apunkt/MetaGPT
1
0
Fork 0
mirror of https://github.com/FoundationAgents/MetaGPT.git synced 2026-06-17 15:35:21 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Reflect测试第一版本

Browse source 
1. 能通过
2. insight不稳定
This commit is contained in:
didi 2023-10-06 01:44:45 +08:00
parent 1ec2b922c1
commit e40e3bacc5
4 changed files with 74 additions and 43 deletions
Download patch file Download diff file Expand all files Collapse all files

63
examples/st_game/actions/run_reflect_action.py
View file

@ -69,16 +69,20 @@ class AgentInsightAndGuidance(STAction):
return False
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> dict:
llm_resp = "1. " + llm_resp.strip()
ret = dict()
for i in llm_resp.split("\n"):
row = i.split(". ")[-1]
thought = row.split("(because of ")[0].strip()
evi_raw = row.split("(because of ")[1].split(")")[0].strip()
evi_raw = re.findall(r'\d+', evi_raw)
evi_raw = [int(i.strip()) for i in evi_raw]
ret[thought] = evi_raw
return ret
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"):
row = i.split(". ")[-1]
thought = row.split("(because of ")[0].strip()
evi_raw = row.split("(because of ")[1].split(")")[0].strip()
evi_raw = re.findall(r'\d+', evi_raw)
evi_raw = [int(i.strip()) for i in evi_raw]
ret[thought] = evi_raw
return ret
except Exception as exp:
logger.error(f"AGent Insight 报错{exp}")
def _func_fail_default_resp(self) -> str:
pass
@ -112,9 +116,15 @@ class AgentEventTriple(STAction):
return True
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> list:
cr = llm_resp.strip()
cr = [i.strip() for i in cr.split(")")[0].split(",")]
return cr
try:
cr = llm_resp.strip()
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}")
def _func_fail_default_resp(self) -> str:
pass
@ -152,9 +162,11 @@ class AgentEventPoignancy(STAction):
return False
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> int:
llm_resp = int(llm_resp.strip())
return llm_resp
try:
llm_resp = int(llm_resp.strip())
return llm_resp
except Exception as exp:
logger.error(f"AGent Event poignancy 报错{exp}")
def _func_fail_default_resp(self) -> str:
pass
@ -193,9 +205,11 @@ class AgentChatPoignancy(STAction):
return False
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> int:
llm_resp = int(llm_resp.strip())
return llm_resp
try:
llm_resp = int(llm_resp.strip())
return llm_resp
except Exception as exp:
logger.error(f"AGent Chat poignancy 报错{exp}")
def _func_fail_default_resp(self) -> str:
pass
@ -234,7 +248,10 @@ class AgentPlanThoughtOnConvo(STAction):
return False
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
return llm_resp.split('"')[0].strip()
try:
return llm_resp.split('"')[0].strip()
except Exception as exp:
logger.error(f"AGent PlanThought 报错{exp}")
def _func_fail_default_resp(self) -> str:
pass
@ -270,8 +287,10 @@ class AgentMemoryOnConvo(STAction):
return False
def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
return llm_resp.split('"')[0].strip()
try:
return llm_resp.split('"')[0].strip()
except Exception as exp:
logger.error(f"AGent MemoryOnconvo 报错{exp}")
def _func_fail_default_resp(self) -> str:
pass

2
examples/st_game/prompts/insight_and_evidence_v1.txt
View file

@ -8,5 +8,5 @@ Variables:
Input:
!<INPUT 0>!
What !<INPUT 1>! high-level insights can you infer from the above statements? (example format: insight (because of 1, 5, 3))
What !<INPUT 1>! high-level insights can you infer from the above statements? Please ensure it includes 'because of' and generates according to the example format.(example format: insight (because of 1, 5, 3)) .
1.

27
examples/st_game/reflect/reflect.py
View file

@ -37,12 +37,14 @@ def generate_insights_and_evidence(role, nodes, n=5):
ret = run_insight_and_guidance.run(role, statements, n)
logger.info(ret)
try:
for thought, evi_raw in ret.items():
evidence_node_id = [nodes[i].node_id for i in evi_raw]
evidence_node_id = [nodes[i].memory_id for i in evi_raw]
ret[thought] = evidence_node_id
return ret
except:
except Exception as exp:
logger.info(f"insight处理错误为{exp}")
return {"this is blank": "node_1"}
@ -57,6 +59,7 @@ 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
@ -68,11 +71,11 @@ def generate_poig_score(role: "STRole", event_type, description):
if event_type == "event" or event_type == "thought":
run_event_poignancy = AgentEventPoignancy()
return run_event_poignancy.run(role, description)[0]
return run_event_poignancy.run(role, description)
elif event_type == "chat":
run_chat_poignancy = AgentChatPoignancy()
return run_chat_poignancy.run(role,
role.scratch.act_description)[0]
role.scratch.act_description)
def generate_planning_thought_on_convo(role, all_utt):
@ -98,24 +101,24 @@ def run_reflect(role: "STRole"):
"""
# Reflection requires certain focal points. Generate that first.
focal_points = generate_focal_points(role, 3)
# Retrieve the relevant Nodes object for each of the focal points.
# Retrieve the relevant Nodesobject for each of the focal points.
# <retrieved> has keys of focal points, and values of the associated Nodes.
retrieved = role.retrieve(focal_points)
# For each of the focal points, generate thoughts and save it in the
# agent's memory.
for focal_pt, nodes in retrieved.items():
xx = [i.embedding_key for i in nodes]
for xxx in xx: logger.info(xxx)
logger.info(f"检索结果为{nodes}")
# xx = [i.embedding_key for i in nodes]
# for xxx in xx: logger.info(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)
keywords = {[s, p, o]}
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))
@ -193,7 +196,7 @@ def role_reflect(role: "STRole"):
created = role.scratch.curr_time
expiration = created + datetime.timedelta(days=30)
s, p, o = generate_action_event_triple(planning_thought, role)
keywords = {[s, p, o]}
keywords = set([s, p, o])
thought_poignancy = generate_poig_score(role, "thought", planning_thought)
thought_embedding_pair = (planning_thought, get_embedding(planning_thought))
@ -208,7 +211,7 @@ def role_reflect(role: "STRole"):
created = role.scratch.curr_time
expiration = created + datetime.timedelta(days=30)
s, p, o = generate_action_event_triple(memo_thought, role)
keywords = {[s, p, o]}
keywords = set([s, p, o])
thought_poignancy = generate_poig_score(role, "thought", memo_thought)
thought_embedding_pair = (memo_thought, get_embedding(memo_thought))

25
examples/st_game/tests/test_reflect.py
View file

@ -10,32 +10,41 @@ class TestReflectFunction:
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')
logger.info(role.scratch.name)
logger.info(f"记忆长度为{len(role.memory.storage)}")
return role
def test_function_focus_and_insight_action(self,init_agent):
"""
单个Action测试样例
"""
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
run_focus.run(init_agent, statements, n=3)
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_insight = AgentInsightAndGuidance()
# 这里主要需要查看，Looger.info(filling)
# 完善代码
"""
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)
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
"""
run_triple = AgentEventTriple()
statements = "(Klaus Mueller is academically inclined)"
run_triple.run(statements,init_agent)
pass
def test_poignancy_action(self,init_agent):
"""
测试两个关于poignancy的Action
"""
pass
def test_convo_action(self,init_agent):