From 7c50e333692c878807d66c58db2986e7ac90b0cd Mon Sep 17 00:00:00 2001
From: SengokuCola <1026294844@qq.com>
Date: Fri, 21 Mar 2025 16:24:28 +0800
Subject: [PATCH] =?UTF-8?q?better=20=E6=9B=B4=E5=A5=BD=E7=9A=84logger?=
=?UTF-8?q?=E6=A0=BC=E5=BC=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
src/common/logger.py | 21 ++++----
src/plugins/chat/bot.py | 2 +-
src/plugins/chat/message_sender.py | 2 +-
src/plugins/chat/utils.py | 17 +++----
src/plugins/chat/utils_image.py | 2 +-
src/plugins/memory_system/memory.py | 77 ++++++++++++++++++++---------
6 files changed, 73 insertions(+), 48 deletions(-)
diff --git a/src/common/logger.py b/src/common/logger.py
index 2673275a5..91f1a1da0 100644
--- a/src/common/logger.py
+++ b/src/common/logger.py
@@ -31,10 +31,10 @@ _handler_registry: Dict[str, List[int]] = {}
current_file_path = Path(__file__).resolve()
LOG_ROOT = "logs"
-ENABLE_ADVANCE_OUTPUT = os.getenv("SIMPLE_OUTPUT", "false")
-print(f"ENABLE_ADVANCE_OUTPUT: {ENABLE_ADVANCE_OUTPUT}")
+SIMPLE_OUTPUT = os.getenv("SIMPLE_OUTPUT", "false")
+print(f"SIMPLE_OUTPUT: {SIMPLE_OUTPUT}")
-if not ENABLE_ADVANCE_OUTPUT:
+if not SIMPLE_OUTPUT:
# 默认全局配置
DEFAULT_CONFIG = {
# 日志级别配置
@@ -86,7 +86,6 @@ MEMORY_STYLE_CONFIG = {
},
}
-# 海马体日志样式配置
SENDER_STYLE_CONFIG = {
"advanced": {
"console_format": (
@@ -153,17 +152,17 @@ CHAT_STYLE_CONFIG = {
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 见闻 | {message}"),
},
"simple": {
- "console_format": ("{time:MM-DD HH:mm} | 见闻 | {message}"),
+ "console_format": ("{time:MM-DD HH:mm} | 见闻 | {message}"), # noqa: E501
"file_format": ("{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {extra[module]: <15} | 见闻 | {message}"),
},
}
-# 根据ENABLE_ADVANCE_OUTPUT选择配置
-MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["advanced"] if ENABLE_ADVANCE_OUTPUT else MEMORY_STYLE_CONFIG["simple"]
-TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["advanced"] if ENABLE_ADVANCE_OUTPUT else TOPIC_STYLE_CONFIG["simple"]
-SENDER_STYLE_CONFIG = SENDER_STYLE_CONFIG["advanced"] if ENABLE_ADVANCE_OUTPUT else SENDER_STYLE_CONFIG["simple"]
-LLM_STYLE_CONFIG = LLM_STYLE_CONFIG["advanced"] if ENABLE_ADVANCE_OUTPUT else LLM_STYLE_CONFIG["simple"]
-CHAT_STYLE_CONFIG = CHAT_STYLE_CONFIG["advanced"] if ENABLE_ADVANCE_OUTPUT else CHAT_STYLE_CONFIG["simple"]
+# 根据SIMPLE_OUTPUT选择配置
+MEMORY_STYLE_CONFIG = MEMORY_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else MEMORY_STYLE_CONFIG["advanced"]
+TOPIC_STYLE_CONFIG = TOPIC_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else TOPIC_STYLE_CONFIG["advanced"]
+SENDER_STYLE_CONFIG = SENDER_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else SENDER_STYLE_CONFIG["advanced"]
+LLM_STYLE_CONFIG = LLM_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else LLM_STYLE_CONFIG["advanced"]
+CHAT_STYLE_CONFIG = CHAT_STYLE_CONFIG["simple"] if SIMPLE_OUTPUT else CHAT_STYLE_CONFIG["advanced"]
def is_registered_module(record: dict) -> bool:
diff --git a/src/plugins/chat/bot.py b/src/plugins/chat/bot.py
index 24b7bdbff..38450f903 100644
--- a/src/plugins/chat/bot.py
+++ b/src/plugins/chat/bot.py
@@ -154,7 +154,7 @@ class ChatBot:
)
# 开始思考的时间点
thinking_time_point = round(time.time(), 2)
- logger.info(f"开始思考的时间点: {thinking_time_point}")
+ # logger.debug(f"开始思考的时间点: {thinking_time_point}")
think_id = "mt" + str(thinking_time_point)
thinking_message = MessageThinking(
message_id=think_id,
diff --git a/src/plugins/chat/message_sender.py b/src/plugins/chat/message_sender.py
index 741cc2889..d79e9e7ab 100644
--- a/src/plugins/chat/message_sender.py
+++ b/src/plugins/chat/message_sender.py
@@ -220,7 +220,7 @@ class MessageManager:
message_timeout = container.get_timeout_messages()
if message_timeout:
- logger.warning(f"发现{len(message_timeout)}条超时消息")
+ logger.debug(f"发现{len(message_timeout)}条超时消息")
for msg in message_timeout:
if msg == message_earliest:
continue
diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py
index 8b728ee4d..1563ea526 100644
--- a/src/plugins/chat/utils.py
+++ b/src/plugins/chat/utils.py
@@ -76,18 +76,11 @@ def calculate_information_content(text):
def get_closest_chat_from_db(length: int, timestamp: str):
- """从数据库中获取最接近指定时间戳的聊天记录
-
- Args:
- length: 要获取的消息数量
- timestamp: 时间戳
-
- Returns:
- list: 消息记录列表，每个记录包含时间和文本信息
- """
+ # print(f"获取最接近指定时间戳的聊天记录，长度: {length}, 时间戳: {timestamp}")
+ # print(f"当前时间: {timestamp},转换后时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))}")
chat_records = []
closest_record = db.messages.find_one({"time": {"$lte": timestamp}}, sort=[("time", -1)])
-
+ # print(f"最接近的记录: {closest_record}")
if closest_record:
closest_time = closest_record["time"]
chat_id = closest_record["chat_id"] # 获取chat_id
@@ -102,7 +95,9 @@ def get_closest_chat_from_db(length: int, timestamp: str):
.sort("time", 1)
.limit(length)
)
-
+ # print(f"获取到的记录: {chat_records}")
+ length = len(chat_records)
+ # print(f"获取到的记录长度: {length}")
# 转换记录格式
formatted_records = []
for record in chat_records:
diff --git a/src/plugins/chat/utils_image.py b/src/plugins/chat/utils_image.py
index ea0c160eb..521795024 100644
--- a/src/plugins/chat/utils_image.py
+++ b/src/plugins/chat/utils_image.py
@@ -112,7 +112,7 @@ class ImageManager:
# 查询缓存的描述
cached_description = self._get_description_from_db(image_hash, "emoji")
if cached_description:
- logger.info(f"缓存表情包描述: {cached_description}")
+ logger.debug(f"缓存表情包描述: {cached_description}")
return f"[表情包：{cached_description}]"
# 调用AI获取描述
diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py
index 1f69dd3cf..f5012c828 100644
--- a/src/plugins/memory_system/memory.py
+++ b/src/plugins/memory_system/memory.py
@@ -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):