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feat；优化了记忆检索的速度和token消耗（将question提出交给planenr)

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SengokuCola
2025-12-24 18:43:32 +08:00
parent 490589b0ad
commit 0852af49f9
15 changed files with 448 additions and 152 deletions
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33
src/chat/utils/statistic.py
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@@ -743,13 +743,13 @@ class StatisticOutputTask(AsyncTask):
"""
if stats[TOTAL_REQ_CNT] <= 0:
return ""
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12}"
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12} {:>12}"
total_replies = stats.get(TOTAL_REPLY_CNT, 0)
output = [
"按模型分类统计:",
" 模型名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒) 每次回复平均调用次数 每次回复平均Token数",
" 模型名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒) 每次回复平均调用次数 每次回复平均Token数 每次调用平均Token",
]
for model_name, count in sorted(stats[REQ_CNT_BY_MODEL].items()):
name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name
@@ -764,6 +764,9 @@ class StatisticOutputTask(AsyncTask):
avg_count_per_reply = count / total_replies if total_replies > 0 else 0.0
avg_tokens_per_reply = tokens / total_replies if total_replies > 0 else 0.0
# 计算每次调用平均token
avg_tokens_per_call = tokens / count if count > 0 else 0.0
# 格式化大数字
formatted_count = _format_large_number(count)
formatted_in_tokens = _format_large_number(in_tokens)
@@ -771,6 +774,7 @@ class StatisticOutputTask(AsyncTask):
formatted_tokens = _format_large_number(tokens)
formatted_avg_count = _format_large_number(avg_count_per_reply) if total_replies > 0 else "N/A"
formatted_avg_tokens = _format_large_number(avg_tokens_per_reply) if total_replies > 0 else "N/A"
formatted_avg_tokens_per_call = _format_large_number(avg_tokens_per_call) if count > 0 else "N/A"
output.append(
data_fmt.format(
@@ -784,6 +788,7 @@ class StatisticOutputTask(AsyncTask):
std_time_cost,
formatted_avg_count,
formatted_avg_tokens,
formatted_avg_tokens_per_call,
)
)
@@ -797,13 +802,13 @@ class StatisticOutputTask(AsyncTask):
"""
if stats[TOTAL_REQ_CNT] <= 0:
return ""
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12}"
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.2f}¥ {:>10.1f} {:>10.1f} {:>12} {:>12} {:>12}"
total_replies = stats.get(TOTAL_REPLY_CNT, 0)
output = [
"按模块分类统计:",
" 模块名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒) 每次回复平均调用次数 每次回复平均Token数",
" 模块名称 调用次数 输入Token 输出Token Token总量 累计花费 平均耗时(秒) 标准差(秒) 每次回复平均调用次数 每次回复平均Token数 每次调用平均Token",
]
for module_name, count in sorted(stats[REQ_CNT_BY_MODULE].items()):
name = f"{module_name[:29]}..." if len(module_name) > 32 else module_name
@@ -818,6 +823,9 @@ class StatisticOutputTask(AsyncTask):
avg_count_per_reply = count / total_replies if total_replies > 0 else 0.0
avg_tokens_per_reply = tokens / total_replies if total_replies > 0 else 0.0
# 计算每次调用平均token
avg_tokens_per_call = tokens / count if count > 0 else 0.0
# 格式化大数字
formatted_count = _format_large_number(count)
formatted_in_tokens = _format_large_number(in_tokens)
@@ -825,6 +833,7 @@ class StatisticOutputTask(AsyncTask):
formatted_tokens = _format_large_number(tokens)
formatted_avg_count = _format_large_number(avg_count_per_reply) if total_replies > 0 else "N/A"
formatted_avg_tokens = _format_large_number(avg_tokens_per_reply) if total_replies > 0 else "N/A"
formatted_avg_tokens_per_call = _format_large_number(avg_tokens_per_call) if count > 0 else "N/A"
output.append(
data_fmt.format(
@@ -838,6 +847,7 @@ class StatisticOutputTask(AsyncTask):
std_time_cost,
formatted_avg_count,
formatted_avg_tokens,
formatted_avg_tokens_per_call,
)
)
@@ -935,11 +945,12 @@ class StatisticOutputTask(AsyncTask):
f"<td>{stat_data[STD_TIME_COST_BY_MODEL][model_name]:.1f} 秒</td>"
f"<td>{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}</td>"
f"<td>{_format_large_number(stat_data[TOTAL_TOK_BY_MODEL][model_name] / total_replies, html=True) if total_replies > 0 else 'N/A'}</td>"
f"<td>{_format_large_number(stat_data[TOTAL_TOK_BY_MODEL][model_name] / count, html=True) if count > 0 else 'N/A'}</td>"
f"</tr>"
for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
]
if stat_data[REQ_CNT_BY_MODEL]
else ["<tr><td colspan='10' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
else ["<tr><td colspan='11' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
)
# 按请求类型分类统计
type_rows = "\n".join(
@@ -955,11 +966,12 @@ class StatisticOutputTask(AsyncTask):
f"<td>{stat_data[STD_TIME_COST_BY_TYPE][req_type]:.1f} 秒</td>"
f"<td>{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}</td>"
f"<td>{_format_large_number(stat_data[TOTAL_TOK_BY_TYPE][req_type] / total_replies, html=True) if total_replies > 0 else 'N/A'}</td>"
f"<td>{_format_large_number(stat_data[TOTAL_TOK_BY_TYPE][req_type] / count, html=True) if count > 0 else 'N/A'}</td>"
f"</tr>"
for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
]
if stat_data[REQ_CNT_BY_TYPE]
else ["<tr><td colspan='10' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
else ["<tr><td colspan='11' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
)
# 按模块分类统计
module_rows = "\n".join(
@@ -975,11 +987,12 @@ class StatisticOutputTask(AsyncTask):
f"<td>{stat_data[STD_TIME_COST_BY_MODULE][module_name]:.1f} 秒</td>"
f"<td>{_format_large_number(count / total_replies, html=True) if total_replies > 0 else 'N/A'}</td>"
f"<td>{_format_large_number(stat_data[TOTAL_TOK_BY_MODULE][module_name] / total_replies, html=True) if total_replies > 0 else 'N/A'}</td>"
f"<td>{_format_large_number(stat_data[TOTAL_TOK_BY_MODULE][module_name] / count, html=True) if count > 0 else 'N/A'}</td>"
f"</tr>"
for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
]
if stat_data[REQ_CNT_BY_MODULE]
else ["<tr><td colspan='10' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
else ["<tr><td colspan='11' style='text-align: center; color: #999;'>暂无数据</td></tr>"]
)
# 聊天消息统计
@@ -1054,7 +1067,7 @@ class StatisticOutputTask(AsyncTask):
<h2>按模型分类统计</h2>
<div class=\"table-wrap\">
<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><th>每次回复平均调用次数</th><th>每次回复平均Token数</th></tr></thead>
<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><th>每次回复平均调用次数</th><th>每次回复平均Token数</th><th>每次调用平均Token</th></tr></thead>
<tbody>
{model_rows}
</tbody>
@@ -1065,7 +1078,7 @@ class StatisticOutputTask(AsyncTask):
<div class=\"table-wrap\">
<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><th>每次回复平均调用次数</th><th>每次回复平均Token数</th></tr>
<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><th>每次回复平均调用次数</th><th>每次回复平均Token数</th><th>每次调用平均Token</th></tr>
</thead>
<tbody>
{module_rows}
@@ -1077,7 +1090,7 @@ class StatisticOutputTask(AsyncTask):
<div class=\"table-wrap\">
<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><th>每次回复平均调用次数</th><th>每次回复平均Token数</th></tr>
<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><th>每次回复平均调用次数</th><th>每次回复平均Token数</th><th>每次调用平均Token</th></tr>
</thead>
<tbody>
{type_rows}