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feat: add llmage module stat cards - model count, today's usage, amount, catalog count

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- Create stats.py with get_llmage_stats() helper function
- Add 4 stat widgets: stat_total_models, stat_today_calls, stat_today_amount, stat_catelog_count
- Update index.ui to display stat cards row above navigation cards
- Register get_llmage_stats in load_llmage()
This commit is contained in:
yumoqing 2026-05-25 18:43:35 +08:00
parent ae61193454
commit 6d14347ebc
9 changed files with 370 additions and 0 deletions
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53
dat/qwen3.7-max.txt Normal file
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已生成完整的 qwen3.7-max 配置SQL。以下是配置方案：
模型摘要
- 模型名称: 千问3.7-Max
- API model: qwen3.7-max (严格按API文档填写)
- 分类: text2text (文生文)
- 供应商: 阿里百炼 (ali-qwen)
- 接口: OpenAI兼容 /chat/completions，同步流式
复用vs新建
- upapp: 复用 ali-qwen (阿里百炼)
- upappkey: 复用 gCMl-BnrTrfoqWAEPPoH8 (API Key已配置)
- uapi: 复用 t2t (BY2cA4RD_axNxTG4m9Coa)
- uapiio: 复用 Is8l4TGkcZcqFSjbbeIK2 (文本会话)
- llm: 新建
- llm_api_map: 新建
- pricing_program: 新建
- pricing_program_timing: 新建
生成的SQL (4条)
sql
-- 1. llm表
INSERT INTO llm VALUES ('Jt26VKlUFsJxABuVLauBc','千问3.7-Max','qwen3.7-max','通义千问3.7系列旗舰模型，相较3.6版本在推理能力、代码能力、多模态理解等方面全面升级。适用于复杂任务处理、智能体开发、代码生成等高级应用场景。','text2text','qwen','ali-qwen','t2t','6fadgewjraOyvxC_EkHou','0','2026-05-23','9999-12-31','',NULL,10.00);
-- 2. llm_api_map表
INSERT INTO llm_api_map VALUES ('rFS0Ag-epBUxE5LCF6glW','Jt26VKlUFsJxABuVLauBc','text2text','t2t',NULL,NULL,'0B-JNSUEelsY5TQ_Sawa7','1');
-- 3. pricing_program表
INSERT INTO pricing_program VALUES ('0B-JNSUEelsY5TQ_Sawa7','qwen3.7-max定价','fields:\r\n formula:\r\n label: 计算公式\r\n type: str\r\n model:\r\n label: 模型\r\n type: str\r\n prompt_tokens:\r\n label: 输入词元数\r\n type: int\r\n value_mode: between\r\nformula: null\r\npricings:\r\n- formula: 2.5 prompt_tokens / 1000000 + completion_tokens 10 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: -0.1 ~= 32000\r\n- formula: 4 prompt_tokens / 1000000 + completion_tokens 16 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: 32000 ~= 128000\r\n- formula: 7 prompt_tokens / 1000000 + completion_tokens 28 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: 128000 ~= 256000\r\n- formula: 10 prompt_tokens / 1000000 + completion_tokens 40 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: 256000 ~= 1000000','2026-05-23','9999-12-31');
-- 4. pricing_program_timing表
INSERT INTO pricing_program_timing VALUES ('C-IIEG_9_O9beN8S2vdUI','0B-JNSUEelsY5TQ_Sawa7','qwen3.7-max计费','fields:\r\n formula:\r\n label: 计算公式\r\n type: str\r\n model:\r\n label: 模型\r\n type: str\r\n prompt_tokens:\r\n label: 输入词元数\r\n type: int\r\n value_mode: between\r\nformula: null\r\npricings:\r\n- formula: 2.5 prompt_tokens / 1000000 + completion_tokens 10 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: -0.1 ~= 32000\r\n- formula: 4 prompt_tokens / 1000000 + completion_tokens 16 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: 32000 ~= 128000\r\n- formula: 7 prompt_tokens / 1000000 + completion_tokens 28 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: 128000 ~= 256000\r\n- formula: 10 prompt_tokens / 1000000 + completion_tokens 40 /1000000\r\n model: qwen3.7-max\r\n prompt_tokens: 256000 ~= 1000000','2026-05-23','9999-12-31');
验证查询
sql
SELECT id, name, model, upappid, apiname FROM llm WHERE model = 'qwen3.7-max';
SELECT id, llmid, llmcatelogid, apiname, ppid FROM llm_api_map WHERE llmid = 'Jt26VKlUFsJxABuVLauBc';
SELECT id, name FROM pricing_program WHERE id = '0B-JNSUEelsY5TQ_Sawa7';
ppid三处一致
- pricing_program.id = 0B-JNSUEelsY5TQ_Sawa7
- pricing_program_timing.ppid = 0B-JNSUEelsY5TQ_Sawa7
- llm_api_map.ppid = 0B-JNSUEelsY5TQ_Sawa7
注意事项
1. 定价参考qwen3.6-plus的阶梯式计费模式，具体价格需根据百炼官方定价调整
2. qwen3.7-max使用已有的t2t uapi，OpenAI兼容接口
3. 如果qwen3.7-max有特殊的推理模式（如思考/非思考切换），可能需要额外的配置
4. 之前提到的 startReasoning is not defined 前端报错问题需要单独排查，可能与harnessed_reasoning的bricks前端代码有关

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llmage/__pycache__/accounting.cpython-310.pyc Normal file
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2
llmage/init.py
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@ -32,6 +32,7 @@ from .accounting import (
get_failed_accounting_records,
llm_accoung_failed
)
from .stats import get_llmage_stats
from .asyncinference import (
get_asynctask_status,
@ -63,6 +64,7 @@ def load_llmage():
env.get_llms_by_catelog_to_customer = get_llms_by_catelog_to_customer
env.backup_accounted_llmusage = backup_accounted_llmusage
env.get_failed_accounting_records = get_failed_accounting_records
env.get_llmage_stats = get_llmage_stats
rf = RegisterFunction()
rf.register('jimeng_auth_headers', jimeng_auth_headers)

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llmage/stats.py Normal file
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from sqlor.dbpools import get_sor_context
from appPublic.timeUtils import curDateString, timestampstr
from datetime import datetime, timedelta
from appPublic.log import debug, exception
async def get_llmage_stats(request):
"""Get llmage module statistics"""
env = request._run_ns
userorgid = await env.get_userorgid()
today = curDateString()
tomorrow = (datetime.now() + timedelta(days=1)).strftime('%Y-%m-%d')
stats = {
'total_models': 0,
'today_usage_count': 0,
'today_amount': 0,
'catelog_count': 0
}
async with get_sor_context(env, 'llmage') as sor:
# Total enabled models
sql_models = """
SELECT COUNT(DISTINCT id) as cnt FROM llm
WHERE enabled_date <= ${today}$
AND expired_date > ${today}$
"""
recs = await sor.sqlExe(sql_models, {'today': today})
if recs:
stats['total_models'] = int(recs[0].cnt or 0)
# Today's usage count
sql_usage = """
SELECT COUNT(*) as cnt FROM llmusage
WHERE userorgid = ${userorgid}$
AND use_date >= ${today}$
AND use_date < ${tomorrow}$
"""
recs = await sor.sqlExe(sql_usage, {
'userorgid': userorgid,
'today': today,
'tomorrow': tomorrow
})
if recs:
stats['today_usage_count'] = int(recs[0].cnt or 0)
# Today's total amount
sql_amount = """
SELECT COALESCE(SUM(amount), 0) as total FROM llmusage
WHERE userorgid = ${userorgid}$
AND use_date >= ${today}$
AND use_date < ${tomorrow}$
"""
recs = await sor.sqlExe(sql_amount, {
'userorgid': userorgid,
'today': today,
'tomorrow': tomorrow
})
if recs:
stats['today_amount'] = float(recs[0].total or 0)
# Catalog count
sql_catelog = """
SELECT COUNT(*) as cnt FROM llmcatelog
"""
recs = await sor.sqlExe(sql_catelog, {})
if recs:
stats['catelog_count'] = int(recs[0].cnt or 0)
return stats

34
wwwroot/index.ui
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@ -35,6 +35,40 @@
}
]
},
{
"widgettype": "ResponsableBox",
"options": {
"gap": "16px",
"minWidth": "200px",
"marginBottom": "24px"
},
"subwidgets": [
{
"widgettype": "urlwidget",
"options": {
"url": "{{entire_url('/llmage/stat_total_models.ui')}}"
}
},
{
"widgettype": "urlwidget",
"options": {
"url": "{{entire_url('/llmage/stat_today_calls.ui')}}"
}
},
{
"widgettype": "urlwidget",
"options": {
"url": "{{entire_url('/llmage/stat_today_amount.ui')}}"
}
},
{
"widgettype": "urlwidget",
"options": {
"url": "{{entire_url('/llmage/stat_catelog_count.ui')}}"
}
}
]
},
{
"widgettype": "ResponsableBox",
"options": {

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wwwroot/stat_catelog_count.ui Normal file
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{% set stats = get_llmage_stats(request) %}
{
"widgettype": "VBox",
"options": {
"bgcolor": "#1E293B",
"padding": "20px",
"borderRadius": "12px",
"border": "1px solid #334155",
"flex": "1",
"minHeight": "110px"
},
"subwidgets": [
{
"widgettype": "HBox",
"options": {
"alignItems": "center",
"marginBottom": "12px"
},
"subwidgets": [
{
"widgettype": "Svg",
"options": {
"svg": "<svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"#8B5CF6\" stroke-width=\"2\"><path d=\"M3.75 6A2.25 2.25 0 016 3.75h2.25A2.25 2.25 0 0110.5 6v2.25a2.25 2.25 0 01-2.25 2.25H6a2.25 2.25 0 01-2.25-2.25V6zM3.75 15.75A2.25 2.25 0 016 13.5h2.25a2.25 2.25 0 012.25 2.25V18a2.25 2.25 0 01-2.25 2.25H6A2.25 2.25 0 013.75 18v-2.25zM13.5 6a2.25 2.25 0 012.25-2.25H18A2.25 2.25 0 0120.25 6v2.25A2.25 2.25 0 0118 10.5h-2.25a2.25 2.25 0 01-2.25-2.25V6zM13.5 15.75a2.25 2.25 0 012.25-2.25H18a2.25 2.25 0 012.25 2.25V18A2.25 2.25 0 0118 20.25h-2.25A2.25 2.25 0 0113.5 18v-2.25z\"/></svg>",
"width": "24px",
"height": "24px"
}
},
{
"widgettype": "Filler"
}
]
},
{
"widgettype": "Text",
"options": {
"text": "{{stats.catelog_count}}",
"fontSize": "32px",
"fontWeight": "700",
"color": "#F1F5F9",
"lineHeight": "1.1"
}
},
{
"widgettype": "Text",
"options": {
"text": "模型分类",
"fontSize": "14px",
"color": "#94A3B8",
"marginTop": "4px"
}
}
]
}

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wwwroot/stat_today_amount.ui Normal file
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{% set stats = get_llmage_stats(request) %}
{
"widgettype": "VBox",
"options": {
"bgcolor": "#1E293B",
"padding": "20px",
"borderRadius": "12px",
"border": "1px solid #334155",
"flex": "1",
"minHeight": "110px"
},
"subwidgets": [
{
"widgettype": "HBox",
"options": {
"alignItems": "center",
"marginBottom": "12px"
},
"subwidgets": [
{
"widgettype": "Svg",
"options": {
"svg": "<svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"#F59E0B\" stroke-width=\"2\"><path d=\"M12 6v12m-3-2.818l.879.659c1.171.879 3.07.879 4.242 0 1.172-.879 1.172-2.303 0-3.182C13.536 12.219 12.768 12 12 12c-.725 0-1.45-.22-2.003-.659-1.106-.879-1.106-2.303 0-3.182s2.9-.879 4.006 0l.415.33M21 12a9 9 0 11-18 0 9 9 0 0118 0z\"/></svg>",
"width": "24px",
"height": "24px"
}
},
{
"widgettype": "Filler"
}
]
},
{
"widgettype": "Text",
"options": {
"text": "¥{{'%.2f' % stats.today_amount}}",
"fontSize": "32px",
"fontWeight": "700",
"color": "#F1F5F9",
"lineHeight": "1.1"
}
},
{
"widgettype": "Text",
"options": {
"text": "今日消费",
"fontSize": "14px",
"color": "#94A3B8",
"marginTop": "4px"
}
}
]
}

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wwwroot/stat_today_calls.ui Normal file
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@ -0,0 +1,53 @@
{% set stats = get_llmage_stats(request) %}
{
"widgettype": "VBox",
"options": {
"bgcolor": "#1E293B",
"padding": "20px",
"borderRadius": "12px",
"border": "1px solid #334155",
"flex": "1",
"minHeight": "110px"
},
"subwidgets": [
{
"widgettype": "HBox",
"options": {
"alignItems": "center",
"marginBottom": "12px"
},
"subwidgets": [
{
"widgettype": "Svg",
"options": {
"svg": "<svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"#22C55E\" stroke-width=\"2\"><path d=\"M7.5 21L3 16.5m0 0L7.5 12M12 9v7.5m0 0l4.5-4.5M12 9l4.5 4.5m0 0L12 16.5\"/><path d=\"M21 12h-4.5M12 3v4.5m0 0L7.5 12\"/></svg>",
"width": "24px",
"height": "24px"
}
},
{
"widgettype": "Filler"
}
]
},
{
"widgettype": "Text",
"options": {
"text": "{{stats.today_usage_count}}",
"fontSize": "32px",
"fontWeight": "700",
"color": "#F1F5F9",
"lineHeight": "1.1"
}
},
{
"widgettype": "Text",
"options": {
"text": "今日调用",
"fontSize": "14px",
"color": "#94A3B8",
"marginTop": "4px"
}
}
]
}

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wwwroot/stat_total_models.ui Normal file
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@ -0,0 +1,53 @@
{% set stats = get_llmage_stats(request) %}
{
"widgettype": "VBox",
"options": {
"bgcolor": "#1E293B",
"padding": "20px",
"borderRadius": "12px",
"border": "1px solid #334155",
"flex": "1",
"minHeight": "110px"
},
"subwidgets": [
{
"widgettype": "HBox",
"options": {
"alignItems": "center",
"marginBottom": "12px"
},
"subwidgets": [
{
"widgettype": "Svg",
"options": {
"svg": "<svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"#3B82F6\" stroke-width=\"2\"><path d=\"M9.813 15.904L9 18.75l-.813-2.846a4.5 4.5 0 00-3.09-3.09L2.25 12l2.846-.813a4.5 4.5 0 003.09-3.09L9 5.25l.813 2.846a4.5 4.5 0 003.09 3.09L15.75 12l-2.846.813a4.5 4.5 0 00-3.09 3.09zM18.259 8.715L18 9.75l-.259-1.035a3.375 3.375 0 00-2.455-2.456L14.25 6l1.036-.259a3.375 3.375 0 002.455-2.456L18 2.25l.259 1.035a3.375 3.375 0 002.455 2.456L21.75 6l-1.036.259a3.375 3.375 0 00-2.455 2.456zM16.894 20.567L16.5 21.75l-.394-1.183a2.25 2.25 0 00-1.423-1.423L13.5 18.75l1.183-.394a2.25 2.25 0 001.423-1.423l.394-1.183.394 1.183a2.25 2.25 0 001.423 1.423l1.183.394-1.183.394a2.25 2.25 0 00-1.423 1.423z\"/></svg>",
"width": "24px",
"height": "24px"
}
},
{
"widgettype": "Filler"
}
]
},
{
"widgettype": "Text",
"options": {
"text": "{{stats.total_models}}",
"fontSize": "32px",
"fontWeight": "700",
"color": "#F1F5F9",
"lineHeight": "1.1"
}
},
{
"widgettype": "Text",
"options": {
"text": "可用模型数",
"fontSize": "14px",
"color": "#94A3B8",
"marginTop": "4px"
}
}
]
}