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feat(statistic): 为供应商统计增加平均耗时和标准差指标

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- 为按供应商分类的统计数据新增了平均请求耗时和耗时标准差的计算与展示。
- 重构了统计数据计算逻辑，统一使用 `defaultdict` 的直接索引访问替代 `.get()` 方法，使代码更简洁并提高了健壮性。
- 标准化了与耗时相关的统计键名，以提高代码的一致性和可读性。
This commit is contained in:
minecraft1024a
2025-11-14 12:23:38 +08:00
parent 7ff9edd35a
commit ec1ce9db8e
2 changed files with 23 additions and 28 deletions
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47
src/chat/utils/statistic.py
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@@ -157,7 +157,6 @@ class StatisticOutputTask(AsyncTask):
:param now: 基准当前时间
"""
# 输出最近一小时的统计数据
output = [
self.SEP_LINE,
f" 最近1小时的统计数据 (自{now.strftime('%Y-%m-%d %H:%M:%S')}开始，详细信息见文件：{self.record_file_path})",
@@ -279,6 +278,8 @@ class StatisticOutputTask(AsyncTask):
STD_TIME_COST_BY_USER: defaultdict(float),
STD_TIME_COST_BY_MODEL: defaultdict(float),
STD_TIME_COST_BY_MODULE: defaultdict(float),
AVG_TIME_COST_BY_PROVIDER: defaultdict(float),
STD_TIME_COST_BY_PROVIDER: defaultdict(float),
# New calculated fields
TPS_BY_MODEL: defaultdict(float),
COST_PER_KTOK_BY_MODEL: defaultdict(float),
@@ -377,9 +378,9 @@ class StatisticOutputTask(AsyncTask):
for period_key, period_stats in stats.items():
# 计算模型相关指标
for model_name, req_count in period_stats[REQ_CNT_BY_MODEL].items():
total_tok = period_stats[TOTAL_TOK_BY_MODEL].get(model_name, 0)
total_cost = period_stats[COST_BY_MODEL].get(model_name, 0.0)
time_costs = period_stats[TIME_COST_BY_MODEL].get(model_name, [])
total_tok = period_stats[TOTAL_TOK_BY_MODEL][model_name] or 0
total_cost = period_stats[COST_BY_MODEL][model_name] or 0
time_costs = period_stats[TIME_COST_BY_MODEL][model_name] or []
total_time_cost = sum(time_costs)
# TPS
@@ -393,9 +394,9 @@ class StatisticOutputTask(AsyncTask):
# 计算供应商相关指标
for provider_name, req_count in period_stats[REQ_CNT_BY_PROVIDER].items():
total_tok = period_stats[TOTAL_TOK_BY_PROVIDER].get(provider_name, 0)
total_cost = period_stats[COST_BY_PROVIDER].get(provider_name, 0.0)
time_costs = period_stats[TIME_COST_BY_PROVIDER].get(provider_name, [])
total_tok = period_stats[TOTAL_TOK_BY_PROVIDER][provider_name]
total_cost = period_stats[COST_BY_PROVIDER][provider_name]
time_costs = period_stats[TIME_COST_BY_PROVIDER][provider_name]
total_time_cost = sum(time_costs)
# TPS
@@ -407,23 +408,16 @@ class StatisticOutputTask(AsyncTask):
# 计算平均耗时和标准差
for category_key, items in [
(REQ_CNT_BY_TYPE, "type"),
(REQ_CNT_BY_USER, "user"),
(REQ_CNT_BY_MODEL, "model"),
(REQ_CNT_BY_MODULE, "module"),
(REQ_CNT_BY_PROVIDER, "provider"),
]:
time_cost_key = f"TIME_COST_BY_{items.upper()}"
avg_key = f"AVG_TIME_COST_BY_{items.upper()}"
std_key = f"STD_TIME_COST_BY_{items.upper()}"
# Ensure the stat dicts exist before trying to access them, making the process more robust.
period_stats.setdefault(time_cost_key, defaultdict(list))
period_stats.setdefault(avg_key, defaultdict(float))
period_stats.setdefault(std_key, defaultdict(float))
for item_name in period_stats.get(category_key, {}):
time_costs = period_stats[time_cost_key].get(item_name, [])
time_cost_key = f"time_costs_by_{items.lower()}"
avg_key = f"avg_time_costs_by_{items.lower()}"
std_key = f"std_time_costs_by_{items.lower()}"
for item_name in period_stats[category_key]:
time_costs = period_stats[time_cost_key][item_name]
if time_costs:
avg_time = sum(time_costs) / len(time_costs)
period_stats[avg_key][item_name] = round(avg_time, 3)
@@ -622,7 +616,6 @@ class StatisticOutputTask(AsyncTask):
stat[period_key].update(model_req_stat.get(period_key, {}))
stat[period_key].update(online_time_stat.get(period_key, {}))
stat[period_key].update(message_count_stat.get(period_key, {}))
if last_all_time_stat:
# 若存在上次完整统计数据，则将其与当前统计数据合并
for key, val in last_all_time_stat.items():
@@ -706,14 +699,14 @@ class StatisticOutputTask(AsyncTask):
output = [
" 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)",
]
for model_name, count in sorted(stats.get(REQ_CNT_BY_MODEL, {}).items()):
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
in_tokens = stats.get(IN_TOK_BY_MODEL, {}).get(model_name, 0)
out_tokens = stats.get(OUT_TOK_BY_MODEL, {}).get(model_name, 0)
tokens = stats.get(TOTAL_TOK_BY_MODEL, {}).get(model_name, 0)
cost = stats.get(COST_BY_MODEL, {}).get(model_name, 0.0)
avg_time_cost = stats.get(AVG_TIME_COST_BY_MODEL, {}).get(model_name, 0.0)
std_time_cost = stats.get(STD_TIME_COST_BY_MODEL, {}).get(model_name, 0.0)
in_tokens = stats[IN_TOK_BY_MODEL][model_name]
out_tokens = stats[OUT_TOK_BY_MODEL][model_name]
tokens = stats[TOTAL_TOK_BY_MODEL][model_name]
cost = stats[COST_BY_MODEL][model_name]
avg_time_cost = stats[AVG_TIME_COST_BY_MODEL][model_name]
std_time_cost = stats[STD_TIME_COST_BY_MODEL][model_name]
output.append(
data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost, avg_time_cost, std_time_cost)
)