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PEP8格式修改
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didi
parent
e40e3bacc5
commit
da44e12677
3 changed files with 53 additions and 51 deletions
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@ -23,16 +23,14 @@ class AgentFocusPt(STAction):
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except:
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return False
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def _func_cleanup(self, llm_resp: str, prompt: str = "") -> list:
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def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
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try:
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"""
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Cleanup handling has been completed for run_v2
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"""
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return llm_resp
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# llm_resp = "1) " + llm_resp.strip()
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# ret = []
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# for i in llm_resp.split("\n"):
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# ret += [i.split(") ")[-1]]
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# return ret
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except Exception as exp:
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print(f"cleanup 错误 {exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -70,7 +68,6 @@ class AgentInsightAndGuidance(STAction):
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def _func_cleanup(self, llm_resp: str, prompt: str = "") -> dict:
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try:
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logger.info(f"Run prompt Insight get {llm_resp}")
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llm_resp = "1. " + llm_resp.strip()
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ret = dict()
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for i in llm_resp.split("\n"):
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@ -82,7 +79,7 @@ class AgentInsightAndGuidance(STAction):
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ret[thought] = evi_raw
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return ret
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except Exception as exp:
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logger.error(f"AGent Insight 报错{exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -121,10 +118,9 @@ class AgentEventTriple(STAction):
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cr = [i.strip() for i in cr.split(")")[0].split(",")]
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if len(cr) != 2:
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return cr[-2:]
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logger.info(f"cr结果为{cr}")
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return cr
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except Exception as exp:
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logger.error(f"AGent Triple 报错{exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -166,7 +162,8 @@ class AgentEventPoignancy(STAction):
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llm_resp = int(llm_resp.strip())
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return llm_resp
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except Exception as exp:
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logger.error(f"AGent Event poignancy 报错{exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -209,7 +206,8 @@ class AgentChatPoignancy(STAction):
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llm_resp = int(llm_resp.strip())
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return llm_resp
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except Exception as exp:
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logger.error(f"AGent Chat poignancy 报错{exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -251,7 +249,7 @@ class AgentPlanThoughtOnConvo(STAction):
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try:
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return llm_resp.split('"')[0].strip()
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except Exception as exp:
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logger.error(f"AGent PlanThought 报错{exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -290,7 +288,8 @@ class AgentMemoryOnConvo(STAction):
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try:
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return llm_resp.split('"')[0].strip()
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except Exception as exp:
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logger.error(f"AGent MemoryOnconvo 报错{exp}")
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logger.error(f"{self.__class__.__name__} with error {exp}")
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def _func_fail_default_resp(self) -> str:
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pass
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@ -44,7 +44,7 @@ def generate_insights_and_evidence(role, nodes, n=5):
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ret[thought] = evidence_node_id
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return ret
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except Exception as exp:
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logger.info(f"insight处理错误为{exp}")
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logger.error(f"generate_insights_and_evidence error:{exp}")
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return {"this is blank": "node_1"}
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@ -59,7 +59,6 @@ def generate_action_event_triple(act_desp, role):
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EXAMPLE OUTPUT:
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"🧈🍞"
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"""
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logger.info(f"event Triple 输入为：{act_desp}")
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run_event_triple = AgentEventTriple()
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result = run_event_triple.run(act_desp, role)
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return result
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@ -108,16 +107,15 @@ def run_reflect(role: "STRole"):
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# For each of the focal points, generate thoughts and save it in the
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# agent's memory.
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for focal_pt, nodes in retrieved.items():
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logger.info(f"检索结果为{nodes}")
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# xx = [i.embedding_key for i in nodes]
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# for xxx in xx: logger.info(xxx)
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xx = [i.embedding_key for i in nodes]
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for xxx in xx: logger.info(f"Nodes retrieved for {focal_pt} are {xxx}.")
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thoughts = generate_insights_and_evidence(role, nodes, 5)
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# 生成的是字典类型
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for thought, evidence in thoughts.items():
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created = role.scratch.curr_time
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expiration = created + datetime.timedelta(days=30)
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s, p, o = generate_action_event_triple("("+thought+")", role)
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s, p, o = generate_action_event_triple("(" + thought + ")", role)
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keywords = set([s, p, o])
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thought_poignancy = generate_poig_score(role, "thought", thought)
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thought_embedding_pair = (thought, get_embedding(thought))
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@ -126,7 +124,7 @@ def run_reflect(role: "STRole"):
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created, expiration, s, p, o, thought, keywords,
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thought_poignancy, thought_embedding_pair, evidence
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)
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logger.info(f"主谓宾为：{s}{p}{o},描述为{thought}")
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logger.info(f"add thought memory: {thought}")
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def reflection_trigger(role: "STRole"):
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@ -1,60 +1,65 @@
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import pytest
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from examples.st_game.roles.st_role import STRole
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from examples.st_game.actions.run_reflect_action import AgentFocusPt, AgentInsightAndGuidance, AgentEventTriple, AgentEventPoignancy, AgentChatPoignancy, AgentPlanThoughtOnConvo, AgentMemoryOnConvo
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from examples.st_game.actions.run_reflect_action import AgentFocusPt, AgentInsightAndGuidance, AgentEventTriple, \
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AgentEventPoignancy, AgentChatPoignancy, AgentPlanThoughtOnConvo, AgentMemoryOnConvo
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from metagpt.logs import logger
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class TestReflectFunction:
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@pytest.fixture
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def init_agent(self):
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# 创建一个STRole实例，注意从GA中copy过来JSON文件
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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')
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"""
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init STRole form local json, set sim_code(path),curr_time & start_date
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"""
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role = STRole(sim_code="July1_the_ville_isabella_maria_klaus-step-3-11", start_date='February 13, 2023',
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curr_time='February 13, 2023, 14:53:10')
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return role
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def test_function_focus_and_insight_action(self,init_agent):
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def test_function_focus_and_insight_action(self, init_agent):
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"""
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单个Action测试样例
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test for AgentFocusPt & AgentInsightAndGuidance
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"""
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logger.info(f"{__name__}函数启动")
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# run_focus = AgentFocusPt()
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# 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
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# out_put = run_focus.run(init_agent, statements, n=3)
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run_focus = AgentFocusPt()
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statements = ""
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run_focus.run(init_agent, statements, n=3)
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"""
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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
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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]}
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"""
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# run_insight = AgentInsightAndGuidance()
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# 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]"
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# run_insight.run(init_agent, statements, n=5)
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run_insight = AgentInsightAndGuidance()
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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]"
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run_insight.run(init_agent, statements, n=5)
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def test_event_triple_action(self,init_agent):
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def test_event_triple_action(self, init_agent):
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"""
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测试tripleAgent Action
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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
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Prompt同样存在问题，但是我做了处理
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test for AgentEventTriple
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error：遇到复杂情况时（多个Object）无法正确归纳，
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solution: 限制MaxTokens为15
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"""
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run_triple = AgentEventTriple()
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statements = "(Klaus Mueller is academically inclined)"
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run_triple.run(statements,init_agent)
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pass
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run_triple.run(statements, init_agent)
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def test_poignancy_action(self,init_agent):
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def test_poignancy_action(self, init_agent):
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"""
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测试两个关于poignancy的Action
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"""
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pass
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def test_convo_action(self,init_agent):
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"""
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测试两个convo相关的类
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test for AgentEventPoignancy, AgentChatPoignancy
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done in reflect test
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"""
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pass
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def test_convo_action(self, init_agent):
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"""
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test for AgentPlanThoughtOnConvo, AgentMemoryOnConvo
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TODO Undone
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"""
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pass
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# 测试全部Reflection功能
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def test_reflect_function(self, init_agent):
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# 修改 近期 importace 确保Reflect机制能够触发
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"""
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test for reflection
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modify importance_trigger_curr to trigger reflect
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"""
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init_agent.scratch.importance_trigger_curr = -1
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init_agent.reflect()
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