From ba7897a2ab29848980a201e4a5f8feb964f3dba7 Mon Sep 17 00:00:00 2001
From: didi <2020201387@ruc.edu.cn>
Date: Tue, 3 Oct 2023 23:00:22 +0800
Subject: [PATCH] Retrieve & Reflect bug fix
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
修复了Retrieve与Reflect函数中的bug
---
.../st_game/actions/run_reflect_action.py | 88 +++++++++---------
examples/st_game/actions/st_action.py | 2 +-
examples/st_game/memory/agent_memory.py | 6 ++
examples/st_game/memory/retrieve.py | 23 +++--
.../st_game/reflect/ga_prompt_generator.py | 64 -------------
examples/st_game/reflect/reflect.py | 58 ++++++------
examples/st_game/reflect/st_reflect.py | 92 -------------------
examples/st_game/roles/st_role.py | 21 +++--
.../st_game/tests/actions/test_reflect.py | 0
.../st_game/tests/actions/test_retrieve.py | 0
examples/st_game/tests/test_memory.py | 2 +-
11 files changed, 105 insertions(+), 251 deletions(-)
delete mode 100644 examples/st_game/reflect/ga_prompt_generator.py
delete mode 100644 examples/st_game/reflect/st_reflect.py
delete mode 100644 examples/st_game/tests/actions/test_reflect.py
delete mode 100644 examples/st_game/tests/actions/test_retrieve.py
diff --git a/examples/st_game/actions/run_reflect_action.py b/examples/st_game/actions/run_reflect_action.py
index 923cf68eb..4f53e5647 100644
--- a/examples/st_game/actions/run_reflect_action.py
+++ b/examples/st_game/actions/run_reflect_action.py
@@ -7,6 +7,7 @@ from metagpt.logs import logger
from examples.st_game.actions.st_action import STAction
from examples.st_game.memory.agent_memory import BasicMemory
+from examples.st_game.roles.st_role import STRole
# Run GPT Prompt Focal Point method
@@ -32,8 +33,8 @@ 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):
+ 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
@@ -43,9 +44,9 @@ class AgentFocusPt(STAction):
example_output = '["What should Jane do for lunch", "Does Jane like strawberry", "Who is Jane"]'
special_instruction = "Output must be a list of str."
- output = await self._run_v2(prompt,
- example_output,
- special_instruction)
+ output = self._run_v2(prompt,
+ example_output,
+ special_instruction)
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
return output
@@ -63,7 +64,7 @@ class AgentInsightAndGuidance(STAction):
except:
return False
- def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
+ 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"):
@@ -78,8 +79,8 @@ 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):
+ def run(self, role: STRole, statements: str, n: int, test_input=None) -> dict:
+ def create_prompt_input(role, statements, n, test_input=None):
prompt_input = [statements, str(n)]
return prompt_input
@@ -87,7 +88,7 @@ class AgentInsightAndGuidance(STAction):
prompt = self.generate_prompt_with_tmpl_filename(prompt_input,
"insight_and_evidence_v1.txt")
- output = await self._run_v1(prompt)
+ output = self._run_v1(prompt)
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
return output
@@ -106,7 +107,7 @@ class AgentEventTriple(STAction):
return False
return True
- def _func_cleanup(self, llm_resp: str, prompt: str = "") -> str:
+ 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
@@ -114,23 +115,24 @@ class AgentEventTriple(STAction):
def _func_fail_default_resp(self) -> str:
pass
- async def run(self, statements: str, role: "STRole", verbose=False) -> str:
+ 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]
- prompt_input = [role._rc.scratch.name,
+ prompt_input = [role.scratch.name,
statements,
- role._rc.scratch.name]
+ role.scratch.name]
return prompt_input
prompt_input = create_prompt_input(statements, role)
prompt = self.generate_prompt_with_tmpl_filename(prompt_input,
"generate_event_triple_v1.txt")
- output = await self._run_v1(prompt)
+ output = self._run_v1(prompt)
+ output = (role.scratch.name,output[0],output[1])
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
- return output[0]
+ return output
# Run GPT Prompt Event Poignancy
@@ -152,11 +154,11 @@ 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):
- prompt_input = [role._rc.scratch.name,
- role._rc.scratch.get_str_iss(),
- role._rc.scratch.name,
+ 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.scratch.name,
+ role.scratch.get_str_iss(),
+ role.scratch.name,
statements]
return prompt_input
@@ -166,9 +168,9 @@ class AgentEventPoignancy(STAction):
example_output = "5" # ########
special_instruction = "The output should ONLY contain ONE integer value on the scale of 1 to 10."
- output = await self._run_v2(prompt,
- example_output,
- special_instruction)
+ output = self._run_v2(prompt,
+ example_output,
+ special_instruction)
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
return output
@@ -193,11 +195,11 @@ 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):
- prompt_input = [role._rc.scratch.name,
- role._rc.scratch.get_str_iss(),
- role._rc.scratch.name,
+ 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.scratch.name,
+ role.scratch.get_str_iss(),
+ role.scratch.name,
statements]
return prompt_input
@@ -207,9 +209,9 @@ class AgentChatPoignancy(STAction):
example_output = "5" # ########
special_instruction = "The output should ONLY contain ONE integer value on the scale of 1 to 10."
- output = await self._run_v2(prompt,
- example_output,
- special_instruction)
+ output = self._run_v2(prompt,
+ example_output,
+ special_instruction)
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
return output
@@ -233,19 +235,19 @@ 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:
+ 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,
- role._rc.scratch.name,
- role._rc.scratch.name]
+ role.scratch.name,
+ role.scratch.name,
+ role.scratch.name]
return prompt_input
prompt_input = create_prompt_input(role, statements)
prompt = self.generate_prompt_with_tmpl_filename(prompt_input,
"planning_thought_on_convo_v1.txt")
- output = await self._run_v1(prompt)
+ output = self._run_v1(prompt)
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
return output
@@ -269,12 +271,12 @@ 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:
+ 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,
- role._rc.scratch.name,
- role._rc.scratch.name]
+ role.scratch.name,
+ role.scratch.name,
+ role.scratch.name]
return prompt_input
prompt_input = create_prompt_input(role, statements)
@@ -282,9 +284,9 @@ class AgentMemoryOnConvo(STAction):
"memo_on_convo_v1.txt")
example_output = 'Jane Doe was interesting to talk to.'
special_instruction = 'The output should ONLY contain a string that summarizes anything interesting that the agent may have noticed'
- output = await self._run_v2(prompt,
- example_output,
- special_instruction)
+ output = self._run_v2(prompt,
+ example_output,
+ special_instruction)
logger.info(f"Run action: {self.__class__.__name__} with result: {output}")
return output
diff --git a/examples/st_game/actions/st_action.py b/examples/st_game/actions/st_action.py
index 60f73f36d..e5c048b4e 100644
--- a/examples/st_game/actions/st_action.py
+++ b/examples/st_game/actions/st_action.py
@@ -56,7 +56,7 @@ class STAction(Action):
def _ask(self, prompt: str, system_msgs: Optional[list[str]] = None) -> str:
return self.llm.ask(prompt)
- def _run_v1(self, prompt: str, retry: int = 3) -> str:
+ def _run_v1(self, prompt: str, retry: int = 3) :
"""
same with `gpt_structure.safe_generate_response`
default post-preprocess operations of LLM response
diff --git a/examples/st_game/memory/agent_memory.py b/examples/st_game/memory/agent_memory.py
index 617603195..5c23c9e24 100644
--- a/examples/st_game/memory/agent_memory.py
+++ b/examples/st_game/memory/agent_memory.py
@@ -7,6 +7,7 @@ from datetime import datetime
from metagpt.memory.memory import Memory
from metagpt.schema import Message
+from metagpt.logs import logger
class BasicMemory(Message):
@@ -114,6 +115,9 @@ class AgentMemory(Memory):
self.kw_strength_event = dict() # 关键词影响存储
self.kw_strength_thought = dict()
+ self.memory_saved = None
+ self.embeddings = None
+
# self.load(memory_saved)
def set_mem_path(self, memory_saved: str):
@@ -129,6 +133,7 @@ class AgentMemory(Memory):
memory_json = dict()
for i in range(len(self.storage)):
memory_node = self.storage[i]
+ memory_node = memory_node.save_to_dict()
memory_json.update(memory_node)
with open(memory_saved + "/nodes.json", "w") as outfile:
json.dump(memory_json, outfile)
@@ -253,6 +258,7 @@ class AgentMemory(Memory):
depth_list = [memory_node.depth for memory_node in self.storage if memory_node.memory_id in filling]
depth += max(depth_list)
except Exception as exp:
+ logger.warning(f"filling init occur {exp}")
pass
memory_node = BasicMemory(memory_id, memory_count, type_count, memory_type, depth,
diff --git a/examples/st_game/memory/retrieve.py b/examples/st_game/memory/retrieve.py
index 0656d5c05..55e5b873f 100644
--- a/examples/st_game/memory/retrieve.py
+++ b/examples/st_game/memory/retrieve.py
@@ -7,12 +7,13 @@ import datetime
from numpy import dot
from numpy.linalg import norm
-from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory
+from examples.st_game.memory.agent_memory import BasicMemory
from examples.st_game.utils.utils import get_embedding
+from examples.st_game.roles.st_role import STRole
-def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memory_forget: float, query: str,
- topk: int = 4) -> list[BasicMemory]:
+def agent_retrieve(curr_time: datetime.datetime, memory_forget: float, query: str, nodes: list[BasicMemory],
+ 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
@@ -26,7 +27,7 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo
"relevance": 搜索结果
}
"""
- memories = agent_memory.storage
+ memories = nodes
memories = sorted(memories, key=lambda memory_node: memory_node.last_accessed, reverse=True)
score_list = []
@@ -49,25 +50,27 @@ def agent_retrieve(agent_memory: AgentMemory, curr_time: datetime.datetime, memo
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(role: STRole, focus_points: list, n_count=30) -> dict:
"""
- 输入为Strole，关注点列表,返回记忆数量
+ 输入为role，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:
nodes = [[i.last_accessed, i]
- for i in strole._rc.memory.event_list + strole._rc.memory.thought_list
+ for i in role.memory.event_list + role.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)
+ results = agent_retrieve(role.scratch.curr_time, role.scratch.recency_decay,
+ focal_pt, nodes, n_count)
for n in results:
- n.last_accessed = strole._rc.scratch.curr_time
+ n.last_accessed = role.scratch.curr_time
retrieved[focal_pt] = results
+ return retrieved
+
def top_highest_x_values(d, x):
"""
diff --git a/examples/st_game/reflect/ga_prompt_generator.py b/examples/st_game/reflect/ga_prompt_generator.py
deleted file mode 100644
index 81ed5e7f7..000000000
--- a/examples/st_game/reflect/ga_prompt_generator.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# author: didi
-# Date:9.25
-
-import openai
-from metagpt.llm import DEFAULT_LLM
-# 直接调用Prompt生成
-# ga的prompt构建格式和metagpt完全不同。没有办法融合。
-
-
-# 特殊指令加入Prompt生成
-
-
-async def final_response(prompt, special_instruction, example_output=None):
- """
- 通过将特殊指令加入Prompt生成最终的响应。
-
- 参数：
- - prompt：要生成响应的提示文本。
- - special_instruction：要加入Prompt的特殊指令。
- - example_output（可选）：示例输出的JSON字符串。
-
- 返回：
- 生成的最终响应。
-
- """
- prompt = '"""\n' + prompt + '\n"""\n'
- prompt += f"Output the response to the prompt above in json. {special_instruction}\n"
- if example_output:
- prompt += "Example output json:\n"
- prompt += '{"output": "' + str(example_output) + '"}'
- return await DEFAULT_LLM.aask(prompt)
-
-# prompt填充模板
-
-
-def prompt_generate(curr_input, prompt_lib_file):
- """
- 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: !! -- 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.
- """
- if isinstance(curr_input, str):
- curr_input = [curr_input]
- curr_input = [str(i) for i in curr_input]
-
- f = open(prompt_lib_file, "r")
- prompt = f.read()
- f.close()
- for count, i in enumerate(curr_input):
- prompt = prompt.replace(f"!!", i)
- if "###" in prompt:
- prompt = prompt.split(
- "###")[1]
- return prompt.strip()
diff --git a/examples/st_game/reflect/reflect.py b/examples/st_game/reflect/reflect.py
index 6cb7d86f2..1ff0c16ab 100644
--- a/examples/st_game/reflect/reflect.py
+++ b/examples/st_game/reflect/reflect.py
@@ -17,14 +17,14 @@ from examples.st_game.actions.run_reflect_action import (
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
+ role.memory.event_list + role.memory.thought_list
if "idle" not in i.embedding_key
]
nodes = sorted(nodes, key=lambda x: x[0])
nodes = [i for _, i in nodes]
statements = ""
- for node in nodes[-1 * role._rc.scratch.importance_ele_n:]:
+ for node in nodes[-1 * role.scratch.importance_ele_n:]:
statements += node.embedding_key + "\n"
run_focal_pt = AgentFocusPt()
return run_focal_pt.run(role, statements, n)
@@ -59,7 +59,8 @@ def generate_action_event_triple(act_desp, role):
"🧈🍞"
"""
run_event_triple = AgentEventTriple()
- return AgentEventTriple(act_desp, role)
+ result = run_event_triple.run(act_desp, role)
+ return result
def generate_poig_score(role: STRole, event_type, description):
@@ -72,7 +73,7 @@ def generate_poig_score(role: STRole, event_type, description):
elif event_type == "chat":
run_chat_poignancy = AgentChatPoignancy()
return run_chat_poignancy.run(role,
- role._rc.scratch.act_description)[0]
+ role.scratch.act_description)[0]
def generate_planning_thought_on_convo(role, all_utt):
@@ -114,17 +115,16 @@ def run_reflect(role: "STRole"):
created = role.scratch.curr_time
expiration = created + datetime.timedelta(days=30)
s, p, o = generate_action_event_triple(thought, role)
- keywords = set([s, p, o])
+ keywords = {[s, p, o]}
thought_poignancy = generate_poig_score(role, "thought", thought)
thought_embedding_pair = (thought, get_embedding(thought))
- role._rc.memory.add_thought(
+ role.memory.add_thought(
created, expiration, s, p, o, thought, keywords,
thought_poignancy, thought_embedding_pair, evidence
)
-# Done
def reflection_trigger(role: "STRole"):
"""
Given the current role, determine whether the role should run a
@@ -140,13 +140,13 @@ def reflection_trigger(role: "STRole"):
False otherwise.
"""
logger.info(
- role._rc.scratch.name, "role.scratch.importance_trigger_curr::",
- role._rc.scratch.importance_trigger_curr
+ role.scratch.name, "role.scratch.importance_trigger_curr::",
+ role.scratch.importance_trigger_curr
)
- logger.info(role._rc.scratch.importance_trigger_max)
+ logger.info(role.scratch.importance_trigger_max)
- if (role._rc.scratch.importance_trigger_curr <= 0 and
- [] != role._rc.memory.seq_event + role._rc.memory.seq_thought):
+ if (role.scratch.importance_trigger_curr <= 0 and
+ [] != role.memory.seq_event + role.memory.seq_thought):
return True
return False
@@ -161,12 +161,11 @@ def reset_reflection_counter(role: "STRole"):
Output:
None
"""
- role_imt_max = role._rc.scratch.importance_trigger_max
- role._rc.scratch.importance_trigger_curr = role_imt_max
- role._rc.scratch.importance_ele_n = 0
+ role_imt_max = role.scratch.importance_trigger_max
+ role.scratch.importance_trigger_curr = role_imt_max
+ role.scratch.importance_ele_n = 0
-# Question 1 chat函数
def role_reflect(role: "STRole"):
"""
The main reflection module for the role. We first check if the trigger
@@ -182,42 +181,41 @@ def role_reflect(role: "STRole"):
run_reflect(role)
reset_reflection_counter(role)
- if role._rc.scratch.chatting_end_time:
- if role._rc.scratch.curr_time + datetime.timedelta(0, 10) == role._rc.scratch.chatting_end_time:
+ if role.scratch.chatting_end_time:
+ if role.scratch.curr_time + datetime.timedelta(0, 10) == role.scratch.chatting_end_time:
all_utt = ""
- if role._rc.scratch.chat:
- for row in role._rc.scratch.chat:
+ if role.scratch.chat:
+ for row in role.scratch.chat:
all_utt += f"{row[0]}: {row[1]}\n"
- # Question memory添加对话函数
- evidence = [role._rc.memory.get_last_chat(role._rc.scratch.chatting_with).memory_id]
+ evidence = [role.memory.get_last_chat(role.scratch.chatting_with).memory_id]
planning_thought = generate_planning_thought_on_convo(role, all_utt)
- planning_thought = f"For {role._rc.scratch.name}'s planning: {planning_thought}"
+ planning_thought = f"For {role.scratch.name}'s planning: {planning_thought}"
- created = role._rc.scratch.curr_time
+ created = role.scratch.curr_time
expiration = created + datetime.timedelta(days=30)
s, p, o = generate_action_event_triple(planning_thought, role)
- keywords = set([s, p, o])
+ keywords = {[s, p, o]}
thought_poignancy = generate_poig_score(role, "thought", planning_thought)
thought_embedding_pair = (planning_thought, get_embedding(planning_thought))
- role._rc.memory.add_thought(
+ role.memory.add_thought(
created, expiration, s, p, o, planning_thought, keywords,
thought_poignancy, thought_embedding_pair, evidence
)
memo_thought = generate_memo_on_convo(role, all_utt)
- memo_thought = f"{role._rc.scratch.name} {memo_thought}"
+ memo_thought = f"{role.scratch.name} {memo_thought}"
- created = role._rc.scratch.curr_time
+ created = role.scratch.curr_time
expiration = created + datetime.timedelta(days=30)
s, p, o = generate_action_event_triple(memo_thought, role)
- keywords = set([s, p, o])
+ keywords = {[s, p, o]}
thought_poignancy = generate_poig_score(role, "thought", memo_thought)
thought_embedding_pair = (memo_thought, get_embedding(memo_thought))
- role._rc.memory.add_thought(
+ role.memory.add_thought(
created, expiration, s, p, o, memo_thought, keywords,
thought_poignancy, thought_embedding_pair, evidence
)
diff --git a/examples/st_game/reflect/st_reflect.py b/examples/st_game/reflect/st_reflect.py
deleted file mode 100644
index f4b4c448c..000000000
--- a/examples/st_game/reflect/st_reflect.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Desc : st's reflection execution
-
-import asyncio
-import json
-import time
-from metagpt.logs import logger
-
-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):
- """
- 代理反思函数：生成关注点并生成洞察和证据
-
- """
- A = await generate_focus_point(memories_list)
-
- for i in A:
- B = await generate_insights_and_evidence(memories_list, question=i)
-
-
-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
- """
- wait_sorted_mem = [[i.accessed_time, i] for i in memories_list]
- sorted_memories = sorted(wait_sorted_mem, key=lambda x: x[0])
- memorys = [i for created, i in sorted_memories]
- statements = ''
- for i in memorys:
- statements += i.description + "\n"
- prompt = '''
- {statements}
- Given only the information above, what are {num_question} most salient high-level questions we can answer about the subjects grounded in the statements?
- '''
- example_output = '["What should Jane do for lunch", "Does Jane like strawberry", "Who is Jane"]'
- out = await final_response(prompt.format(statements=statements, num_question=n),
- "Output must be a list of str.", example_output)
- try:
- poi_dict = json.loads(out)
- return poi_dict['output']
- except ValueError:
- print(out)
- logger.error('无法返回正常结果')
- return out
-
-
-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
- """
- memories_list = await agent_retrieve(agent, question, 50, 10)
- statements = ""
- for count, mem in enumerate(memories_list):
- statements += f'{str(count)}. {mem.description}\n'
- prompt = '''
- Input:
- {statements}
-
- What {n} high-level insights can you infer from the above statements?
- You should return a list of list[str,list]. The first element is the insight you have found. The second element is the
- '''
-
- ret = final_response(prompt.format(
- question=question, statements=statements, n=n), "['insightA',[1,2,3]]")
- try:
- insight_list = json.loads(ret)
- for insight, index in insight_list:
- agent.memory_list.append(BasicMemory(
- time.time(), None, insight, None, None))
- return insight_list
- except:
- logger.error('我们无法获得想要的返回。')
- return ret
-
-
-""" if __name__ == "__main__":
- # 例子，构建John Agent，实现retrive
- John_iss = "John Lin is a pharmacy shopkeeper at the Willow Market and Pharmacy who loves to help people. He is always looking for ways to make the process of getting medication easier for his customers; John Lin is living with his wife, Mei Lin, who is a college professor, and son, Eddy Lin, who is a student studying music theory; John Lin loves his family very much; John Lin has known the old couple next-door, Sam Moore and Jennifer Moore, for a few years; John Lin thinks Sam Moore is a kind and nice man; John Lin knows his neighbor, Yuriko Yamamoto, well; John Lin knows of his neighbors, Tamara Taylor and Carmen Ortiz, but has not met them before; John Lin and Tom Moreno are colleagues at The Willows Market and Pharmacy; John Lin and Tom Moreno are friends and like to discuss local politics together; John Lin knows the Moreno family somewhat well — the husband Tom Moreno and the wife Jane Moreno."
- John = AgentMemory(
- "John", John_iss, memory_path="agent_memories/John_memory.json")
-
- # John的相关信息：{'Had a friendly chat with Yuriko about her garden.': 2.4992317730827667, 'Helped Mrs. Moore carry groceries into her house.': 1.957656720441911, 'Discussed local politics with Tom Moreno.': 1.9458268038234035}
- asyncio.run(agent_reflect(John))
- '''
- 这里是输出,list形式，返回给记忆。
- [['The pharmacy is a friendly and helpful community.', [0, 2, 9, 12]], ['The pharmacy is a place where people come for more than just medication.', [3, 5, 13, 14]], ['The pharmacy is a place where people come for advice and conversation.', [0, 2, 6, 9, 12]], ['The pharmacy is a place where people come for assistance with daily tasks.', [3, 5, 13, 14]], ['The pharmacy is a place where people come for political discussions.', [1]]]
- '''
- """
diff --git a/examples/st_game/roles/st_role.py b/examples/st_game/roles/st_role.py
index 1ca6db09d..ad53dc4c7 100644
--- a/examples/st_game/roles/st_role.py
+++ b/examples/st_game/roles/st_role.py
@@ -104,6 +104,10 @@ class STRole(Role):
def scratch(self):
return self._rc.scratch
+ @property
+ def memory(self):
+ return self._rc.memory
+
def load_from(self, folder: Path):
"""
load role data from `storage/{simulation_name}/personas/{role_name}
@@ -123,8 +127,6 @@ class STRole(Role):
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 self._rc.news[0].cause_by == UserRequirement:
- # add inner voice
- # TODO
self.add_inner_voice(self._rc.news[0].content)
logger.warning(f"Role: {self.name} add inner voice: {self._rc.news[0].content}")
@@ -132,25 +134,24 @@ class STRole(Role):
def add_inner_voice(self, whisper):
# TODO
- def generate_inner_thought(strole: STRole, whisper):
+ def generate_inner_thought(role: STRole, whisper):
run_whisper_thought = AgentWhisperThoughtAction()
inner_thought = run_whisper_thought.run(self, whisper)
return inner_thought
-
+
whisper = input("Enter Input: ")
thought = generate_inner_thought(whisper)
created = self._rc.scratch.curr_time
expiration = self._rc.scratch.curr_time + datetime.timedelta(days=30)
run_event_triple = AgentEventTriple()
- s, p, o = run_event_triple(thought, self)
+ s, p, o = run_event_triple.run(thought, self)
keywords = set([s, p, o])
thought_poignancy = generate_poig_score(self, "event", whisper)
thought_embedding_pair = (thought, get_embedding(thought))
- self._rc.memory.add_thought(created, expiration, s, p, o,
- thought, keywords, thought_poignancy,
- thought_embedding_pair, None)
-
+ self._rc.memory.add_thought(created, expiration, s, p, o,
+ thought, keywords, thought_poignancy,
+ thought_embedding_pair, None)
async def observe(self) -> list[BasicMemory]:
# TODO observe info from maze_env
@@ -293,7 +294,7 @@ class STRole(Role):
return ret_events
- async def retrieve(self, focus_points, n=30):
+ def retrieve(self, focus_points, n=30) -> dict:
# TODO retrieve memories from agent_memory
retrieve_memories = new_agent_retrieve(self, focus_points, n)
return retrieve_memories
diff --git a/examples/st_game/tests/actions/test_reflect.py b/examples/st_game/tests/actions/test_reflect.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/examples/st_game/tests/actions/test_retrieve.py b/examples/st_game/tests/actions/test_retrieve.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/examples/st_game/tests/test_memory.py b/examples/st_game/tests/test_memory.py
index 9c0354b36..451addc8b 100644
--- a/examples/st_game/tests/test_memory.py
+++ b/examples/st_game/tests/test_memory.py
@@ -1,6 +1,6 @@
from datetime import datetime
from metagpt.logs import logger
-from ..memory.agent_memory import AgentMemory, BasicMemory
+from examples.st_game.memory.agent_memory import AgentMemory, BasicMemory
# Create some sample BasicMemory instances
memory1 = BasicMemory(