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ruff reformatted

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春河晴
2025-04-08 15:31:13 +09:00
parent 0d7068acab
commit 7840a6080d
40 changed files with 1227 additions and 1336 deletions
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30
src/heart_flow/heartflow.py
View File

@@ -42,7 +42,6 @@ class Heartflow:
self._subheartflows = {}
self.active_subheartflows_nums = 0
async def _cleanup_inactive_subheartflows(self):
"""定期清理不活跃的子心流"""
while True:
@@ -84,25 +83,22 @@ class Heartflow:
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
personality_info = prompt_personality
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
related_memory_info = "memory"
@@ -146,22 +142,20 @@ class Heartflow:
async def minds_summary(self, minds_str):
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
personality_info = prompt_personality
mood_info = self.current_state.mood
@@ -183,7 +177,7 @@ class Heartflow:
添加一个SubHeartflow实例到self._subheartflows字典中
并根据subheartflow_id为子心流创建一个观察对象
"""
try:
if subheartflow_id not in self._subheartflows:
logger.debug(f"创建 subheartflow: {subheartflow_id}")

22
src/heart_flow/observation.py
View File

@@ -7,6 +7,7 @@ from src.common.database import db
from src.individuality.individuality import Individuality
import random
# 所有观察的基类
class Observation:
def __init__(self, observe_type, observe_id):
@@ -24,7 +25,7 @@ class ChattingObservation(Observation):
self.talking_message = []
self.talking_message_str = ""
self.name = global_config.BOT_NICKNAME
self.nick_name = global_config.BOT_ALIAS_NAMES
@@ -57,7 +58,7 @@ class ChattingObservation(Observation):
for msg in new_messages:
if "detailed_plain_text" in msg:
new_messages_str += f"{msg['detailed_plain_text']}"
# print(f"new_messages_str：{new_messages_str}")
# 将新消息添加到talking_message，同时保持列表长度不超过20条
@@ -117,26 +118,22 @@ class ChattingObservation(Observation):
# print(f"更新聊天总结：{self.talking_summary}")
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
personality_info = prompt_personality
prompt = ""
prompt += f"{personality_info}，请注意识别你自己的聊天发言"
prompt += f"你的名字叫：{self.name}，你的昵称是：{self.nick_name}\n"
@@ -148,7 +145,6 @@ class ChattingObservation(Observation):
self.observe_info, reasoning_content = await self.llm_summary.generate_response_async(prompt)
print(f"prompt：{prompt}")
print(f"self.observe_info：{self.observe_info}")
def translate_message_list_to_str(self):
self.talking_message_str = ""

117
src/heart_flow/sub_heartflow.py
View File

@@ -53,11 +53,10 @@ class SubHeartflow:
if not self.current_mind:
self.current_mind = "你什么也没想"
self.is_active = False
self.observations: list[Observation] = []
self.running_knowledges = []
def add_observation(self, observation: Observation):
@@ -86,7 +85,9 @@ class SubHeartflow:
async def subheartflow_start_working(self):
while True:
current_time = time.time()
if current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time: # 120秒无回复/不在场，冻结
if (
current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time
): # 120秒无回复/不在场，冻结
self.is_active = False
await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 每60秒检查一次
else:
@@ -100,7 +101,9 @@ class SubHeartflow:
await asyncio.sleep(global_config.sub_heart_flow_update_interval)
# 检查是否超过10分钟没有激活
if current_time - self.last_active_time > global_config.sub_heart_flow_stop_time: # 5分钟无回复/不在场，销毁
if (
current_time - self.last_active_time > global_config.sub_heart_flow_stop_time
): # 5分钟无回复/不在场，销毁
logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...")
break # 退出循环，销毁自己
@@ -147,11 +150,11 @@ class SubHeartflow:
# self.current_mind = reponse
# logger.debug(f"prompt:\n{prompt}\n")
# logger.info(f"麦麦的脑内状态：{self.current_mind}")
async def do_observe(self):
observation = self.observations[0]
await observation.observe()
async def do_thinking_before_reply(self, message_txt):
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -162,23 +165,20 @@ class SubHeartflow:
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# 调取记忆
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
@@ -191,7 +191,7 @@ class SubHeartflow:
else:
related_memory_info = ""
related_info,grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4)
related_info, grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4)
# print(related_info)
for _topic, results in grouped_results.items():
for result in results:
@@ -227,25 +227,23 @@ class SubHeartflow:
async def do_thinking_after_reply(self, reply_content, chat_talking_prompt):
# print("麦麦回复之后脑袋转起来了")
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -279,22 +277,20 @@ class SubHeartflow:
async def judge_willing(self):
# 开始构建prompt
prompt_personality = "你"
#person
# person
individuality = Individuality.get_instance()
personality_core = individuality.personality.personality_core
prompt_personality += personality_core
personality_sides = individuality.personality.personality_sides
random.shuffle(personality_sides)
prompt_personality += f",{personality_sides[0]}"
identity_detail = individuality.identity.identity_detail
random.shuffle(identity_detail)
prompt_personality += f",{identity_detail[0]}"
# print("麦麦闹情绪了1")
current_thinking_info = self.current_mind
mood_info = self.current_state.mood
@@ -320,13 +316,12 @@ class SubHeartflow:
def update_current_mind(self, reponse):
self.past_mind.append(self.current_mind)
self.current_mind = reponse
async def get_prompt_info(self, message: str, threshold: float):
start_time = time.time()
related_info = ""
logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
# 1. 先从LLM获取主题，类似于记忆系统的做法
topics = []
# try:
@@ -334,7 +329,7 @@ class SubHeartflow:
# hippocampus = HippocampusManager.get_instance()._hippocampus
# topic_num = min(5, max(1, int(len(message) * 0.1)))
# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
# # 提取关键词
# topics = re.findall(r"<([^>]+)>", topics_response[0])
# if not topics:
@@ -345,7 +340,7 @@ class SubHeartflow:
# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
# if topic.strip()
# ]
# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
# except Exception as e:
# logger.error(f"从LLM提取主题失败: {str(e)}")
@@ -353,7 +348,7 @@ class SubHeartflow:
# words = jieba.cut(message)
# topics = [word for word in words if len(word) > 1][:5]
# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
# 如果无法提取到主题，直接使用整个消息
if not topics:
logger.debug("未能提取到任何主题，使用整个消息进行查询")
@@ -361,26 +356,26 @@ class SubHeartflow:
if not embedding:
logger.error("获取消息嵌入向量失败")
return ""
related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
return related_info, {}
# 2. 对每个主题进行知识库查询
logger.info(f"开始处理{len(topics)}个主题的知识库查询")
# 优化：批量获取嵌入向量，减少API调用
embeddings = {}
topics_batch = [topic for topic in topics if len(topic) > 0]
if message: # 确保消息非空
topics_batch.append(message)
# 批量获取嵌入向量
embed_start_time = time.time()
for text in topics_batch:
if not text or len(text.strip()) == 0:
continue
try:
embedding = await get_embedding(text, request_type="info_retrieval")
if embedding:
@@ -389,17 +384,17 @@ class SubHeartflow:
logger.warning(f"获取'{text}'的嵌入向量失败")
except Exception as e:
logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
if not embeddings:
logger.error("所有嵌入向量获取失败")
return ""
# 3. 对每个主题进行知识库查询
all_results = []
query_start_time = time.time()
# 首先添加原始消息的查询结果
if message in embeddings:
original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
@@ -408,12 +403,12 @@ class SubHeartflow:
result["topic"] = "原始消息"
all_results.extend(original_results)
logger.info(f"原始消息查询到{len(original_results)}条结果")
# 然后添加每个主题的查询结果
for topic in topics:
if not topic or topic not in embeddings:
continue
try:
topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
if topic_results:
@@ -424,9 +419,9 @@ class SubHeartflow:
logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
except Exception as e:
logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果")
# 4. 去重和过滤
process_start_time = time.time()
unique_contents = set()
@@ -436,14 +431,16 @@ class SubHeartflow:
if content not in unique_contents:
unique_contents.add(content)
filtered_results.append(result)
# 5. 按相似度排序
filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
# 6. 限制总数量（最多10条）
filtered_results = filtered_results[:10]
logger.info(f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果")
logger.info(
f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果"
)
# 7. 格式化输出
if filtered_results:
format_start_time = time.time()
@@ -453,7 +450,7 @@ class SubHeartflow:
if topic not in grouped_results:
grouped_results[topic] = []
grouped_results[topic].append(result)
# 按主题组织输出
for topic, results in grouped_results.items():
related_info += f"【主题: {topic}】\n"
@@ -464,13 +461,15 @@ class SubHeartflow:
# related_info += f"{i}. [{similarity:.2f}] {content}\n"
related_info += f"{content}\n"
related_info += "\n"
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info,grouped_results
def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> Union[str, list]:
logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
return related_info, grouped_results
def get_info_from_db(
self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
) -> Union[str, list]:
if not query_embedding:
return "" if not return_raw else []
# 使用余弦相似度计算