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修复了记忆时间bug,config添加了记忆屏蔽关键词

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SengokuCola
2025-03-09 22:45:32 +08:00
parent c6b75c9b5c
commit 6a71ea4f2e
7 changed files with 367 additions and 130 deletions
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100
src/plugins/memory_system/memory.py
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@@ -157,46 +157,88 @@ class Hippocampus:
nodes = sorted([source, target])
return hash(f"{nodes[0]}:{nodes[1]}")
def get_memory_sample(self,chat_size=20,time_frequency:dict={'near':2,'mid':4,'far':3}):
def get_memory_sample(self, chat_size=20, time_frequency:dict={'near':2,'mid':4,'far':3}):
"""获取记忆样本
Returns:
list: 消息记录列表，每个元素是一个消息记录字典列表
"""
current_timestamp = datetime.datetime.now().timestamp()
chat_text = []
#短期：1h 中期：4h 长期：24h
for _ in range(time_frequency.get('near')): # 循环10次
random_time = current_timestamp - random.randint(1, 3600) # 随机时间
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
chat_text.append(chat_)
for _ in range(time_frequency.get('mid')): # 循环10次
random_time = current_timestamp - random.randint(3600, 3600*4) # 随机时间
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
chat_text.append(chat_)
for _ in range(time_frequency.get('far')): # 循环10次
random_time = current_timestamp - random.randint(3600*4, 3600*24) # 随机时间
chat_ = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
chat_text.append(chat_)
return [text for text in chat_text if text]
async def memory_compress(self, input_text, compress_rate=0.1):
chat_samples = []
# 短期：1h 中期：4h 长期：24h
for _ in range(time_frequency.get('near')):
random_time = current_timestamp - random.randint(1, 3600)
messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
if messages:
chat_samples.append(messages)
for _ in range(time_frequency.get('mid')):
random_time = current_timestamp - random.randint(3600, 3600*4)
messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
if messages:
chat_samples.append(messages)
for _ in range(time_frequency.get('far')):
random_time = current_timestamp - random.randint(3600*4, 3600*24)
messages = get_cloest_chat_from_db(db=self.memory_graph.db, length=chat_size, timestamp=random_time)
if messages:
chat_samples.append(messages)
return chat_samples
async def memory_compress(self, messages: list, compress_rate=0.1):
"""压缩消息记录为记忆
Args:
messages: 消息记录字典列表，每个字典包含text和time字段
compress_rate: 压缩率
Returns:
set: (话题, 记忆) 元组集合
"""
if not messages:
return set()
# 合并消息文本，同时保留时间信息
input_text = ""
time_info = ""
# 计算最早和最晚时间
earliest_time = min(msg['time'] for msg in messages)
latest_time = max(msg['time'] for msg in messages)
earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
latest_dt = datetime.datetime.fromtimestamp(latest_time)
# 如果是同一年
if earliest_dt.year == latest_dt.year:
earliest_str = earliest_dt.strftime("%m-%d %H:%M:%S")
latest_str = latest_dt.strftime("%m-%d %H:%M:%S")
time_info += f"是在{earliest_dt.year}年，{earliest_str} 到 {latest_str} 的对话:\n"
else:
earliest_str = earliest_dt.strftime("%Y-%m-%d %H:%M:%S")
latest_str = latest_dt.strftime("%Y-%m-%d %H:%M:%S")
time_info += f"是从 {earliest_str} 到 {latest_str} 的对话:\n"
for msg in messages:
input_text += f"{msg['text']}\n"
print(input_text)
#获取topics
topic_num = self.calculate_topic_num(input_text, compress_rate)
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
# 修改话题处理逻辑
# 定义需要过滤的关键词
filter_keywords = ['表情包', '图片', '回复', '聊天记录']
# 过滤topics
filter_keywords = global_config.memory_ban_words
topics = [topic.strip() for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
# print(f"原始话题: {topics}")
print(f"过滤后话题: {filtered_topics}")
# 使用过滤后的话题继续处理
# 创建所有话题的请求任务
tasks = []
for topic in filtered_topics:
topic_what_prompt = self.topic_what(input_text, topic)
# 创建异步任务
topic_what_prompt = self.topic_what(input_text, topic, time_info)
task = self.llm_summary_by_topic.generate_response_async(topic_what_prompt)
tasks.append((topic.strip(), task))
@@ -440,7 +482,7 @@ class Hippocampus:
print(f"选择的记忆:\n{merged_text}")
# 使用memory_compress生成新的压缩记忆
compressed_memories = await self.memory_compress(merged_text, 0.1)
compressed_memories = await self.memory_compress(selected_memories, 0.1)
# 从原记忆列表中移除被选中的记忆
for memory in selected_memories:
@@ -494,8 +536,8 @@ class Hippocampus:
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
return prompt
def topic_what(self,text, topic):
prompt = f'这是一段文字：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
def topic_what(self,text, topic, time_info):
prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
return prompt
async def _identify_topics(self, text: str) -> list: