move stanford_town folder

tests/metagpt/ext/__init__.py

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

tests/metagpt/ext/stanford_town/__init__.py

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

tests/metagpt/ext/stanford_town/actions/__init__.py

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

tests/metagpt/ext/stanford_town/actions/test_gen_action_details.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : unittest of actions/gen_action_details.py
+
+import pytest
+
+from metagpt.environment import StanfordTownEnv
+from metagpt.environment.api.env_api import EnvAPIAbstract
+from metagpt.ext.stanford_town.actions.gen_action_details import (
+    GenActionArena,
+    GenActionDetails,
+    GenActionObject,
+    GenActionSector,
+    GenActObjDescription,
+)
+from metagpt.ext.stanford_town.roles.st_role import STRole
+from metagpt.ext.stanford_town.utils.const import MAZE_ASSET_PATH
+
+
+@pytest.mark.asyncio
+async def test_gen_action_details():
+    role = STRole(
+        name="Klaus Mueller",
+        start_time="February 13, 2023",
+        curr_time="February 13, 2023, 00:00:00",
+        sim_code="base_the_ville_isabella_maria_klaus",
+    )
+    role.set_env(StanfordTownEnv(maze_asset_path=MAZE_ASSET_PATH))
+    await role.init_curr_tile()
+
+    act_desp = "sleeping"
+    act_dura = "120"
+
+    access_tile = await role.rc.env.read_from_api(
+        EnvAPIAbstract(api_name="access_tile", kwargs={"tile": role.scratch.curr_tile})
+    )
+    act_world = access_tile["world"]
+    assert act_world == "the Ville"
+
+    sector = await GenActionSector().run(role, access_tile, act_desp)
+    arena = await GenActionArena().run(role, act_desp, act_world, sector)
+    temp_address = f"{act_world}:{sector}:{arena}"
+    print(temp_address)
+    obj = await GenActionObject().run(role, act_desp, temp_address)
+
+    act_obj_desp = await GenActObjDescription().run(role, obj, act_desp)
+
+    result_dict = await GenActionDetails().run(role, act_desp, act_dura)
+
+    # gen_action_sector
+    assert isinstance(sector, str)
+    assert sector in role.s_mem.get_str_accessible_sectors(act_world)
+
+    # gen_action_arena
+    assert isinstance(arena, str)
+    assert arena in role.s_mem.get_str_accessible_sector_arenas(f"{act_world}:{sector}")
+
+    # gen_action_obj
+    assert isinstance(obj, str)
+    assert obj in role.s_mem.get_str_accessible_arena_game_objects(temp_address)
+
+    if result_dict:
+        for key in [
+            "action_address",
+            "action_duration",
+            "action_description",
+            "action_pronunciatio",
+            "action_event",
+            "chatting_with",
+            "chat",
+            "chatting_with_buffer",
+            "chatting_end_time",
+            "act_obj_description",
+            "act_obj_pronunciatio",
+            "act_obj_event",
+        ]:
+            assert key in result_dict
+    assert result_dict["action_address"] == f"{temp_address}:{obj}"
+    assert result_dict["action_duration"] == int(act_dura)
+    assert result_dict["act_obj_description"] == act_obj_desp

tests/metagpt/ext/stanford_town/actions/test_summarize_conv.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : unittest of actions/summarize_conv
+
+import pytest
+
+from metagpt.ext.stanford_town.actions.summarize_conv import SummarizeConv
+
+
+@pytest.mark.asyncio
+async def test_summarize_conv():
+    conv = [("Role_A", "what's the weather today?"), ("Role_B", "It looks pretty good, and I will take a walk then.")]
+
+    output = await SummarizeConv().run(conv)
+    assert "weather" in output

tests/metagpt/ext/stanford_town/memory/__init__.py

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

tests/metagpt/ext/stanford_town/memory/test_agent_memory.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : the unittest of AgentMemory
+
+from datetime import datetime, timedelta
+
+import pytest
+
+from metagpt.ext.stanford_town.memory import AgentMemory
+from metagpt.ext.stanford_town.memory.retrieve import agent_retrieve
+from metagpt.ext.stanford_town.utils.const import STORAGE_PATH
+from metagpt.logs import logger
+
+"""
+memory测试思路
+1. Basic Memory测试
+2. Agent Memory测试
+    2.1 Load & Save方法测试; Load方法中使用了add方法，验证Load即可验证所有add
+    2.2 Get方法测试
+"""
+memory_easy_storage_path = STORAGE_PATH.joinpath(
+    "base_the_ville_isabella_maria_klaus/personas/Isabella Rodriguez/bootstrap_memory/associative_memory",
+)
+memroy_chat_storage_path = STORAGE_PATH.joinpath(
+    "base_the_ville_isabella_maria_klaus/personas/Isabella Rodriguez/bootstrap_memory/associative_memory",
+)
+memory_save_easy_test_path = STORAGE_PATH.joinpath(
+    "base_the_ville_isabella_maria_klaus/personas/Isabella Rodriguez/bootstrap_memory/test_memory",
+)
+memory_save_chat_test_path = STORAGE_PATH.joinpath(
+    "base_the_ville_isabella_maria_klaus/personas/Isabella Rodriguez/bootstrap_memory/test_memory",
+)
+
+
+class TestAgentMemory:
+    @pytest.fixture
+    def agent_memory(self):
+        # 创建一个AgentMemory实例并返回，可以在所有测试用例中共享
+        test_agent_memory = AgentMemory()
+        test_agent_memory.set_mem_path(memroy_chat_storage_path)
+        return test_agent_memory
+
+    def test_load(self, agent_memory):
+        logger.info(f"存储路径为：{agent_memory.memory_saved}")
+        logger.info(f"存储记忆条数为：{len(agent_memory.storage)}")
+        logger.info(f"kw_strength为{agent_memory.kw_strength_event},{agent_memory.kw_strength_thought}")
+        logger.info(f"embeeding.json条数为{len(agent_memory.embeddings)}")
+
+        assert agent_memory.embeddings is not None
+
+    def test_save(self, agent_memory):
+        try:
+            agent_memory.save(memory_save_chat_test_path)
+            logger.info("成功存储")
+        except:
+            pass
+
+    def test_summary_function(self, agent_memory):
+        logger.info(f"event长度为{len(agent_memory.event_list)}")
+        logger.info(f"thought长度为{len(agent_memory.thought_list)}")
+        logger.info(f"chat长度为{len(agent_memory.chat_list)}")
+        result1 = agent_memory.get_summarized_latest_events(4)
+        logger.info(f"总结最近事件结果为:{result1}")
+
+    def test_get_last_chat_function(self, agent_memory):
+        result2 = agent_memory.get_last_chat("customers")
+        logger.info(f"上一次对话是{result2}")
+
+    def test_retrieve_function(self, agent_memory):
+        focus_points = ["who i love?"]
+        retrieved = dict()
+        for focal_pt in focus_points:
+            nodes = [
+                [i.last_accessed, i]
+                for i in agent_memory.event_list + agent_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(agent_memory, datetime.now() - timedelta(days=120), 0.99, focal_pt, nodes, 5)
+            final_result = []
+            for n in results:
+                for i in agent_memory.storage:
+                    if i.memory_id == n:
+                        i.last_accessed = datetime.now() - timedelta(days=120)
+                        final_result.append(i)
+
+            retrieved[focal_pt] = final_result
+        logger.info(f"检索结果为{retrieved}")

tests/metagpt/ext/stanford_town/memory/test_basic_memory.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : the unittest of BasicMemory
+
+from datetime import datetime, timedelta
+
+import pytest
+
+from metagpt.ext.stanford_town.memory.agent_memory import BasicMemory
+from metagpt.logs import logger
+
+"""
+memory测试思路
+1. Basic Memory测试
+2. Agent Memory测试
+    2.1 Load & Save方法测试
+    2.2 Add方法测试
+    2.3 Get方法测试
+"""
+
+# Create some sample BasicMemory instances
+memory1 = BasicMemory(
+    memory_id="1",
+    memory_count=1,
+    type_count=1,
+    memory_type="event",
+    depth=1,
+    created=datetime.now(),
+    expiration=datetime.now() + timedelta(days=30),
+    subject="Subject1",
+    predicate="Predicate1",
+    object="Object1",
+    content="This is content 1",
+    embedding_key="embedding_key_1",
+    poignancy=1,
+    keywords=["keyword1", "keyword2"],
+    filling=["memory_id_2"],
+)
+memory2 = BasicMemory(
+    memory_id="2",
+    memory_count=2,
+    type_count=2,
+    memory_type="thought",
+    depth=2,
+    created=datetime.now(),
+    expiration=datetime.now() + timedelta(days=30),
+    subject="Subject2",
+    predicate="Predicate2",
+    object="Object2",
+    content="This is content 2",
+    embedding_key="embedding_key_2",
+    poignancy=2,
+    keywords=["keyword3", "keyword4"],
+    filling=[],
+)
+
+
+@pytest.fixture
+def basic_mem_set():
+    basic_mem2 = memory2
+    yield basic_mem2
+
+
+def test_basic_mem_function(basic_mem_set):
+    a, b, c = basic_mem_set.summary()
+    logger.info(f"{a}{b}{c}")
+    assert a == "Subject2"
+
+
+def test_basic_mem_save(basic_mem_set):
+    result = basic_mem_set.save_to_dict()
+    logger.info(f"save结果为{result}")
+
+
+if __name__ == "__main__":
+    pytest.main()

tests/metagpt/ext/stanford_town/memory/test_spatial_memory.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : the unittest of MemoryTree
+
+from metagpt.ext.stanford_town.memory.spatial_memory import MemoryTree
+from metagpt.ext.stanford_town.utils.const import STORAGE_PATH
+
+
+def test_spatial_memory():
+    f_path = STORAGE_PATH.joinpath(
+        "base_the_ville_isabella_maria_klaus/personas/Isabella Rodriguez/bootstrap_memory/spatial_memory.json"
+    )
+    x = MemoryTree()
+    x.set_mem_path(f_path)
+    assert x.tree
+    assert "the Ville" in x.tree
+    assert "Isabella Rodriguez's apartment" in x.get_str_accessible_sectors("the Ville")

tests/metagpt/ext/stanford_town/plan/__init__.py

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

tests/metagpt/ext/stanford_town/plan/test_conversation.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : unittest of roles conversation
+
+from typing import Tuple
+
+import pytest
+
+from metagpt.environment import StanfordTownEnv
+from metagpt.ext.stanford_town.plan.converse import agent_conversation
+from metagpt.ext.stanford_town.roles.st_role import STRole
+from metagpt.ext.stanford_town.utils.const import MAZE_ASSET_PATH, STORAGE_PATH
+from metagpt.ext.stanford_town.utils.mg_ga_transform import get_reverie_meta
+from metagpt.ext.stanford_town.utils.utils import copy_folder
+
+
+async def init_two_roles(fork_sim_code: str = "base_the_ville_isabella_maria_klaus") -> Tuple["STRole"]:
+    sim_code = "unittest_sim"
+
+    copy_folder(str(STORAGE_PATH.joinpath(fork_sim_code)), str(STORAGE_PATH.joinpath(sim_code)))
+
+    reverie_meta = get_reverie_meta(fork_sim_code)
+    role_ir_name = "Isabella Rodriguez"
+    role_km_name = "Klaus Mueller"
+
+    env = StanfordTownEnv(maze_asset_path=MAZE_ASSET_PATH)
+
+    role_ir = STRole(
+        name=role_ir_name,
+        sim_code=sim_code,
+        profile=role_ir_name,
+        step=reverie_meta.get("step"),
+        start_time=reverie_meta.get("start_date"),
+        curr_time=reverie_meta.get("curr_time"),
+        sec_per_step=reverie_meta.get("sec_per_step"),
+    )
+    role_ir.set_env(env)
+    await role_ir.init_curr_tile()
+
+    role_km = STRole(
+        name=role_km_name,
+        sim_code=sim_code,
+        profile=role_km_name,
+        step=reverie_meta.get("step"),
+        start_time=reverie_meta.get("start_date"),
+        curr_time=reverie_meta.get("curr_time"),
+        sec_per_step=reverie_meta.get("sec_per_step"),
+    )
+    role_km.set_env(env)
+    await role_km.init_curr_tile()
+
+    return role_ir, role_km
+
+
+@pytest.mark.asyncio
+async def test_agent_conversation():
+    role_ir, role_km = await init_two_roles()
+
+    curr_chat = await agent_conversation(role_ir, role_km, conv_rounds=2)
+    assert len(curr_chat) % 2 == 0
+
+    meet = False
+    for conv in curr_chat:
+        if "Valentine's Day party" in conv[1]:
+            # conv[0] speaker, conv[1] utterance
+            meet = True
+    assert meet

tests/metagpt/ext/stanford_town/plan/test_st_plan.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : unittest of st_plan
+
+import pytest
+
+from metagpt.ext.stanford_town.plan.st_plan import (
+    _choose_retrieved,
+    _should_react,
+    _wait_react,
+)
+from tests.metagpt.ext.stanford_town.plan.test_conversation import init_two_roles
+
+
+def test_should_react():
+    role_ir, role_km = init_two_roles()
+    roles = {role_ir.name: role_ir, role_km.name: role_km}
+
+    observed = role_ir.observe()
+    retrieved = role_ir.retrieve(observed)
+
+    focused_event = _choose_retrieved(role_ir.name, retrieved)
+
+    if focused_event:
+        reaction_mode = _should_react(role_ir, focused_event, roles)  # chat with Isabella Rodriguez
+        assert "chat with" in reaction_mode
+
+
+@pytest.mark.asyncio
+async def test_wait_react():
+    role_ir, role_km = init_two_roles("base_the_ville_isabella_maria_klaus")
+    reaction_mode = "wait: February 13, 2023, 00:01:30"
+    f_daily_schedule = role_ir.scratch.f_daily_schedule
+    # [['sleeping', 360], ['waking up and completing her morning routine (getting out of bed)', 5], ['sleeping', 180]]
+
+    await _wait_react(role_ir, reaction_mode)
+    new_f_daily_schedule = role_ir.scratch.f_daily_schedule
+    # [['sleeping', 360], ['waking up and completing her morning routine (getting out of bed)', 5],
+    # ['waking up and completing her morning routine (brushing her teeth)', 5], ['sleeping', 180]]
+    assert len(f_daily_schedule) == len(new_f_daily_schedule)

tests/metagpt/ext/stanford_town/roles/__init__.py

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

tests/metagpt/ext/stanford_town/roles/test_st_role.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : the unittest of STRole
+
+import pytest
+
+from metagpt.environment import StanfordTownEnv
+from metagpt.ext.stanford_town.memory.agent_memory import BasicMemory
+from metagpt.ext.stanford_town.roles.st_role import STRole
+from metagpt.ext.stanford_town.utils.const import MAZE_ASSET_PATH
+
+
+@pytest.mark.asyncio
+async def test_observe():
+    role = STRole(
+        sim_code="base_the_ville_isabella_maria_klaus",
+        start_time="February 13, 2023",
+        curr_time="February 13, 2023, 00:00:00",
+    )
+    role.set_env(StanfordTownEnv(maze_asset_path=MAZE_ASSET_PATH))
+    await role.init_curr_tile()
+
+    ret_events = await role.observe()
+    assert ret_events
+    for event in ret_events:
+        assert isinstance(event, BasicMemory)

tests/metagpt/ext/stanford_town/test_reflect.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : the unittest of reflection
+
+import pytest
+
+from metagpt.environment import StanfordTownEnv
+from metagpt.ext.stanford_town.actions.run_reflect_action import (
+    AgentEventTriple,
+    AgentFocusPt,
+    AgentInsightAndGuidance,
+)
+from metagpt.ext.stanford_town.roles.st_role import STRole
+from metagpt.ext.stanford_town.utils.const import MAZE_ASSET_PATH
+
+
+@pytest.mark.asyncio
+async def test_reflect():
+    """
+    init STRole form local json, set sim_code(path),curr_time & start_time
+    """
+    role = STRole(
+        sim_code="base_the_ville_isabella_maria_klaus",
+        start_time="February 13, 2023",
+        curr_time="February 13, 2023, 00:00:00",
+    )
+    role.set_env(StanfordTownEnv(maze_asset_path=MAZE_ASSET_PATH))
+    role.init_curr_tile()
+
+    run_focus = AgentFocusPt()
+    statements = ""
+    await run_focus.run(role, statements, n=3)
+
+    """
+    这里有通过测试的结果，但是更多时候LLM生成的结果缺少了because of；考虑修改一下prompt
+    result = {'Klaus Mueller and Maria Lopez have a close relationship because they have been friends for a long time and have a strong bond': [1, 2, 5, 9, 11, 14], 'Klaus Mueller has a crush on Maria Lopez': [8, 15, 24], 'Klaus Mueller is academically inclined and actively researching a topic': [13, 20], 'Klaus Mueller is socially active and acquainted with Isabella Rodriguez': [17, 21, 22], 'Klaus Mueller is organized and prepared': [19]}
+    """
+    run_insight = AgentInsightAndGuidance()
+    statements = "[user: Klaus Mueller has a close relationship with Maria Lopez, user:s Mueller and Maria Lopez have a close relationship, user: Klaus Mueller has a close relationship with Maria Lopez, user: Klaus Mueller has a close relationship with Maria Lopez, user: Klaus Mueller and Maria Lopez have a strong relationship, user: Klaus Mueller is a dormmate of Maria Lopez., user: Klaus Mueller and Maria Lopez have a strong bond, user: Klaus Mueller has a crush on Maria Lopez, user: Klaus Mueller and Maria Lopez have been friends for more than 2 years., user: Klaus Mueller has a close relationship with Maria Lopez, user: Klaus Mueller Maria Lopez is heading off to college., user: Klaus Mueller and Maria Lopez have a close relationship, user: Klaus Mueller is actively researching a topic, user: Klaus Mueller is close friends and classmates with Maria Lopez., user: Klaus Mueller is socially active, user: Klaus Mueller has a crush on Maria Lopez., user: Klaus Mueller and Maria Lopez have been friends for a long time, user: Klaus Mueller is academically inclined, user: For Klaus Mueller's planning: should remember to ask Maria Lopez about her research paper, as she found it interesting that he mentioned it., user: Klaus Mueller is acquainted with Isabella Rodriguez, user: Klaus Mueller is organized and prepared, user: Maria Lopez is conversing about conversing about Maria's research paper mentioned by Klaus, user: Klaus Mueller is conversing about conversing about Maria's research paper mentioned by Klaus, user: Klaus Mueller is a student, user: Klaus Mueller is a student, user: Klaus Mueller is conversing about two friends named Klaus Mueller and Maria Lopez discussing their morning plans and progress on a research paper before Maria heads off to college., user: Klaus Mueller is socially active, user: Klaus Mueller is socially active, user: Klaus Mueller is socially active and acquainted with Isabella Rodriguez, user: Klaus Mueller has a crush on Maria Lopez]"
+    await run_insight.run(role, statements, n=5)
+
+    run_triple = AgentEventTriple()
+    statements = "(Klaus Mueller is academically inclined)"
+    await run_triple.run(statements, role)
+
+    role.scratch.importance_trigger_curr = -1
+    role.reflect()