fix(llmage): 修复备份SQL条件 use_date < today（非yesterday）+ 提高备份频率到每5分钟

dat/qwen3.7-max.txt

@@ -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
+

llmage/accounting.py

@@ -240,19 +240,19 @@ async def llm_accoung_failed(luid, reason=None):
 
 
 async def backup_accounted_llmusage():
-	"""Backup yesterday's accounted records to history table and remove from llmusage."""
+	"""Backup accounted records with use_date before today to history table."""
 	env = ServerEnv()
-	yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')
+	today = datetime.now().strftime('%Y-%m-%d')
 	ts = env.timestampstr()
 	batched = 0
 	async with get_sor_context(env, 'llmage') as sor:
-		# Select yesterday's accounted records
+		# Select records with use_date < today (i.e. yesterday and earlier)
 		sql = """select * from llmusage 
 where accounting_status='accounted' 
-	and use_date < ${yesterday}$"""
+	and use_date < ${today}$"""
-		recs = await sor.sqlExe(sql, {'yesterday': yesterday})
+		recs = await sor.sqlExe(sql, {'today': today})
 		if not recs:
-			debug(f'backup_accounted_llmusage: no records to backup for use_date < {yesterday}')
+			debug(f'backup_accounted_llmusage: no records to backup for use_date < {today}')
 			return 0
 		debug(f'backup_accounted_llmusage: {len(recs)} records to backup')
 		for r in recs:
@@ -356,9 +356,9 @@ async def backend_accounting():
 				exception(f'{e}, {lu.id=}')
 				await llm_accoung_failed(lu.id, reason=str(e))
 
-		# Run backup every 100 iterations (roughly every ~1000 seconds)
+		# Run backup every 30 iterations (~5 minutes)
 		backup_counter += 1
-		if backup_counter >= 100:
+		if backup_counter >= 30:
 			backup_counter = 0
 			try:
 				await backup_accounted_llmusage()