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llm统计记录模型反应时间

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雅诺狐
2025-08-16 14:26:18 +08:00
parent 8277e19728
commit 7dfaf54c9c
4 changed files with 89 additions and 8 deletions
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78
src/chat/utils/statistic.py
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@@ -78,6 +78,18 @@ COST_BY_MODULE = "costs_by_module"
ONLINE_TIME = "online_time"
TOTAL_MSG_CNT = "total_messages"
MSG_CNT_BY_CHAT = "messages_by_chat"
TIME_COST_BY_TYPE = "time_costs_by_type"
TIME_COST_BY_USER = "time_costs_by_user"
TIME_COST_BY_MODEL = "time_costs_by_model"
TIME_COST_BY_MODULE = "time_costs_by_module"
AVG_TIME_COST_BY_TYPE = "avg_time_costs_by_type"
AVG_TIME_COST_BY_USER = "avg_time_costs_by_user"
AVG_TIME_COST_BY_MODEL = "avg_time_costs_by_model"
AVG_TIME_COST_BY_MODULE = "avg_time_costs_by_module"
STD_TIME_COST_BY_TYPE = "std_time_costs_by_type"
STD_TIME_COST_BY_USER = "std_time_costs_by_user"
STD_TIME_COST_BY_MODEL = "std_time_costs_by_model"
STD_TIME_COST_BY_MODULE = "std_time_costs_by_module"
class OnlineTimeRecordTask(AsyncTask):
@@ -338,6 +350,18 @@ class StatisticOutputTask(AsyncTask):
COST_BY_USER: defaultdict(float),
COST_BY_MODEL: defaultdict(float),
COST_BY_MODULE: defaultdict(float),
TIME_COST_BY_TYPE: defaultdict(list),
TIME_COST_BY_USER: defaultdict(list),
TIME_COST_BY_MODEL: defaultdict(list),
TIME_COST_BY_MODULE: defaultdict(list),
AVG_TIME_COST_BY_TYPE: defaultdict(float),
AVG_TIME_COST_BY_USER: defaultdict(float),
AVG_TIME_COST_BY_MODEL: defaultdict(float),
AVG_TIME_COST_BY_MODULE: defaultdict(float),
STD_TIME_COST_BY_TYPE: defaultdict(float),
STD_TIME_COST_BY_USER: defaultdict(float),
STD_TIME_COST_BY_MODEL: defaultdict(float),
STD_TIME_COST_BY_MODULE: defaultdict(float),
}
for period_key, _ in collect_period
}
@@ -394,7 +418,40 @@ class StatisticOutputTask(AsyncTask):
stats[period_key][COST_BY_USER][user_id] += cost
stats[period_key][COST_BY_MODEL][model_name] += cost
stats[period_key][COST_BY_MODULE][module_name] += cost
# 收集time_cost数据
time_cost = record.time_cost or 0.0
if time_cost > 0: # 只记录有效的time_cost
stats[period_key][TIME_COST_BY_TYPE][request_type].append(time_cost)
stats[period_key][TIME_COST_BY_USER][user_id].append(time_cost)
stats[period_key][TIME_COST_BY_MODEL][model_name].append(time_cost)
stats[period_key][TIME_COST_BY_MODULE][module_name].append(time_cost)
break
# 计算平均耗时和标准差
for period_key in stats:
for category in [REQ_CNT_BY_TYPE, REQ_CNT_BY_USER, REQ_CNT_BY_MODEL, REQ_CNT_BY_MODULE]:
time_cost_key = f"time_costs_by_{category.split('_')[-1]}"
avg_key = f"avg_time_costs_by_{category.split('_')[-1]}"
std_key = f"std_time_costs_by_{category.split('_')[-1]}"
for item_name in stats[period_key][category]:
time_costs = stats[period_key][time_cost_key].get(item_name, [])
if time_costs:
# 计算平均耗时
avg_time_cost = sum(time_costs) / len(time_costs)
stats[period_key][avg_key][item_name] = round(avg_time_cost, 3)
# 计算标准差
if len(time_costs) > 1:
variance = sum((x - avg_time_cost) ** 2 for x in time_costs) / len(time_costs)
std_time_cost = variance ** 0.5
stats[period_key][std_key][item_name] = round(std_time_cost, 3)
else:
stats[period_key][std_key][item_name] = 0.0
else:
stats[period_key][avg_key][item_name] = 0.0
stats[period_key][std_key][item_name] = 0.0
return stats
@staticmethod
@@ -626,11 +683,10 @@ class StatisticOutputTask(AsyncTask):
"""
if stats[TOTAL_REQ_CNT] <= 0:
return ""
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.4f}¥"
data_fmt = "{:<32} {:>10} {:>12} {:>12} {:>12} {:>9.4f}¥ {:>10} {:>10}"
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",
" 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[REQ_CNT_BY_MODEL].items()):
name = f"{model_name[:29]}..." if len(model_name) > 32 else model_name
@@ -638,7 +694,9 @@ class StatisticOutputTask(AsyncTask):
out_tokens = stats[OUT_TOK_BY_MODEL][model_name]
tokens = stats[TOTAL_TOK_BY_MODEL][model_name]
cost = stats[COST_BY_MODEL][model_name]
output.append(data_fmt.format(name, count, in_tokens, out_tokens, tokens, cost))
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))
output.append("")
return "\n".join(output)
@@ -723,6 +781,8 @@ class StatisticOutputTask(AsyncTask):
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"</tr>"
for model_name, count in sorted(stat_data[REQ_CNT_BY_MODEL].items())
]
@@ -737,6 +797,8 @@ class StatisticOutputTask(AsyncTask):
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"</tr>"
for req_type, count in sorted(stat_data[REQ_CNT_BY_TYPE].items())
]
@@ -751,6 +813,8 @@ class StatisticOutputTask(AsyncTask):
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"</tr>"
for module_name, count in sorted(stat_data[REQ_CNT_BY_MODULE].items())
]
@@ -777,7 +841,7 @@ class StatisticOutputTask(AsyncTask):
<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></tr></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>
<tbody>
{model_rows}
</tbody>
@@ -786,7 +850,7 @@ class StatisticOutputTask(AsyncTask):
<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></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></tr>
</thead>
<tbody>
{module_rows}
@@ -796,7 +860,7 @@ class StatisticOutputTask(AsyncTask):
<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></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></tr>
</thead>
<tbody>
{type_rows}

8
src/common/database/sqlalchemy_models.py
View File

@@ -30,6 +30,8 @@ def get_string_field(max_length=255, **kwargs):
else:
return Text(**kwargs)
class SessionProxy:
"""线程安全的Session代理类，自动管理session生命周期"""
@@ -155,11 +157,14 @@ class LLMUsage(Base):
id = Column(Integer, primary_key=True, autoincrement=True)
model_name = Column(get_string_field(100), nullable=False, index=True)
model_assign_name = Column(get_string_field(100), index=True) # 添加索引
model_api_provider = Column(get_string_field(100), index=True) # 添加索引
user_id = Column(get_string_field(50), nullable=False, index=True)
request_type = Column(get_string_field(50), nullable=False, index=True)
endpoint = Column(Text, nullable=False)
prompt_tokens = Column(Integer, nullable=False)
completion_tokens = Column(Integer, nullable=False)
time_cost = Column(Float, nullable=True)
total_tokens = Column(Integer, nullable=False)
cost = Column(Float, nullable=False)
status = Column(Text, nullable=False)
@@ -167,6 +172,9 @@ class LLMUsage(Base):
__table_args__ = (
Index('idx_llmusage_model_name', 'model_name'),
Index('idx_llmusage_model_assign_name', 'model_assign_name'),
Index('idx_llmusage_model_api_provider', 'model_api_provider'),
Index('idx_llmusage_time_cost', 'time_cost'),
Index('idx_llmusage_user_id', 'user_id'),
Index('idx_llmusage_request_type', 'request_type'),
Index('idx_llmusage_timestamp', 'timestamp'),

5
src/llm_models/utils.py
View File

@@ -147,7 +147,7 @@ class LLMUsageRecorder:
def record_usage_to_database(
self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str
self, model_info: ModelInfo, model_usage: UsageRecord, user_id: str, request_type: str, endpoint: str, time_cost: float = 0.0
):
input_cost = (model_usage.prompt_tokens / 1000000) * model_info.price_in
output_cost = (model_usage.completion_tokens / 1000000) * model_info.price_out
@@ -160,6 +160,8 @@ class LLMUsageRecorder:
usage_record = LLMUsage(
model_name=model_info.model_identifier,
model_assign_name=model_info.name,
model_api_provider=model_info.api_provider,
user_id=user_id,
request_type=request_type,
endpoint=endpoint,
@@ -167,6 +169,7 @@ class LLMUsageRecorder:
completion_tokens=model_usage.completion_tokens or 0,
total_tokens=model_usage.total_tokens or 0,
cost=total_cost or 0.0,
time_cost = round(time_cost or 0.0, 3),
status="success",
timestamp=datetime.now(), # SQLAlchemy 会处理 DateTime 字段
)

6
src/llm_models/utils_model.py
View File

@@ -115,6 +115,7 @@ class LLMRequest:
normalized_format = _normalize_image_format(image_format)
# 模型选择
start_time = time.time()
model_info, api_provider, client = self._select_model()
# 请求体构建
@@ -147,6 +148,7 @@ class LLMRequest:
model_info=model_info,
model_usage=usage,
user_id="system",
time_cost=time.time() - start_time,
request_type=self.request_type,
endpoint="/chat/completions",
)
@@ -240,6 +242,7 @@ class LLMRequest:
) -> Tuple[str, Tuple[str, str, Optional[List[ToolCall]]]]:
"""执行单次请求"""
# 模型选择和请求准备
start_time = time.time()
model_info, api_provider, client = self._select_model()
processed_prompt = self._apply_content_obfuscation(prompt, api_provider)
@@ -293,6 +296,7 @@ class LLMRequest:
llm_usage_recorder.record_usage_to_database(
model_info=model_info,
model_usage=usage,
time_cost=time.time() - start_time,
user_id="system",
request_type=self.request_type,
endpoint="/chat/completions",
@@ -331,6 +335,7 @@ class LLMRequest:
(Tuple[List[float], str]): (嵌入向量，使用的模型名称)
"""
# 无需构建消息体，直接使用输入文本
start_time = time.time()
model_info, api_provider, client = self._select_model()
# 请求并处理返回值
@@ -347,6 +352,7 @@ class LLMRequest:
if usage := response.usage:
llm_usage_recorder.record_usage_to_database(
model_info=model_info,
time_cost=time.time() - start_time,
model_usage=usage,
user_id="system",
request_type=self.request_type,