feat(llmage): 日期变更触发备份(use_date<昨天) + llmusage添加(accounting_status,use_date)组合索引
This commit is contained in:
parent
8755ab5d6d
commit
c7acd449f1
53
dat/qwen3.7-max.txt
Normal file
53
dat/qwen3.7-max.txt
Normal file
@ -0,0 +1,53 @@
|
||||
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
|
||||
|
||||
BIN
llmage/__pycache__/accounting.cpython-310.pyc
Normal file
BIN
llmage/__pycache__/accounting.cpython-310.pyc
Normal file
Binary file not shown.
@ -1,7 +1,7 @@
|
||||
import asyncio
|
||||
import json
|
||||
import time
|
||||
from datetime import datetime
|
||||
from datetime import datetime, timedelta
|
||||
from appPublic.log import exception, debug
|
||||
from appPublic.uniqueID import getID
|
||||
from appPublic.dictObject import DictObject
|
||||
@ -239,20 +239,18 @@ async def llm_accoung_failed(luid, reason=None):
|
||||
await sor.C('llmusage_accounting_failed', failed_rec)
|
||||
|
||||
|
||||
async def backup_accounted_llmusage():
|
||||
"""Backup accounted records with use_date before today to history table."""
|
||||
async def backup_accounted_llmusage(cutoff_date):
|
||||
"""Backup accounted records with use_date < cutoff_date to history table."""
|
||||
env = ServerEnv()
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
ts = env.timestampstr()
|
||||
batched = 0
|
||||
async with get_sor_context(env, 'llmage') as sor:
|
||||
# Select records with use_date < today (i.e. yesterday and earlier)
|
||||
sql = """select * from llmusage
|
||||
where accounting_status='accounted'
|
||||
and use_date < ${today}$"""
|
||||
recs = await sor.sqlExe(sql, {'today': today})
|
||||
and use_date < ${cutoff_date}$"""
|
||||
recs = await sor.sqlExe(sql, {'cutoff_date': cutoff_date})
|
||||
if not recs:
|
||||
debug(f'backup_accounted_llmusage: no records to backup for use_date < {today}')
|
||||
debug(f'backup_accounted_llmusage: no records to backup for use_date < {cutoff_date}')
|
||||
return 0
|
||||
debug(f'backup_accounted_llmusage: {len(recs)} records to backup')
|
||||
for r in recs:
|
||||
@ -280,7 +278,7 @@ where accounting_status='accounted'
|
||||
# Delete from main table
|
||||
await sor.D('llmusage', {'id': r.id})
|
||||
batched += 1
|
||||
debug(f'backup_accounted_llmusage: backed up {batched} records')
|
||||
debug(f'backup_accounted_llmusage: backed up {batched} records for use_date < {cutoff_date}')
|
||||
return batched
|
||||
|
||||
|
||||
@ -335,14 +333,13 @@ order by failed_time desc limit {page_size} offset {offset}"""
|
||||
async def backend_accounting():
|
||||
env = ServerEnv()
|
||||
debug(f'backend accounting started ...')
|
||||
backup_counter = 0
|
||||
last_backup_date = None
|
||||
while True:
|
||||
try:
|
||||
lus = await get_accounting_llmusages()
|
||||
except Exception as e:
|
||||
exception(f'{e}')
|
||||
lus = []
|
||||
# debug(f'{len(lus)=} need to accounting........')
|
||||
for lu in lus:
|
||||
try:
|
||||
tpac = await get_user_tpac(lu.userid)
|
||||
@ -356,12 +353,14 @@ async def backend_accounting():
|
||||
exception(f'{e}, {lu.id=}')
|
||||
await llm_accoung_failed(lu.id, reason=str(e))
|
||||
|
||||
# Run backup every 30 iterations (~5 minutes)
|
||||
backup_counter += 1
|
||||
if backup_counter >= 30:
|
||||
backup_counter = 0
|
||||
# Check if date changed, trigger backup once per day
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
if today != last_backup_date:
|
||||
yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
|
||||
last_backup_date = today
|
||||
try:
|
||||
await backup_accounted_llmusage()
|
||||
debug(f'date changed to {today}, triggering backup for use_date < {yesterday}')
|
||||
await backup_accounted_llmusage(yesterday)
|
||||
except Exception as e:
|
||||
exception(f'backup_accounted_llmusage failed: {e}')
|
||||
|
||||
|
||||
@ -123,6 +123,14 @@
|
||||
"idxfields": [
|
||||
"userid"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "idx_llmusage_accounting",
|
||||
"idxtype": "index",
|
||||
"idxfields": [
|
||||
"accounting_status",
|
||||
"use_date"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -69,6 +69,9 @@ CREATE INDEX idx_laf_llmid ON llmusage_accounting_failed(llmid);
|
||||
CREATE INDEX idx_laf_handled ON llmusage_accounting_failed(handled);
|
||||
CREATE INDEX idx_laf_failed_time ON llmusage_accounting_failed(failed_time);
|
||||
|
||||
-- 3. 为 llmusage 表添加组合索引（优化备份查询: accounting_status + use_date）
|
||||
CREATE INDEX idx_llmusage_accounting ON llmusage(accounting_status, use_date);
|
||||
|
||||
-- ============================================================
|
||||
-- 验证步骤（执行后运行）:
|
||||
-- 1. 确认表创建成功:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user