diff --git a/examples/st_game/actions/run_reflect_action.py b/examples/st_game/actions/run_reflect_action.py index 10e22dbcf..d29b654f8 100644 --- a/examples/st_game/actions/run_reflect_action.py +++ b/examples/st_game/actions/run_reflect_action.py @@ -23,16 +23,14 @@ class AgentFocusPt(STAction): except: return False - def _func_cleanup(self, llm_resp: str, prompt: str = "") -> list: + def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str: try: + """ + Cleanup handling has been completed for run_v2 + """ return llm_resp - # llm_resp = "1) " + llm_resp.strip() - # ret = [] - # for i in llm_resp.split("\n"): - # ret += [i.split(") ")[-1]] - # return ret except Exception as exp: - print(f"cleanup 错误 {exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") def _func_fail_default_resp(self) -> str: pass @@ -70,7 +68,6 @@ class AgentInsightAndGuidance(STAction): def _func_cleanup(self, llm_resp: str, prompt: str = "") -> dict: try: - logger.info(f"Run prompt Insight get {llm_resp}") llm_resp = "1. " + llm_resp.strip() ret = dict() for i in llm_resp.split("\n"): @@ -82,7 +79,7 @@ class AgentInsightAndGuidance(STAction): ret[thought] = evi_raw return ret except Exception as exp: - logger.error(f"AGent Insight 报错{exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") def _func_fail_default_resp(self) -> str: pass @@ -121,10 +118,9 @@ class AgentEventTriple(STAction): cr = [i.strip() for i in cr.split(")")[0].split(",")] if len(cr) != 2: return cr[-2:] - logger.info(f"cr结果为{cr}") return cr except Exception as exp: - logger.error(f"AGent Triple 报错{exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") def _func_fail_default_resp(self) -> str: pass @@ -166,7 +162,8 @@ class AgentEventPoignancy(STAction): llm_resp = int(llm_resp.strip()) return llm_resp except Exception as exp: - logger.error(f"AGent Event poignancy 报错{exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") + def _func_fail_default_resp(self) -> str: pass @@ -209,7 +206,8 @@ class AgentChatPoignancy(STAction): llm_resp = int(llm_resp.strip()) return llm_resp except Exception as exp: - logger.error(f"AGent Chat poignancy 报错{exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") + def _func_fail_default_resp(self) -> str: pass @@ -251,7 +249,7 @@ class AgentPlanThoughtOnConvo(STAction): try: return llm_resp.split('"')[0].strip() except Exception as exp: - logger.error(f"AGent PlanThought 报错{exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") def _func_fail_default_resp(self) -> str: pass @@ -290,7 +288,8 @@ class AgentMemoryOnConvo(STAction): try: return llm_resp.split('"')[0].strip() except Exception as exp: - logger.error(f"AGent MemoryOnconvo 报错{exp}") + logger.error(f"{self.__class__.__name__} with error {exp}") + def _func_fail_default_resp(self) -> str: pass diff --git a/examples/st_game/reflect/reflect.py b/examples/st_game/reflect/reflect.py index 5423e52bf..62f835e1a 100644 --- a/examples/st_game/reflect/reflect.py +++ b/examples/st_game/reflect/reflect.py @@ -44,7 +44,7 @@ def generate_insights_and_evidence(role, nodes, n=5): ret[thought] = evidence_node_id return ret except Exception as exp: - logger.info(f"insight处理错误为{exp}") + logger.error(f"generate_insights_and_evidence error:{exp}") return {"this is blank": "node_1"} @@ -59,7 +59,6 @@ def generate_action_event_triple(act_desp, role): EXAMPLE OUTPUT: "🧈🍞" """ - logger.info(f"event Triple 输入为：{act_desp}") run_event_triple = AgentEventTriple() result = run_event_triple.run(act_desp, role) return result @@ -108,16 +107,15 @@ def run_reflect(role: "STRole"): # For each of the focal points, generate thoughts and save it in the # agent's memory. for focal_pt, nodes in retrieved.items(): - logger.info(f"检索结果为{nodes}") - # xx = [i.embedding_key for i in nodes] - # for xxx in xx: logger.info(xxx) + xx = [i.embedding_key for i in nodes] + for xxx in xx: logger.info(f"Nodes retrieved for {focal_pt} are {xxx}.") thoughts = generate_insights_and_evidence(role, nodes, 5) # 生成的是字典类型 for thought, evidence in thoughts.items(): created = role.scratch.curr_time expiration = created + datetime.timedelta(days=30) - s, p, o = generate_action_event_triple("("+thought+")", role) + s, p, o = generate_action_event_triple("(" + thought + ")", role) keywords = set([s, p, o]) thought_poignancy = generate_poig_score(role, "thought", thought) thought_embedding_pair = (thought, get_embedding(thought)) @@ -126,7 +124,7 @@ def run_reflect(role: "STRole"): created, expiration, s, p, o, thought, keywords, thought_poignancy, thought_embedding_pair, evidence ) - logger.info(f"主谓宾为：{s}{p}{o},描述为{thought}") + logger.info(f"add thought memory: {thought}") def reflection_trigger(role: "STRole"): diff --git a/examples/st_game/tests/test_reflect.py b/examples/st_game/tests/test_reflect.py index 1593d9aaa..191e46279 100644 --- a/examples/st_game/tests/test_reflect.py +++ b/examples/st_game/tests/test_reflect.py @@ -1,60 +1,65 @@ - import pytest from examples.st_game.roles.st_role import STRole -from examples.st_game.actions.run_reflect_action import AgentFocusPt, AgentInsightAndGuidance, AgentEventTriple, AgentEventPoignancy, AgentChatPoignancy, AgentPlanThoughtOnConvo, AgentMemoryOnConvo +from examples.st_game.actions.run_reflect_action import AgentFocusPt, AgentInsightAndGuidance, AgentEventTriple, \ + AgentEventPoignancy, AgentChatPoignancy, AgentPlanThoughtOnConvo, AgentMemoryOnConvo from metagpt.logs import logger class TestReflectFunction: @pytest.fixture def init_agent(self): - # 创建一个STRole实例，注意从GA中copy过来JSON文件 - role = STRole(sim_code="July1_the_ville_isabella_maria_klaus-step-3-11", start_date='February 13, 2023', curr_time='February 13, 2023, 14:53:10') + """ + init STRole form local json, set sim_code(path),curr_time & start_date + """ + role = STRole(sim_code="July1_the_ville_isabella_maria_klaus-step-3-11", start_date='February 13, 2023', + curr_time='February 13, 2023, 14:53:10') return role - def test_function_focus_and_insight_action(self,init_agent): + def test_function_focus_and_insight_action(self, init_agent): """ - 单个Action测试样例 + test for AgentFocusPt & AgentInsightAndGuidance """ logger.info(f"{__name__}函数启动") - # run_focus = AgentFocusPt() - # statements = "" # 这个statements 与 n 设置是遵循reflect里面实际设置# 来的，你写的时候可以对应代码看一下 - # out_put = run_focus.run(init_agent, statements, n=3) + run_focus = AgentFocusPt() + statements = "" + run_focus.run(init_agent, 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]" - # run_insight.run(init_agent, statements, n=5) + 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]" + run_insight.run(init_agent, statements, n=5) - def test_event_triple_action(self,init_agent): + def test_event_triple_action(self, init_agent): """ - 测试tripleAgent Action - 我们需要限制生成字数在15之内，生成字数没有限制的时候很容易跑通 - Prompt同样存在问题，但是我做了处理 + test for AgentEventTriple + error：遇到复杂情况时（多个Object）无法正确归纳， + solution: 限制MaxTokens为15 """ run_triple = AgentEventTriple() statements = "(Klaus Mueller is academically inclined)" - run_triple.run(statements,init_agent) - pass + run_triple.run(statements, init_agent) - def test_poignancy_action(self,init_agent): + def test_poignancy_action(self, init_agent): """ - 测试两个关于poignancy的Action - """ - - pass - - def test_convo_action(self,init_agent): - """ - 测试两个convo相关的类 + test for AgentEventPoignancy, AgentChatPoignancy + done in reflect test + """ + pass + + def test_convo_action(self, init_agent): + """ + test for AgentPlanThoughtOnConvo, AgentMemoryOnConvo + TODO Undone """ pass - # 测试全部Reflection功能 def test_reflect_function(self, init_agent): - # 修改 近期 importace 确保Reflect机制能够触发 + """ + test for reflection + modify importance_trigger_curr to trigger reflect + """ init_agent.scratch.importance_trigger_curr = -1 init_agent.reflect()