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v0.3.3 异步处理记忆，修复了GUI

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This commit is contained in:
SengokuCola
2025-03-02 18:36:36 +08:00
parent 10c3f90720
commit b98314da4f
8 changed files with 116 additions and 84 deletions
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44
src/plugins/memory_system/memory.py
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@@ -193,7 +193,25 @@ class Hippocampus:
chat_text.append(chat_)
return chat_text
def build_memory(self,chat_size=12):
async def memory_compress(self, input_text, rate=1):
information_content = calculate_information_content(input_text)
print(f"文本的信息量（熵）: {information_content:.4f} bits")
topic_num = max(1, min(5, int(information_content * rate / 4)))
topic_prompt = find_topic(input_text, topic_num)
topic_response = await self.llm_model.generate_response(topic_prompt)
# 检查 topic_response 是否为元组
if isinstance(topic_response, tuple):
topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串
else:
topics = topic_response.split(",")
compressed_memory = set()
for topic in topics:
topic_what_prompt = topic_what(input_text,topic)
topic_what_response = await self.llm_model_small.generate_response(topic_what_prompt)
compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储
return compressed_memory
async def build_memory(self,chat_size=12):
#最近消息获取频率
time_frequency = {'near':1,'mid':2,'far':2}
memory_sample = self.get_memory_sample(chat_size,time_frequency)
@@ -208,9 +226,7 @@ class Hippocampus:
if input_text:
# 生成压缩后记忆
first_memory = set()
first_memory = self.memory_compress(input_text, 2.5)
# 延时防止访问超频
# time.sleep(5)
first_memory = await self.memory_compress(input_text, 2.5)
#将记忆加入到图谱中
for topic, memory in first_memory:
topics = segment_text(topic)
@@ -224,26 +240,6 @@ class Hippocampus:
else:
print(f"空消息 跳过")
self.memory_graph.save_graph_to_db()
def memory_compress(self, input_text, rate=1):
information_content = calculate_information_content(input_text)
print(f"文本的信息量（熵）: {information_content:.4f} bits")
topic_num = max(1, min(5, int(information_content * rate / 4)))
# print(topic_num)
topic_prompt = find_topic(input_text, topic_num)
topic_response = self.llm_model.generate_response(topic_prompt)
# 检查 topic_response 是否为元组
if isinstance(topic_response, tuple):
topics = topic_response[0].split(",") # 假设第一个元素是我们需要的字符串
else:
topics = topic_response.split(",")
# print(topics)
compressed_memory = set()
for topic in topics:
topic_what_prompt = topic_what(input_text,topic)
topic_what_response = self.llm_model_small.generate_response(topic_what_prompt)
compressed_memory.add((topic.strip(), topic_what_response[0])) # 将话题和记忆作为元组存储
return compressed_memory
def segment_text(text):