRole(Role):
         """
         pass
 
+    async def _observe(self) -> int:
+        if not self._rc.env:
+            return 0
+
+        observed = self._rc.env.memory.get_by_actions(self._rc.watch)
+        self._rc.news = self._rc.memory.remember(observed)
+        if len(self._rc.news) == 1 and isinstance(self._rc.news[0], UserRequirement):
+            # add inner voice
+            # TODO
+            logger.warning(f"Role: {self.name} add inner voice: {self._rc.news[0].content}")
+
+        return 1  # always return 1 to execute role's `_react`
+
     async def observe(self) -> list[BasicMemory]:
         # TODO observe info from maze_env
         """
@@ -247,7 +269,7 @@ class STRole(Role):
 
     async def retrieve(self, focus_points, n=30):
         # TODO retrieve memories from agent_memory
-        retrieve_memories = new_agent_retrieve(self,focus_points,n)
+        retrieve_memories = new_agent_retrieve(self, focus_points, n)
         return retrieve_memories
 
     async def plan(self):
@@ -435,7 +457,7 @@ class STRole(Role):
         ret = self.update_role_env()
         if not ret:
             # TODO add message
-            return
+            return DummyMessage()
 
         # TODO observe
         # get maze_env from self._rc.env, and observe env info
@@ -443,20 +465,23 @@ class STRole(Role):
         # TODO retrieve, use self._rc.memory 's retrieve functions
 
         # TODO plan
-        plan = self.plan()
-
-        # TODO reflect
-
-        # TODO execute(feed-back into maze_env)
-        next_tile, pronunciatio, description = self.execute(plan)
-        role_move = {
-            "movement": next_tile,
-            "pronunciatio": pronunciatio,
-            "description": description,
-            "chat": self.scratch.chat
-        }
-        save_movement(self.name, role_move, step=self.step, sim_code=self.sim_code, curr_time=self.curr_time)
+        # plan = self.plan()
+        #
+        # # TODO reflect
+        #
+        # # TODO execute(feed-back into maze_env)
+        # next_tile, pronunciatio, description = self.execute(plan)
+        # role_move = {
+        #     "movement": next_tile,
+        #     "pronunciatio": pronunciatio,
+        #     "description": description,
+        #     "chat": self.scratch.chat
+        # }
+        # save_movement(self.name, role_move, step=self.step, sim_code=self.sim_code, curr_time=self.curr_time)
 
         # step update
+        logger.info(f"Role: {self.name} run at {self.step} step on {self.curr_time}")
         self.step += 1
         self.curr_time += datetime.timedelta(seconds=self.sec_per_step)
+
+        return DummyMessage()

examples/st_game/run_st_game.py

@@ -9,6 +9,7 @@ from stanford_town import StanfordTown
 from roles.st_role import STRole
 from utils.mg_ga_transform import get_reverie_meta
 from utils.const import STORAGE_PATH
+from utils.utils import copy_folder
 
 
 async def startup(idea: str,
@@ -16,6 +17,8 @@ async def startup(idea: str,
                   sim_code: str,
                   investment: float = 30.0,
                   n_round: int = 500):
+    # copy `storage/{fork_sim_code}` to `storage/{sim_code}`
+    copy_folder(str(STORAGE_PATH.joinpath(fork_sim_code)), str(STORAGE_PATH.joinpath(sim_code)))
 
     # get role names from `storage/{simulation_name}/reverie/meta.json` and then init roles
     reverie_meta = get_reverie_meta(fork_sim_code)

examples/st_game/tests/actions/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

examples/st_game/tests/actions/test_summarize_conv.py

@@ -2,6 +2,10 @@
 # -*- coding: utf-8 -*-
 # @Desc   : unittest of actions/summarize_conv
 
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.dirname(__file__) + "./../../../"))
+
 from metagpt.logs import logger
 
 from st_game.actions.summarize_conv import SummarizeConv

examples/st_game/utils/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

examples/st_game/utils/mg_ga_transform.py

@@ -6,8 +6,8 @@ import json
 
 from metagpt.logs import logger
 
-from .const import STORAGE_PATH
-from .utils import read_json_file, write_json_file
+from examples.st_game.utils.const import STORAGE_PATH
+from examples.st_game.utils.utils import read_json_file, write_json_file
 
 
 def get_reverie_meta(sim_code: str) -> dict:

examples/st_game/utils/utils.py

@@ -2,13 +2,17 @@
 # -*- coding: utf-8 -*-
 # @Desc   : utils
 
-from typing import Any, Union
-import os
-import json
-import openai
-from pathlib import Path
 import csv
-from ..prompts.run_gpt_prompts import get_poignancy_action, get_poignancy_chat
+import errno
+import json
+import os
+import shutil
+from pathlib import Path
+from typing import Any, Union
+
+import openai
+
+from metagpt.logs import logger
 
 
 def read_json_file(json_file: str, encoding=None) -> list[Any]:
@@ -37,23 +41,24 @@ def read_csv_to_list(curr_file: str, header=False, strip_trail=True):
     RETURNS:
       List of list where the component lists are the rows of the file.
     """
+    logger.info(f"start read csv: {curr_file}")
     if not header:
         analysis_list = []
         with open(curr_file) as f_analysis_file:
             data_reader = csv.reader(f_analysis_file, delimiter=",")
-        for count, row in enumerate(data_reader):
-            if strip_trail:
-                row = [i.strip() for i in row]
-            analysis_list += [row]
+            for count, row in enumerate(data_reader):
+                if strip_trail:
+                    row = [i.strip() for i in row]
+                analysis_list += [row]
         return analysis_list
     else:
         analysis_list = []
         with open(curr_file) as f_analysis_file:
             data_reader = csv.reader(f_analysis_file, delimiter=",")
-        for count, row in enumerate(data_reader):
-            if strip_trail:
-                row = [i.strip() for i in row]
-            analysis_list += [row]
+            for count, row in enumerate(data_reader):
+                if strip_trail:
+                    row = [i.strip() for i in row]
+                analysis_list += [row]
         return analysis_list[0], analysis_list[1:]
 
 
@@ -65,15 +70,6 @@ def get_embedding(text, model: str = "text-embedding-ada-002"):
         input=[text], model=model)['data'][0]['embedding']
 
 
-def generate_poig_score(scratch, event_type, description):
-    if "is idle" in description:
-        return 1
-    if event_type == "action":
-        return get_poignancy_action(scratch, description)[0]
-    elif event_type == "chat":
-        return get_poignancy_chat(scratch, description)[0]
-
-
 def extract_first_json_dict(data_str: str) -> Union[None, dict]:
     # Find the first occurrence of a JSON object within the string
     start_idx = data_str.find("{")
@@ -178,6 +174,7 @@ def path_finder(maze: "Maze", start: list[int], end: list[int], collision_block_
 
     return path
 
+
 def create_folder_if_not_there(curr_path):
     """
     Checks if a folder in the curr_path exists. If it does not exist, creates
@@ -207,6 +204,7 @@ def create_folder_if_not_there(curr_path):
 
     return False
 
+
 def find_filenames(path_to_dir, suffix=".csv"):
     """
     Given a directory, find all files that end with the provided suffix and
@@ -219,4 +217,21 @@ def find_filenames(path_to_dir, suffix=".csv"):
     """
     filenames = os.listdir(path_to_dir)
     return [path_to_dir + "/" + filename
-            for filename in filenames if filename.endswith(suffix)]
+            for filename in filenames if filename.endswith(suffix)]
+
+
+def check_if_file_exists(curr_file: str):
+    return Path(curr_file).exists()
+
+
+def copy_folder(src_folder: str, dest_folder: str):
+    try:
+        if Path(dest_folder).exists():
+            logger.warning(f"{dest_folder} exist, start to remove.")
+            shutil.rmtree(dest_folder)
+        shutil.copytree(src_folder, dest_folder)
+    except OSError as exc:  # python >2.5
+        if exc.errno in (errno.ENOTDIR, errno.EINVAL):
+            shutil.copy(src_folder, dest_folder)
+        else:
+            raise