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大修_execute_request炸程序的问题

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UnCLAS-Prommer
2025-04-09 13:52:12 +08:00
parent a42d1b3664
commit e35c2bb9b4
9 changed files with 93 additions and 46 deletions
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31
src/heart_flow/heartflow.py
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@@ -18,7 +18,7 @@ heartflow_config = LogConfig(
logger = get_module_logger("heartflow", config=heartflow_config)
class CuttentState:
class CurrentState:
def __init__(self):
self.willing = 0
self.current_state_info = ""
@@ -34,7 +34,7 @@ class Heartflow:
def __init__(self):
self.current_mind = "你什么也没想"
self.past_mind = []
self.current_state: CuttentState = CuttentState()
self.current_state: CurrentState = CurrentState()
self.llm_model = LLM_request(
model=global_config.llm_heartflow, temperature=0.6, max_tokens=1000, request_type="heart_flow"
)
@@ -102,7 +102,11 @@ class Heartflow:
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
related_memory_info = "memory"
try:
sub_flows_info = await self.get_all_subheartflows_minds()
except Exception as e:
logger.error(f"获取子心流的想法失败: {e}")
return
schedule_info = bot_schedule.get_current_num_task(num=4, time_info=True)
@@ -111,26 +115,29 @@ class Heartflow:
prompt += f"{personality_info}\n"
prompt += f"你想起来{related_memory_info}。"
prompt += f"刚刚你的主要想法是{current_thinking_info}。"
prompt += f"你还有一些小想法，因为你在参加不同的群聊天，是你正在做的事情：{sub_flows_info}\n"
prompt += f"你还有一些小想法，因为你在参加不同的群聊天，这是你正在做的事情：{sub_flows_info}\n"
prompt += f"你现在{mood_info}。"
prompt += "现在你接下去继续思考，产生新的想法，但是要基于原有的主要想法，不要分点输出，"
prompt += "输出连贯的内心独白，不要太长，但是记得结合上述的消息，关注新内容:"
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"内心独白获取失败: {e}")
return
self.update_current_mind(response)
self.update_current_mind(reponse)
self.current_mind = reponse
self.current_mind = response
logger.info(f"麦麦的总体脑内状态：{self.current_mind}")
# logger.info("麦麦想了想，当前活动:")
# await bot_schedule.move_doing(self.current_mind)
for _, subheartflow in self._subheartflows.items():
subheartflow.main_heartflow_info = reponse
subheartflow.main_heartflow_info = response
def update_current_mind(self, reponse):
def update_current_mind(self, response):
self.past_mind.append(self.current_mind)
self.current_mind = reponse
self.current_mind = response
async def get_all_subheartflows_minds(self):
sub_minds = ""
@@ -167,9 +174,9 @@ class Heartflow:
prompt += """现在请你总结这些聊天内容，注意关注聊天内容对原有的想法的影响，输出连贯的内心独白
不要太长，但是记得结合上述的消息，要记得你的人设，关注新内容:"""
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
return reponse
return response
def create_subheartflow(self, subheartflow_id):
"""

4
src/heart_flow/observation.py
View File

@@ -142,7 +142,11 @@ class ChattingObservation(Observation):
prompt += """以上是群里在进行的聊天，请你对这个聊天内容进行总结，总结内容要包含聊天的大致内容，
以及聊天中的一些重要信息，注意识别你自己的发言，记得不要分点，不要太长，精简的概括成一段文本\n"""
prompt += "总结概括："
try:
self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
except Exception as e:
print(f"获取总结失败: {e}")
self.observe_info = ""
print(f"prompt：{prompt}")
print(f"self.observe_info：{self.observe_info}")

42
src/heart_flow/sub_heartflow.py
View File

@@ -22,7 +22,7 @@ subheartflow_config = LogConfig(
logger = get_module_logger("subheartflow", config=subheartflow_config)
class CuttentState:
class CurrentState:
def __init__(self):
self.willing = 0
self.current_state_info = ""
@@ -40,7 +40,7 @@ class SubHeartflow:
self.current_mind = ""
self.past_mind = []
self.current_state: CuttentState = CuttentState()
self.current_state: CurrentState = CurrentState()
self.llm_model = LLM_request(
model=global_config.llm_sub_heartflow, temperature=0.7, max_tokens=600, request_type="sub_heart_flow"
)
@@ -143,11 +143,11 @@ class SubHeartflow:
# prompt += f"你现在{mood_info}\n"
# prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
# prompt += "但是记得结合上述的消息，要记得维持住你的人设，关注聊天和新内容，不要思考太多:"
# reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
# response, reasoning_content = await self.llm_model.generate_response_async(prompt)
# self.update_current_mind(reponse)
# self.update_current_mind(response)
# self.current_mind = reponse
# self.current_mind = response
# logger.debug(f"prompt:\n{prompt}\n")
# logger.info(f"麦麦的脑内状态：{self.current_mind}")
@@ -217,11 +217,14 @@ class SubHeartflow:
prompt += f"你注意到有人刚刚说：{message_txt}\n"
prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，"
prompt += "记得结合上述的消息，要记得维持住你的人设，注意自己的名字，关注有人刚刚说的内容，不要思考太多:"
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"回复前内心独白获取失败: {e}")
response = ""
self.update_current_mind(response)
self.update_current_mind(reponse)
self.current_mind = reponse
self.current_mind = response
logger.debug(f"prompt:\n{prompt}\n")
logger.info(f"麦麦的思考前脑内状态：{self.current_mind}")
@@ -264,12 +267,14 @@ class SubHeartflow:
prompt += f"你现在{mood_info}"
prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白"
prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:"
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
except Exception as e:
logger.error(f"回复后内心独白获取失败: {e}")
response = ""
self.update_current_mind(response)
reponse, reasoning_content = await self.llm_model.generate_response_async(prompt)
self.update_current_mind(reponse)
self.current_mind = reponse
self.current_mind = response
logger.info(f"麦麦回复后的脑内状态：{self.current_mind}")
self.last_reply_time = time.time()
@@ -302,10 +307,13 @@ class SubHeartflow:
prompt += f"你现在{mood_info}。"
prompt += "现在请你思考，你想不想发言或者回复，请你输出一个数字，1-10，1表示非常不想，10表示非常想。"
prompt += "请你用<>包裹你的回复意愿，输出<1>表示不想回复，输出<10>表示非常想回复。请你考虑，你完全可以不回复"
try:
response, reasoning_content = await self.llm_model.generate_response_async(prompt)
# 解析willing值
willing_match = re.search(r"<(\d+)>", response)
except Exception as e:
logger.error(f"意愿判断获取失败: {e}")
willing_match = None
if willing_match:
self.current_state.willing = int(willing_match.group(1))
else:
@@ -313,9 +321,9 @@ class SubHeartflow:
return self.current_state.willing
def update_current_mind(self, reponse):
def update_current_mind(self, response):
self.past_mind.append(self.current_mind)
self.current_mind = reponse
self.current_mind = response
async def get_prompt_info(self, message: str, threshold: float):
start_time = time.time()

5
src/plugins/PFC/pfc.py
View File

@@ -117,9 +117,12 @@ class GoalAnalyzer:
}}"""
logger.debug(f"发送到LLM的提示词: {prompt}")
try:
content, _ = await self.llm.generate_response_async(prompt)
logger.debug(f"LLM原始返回内容: {content}")
except Exception as e:
logger.error(f"分析对话目标时出错: {str(e)}")
content = ""
# 使用简化函数提取JSON内容
success, result = get_items_from_json(
content,

3
src/plugins/chat/emoji_manager.py
View File

@@ -340,6 +340,9 @@ class EmojiManager:
if description is not None:
embedding = await get_embedding(description, request_type="emoji")
if not embedding:
logger.error("获取消息嵌入向量失败")
raise ValueError("获取消息嵌入向量失败")
# 准备数据库记录
emoji_record = {
"filename": filename,

8
src/plugins/chat/utils.py
View File

@@ -79,7 +79,13 @@ async def get_embedding(text, request_type="embedding"):
"""获取文本的embedding向量"""
llm = LLM_request(model=global_config.embedding, request_type=request_type)
# return llm.get_embedding_sync(text)
return await llm.get_embedding(text)
try:
embedding = await llm.get_embedding(text)
except Exception as e:
logger.error(f"获取embedding失败: {str(e)}")
embedding = None
return embedding
async def get_recent_group_messages(chat_id: str, limit: int = 12) -> list:

17
src/plugins/memory_system/Hippocampus.py
View File

@@ -1316,15 +1316,24 @@ class HippocampusManager:
"""从文本中获取相关记忆的公共接口"""
if not self._initialized:
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
return await self._hippocampus.get_memory_from_text(
text, max_memory_num, max_memory_length, max_depth, fast_retrieval
)
try:
response = await self._hippocampus.get_memory_from_text(text, max_memory_num, max_memory_length, max_depth, fast_retrieval)
except Exception as e:
logger.error(f"文本激活记忆失败: {e}")
response = []
return response
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> float:
"""从文本中获取激活值的公共接口"""
if not self._initialized:
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
return await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
try:
response = await self._hippocampus.get_activate_from_text(text, max_depth, fast_retrieval)
except Exception as e:
logger.error(f"文本产生激活值失败: {e}")
response = 0.0
return response
def get_memory_from_keyword(self, keyword: str, max_depth: int = 2) -> list:
"""从关键词获取相关记忆的公共接口"""

4
src/plugins/schedule/schedule_generator.py
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@@ -121,7 +121,11 @@ class ScheduleGenerator:
self.today_done_list = []
if not self.today_schedule_text:
logger.info(f"{today.strftime('%Y-%m-%d')}的日程不存在，准备生成新的日程")
try:
self.today_schedule_text = await self.generate_daily_schedule(target_date=today)
except Exception as e:
logger.error(f"生成日程时发生错误: {str(e)}")
self.today_schedule_text = ""
self.save_today_schedule_to_db()

7
src/plugins/topic_identify/topic_identifier.py
View File

@@ -29,10 +29,13 @@ class TopicIdentifier:
消息内容：{text}"""
# 使用 LLM_request 类进行请求
try:
topic, _, _ = await self.llm_topic_judge.generate_response(prompt)
except Exception as e:
logger.error(f"LLM 请求topic失败: {e}")
return None
if not topic:
logger.error("LLM API 返回为空")
logger.error("LLM 得到的topic为空")
return None
# 直接在这里处理主题解析