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better 更好的logger格式

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This commit is contained in:
SengokuCola
2025-03-21 16:24:28 +08:00
parent 432104f582
commit 7c50e33369
6 changed files with 73 additions and 48 deletions
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77
src/plugins/memory_system/memory.py
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@@ -26,6 +26,11 @@ memory_config = LogConfig(
console_format=MEMORY_STYLE_CONFIG["console_format"],
file_format=MEMORY_STYLE_CONFIG["file_format"],
)
# print(f"memory_config: {memory_config}")
# print(f"MEMORY_STYLE_CONFIG: {MEMORY_STYLE_CONFIG}")
# print(f"MEMORY_STYLE_CONFIG['console_format']: {MEMORY_STYLE_CONFIG['console_format']}")
# print(f"MEMORY_STYLE_CONFIG['file_format']: {MEMORY_STYLE_CONFIG['file_format']}")
logger = get_module_logger("memory_system", config=memory_config)
@@ -198,13 +203,15 @@ class Hippocampus:
def random_get_msg_snippet(self, target_timestamp: float, chat_size: int, max_memorized_time_per_msg: int) -> list:
try_count = 0
# 最多尝试2次抽取
while try_count < 2:
while try_count < 3:
messages = get_closest_chat_from_db(length=chat_size, timestamp=target_timestamp)
if messages:
# print(f"抽取到的消息: {messages}")
# 检查messages是否均没有达到记忆次数限制
for message in messages:
if message["memorized_times"] >= max_memorized_time_per_msg:
messages = None
# print(f"抽取到的消息提取次数达到限制，跳过")
break
if messages:
# 成功抽取短期消息样本
@@ -235,8 +242,10 @@ class Hippocampus:
# 生成时间戳数组
timestamps = scheduler.get_timestamp_array()
logger.debug(f"生成的时间戳数组: {timestamps}")
# logger.debug(f"生成的时间戳数组: {timestamps}")
# print(f"生成的时间戳数组: {timestamps}")
# print(f"时间戳的实际时间: {[time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ts)) for ts in timestamps]}")
logger.info(f"回忆往事: {[time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(ts)) for ts in timestamps]}")
chat_samples = []
for timestamp in timestamps:
messages = self.random_get_msg_snippet(
@@ -247,18 +256,14 @@ class Hippocampus:
if messages:
time_diff = (datetime.datetime.now().timestamp() - timestamp) / 3600
logger.debug(f"成功抽取 {time_diff:.1f} 小时前的消息样本，共{len(messages)}条")
# print(f"成功抽取 {time_diff:.1f} 小时前的消息样本，共{len(messages)}条")
chat_samples.append(messages)
else:
logger.warning(f"时间戳 {timestamp} 的消息样本抽取失败")
logger.debug(f"时间戳 {timestamp} 的消息样本抽取失败")
return chat_samples
async def memory_compress(self, messages: list, compress_rate=0.1):
"""压缩消息记录为记忆
Returns:
tuple: (压缩记忆集合, 相似主题字典)
"""
if not messages:
return set(), {}
@@ -291,15 +296,23 @@ class Hippocampus:
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(input_text, topic_num))
# 过滤topics
# 从配置文件获取需要过滤的关键词列表
filter_keywords = global_config.memory_ban_words
# 将topics_response[0]中的中文逗号、顿号、空格都替换成英文逗号
# 然后按逗号分割成列表,并去除每个topic前后的空白字符
topics = [
topic.strip()
for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
if topic.strip()
]
# 过滤掉包含禁用关键词的topic
# any()检查topic中是否包含任何一个filter_keywords中的关键词
# 只保留不包含禁用关键词的topic
filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
logger.info(f"过滤后话题: {filtered_topics}")
logger.debug(f"过滤后话题: {filtered_topics}")
# 创建所有话题的请求任务
tasks = []
@@ -309,31 +322,42 @@ class Hippocampus:
tasks.append((topic.strip(), task))
# 等待所有任务完成
compressed_memory = set()
# 初始化压缩后的记忆集合和相似主题字典
compressed_memory = set() # 存储压缩后的(主题,内容)元组
similar_topics_dict = {} # 存储每个话题的相似主题列表
# 遍历每个主题及其对应的LLM任务
for topic, task in tasks:
response = await task
if response:
# 将主题和LLM生成的内容添加到压缩记忆中
compressed_memory.add((topic, response[0]))
# 为每个话题查找相似的已存在主题
# 为当前主题寻找相似的已存在主题
existing_topics = list(self.memory_graph.G.nodes())
similar_topics = []
# 计算当前主题与每个已存在主题的相似度
for existing_topic in existing_topics:
# 使用jieba分词,将主题转换为词集合
topic_words = set(jieba.cut(topic))
existing_words = set(jieba.cut(existing_topic))
all_words = topic_words | existing_words
v1 = [1 if word in topic_words else 0 for word in all_words]
v2 = [1 if word in existing_words else 0 for word in all_words]
# 构建词向量用于计算余弦相似度
all_words = topic_words | existing_words # 所有不重复的词
v1 = [1 if word in topic_words else 0 for word in all_words] # 当前主题的词向量
v2 = [1 if word in existing_words else 0 for word in all_words] # 已存在主题的词向量
# 计算余弦相似度
similarity = cosine_similarity(v1, v2)
if similarity >= 0.6:
# 如果相似度超过阈值,添加到相似主题列表
if similarity >= 0.7:
similar_topics.append((existing_topic, similarity))
# 按相似度降序排序,只保留前3个最相似的主题
similar_topics.sort(key=lambda x: x[1], reverse=True)
similar_topics = similar_topics[:5]
similar_topics = similar_topics[:3]
similar_topics_dict[topic] = similar_topics
return compressed_memory, similar_topics_dict
@@ -352,7 +376,8 @@ class Hippocampus:
async def operation_build_memory(self):
memory_samples = self.get_memory_sample()
all_added_nodes = []
all_added_edges = []
for i, messages in enumerate(memory_samples, 1):
all_topics = []
# 加载进度可视化
@@ -364,12 +389,13 @@ class Hippocampus:
compress_rate = global_config.memory_compress_rate
compressed_memory, similar_topics_dict = await self.memory_compress(messages, compress_rate)
logger.info(f"压缩后记忆数量: {len(compressed_memory)}，似曾相识的话题: {len(similar_topics_dict)}")
logger.debug(f"压缩后记忆数量: {compressed_memory}，似曾相识的话题: {similar_topics_dict}")
current_time = datetime.datetime.now().timestamp()
logger.debug(f"添加节点: {', '.join(topic for topic, _ in compressed_memory)}")
all_added_nodes.extend(topic for topic, _ in compressed_memory)
for topic, memory in compressed_memory:
logger.info(f"添加节点: {topic}")
self.memory_graph.add_dot(topic, memory)
all_topics.append(topic)
@@ -379,7 +405,8 @@ class Hippocampus:
for similar_topic, similarity in similar_topics:
if topic != similar_topic:
strength = int(similarity * 10)
logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
logger.debug(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
all_added_edges.append(f"{topic}-{similar_topic}")
self.memory_graph.G.add_edge(
topic,
similar_topic,
@@ -391,9 +418,13 @@ class Hippocampus:
# 连接同批次的相关话题
for i in range(len(all_topics)):
for j in range(i + 1, len(all_topics)):
logger.info(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
logger.debug(f"连接同批次节点: {all_topics[i]} 和 {all_topics[j]}")
all_added_edges.append(f"{all_topics[i]}-{all_topics[j]}")
self.memory_graph.connect_dot(all_topics[i], all_topics[j])
logger.success(f"更新记忆: {', '.join(all_added_nodes)}")
logger.success(f"强化连接: {', '.join(all_added_edges)}")
# logger.success(f"强化连接: {', '.join(all_added_edges)}")
self.sync_memory_to_db()
def sync_memory_to_db(self):