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better：美化logger

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This commit is contained in:
SengokuCola
2025-08-24 15:26:24 +08:00
parent e860b7033a
commit d10e08f15d
9 changed files with 89 additions and 109 deletions
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6
src/chat/heart_flow/heartflow_message_processor.py
View File

@@ -140,10 +140,8 @@ class HeartFCMessageReceiver:
replace_bot_name=True
)
if keywords:
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[兴趣度：{interested_rate:.2f}][关键词：{keywords}]") # type: ignore
else:
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[兴趣度：{interested_rate:.2f}]") # type: ignore
logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[{interested_rate:.2f}]") # type: ignore
_ = Person.register_person(platform=message.message_info.platform, user_id=message.message_info.user_info.user_id,nickname=userinfo.user_nickname) # type: ignore

2
src/chat/message_receive/uni_message_sender.py
View File

@@ -17,7 +17,7 @@ logger = get_logger("sender")
async def send_message(message: MessageSending, show_log=True) -> bool:
"""合并后的消息发送函数，包含WS发送和日志记录"""
message_preview = truncate_message(message.processed_plain_text, max_length=120)
message_preview = truncate_message(message.processed_plain_text, max_length=200)
try:
# 直接调用API发送消息

2
src/chat/planner_actions/action_modifier.py
View File

@@ -131,7 +131,7 @@ class ActionModifier:
available_actions = list(self.action_manager.get_using_actions().keys())
available_actions_text = "、".join(available_actions) if available_actions else "无"
logger.info(
logger.debug(
f"{self.log_prefix} 当前可用动作: {available_actions_text}||移除: {removals_summary}"
)

3
src/chat/planner_actions/planner.py
View File

@@ -512,7 +512,6 @@ class ActionPlanner:
self.last_obs_time_mark = time.time()
try:
logger.info(f"{self.log_prefix}开始构建副Planner")
sub_planner_actions: Dict[str, ActionInfo] = {}
for action_name, action_info in available_actions.items():
@@ -537,7 +536,7 @@ class ActionPlanner:
sub_planner_size = int(global_config.chat.planner_size) + 1
sub_planner_num = math.ceil(sub_planner_actions_num / sub_planner_size)
logger.info(f"{self.log_prefix}副规划器数量: {sub_planner_num}, 副规划器大小: {sub_planner_size}")
logger.info(f"{self.log_prefix}使用{sub_planner_num}个小脑进行思考（尺寸:{sub_planner_size}）")
# 将sub_planner_actions随机分配到sub_planner_num个List中
sub_planner_lists: List[List[Tuple[str, ActionInfo]]] = []

128
src/chat/utils/statistic.py
View File

@@ -612,7 +612,7 @@ class StatisticOutputTask(AsyncTask):
f"总在线时间: {_format_online_time(stats[ONLINE_TIME])}",
f"总消息数: {stats[TOTAL_MSG_CNT]}",
f"总请求数: {stats[TOTAL_REQ_CNT]}",
f"总花费: {stats[TOTAL_COST]:.4f}¥",
f"总花费: {stats[TOTAL_COST]:.2f}¥",
"",
]
@@ -625,7 +625,7 @@ class StatisticOutputTask(AsyncTask):
"""
if stats[TOTAL_REQ_CNT] <= 0:
return ""
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.4f}¥ {:>10} {:>10}"
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f}"
output = [
"按模型分类统计:",
@@ -723,9 +723,9 @@ class StatisticOutputTask(AsyncTask):
f"<td>{stat_data[IN_TOK_BY_MODEL][model_name]}</td>"
f"<td>{stat_data[OUT_TOK_BY_MODEL][model_name]}</td>"
f"<td>{stat_data[TOTAL_TOK_BY_MODEL][model_name]}</td>"
f"<td>{stat_data[COST_BY_MODEL][model_name]:.4f} ¥</td>"
f"<td>{stat_data[AVG_TIME_COST_BY_MODEL][model_name]:.3f} 秒</td>"
f"<td>{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.3f} 秒</td>"
f"<td>{stat_data[COST_BY_MODEL][model_name]:.2f} ¥</td>"
f"<td>{stat_data[AVG_TIME_COST_BY_MODEL][model_name]:.1f} 秒</td>"
f"<td>{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.1f} 秒</td>"
f"</tr>"
for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
]
@@ -739,9 +739,9 @@ class StatisticOutputTask(AsyncTask):
f"<td>{stat_data[IN_TOK_BY_TYPE][req_type]}</td>"
f"<td>{stat_data[OUT_TOK_BY_TYPE][req_type]}</td>"
f"<td>{stat_data[TOTAL_TOK_BY_TYPE][req_type]}</td>"
f"<td>{stat_data[COST_BY_TYPE][req_type]:.4f} ¥</td>"
f"<td>{stat_data[AVG_TIME_COST_BY_TYPE][req_type]:.3f} 秒</td>"
f"<td>{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.3f} 秒</td>"
f"<td>{stat_data[COST_BY_TYPE][req_type]:.2f} ¥</td>"
f"<td>{stat_data[AVG_TIME_COST_BY_TYPE][req_type]:.1f} 秒</td>"
f"<td>{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.1f} 秒</td>"
f"</tr>"
for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
]
@@ -755,9 +755,9 @@ class StatisticOutputTask(AsyncTask):
f"<td>{stat_data[IN_TOK_BY_MODULE][module_name]}</td>"
f"<td>{stat_data[OUT_TOK_BY_MODULE][module_name]}</td>"
f"<td>{stat_data[TOTAL_TOK_BY_MODULE][module_name]}</td>"
f"<td>{stat_data[COST_BY_MODULE][module_name]:.4f} ¥</td>"
f"<td>{stat_data[AVG_TIME_COST_BY_MODULE][module_name]:.3f} 秒</td>"
f"<td>{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.3f} 秒</td>"
f"<td>{stat_data[COST_BY_MODULE][module_name]:.2f} ¥</td>"
f"<td>{stat_data[AVG_TIME_COST_BY_MODULE][module_name]:.1f} 秒</td>"
f"<td>{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.1f} 秒</td>"
f"</tr>"
for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
]
@@ -780,79 +780,47 @@ class StatisticOutputTask(AsyncTask):
<p class=\"info-item\"><strong>总在线时间: </strong>{_format_online_time(stat_data[ONLINE_TIME])}</p>
<p class=\"info-item\"><strong>总消息数: </strong>{stat_data[TOTAL_MSG_CNT]}</p>
<p class=\"info-item\"><strong>总请求数: </strong>{stat_data[TOTAL_REQ_CNT]}</p>
<p class=\"info-item\"><strong>总花费: </strong>{stat_data[TOTAL_COST]:.4f} ¥</p>
<p class=\"info-item\"><strong>总花费: </strong>{stat_data[TOTAL_COST]:.2f} ¥</p>
<div style="display: flex; flex-wrap: wrap; gap: 20px; margin: 20px 0;">
<div style="flex: 1; min-width: 300px;">
<h2>按模型分类统计</h2>
<table>
<thead><tr><th>模型名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr></thead>
<tbody>
{model_rows}
</tbody>
</table>
</div>
<div style="flex: 1; min-width: 300px;">
<h3>模型调用次数分布</h3>
<canvas id="modelPieChart_{div_id}" width="300" height="300"></canvas>
</div>
</div>
<h2>按模型分类统计</h2>
<table>
<thead><tr><th>模型名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr></thead>
<tbody>
{model_rows}
</tbody>
</table>
<div style="display: flex; flex-wrap: wrap; gap: 20px; margin: 20px 0;">
<div style="flex: 1; min-width: 300px;">
<h2>按模块分类统计</h2>
<table>
<thead>
<tr><th>模块名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr>
</thead>
<tbody>
{module_rows}
</tbody>
</table>
</div>
<div style="flex: 1; min-width: 300px;">
<h3>模块调用次数分布</h3>
<canvas id="modulePieChart_{div_id}" width="300" height="300"></canvas>
</div>
</div>
<h2>按模块分类统计</h2>
<table>
<thead>
<tr><th>模块名称</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr>
</thead>
<tbody>
{module_rows}
</tbody>
</table>
<div style="display: flex; flex-wrap: wrap; gap: 20px; margin: 20px 0;">
<div style="flex: 1; min-width: 300px;">
<h2>按请求类型分类统计</h2>
<table>
<thead>
<tr><th>请求类型</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr>
</thead>
<tbody>
{type_rows}
</tbody>
</table>
</div>
<div style="flex: 1; min-width: 300px;">
<h3>请求类型分布</h3>
<canvas id="typePieChart_{div_id}" width="300" height="300"></canvas>
</div>
</div>
<h2>按请求类型分类统计</h2>
<table>
<thead>
<tr><th>请求类型</th><th>调用次数</th><th>输入Token</th><th>输出Token</th><th>Token总量</th><th>累计花费</th><th>平均耗时(秒)</th><th>标准差(秒)</th></tr>
</thead>
<tbody>
{type_rows}
</tbody>
</table>
<div style="display: flex; flex-wrap: wrap; gap: 20px; margin: 20px 0;">
<div style="flex: 1; min-width: 300px;">
<h2>聊天消息统计</h2>
<table>
<thead>
<tr><th>联系人/群组名称</th><th>消息数量</th></tr>
</thead>
<tbody>
{chat_rows}
</tbody>
</table>
</div>
<div style="flex: 1; min-width: 300px;">
<h3>消息分布</h3>
<canvas id="chatPieChart_{div_id}" width="300" height="300"></canvas>
</div>
</div>
<h2>聊天消息统计</h2>
<table>
<thead>
<tr><th>联系人/群组名称</th><th>消息数量</th></tr>
</thead>
<tbody>
{chat_rows}
</tbody>
</table>
<script>
// 为当前统计卡片创建饼图
createPieCharts_{div_id}();
@@ -991,7 +959,7 @@ class StatisticOutputTask(AsyncTask):
}}
}});
}}
</script>
</div>
"""