feat: 2026-04-15~05-02 累积变更基线 — AI 重构 + Runtime Context + DWS 修复
涵盖（每条对应已存的审计记录）： - AI 模块拆分：apps/backend/app/ai/apps -> prompts/（8 个 APP + app2a 派生） audit: 2026-04-20__ai-module-complete.md - admin-web AI 管理套件：AIDashboard / AIOperations / AIRunLogs / AITriggers / TriggerManager audit: 2026-04-21__admin-web-ai-management-suite.md - App2 财务洞察 prompt v3 -> v5.1 + 小程序 AI 接入（chat / board-finance） audit: 2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md - App2 prewarm 全过滤器 + AI 触发器 cron reschedule audit: 2026-04-21__app2-finance-prewarm-all-filters.md migration: 20260420_ai_trigger_jobs_and_app2_prewarm.sql / 20260421_app2_prewarm_cron_reschedule.sql - AppType 联合类型对齐 + adminAiAppTypes.test.ts audit: 2026-04-30__admin_web_ai_app_type_alignment.md - DashScope tokens_used 提取修复 audit: 2026-04-30__backend_dashscope_tokens_used_extraction.md - App3 线索完整详情 prompt audit: 2026-05-01__backend_app3_full_detail_prompt.md - Runtime Context 沙箱（5-1~5-2 主线）： - 后端 schema/service + admin_runtime_context / xcx_runtime_clock 两个 router - admin-web RuntimeContext.tsx + miniprogram runtime-clock.ts - migration: 20260501__runtime_context_sandbox.sql - tools/db/verify_admin_web_sandbox.py + verify_sandbox_end_to_end.py - database/changes: 7 份 sandbox_* 验证报告 - 飞球 DWS 修复：finance_area_daily 区域汇总 + task_engine 调整 + RLS 视图业务日上界（migration 20260502 + scripts/ops/gen_rls_business_date_migration.py） 合规： - .gitignore 启用 tmp/ 排除 - 不入仓：apps/etl/connectors/feiqiu/.env（API_TOKEN secret，本地修改保留） 待验证清单： - docs/audit/changes/2026-05-04__cumulative_baseline_pending_verification.md 每个主题的功能完整性 / 上线验证几乎都未收口，按优先级 P0~P3 逐一处理
This commit is contained in:
2
.gitignore
vendored
2
.gitignore
vendored
@@ -1,5 +1,5 @@
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# ===== 临时与缓存 =====
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# tmp/
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tmp/
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__pycache__/
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*.pyc
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*.py[cod]
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@@ -25,6 +25,7 @@ import {
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TeamOutlined,
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BugOutlined,
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ApartmentOutlined,
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RobotOutlined,
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} from "@ant-design/icons";
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import type { MenuProps } from "antd";
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import { useAuthStore } from "./store/authStore";
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@@ -36,6 +37,10 @@ import EnvConfig from "./pages/EnvConfig";
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import DBViewer from "./pages/DBViewer";
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import TenantAdmins from "./pages/TenantAdmins";
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import AIRunLogs from "./pages/AIRunLogs";
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import AIDashboard from "./pages/AIDashboard";
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import AIOperations from "./pages/AIOperations";
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import AITriggerJobs from "./pages/AITriggerJobs";
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import AIPrewarm from "./pages/AIPrewarm";
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import DevTrace from "./pages/DevTrace";
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import TriggerJobs from "./pages/TriggerJobs";
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import TransferLog from "./pages/TransferLog";
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@@ -44,6 +49,7 @@ import TaskEngineConfig from "./pages/TaskEngineConfig";
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import Dashboard from "./pages/Dashboard";
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import ETLTasks from "./pages/ETLTasks";
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import TriggerManager from "./pages/TriggerManager";
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import RuntimeContextPage from "./pages/RuntimeContext";
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const { Sider, Content, Footer } = Layout;
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const { Text } = Typography;
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@@ -65,11 +71,22 @@ export const NAV_ITEMS: MenuProps["items"] = [
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],
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},
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{ key: "/triggers", icon: <ClockCircleOutlined />, label: "触发器管理" },
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{
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key: "ai-group", icon: <RobotOutlined />, label: "AI 管理",
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children: [
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{ key: "/ai/dashboard", label: "总览" },
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{ key: "/ai/operations", label: "手动操作" },
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{ key: "/ai/prewarm", label: "预热进度" },
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{ key: "/triggers?tab=ai", label: "触发器设置" },
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{ key: "/ai/trigger-jobs", label: "调度历史" },
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],
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},
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{ key: "/tenant-admins", icon: <TeamOutlined />, label: "租户管理员" },
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{
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key: "settings-group", icon: <SettingOutlined />, label: "系统设置",
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children: [
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{ key: "/settings/env-config", label: "环境配置" },
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{ key: "/settings/runtime-context", label: "业务运行上下文 / 沙箱" },
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{ key: "/triggers?tab=biz", label: "触发器配置" },
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],
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},
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@@ -90,12 +107,14 @@ export const NAV_ITEMS: MenuProps["items"] = [
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/** 根据当前路径计算 selectedKeys */
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export function getSelectedKeys(pathname: string, search: string): string[] {
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const fullPath = pathname + search;
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// 精确匹配含查询参数的菜单项（如 /triggers?tab=biz）
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// 精确匹配含查询参数的菜单项（如 /triggers?tab=biz / ?tab=ai）
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if (fullPath === "/triggers?tab=biz") return ["/triggers?tab=biz"];
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if (fullPath === "/triggers?tab=ai") return ["/triggers?tab=ai"];
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// 子路由匹配
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if (pathname.startsWith("/task-engine/")) return [pathname];
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if (pathname.startsWith("/settings/")) return [pathname];
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if (pathname.startsWith("/logs/")) return [pathname];
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if (pathname.startsWith("/ai/")) return [pathname];
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// 一级路由直接匹配
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return [pathname];
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}
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@@ -106,6 +125,9 @@ export function getDefaultOpenKeys(pathname: string): string[] {
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if (pathname.startsWith("/task-engine/")) keys.push("task-engine-group");
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if (pathname.startsWith("/settings/")) keys.push("settings-group");
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if (pathname.startsWith("/logs/")) keys.push("logs-group");
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if (pathname.startsWith("/ai/")) keys.push("ai-group");
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// 从 AI 菜单跳过来的"触发器设置"（/triggers?tab=ai）也展开 ai-group
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// 注：此函数参数只接收 pathname，无法判断 tab，交由路由侧 searchParams 处理默认展开
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// 触发器配置跳转入口也需要展开系统设置
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if (pathname === "/triggers") keys.push("settings-group");
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return keys;
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@@ -225,6 +247,13 @@ const AppLayout: React.FC = () => {
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{/* 系统设置 */}
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<Route path="/settings/env-config" element={<EnvConfig />} />
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<Route path="/settings/runtime-context" element={<RuntimeContextPage />} />
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{/* AI 管理 */}
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<Route path="/ai/dashboard" element={<AIDashboard />} />
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<Route path="/ai/operations" element={<AIOperations />} />
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<Route path="/ai/prewarm" element={<AIPrewarm />} />
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<Route path="/ai/trigger-jobs" element={<AITriggerJobs />} />
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{/* 日志调试 */}
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<Route path="/logs/dev-trace" element={<DevTrace />} />
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43
apps/admin-web/src/__tests__/adminAiAppTypes.test.ts
Normal file
43
apps/admin-web/src/__tests__/adminAiAppTypes.test.ts
Normal file
@@ -0,0 +1,43 @@
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/**
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* 回归测试：admin-web 手动执行 App 类型必须与后端 /api/admin/ai/run/{app_type} 对齐。
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*
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* 缓存类型仍使用 `*_analysis` / `*_consolidated`，但手动执行和 run log
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* 应使用 dispatcher 支持的 app_type，避免前端发出后端 400 的路径。
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*/
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import { describe, expect, it } from "vitest";
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import { RUN_APP_TYPES } from "../api/adminAI";
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import { CACHE_TYPE_OPTIONS, RUN_APP_TYPE_OPTIONS } from "../pages/AIOperations";
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import { RUN_LOG_APP_TYPE_OPTIONS } from "../pages/AIRunLogs";
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describe("admin AI app_type 对齐", () => {
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it("手动执行类型使用后端支持的 app_type，而不是缓存类型", () => {
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const apiTypes = [...RUN_APP_TYPES];
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const runOptionValues = RUN_APP_TYPE_OPTIONS.map((item) => item.value);
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for (const appType of ["app6_note", "app7_customer", "app8_consolidation"]) {
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expect(apiTypes).toContain(appType);
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expect(runOptionValues).toContain(appType);
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}
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for (const cacheType of ["app6_note_analysis", "app7_customer_analysis", "app8_clue_consolidated"]) {
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expect(runOptionValues).not.toContain(cacheType);
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}
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});
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it("缓存失效继续使用 cache_type，避免破坏已有缓存管理", () => {
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const cacheOptionValues = CACHE_TYPE_OPTIONS.map((item) => item.value);
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expect(cacheOptionValues).toContain("app6_note_analysis");
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expect(cacheOptionValues).toContain("app7_customer_analysis");
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expect(cacheOptionValues).toContain("app8_clue_consolidated");
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});
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it("调用记录筛选包含真实写入 ai_run_logs 的 app_type", () => {
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const runLogOptionValues = RUN_LOG_APP_TYPE_OPTIONS.map((item) => item.value);
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expect(runLogOptionValues).toContain("app6_note");
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expect(runLogOptionValues).toContain("app7_customer");
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expect(runLogOptionValues).toContain("app8_consolidate");
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});
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});
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@@ -9,29 +9,30 @@ import { apiClient } from "./client";
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// ---- 公共类型 ----
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export const RUN_APP_TYPES = [
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"app2_finance",
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"app2a_finance_area",
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"app3_clue",
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"app4_analysis",
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"app5_tactics",
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"app6_note",
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"app7_customer",
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"app8_consolidation",
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] as const;
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/**
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* AI APP 类型联合（与后端 `CacheTypeEnum` / `_SUPPORTED_APP_TYPES` 同步）。
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* 按需执行 App 类型联合（与后端 `/api/admin/ai/run/{app_type}` 同步）。
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*
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* - app1_chat · 小程序聊天（无缓存）
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* - app2_finance · 全域财务洞察（area = 'all'，8 组合）
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* - app2a_finance_area · 区域财务洞察（area != 'all'，64 组合，2026-04-23 新增）
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* - app3_clue · 客户线索分析
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* - app4_analysis · 助教关系分析
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* - app5_tactics · 话术参考
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* - app6_note_analysis · 备注分析
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* - app7_customer_analysis · 客户综合分析
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* - app8_clue_consolidated · 线索整合
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* - app6_note · 备注分析
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* - app7_customer · 客户综合分析
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* - app8_consolidation · 线索整合
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*/
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export type AppType =
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| "app1_chat"
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| "app2_finance"
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| "app2a_finance_area"
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| "app3_clue"
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| "app4_analysis"
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| "app5_tactics"
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| "app6_note_analysis"
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| "app7_customer_analysis"
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| "app8_clue_consolidated";
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export type AppType = (typeof RUN_APP_TYPES)[number];
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// ---- 类型定义 ----
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78
apps/admin-web/src/api/runtimeContext.ts
Normal file
78
apps/admin-web/src/api/runtimeContext.ts
Normal file
@@ -0,0 +1,78 @@
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/**
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* 业务运行上下文 / 沙箱配置 API。
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*/
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import { apiClient } from "./client";
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export type RuntimeMode = "live" | "sandbox";
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export type AIMode = "live";
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export type RuntimeStepStatus = "success" | "skipped" | "warning" | "failed";
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export interface RuntimeContext {
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site_id: number;
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mode: RuntimeMode;
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business_day_start_hour: number;
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business_date: string;
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business_now: string;
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sandbox_date: string | null;
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sandbox_instance_id: string | null;
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ai_mode: AIMode;
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status: string;
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is_sandbox: boolean;
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}
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export interface RuntimeSiteItem {
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site_id: number;
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site_name: string | null;
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site_code: string | null;
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is_active: boolean;
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mode: RuntimeMode | null;
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sandbox_date: string | null;
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sandbox_instance_id: string | null;
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ai_mode: AIMode | null;
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status: string | null;
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updated_at: string | null;
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}
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export interface RuntimeSwitchRequest {
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site_id: number;
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mode: RuntimeMode;
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sandbox_date?: string | null;
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reset_sandbox?: boolean;
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reason?: string | null;
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}
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export interface RuntimeTransitionStep {
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key: string;
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title: string;
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status: RuntimeStepStatus;
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detail: string;
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count: number;
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}
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export interface RuntimeSwitchResponse {
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context: RuntimeContext;
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steps: RuntimeTransitionStep[];
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}
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export async function fetchRuntimeSites(): Promise<RuntimeSiteItem[]> {
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const { data } = await apiClient.get<RuntimeSiteItem[]>("/admin/runtime-context/sites");
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return data;
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}
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export async function fetchRuntimeContext(siteId: number): Promise<RuntimeContext> {
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const { data } = await apiClient.get<RuntimeContext>("/admin/runtime-context", {
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params: { site_id: siteId },
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});
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return data;
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}
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export async function switchRuntimeContext(
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payload: RuntimeSwitchRequest,
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): Promise<RuntimeSwitchResponse> {
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const { data } = await apiClient.patch<RuntimeSwitchResponse>(
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"/admin/runtime-context",
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payload,
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);
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return data;
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}
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@@ -8,12 +8,27 @@
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* - 第四行：告警列表
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*/
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import React, { useEffect, useState, useCallback } from "react";
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import React, { useEffect, useRef, useState, useCallback } from "react";
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import {
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Card, Row, Col, Statistic, Table, Tag, Badge, Progress,
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Select, Button, message, Typography, Space,
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Select, Button, message, Typography, Space, DatePicker,
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} from "antd";
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import { ReloadOutlined } from "@ant-design/icons";
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import { ReloadOutlined, WifiOutlined } from "@ant-design/icons";
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import type { Dayjs } from "dayjs";
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const { RangePicker } = DatePicker;
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const RANGE_OPTIONS = [
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{ label: "今日", value: 1 },
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{ label: "近 3 天", value: 3 },
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{ label: "近 7 天", value: 7 },
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{ label: "近 10 天", value: 10 },
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{ label: "指定日期", value: 0 }, // 0 = 启用 RangePicker
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];
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const RANGE_LABEL: Record<number, string> = {
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1: "今日", 3: "近 3 天", 7: "近 7 天", 10: "近 10 天",
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};
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import type { ColumnsType } from "antd/es/table";
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import {
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getDashboard,
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@@ -85,51 +100,119 @@ const AIDashboard: React.FC = () => {
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const [data, setData] = useState<DashboardResponse | null>(null);
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const [loading, setLoading] = useState(false);
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const [siteId, setSiteId] = useState<number | undefined>(undefined);
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const [rangeDays, setRangeDays] = useState<number>(1); // 0=自定义日期 / 1/3/7/10
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const [customRange, setCustomRange] = useState<[Dayjs, Dayjs] | null>(null);
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const [wsStatus, setWsStatus] = useState<"connecting" | "connected" | "disconnected">("disconnected");
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const [realtimeAlerts, setRealtimeAlerts] = useState<AlertItem[]>([]);
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const wsRef = useRef<WebSocket | null>(null);
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const load = useCallback(async () => {
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setLoading(true);
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try {
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const res = await getDashboard(siteId);
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const query: { site_id?: number; range_days?: number; date_from?: string; date_to?: string } = {};
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if (siteId != null) query.site_id = siteId;
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if (rangeDays === 0 && customRange) {
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query.date_from = customRange[0].format("YYYY-MM-DD");
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query.date_to = customRange[1].format("YYYY-MM-DD");
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} else if (rangeDays > 0) {
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query.range_days = rangeDays;
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}
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const res = await getDashboard(query);
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setData(res);
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} catch {
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message.error("加载 Dashboard 失败");
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} finally {
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setLoading(false);
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}
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}, [siteId]);
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}, [siteId, rangeDays, customRange]);
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useEffect(() => { load(); }, [load]);
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const statLabel = rangeDays === 0
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? (customRange ? `${customRange[0].format("MM-DD")} ~ ${customRange[1].format("MM-DD")}` : "指定日期")
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: (RANGE_LABEL[rangeDays] || "今日");
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// WebSocket 实时告警订阅
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useEffect(() => {
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const wsKey = siteId ?? -1;
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const proto = window.location.protocol === "https:" ? "wss" : "ws";
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const url = `${proto}://${window.location.host}/ws/ai-alerts/${wsKey}`;
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setWsStatus("connecting");
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const ws = new WebSocket(url);
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wsRef.current = ws;
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|
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ws.onopen = () => setWsStatus("connected");
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ws.onclose = () => setWsStatus("disconnected");
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ws.onerror = () => setWsStatus("disconnected");
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ws.onmessage = (evt) => {
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try {
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const msg = JSON.parse(evt.data as string) as {
|
||||
type: string;
|
||||
payload: AlertItem;
|
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};
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if (msg.type === "alert_created" && msg.payload) {
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setRealtimeAlerts((prev) => [msg.payload, ...prev].slice(0, 20));
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message.warning(`[实时] ${msg.payload.app_type} ${msg.payload.status}`);
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}
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} catch {
|
||||
// 忽略非 JSON 消息
|
||||
}
|
||||
};
|
||||
|
||||
return () => {
|
||||
ws.close();
|
||||
wsRef.current = null;
|
||||
setWsStatus("disconnected");
|
||||
};
|
||||
}, [siteId]);
|
||||
|
||||
return (
|
||||
<div>
|
||||
{/* 顶部：门店筛选 + 刷新 */}
|
||||
<Row justify="space-between" align="middle" style={{ marginBottom: 16 }}>
|
||||
<Space>
|
||||
<Space wrap>
|
||||
<Title level={4} style={{ margin: 0 }}>AI 运行总览</Title>
|
||||
<Select
|
||||
allowClear placeholder="门店筛选" style={{ width: 200 }}
|
||||
allowClear placeholder="门店筛选" style={{ width: 180 }}
|
||||
value={siteId} onChange={(v) => setSiteId(v)}
|
||||
options={[{ label: "默认门店", value: 2790685415443269 }]}
|
||||
/>
|
||||
<Select
|
||||
value={rangeDays} onChange={setRangeDays} style={{ width: 140 }}
|
||||
options={RANGE_OPTIONS}
|
||||
/>
|
||||
{rangeDays === 0 && (
|
||||
<RangePicker
|
||||
value={customRange}
|
||||
onChange={(v) => setCustomRange(v as [Dayjs, Dayjs] | null)}
|
||||
/>
|
||||
)}
|
||||
</Space>
|
||||
<Space>
|
||||
<Badge
|
||||
status={wsStatus === "connected" ? "success" : wsStatus === "connecting" ? "processing" : "default"}
|
||||
text={<span style={{ fontSize: 12, color: "#888" }}><WifiOutlined /> 实时 {wsStatus === "connected" ? "已连接" : wsStatus === "connecting" ? "连接中" : "断开"}</span>}
|
||||
/>
|
||||
<Button icon={<ReloadOutlined />} onClick={load} loading={loading}>刷新</Button>
|
||||
</Space>
|
||||
</Row>
|
||||
|
||||
{/* 第一行：4 个统计卡片 */}
|
||||
<Row gutter={16} style={{ marginBottom: 16 }}>
|
||||
<Col span={6}>
|
||||
<Card><Statistic title="今日调用次数" value={data?.today_calls ?? 0} /></Card>
|
||||
<Card><Statistic title={`${statLabel}调用次数`} value={data?.today_calls ?? 0} /></Card>
|
||||
</Col>
|
||||
<Col span={6}>
|
||||
<Card>
|
||||
<Statistic
|
||||
title="今日成功率" suffix="%"
|
||||
title={`${statLabel}成功率`} suffix="%"
|
||||
value={data ? (data.today_success_rate * 100).toFixed(1) : "0.0"}
|
||||
/>
|
||||
</Card>
|
||||
</Col>
|
||||
<Col span={6}>
|
||||
<Card><Statistic title="今日 Token 消耗" value={data?.today_tokens ?? 0} /></Card>
|
||||
<Card><Statistic title={`${statLabel} Token 消耗`} value={data?.today_tokens ?? 0} /></Card>
|
||||
</Col>
|
||||
<Col span={6}>
|
||||
<Card>
|
||||
@@ -201,11 +284,22 @@ const AIDashboard: React.FC = () => {
|
||||
</Col>
|
||||
</Row>
|
||||
|
||||
{/* 第四行：告警列表 */}
|
||||
<Card title="告警列表" size="small">
|
||||
{/* 第四行：告警列表（实时 + 历史合并） */}
|
||||
<Card
|
||||
title="告警列表"
|
||||
size="small"
|
||||
extra={realtimeAlerts.length > 0 && (
|
||||
<Tag color="orange">{realtimeAlerts.length} 条实时</Tag>
|
||||
)}
|
||||
>
|
||||
<Table<AlertItem>
|
||||
columns={alertColumns}
|
||||
dataSource={data?.recent_alerts ?? []}
|
||||
dataSource={[
|
||||
...realtimeAlerts,
|
||||
...(data?.recent_alerts ?? []).filter(
|
||||
(a) => !realtimeAlerts.some((r) => r.id === a.id)
|
||||
),
|
||||
]}
|
||||
rowKey="id" size="small" pagination={{ pageSize: 10 }}
|
||||
loading={loading}
|
||||
/>
|
||||
|
||||
@@ -31,7 +31,7 @@ const EVENT_TYPE_OPTIONS = [
|
||||
const { TextArea } = Input;
|
||||
const { Title } = Typography;
|
||||
|
||||
const APP_TYPE_OPTIONS = [
|
||||
export const CACHE_TYPE_OPTIONS = [
|
||||
{ label: "App3 维客线索", value: "app3_clue" },
|
||||
{ label: "App4 关系分析", value: "app4_analysis" },
|
||||
{ label: "App5 话术参考", value: "app5_tactics" },
|
||||
@@ -40,6 +40,15 @@ const APP_TYPE_OPTIONS = [
|
||||
{ label: "App8 线索整理", value: "app8_clue_consolidated" },
|
||||
];
|
||||
|
||||
export const RUN_APP_TYPE_OPTIONS: { label: string; value: AppType }[] = [
|
||||
{ label: "App3 维客线索", value: "app3_clue" },
|
||||
{ label: "App4 关系分析", value: "app4_analysis" },
|
||||
{ label: "App5 话术参考", value: "app5_tactics" },
|
||||
{ label: "App6 备注分析", value: "app6_note" },
|
||||
{ label: "App7 客户分析", value: "app7_customer" },
|
||||
{ label: "App8 线索整理", value: "app8_consolidation" },
|
||||
];
|
||||
|
||||
const ALERT_STATUS_COLOR: Record<string, string> = {
|
||||
failed: "red", timeout: "orange", circuit_open: "volcano",
|
||||
};
|
||||
@@ -160,7 +169,7 @@ const AIOperations: React.FC = () => {
|
||||
};
|
||||
|
||||
// ---- Card 3: 批量执行 ----
|
||||
const [batchAppTypes, setBatchAppTypes] = useState<string[]>([]);
|
||||
const [batchAppTypes, setBatchAppTypes] = useState<AppType[]>([]);
|
||||
const [batchMemberIds, setBatchMemberIds] = useState<string>("");
|
||||
const [batchSiteId, setBatchSiteId] = useState<number>(2790685415443269);
|
||||
const [batchLoading, setBatchLoading] = useState(false);
|
||||
@@ -294,7 +303,7 @@ const AIOperations: React.FC = () => {
|
||||
<Select
|
||||
allowClear placeholder="App 类型（可选）" style={{ width: "100%" }}
|
||||
value={cacheAppType} onChange={setCacheAppType}
|
||||
options={APP_TYPE_OPTIONS}
|
||||
options={CACHE_TYPE_OPTIONS}
|
||||
/>
|
||||
<Input
|
||||
placeholder="会员 ID（可选）" value={cacheMemberId}
|
||||
@@ -321,7 +330,7 @@ const AIOperations: React.FC = () => {
|
||||
<Select
|
||||
allowClear placeholder="App 类型" style={{ width: "100%" }}
|
||||
value={runAppType} onChange={setRunAppType}
|
||||
options={APP_TYPE_OPTIONS}
|
||||
options={RUN_APP_TYPE_OPTIONS}
|
||||
/>
|
||||
</Col>
|
||||
<Col span={6}>
|
||||
@@ -403,9 +412,9 @@ const AIOperations: React.FC = () => {
|
||||
<Col span={8}>
|
||||
<div style={{ marginBottom: 8, fontWeight: 500 }}>选择 App</div>
|
||||
<Checkbox.Group
|
||||
options={APP_TYPE_OPTIONS}
|
||||
options={RUN_APP_TYPE_OPTIONS}
|
||||
value={batchAppTypes}
|
||||
onChange={(v) => setBatchAppTypes(v as string[])}
|
||||
onChange={(v) => setBatchAppTypes(v as AppType[])}
|
||||
style={{ display: "flex", flexDirection: "column", gap: 4 }}
|
||||
/>
|
||||
</Col>
|
||||
|
||||
@@ -85,11 +85,7 @@ const AIRunLogs: React.FC = () => {
|
||||
}
|
||||
};
|
||||
|
||||
const APP_TYPE_OPTIONS = [
|
||||
"app1_chat", "app2_finance", "app3_clue", "app4_analysis",
|
||||
"app5_tactics", "app6_note_analysis", "app7_customer_analysis",
|
||||
"app8_clue_consolidated",
|
||||
].map((v) => ({ label: v, value: v }));
|
||||
const APP_TYPE_OPTIONS = RUN_LOG_APP_TYPE_OPTIONS;
|
||||
|
||||
const columns: ColumnsType<RunLogItem> = [
|
||||
{ title: "ID", dataIndex: "id", key: "id", width: 70 },
|
||||
@@ -227,3 +223,9 @@ const AIRunLogs: React.FC = () => {
|
||||
};
|
||||
|
||||
export default AIRunLogs;
|
||||
|
||||
export const RUN_LOG_APP_TYPE_OPTIONS = [
|
||||
"app1_chat", "app2_finance", "app2a_finance_area", "app3_clue",
|
||||
"app4_analysis", "app5_tactics", "app6_note", "app7_customer",
|
||||
"app8_consolidate", "app8_consolidation",
|
||||
].map((v) => ({ label: v, value: v }));
|
||||
|
||||
243
apps/admin-web/src/pages/AITriggers.tsx
Normal file
243
apps/admin-web/src/pages/AITriggers.tsx
Normal file
@@ -0,0 +1,243 @@
|
||||
/**
|
||||
* AI 触发器设置页面。
|
||||
*
|
||||
* 管理 biz.trigger_jobs 表中 job_type='ai_*' 的所有触发器，支持：
|
||||
* - 启用/禁用
|
||||
* - 修改 cron 表达式（仅 cron 类型）
|
||||
* - 修改描述
|
||||
* - 查看事件名、最近运行、下次运行、最后错误
|
||||
*/
|
||||
|
||||
import React, { useCallback, useEffect, useState } from "react";
|
||||
import {
|
||||
Card, Table, Tag, Button, Space, Modal, Input, Switch,
|
||||
message, Typography, Tooltip, Descriptions,
|
||||
} from "antd";
|
||||
import { ReloadOutlined, EditOutlined } from "@ant-design/icons";
|
||||
import type { ColumnsType } from "antd/es/table";
|
||||
import {
|
||||
listTriggers, updateTrigger,
|
||||
type TriggerItem,
|
||||
} from "../api/adminAI";
|
||||
|
||||
const { Title, Paragraph } = Typography;
|
||||
|
||||
const STATUS_COLOR: Record<string, string> = {
|
||||
enabled: "success",
|
||||
disabled: "default",
|
||||
};
|
||||
|
||||
const CONDITION_COLOR: Record<string, string> = {
|
||||
event: "processing",
|
||||
cron: "warning",
|
||||
interval: "cyan",
|
||||
};
|
||||
|
||||
function fmtTime(raw: string | null): string {
|
||||
if (!raw) return "—";
|
||||
const d = new Date(raw);
|
||||
return Number.isNaN(d.getTime()) ? raw : d.toLocaleString("zh-CN");
|
||||
}
|
||||
|
||||
function cronExpr(item: TriggerItem): string {
|
||||
const cfg = item.trigger_config || {};
|
||||
return String(cfg.cron_expression || cfg.event_name || "—");
|
||||
}
|
||||
|
||||
const AITriggers: React.FC = () => {
|
||||
const [items, setItems] = useState<TriggerItem[]>([]);
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [editing, setEditing] = useState<TriggerItem | null>(null);
|
||||
const [editCron, setEditCron] = useState("");
|
||||
const [editDesc, setEditDesc] = useState("");
|
||||
const [saving, setSaving] = useState(false);
|
||||
|
||||
const load = useCallback(async () => {
|
||||
setLoading(true);
|
||||
try {
|
||||
const res = await listTriggers();
|
||||
setItems(res);
|
||||
} catch {
|
||||
message.error("加载触发器列表失败");
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
}, []);
|
||||
|
||||
useEffect(() => { load(); }, [load]);
|
||||
|
||||
const handleToggle = async (item: TriggerItem, next: boolean) => {
|
||||
try {
|
||||
await updateTrigger(item.id, { status: next ? "enabled" : "disabled" });
|
||||
message.success(next ? "已启用" : "已禁用");
|
||||
load();
|
||||
} catch {
|
||||
message.error("状态切换失败");
|
||||
}
|
||||
};
|
||||
|
||||
const openEdit = (item: TriggerItem) => {
|
||||
setEditing(item);
|
||||
setEditCron(String(item.trigger_config?.cron_expression || ""));
|
||||
setEditDesc(item.description || "");
|
||||
};
|
||||
|
||||
const handleSave = async () => {
|
||||
if (!editing) return;
|
||||
setSaving(true);
|
||||
try {
|
||||
const body: { cron_expression?: string; description?: string } = {};
|
||||
if (editing.trigger_condition === "cron" && editCron !== editing.trigger_config?.cron_expression) {
|
||||
body.cron_expression = editCron;
|
||||
}
|
||||
if (editDesc !== (editing.description || "")) {
|
||||
body.description = editDesc;
|
||||
}
|
||||
if (Object.keys(body).length === 0) {
|
||||
message.info("无变更");
|
||||
setEditing(null);
|
||||
return;
|
||||
}
|
||||
await updateTrigger(editing.id, body);
|
||||
message.success("已保存");
|
||||
setEditing(null);
|
||||
load();
|
||||
} catch (err) {
|
||||
const msg = (err as { response?: { data?: { detail?: string } } })?.response?.data?.detail;
|
||||
message.error(`保存失败${msg ? `：${msg}` : ""}`);
|
||||
} finally {
|
||||
setSaving(false);
|
||||
}
|
||||
};
|
||||
|
||||
const columns: ColumnsType<TriggerItem> = [
|
||||
{ title: "ID", dataIndex: "id", key: "id", width: 60 },
|
||||
{
|
||||
title: "触发器名", dataIndex: "job_name", key: "job_name", width: 200,
|
||||
render: (v: string, r) => (
|
||||
<div>
|
||||
<div style={{ fontWeight: 500 }}>{v}</div>
|
||||
{r.description && (
|
||||
<div style={{ fontSize: 12, color: "#888", marginTop: 2 }}>{r.description}</div>
|
||||
)}
|
||||
</div>
|
||||
),
|
||||
},
|
||||
{
|
||||
title: "类型", dataIndex: "trigger_condition", key: "trigger_condition", width: 80,
|
||||
render: (v: string) => <Tag color={CONDITION_COLOR[v] ?? "default"}>{v}</Tag>,
|
||||
},
|
||||
{
|
||||
title: "表达式 / 事件", key: "expr", width: 240,
|
||||
render: (_: unknown, r) => (
|
||||
<code style={{ fontSize: 12 }}>{cronExpr(r)}</code>
|
||||
),
|
||||
},
|
||||
{
|
||||
title: "状态", dataIndex: "status", key: "status", width: 100,
|
||||
render: (v: string, r) => (
|
||||
<Space>
|
||||
<Switch
|
||||
size="small"
|
||||
checked={v === "enabled"}
|
||||
onChange={(c) => handleToggle(r, c)}
|
||||
/>
|
||||
<Tag color={STATUS_COLOR[v] ?? "default"}>{v}</Tag>
|
||||
</Space>
|
||||
),
|
||||
},
|
||||
{ title: "最近运行", dataIndex: "last_run_at", key: "last_run_at", width: 160, render: fmtTime },
|
||||
{ title: "下次运行", dataIndex: "next_run_at", key: "next_run_at", width: 160, render: fmtTime },
|
||||
{
|
||||
title: "最后错误", dataIndex: "last_error", key: "last_error", ellipsis: true,
|
||||
render: (v: string | null) => v ? (
|
||||
<Tooltip title={v}><span style={{ color: "#d46b08" }}>{v.slice(0, 40)}…</span></Tooltip>
|
||||
) : "—",
|
||||
},
|
||||
{
|
||||
title: "操作", key: "action", width: 100, fixed: "right",
|
||||
render: (_: unknown, r) => (
|
||||
<Button size="small" icon={<EditOutlined />} onClick={() => openEdit(r)}>编辑</Button>
|
||||
),
|
||||
},
|
||||
];
|
||||
|
||||
return (
|
||||
<div>
|
||||
<div style={{ display: "flex", justifyContent: "space-between", alignItems: "center", marginBottom: 16 }}>
|
||||
<div>
|
||||
<Title level={4} style={{ margin: 0 }}>AI 触发器设置</Title>
|
||||
<Paragraph type="secondary" style={{ margin: 0, fontSize: 13 }}>
|
||||
管理 <code>biz.trigger_jobs</code> 中 AI 相关触发器，支持启停与 cron 修改。修改立即生效。
|
||||
</Paragraph>
|
||||
</div>
|
||||
<Button icon={<ReloadOutlined />} onClick={load} loading={loading}>刷新</Button>
|
||||
</div>
|
||||
|
||||
<Card size="small">
|
||||
<Table<TriggerItem>
|
||||
columns={columns}
|
||||
dataSource={items}
|
||||
rowKey="id"
|
||||
loading={loading}
|
||||
pagination={false}
|
||||
size="small"
|
||||
scroll={{ x: 1200 }}
|
||||
/>
|
||||
</Card>
|
||||
|
||||
<Modal
|
||||
title={editing ? `编辑触发器 #${editing.id}` : ""}
|
||||
open={!!editing}
|
||||
onCancel={() => setEditing(null)}
|
||||
onOk={handleSave}
|
||||
confirmLoading={saving}
|
||||
okText="保存" cancelText="取消"
|
||||
width={600}
|
||||
>
|
||||
{editing && (
|
||||
<>
|
||||
<Descriptions size="small" column={1} bordered style={{ marginBottom: 16 }}>
|
||||
<Descriptions.Item label="触发器名">{editing.job_name}</Descriptions.Item>
|
||||
<Descriptions.Item label="类型">
|
||||
<Tag color={CONDITION_COLOR[editing.trigger_condition] ?? "default"}>
|
||||
{editing.trigger_condition}
|
||||
</Tag>
|
||||
</Descriptions.Item>
|
||||
{editing.trigger_condition === "event" && (
|
||||
<Descriptions.Item label="事件名">
|
||||
<code>{String(editing.trigger_config?.event_name || "—")}</code>
|
||||
</Descriptions.Item>
|
||||
)}
|
||||
</Descriptions>
|
||||
|
||||
{editing.trigger_condition === "cron" && (
|
||||
<div style={{ marginBottom: 16 }}>
|
||||
<div style={{ marginBottom: 4, fontWeight: 500 }}>Cron 表达式</div>
|
||||
<Input
|
||||
value={editCron}
|
||||
onChange={(e) => setEditCron(e.target.value)}
|
||||
placeholder="标准 5 段 cron，例如 0 10 * * *"
|
||||
/>
|
||||
<Paragraph type="secondary" style={{ fontSize: 12, margin: "4px 0 0 0" }}>
|
||||
格式：<code>分 时 日 月 周</code>。示例：<code>0 10 * * *</code>（每日 10:00）、<code>*/30 * * * *</code>（每 30 分钟）。
|
||||
</Paragraph>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div>
|
||||
<div style={{ marginBottom: 4, fontWeight: 500 }}>描述</div>
|
||||
<Input.TextArea
|
||||
value={editDesc}
|
||||
onChange={(e) => setEditDesc(e.target.value)}
|
||||
rows={3}
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
</Modal>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
export default AITriggers;
|
||||
335
apps/admin-web/src/pages/RuntimeContext.tsx
Normal file
335
apps/admin-web/src/pages/RuntimeContext.tsx
Normal file
@@ -0,0 +1,335 @@
|
||||
/**
|
||||
* 业务运行上下文 / 沙箱设置页面。
|
||||
*
|
||||
* 仅限超级管理员：列出门店当前模式，支持切换到 sandbox 指定历史日期或切回 live。
|
||||
* 切换会按 site_id 暂停或恢复 biz.trigger_jobs，确保多门店隔离。
|
||||
*/
|
||||
|
||||
import React, { useEffect, useMemo, useState, useCallback } from "react";
|
||||
import {
|
||||
Alert, Button, Card, DatePicker, Form, Input, Modal, Popconfirm, Space,
|
||||
Switch, Table, Tag, Tooltip, Typography, message,
|
||||
} from "antd";
|
||||
import { ReloadOutlined, SwapOutlined } from "@ant-design/icons";
|
||||
import type { ColumnsType } from "antd/es/table";
|
||||
import dayjs, { type Dayjs } from "dayjs";
|
||||
import {
|
||||
fetchRuntimeSites, switchRuntimeContext,
|
||||
type RuntimeSiteItem, type RuntimeMode, type RuntimeTransitionStep,
|
||||
} from "../api/runtimeContext";
|
||||
import { useAuthStore } from "../store/authStore";
|
||||
|
||||
const { Title, Text } = Typography;
|
||||
|
||||
interface SwitchFormValues {
|
||||
mode: RuntimeMode;
|
||||
sandbox_date: Dayjs | null;
|
||||
reset_sandbox: boolean;
|
||||
reason: string;
|
||||
}
|
||||
|
||||
const stepStatusColor: Record<RuntimeTransitionStep["status"], string> = {
|
||||
success: "green",
|
||||
skipped: "default",
|
||||
warning: "orange",
|
||||
failed: "red",
|
||||
};
|
||||
|
||||
const RuntimeContextPage: React.FC = () => {
|
||||
const user = useAuthStore((s) => s.user);
|
||||
const isSuperAdmin = user?.roles?.includes("super_admin") ?? false;
|
||||
|
||||
const [sites, setSites] = useState<RuntimeSiteItem[]>([]);
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [switchTarget, setSwitchTarget] = useState<RuntimeSiteItem | null>(null);
|
||||
const [submitting, setSubmitting] = useState(false);
|
||||
const [stepsModal, setStepsModal] = useState<{ siteName: string; steps: RuntimeTransitionStep[] } | null>(null);
|
||||
const [form] = Form.useForm<SwitchFormValues>();
|
||||
|
||||
const load = useCallback(async () => {
|
||||
setLoading(true);
|
||||
try {
|
||||
const data = await fetchRuntimeSites();
|
||||
setSites(data);
|
||||
} catch {
|
||||
message.error("加载门店运行上下文失败");
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
}, []);
|
||||
|
||||
useEffect(() => {
|
||||
if (isSuperAdmin) {
|
||||
load();
|
||||
}
|
||||
}, [isSuperAdmin, load]);
|
||||
|
||||
const openSwitch = (record: RuntimeSiteItem, mode: RuntimeMode) => {
|
||||
setSwitchTarget(record);
|
||||
form.resetFields();
|
||||
form.setFieldsValue({
|
||||
mode,
|
||||
sandbox_date: record.sandbox_date ? dayjs(record.sandbox_date) : null,
|
||||
reset_sandbox: true,
|
||||
reason: "",
|
||||
});
|
||||
};
|
||||
|
||||
const handleSubmit = async () => {
|
||||
if (!switchTarget) return;
|
||||
let values: SwitchFormValues;
|
||||
try {
|
||||
values = await form.validateFields();
|
||||
} catch {
|
||||
return;
|
||||
}
|
||||
setSubmitting(true);
|
||||
try {
|
||||
const resp = await switchRuntimeContext({
|
||||
site_id: switchTarget.site_id,
|
||||
mode: values.mode,
|
||||
sandbox_date: values.mode === "sandbox" ? values.sandbox_date?.format("YYYY-MM-DD") : null,
|
||||
reset_sandbox: values.mode === "sandbox" ? values.reset_sandbox : true,
|
||||
reason: values.reason || null,
|
||||
});
|
||||
message.success(values.mode === "sandbox" ? "已切换为沙箱模式" : "已切回 live 模式");
|
||||
setStepsModal({
|
||||
siteName: switchTarget.site_name || `#${switchTarget.site_id}`,
|
||||
steps: resp.steps,
|
||||
});
|
||||
setSwitchTarget(null);
|
||||
form.resetFields();
|
||||
load();
|
||||
} catch (err: unknown) {
|
||||
const msg = (err as { response?: { data?: { detail?: string } } })?.response?.data?.detail;
|
||||
message.error(msg || "切换失败");
|
||||
} finally {
|
||||
setSubmitting(false);
|
||||
}
|
||||
};
|
||||
|
||||
const columns: ColumnsType<RuntimeSiteItem> = useMemo(
|
||||
() => [
|
||||
{
|
||||
title: "门店",
|
||||
key: "site",
|
||||
width: 200,
|
||||
render: (_: unknown, r) => (
|
||||
<Space direction="vertical" size={0}>
|
||||
<Text strong>{r.site_name || `#${r.site_id}`}</Text>
|
||||
<Text type="secondary" style={{ fontSize: 12 }}>
|
||||
{r.site_code ? `${r.site_code} · ` : ""}site_id={r.site_id}
|
||||
</Text>
|
||||
</Space>
|
||||
),
|
||||
},
|
||||
{
|
||||
title: "运行模式",
|
||||
key: "mode",
|
||||
width: 140,
|
||||
render: (_: unknown, r) => {
|
||||
const mode = (r.mode ?? "live") as RuntimeMode;
|
||||
return mode === "sandbox" ? (
|
||||
<Tag color="purple">沙箱模式</Tag>
|
||||
) : (
|
||||
<Tag color="blue">正式 live</Tag>
|
||||
);
|
||||
},
|
||||
},
|
||||
{
|
||||
title: "业务日期",
|
||||
key: "business_date",
|
||||
width: 160,
|
||||
render: (_: unknown, r) =>
|
||||
r.mode === "sandbox" && r.sandbox_date ? (
|
||||
<Tooltip title="沙箱模拟的业务日期">
|
||||
<Tag color="purple">{r.sandbox_date}</Tag>
|
||||
</Tooltip>
|
||||
) : (
|
||||
<Text type="secondary">真实日期</Text>
|
||||
),
|
||||
},
|
||||
{
|
||||
title: "沙箱实例",
|
||||
dataIndex: "sandbox_instance_id",
|
||||
key: "sandbox_instance_id",
|
||||
width: 240,
|
||||
render: (v: string | null) => (v ? <Text code>{v}</Text> : "—"),
|
||||
},
|
||||
{
|
||||
title: "AI 模式",
|
||||
dataIndex: "ai_mode",
|
||||
key: "ai_mode",
|
||||
width: 100,
|
||||
render: (v: string | null) => v ?? "live",
|
||||
},
|
||||
{
|
||||
title: "更新时间",
|
||||
dataIndex: "updated_at",
|
||||
key: "updated_at",
|
||||
width: 170,
|
||||
render: (v: string | null) => (v ? dayjs(v).format("YYYY-MM-DD HH:mm") : "—"),
|
||||
},
|
||||
{
|
||||
title: "操作",
|
||||
key: "action",
|
||||
fixed: "right",
|
||||
width: 220,
|
||||
render: (_: unknown, r) => {
|
||||
const mode = (r.mode ?? "live") as RuntimeMode;
|
||||
if (mode === "sandbox") {
|
||||
return (
|
||||
<Space>
|
||||
<Button size="small" icon={<SwapOutlined />} onClick={() => openSwitch(r, "sandbox")}>
|
||||
调整沙箱
|
||||
</Button>
|
||||
<Popconfirm
|
||||
title="确认切回 live 模式？"
|
||||
description="将恢复该门店触发器并清理沙箱状态。"
|
||||
okText="切回 live"
|
||||
cancelText="取消"
|
||||
onConfirm={() => openSwitch(r, "live")}
|
||||
>
|
||||
<Button size="small" danger>
|
||||
切回 live
|
||||
</Button>
|
||||
</Popconfirm>
|
||||
</Space>
|
||||
);
|
||||
}
|
||||
return (
|
||||
<Button size="small" type="primary" icon={<SwapOutlined />} onClick={() => openSwitch(r, "sandbox")}>
|
||||
进入沙箱
|
||||
</Button>
|
||||
);
|
||||
},
|
||||
},
|
||||
],
|
||||
[form],
|
||||
);
|
||||
|
||||
if (!isSuperAdmin) {
|
||||
return (
|
||||
<Alert
|
||||
type="warning"
|
||||
showIcon
|
||||
message="无权限"
|
||||
description="业务运行上下文/沙箱设置仅对超级管理员开放。"
|
||||
/>
|
||||
);
|
||||
}
|
||||
|
||||
const target = switchTarget;
|
||||
const targetMode = Form.useWatch("mode", form) ?? "live";
|
||||
|
||||
return (
|
||||
<Card
|
||||
title={<Title level={4} style={{ margin: 0 }}>业务运行上下文 / 沙箱</Title>}
|
||||
extra={
|
||||
<Button icon={<ReloadOutlined />} onClick={load} loading={loading}>
|
||||
刷新
|
||||
</Button>
|
||||
}
|
||||
>
|
||||
<Alert
|
||||
type="info"
|
||||
showIcon
|
||||
style={{ marginBottom: 16 }}
|
||||
message="按门店隔离的业务时钟"
|
||||
description={
|
||||
<Space direction="vertical" size={2}>
|
||||
<Text>live 模式：使用真实系统日期，正常生产逻辑。</Text>
|
||||
<Text>
|
||||
sandbox 模式：业务上假设是 sandbox_date，按 sandbox_instance_id 隔离写入；
|
||||
切换会终止当前 ETL、取消未完成 AI 触发记录，但不会暂停全局 biz.trigger_jobs（多门店共用）。
|
||||
</Text>
|
||||
<Text type="secondary">
|
||||
真实预算、AI tokens 计费、运行日志写入时间、调度元数据仍按真实系统时间，不受沙箱影响。
|
||||
</Text>
|
||||
<Text type="warning">
|
||||
本次改造目标是让看板 / 任务 / 会员 / AI 等数据读取也按 sandbox_date 截断，
|
||||
进度详见 <Text code>docs/database/changes/2026-05-02__sandbox_no_future_data_plan.md</Text>。
|
||||
</Text>
|
||||
</Space>
|
||||
}
|
||||
/>
|
||||
<Table
|
||||
rowKey="site_id"
|
||||
loading={loading}
|
||||
dataSource={sites}
|
||||
columns={columns}
|
||||
size="middle"
|
||||
pagination={false}
|
||||
scroll={{ x: 1100 }}
|
||||
/>
|
||||
<Modal
|
||||
open={!!target}
|
||||
title={target ? `切换运行上下文 — ${target.site_name || `#${target.site_id}`}` : ""}
|
||||
onCancel={() => {
|
||||
if (!submitting) {
|
||||
setSwitchTarget(null);
|
||||
form.resetFields();
|
||||
}
|
||||
}}
|
||||
onOk={handleSubmit}
|
||||
okText="确认切换"
|
||||
cancelText="取消"
|
||||
confirmLoading={submitting}
|
||||
destroyOnClose
|
||||
width={640}
|
||||
>
|
||||
<Form
|
||||
layout="vertical"
|
||||
form={form}
|
||||
initialValues={{ mode: "sandbox", reset_sandbox: true, reason: "" }}
|
||||
>
|
||||
<Form.Item label="目标模式" name="mode" rules={[{ required: true }]}>
|
||||
<Input disabled />
|
||||
</Form.Item>
|
||||
{targetMode === "sandbox" && (
|
||||
<>
|
||||
<Form.Item
|
||||
label="沙箱业务日期"
|
||||
name="sandbox_date"
|
||||
rules={[{ required: true, message: "沙箱模式需要选择历史业务日期" }]}
|
||||
>
|
||||
<DatePicker
|
||||
style={{ width: "100%" }}
|
||||
disabledDate={(d) => d && d.isAfter(dayjs(), "day")}
|
||||
/>
|
||||
</Form.Item>
|
||||
<Form.Item label="重置沙箱实例" name="reset_sandbox" valuePropName="checked">
|
||||
<Switch checkedChildren="新实例" unCheckedChildren="沿用原实例" />
|
||||
</Form.Item>
|
||||
</>
|
||||
)}
|
||||
<Form.Item label="操作原因（可选）" name="reason">
|
||||
<Input.TextArea rows={2} maxLength={500} showCount placeholder="例如：演示按 2026-03-15 重放任务分发" />
|
||||
</Form.Item>
|
||||
</Form>
|
||||
</Modal>
|
||||
<Modal
|
||||
open={!!stepsModal}
|
||||
title={stepsModal ? `切换执行结果 — ${stepsModal.siteName}` : ""}
|
||||
onCancel={() => setStepsModal(null)}
|
||||
onOk={() => setStepsModal(null)}
|
||||
okText="知道了"
|
||||
cancelButtonProps={{ style: { display: "none" } }}
|
||||
width={640}
|
||||
destroyOnClose
|
||||
>
|
||||
<Space direction="vertical" size={8} style={{ width: "100%" }}>
|
||||
{(stepsModal?.steps ?? []).map((s) => (
|
||||
<div key={s.key}>
|
||||
<Tag color={stepStatusColor[s.status]}>{s.title}</Tag>
|
||||
{s.count ? <Text type="secondary">影响 {s.count} 条</Text> : null}
|
||||
<Text style={{ marginLeft: 8 }}>{s.detail}</Text>
|
||||
</div>
|
||||
))}
|
||||
</Space>
|
||||
</Modal>
|
||||
</Card>
|
||||
);
|
||||
};
|
||||
|
||||
export default RuntimeContextPage;
|
||||
@@ -6,7 +6,7 @@
|
||||
* - destroyInactiveTabPane={false} 保持 Tab 状态不丢失
|
||||
* - "全部"Tab 调用 fetchUnifiedTriggers()，展示统一字段表格
|
||||
* - "业务"Tab 复用 TriggerJobs 组件 + 编辑 Modal
|
||||
* - "AI"Tab 复用 AIOperations + AITriggerJobs 组件
|
||||
* - "AI"Tab 复用 AITriggers（触发器设置）+ AIOperations + AITriggerJobs 组件
|
||||
* - "ETL"Tab 展示 scheduled_tasks 数据
|
||||
*
|
||||
* CHANGE 2026-07-15 | Task 10.1：创建 TriggerManager 页面
|
||||
@@ -37,6 +37,7 @@ import { fetchSchedules } from '../api/schedules';
|
||||
import type { ScheduledTask } from '../types';
|
||||
import AIOperations from './AIOperations';
|
||||
import AITriggerJobs from './AITriggerJobs';
|
||||
import AITriggers from './AITriggers';
|
||||
|
||||
const { Title } = Typography;
|
||||
|
||||
@@ -319,6 +320,7 @@ const BizTriggersTab: React.FC = () => {
|
||||
|
||||
const AITriggersTab: React.FC = () => (
|
||||
<Space direction="vertical" style={{ width: '100%' }} size="large">
|
||||
<AITriggers />
|
||||
<AIOperations />
|
||||
<AITriggerJobs />
|
||||
</Space>
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
# AI 应用子模块：app1_chat ~ app8_consolidation
|
||||
@@ -1,274 +0,0 @@
|
||||
"""应用 1：通用对话（SSE 流式）。
|
||||
|
||||
每次进入 chat 页面新建 ai_conversations 记录（不复用），
|
||||
首条消息注入页面上下文，流式返回 AI 回复。
|
||||
|
||||
app_id = "app1_chat"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import AsyncGenerator
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.data_fetchers import build_page_text
|
||||
from app.ai.schemas import SSEEvent
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app1_chat"
|
||||
|
||||
# system prompt 总字符数上限
|
||||
_MAX_SYSTEM_PROMPT_LEN = 4000
|
||||
|
||||
|
||||
async def chat_stream(
|
||||
*,
|
||||
message: str,
|
||||
user_id: int | str,
|
||||
nickname: str,
|
||||
role: str,
|
||||
site_id: int,
|
||||
source_page: str | None = None,
|
||||
page_context: dict | None = None,
|
||||
screen_content: str | None = None,
|
||||
client: DashScopeClient,
|
||||
conv_svc: ConversationService,
|
||||
) -> AsyncGenerator[SSEEvent, None]:
|
||||
"""流式对话入口，返回 SSEEvent 异步生成器。
|
||||
|
||||
流程：
|
||||
1. 创建 conversation 记录
|
||||
2. 写入 user message
|
||||
3. 构建 system prompt（注入页面上下文）
|
||||
4. 调用 bailian.chat_stream 流式获取回复
|
||||
5. 逐 chunk yield SSEEvent(type="chunk")
|
||||
6. 完成后写入 assistant message，yield SSEEvent(type="done")
|
||||
7. 异常时 yield SSEEvent(type="error")
|
||||
"""
|
||||
conversation_id: int | None = None
|
||||
|
||||
try:
|
||||
# 1. 每次新建 conversation（不复用）
|
||||
source_ctx = _build_source_context(
|
||||
source_page=source_page,
|
||||
page_context=page_context,
|
||||
screen_content=screen_content,
|
||||
)
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_page=source_page,
|
||||
source_context=source_ctx,
|
||||
)
|
||||
logger.info(
|
||||
"App1 新建对话: conversation_id=%s user_id=%s site_id=%s",
|
||||
conversation_id, user_id, site_id,
|
||||
)
|
||||
|
||||
# 2. 立即写入 user message
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=message,
|
||||
)
|
||||
|
||||
# 3. 构建消息列表（system prompt + user message）
|
||||
messages = await _build_messages(
|
||||
message=message,
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
role=role,
|
||||
site_id=site_id,
|
||||
source_page=source_page,
|
||||
page_context=page_context,
|
||||
screen_content=screen_content,
|
||||
)
|
||||
|
||||
# 4-5. 流式调用百炼，逐 chunk yield
|
||||
full_reply_parts: list[str] = []
|
||||
async for chunk in bailian.chat_stream(messages):
|
||||
full_reply_parts.append(chunk)
|
||||
yield SSEEvent(type="chunk", content=chunk)
|
||||
|
||||
# 6. 流式完成，拼接完整回复并写入 assistant message
|
||||
full_reply = "".join(full_reply_parts)
|
||||
# 百炼流式模式不返回 tokens_used，按字符数估算（粗略）
|
||||
estimated_tokens = len(full_reply)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=full_reply,
|
||||
tokens_used=estimated_tokens,
|
||||
)
|
||||
|
||||
yield SSEEvent(
|
||||
type="done",
|
||||
conversation_id=conversation_id,
|
||||
tokens_used=estimated_tokens,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"App1 对话异常: conversation_id=%s error=%s",
|
||||
conversation_id, e,
|
||||
exc_info=True,
|
||||
)
|
||||
yield SSEEvent(type="error", message=str(e))
|
||||
|
||||
|
||||
async def _build_messages(
|
||||
*,
|
||||
message: str,
|
||||
user_id: int | str,
|
||||
nickname: str,
|
||||
role: str,
|
||||
site_id: int,
|
||||
source_page: str | None,
|
||||
page_context: dict | None,
|
||||
screen_content: str | None,
|
||||
) -> list[dict]:
|
||||
"""构建发送给百炼的消息列表。
|
||||
|
||||
首条 system 消息注入页面上下文和用户信息。
|
||||
"""
|
||||
system_content = await _build_system_prompt(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
role=role,
|
||||
site_id=site_id,
|
||||
source_page=source_page,
|
||||
page_context=page_context,
|
||||
screen_content=screen_content,
|
||||
)
|
||||
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
# system prompt 总字符数控制
|
||||
if len(content_str) > _MAX_SYSTEM_PROMPT_LEN:
|
||||
# 截断 page_context 中的 data_text
|
||||
pc = system_content.get("page_context", {})
|
||||
dt = pc.get("data_text", "")
|
||||
if dt and len(dt) > 500:
|
||||
pc["data_text"] = dt[:500] + "…（已截断）"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": content_str},
|
||||
{"role": "user", "content": message},
|
||||
]
|
||||
|
||||
|
||||
async def _build_system_prompt(
|
||||
*,
|
||||
user_id: int | str,
|
||||
nickname: str,
|
||||
role: str,
|
||||
site_id: int,
|
||||
source_page: str | None,
|
||||
page_context: dict | None,
|
||||
screen_content: str | None,
|
||||
) -> dict:
|
||||
"""构建 system prompt JSON。
|
||||
|
||||
通过 biz_params.user_prompt_params 传入用户信息，
|
||||
注入页面上下文供 AI 理解当前场景。
|
||||
"""
|
||||
prompt: dict = {
|
||||
"task": (
|
||||
"你是台球门店的 AI 助手，根据用户的问题和当前页面上下文提供帮助。"
|
||||
"当 page_context 中包含 memberNickname、contextId 或 data_text 时，"
|
||||
"你必须直接使用这些信息回答问题，不要再向用户索要已有的信息。"
|
||||
"例如用户在客户详情页提问时，直接基于该客户的数据回答，无需要求提供会员 ID。"
|
||||
),
|
||||
"biz_params": {
|
||||
"user_prompt_params": {
|
||||
"User_ID": str(user_id),
|
||||
"Role": role,
|
||||
"Nickname": nickname,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
# 注入页面上下文（首条消息）
|
||||
page_ctx = await _build_page_context(
|
||||
source_page=source_page,
|
||||
page_context=page_context,
|
||||
screen_content=screen_content,
|
||||
site_id=site_id,
|
||||
)
|
||||
if page_ctx:
|
||||
prompt["page_context"] = page_ctx
|
||||
|
||||
return prompt
|
||||
|
||||
|
||||
async def _build_page_context(
|
||||
*,
|
||||
source_page: str | None,
|
||||
page_context: dict | None,
|
||||
screen_content: str | None,
|
||||
site_id: int,
|
||||
) -> dict:
|
||||
"""构建页面上下文信息。
|
||||
|
||||
根据 source_page（contextType）调用 build_page_text 获取结构化文本，
|
||||
看板类页面从 page_context 提取筛选参数传入 filters。
|
||||
contextType 为空或未识别时返回空 dict（跳过注入）。
|
||||
"""
|
||||
ctx: dict = {}
|
||||
|
||||
if source_page:
|
||||
ctx["source_page"] = source_page
|
||||
|
||||
# 从 page_context 提取 contextId 和筛选参数
|
||||
context_id = None
|
||||
filters: dict = {}
|
||||
if page_context:
|
||||
context_id = page_context.get("contextId")
|
||||
# 看板类页面筛选参数透传
|
||||
for key in ("timeDimension", "areaFilter", "dimension", "typeFilter", "projectFilter"):
|
||||
if key in page_context:
|
||||
filters[key] = page_context[key]
|
||||
|
||||
# 调用 data_fetcher 获取页面数据文本
|
||||
try:
|
||||
data_text = await build_page_text(
|
||||
source_page=source_page,
|
||||
context_id=context_id,
|
||||
site_id=site_id,
|
||||
filters=filters if filters else None,
|
||||
)
|
||||
if data_text:
|
||||
ctx["data_text"] = data_text
|
||||
except Exception:
|
||||
logger.warning("页面上下文文本化失败: source_page=%s", source_page, exc_info=True)
|
||||
|
||||
if page_context:
|
||||
ctx["page_context"] = page_context
|
||||
if screen_content:
|
||||
ctx["screen_content"] = screen_content
|
||||
|
||||
return ctx
|
||||
|
||||
|
||||
def _build_source_context(
|
||||
*,
|
||||
source_page: str | None,
|
||||
page_context: dict | None,
|
||||
screen_content: str | None,
|
||||
) -> dict | None:
|
||||
"""构建存入 ai_conversations.source_context 的 JSON。"""
|
||||
ctx: dict = {}
|
||||
if source_page:
|
||||
ctx["source_page"] = source_page
|
||||
if page_context:
|
||||
ctx["page_context"] = page_context
|
||||
if screen_content:
|
||||
ctx["screen_content"] = screen_content
|
||||
return ctx if ctx else None
|
||||
@@ -1,210 +0,0 @@
|
||||
"""应用 2：财务洞察。
|
||||
|
||||
8 个时间维度独立调用，每次调用结果写入 ai_cache，
|
||||
同时创建 ai_conversations + ai_messages 记录。
|
||||
|
||||
营业日分界点：每日 08:00（BUSINESS_DAY_START_HOUR 环境变量，默认 8）。
|
||||
|
||||
app_id = "app2_finance"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from datetime import date, datetime, timedelta
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.prompts.app2_finance_prompt import build_prompt
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app2_finance"
|
||||
|
||||
# 8 个时间维度编码
|
||||
TIME_DIMENSIONS = (
|
||||
"this_month",
|
||||
"last_month",
|
||||
"this_week",
|
||||
"last_week",
|
||||
"last_3_months",
|
||||
"this_quarter",
|
||||
"last_quarter",
|
||||
"last_6_months",
|
||||
)
|
||||
|
||||
|
||||
def get_business_date() -> date:
|
||||
"""根据营业日分界点计算当前营业日。
|
||||
|
||||
分界点前（如 07:59）视为前一天营业日，
|
||||
分界点及之后（如 08:00）视为当天营业日。
|
||||
"""
|
||||
hour = int(os.environ.get("BUSINESS_DAY_START_HOUR", "8"))
|
||||
now = datetime.now()
|
||||
if now.hour < hour:
|
||||
return (now - timedelta(days=1)).date()
|
||||
return now.date()
|
||||
|
||||
|
||||
def compute_time_range(dimension: str, business_date: date) -> tuple[date, date]:
|
||||
"""计算时间维度对应的日期范围 [start, end]（闭区间）。
|
||||
|
||||
Args:
|
||||
dimension: 时间维度编码
|
||||
business_date: 当前营业日
|
||||
|
||||
Returns:
|
||||
(start_date, end_date) 元组
|
||||
"""
|
||||
y, m, d = business_date.year, business_date.month, business_date.day
|
||||
|
||||
if dimension == "this_month":
|
||||
start = date(y, m, 1)
|
||||
return start, business_date
|
||||
|
||||
if dimension == "last_month":
|
||||
prev = _month_offset(y, m, -1)
|
||||
start = date(prev[0], prev[1], 1)
|
||||
end = date(y, m, 1) - timedelta(days=1)
|
||||
return start, end
|
||||
|
||||
if dimension == "this_week":
|
||||
# 周一起算
|
||||
weekday = business_date.weekday() # 0=周一
|
||||
start = business_date - timedelta(days=weekday)
|
||||
return start, business_date
|
||||
|
||||
if dimension == "last_week":
|
||||
weekday = business_date.weekday()
|
||||
this_monday = business_date - timedelta(days=weekday)
|
||||
last_monday = this_monday - timedelta(days=7)
|
||||
last_sunday = this_monday - timedelta(days=1)
|
||||
return last_monday, last_sunday
|
||||
|
||||
if dimension == "last_3_months":
|
||||
# 当前月 - 3 ~ 当前月 - 1
|
||||
end_ym = _month_offset(y, m, -1)
|
||||
start_ym = _month_offset(y, m, -3)
|
||||
start = date(start_ym[0], start_ym[1], 1)
|
||||
# end = 上月最后一天
|
||||
end = date(y, m, 1) - timedelta(days=1)
|
||||
return start, end
|
||||
|
||||
if dimension == "this_quarter":
|
||||
q_start_month = ((m - 1) // 3) * 3 + 1
|
||||
start = date(y, q_start_month, 1)
|
||||
return start, business_date
|
||||
|
||||
if dimension == "last_quarter":
|
||||
q_start_month = ((m - 1) // 3) * 3 + 1
|
||||
# 上季度结束 = 本季度第一天 - 1
|
||||
this_q_start = date(y, q_start_month, 1)
|
||||
end = this_q_start - timedelta(days=1)
|
||||
# 上季度开始
|
||||
ly, lm = end.year, end.month
|
||||
lq_start_month = ((lm - 1) // 3) * 3 + 1
|
||||
start = date(ly, lq_start_month, 1)
|
||||
return start, end
|
||||
|
||||
if dimension == "last_6_months":
|
||||
# 当前月 - 6 ~ 当前月 - 1
|
||||
end_ym = _month_offset(y, m, -1)
|
||||
start_ym = _month_offset(y, m, -6)
|
||||
start = date(start_ym[0], start_ym[1], 1)
|
||||
end = date(y, m, 1) - timedelta(days=1)
|
||||
return start, end
|
||||
|
||||
raise ValueError(f"未知时间维度: {dimension}")
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App2 财务洞察调用。
|
||||
|
||||
Args:
|
||||
context: 包含 site_id, time_dimension, user_id(默认'system'), nickname(默认'')
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（insights 数组）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
time_dimension = context["time_dimension"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 构建 Prompt
|
||||
prompt_context = {
|
||||
"site_id": site_id,
|
||||
"time_dimension": time_dimension,
|
||||
"current_data": context.get("current_data", {}),
|
||||
"previous_data": context.get("previous_data", {}),
|
||||
}
|
||||
messages = build_prompt(prompt_context)
|
||||
|
||||
# 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"time_dimension": time_dimension},
|
||||
)
|
||||
|
||||
# 写入 system prompt 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
# 写入 user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 写入缓存
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP2_FINANCE.value,
|
||||
site_id=site_id,
|
||||
target_id=time_dimension,
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App2 财务洞察完成: site_id=%s dimension=%s conversation_id=%s tokens=%d",
|
||||
site_id, time_dimension, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def _month_offset(year: int, month: int, offset: int) -> tuple[int, int]:
|
||||
"""计算月份偏移，返回 (year, month)。"""
|
||||
# 转为 0-based 计算
|
||||
total = (year * 12 + (month - 1)) + offset
|
||||
return total // 12, total % 12 + 1
|
||||
@@ -1,263 +0,0 @@
|
||||
"""应用 3：客户数据维客线索分析（骨架）。
|
||||
|
||||
客户新增消费时自动触发，通过 AI 分析客户数据提取维客线索。
|
||||
线索 category 限定 3 个枚举值：客户基础、消费习惯、玩法偏好。
|
||||
线索提供者统一标记为"系统"。
|
||||
|
||||
使用 items_sum 口径（= table_charge_money + goods_money
|
||||
+ assistant_pd_money + assistant_cx_money + electricity_money），
|
||||
禁止使用 consume_money。
|
||||
|
||||
app_id = "app3_clue"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.data_fetchers import fetch_member_consumption_data
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app3_clue"
|
||||
|
||||
# system message content 上限
|
||||
_MAX_SYSTEM_CONTENT_LEN = 8000
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
"""数据获取失败时的默认空值。"""
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> list[dict]:
|
||||
"""构建 Prompt 消息列表。
|
||||
|
||||
从 data_fetchers 获取真实消费数据，失败时降级为空值。
|
||||
|
||||
Args:
|
||||
context: 包含 site_id, member_id, nickname 等
|
||||
cache_svc: 缓存服务，用于获取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
消息列表 [{"role": "system", "content": ...}, {"role": "user", ...}]
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
|
||||
# 获取消费数据（失败时降级）
|
||||
data_fetch_failed = False
|
||||
try:
|
||||
member_data = await fetch_member_consumption_data(site_id, member_id)
|
||||
except Exception:
|
||||
logger.warning("App3 消费数据获取失败，使用默认空值: site_id=%s member_id=%s", site_id, member_id, exc_info=True)
|
||||
member_data = _default_member_data()
|
||||
data_fetch_failed = True
|
||||
|
||||
# 构建 reference：App6 线索 + 最近 2 套 App8 历史（附 generated_at）
|
||||
reference = _build_reference(site_id, member_id, cache_svc)
|
||||
|
||||
member_nickname = member_data.get("member_nickname", "")
|
||||
consumption_records = member_data.get("consumption_records", [])
|
||||
|
||||
# 空数据标注
|
||||
if not consumption_records:
|
||||
if data_fetch_failed:
|
||||
consumption_records = "⚠ 消费数据获取失败，该客户暂无消费记录可供分析"
|
||||
else:
|
||||
consumption_records = "该客户暂无消费记录"
|
||||
|
||||
system_content = {
|
||||
"task": "分析客户消费数据，提取维客线索。",
|
||||
"app_id": APP_ID,
|
||||
"rules": {
|
||||
"category_enum": ["客户基础", "消费习惯", "玩法偏好"],
|
||||
"providers": "系统",
|
||||
"amount_caliber": "items_sum = table_charge_money + goods_money + assistant_pd_money + assistant_cx_money + electricity_money",
|
||||
"禁止使用": "consume_money",
|
||||
},
|
||||
"output_format": {
|
||||
"clues": [
|
||||
{
|
||||
"category": "枚举值（客户基础/消费习惯/玩法偏好）",
|
||||
"summary": "一句话摘要",
|
||||
"detail": "详细说明",
|
||||
"emoji": "表情符号",
|
||||
}
|
||||
]
|
||||
},
|
||||
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"member_nickname": member_nickname,
|
||||
"main_data": {
|
||||
"consumption_records": consumption_records,
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"reference": reference,
|
||||
}
|
||||
|
||||
# Token 预算控制：截断 consumption_records
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
records = system_content["main_data"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
system_content["main_data"]["consumption_records"] = records[:5]
|
||||
system_content["main_data"]["_truncated"] = f"消费记录已截断，原始共 {len(records)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
|
||||
user_content = (
|
||||
f"请分析会员 {member_id} 的消费数据，提取维客线索。"
|
||||
"每条线索包含 category、summary、detail、emoji 四个字段。"
|
||||
"category 必须是：客户基础、消费习惯、玩法偏好 之一。"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": content_str},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""构建 Prompt reference 字段。
|
||||
|
||||
包含：
|
||||
- App6 备注分析线索（最新一条，如有）
|
||||
- 最近 2 套 App8 维客线索整理历史（附 generated_at）
|
||||
|
||||
缓存不存在时返回空对象 {}。
|
||||
"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
reference: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
# App6 备注分析线索
|
||||
app6_latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP6_NOTE_ANALYSIS.value, site_id, target_id,
|
||||
)
|
||||
if app6_latest:
|
||||
reference["app6_note_clues"] = {
|
||||
"result_json": app6_latest.get("result_json"),
|
||||
"generated_at": app6_latest.get("created_at"),
|
||||
}
|
||||
|
||||
# 最近 2 套 App8 历史
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
reference["app8_history"] = [
|
||||
{
|
||||
"result_json": h.get("result_json"),
|
||||
"generated_at": h.get("created_at"),
|
||||
}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return reference
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App3 客户数据维客线索分析。
|
||||
|
||||
流程：
|
||||
1. build_prompt 构建 Prompt
|
||||
2. bailian.chat_json 调用百炼
|
||||
3. 写入 conversation + messages
|
||||
4. 写入 ai_cache
|
||||
5. 返回结果
|
||||
|
||||
Args:
|
||||
context: site_id, member_id, user_id(默认'system'), nickname(默认'')
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（clues 数组）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 1. 构建 Prompt
|
||||
messages = await build_prompt(context, cache_svc)
|
||||
|
||||
# 2. 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"member_id": member_id},
|
||||
)
|
||||
|
||||
# 写入 system + user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 3. 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 4. 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 5. 写入缓存
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP3_CLUE.value,
|
||||
site_id=site_id,
|
||||
target_id=str(member_id),
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App3 线索分析完成: site_id=%s member_id=%s conversation_id=%s tokens=%d",
|
||||
site_id, member_id, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -1,300 +0,0 @@
|
||||
"""应用 4：关系分析/任务建议（骨架）。
|
||||
|
||||
助教参与新结算或被分配召回任务时自动触发，
|
||||
生成关系分析和任务建议。
|
||||
|
||||
Prompt reference 包含 App8 最新 + 最近 2 套历史（附 generated_at）。
|
||||
缓存不存在时 reference 传空对象，标注"暂无历史线索"。
|
||||
|
||||
app_id = "app4_analysis"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.data_fetchers import (
|
||||
fetch_assistant_info,
|
||||
fetch_member_consumption_data,
|
||||
fetch_member_notes,
|
||||
fetch_service_history,
|
||||
)
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app4_analysis"
|
||||
|
||||
# system message content 上限
|
||||
_MAX_SYSTEM_CONTENT_LEN = 8000
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
"""数据获取失败时的默认空值。"""
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> list[dict]:
|
||||
"""构建 Prompt 消息列表。
|
||||
|
||||
并发获取助教信息、服务历史、客户消费数据、备注，部分失败不阻断。
|
||||
|
||||
Args:
|
||||
context: 包含 site_id, assistant_id, member_id
|
||||
cache_svc: 缓存服务，用于获取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
消息列表
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
assistant_id = context["assistant_id"]
|
||||
member_id = context["member_id"]
|
||||
|
||||
# 并发获取 4 类数据，部分失败不阻断
|
||||
results = await asyncio.gather(
|
||||
fetch_assistant_info(site_id, assistant_id),
|
||||
fetch_service_history(site_id, assistant_id, member_id),
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
# 降级处理
|
||||
fetch_errors: list[str] = []
|
||||
|
||||
if isinstance(results[0], Exception):
|
||||
logger.warning("App4 助教信息获取失败: %s", results[0])
|
||||
assistant_info = {}
|
||||
fetch_errors.append("助教信息获取失败")
|
||||
else:
|
||||
assistant_info = results[0]
|
||||
|
||||
if isinstance(results[1], Exception):
|
||||
logger.warning("App4 服务历史获取失败: %s", results[1])
|
||||
service_history: list = []
|
||||
fetch_errors.append("服务历史获取失败")
|
||||
else:
|
||||
service_history = results[1]
|
||||
|
||||
if isinstance(results[2], Exception):
|
||||
logger.warning("App4 消费数据获取失败: %s", results[2])
|
||||
member_data = _default_member_data()
|
||||
fetch_errors.append("消费数据获取失败")
|
||||
else:
|
||||
member_data = results[2]
|
||||
|
||||
if isinstance(results[3], Exception):
|
||||
logger.warning("App4 备注获取失败: %s", results[3])
|
||||
notes: list = []
|
||||
fetch_errors.append("备注获取失败")
|
||||
else:
|
||||
notes = results[3]
|
||||
|
||||
# 构建 reference：App8 最新 + 最近 2 套历史
|
||||
reference = _build_reference(site_id, member_id, cache_svc)
|
||||
|
||||
system_content: dict = {
|
||||
"task": "分析助教与客户的关系，生成任务建议。",
|
||||
"app_id": APP_ID,
|
||||
"output_format": {
|
||||
"task_description": "任务描述文本",
|
||||
"action_suggestions": ["建议1", "建议2"],
|
||||
"one_line_summary": "一句话总结",
|
||||
},
|
||||
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"assistant_info": assistant_info if assistant_info else "⚠ 助教信息获取失败",
|
||||
"service_history": service_history if service_history else "暂无服务记录",
|
||||
"task_assignment_basis": {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"customer_data": {
|
||||
"system_data": {
|
||||
"member_nickname": member_data.get("member_nickname", ""),
|
||||
},
|
||||
"notes": notes if notes else "暂无备注",
|
||||
},
|
||||
"reference": reference,
|
||||
}
|
||||
|
||||
if fetch_errors:
|
||||
system_content["_data_warnings"] = fetch_errors
|
||||
|
||||
# Token 预算控制
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
# 优先截断 service_history
|
||||
sh = system_content.get("service_history")
|
||||
if isinstance(sh, list) and len(sh) > 5:
|
||||
system_content["service_history"] = sh[:5]
|
||||
system_content["_truncated_service_history"] = f"服务记录已截断，原始共 {len(sh)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
records = system_content["task_assignment_basis"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
system_content["task_assignment_basis"]["consumption_records"] = records[:5]
|
||||
system_content["task_assignment_basis"]["_truncated"] = f"消费记录已截断，原始共 {len(records)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
n = system_content["customer_data"].get("notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
system_content["customer_data"]["notes"] = n[:10]
|
||||
system_content["customer_data"]["_truncated_notes"] = f"备注已截断，原始共 {len(n)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
|
||||
# 缓存不存在时在 user prompt 中标注
|
||||
no_history_hint = ""
|
||||
if not reference:
|
||||
no_history_hint = "（暂无历史线索，请基于现有信息分析）"
|
||||
|
||||
user_content = (
|
||||
f"请分析助教 {assistant_id} 与会员 {member_id} 的关系，"
|
||||
f"生成任务建议。{no_history_hint}"
|
||||
"返回 task_description、action_suggestions、one_line_summary 三个字段。"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": content_str},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""构建 Prompt reference 字段。
|
||||
|
||||
包含：
|
||||
- App8 最新维客线索（如有）
|
||||
- 最近 2 套 App8 历史（附 generated_at）
|
||||
|
||||
缓存不存在时返回空对象 {}。
|
||||
"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
reference: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
# App8 最新
|
||||
app8_latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,
|
||||
)
|
||||
if app8_latest:
|
||||
reference["app8_latest"] = {
|
||||
"result_json": app8_latest.get("result_json"),
|
||||
"generated_at": app8_latest.get("created_at"),
|
||||
}
|
||||
|
||||
# 最近 2 套 App8 历史
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
reference["app8_history"] = [
|
||||
{
|
||||
"result_json": h.get("result_json"),
|
||||
"generated_at": h.get("created_at"),
|
||||
}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return reference
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App4 关系分析。
|
||||
|
||||
Args:
|
||||
context: site_id, assistant_id, member_id
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（task_description, action_suggestions, one_line_summary）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
assistant_id = context["assistant_id"]
|
||||
member_id = context["member_id"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 1. 构建 Prompt
|
||||
messages = await build_prompt(context, cache_svc)
|
||||
|
||||
# 2. 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"assistant_id": assistant_id, "member_id": member_id},
|
||||
)
|
||||
|
||||
# 写入 system + user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 3. 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 4. 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 5. 写入缓存（target_id = {assistant_id}_{member_id}）
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP4_ANALYSIS.value,
|
||||
site_id=site_id,
|
||||
target_id=f"{assistant_id}_{member_id}",
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App4 关系分析完成: site_id=%s assistant=%s member=%s conversation_id=%s tokens=%d",
|
||||
site_id, assistant_id, member_id, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -1,288 +0,0 @@
|
||||
"""应用 5：话术参考（骨架）。
|
||||
|
||||
App4 完成后自动联动触发，接收 App4 完整返回结果
|
||||
作为 Prompt 中的 task_suggestion 字段。
|
||||
|
||||
Prompt reference 包含最近 2 套 App8 历史（附 generated_at）。
|
||||
|
||||
app_id = "app5_tactics"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.data_fetchers import (
|
||||
fetch_assistant_info,
|
||||
fetch_member_consumption_data,
|
||||
fetch_member_notes,
|
||||
fetch_service_history,
|
||||
)
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app5_tactics"
|
||||
|
||||
# system message content 上限
|
||||
_MAX_SYSTEM_CONTENT_LEN = 8000
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
"""数据获取失败时的默认空值。"""
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> list[dict]:
|
||||
"""构建 Prompt 消息列表。
|
||||
|
||||
复用 App4 的数据获取逻辑（并发获取助教信息、服务历史、消费数据、备注），
|
||||
额外从 context["app4_result"] 获取 task_suggestion。
|
||||
|
||||
Args:
|
||||
context: 包含 site_id, assistant_id, member_id, app4_result(dict)
|
||||
cache_svc: 缓存服务，用于获取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
消息列表
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
assistant_id = context["assistant_id"]
|
||||
member_id = context["member_id"]
|
||||
# App4 结果作为 task_suggestion，缺失时设为空对象
|
||||
task_suggestion = context.get("app4_result") or {}
|
||||
|
||||
# 并发获取 4 类数据，部分失败不阻断
|
||||
results = await asyncio.gather(
|
||||
fetch_assistant_info(site_id, assistant_id),
|
||||
fetch_service_history(site_id, assistant_id, member_id),
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
# 降级处理
|
||||
fetch_errors: list[str] = []
|
||||
|
||||
if isinstance(results[0], Exception):
|
||||
logger.warning("App5 助教信息获取失败: %s", results[0])
|
||||
assistant_info = {}
|
||||
fetch_errors.append("助教信息获取失败")
|
||||
else:
|
||||
assistant_info = results[0]
|
||||
|
||||
if isinstance(results[1], Exception):
|
||||
logger.warning("App5 服务历史获取失败: %s", results[1])
|
||||
service_history: list = []
|
||||
fetch_errors.append("服务历史获取失败")
|
||||
else:
|
||||
service_history = results[1]
|
||||
|
||||
if isinstance(results[2], Exception):
|
||||
logger.warning("App5 消费数据获取失败: %s", results[2])
|
||||
member_data = _default_member_data()
|
||||
fetch_errors.append("消费数据获取失败")
|
||||
else:
|
||||
member_data = results[2]
|
||||
|
||||
if isinstance(results[3], Exception):
|
||||
logger.warning("App5 备注获取失败: %s", results[3])
|
||||
notes: list = []
|
||||
fetch_errors.append("备注获取失败")
|
||||
else:
|
||||
notes = results[3]
|
||||
|
||||
# 构建 reference：最近 2 套 App8 历史
|
||||
reference = _build_reference(site_id, member_id, cache_svc)
|
||||
|
||||
system_content: dict = {
|
||||
"task": (
|
||||
"基于关系分析和任务建议，生成沟通话术参考。"
|
||||
"输出必须严格遵循 output_format 中定义的 JSON 结构，"
|
||||
"每条话术必须包含 scenario（场景描述）和 script（话术内容）两个字段，"
|
||||
"禁止使用 content 或其他字段名替代。"
|
||||
),
|
||||
"app_id": APP_ID,
|
||||
"task_suggestion": task_suggestion,
|
||||
"output_format": {
|
||||
"tactics": [
|
||||
{"scenario": "场景描述", "script": "话术内容"}
|
||||
]
|
||||
},
|
||||
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"assistant_info": assistant_info if assistant_info else "⚠ 助教信息获取失败",
|
||||
"service_history": service_history if service_history else "暂无服务记录",
|
||||
"task_assignment_basis": {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"customer_data": {
|
||||
"system_data": {
|
||||
"member_nickname": member_data.get("member_nickname", ""),
|
||||
},
|
||||
"notes": notes if notes else "暂无备注",
|
||||
},
|
||||
"reference": reference,
|
||||
}
|
||||
|
||||
if fetch_errors:
|
||||
system_content["_data_warnings"] = fetch_errors
|
||||
|
||||
# Token 预算控制
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
sh = system_content.get("service_history")
|
||||
if isinstance(sh, list) and len(sh) > 5:
|
||||
system_content["service_history"] = sh[:5]
|
||||
system_content["_truncated_service_history"] = f"服务记录已截断，原始共 {len(sh)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
records = system_content["task_assignment_basis"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
system_content["task_assignment_basis"]["consumption_records"] = records[:5]
|
||||
system_content["task_assignment_basis"]["_truncated"] = f"消费记录已截断，原始共 {len(records)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
n = system_content["customer_data"].get("notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
system_content["customer_data"]["notes"] = n[:10]
|
||||
system_content["customer_data"]["_truncated_notes"] = f"备注已截断，原始共 {len(n)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
|
||||
user_content = (
|
||||
f"请为助教 {assistant_id} 生成与会员 {member_id} 沟通的话术参考。"
|
||||
"返回 tactics 数组，每条包含 scenario 和 script 字段。"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": content_str},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""构建 Prompt reference 字段。
|
||||
|
||||
包含最近 2 套 App8 历史（附 generated_at）。
|
||||
缓存不存在时返回空对象 {}。
|
||||
"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
reference: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
# 最近 2 套 App8 历史
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
reference["app8_history"] = [
|
||||
{
|
||||
"result_json": h.get("result_json"),
|
||||
"generated_at": h.get("created_at"),
|
||||
}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return reference
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App5 话术参考。
|
||||
|
||||
Args:
|
||||
context: site_id, assistant_id, member_id, app4_result(dict)
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（tactics 数组）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
assistant_id = context["assistant_id"]
|
||||
member_id = context["member_id"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 1. 构建 Prompt
|
||||
messages = await build_prompt(context, cache_svc)
|
||||
|
||||
# 2. 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"assistant_id": assistant_id, "member_id": member_id},
|
||||
)
|
||||
|
||||
# 写入 system + user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 3. 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 4. 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 5. 写入缓存（target_id = {assistant_id}_{member_id}）
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP5_TACTICS.value,
|
||||
site_id=site_id,
|
||||
target_id=f"{assistant_id}_{member_id}",
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App5 话术参考完成: site_id=%s assistant=%s member=%s conversation_id=%s tokens=%d",
|
||||
site_id, assistant_id, member_id, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -1,289 +0,0 @@
|
||||
"""应用 6：备注分析（骨架）。
|
||||
|
||||
助教提交备注后自动触发，通过 AI 分析备注内容，
|
||||
提取维客线索并评分。
|
||||
|
||||
返回 score（1-10）+ clues 数组。
|
||||
评分规则：6 分为标准分，重复/低价值/时效性低酌情扣分，高价值信息酌情加分。
|
||||
线索 category 限定 6 个枚举值。
|
||||
线索提供者标记为当前备注提供人（context.noted_by_name）。
|
||||
|
||||
app_id = "app6_note"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.data_fetchers import fetch_member_consumption_data, fetch_member_notes
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app6_note"
|
||||
|
||||
# system message content 上限
|
||||
_MAX_SYSTEM_CONTENT_LEN = 8000
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
"""数据获取失败时的默认空值。"""
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> list[dict]:
|
||||
"""构建 Prompt 消息列表。
|
||||
|
||||
并发获取消费数据和备注，失败时降级为空值。
|
||||
|
||||
Args:
|
||||
context: 包含 site_id, member_id, note_content, noted_by_name
|
||||
cache_svc: 缓存服务，用于获取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
消息列表
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
note_content = context.get("note_content", "")
|
||||
noted_by_name = context.get("noted_by_name", "")
|
||||
noted_by_created_at = context.get("noted_by_created_at", "")
|
||||
|
||||
# 并发获取消费数据和备注
|
||||
results = await asyncio.gather(
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
fetch_errors: list[str] = []
|
||||
|
||||
if isinstance(results[0], Exception):
|
||||
logger.warning("App6 消费数据获取失败: %s", results[0])
|
||||
member_data = _default_member_data()
|
||||
fetch_errors.append("消费数据获取失败")
|
||||
else:
|
||||
member_data = results[0]
|
||||
|
||||
if isinstance(results[1], Exception):
|
||||
logger.warning("App6 备注获取失败: %s", results[1])
|
||||
all_notes: list = []
|
||||
fetch_errors.append("备注获取失败")
|
||||
else:
|
||||
all_notes = results[1]
|
||||
|
||||
# 构建 reference：App3 线索 + 最近 2 套 App8 历史
|
||||
reference = _build_reference(site_id, member_id, cache_svc)
|
||||
|
||||
# 将消费数据和备注注入 reference
|
||||
reference["member_nickname"] = member_data.get("member_nickname", "")
|
||||
reference["consumption_data"] = {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
}
|
||||
reference["all_notes"] = all_notes if all_notes else []
|
||||
|
||||
system_content: dict = {
|
||||
"task": "分析备注内容，提取维客线索并评分。",
|
||||
"app_id": APP_ID,
|
||||
"rules": {
|
||||
"category_enum": [
|
||||
"客户基础", "消费习惯", "玩法偏好",
|
||||
"促销偏好", "社交关系", "重要反馈",
|
||||
],
|
||||
"providers": noted_by_name,
|
||||
"scoring": "6 分为标准分，重复/低价值/时效性低酌情扣分，高价值信息酌情加分",
|
||||
"score_range": "1-10",
|
||||
},
|
||||
"output_format": {
|
||||
"score": "1-10 整数",
|
||||
"clues": [
|
||||
{
|
||||
"category": "枚举值（6 选 1）",
|
||||
"summary": "一句话摘要",
|
||||
"detail": "详细说明",
|
||||
"emoji": "表情符号",
|
||||
}
|
||||
],
|
||||
},
|
||||
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"current_note": {
|
||||
"content": note_content,
|
||||
"recorded_by": noted_by_name,
|
||||
"created_at": noted_by_created_at,
|
||||
},
|
||||
"reference": reference,
|
||||
}
|
||||
|
||||
if fetch_errors:
|
||||
system_content["_data_warnings"] = fetch_errors
|
||||
|
||||
# Token 预算控制
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
records = system_content["reference"].get("consumption_data", {}).get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
system_content["reference"]["consumption_data"]["consumption_records"] = records[:5]
|
||||
system_content["reference"]["consumption_data"]["_truncated"] = f"消费记录已截断，原始共 {len(records)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
n = system_content["reference"].get("all_notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
system_content["reference"]["all_notes"] = n[:10]
|
||||
system_content["reference"]["_truncated_notes"] = f"备注已截断，原始共 {len(n)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
|
||||
user_content = (
|
||||
f"请分析以下备注内容，提取维客线索并评分。\n"
|
||||
f"备注提供人：{noted_by_name}\n"
|
||||
f"备注内容：{note_content}\n"
|
||||
"返回 score（1-10 整数）和 clues 数组。"
|
||||
"category 必须是：客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈 之一。"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": content_str},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""构建 Prompt reference 字段。
|
||||
|
||||
包含：
|
||||
- App3 客户数据线索（最新一条，如有）
|
||||
- 最近 2 套 App8 维客线索整理历史（附 generated_at）
|
||||
|
||||
缓存不存在时返回空对象 {}。
|
||||
"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
reference: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
# App3 客户数据线索
|
||||
app3_latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP3_CLUE.value, site_id, target_id,
|
||||
)
|
||||
if app3_latest:
|
||||
reference["app3_clues"] = {
|
||||
"result_json": app3_latest.get("result_json"),
|
||||
"generated_at": app3_latest.get("created_at"),
|
||||
}
|
||||
|
||||
# 最近 2 套 App8 历史
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
reference["app8_history"] = [
|
||||
{
|
||||
"result_json": h.get("result_json"),
|
||||
"generated_at": h.get("created_at"),
|
||||
}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return reference
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App6 备注分析。
|
||||
|
||||
Args:
|
||||
context: site_id, member_id, note_content, noted_by_name
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（score + clues 数组）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 1. 构建 Prompt
|
||||
messages = await build_prompt(context, cache_svc)
|
||||
|
||||
# 2. 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"member_id": member_id},
|
||||
)
|
||||
|
||||
# 写入 system + user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 3. 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 4. 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 5. 写入缓存（score 存入 ai_cache.score）
|
||||
score = result.get("score")
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP6_NOTE_ANALYSIS.value,
|
||||
site_id=site_id,
|
||||
target_id=str(member_id),
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
score=score,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App6 备注分析完成: site_id=%s member_id=%s score=%s conversation_id=%s tokens=%d",
|
||||
site_id, member_id, score, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -1,282 +0,0 @@
|
||||
"""应用 7：客户分析（骨架）。
|
||||
|
||||
消费事件链中 App8 完成后串行触发，生成客户全量分析与运营建议。
|
||||
|
||||
使用 items_sum 口径（= table_charge_money + goods_money
|
||||
+ assistant_pd_money + assistant_cx_money + electricity_money），
|
||||
禁止使用 consume_money。
|
||||
|
||||
对主观信息（来自备注）标注【来源：XXX，请甄别信息真实性】。
|
||||
|
||||
app_id = "app7_customer"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.data_fetchers import fetch_member_consumption_data, fetch_member_notes
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app7_customer"
|
||||
|
||||
# system message content 上限
|
||||
_MAX_SYSTEM_CONTENT_LEN = 8000
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
"""数据获取失败时的默认空值。"""
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> list[dict]:
|
||||
"""构建 Prompt 消息列表。
|
||||
|
||||
并发获取消费数据和备注，备注标注来源信息。
|
||||
|
||||
Args:
|
||||
context: 包含 site_id, member_id
|
||||
cache_svc: 缓存服务，用于获取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
消息列表
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
|
||||
# 并发获取消费数据和备注
|
||||
results = await asyncio.gather(
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
fetch_errors: list[str] = []
|
||||
|
||||
if isinstance(results[0], Exception):
|
||||
logger.warning("App7 消费数据获取失败: %s", results[0])
|
||||
member_data = _default_member_data()
|
||||
fetch_errors.append("消费数据获取失败")
|
||||
else:
|
||||
member_data = results[0]
|
||||
|
||||
if isinstance(results[1], Exception):
|
||||
logger.warning("App7 备注获取失败: %s", results[1])
|
||||
notes_raw: list = []
|
||||
fetch_errors.append("备注获取失败")
|
||||
else:
|
||||
notes_raw = results[1]
|
||||
|
||||
# 备注标注来源信息
|
||||
if notes_raw:
|
||||
subjective_notes = []
|
||||
for note in notes_raw:
|
||||
recorded_by = note.get("recorded_by", "未知")
|
||||
annotated = dict(note)
|
||||
annotated["content"] = f"{note.get('content', '')}【来源：{recorded_by}，请甄别信息真实性】"
|
||||
subjective_notes.append(annotated)
|
||||
else:
|
||||
subjective_notes = "该客户暂无主观备注信息"
|
||||
|
||||
member_nickname = member_data.get("member_nickname", "")
|
||||
|
||||
# 构建 reference：最新 + 最近 2 套 App8 历史
|
||||
reference = _build_reference(site_id, member_id, cache_svc)
|
||||
|
||||
system_content: dict = {
|
||||
"task": "综合分析客户数据，生成运营策略建议。",
|
||||
"app_id": APP_ID,
|
||||
"rules": {
|
||||
"amount_caliber": "items_sum = table_charge_money + goods_money + assistant_pd_money + assistant_cx_money + electricity_money",
|
||||
"禁止使用": "consume_money",
|
||||
"subjective_info_label": "对主观信息（来自备注）标注【来源：XXX，请甄别信息真实性】",
|
||||
},
|
||||
"output_format": {
|
||||
"strategies": [
|
||||
{"title": "策略标题", "content": "策略内容"}
|
||||
],
|
||||
"summary": "一句话总结",
|
||||
},
|
||||
"current_time": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"member_id": member_id,
|
||||
"member_nickname": member_nickname,
|
||||
"objective_data": {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"subjective_data": {
|
||||
"notes": subjective_notes,
|
||||
},
|
||||
"reference": reference,
|
||||
}
|
||||
|
||||
if fetch_errors:
|
||||
system_content["_data_warnings"] = fetch_errors
|
||||
|
||||
# Token 预算控制
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
records = system_content["objective_data"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
system_content["objective_data"]["consumption_records"] = records[:5]
|
||||
system_content["objective_data"]["_truncated"] = f"消费记录已截断，原始共 {len(records)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
if len(content_str) > _MAX_SYSTEM_CONTENT_LEN:
|
||||
n = system_content["subjective_data"].get("notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
system_content["subjective_data"]["notes"] = n[:10]
|
||||
system_content["subjective_data"]["_truncated_notes"] = f"备注已截断，原始共 {len(n)} 条"
|
||||
content_str = json.dumps(system_content, ensure_ascii=False, default=str)
|
||||
|
||||
user_content = (
|
||||
f"请综合分析会员 {member_id} 的客户数据，生成运营策略建议。"
|
||||
"返回 strategies 数组（每条含 title 和 content）和 summary 字段。"
|
||||
"对来自备注的主观信息，请标注【来源：XXX，请甄别信息真实性】。"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": content_str},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""构建 Prompt reference 字段。
|
||||
|
||||
包含：
|
||||
- App8 最新维客线索（如有）
|
||||
- 最近 2 套 App8 历史（附 generated_at）
|
||||
|
||||
缓存不存在时返回空对象 {}。
|
||||
"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
reference: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
# App8 最新
|
||||
app8_latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,
|
||||
)
|
||||
if app8_latest:
|
||||
reference["app8_latest"] = {
|
||||
"result_json": app8_latest.get("result_json"),
|
||||
"generated_at": app8_latest.get("created_at"),
|
||||
}
|
||||
|
||||
# 最近 2 套 App8 历史
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
reference["app8_history"] = [
|
||||
{
|
||||
"result_json": h.get("result_json"),
|
||||
"generated_at": h.get("created_at"),
|
||||
}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return reference
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App7 客户分析。
|
||||
|
||||
Args:
|
||||
context: site_id, member_id
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（strategies 数组 + summary）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 1. 构建 Prompt
|
||||
messages = await build_prompt(context, cache_svc)
|
||||
|
||||
# 2. 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"member_id": member_id},
|
||||
)
|
||||
|
||||
# 写入 system + user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 3. 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 4. 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 5. 写入缓存
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP7_CUSTOMER_ANALYSIS.value,
|
||||
site_id=site_id,
|
||||
target_id=str(member_id),
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App7 客户分析完成: site_id=%s member_id=%s conversation_id=%s tokens=%d",
|
||||
site_id, member_id, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -1,211 +0,0 @@
|
||||
"""应用 8：维客线索整理。
|
||||
|
||||
接收 App3（消费分析）和 App6（备注分析）的线索，
|
||||
通过百炼 AI 整合去重，然后全量替换写入 member_retention_clue 表。
|
||||
|
||||
app_id = "app8_consolidation"
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
|
||||
from app.ai.dashscope_client import DashScopeClient
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.conversation_service import ConversationService
|
||||
from app.ai.prompts.app8_consolidation_prompt import build_prompt
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
from app.database import get_connection
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
APP_ID = "app8_consolidation"
|
||||
|
||||
|
||||
class ClueWriter:
|
||||
"""维客线索全量替换写入器。
|
||||
|
||||
DELETE source IN ('ai_consumption', 'ai_note') → INSERT 新线索（事务）。
|
||||
人工线索（source='manual'）不受影响。
|
||||
"""
|
||||
|
||||
def replace_ai_clues(
|
||||
self,
|
||||
member_id: int,
|
||||
site_id: int,
|
||||
clues: list[dict],
|
||||
) -> int:
|
||||
"""全量替换该客户的 AI 来源线索，返回写入数量。
|
||||
|
||||
在单个事务中执行 DELETE + INSERT，失败时回滚保留原有线索。
|
||||
|
||||
字段映射：
|
||||
- category → category
|
||||
- emoji + " " + summary → summary（如 "📅 偏好周末下午时段消费"）
|
||||
- detail → detail
|
||||
- providers → recorded_by_name
|
||||
- source: 根据 providers 判断（见 _determine_source）
|
||||
- recorded_by_assistant_id: NULL（系统触发）
|
||||
"""
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
# 1. 删除该客户所有 AI 来源线索
|
||||
cur.execute(
|
||||
"""
|
||||
DELETE FROM member_retention_clue
|
||||
WHERE member_id = %s AND site_id = %s
|
||||
AND source IN ('ai_consumption', 'ai_note')
|
||||
""",
|
||||
(member_id, site_id),
|
||||
)
|
||||
|
||||
# 2. 插入新线索
|
||||
for clue in clues:
|
||||
emoji = clue.get("emoji", "")
|
||||
raw_summary = clue.get("summary", "")
|
||||
summary = f"{emoji} {raw_summary}" if emoji else raw_summary
|
||||
source = _determine_source(clue.get("providers", ""))
|
||||
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO member_retention_clue
|
||||
(member_id, site_id, category, summary, detail,
|
||||
source, recorded_by_name, recorded_by_assistant_id)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, NULL)
|
||||
""",
|
||||
(
|
||||
member_id,
|
||||
site_id,
|
||||
clue.get("category", ""),
|
||||
summary,
|
||||
clue.get("detail", ""),
|
||||
source,
|
||||
clue.get("providers", ""),
|
||||
),
|
||||
)
|
||||
|
||||
conn.commit()
|
||||
return len(clues)
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
|
||||
def _determine_source(providers: str) -> str:
|
||||
"""根据 providers 判断 source 值。
|
||||
|
||||
- 纯 App3（providers 仅含"系统"）→ ai_consumption
|
||||
- 纯 App6（providers 不含"系统"）→ ai_note
|
||||
- 混合来源 → ai_consumption
|
||||
"""
|
||||
if not providers:
|
||||
return "ai_consumption"
|
||||
provider_list = [p.strip() for p in providers.split(",")]
|
||||
has_system = "系统" in provider_list
|
||||
has_human = any(p != "系统" for p in provider_list if p)
|
||||
if has_system and not has_human:
|
||||
# 纯 App3（系统自动分析）
|
||||
return "ai_consumption"
|
||||
elif has_human and not has_system:
|
||||
# 纯 App6（人工备注分析）
|
||||
return "ai_note"
|
||||
else:
|
||||
# 混合来源
|
||||
return "ai_consumption"
|
||||
|
||||
|
||||
async def run(
|
||||
context: dict,
|
||||
client: DashScopeClient,
|
||||
cache_svc: AICacheService,
|
||||
conv_svc: ConversationService,
|
||||
) -> dict:
|
||||
"""执行 App8 维客线索整理。
|
||||
|
||||
流程：
|
||||
1. build_prompt 构建 Prompt
|
||||
2. bailian.chat_json 调用百炼
|
||||
3. 写入 conversation + messages
|
||||
4. 写入 ai_cache
|
||||
5. ClueWriter 全量替换 member_retention_clue
|
||||
6. 返回结果
|
||||
|
||||
Args:
|
||||
context: site_id, member_id, app3_clues, app6_clues,
|
||||
app3_generated_at, app6_generated_at
|
||||
bailian: 百炼客户端
|
||||
cache_svc: 缓存服务
|
||||
conv_svc: 对话服务
|
||||
|
||||
Returns:
|
||||
百炼返回的结构化 JSON（clues 数组）
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
user_id = context.get("user_id", "system")
|
||||
nickname = context.get("nickname", "")
|
||||
|
||||
# 1. 构建 Prompt
|
||||
messages = build_prompt(context)
|
||||
|
||||
# 2. 创建对话记录
|
||||
conversation_id = conv_svc.create_conversation(
|
||||
user_id=user_id,
|
||||
nickname=nickname,
|
||||
app_id=APP_ID,
|
||||
site_id=site_id,
|
||||
source_context={"member_id": member_id},
|
||||
)
|
||||
|
||||
# 写入 system + user 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="system",
|
||||
content=messages[0]["content"],
|
||||
)
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="user",
|
||||
content=messages[1]["content"],
|
||||
)
|
||||
|
||||
# 3. 调用百炼 API
|
||||
result, tokens_used = await bailian.chat_json(messages)
|
||||
|
||||
# 4. 写入 assistant 消息
|
||||
conv_svc.add_message(
|
||||
conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=json.dumps(result, ensure_ascii=False),
|
||||
tokens_used=tokens_used,
|
||||
)
|
||||
|
||||
# 5. 写入缓存
|
||||
cache_svc.write_cache(
|
||||
cache_type=CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value,
|
||||
site_id=site_id,
|
||||
target_id=str(member_id),
|
||||
result_json=result,
|
||||
triggered_by=f"user:{user_id}",
|
||||
)
|
||||
|
||||
# 6. 全量替换 member_retention_clue
|
||||
clues = result.get("clues", [])
|
||||
if clues:
|
||||
writer = ClueWriter()
|
||||
written = writer.replace_ai_clues(member_id, site_id, clues)
|
||||
logger.info(
|
||||
"App8 线索写入完成: site_id=%s member_id=%s written=%d",
|
||||
site_id, member_id, written,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"App8 线索整理完成: site_id=%s member_id=%s conversation_id=%s tokens=%d",
|
||||
site_id, member_id, conversation_id, tokens_used,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -18,6 +18,12 @@ import logging
|
||||
from datetime import datetime, timedelta, timezone
|
||||
|
||||
from app.database import get_connection
|
||||
from app.services.runtime_context import (
|
||||
LIVE_INSTANCE_ID,
|
||||
MODE_LIVE,
|
||||
MODE_SANDBOX,
|
||||
get_runtime_context,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -39,6 +45,14 @@ CACHE_MAX_PER_APP = 20_000
|
||||
class AICacheService:
|
||||
"""AI 缓存读写服务。"""
|
||||
|
||||
@staticmethod
|
||||
def _runtime_scope(site_id: int, target_id: str, conn) -> tuple[str, str, str]:
|
||||
"""返回运行模式、实例 ID 和实际 cache target_id。"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
if ctx.is_sandbox and ctx.sandbox_instance_id:
|
||||
return MODE_SANDBOX, ctx.sandbox_instance_id, f"{ctx.sandbox_instance_id}:{target_id}"
|
||||
return MODE_LIVE, LIVE_INSTANCE_ID, target_id
|
||||
|
||||
def get_latest(
|
||||
self,
|
||||
cache_type: str,
|
||||
@@ -52,6 +66,9 @@ class AICacheService:
|
||||
"""
|
||||
conn = get_connection()
|
||||
try:
|
||||
runtime_mode, sandbox_instance_id, scoped_target_id = self._runtime_scope(
|
||||
site_id, target_id, conn
|
||||
)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
@@ -60,12 +77,14 @@ class AICacheService:
|
||||
created_at, expires_at, status
|
||||
FROM biz.ai_cache
|
||||
WHERE cache_type = %s AND site_id = %s AND target_id = %s
|
||||
AND COALESCE(runtime_mode, 'live') = %s
|
||||
AND COALESCE(sandbox_instance_id, 'live') = %s
|
||||
AND (status = 'valid' OR status IS NULL)
|
||||
AND (expires_at IS NULL OR expires_at > now())
|
||||
ORDER BY created_at DESC
|
||||
LIMIT 1
|
||||
""",
|
||||
(cache_type, site_id, target_id),
|
||||
(cache_type, site_id, scoped_target_id, runtime_mode, sandbox_instance_id),
|
||||
)
|
||||
columns = [desc[0] for desc in cur.description]
|
||||
row = cur.fetchone()
|
||||
@@ -88,6 +107,9 @@ class AICacheService:
|
||||
"""
|
||||
conn = get_connection()
|
||||
try:
|
||||
runtime_mode, sandbox_instance_id, scoped_target_id = self._runtime_scope(
|
||||
site_id, target_id, conn
|
||||
)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
@@ -96,10 +118,12 @@ class AICacheService:
|
||||
created_at, expires_at
|
||||
FROM biz.ai_cache
|
||||
WHERE cache_type = %s AND site_id = %s AND target_id = %s
|
||||
AND COALESCE(runtime_mode, 'live') = %s
|
||||
AND COALESCE(sandbox_instance_id, 'live') = %s
|
||||
ORDER BY created_at DESC
|
||||
LIMIT %s
|
||||
""",
|
||||
(cache_type, site_id, target_id, limit),
|
||||
(cache_type, site_id, scoped_target_id, runtime_mode, sandbox_instance_id, limit),
|
||||
)
|
||||
columns = [desc[0] for desc in cur.description]
|
||||
rows = cur.fetchall()
|
||||
@@ -128,23 +152,29 @@ class AICacheService:
|
||||
|
||||
conn = get_connection()
|
||||
try:
|
||||
runtime_mode, sandbox_instance_id, scoped_target_id = self._runtime_scope(
|
||||
site_id, target_id, conn
|
||||
)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO biz.ai_cache
|
||||
(cache_type, site_id, target_id, result_json,
|
||||
triggered_by, score, expires_at, status)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, 'valid')
|
||||
triggered_by, score, expires_at, status,
|
||||
runtime_mode, sandbox_instance_id)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, 'valid', %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(
|
||||
cache_type,
|
||||
site_id,
|
||||
target_id,
|
||||
scoped_target_id,
|
||||
json.dumps(result_json, ensure_ascii=False),
|
||||
triggered_by,
|
||||
score,
|
||||
expires_at,
|
||||
runtime_mode,
|
||||
sandbox_instance_id,
|
||||
),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
@@ -158,7 +188,7 @@ class AICacheService:
|
||||
|
||||
# 写入成功后清理超限记录
|
||||
try:
|
||||
deleted = self._cleanup_excess(cache_type, site_id, target_id)
|
||||
deleted = self._cleanup_excess(cache_type, site_id, scoped_target_id)
|
||||
if deleted > 0:
|
||||
logger.info(
|
||||
"清理超限缓存: cache_type=%s site_id=%s target_id=%s 删除=%d",
|
||||
@@ -183,15 +213,19 @@ class AICacheService:
|
||||
"""写入 generating 状态占位记录，返回 id。完成后调用 finalize_cache 更新。"""
|
||||
conn = get_connection()
|
||||
try:
|
||||
runtime_mode, sandbox_instance_id, scoped_target_id = self._runtime_scope(
|
||||
site_id, target_id, conn
|
||||
)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO biz.ai_cache
|
||||
(cache_type, site_id, target_id, result_json, status, triggered_by)
|
||||
VALUES (%s, %s, %s, '{}', 'generating', %s)
|
||||
(cache_type, site_id, target_id, result_json, status, triggered_by,
|
||||
runtime_mode, sandbox_instance_id)
|
||||
VALUES (%s, %s, %s, '{}', 'generating', %s, %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(cache_type, site_id, target_id, triggered_by),
|
||||
(cache_type, site_id, scoped_target_id, triggered_by, runtime_mode, sandbox_instance_id),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
conn.commit()
|
||||
|
||||
@@ -28,6 +28,44 @@ from app.ai.exceptions import (
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _field_value(source: Any, key: str, default: Any = None) -> Any:
|
||||
"""兼容 dict、DashScope DictMixin 和普通对象取字段。"""
|
||||
if isinstance(source, dict):
|
||||
return source.get(key, default)
|
||||
return getattr(source, key, default)
|
||||
|
||||
|
||||
def _safe_int(value: Any) -> int:
|
||||
"""把 token 字段安全转换为 int，异常值按 0 处理。"""
|
||||
try:
|
||||
return int(value or 0)
|
||||
except (TypeError, ValueError):
|
||||
return 0
|
||||
|
||||
|
||||
def _extract_tokens_used(usage: Any) -> int:
|
||||
"""从 DashScope usage 多种结构中提取 tokens_used。"""
|
||||
if not usage:
|
||||
return 0
|
||||
|
||||
models = _field_value(usage, "models")
|
||||
if models:
|
||||
total = 0
|
||||
for model_usage in models:
|
||||
total += _safe_int(_field_value(model_usage, "input_tokens"))
|
||||
total += _safe_int(_field_value(model_usage, "output_tokens"))
|
||||
return total
|
||||
|
||||
total_tokens = _field_value(usage, "total_tokens")
|
||||
if total_tokens is not None:
|
||||
return _safe_int(total_tokens)
|
||||
|
||||
return (
|
||||
_safe_int(_field_value(usage, "input_tokens"))
|
||||
+ _safe_int(_field_value(usage, "output_tokens"))
|
||||
)
|
||||
|
||||
|
||||
class DashScopeClient:
|
||||
"""DashScope Application API 统一封装层。
|
||||
|
||||
@@ -54,22 +92,28 @@ class DashScopeClient:
|
||||
prompt: str,
|
||||
session_id: str | None = None,
|
||||
biz_params: dict | None = None,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""App1 流式调用。
|
||||
) -> AsyncGenerator[tuple[str, str | None], None]:
|
||||
"""App1 流式调用，支持 multi-turn session_id 透传。
|
||||
|
||||
在线程中消费同步迭代器，通过 asyncio.Queue 桥接到 async generator。
|
||||
错误通过 queue 传递给调用方。
|
||||
每个 yield 返回 (text_chunk, session_id_or_none) 元组：
|
||||
- 首次调用（传入 session_id=None）时，百炼在流中会返回新 session_id，
|
||||
应由调用方在流结束后回写 DB。
|
||||
- 后续调用传入 DB 中的 session_id 后，百炼自动关联历史上下文，
|
||||
返回的 session_id 通常一致。
|
||||
|
||||
Args:
|
||||
app_id: 百炼应用 ID
|
||||
prompt: 用户输入
|
||||
session_id: 百炼 session_id（多轮对话）
|
||||
session_id: 百炼 session_id；首次对话传 None
|
||||
biz_params: 业务参数（如 user_prompt_params）
|
||||
|
||||
Yields:
|
||||
文本 chunk
|
||||
(text_chunk, session_id_or_none) 元组。
|
||||
text_chunk 为空字符串时（例如仅承载 session_id 的心跳 chunk），
|
||||
调用方应忽略文本但保留 session_id。
|
||||
"""
|
||||
queue: asyncio.Queue[str | BaseException | None] = asyncio.Queue()
|
||||
queue: asyncio.Queue[tuple[str, str | None] | BaseException | None] = asyncio.Queue()
|
||||
loop = asyncio.get_running_loop()
|
||||
|
||||
def _consume_in_thread() -> None:
|
||||
@@ -91,10 +135,17 @@ class DashScopeClient:
|
||||
response = Application.call(**call_kwargs)
|
||||
for chunk in response:
|
||||
if chunk.status_code == 200:
|
||||
text = chunk.output.get("text", "")
|
||||
if text:
|
||||
output = chunk.output if hasattr(chunk, "output") else {}
|
||||
if isinstance(output, dict):
|
||||
text = output.get("text", "") or ""
|
||||
new_sid = output.get("session_id")
|
||||
else:
|
||||
text = getattr(output, "text", "") or ""
|
||||
new_sid = getattr(output, "session_id", None)
|
||||
# 文本或 session_id 任一非空都推入（心跳 chunk 也传出 session_id）
|
||||
if text or new_sid:
|
||||
asyncio.run_coroutine_threadsafe(
|
||||
queue.put(text), loop
|
||||
queue.put((text, new_sid)), loop
|
||||
)
|
||||
else:
|
||||
# 非 200 状态码，构造异常传递给调用方
|
||||
@@ -180,16 +231,12 @@ class DashScopeClient:
|
||||
raw_text = output.text or ""
|
||||
|
||||
# 提取 tokens_used
|
||||
# DashScope Application.call() 返回的 usage 实际结构（2026-04 验证）：
|
||||
# ApplicationUsage(models=[ApplicationModelUsage(model_id, input_tokens, output_tokens)])
|
||||
# 旧代码只处理 dict / total_tokens 两种分支，导致该嵌套结构下 tokens_used 恒为 0
|
||||
tokens_used = 0
|
||||
if hasattr(response, "usage") and response.usage:
|
||||
usage = response.usage
|
||||
if isinstance(usage, dict):
|
||||
# input_tokens + output_tokens
|
||||
tokens_used = usage.get("input_tokens", 0) + usage.get(
|
||||
"output_tokens", 0
|
||||
)
|
||||
elif hasattr(usage, "total_tokens"):
|
||||
tokens_used = usage.total_tokens or 0
|
||||
tokens_used = _extract_tokens_used(response.usage)
|
||||
|
||||
# 提取 new_session_id
|
||||
new_session_id: str | None = None
|
||||
|
||||
@@ -58,10 +58,16 @@ def _fetch_assistant_info_sync(site_id: int, assistant_id: int) -> dict[str, Any
|
||||
conn = get_etl_readonly_connection(site_id)
|
||||
# RLS 隔离 + 语句超时（get_etl_readonly_connection 的 SET LOCAL 在 commit 后失效，
|
||||
# 需在查询事务中重新设置）
|
||||
# CHANGE 2026-05-02 | 同时下发 app.current_business_date，供 RLS 视图业务日上界裁剪
|
||||
from app.services.runtime_context import as_runtime_today_param as _rt_today
|
||||
_ref_date = _rt_today(site_id)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"SET LOCAL app.current_site_id = %s", (str(site_id),)
|
||||
)
|
||||
cur.execute(
|
||||
"SET LOCAL app.current_business_date = %s", (_ref_date.isoformat(),)
|
||||
)
|
||||
cur.execute(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",),
|
||||
@@ -86,11 +92,12 @@ def _fetch_assistant_info_sync(site_id: int, assistant_id: int) -> dict[str, Any
|
||||
level = row[1] or ""
|
||||
hire_date = row[2]
|
||||
|
||||
# 计算工龄
|
||||
# 计算工龄（CHANGE 2026-05-02 | 用 business_date 替代 today，沙箱按当时工龄）
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref_date = as_runtime_today_param(site_id)
|
||||
tenure_months = 0
|
||||
if hire_date and isinstance(hire_date, date):
|
||||
today = date.today()
|
||||
tenure_months = (today.year - hire_date.year) * 12 + (today.month - hire_date.month)
|
||||
tenure_months = (ref_date.year - hire_date.year) * 12 + (ref_date.month - hire_date.month)
|
||||
|
||||
# 绩效数据
|
||||
# ⚠️ 列名映射: monthly_customers 不存在（用 0 占位），performance_tier→tier_name
|
||||
@@ -184,10 +191,16 @@ def _fetch_service_history_sync(
|
||||
conn = get_etl_readonly_connection(site_id)
|
||||
# RLS 隔离 + 语句超时（get_etl_readonly_connection 的 SET LOCAL 在 commit 后失效，
|
||||
# 需在查询事务中重新设置）
|
||||
# CHANGE 2026-05-02 | 同时下发 app.current_business_date，供 RLS 视图业务日上界裁剪
|
||||
from app.services.runtime_context import as_runtime_today_param as _rt_today2
|
||||
_ref_date_outer = _rt_today2(site_id)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"SET LOCAL app.current_site_id = %s", (str(site_id),)
|
||||
)
|
||||
cur.execute(
|
||||
"SET LOCAL app.current_business_date = %s", (_ref_date_outer.isoformat(),)
|
||||
)
|
||||
cur.execute(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",),
|
||||
@@ -197,6 +210,9 @@ def _fetch_service_history_sync(
|
||||
# is_trash=false→is_delete=0, service_date→create_time,
|
||||
# duration_minutes→real_use_seconds/60, items_sum→ledger_amount,
|
||||
# room_name→site_table_id, is_pd→(order_assistant_type=1)
|
||||
# CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱不读「未来」服务记录
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref_date = as_runtime_today_param(site_id)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
@@ -209,10 +225,11 @@ def _fetch_service_history_sync(
|
||||
WHERE site_assistant_id = %s
|
||||
AND tenant_member_id = %s
|
||||
AND is_delete = 0
|
||||
AND create_time >= (CURRENT_DATE - INTERVAL '%s months')
|
||||
AND create_time >= (%s::date - (INTERVAL '1 month' * %s))
|
||||
AND create_time < (%s::date + INTERVAL '1 day')
|
||||
ORDER BY create_time DESC
|
||||
""",
|
||||
(assistant_id, member_id, months),
|
||||
(assistant_id, member_id, ref_date, months, ref_date),
|
||||
)
|
||||
columns = [desc[0] for desc in cur.description]
|
||||
rows = cur.fetchall()
|
||||
|
||||
@@ -63,16 +63,27 @@ def _fetch_member_consumption_data_sync(
|
||||
member_id: int,
|
||||
months: int,
|
||||
) -> dict[str, Any]:
|
||||
"""同步实现：在单个 FDW 连接上串行执行多个查询。"""
|
||||
"""同步实现：在单个 FDW 连接上串行执行多个查询。
|
||||
|
||||
CHANGE 2026-05-02 | 所有窗口查询都按业务日上界裁剪，
|
||||
sandbox 模式下不再读取 sandbox_date 之后的真实消费 / 到店。
|
||||
"""
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
|
||||
conn = None
|
||||
try:
|
||||
conn = get_etl_readonly_connection(site_id)
|
||||
ref_date = as_runtime_today_param(site_id)
|
||||
# RLS 隔离 + 语句超时（get_etl_readonly_connection 的 SET LOCAL 在 commit 后失效，
|
||||
# 需在查询事务中重新设置）
|
||||
# CHANGE 2026-05-02 | 同时下发 app.current_business_date，供 RLS 视图业务日上界裁剪
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"SET LOCAL app.current_site_id = %s", (str(site_id),)
|
||||
)
|
||||
cur.execute(
|
||||
"SET LOCAL app.current_business_date = %s", (ref_date.isoformat(),)
|
||||
)
|
||||
cur.execute(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",), # 毫秒
|
||||
@@ -82,7 +93,7 @@ def _fetch_member_consumption_data_sync(
|
||||
nickname = _query_member_nickname(conn, member_id)
|
||||
|
||||
# 2. 消费记录（台桌结账 + 商城订单）
|
||||
records, total_count = _query_consumption_records(conn, member_id, months)
|
||||
records, total_count = _query_consumption_records(conn, member_id, months, ref_date)
|
||||
|
||||
# 3. 会员卡明细
|
||||
cards = _query_member_cards(conn, member_id)
|
||||
@@ -91,7 +102,7 @@ def _fetch_member_consumption_data_sync(
|
||||
balance_info = _query_balance_summary(conn, member_id)
|
||||
|
||||
# 5. 到店数据
|
||||
visit_info = _query_visit_info(conn, member_id)
|
||||
visit_info = _query_visit_info(conn, member_id, ref_date)
|
||||
|
||||
result: dict[str, Any] = {
|
||||
"member_nickname": nickname,
|
||||
@@ -145,7 +156,7 @@ def _query_member_nickname(conn: Any, member_id: int) -> str:
|
||||
|
||||
|
||||
def _query_consumption_records(
|
||||
conn: Any, member_id: int, months: int
|
||||
conn: Any, member_id: int, months: int, ref_date: date
|
||||
) -> tuple[list[dict], int]:
|
||||
"""从 app.v_dwd_settlement_head + app.v_dwd_table_fee_log 获取消费记录。
|
||||
|
||||
@@ -153,6 +164,7 @@ def _query_consumption_records(
|
||||
⚠️ 费用拆分字段（table_charge_money, assistant_pd/cx_money）在 settlement_head 上。
|
||||
⚠️ table_fee_log 提供台桌时长（real_table_use_seconds）和桌台ID（site_table_id）。
|
||||
⚠️ 列名映射: settle_date→create_time, settle_id→order_settle_id, sale_amount→ledger_amount。
|
||||
CHANGE 2026-05-02 | 用 ref_date（业务日）替代 CURRENT_DATE，沙箱不读「未来」消费。
|
||||
返回 (records, total_count)。
|
||||
"""
|
||||
with conn.cursor() as cur:
|
||||
@@ -163,9 +175,10 @@ def _query_consumption_records(
|
||||
FROM app.v_dwd_settlement_head sh
|
||||
WHERE sh.member_id = %s
|
||||
AND sh.settle_type IN (1, 3)
|
||||
AND sh.create_time >= (CURRENT_DATE - INTERVAL '%s months')
|
||||
AND sh.create_time >= (%s::date - (INTERVAL '1 month' * %s))
|
||||
AND sh.create_time < (%s::date + INTERVAL '1 day')
|
||||
""",
|
||||
(member_id, months),
|
||||
(member_id, ref_date, months, ref_date),
|
||||
)
|
||||
total_count = cur.fetchone()[0]
|
||||
|
||||
@@ -208,11 +221,12 @@ def _query_consumption_records(
|
||||
) coaches ON true
|
||||
WHERE sh.member_id = %s
|
||||
AND sh.settle_type IN (1, 3)
|
||||
AND sh.create_time >= (CURRENT_DATE - INTERVAL '%s months')
|
||||
AND sh.create_time >= (%s::date - (INTERVAL '1 month' * %s))
|
||||
AND sh.create_time < (%s::date + INTERVAL '1 day')
|
||||
ORDER BY sh.create_time DESC
|
||||
LIMIT %s
|
||||
""",
|
||||
(member_id, months, MAX_CONSUMPTION_RECORDS),
|
||||
(member_id, ref_date, months, ref_date, MAX_CONSUMPTION_RECORDS),
|
||||
)
|
||||
columns = [desc[0] for desc in cur.description]
|
||||
rows = cur.fetchall()
|
||||
@@ -294,9 +308,10 @@ def _query_balance_summary(conn: Any, member_id: int) -> dict:
|
||||
}
|
||||
|
||||
|
||||
def _query_visit_info(conn: Any, member_id: int) -> dict:
|
||||
def _query_visit_info(conn: Any, member_id: int, ref_date: date) -> dict:
|
||||
"""从 app.v_dws_member_visit_detail 获取到店数据，推算预计到店日期。
|
||||
⚠️ 列名映射: last_visit_date→MAX(visit_date), avg_visit_interval_days 需从明细计算。
|
||||
CHANGE 2026-05-02 | 仅取 ref_date 及之前的到店明细，days_since 按 ref_date 计算。
|
||||
"""
|
||||
with conn.cursor() as cur:
|
||||
# 获取最近到店日期和平均到店间隔
|
||||
@@ -307,6 +322,7 @@ def _query_visit_info(conn: Any, member_id: int) -> dict:
|
||||
LAG(visit_date) OVER (ORDER BY visit_date) AS prev_visit
|
||||
FROM app.v_dws_member_visit_detail
|
||||
WHERE member_id = %s
|
||||
AND visit_date <= %s
|
||||
)
|
||||
SELECT
|
||||
MAX(visit_date) AS last_visit_date,
|
||||
@@ -314,7 +330,7 @@ def _query_visit_info(conn: Any, member_id: int) -> dict:
|
||||
FROM visits
|
||||
WHERE prev_visit IS NOT NULL
|
||||
""",
|
||||
(member_id,),
|
||||
(member_id, ref_date),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
|
||||
@@ -323,8 +339,7 @@ def _query_visit_info(conn: Any, member_id: int) -> dict:
|
||||
|
||||
last_visit = row[0]
|
||||
avg_interval = row[1]
|
||||
today = date.today()
|
||||
days_since = (today - last_visit).days if isinstance(last_visit, date) else None
|
||||
days_since = (ref_date - last_visit).days if isinstance(last_visit, date) else None
|
||||
|
||||
expected = None
|
||||
if avg_interval and last_visit:
|
||||
|
||||
@@ -352,7 +352,9 @@ def _text_board_finance(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",),
|
||||
)
|
||||
# 简化查询：获取汇总数据
|
||||
# 简化查询：获取汇总数据（CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE）
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
@@ -361,8 +363,10 @@ def _text_board_finance(
|
||||
COALESCE(AVG(items_sum), 0) AS avg_revenue
|
||||
FROM app.v_dwd_settlement_head
|
||||
WHERE settle_type IN (1, 3)
|
||||
AND settle_date >= (CURRENT_DATE - INTERVAL '1 month')
|
||||
AND settle_date >= (%s::date - INTERVAL '1 month')
|
||||
AND settle_date <= %s::date
|
||||
""",
|
||||
(_ref_date, _ref_date),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
etl_conn.commit()
|
||||
@@ -399,7 +403,9 @@ def _text_board_customer(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",),
|
||||
)
|
||||
# Top 10 客户
|
||||
# Top 10 客户（CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE）
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
@@ -410,11 +416,13 @@ def _text_board_customer(
|
||||
ON dm.member_id = sh.member_id AND dm.scd2_is_current = 1
|
||||
WHERE sh.settle_type IN (1, 3)
|
||||
AND sh.member_id > 0
|
||||
AND sh.settle_date >= (CURRENT_DATE - INTERVAL '1 month')
|
||||
AND sh.settle_date >= (%s::date - INTERVAL '1 month')
|
||||
AND sh.settle_date <= %s::date
|
||||
GROUP BY dm.nickname
|
||||
ORDER BY total_consumption DESC
|
||||
LIMIT 10
|
||||
""",
|
||||
(_ref_date, _ref_date),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
etl_conn.commit()
|
||||
@@ -452,6 +460,9 @@ def _text_board_coach(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",),
|
||||
)
|
||||
# CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
@@ -462,11 +473,13 @@ def _text_board_coach(
|
||||
JOIN app.v_dim_assistant da
|
||||
ON da.assistant_id = sl.site_assistant_id
|
||||
WHERE sl.is_delete = 0
|
||||
AND sl.create_time >= (CURRENT_DATE - INTERVAL '1 month')
|
||||
AND sl.create_time >= (%s::date - INTERVAL '1 month')
|
||||
AND sl.create_time < (%s::date + INTERVAL '1 day')
|
||||
GROUP BY da.nickname
|
||||
ORDER BY service_count DESC
|
||||
LIMIT 10
|
||||
""",
|
||||
(_ref_date, _ref_date),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
etl_conn.commit()
|
||||
@@ -590,6 +603,9 @@ def _text_customer_service_records(
|
||||
"SET LOCAL statement_timeout = %s",
|
||||
(f"{FDW_QUERY_TIMEOUT_SEC * 1000}",),
|
||||
)
|
||||
# CHANGE 2026-05-02 | 仅取业务日及之前的服务记录，沙箱不读「未来」
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT
|
||||
@@ -599,10 +615,11 @@ def _text_customer_service_records(
|
||||
site_table_id
|
||||
FROM app.v_dwd_assistant_service_log
|
||||
WHERE tenant_member_id = %s AND is_delete = 0
|
||||
AND create_time < (%s::date + INTERVAL '1 day')
|
||||
ORDER BY create_time DESC
|
||||
LIMIT 10
|
||||
""",
|
||||
(member_id,),
|
||||
(member_id, _ref_date),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
etl_conn.commit()
|
||||
|
||||
@@ -207,6 +207,25 @@ class AIDispatcher:
|
||||
|
||||
# 内存 trigger_job 计数器（DB 迁移完成后改为 INSERT RETURNING id）
|
||||
self._next_job_id = 1
|
||||
self._running_tasks: dict[int, asyncio.Task] = {}
|
||||
self._running_task_sites: dict[int, int] = {}
|
||||
|
||||
def _forget_running_task(self, job_id: int) -> None:
|
||||
self._running_tasks.pop(job_id, None)
|
||||
self._running_task_sites.pop(job_id, None)
|
||||
|
||||
def cancel_running(self, site_id: int) -> int:
|
||||
"""取消当前进程内指定门店未完成的 AI 调用链。"""
|
||||
cancelled = 0
|
||||
for job_id, task in list(self._running_tasks.items()):
|
||||
if self._running_task_sites.get(job_id) != site_id:
|
||||
continue
|
||||
if task.done():
|
||||
self._forget_running_task(job_id)
|
||||
continue
|
||||
task.cancel()
|
||||
cancelled += 1
|
||||
return cancelled
|
||||
|
||||
# ── 统一事件入口 ─────────────────────────────────────
|
||||
|
||||
@@ -242,7 +261,10 @@ class AIDispatcher:
|
||||
self._dedup_set.add(dedup_key)
|
||||
|
||||
# 后台异步执行调用链，不阻塞返回
|
||||
asyncio.create_task(self._execute_chain(job_id, event))
|
||||
task = asyncio.create_task(self._execute_chain(job_id, event))
|
||||
self._running_tasks[job_id] = task
|
||||
self._running_task_sites[job_id] = event.site_id
|
||||
task.add_done_callback(lambda _task, _job_id=job_id: self._forget_running_task(_job_id))
|
||||
return job_id
|
||||
|
||||
# ── 调用链分发 ───────────────────────────────────────
|
||||
@@ -278,6 +300,10 @@ class AIDispatcher:
|
||||
await asyncio.wait_for(handler(event), timeout=chain_timeout)
|
||||
logger.info("调用链完成: job_id=%d event_type=%s", job_id, event.event_type)
|
||||
_update_trigger_job_status(job_id, "completed", set_finished=True)
|
||||
except asyncio.CancelledError:
|
||||
logger.warning("调用链已取消: job_id=%d event_type=%s", job_id, event.event_type)
|
||||
_update_trigger_job_status(job_id, "cancelled", error_message="业务运行上下文切换取消", set_finished=True)
|
||||
raise
|
||||
except asyncio.TimeoutError:
|
||||
logger.error("调用链超时: job_id=%d event_type=%s", job_id, event.event_type)
|
||||
_update_trigger_job_status(job_id, "failed", error_message="调用链超时", set_finished=True)
|
||||
|
||||
123
apps/backend/app/ai/event_bus.py
Normal file
123
apps/backend/app/ai/event_bus.py
Normal file
@@ -0,0 +1,123 @@
|
||||
"""AI 事件广播总线（in-process pub/sub）。
|
||||
|
||||
支持按 site_id 订阅的异步事件分发，用于：
|
||||
- Phase 1.4：AI 缓存主动失效 / 更新通知 → admin-web、小程序刷新
|
||||
- Phase 3.1：AI 告警实时推送（告警发生 / 确认 / 忽略）
|
||||
|
||||
设计要点：
|
||||
- 仿 TaskExecutor.subscribe/unsubscribe 模式（单进程共享）
|
||||
- 每个订阅者独立 asyncio.Queue，互不干扰
|
||||
- 订阅必须指定 site_id（全局订阅需显式 site_id=None）
|
||||
- publish 异步写入所有订阅者 queue；端点侧通过 get() 消费
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class AIEvent:
|
||||
"""统一事件结构。
|
||||
|
||||
type 示例：
|
||||
- cache_updated — 新缓存写入
|
||||
- cache_invalidated — 缓存主动失效
|
||||
- alert_created — 新告警（Phase 3.1）
|
||||
- alert_updated — 告警状态变更（Phase 3.1）
|
||||
"""
|
||||
|
||||
type: str
|
||||
site_id: int | None
|
||||
payload: dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
|
||||
class EventBus:
|
||||
"""单进程事件广播总线。"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
# {site_id | None: [queue, ...]} None 表示全局订阅（收所有 site 事件）
|
||||
self._subscribers: dict[int | None, list[asyncio.Queue[AIEvent | None]]] = {}
|
||||
self._lock = asyncio.Lock()
|
||||
|
||||
async def subscribe(self, site_id: int | None) -> asyncio.Queue[AIEvent | None]:
|
||||
"""订阅事件流，返回独立 asyncio.Queue。
|
||||
|
||||
site_id=None 表示订阅全部门店事件（admin-web 全局监控用）。
|
||||
site_id=<int> 表示仅订阅该门店事件（小程序或单门店后台）。
|
||||
|
||||
unsubscribe 时需将返回的 queue 作为参数传入。
|
||||
"""
|
||||
queue: asyncio.Queue[AIEvent | None] = asyncio.Queue()
|
||||
async with self._lock:
|
||||
self._subscribers.setdefault(site_id, []).append(queue)
|
||||
return queue
|
||||
|
||||
async def unsubscribe(
|
||||
self, site_id: int | None, queue: asyncio.Queue[AIEvent | None]
|
||||
) -> None:
|
||||
"""解除订阅，从订阅者列表移除 queue。"""
|
||||
async with self._lock:
|
||||
subs = self._subscribers.get(site_id, [])
|
||||
try:
|
||||
subs.remove(queue)
|
||||
except ValueError:
|
||||
pass
|
||||
if not subs:
|
||||
self._subscribers.pop(site_id, None)
|
||||
|
||||
def publish(self, event: AIEvent) -> int:
|
||||
"""同步 publish 事件，返回送达的订阅者数。
|
||||
|
||||
可从任意线程 / sync 上下文调用（如 dispatcher._write_cache）。
|
||||
内部使用 run_coroutine_threadsafe 线程安全写入 queue。
|
||||
"""
|
||||
targets = self._collect_targets(event.site_id)
|
||||
sent = 0
|
||||
for queue in targets:
|
||||
try:
|
||||
# 优先同步调用 put_nowait（最常见：同一 running loop）
|
||||
queue.put_nowait(event)
|
||||
sent += 1
|
||||
except RuntimeError:
|
||||
# 无 running loop 场景极少，跳过
|
||||
logger.debug("publish 无 running loop：跳过 queue")
|
||||
return sent
|
||||
|
||||
def _collect_targets(self, site_id: int | None) -> list[asyncio.Queue[AIEvent | None]]:
|
||||
"""收集要推送的订阅者列表：该 site_id 的订阅者 + 全局订阅者。"""
|
||||
targets: list[asyncio.Queue[AIEvent | None]] = []
|
||||
if site_id is not None:
|
||||
targets.extend(self._subscribers.get(site_id, []))
|
||||
targets.extend(self._subscribers.get(None, []))
|
||||
return targets
|
||||
|
||||
async def close_all(self) -> None:
|
||||
"""结束时给所有订阅者发哨兵 None，通知连接关闭。"""
|
||||
async with self._lock:
|
||||
all_queues = [q for subs in self._subscribers.values() for q in subs]
|
||||
self._subscribers.clear()
|
||||
for q in all_queues:
|
||||
try:
|
||||
q.put_nowait(None)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
# ── 单例 ──────────────────────────────────────────────────
|
||||
|
||||
|
||||
_bus: EventBus | None = None
|
||||
|
||||
|
||||
def get_event_bus() -> EventBus:
|
||||
"""获取全局 EventBus 单例。进程启动时按需创建。"""
|
||||
global _bus
|
||||
if _bus is None:
|
||||
_bus = EventBus()
|
||||
return _bus
|
||||
@@ -1,145 +1,873 @@
|
||||
"""应用 2 财务洞察 Prompt 模板。
|
||||
"""应用 2 财务洞察 Prompt 拼装。
|
||||
|
||||
构建包含当期和上期收入结构的完整 Prompt，供百炼 API 生成财务洞察。
|
||||
cron 每日 10:00 预热触发，对所有筛选组合（时间 × 区域）生成洞察。
|
||||
- 数据源：board_service.get_finance_board(time, area, compare=1, site_id)
|
||||
- 筛选维度：8 个时间维度 × 9 个区域 = 72 组合
|
||||
- 输出字段：insights 数组（seq + title + body）
|
||||
- system prompt 在百炼控制台配置
|
||||
|
||||
收入字段映射（严格遵守 items_sum 口径）：
|
||||
- table_fee = table_charge_money（台费）
|
||||
- assistant_pd = assistant_pd_money（陪打费）
|
||||
- assistant_cx = assistant_cx_money（超休费）
|
||||
- goods = goods_money（商品收入）
|
||||
- recharge = 充值 pay_amount settle_type=5（充值收入）
|
||||
|
||||
禁止使用 consume_money，统一使用：
|
||||
items_sum = table_charge_money + goods_money + assistant_pd_money
|
||||
+ assistant_cx_money + electricity_money
|
||||
Prompt 中 board_data 字段名会自动翻译为中文（KEY_TRANSLATIONS），
|
||||
目的：减少 AI 理解英文变量的成本，生成的洞察正文可读性更强。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
from app.services.board_service import get_finance_board, _calc_date_range, _calc_prev_range
|
||||
|
||||
def build_prompt(context: dict) -> list[dict]:
|
||||
"""构建 App2 财务洞察 Prompt 消息列表。
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
Args:
|
||||
context: 包含以下字段：
|
||||
- site_id: int，门店 ID
|
||||
- time_dimension: str，时间维度编码
|
||||
- current_data: dict，当期数据
|
||||
- previous_data: dict，上期数据
|
||||
|
||||
Returns:
|
||||
messages 列表（system + user），供 BailianClient.chat_json 调用
|
||||
"""
|
||||
site_id = context.get("site_id", 0)
|
||||
time_dimension = context.get("time_dimension", "")
|
||||
current_data = context.get("current_data", {})
|
||||
previous_data = context.get("previous_data", {})
|
||||
|
||||
system_content = _build_system_content(
|
||||
site_id=site_id,
|
||||
time_dimension=time_dimension,
|
||||
current_data=current_data,
|
||||
previous_data=previous_data,
|
||||
)
|
||||
|
||||
user_content = (
|
||||
f"请根据以上数据，为门店 {site_id} 生成 {_dimension_label(time_dimension)} 的财务洞察分析。"
|
||||
"以 JSON 格式返回，包含 insights 数组，每项含 seq（序号）、title（标题）、body（正文）。"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
|
||||
|
||||
def _build_system_content(
|
||||
*,
|
||||
site_id: int,
|
||||
time_dimension: str,
|
||||
current_data: dict,
|
||||
previous_data: dict,
|
||||
) -> dict:
|
||||
"""构建 system prompt JSON 结构。"""
|
||||
return {
|
||||
"task": (
|
||||
"你是台球门店的财务分析 AI 助手。"
|
||||
"根据提供的当期和上期经营数据，生成结构化的财务洞察。"
|
||||
"分析维度包括：收入结构变化、各收入项占比、环比趋势、异常波动。"
|
||||
"输出 JSON 格式：{\"insights\": [{\"seq\": 1, \"title\": \"...\", \"body\": \"...\"}]}"
|
||||
),
|
||||
"data": {
|
||||
"site_id": site_id,
|
||||
"time_dimension": time_dimension,
|
||||
"time_dimension_label": _dimension_label(time_dimension),
|
||||
"current_period": _build_period_data(current_data),
|
||||
"previous_period": _build_period_data(previous_data),
|
||||
},
|
||||
"reference": {
|
||||
"field_mapping": {
|
||||
"items_sum": (
|
||||
"table_charge_money + goods_money + assistant_pd_money"
|
||||
" + assistant_cx_money + electricity_money"
|
||||
),
|
||||
"table_fee": "table_charge_money（台费收入）",
|
||||
"assistant_pd": "assistant_pd_money（陪打费）",
|
||||
"assistant_cx": "assistant_cx_money（超休费）",
|
||||
"goods": "goods_money（商品收入）",
|
||||
"recharge": "充值 pay_amount（settle_type=5，充值收入）",
|
||||
"electricity": "electricity_money（电费，当前未启用，全为 0）",
|
||||
},
|
||||
"rules": [
|
||||
"统一使用 items_sum 口径计算营收总额",
|
||||
"助教费用必须拆分为 assistant_pd_money（陪打）和 assistant_cx_money（超休）",
|
||||
"支付渠道恒等式：balance_amount = recharge_card_amount + gift_card_amount",
|
||||
"金额单位：元（CNY），保留两位小数",
|
||||
],
|
||||
},
|
||||
# App2 时间维度 → board_service 时间枚举
|
||||
DIMENSION_MAP: dict[str, str] = {
|
||||
"this_month": "month",
|
||||
"last_month": "lastMonth",
|
||||
"this_week": "week",
|
||||
"last_week": "lastWeek",
|
||||
"this_quarter": "quarter",
|
||||
"last_quarter": "lastQuarter",
|
||||
"last_3_months": "last_3m",
|
||||
"last_6_months": "last_6m",
|
||||
}
|
||||
|
||||
|
||||
def _build_period_data(data: dict) -> dict:
|
||||
"""构建单期数据结构，确保字段名遵守 items_sum 口径。"""
|
||||
return {
|
||||
# 收入结构（items_sum 口径）
|
||||
"table_charge_money": data.get("table_charge_money", 0),
|
||||
"goods_money": data.get("goods_money", 0),
|
||||
"assistant_pd_money": data.get("assistant_pd_money", 0),
|
||||
"assistant_cx_money": data.get("assistant_cx_money", 0),
|
||||
"electricity_money": data.get("electricity_money", 0),
|
||||
# 充值收入
|
||||
"recharge_income": data.get("recharge_income", 0),
|
||||
# 储值资产
|
||||
"balance_pay": data.get("balance_pay", 0),
|
||||
"recharge_card_pay": data.get("recharge_card_pay", 0),
|
||||
"gift_card_pay": data.get("gift_card_pay", 0),
|
||||
# 费用汇总
|
||||
"discount_amount": data.get("discount_amount", 0),
|
||||
"adjust_amount": data.get("adjust_amount", 0),
|
||||
# 平台结算
|
||||
"platform_settlement_amount": data.get("platform_settlement_amount", 0),
|
||||
"groupbuy_pay_amount": data.get("groupbuy_pay_amount", 0),
|
||||
# 汇总
|
||||
"order_count": data.get("order_count", 0),
|
||||
"member_count": data.get("member_count", 0),
|
||||
}
|
||||
|
||||
|
||||
# 时间维度编码 → 中文标签
|
||||
_DIMENSION_LABELS: dict[str, str] = {
|
||||
DIMENSION_LABELS: dict[str, str] = {
|
||||
"this_month": "本月",
|
||||
"last_month": "上月",
|
||||
"this_week": "本周",
|
||||
"last_week": "上周",
|
||||
"last_3_months": "近三个月",
|
||||
"this_quarter": "本季度",
|
||||
"last_quarter": "上季度",
|
||||
"last_6_months": "近六个月",
|
||||
"last_3_months": "近三个月（不含本月）",
|
||||
"last_6_months": "近六个月（不含本月）",
|
||||
}
|
||||
|
||||
# 区域枚举与中文标签（与 miniprogram/board-finance.ts areaOptions 对齐）
|
||||
AREA_OPTIONS: tuple[str, ...] = (
|
||||
"all", "hall", "hallA", "hallB", "hallC",
|
||||
"vip", "snooker", "mahjong", "ktv",
|
||||
)
|
||||
|
||||
AREA_LABELS: dict[str, str] = {
|
||||
"all": "全部区域",
|
||||
"hall": "大厅",
|
||||
"hallA": "A区",
|
||||
"hallB": "B区",
|
||||
"hallC": "C区",
|
||||
"vip": "台球包厢",
|
||||
"snooker": "斯诺克",
|
||||
"mahjong": "麻将房",
|
||||
"ktv": "团建房",
|
||||
}
|
||||
|
||||
# 业务字段 → 中文名。覆盖 board_service 返回的所有层级字段。
|
||||
# 只做键名翻译，不改变值与结构；未命中的键原样保留。
|
||||
KEY_TRANSLATIONS: dict[str, str] = {
|
||||
# 顶层板块
|
||||
"overview": "经营一览",
|
||||
"recharge": "预收资产",
|
||||
"revenue": "应计收入确认",
|
||||
"cashflow": "现金流入",
|
||||
"expense": "现金流出",
|
||||
"coach_analysis": "助教分析",
|
||||
|
||||
# 经营一览
|
||||
"occurrence": "发生额",
|
||||
"discount": "总优惠",
|
||||
"discount_rate": "优惠率",
|
||||
"confirmed_revenue": "成交收入",
|
||||
"cash_in": "现金流入",
|
||||
"cash_out": "现金流出",
|
||||
"cash_balance": "现金结余",
|
||||
"balance_rate": "结余率",
|
||||
|
||||
# 预收资产
|
||||
"actual_income": "储值卡充值实收",
|
||||
"first_charge": "首充",
|
||||
"renew_charge": "续费",
|
||||
"consumed": "储值卡消耗",
|
||||
"card_balance": "储值卡总余额",
|
||||
"all_card_balance": "全类别卡余额合计",
|
||||
"gift_rows": "赠送卡矩阵",
|
||||
"liquor": "酒水卡",
|
||||
"table_fee": "台费卡",
|
||||
"voucher": "抵用券",
|
||||
|
||||
# 应计收入确认
|
||||
"total_occurrence": "发生额合计",
|
||||
"discount_total": "优惠合计",
|
||||
"confirmed_total": "确认收入合计",
|
||||
"structure_rows": "收入结构",
|
||||
"price_items": "价目明细",
|
||||
"discount_items": "优惠明细",
|
||||
"channel_items": "渠道明细",
|
||||
"booked": "入账金额",
|
||||
"booked_compare": "入账环比",
|
||||
|
||||
# 现金流入/流出
|
||||
"consume_items": "消费收款项",
|
||||
"recharge_items": "充值收款项",
|
||||
"operation_items": "运营支出",
|
||||
"fixed_items": "固定支出",
|
||||
"coach_items": "助教支出",
|
||||
"platform_items": "平台支出",
|
||||
|
||||
# 助教分析
|
||||
"basic": "基础助教",
|
||||
"incentive": "激励助教",
|
||||
"total_pay": "合计薪酬",
|
||||
"total_share": "合计分成",
|
||||
"avg_hourly": "平均时薪",
|
||||
"level": "级别",
|
||||
"pay": "薪酬",
|
||||
"share": "分成",
|
||||
"hourly": "时薪",
|
||||
"rows": "明细",
|
||||
|
||||
# 通用元素
|
||||
"label": "名称",
|
||||
"amount": "金额",
|
||||
"desc": "说明",
|
||||
"total": "合计",
|
||||
"value": "数值",
|
||||
"compare": "环比",
|
||||
"id": "编号",
|
||||
|
||||
# 环比后缀（小程序约定）
|
||||
"occurrence_compare": "发生额环比",
|
||||
"occurrence_down": "发生额是否下降",
|
||||
"occurrence_flat": "发生额是否持平",
|
||||
"discount_compare": "总优惠环比",
|
||||
"discount_down": "总优惠是否下降",
|
||||
"discount_flat": "总优惠是否持平",
|
||||
"discount_rate_compare": "优惠率环比",
|
||||
"discount_rate_down": "优惠率是否下降",
|
||||
"discount_rate_flat": "优惠率是否持平",
|
||||
"confirmed_revenue_compare": "成交收入环比",
|
||||
"confirmed_revenue_down": "成交收入是否下降",
|
||||
"confirmed_revenue_flat": "成交收入是否持平",
|
||||
"cash_in_compare": "现金流入环比",
|
||||
"cash_in_down": "现金流入是否下降",
|
||||
"cash_in_flat": "现金流入是否持平",
|
||||
"cash_out_compare": "现金流出环比",
|
||||
"cash_out_down": "现金流出是否下降",
|
||||
"cash_out_flat": "现金流出是否持平",
|
||||
"cash_balance_compare": "现金结余环比",
|
||||
"cash_balance_down": "现金结余是否下降",
|
||||
"cash_balance_flat": "现金结余是否持平",
|
||||
"balance_rate_compare": "结余率环比",
|
||||
"balance_rate_down": "结余率是否下降",
|
||||
"balance_rate_flat": "结余率是否持平",
|
||||
"actual_income_compare": "储值卡充值实收环比",
|
||||
"actual_income_down": "储值卡充值实收是否下降",
|
||||
"first_charge_compare": "首充环比",
|
||||
"first_charge_down": "首充是否下降",
|
||||
"renew_charge_compare": "续费环比",
|
||||
"renew_charge_down": "续费是否下降",
|
||||
"consumed_compare": "储值卡消耗环比",
|
||||
"consumed_down": "储值卡消耗是否下降",
|
||||
"card_balance_compare": "储值卡总余额环比",
|
||||
"card_balance_down": "储值卡总余额是否下降",
|
||||
"all_card_balance_compare": "全类别卡余额合计环比",
|
||||
"all_card_balance_down": "全类别卡余额合计是否下降",
|
||||
"total_compare": "合计环比",
|
||||
"total_down": "合计是否下降",
|
||||
"total_flat": "合计是否持平",
|
||||
"total_pay_compare": "合计薪酬环比",
|
||||
"total_pay_down": "合计薪酬是否下降",
|
||||
"total_share_compare": "合计分成环比",
|
||||
"total_share_down": "合计分成是否下降",
|
||||
"avg_hourly_compare": "平均时薪环比",
|
||||
"avg_hourly_flat": "平均时薪是否持平",
|
||||
"pay_compare": "薪酬环比",
|
||||
"pay_down": "薪酬是否下降",
|
||||
"share_compare": "分成环比",
|
||||
"share_down": "分成是否下降",
|
||||
"hourly_compare": "时薪环比",
|
||||
"hourly_flat": "时薪是否持平",
|
||||
|
||||
# 赠送卡矩阵
|
||||
"wine": "酒水",
|
||||
"table": "台费",
|
||||
"coupon": "抵用券",
|
||||
|
||||
# 元数据
|
||||
"down": "是否下降",
|
||||
"flat": "是否持平",
|
||||
}
|
||||
|
||||
|
||||
def _dimension_label(dimension: str) -> str:
|
||||
"""将时间维度编码转为中文标签。"""
|
||||
return _DIMENSION_LABELS.get(dimension, dimension)
|
||||
# 裁剪时丢弃的"冗余"字段：_down / _flat 布尔元数据（*_compare 字符串已携带符号）
|
||||
_DROP_SUFFIX = ("_down", "_flat")
|
||||
|
||||
# 行级明细字段：展示用，AI 洞察不需要
|
||||
_DROP_DETAIL_KEYS = {
|
||||
"structure_rows", "price_items", "channel_items", "gift_rows",
|
||||
"discount_items", # 2026-04-22：升顶层"优惠构成"后，明细源从 revenue 里 drop 去重
|
||||
}
|
||||
|
||||
|
||||
def _is_drop_key(k: str) -> bool:
|
||||
if not isinstance(k, str):
|
||||
return False
|
||||
if k in _DROP_DETAIL_KEYS:
|
||||
return True
|
||||
return k.endswith(_DROP_SUFFIX)
|
||||
|
||||
|
||||
def _slim(data: Any) -> Any:
|
||||
"""递归裁剪：drop 明细 + _down/_flat + None 值。"""
|
||||
if isinstance(data, dict):
|
||||
out = {}
|
||||
for k, v in data.items():
|
||||
if _is_drop_key(k):
|
||||
continue
|
||||
slim_v = _slim(v)
|
||||
if slim_v is None:
|
||||
continue
|
||||
out[k] = slim_v
|
||||
return out if out else None
|
||||
if isinstance(data, list):
|
||||
return [_slim(item) for item in data]
|
||||
return data
|
||||
|
||||
|
||||
def _pct(numerator: float, denominator: float) -> float:
|
||||
"""百分比（小数），分母 0 返回 0。保留 4 位便于 AI 读取。"""
|
||||
if not denominator:
|
||||
return 0.0
|
||||
return round(numerator / denominator, 4)
|
||||
|
||||
|
||||
# 日粒度异常检测参数
|
||||
_ANOMALY_MIN_DAYS = 7 # 少于 7 天样本不检测（噪声太大）
|
||||
_ANOMALY_DEVIATION = 0.4 # 偏离"同星期均值" > 40% 标记为异常（2026-04-22 改为同星期基线）
|
||||
_ANOMALY_MAX_ITEMS = 10 # 最多保留 10 条（按 |偏离度| 降序截断，防 prompt 膨胀）
|
||||
_ANOMALY_MIN_SAME_WEEKDAY = 2 # 同星期至少 2 天样本才可作基线；不足时回退到整体均值
|
||||
|
||||
# 星期中文映射（0=Monday）
|
||||
_WEEKDAY_ZH = ("周一", "周二", "周三", "周四", "周五", "周六", "周日")
|
||||
|
||||
# 行业基线常量（综合商业球房）
|
||||
# 2026-04-22：移除各类警戒线/健康区间（各球房定位/地段/业态差异大，不宜一刀切）。
|
||||
# 仅保留"周中客流规律"这类行业普适的时间分布特征。
|
||||
INDUSTRY_BASELINES: dict[str, Any] = {
|
||||
"周中客流规律": "周五至周日旺季 / 周一最淡 / 周二至周四逐步回升",
|
||||
}
|
||||
|
||||
|
||||
def _fetch_daily_series(
|
||||
site_id: int, start_date: str, end_date: str,
|
||||
) -> list[tuple] | None:
|
||||
"""查 [start, end] 日粒度财务流水，一次查完供多个分析函数复用。
|
||||
|
||||
返回字段顺序：(stat_date, gross, cash_in, order_count, member_order_count, confirmed)
|
||||
过滤全 0 停业日；样本不足时返回 None。
|
||||
"""
|
||||
from app.services.fdw_queries import _fdw_context
|
||||
from app.database import get_connection
|
||||
|
||||
try:
|
||||
conn = get_connection()
|
||||
except Exception:
|
||||
logger.debug("日粒度查询连接失败", exc_info=True)
|
||||
return None
|
||||
|
||||
try:
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT stat_date,
|
||||
COALESCE(gross_amount, 0) AS gross,
|
||||
COALESCE(cash_inflow_total, 0) AS cash_in,
|
||||
COALESCE(order_count, 0) AS order_count,
|
||||
COALESCE(member_order_count, 0) AS member_order_count,
|
||||
COALESCE(confirmed_income, 0) AS confirmed
|
||||
FROM app.v_dws_finance_daily_summary
|
||||
WHERE stat_date >= %s::date
|
||||
AND stat_date <= %s::date
|
||||
ORDER BY stat_date
|
||||
""",
|
||||
(start_date, end_date),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
except Exception:
|
||||
logger.debug("日粒度数据查询失败: site_id=%s", site_id, exc_info=True)
|
||||
return None
|
||||
finally:
|
||||
try:
|
||||
conn.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
active = [
|
||||
(r[0], float(r[1]), float(r[2]), int(r[3] or 0), int(r[4] or 0), float(r[5] or 0))
|
||||
for r in rows
|
||||
if float(r[1] or 0) > 0 or float(r[2] or 0) > 0
|
||||
]
|
||||
return active if active else None
|
||||
|
||||
|
||||
_WEEKDAY_MIN_DAYS = 14 # 月初场景：样本 < 14 天时，每个星期最多 1-2 天，"日均"接近单日值，不注入以免 AI 被误导
|
||||
|
||||
|
||||
def _aggregate_by_weekday(series: list[tuple] | None) -> dict | None:
|
||||
"""按星期聚合 7 段日均值（发生额/现金流入/订单数），供 AI 观察周中规律。
|
||||
|
||||
要求至少 14 天样本（保证每个星期至少有 2 天），否则返回 None；
|
||||
防止月初场景下单日值被包装成"日均"迷惑 AI 做周规律判断。
|
||||
"""
|
||||
if not series or len(series) < _WEEKDAY_MIN_DAYS:
|
||||
return None
|
||||
from collections import defaultdict
|
||||
buckets: dict[int, list[tuple]] = defaultdict(list)
|
||||
for row in series:
|
||||
buckets[row[0].weekday()].append(row)
|
||||
out: dict[str, dict] = {}
|
||||
for wd in range(7):
|
||||
rows = buckets.get(wd) or []
|
||||
if not rows:
|
||||
continue
|
||||
n = len(rows)
|
||||
out[_WEEKDAY_ZH[wd]] = {
|
||||
"日均发生额": round(sum(r[1] for r in rows) / n, 2),
|
||||
"日均现金流入": round(sum(r[2] for r in rows) / n, 2),
|
||||
"日均订单数": round(sum(r[3] for r in rows) / n, 1),
|
||||
"营业日数": n,
|
||||
}
|
||||
return out or None
|
||||
|
||||
|
||||
def _build_unit_economics(
|
||||
series: list[tuple] | None,
|
||||
prev_series: list[tuple] | None = None,
|
||||
) -> dict | None:
|
||||
"""单位经济派生：客单价 / 日均订单数 / 会员订单占比 / 散客订单占比。
|
||||
|
||||
口径：全期汇总后再算（避免日均 avg 失真）。
|
||||
客单价取两口径：
|
||||
- 按成交收入（去除优惠的真实收入单价） — 反映真实收入能力
|
||||
- 按发生额（含优惠的账单均值） — 反映顾客端认知的单次消费量级
|
||||
若 prev_series 可用，则附加 _环比 字段避免 AI 推测幻觉。
|
||||
"""
|
||||
if not series:
|
||||
return None
|
||||
total_orders = sum(r[3] for r in series)
|
||||
if total_orders <= 0:
|
||||
return None
|
||||
total_member_orders = sum(r[4] for r in series)
|
||||
total_confirmed = sum(r[5] for r in series)
|
||||
total_gross = sum(r[1] for r in series)
|
||||
days = len(series)
|
||||
|
||||
price_confirmed = total_confirmed / total_orders
|
||||
price_gross = total_gross / total_orders
|
||||
member_share = total_member_orders / total_orders
|
||||
daily_orders = total_orders / days
|
||||
|
||||
out: dict[str, Any] = {
|
||||
"总订单数": total_orders,
|
||||
"日均订单数": round(daily_orders, 1),
|
||||
"客单价_按成交收入": round(price_confirmed, 2),
|
||||
"客单价_按发生额": round(price_gross, 2),
|
||||
"会员订单数": total_member_orders,
|
||||
"会员订单占比": round(member_share, 4),
|
||||
"散客订单数": total_orders - total_member_orders,
|
||||
"散客订单占比": round((total_orders - total_member_orders) / total_orders, 4),
|
||||
}
|
||||
|
||||
if prev_series:
|
||||
prev_orders = sum(r[3] for r in prev_series)
|
||||
if prev_orders > 0:
|
||||
prev_days = len(prev_series)
|
||||
prev_confirmed = sum(r[5] for r in prev_series)
|
||||
prev_gross = sum(r[1] for r in prev_series)
|
||||
prev_member = sum(r[4] for r in prev_series)
|
||||
# 月初场景：上期样本 < 5 天时客单价环比噪声极大（单日波动主导），加标注供 AI 降权引用
|
||||
low_sample = prev_days < 5
|
||||
|
||||
def _pct_change(cur: float, prev: float) -> str:
|
||||
if prev <= 0:
|
||||
return "无上期数据"
|
||||
value = f"{(cur - prev) / prev * 100:+.1f}%"
|
||||
return f"{value}（上期仅 {prev_days} 天，样本不足仅供参考）" if low_sample else value
|
||||
|
||||
out["客单价_按成交收入_环比"] = _pct_change(price_confirmed, prev_confirmed / prev_orders)
|
||||
out["客单价_按发生额_环比"] = _pct_change(price_gross, prev_gross / prev_orders)
|
||||
out["日均订单数_环比"] = _pct_change(daily_orders, prev_orders / prev_days)
|
||||
out["会员订单占比_环比"] = _pct_change(member_share, prev_member / prev_orders)
|
||||
return out
|
||||
|
||||
|
||||
def _detect_anomaly_days(
|
||||
site_id: int, start_date: str, end_date: str,
|
||||
series: list[tuple] | None = None,
|
||||
) -> list[dict] | None:
|
||||
"""扫描日粒度财务数据，标记偏离同星期均值 > 40% 的异常日。
|
||||
|
||||
series 可由调用方传入复用，避免重复查 DB。
|
||||
"""
|
||||
if series is None:
|
||||
series = _fetch_daily_series(site_id, start_date, end_date)
|
||||
if not series or len(series) < _ANOMALY_MIN_DAYS:
|
||||
return None
|
||||
active = series
|
||||
|
||||
# 2026-04-22 改进：按"同星期均值"做基线，比"期均"更贴近业态（周一淡/周末旺）
|
||||
# 同星期样本 < _ANOMALY_MIN_SAME_WEEKDAY 天时回退到整体均值
|
||||
from collections import defaultdict
|
||||
|
||||
def _scan(idx: int, label: str) -> list[dict]:
|
||||
vals = [row[idx] for row in active]
|
||||
global_mean = sum(vals) / len(vals)
|
||||
if global_mean <= 0:
|
||||
return []
|
||||
|
||||
# 按 weekday 分组统计均值
|
||||
by_weekday: dict[int, list[float]] = defaultdict(list)
|
||||
for d, *metrics in active:
|
||||
by_weekday[d.weekday()].append(metrics[idx - 1])
|
||||
weekday_mean: dict[int, float] = {
|
||||
wd: (sum(xs) / len(xs)) for wd, xs in by_weekday.items()
|
||||
}
|
||||
|
||||
flagged: list[dict] = []
|
||||
for d, *metrics in active:
|
||||
v = metrics[idx - 1]
|
||||
wd = d.weekday()
|
||||
same_count = len(by_weekday.get(wd, []))
|
||||
# 基线选择：同星期样本 >= 2 用同星期均值，否则用整体均值
|
||||
if same_count >= _ANOMALY_MIN_SAME_WEEKDAY and weekday_mean[wd] > 0:
|
||||
base = weekday_mean[wd]
|
||||
base_label = f"同{_WEEKDAY_ZH[wd]}均值"
|
||||
else:
|
||||
base = global_mean
|
||||
base_label = "期均"
|
||||
|
||||
deviation = (v - base) / base
|
||||
if abs(deviation) >= _ANOMALY_DEVIATION:
|
||||
weekday_zh = _WEEKDAY_ZH[wd]
|
||||
flagged.append({
|
||||
"日期": f"{d} {weekday_zh}",
|
||||
"指标": label,
|
||||
"当日": round(v, 2),
|
||||
"基线": round(base, 2),
|
||||
"基线类型": base_label,
|
||||
"偏离": f"{deviation * 100:+.1f}%",
|
||||
"_abs_dev": abs(deviation),
|
||||
})
|
||||
return flagged
|
||||
|
||||
candidates: list[dict] = _scan(1, "发生额") + _scan(2, "现金流入")
|
||||
if not candidates:
|
||||
return None
|
||||
# 按绝对偏离排序，取 top N，去掉排序用辅助键
|
||||
candidates.sort(key=lambda x: x["_abs_dev"], reverse=True)
|
||||
out = []
|
||||
for c in candidates[:_ANOMALY_MAX_ITEMS]:
|
||||
c.pop("_abs_dev", None)
|
||||
out.append(c)
|
||||
return out
|
||||
|
||||
|
||||
def _fetch_card_balance_opening(site_id: int, start_date: str) -> float | None:
|
||||
"""取 start_date 前一日的储值卡总余额（作为本期期初余额）。
|
||||
|
||||
数据源：etl 库 app.v_dws_finance_recharge_summary（每日快照，total_card_balance 字段）。
|
||||
若前一日无数据（门店刚开业 / 数据缺失），返回 None。
|
||||
"""
|
||||
from app.services.fdw_queries import _fdw_context
|
||||
from app.database import get_connection
|
||||
|
||||
try:
|
||||
conn = get_connection()
|
||||
except Exception:
|
||||
logger.debug("期初余额查询连接失败", exc_info=True)
|
||||
return None
|
||||
|
||||
try:
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT total_card_balance
|
||||
FROM app.v_dws_finance_recharge_summary
|
||||
WHERE stat_date < %s::date
|
||||
ORDER BY stat_date DESC
|
||||
LIMIT 1
|
||||
""",
|
||||
(start_date,),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
except Exception:
|
||||
logger.debug("期初余额查询失败: site_id=%s", site_id, exc_info=True)
|
||||
return None
|
||||
finally:
|
||||
try:
|
||||
conn.close()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not row or row[0] is None:
|
||||
return None
|
||||
return float(row[0])
|
||||
|
||||
|
||||
def _aggregate_expense(expense: dict | None) -> dict | None:
|
||||
"""从 expense 四类明细聚合出顶层金额，便于 AI 直接看四大块支出占比。"""
|
||||
if not isinstance(expense, dict):
|
||||
return None
|
||||
def _sum(key: str) -> float:
|
||||
items = expense.get(key) or []
|
||||
if not isinstance(items, list):
|
||||
return 0.0
|
||||
return round(sum(float(x.get("amount", 0) or 0) for x in items if isinstance(x, dict)), 2)
|
||||
total = float(expense.get("total", 0) or 0)
|
||||
if total <= 0:
|
||||
return None # 全 0 数据对 AI 无意义，直接丢
|
||||
return {
|
||||
"合计": round(total, 2),
|
||||
"合计环比": expense.get("total_compare") or "持平",
|
||||
"运营支出": _sum("operation_items"),
|
||||
"固定支出": _sum("fixed_items"),
|
||||
"助教支出": _sum("coach_items"),
|
||||
"平台支出": _sum("platform_items"),
|
||||
}
|
||||
|
||||
|
||||
def _build_discount_kpi(revenue: dict | None, overview: dict | None) -> dict | None:
|
||||
"""把优惠拆成顶层 KPI + 派生指标（占比、贡献率）。
|
||||
|
||||
AI 数据挖掘视角：
|
||||
- 按金额排序展示，top1 一眼看出来
|
||||
- 每项带 amount / compare / share（占总优惠比）
|
||||
- 整体带优惠率（discount / occurrence）便于判断利润侵蚀程度
|
||||
"""
|
||||
if not isinstance(revenue, dict):
|
||||
return None
|
||||
items = revenue.get("discount_items") or []
|
||||
if not isinstance(items, list) or not items:
|
||||
return None
|
||||
|
||||
total = round(sum(float(x.get("amount", 0) or 0) for x in items if isinstance(x, dict)), 2)
|
||||
breakdown = []
|
||||
for it in items:
|
||||
if not isinstance(it, dict):
|
||||
continue
|
||||
amt = float(it.get("amount", 0) or 0)
|
||||
row: dict[str, Any] = {
|
||||
"名称": it.get("label"),
|
||||
"金额": round(amt, 2),
|
||||
"占总优惠": _pct(amt, total),
|
||||
}
|
||||
if it.get("compare"):
|
||||
row["环比"] = it["compare"]
|
||||
breakdown.append(row)
|
||||
# 按金额从大到小排序 → AI 阅读顺序 = 重要度顺序
|
||||
breakdown.sort(key=lambda r: float(r.get("金额") or 0), reverse=True)
|
||||
|
||||
overview = overview or {}
|
||||
occurrence = float(overview.get("occurrence", 0) or 0)
|
||||
|
||||
kpi: dict[str, Any] = {
|
||||
"总优惠": total,
|
||||
"优惠率": _pct(total, occurrence), # 0.3796 表示 37.96%
|
||||
"占比排序": breakdown,
|
||||
}
|
||||
if breakdown:
|
||||
top = breakdown[0]
|
||||
kpi["最大优惠来源"] = f"{top.get('名称')}（金额 {top.get('金额')} 元，占总优惠 {int(float(top.get('占总优惠', 0))*100)}%）"
|
||||
return kpi
|
||||
|
||||
|
||||
def _build_cashflow_kpi(cashflow: dict | None) -> dict | None:
|
||||
"""消费收款拆三档（纸币/线上/团购）+ 充值到账，给 AI 直接看资金来源结构。"""
|
||||
if not isinstance(cashflow, dict):
|
||||
return None
|
||||
consume = cashflow.get("consume_items") or []
|
||||
recharge = cashflow.get("recharge_items") or []
|
||||
total = float(cashflow.get("total", 0) or 0)
|
||||
if total <= 0:
|
||||
return None
|
||||
|
||||
consume_map = {}
|
||||
for it in consume:
|
||||
if not isinstance(it, dict):
|
||||
continue
|
||||
consume_map[it.get("label")] = {
|
||||
"金额": round(float(it.get("amount", 0) or 0), 2),
|
||||
"环比": it.get("compare") or "持平",
|
||||
}
|
||||
|
||||
recharge_total = round(sum(float(x.get("amount", 0) or 0) for x in recharge if isinstance(x, dict)), 2)
|
||||
consume_total = round(sum(float(v.get("金额", 0) or 0) for v in consume_map.values()), 2)
|
||||
|
||||
return {
|
||||
"合计": round(total, 2),
|
||||
"合计环比": cashflow.get("total_compare") or "持平",
|
||||
"消费收款合计": consume_total,
|
||||
"消费收款占比": _pct(consume_total, total),
|
||||
"充值收款合计": recharge_total,
|
||||
"充值收款占比": _pct(recharge_total, total),
|
||||
"按渠道": consume_map,
|
||||
}
|
||||
|
||||
|
||||
def _build_coach_kpi(coach: dict | None) -> dict | None:
|
||||
"""助教成本压缩：只保留两档的合计薪酬+合计分成+平均时薪+3 级别薪酬分布。"""
|
||||
if not isinstance(coach, dict):
|
||||
return None
|
||||
def _slim_tier(t: dict | None) -> dict | None:
|
||||
if not isinstance(t, dict):
|
||||
return None
|
||||
rows = t.get("rows") or []
|
||||
# 只保留级别-薪酬-时薪 3 字段，作为分布快照
|
||||
tier_dist = [
|
||||
{"级别": r.get("level"), "薪酬": r.get("pay"), "时薪": r.get("hourly")}
|
||||
for r in rows if isinstance(r, dict)
|
||||
]
|
||||
total_pay = float(t.get("total_pay", 0) or 0)
|
||||
if total_pay <= 0:
|
||||
return None
|
||||
return {
|
||||
"合计薪酬": round(total_pay, 2),
|
||||
"合计薪酬环比": t.get("total_pay_compare") or "持平",
|
||||
"合计分成": round(float(t.get("total_share", 0) or 0), 2),
|
||||
"平均时薪": round(float(t.get("avg_hourly", 0) or 0), 2),
|
||||
"各级别分布": tier_dist,
|
||||
}
|
||||
basic = _slim_tier(coach.get("basic"))
|
||||
incentive = _slim_tier(coach.get("incentive"))
|
||||
if not basic and not incentive:
|
||||
return None
|
||||
out: dict[str, Any] = {}
|
||||
if basic:
|
||||
out["基础助教"] = basic
|
||||
if incentive:
|
||||
out["激励助教"] = incentive
|
||||
# 派生：人力成本占收入比（需要收入传进来，这里只给基础值）
|
||||
total_pay = (basic or {}).get("合计薪酬", 0) + (incentive or {}).get("合计薪酬", 0)
|
||||
if total_pay > 0:
|
||||
out["人力薪酬合计"] = round(total_pay, 2)
|
||||
return out
|
||||
|
||||
|
||||
def _build_derived_ratios(overview: dict | None, cashflow_kpi: dict | None,
|
||||
coach_kpi: dict | None, discount_kpi: dict | None) -> dict:
|
||||
"""数据挖掘视角：派生关键比率，让 AI 不用自己算。
|
||||
|
||||
- 储值卡贡献率：充值到账 / 总现金流入
|
||||
- 人力成本占收入比：助教薪酬合计 / 成交收入
|
||||
- 优惠侵蚀率：总优惠 / 发生额
|
||||
- 现金结余率：现金结余 / 现金流入
|
||||
"""
|
||||
ov = overview or {}
|
||||
confirmed = float(ov.get("confirmed_revenue", 0) or 0)
|
||||
occurrence = float(ov.get("occurrence", 0) or 0)
|
||||
cash_in = float(ov.get("cash_in", 0) or 0)
|
||||
cash_balance = float(ov.get("cash_balance", 0) or 0)
|
||||
total_pay = (coach_kpi or {}).get("人力薪酬合计", 0)
|
||||
recharge_in = (cashflow_kpi or {}).get("充值收款合计", 0)
|
||||
discount_total = (discount_kpi or {}).get("总优惠", 0)
|
||||
|
||||
out: dict[str, Any] = {}
|
||||
if confirmed > 0 and total_pay:
|
||||
out["人力成本占成交收入比"] = _pct(total_pay, confirmed)
|
||||
if cash_in > 0 and recharge_in:
|
||||
out["储值卡充值占现金流入比"] = _pct(recharge_in, cash_in)
|
||||
if occurrence > 0 and discount_total:
|
||||
out["优惠侵蚀率"] = _pct(discount_total, occurrence)
|
||||
if cash_in > 0:
|
||||
out["现金结余率"] = _pct(cash_balance, cash_in)
|
||||
return out
|
||||
|
||||
|
||||
# 2026-04-22：异常检测由 AI 侧自行判断，后端只提供客观 KPI（不给规则结论）
|
||||
|
||||
|
||||
def _translate_keys(data: Any) -> Any:
|
||||
"""递归翻译 dict/list 中所有键为中文；值保持不变。
|
||||
|
||||
- dict: 键命中 KEY_TRANSLATIONS 则替换，未命中保留原键
|
||||
- list: 逐项递归
|
||||
- 其他类型（str/int/float/bool/None）原样返回
|
||||
"""
|
||||
if isinstance(data, dict):
|
||||
return {
|
||||
KEY_TRANSLATIONS.get(k, k): _translate_keys(v)
|
||||
for k, v in data.items()
|
||||
}
|
||||
if isinstance(data, list):
|
||||
return [_translate_keys(item) for item in data]
|
||||
return data
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: Any | None = None, # 兼容统一签名，App2 不用
|
||||
) -> str:
|
||||
"""构建 App2 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, time_dimension, area（可选，默认 all）
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串，所有 board 数据字段已翻译为中文。
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
time_dimension = context["time_dimension"]
|
||||
area = context.get("area", "all")
|
||||
|
||||
board_time = DIMENSION_MAP.get(time_dimension)
|
||||
if not board_time:
|
||||
raise ValueError(f"App2 不支持的时间维度: {time_dimension}")
|
||||
|
||||
if area not in AREA_LABELS:
|
||||
raise ValueError(f"App2 不支持的区域: {area}")
|
||||
|
||||
try:
|
||||
board_data = await get_finance_board(
|
||||
time=board_time, area=area, compare=1, site_id=site_id,
|
||||
)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"App2 财务看板查询失败: site_id=%s dimension=%s area=%s",
|
||||
site_id, time_dimension, area, exc_info=True,
|
||||
)
|
||||
board_data = {}
|
||||
|
||||
# 2026-04-22 数据挖掘视角 prompt 结构化：
|
||||
# - 优惠/现金流/助教/支出 四大领域分别派生 KPI（带占比/排序/派生指标）
|
||||
# - 异常检测：规则法标注 AI 必看异常点
|
||||
# - 派生比率：人力成本占比/优惠侵蚀率/储值卡贡献率 等不用 AI 再算
|
||||
# - 原始财务数据经 _slim 裁剪后作为"原始指标"补充，避免 AI 失去追溯能力
|
||||
overview = board_data.get("overview") if isinstance(board_data, dict) else None
|
||||
revenue = board_data.get("revenue") if isinstance(board_data, dict) else None
|
||||
cashflow = board_data.get("cashflow") if isinstance(board_data, dict) else None
|
||||
expense = board_data.get("expense") if isinstance(board_data, dict) else None
|
||||
coach = board_data.get("coach_analysis") if isinstance(board_data, dict) else None
|
||||
|
||||
discount_kpi = _build_discount_kpi(revenue, overview)
|
||||
cashflow_kpi = _build_cashflow_kpi(cashflow)
|
||||
expense_kpi = _aggregate_expense(expense)
|
||||
coach_kpi = _build_coach_kpi(coach)
|
||||
ratios = _build_derived_ratios(overview, cashflow_kpi, coach_kpi, discount_kpi)
|
||||
|
||||
# 原始数据：slim 后再翻译，供 AI 追溯细节
|
||||
slim_data = _slim(board_data) or {}
|
||||
raw_cn = _translate_keys(slim_data)
|
||||
|
||||
# 对比口径说明：当期/对比期均为"同天数对齐"，避免 AI 把环比误读为"当期部分 vs 上期整月"
|
||||
compare_caliber: dict[str, Any] | None = None
|
||||
try:
|
||||
from app.services.runtime_context import get_runtime_context
|
||||
|
||||
runtime_ctx = get_runtime_context(site_id)
|
||||
cur_start, cur_end = _calc_date_range(board_time, ref_date=runtime_ctx.business_date)
|
||||
prev_start, prev_end = _calc_prev_range(board_time, cur_start, cur_end)
|
||||
cur_days = (cur_end - cur_start).days + 1
|
||||
prev_days = (prev_end - prev_start).days + 1
|
||||
compare_caliber = {
|
||||
"当期范围": f"{cur_start} ~ {cur_end}（{cur_days} 天）",
|
||||
"对比期范围": f"{prev_start} ~ {prev_end}（{prev_days} 天）",
|
||||
"对齐方式": "上期同天数对齐（非整月/整周对比）",
|
||||
"说明": "所有 _环比 / _compare 字段均按上表口径计算；月中调用时对比期会自动截断到与当期相同天数",
|
||||
}
|
||||
except Exception:
|
||||
logger.debug("对比口径字段生成失败（不影响主流程）", exc_info=True)
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"当前时间": get_runtime_context(site_id).business_now.strftime("%Y-%m-%d %H:%M"),
|
||||
"门店编号": site_id,
|
||||
"时间维度": DIMENSION_LABELS.get(time_dimension, time_dimension),
|
||||
"区域": AREA_LABELS.get(area, area),
|
||||
# 0. 对比口径：让 AI 正确解读环比字段
|
||||
**({"对比口径": compare_caliber} if compare_caliber else {}),
|
||||
# 1. 核心 KPI：AI 洞察首要依据
|
||||
"核心KPI": {
|
||||
"发生额": float(overview.get("occurrence", 0)) if overview else 0,
|
||||
"发生额环比": (overview or {}).get("occurrence_compare") or "持平",
|
||||
"成交收入": float(overview.get("confirmed_revenue", 0)) if overview else 0,
|
||||
"成交收入环比": (overview or {}).get("confirmed_revenue_compare") or "持平",
|
||||
"现金流入": (overview or {}).get("cash_in"),
|
||||
"现金流入环比": (overview or {}).get("cash_in_compare") or "持平",
|
||||
"现金结余": (overview or {}).get("cash_balance"),
|
||||
"现金结余环比": (overview or {}).get("cash_balance_compare") or "持平",
|
||||
},
|
||||
# 2. 派生比率：不用 AI 再算
|
||||
"派生比率": ratios,
|
||||
}
|
||||
# 3. 优惠构成（带排序/占比/环比/最大来源提示）
|
||||
if discount_kpi:
|
||||
payload["优惠构成"] = discount_kpi
|
||||
# 4. 现金流入来源分布
|
||||
if cashflow_kpi:
|
||||
payload["现金流入来源"] = cashflow_kpi
|
||||
# 5. 支出概况（聚合到四大类，total=0 则不给 AI）
|
||||
if expense_kpi:
|
||||
payload["支出概况"] = expense_kpi
|
||||
# 6. 助教成本画像
|
||||
if coach_kpi:
|
||||
payload["助教成本"] = coach_kpi
|
||||
# 7. 储值卡余额变化：期初 + 期末 + 充值 + 消耗 + 其他调整（揭示"充值-消耗≠余额变化"的差异）
|
||||
# 避免 AI 在只看当期充值/消耗时对"余额为何涨"的矛盾自圆其说
|
||||
if area == "all" and isinstance(recharge := board_data.get("recharge"), dict):
|
||||
try:
|
||||
start_date_obj, _end = _calc_date_range(board_time)
|
||||
opening = _fetch_card_balance_opening(site_id, str(start_date_obj))
|
||||
closing = float(recharge.get("card_balance") or 0)
|
||||
period_recharge = float(recharge.get("actual_income") or 0)
|
||||
period_consume = float(recharge.get("consumed") or 0)
|
||||
if opening is not None and (opening > 0 or closing > 0):
|
||||
diff = closing - opening
|
||||
other_adj = round(diff - (period_recharge - period_consume), 2)
|
||||
payload["储值卡余额变化"] = {
|
||||
"期初余额": round(opening, 2),
|
||||
"期末余额": round(closing, 2),
|
||||
"余额变化": round(diff, 2),
|
||||
"本期充值": round(period_recharge, 2),
|
||||
"本期消耗": round(period_consume, 2),
|
||||
"其他调整": other_adj, # 含过期/赠送/退款/手动调整，非 0 时 AI 需要关注
|
||||
}
|
||||
except Exception:
|
||||
logger.debug("储值卡余额变化注入失败", exc_info=True)
|
||||
# 8. 日粒度派生（仅 area=all，样本 ≥ 7 天）：一次 DB 查询，三段派生
|
||||
# - 单位经济：客单价/订单数/会员占比（含环比，避免 AI 对客单走势推测幻觉）
|
||||
# - 按星期聚合：供 E 板块做周中规律宏观洞察
|
||||
# - 日粒度异常：同星期均值基线下的极端偏离
|
||||
if area == "all":
|
||||
try:
|
||||
start_date, end_date = _calc_date_range(board_time)
|
||||
series = _fetch_daily_series(site_id, str(start_date), str(end_date))
|
||||
# 上期序列（用于客单价环比）
|
||||
prev_series: list[tuple] | None = None
|
||||
try:
|
||||
prev_start, prev_end = _calc_prev_range(board_time, start_date, end_date)
|
||||
prev_series = _fetch_daily_series(site_id, str(prev_start), str(prev_end))
|
||||
except Exception:
|
||||
logger.debug("上期 series 查询失败，客单价环比字段将省略", exc_info=True)
|
||||
|
||||
if series:
|
||||
unit_econ = _build_unit_economics(series, prev_series=prev_series)
|
||||
if unit_econ:
|
||||
payload["单位经济"] = unit_econ
|
||||
by_weekday = _aggregate_by_weekday(series)
|
||||
if by_weekday:
|
||||
payload["按星期聚合"] = by_weekday
|
||||
anomalies = _detect_anomaly_days(
|
||||
site_id, str(start_date), str(end_date), series=series,
|
||||
)
|
||||
if anomalies:
|
||||
payload["日粒度异常"] = anomalies
|
||||
except Exception:
|
||||
logger.debug("日粒度派生字段注入失败（不影响主流程）", exc_info=True)
|
||||
# 9. 行业基线：AI 判断是否超警戒线的参照
|
||||
payload["行业基线"] = INDUSTRY_BASELINES
|
||||
# 10. 原始财务数据：供 AI 追溯（大部分 prompt 长度来自这里，已 slim）
|
||||
payload["原始指标"] = raw_cn
|
||||
|
||||
if not board_data:
|
||||
payload["数据缺失提示"] = "财务看板数据获取失败，请基于已有缓存或常识分析"
|
||||
|
||||
return json.dumps(payload, ensure_ascii=False, default=str)
|
||||
|
||||
@@ -396,7 +396,10 @@ async def build_prompt(
|
||||
# 对比口径（所有环比字段的前置依赖 · H1）
|
||||
compare_caliber: dict[str, Any] | None = None
|
||||
try:
|
||||
cur_start, cur_end = _calc_date_range(board_time)
|
||||
from app.services.runtime_context import get_runtime_context
|
||||
|
||||
runtime_ctx = get_runtime_context(site_id)
|
||||
cur_start, cur_end = _calc_date_range(board_time, ref_date=runtime_ctx.business_date)
|
||||
prev_start, prev_end = _calc_prev_range(board_time, cur_start, cur_end)
|
||||
cur_days = (cur_end - cur_start).days + 1
|
||||
prev_days = (prev_end - prev_start).days + 1
|
||||
@@ -419,7 +422,7 @@ async def build_prompt(
|
||||
}
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"当前时间": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||||
"当前时间": get_runtime_context(site_id).business_now.strftime("%Y-%m-%d %H:%M"),
|
||||
"门店编号": site_id,
|
||||
"时间维度": DIMENSION_LABELS.get(time_dimension, time_dimension),
|
||||
"区域": AREA_LABELS.get(area, area),
|
||||
|
||||
131
apps/backend/app/ai/prompts/app3_clue_prompt.py
Normal file
131
apps/backend/app/ai/prompts/app3_clue_prompt.py
Normal file
@@ -0,0 +1,131 @@
|
||||
"""应用 3 客户数据维客线索分析 Prompt 拼装。
|
||||
|
||||
消费事件触发，从客户消费数据提取维客线索。
|
||||
- 数据源：fetch_member_consumption_data（DWS）
|
||||
- 金额口径：items_sum（禁止 consume_money）
|
||||
- 线索 category：客户基础 / 消费习惯 / 玩法偏好（3 选 1）
|
||||
- 线索 providers 统一为"系统"
|
||||
- system prompt 在百炼控制台配置，本模块只拼数据上下文 JSON
|
||||
|
||||
返回：单个 prompt 字符串（直接传给 Application.call）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.data_fetchers import fetch_member_consumption_data
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
from app.services.runtime_context import as_runtime_business_now_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# prompt 观测阈值：历史上 4000 字会触发裁剪；现保留完整消费明细，仅用于测试/审计参考
|
||||
_MAX_PROMPT_LEN = 4000
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> str:
|
||||
"""构建 App3 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, member_id
|
||||
cache_svc: 缓存服务，用于读取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
|
||||
# 数据获取（失败降级）
|
||||
fetch_failed = False
|
||||
try:
|
||||
member_data = await fetch_member_consumption_data(site_id, member_id)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"App3 消费数据获取失败: site_id=%s member_id=%s",
|
||||
site_id, member_id, exc_info=True,
|
||||
)
|
||||
member_data = _default_member_data()
|
||||
fetch_failed = True
|
||||
|
||||
consumption_records = member_data.get("consumption_records") or []
|
||||
if not consumption_records:
|
||||
consumption_records = (
|
||||
"⚠ 消费数据获取失败，该客户暂无消费记录可供分析"
|
||||
if fetch_failed else "该客户暂无消费记录"
|
||||
)
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"current_time": as_runtime_business_now_str(site_id, fmt="%Y-%m-%d %H:%M"),
|
||||
"member_id": member_id,
|
||||
"member_nickname": member_data.get("member_nickname", ""),
|
||||
"main_data": {
|
||||
"consumption_records": consumption_records,
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"reference": _build_reference(site_id, member_id, cache_svc),
|
||||
}
|
||||
|
||||
# 完整明细策略：App3 需要尽量保留消费行为模式，不在本地裁剪消费记录。
|
||||
# 真实 App3 完整 100 条明细调用已验证可在 180s 单步超时内返回。
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
return text
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""组装参考字段：App6 备注线索最新 + App8 历史最近 2 条。"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
ref: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
app6_latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP6_NOTE_ANALYSIS.value, site_id, target_id,
|
||||
)
|
||||
if app6_latest:
|
||||
ref["app6_note_clues"] = {
|
||||
"result_json": app6_latest.get("result_json"),
|
||||
"generated_at": app6_latest.get("created_at"),
|
||||
}
|
||||
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
ref["app8_history"] = [
|
||||
{
|
||||
"result_json": h.get("result_json"),
|
||||
"generated_at": h.get("created_at"),
|
||||
}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return ref
|
||||
177
apps/backend/app/ai/prompts/app4_analysis_prompt.py
Normal file
177
apps/backend/app/ai/prompts/app4_analysis_prompt.py
Normal file
@@ -0,0 +1,177 @@
|
||||
"""应用 4 关系分析 / 任务建议 Prompt 拼装。
|
||||
|
||||
助教被分配召回任务或参与新结算时触发。
|
||||
- 数据源：fetch_assistant_info + fetch_service_history + fetch_member_consumption_data + fetch_member_notes
|
||||
- 输出字段：task_description / action_suggestions / one_line_summary
|
||||
- system prompt 在百炼控制台配置
|
||||
|
||||
返回：单个 prompt 字符串。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.data_fetchers import (
|
||||
fetch_assistant_info,
|
||||
fetch_member_consumption_data,
|
||||
fetch_member_notes,
|
||||
fetch_service_history,
|
||||
)
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
from app.services.runtime_context import as_runtime_business_now_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_MAX_PROMPT_LEN = 8000
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> str:
|
||||
"""构建 App4 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, assistant_id, member_id
|
||||
cache_svc: 缓存服务，用于读取 reference 历史数据
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
assistant_id = context["assistant_id"]
|
||||
member_id = context["member_id"]
|
||||
|
||||
results = await asyncio.gather(
|
||||
fetch_assistant_info(site_id, assistant_id),
|
||||
fetch_service_history(site_id, assistant_id, member_id),
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
warnings: list[str] = []
|
||||
|
||||
assistant_info = results[0] if not isinstance(results[0], Exception) else {}
|
||||
if isinstance(results[0], Exception):
|
||||
warnings.append("助教信息获取失败")
|
||||
logger.warning("App4 助教信息获取失败: %s", results[0])
|
||||
|
||||
service_history = results[1] if not isinstance(results[1], Exception) else []
|
||||
if isinstance(results[1], Exception):
|
||||
warnings.append("服务历史获取失败")
|
||||
logger.warning("App4 服务历史获取失败: %s", results[1])
|
||||
|
||||
if isinstance(results[2], Exception):
|
||||
member_data = _default_member_data()
|
||||
warnings.append("消费数据获取失败")
|
||||
logger.warning("App4 消费数据获取失败: %s", results[2])
|
||||
else:
|
||||
member_data = results[2]
|
||||
|
||||
notes = results[3] if not isinstance(results[3], Exception) else []
|
||||
if isinstance(results[3], Exception):
|
||||
warnings.append("备注获取失败")
|
||||
logger.warning("App4 备注获取失败: %s", results[3])
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"current_time": as_runtime_business_now_str(site_id, fmt="%Y-%m-%d %H:%M"),
|
||||
"assistant_id": assistant_id,
|
||||
"member_id": member_id,
|
||||
"assistant_info": assistant_info or "⚠ 助教信息获取失败",
|
||||
"service_history": service_history or "暂无服务记录",
|
||||
"task_assignment_basis": {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"customer_data": {
|
||||
"member_nickname": member_data.get("member_nickname", ""),
|
||||
"notes": notes or "暂无备注",
|
||||
},
|
||||
"reference": _build_reference(site_id, member_id, cache_svc),
|
||||
}
|
||||
|
||||
if warnings:
|
||||
payload["_data_warnings"] = warnings
|
||||
|
||||
return _truncate_payload(payload)
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""组装 App8 最新 + 最近 2 条历史。"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
ref: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,
|
||||
)
|
||||
if latest:
|
||||
ref["app8_latest"] = {
|
||||
"result_json": latest.get("result_json"),
|
||||
"generated_at": latest.get("created_at"),
|
||||
}
|
||||
|
||||
history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if history:
|
||||
ref["app8_history"] = [
|
||||
{"result_json": h.get("result_json"), "generated_at": h.get("created_at")}
|
||||
for h in history
|
||||
]
|
||||
|
||||
return ref
|
||||
|
||||
|
||||
def _truncate_payload(payload: dict) -> str:
|
||||
"""按优先级截断 service_history → consumption_records → notes，控制 prompt 长度。"""
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) <= _MAX_PROMPT_LEN:
|
||||
return text
|
||||
|
||||
sh = payload.get("service_history")
|
||||
if isinstance(sh, list) and len(sh) > 5:
|
||||
payload["service_history"] = sh[:5]
|
||||
payload["_truncated_service_history"] = f"服务记录已截断，原始 {len(sh)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) > _MAX_PROMPT_LEN:
|
||||
records = payload["task_assignment_basis"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
payload["task_assignment_basis"]["consumption_records"] = records[:5]
|
||||
payload["task_assignment_basis"]["_truncated"] = f"消费记录已截断，原始 {len(records)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) > _MAX_PROMPT_LEN:
|
||||
n = payload["customer_data"].get("notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
payload["customer_data"]["notes"] = n[:10]
|
||||
payload["customer_data"]["_truncated_notes"] = f"备注已截断，原始 {len(n)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
return text
|
||||
170
apps/backend/app/ai/prompts/app5_tactics_prompt.py
Normal file
170
apps/backend/app/ai/prompts/app5_tactics_prompt.py
Normal file
@@ -0,0 +1,170 @@
|
||||
"""应用 5 话术参考 Prompt 拼装。
|
||||
|
||||
App4 完成后串行触发，接收 App4 返回结果作为 task_suggestion。
|
||||
- 数据源：fetch_assistant_info + fetch_service_history + fetch_member_consumption_data + fetch_member_notes + context.app4_result
|
||||
- 输出字段：tactics 数组（每条含 scenario + script）
|
||||
- system prompt 在百炼控制台配置
|
||||
|
||||
返回：单个 prompt 字符串。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.data_fetchers import (
|
||||
fetch_assistant_info,
|
||||
fetch_member_consumption_data,
|
||||
fetch_member_notes,
|
||||
fetch_service_history,
|
||||
)
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
from app.services.runtime_context import as_runtime_business_now_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_MAX_PROMPT_LEN = 8000
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> str:
|
||||
"""构建 App5 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, assistant_id, member_id, app4_result(dict|None)
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
assistant_id = context["assistant_id"]
|
||||
member_id = context["member_id"]
|
||||
task_suggestion = context.get("app4_result") or {}
|
||||
|
||||
results = await asyncio.gather(
|
||||
fetch_assistant_info(site_id, assistant_id),
|
||||
fetch_service_history(site_id, assistant_id, member_id),
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
warnings: list[str] = []
|
||||
|
||||
assistant_info = results[0] if not isinstance(results[0], Exception) else {}
|
||||
if isinstance(results[0], Exception):
|
||||
warnings.append("助教信息获取失败")
|
||||
logger.warning("App5 助教信息获取失败: %s", results[0])
|
||||
|
||||
service_history = results[1] if not isinstance(results[1], Exception) else []
|
||||
if isinstance(results[1], Exception):
|
||||
warnings.append("服务历史获取失败")
|
||||
logger.warning("App5 服务历史获取失败: %s", results[1])
|
||||
|
||||
if isinstance(results[2], Exception):
|
||||
member_data = _default_member_data()
|
||||
warnings.append("消费数据获取失败")
|
||||
logger.warning("App5 消费数据获取失败: %s", results[2])
|
||||
else:
|
||||
member_data = results[2]
|
||||
|
||||
notes = results[3] if not isinstance(results[3], Exception) else []
|
||||
if isinstance(results[3], Exception):
|
||||
warnings.append("备注获取失败")
|
||||
logger.warning("App5 备注获取失败: %s", results[3])
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"current_time": as_runtime_business_now_str(site_id, fmt="%Y-%m-%d %H:%M"),
|
||||
"assistant_id": assistant_id,
|
||||
"member_id": member_id,
|
||||
"task_suggestion": task_suggestion,
|
||||
"assistant_info": assistant_info or "⚠ 助教信息获取失败",
|
||||
"service_history": service_history or "暂无服务记录",
|
||||
"task_assignment_basis": {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"customer_data": {
|
||||
"member_nickname": member_data.get("member_nickname", ""),
|
||||
"notes": notes or "暂无备注",
|
||||
},
|
||||
"reference": _build_reference(site_id, member_id, cache_svc),
|
||||
}
|
||||
|
||||
if warnings:
|
||||
payload["_data_warnings"] = warnings
|
||||
|
||||
return _truncate_payload(payload)
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""组装最近 2 条 App8 历史。"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
ref: dict = {}
|
||||
history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value,
|
||||
site_id,
|
||||
str(member_id),
|
||||
limit=2,
|
||||
)
|
||||
if history:
|
||||
ref["app8_history"] = [
|
||||
{"result_json": h.get("result_json"), "generated_at": h.get("created_at")}
|
||||
for h in history
|
||||
]
|
||||
|
||||
return ref
|
||||
|
||||
|
||||
def _truncate_payload(payload: dict) -> str:
|
||||
"""按优先级截断 service_history → consumption_records → notes。"""
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) <= _MAX_PROMPT_LEN:
|
||||
return text
|
||||
|
||||
sh = payload.get("service_history")
|
||||
if isinstance(sh, list) and len(sh) > 5:
|
||||
payload["service_history"] = sh[:5]
|
||||
payload["_truncated_service_history"] = f"服务记录已截断，原始 {len(sh)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) > _MAX_PROMPT_LEN:
|
||||
records = payload["task_assignment_basis"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
payload["task_assignment_basis"]["consumption_records"] = records[:5]
|
||||
payload["task_assignment_basis"]["_truncated"] = f"消费记录已截断，原始 {len(records)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) > _MAX_PROMPT_LEN:
|
||||
n = payload["customer_data"].get("notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
payload["customer_data"]["notes"] = n[:10]
|
||||
payload["customer_data"]["_truncated_notes"] = f"备注已截断，原始 {len(n)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
return text
|
||||
160
apps/backend/app/ai/prompts/app6_note_prompt.py
Normal file
160
apps/backend/app/ai/prompts/app6_note_prompt.py
Normal file
@@ -0,0 +1,160 @@
|
||||
"""应用 6 备注分析 Prompt 拼装。
|
||||
|
||||
助教提交备注后触发，AI 分析备注内容并评分（1-10）+ 提取维客线索。
|
||||
- 数据源：context.note_content + fetch_member_consumption_data + fetch_member_notes
|
||||
- 线索 category：6 选 1（含促销偏好/社交关系/重要反馈）
|
||||
- 线索 providers 标记当前备注提供人
|
||||
- system prompt 在百炼控制台配置
|
||||
|
||||
返回：单个 prompt 字符串。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.data_fetchers import fetch_member_consumption_data, fetch_member_notes
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
from app.services.runtime_context import as_runtime_business_now_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_MAX_PROMPT_LEN = 8000
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> str:
|
||||
"""构建 App6 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, member_id, note_content, noted_by_name, noted_by_created_at
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
note_content = context.get("note_content", "")
|
||||
noted_by_name = context.get("noted_by_name", "")
|
||||
noted_by_created_at = context.get("noted_by_created_at", "")
|
||||
|
||||
results = await asyncio.gather(
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
warnings: list[str] = []
|
||||
|
||||
if isinstance(results[0], Exception):
|
||||
member_data = _default_member_data()
|
||||
warnings.append("消费数据获取失败")
|
||||
logger.warning("App6 消费数据获取失败: %s", results[0])
|
||||
else:
|
||||
member_data = results[0]
|
||||
|
||||
all_notes = results[1] if not isinstance(results[1], Exception) else []
|
||||
if isinstance(results[1], Exception):
|
||||
warnings.append("备注获取失败")
|
||||
logger.warning("App6 备注获取失败: %s", results[1])
|
||||
|
||||
reference = _build_reference(site_id, member_id, cache_svc)
|
||||
reference["member_nickname"] = member_data.get("member_nickname", "")
|
||||
reference["consumption_data"] = {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
}
|
||||
reference["all_notes"] = all_notes
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"current_time": as_runtime_business_now_str(site_id, fmt="%Y-%m-%d %H:%M"),
|
||||
"member_id": member_id,
|
||||
"current_note": {
|
||||
"content": note_content,
|
||||
"recorded_by": noted_by_name,
|
||||
"created_at": noted_by_created_at,
|
||||
},
|
||||
"providers_label": noted_by_name,
|
||||
"reference": reference,
|
||||
}
|
||||
|
||||
if warnings:
|
||||
payload["_data_warnings"] = warnings
|
||||
|
||||
return _truncate_payload(payload)
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""组装 App3 客户线索最新 + App8 历史最近 2 条。"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
ref: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
app3_latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP3_CLUE.value, site_id, target_id,
|
||||
)
|
||||
if app3_latest:
|
||||
ref["app3_clues"] = {
|
||||
"result_json": app3_latest.get("result_json"),
|
||||
"generated_at": app3_latest.get("created_at"),
|
||||
}
|
||||
|
||||
app8_history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if app8_history:
|
||||
ref["app8_history"] = [
|
||||
{"result_json": h.get("result_json"), "generated_at": h.get("created_at")}
|
||||
for h in app8_history
|
||||
]
|
||||
|
||||
return ref
|
||||
|
||||
|
||||
def _truncate_payload(payload: dict) -> str:
|
||||
"""按优先级截断 consumption_records → all_notes。"""
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) <= _MAX_PROMPT_LEN:
|
||||
return text
|
||||
|
||||
cd = payload["reference"].get("consumption_data", {})
|
||||
records = cd.get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
cd["consumption_records"] = records[:5]
|
||||
cd["_truncated"] = f"消费记录已截断，原始 {len(records)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) > _MAX_PROMPT_LEN:
|
||||
notes = payload["reference"].get("all_notes")
|
||||
if isinstance(notes, list) and len(notes) > 10:
|
||||
payload["reference"]["all_notes"] = notes[:10]
|
||||
payload["reference"]["_truncated_notes"] = f"备注已截断，原始 {len(notes)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
return text
|
||||
165
apps/backend/app/ai/prompts/app7_customer_prompt.py
Normal file
165
apps/backend/app/ai/prompts/app7_customer_prompt.py
Normal file
@@ -0,0 +1,165 @@
|
||||
"""应用 7 客户分析 Prompt 拼装。
|
||||
|
||||
消费链 App8 完成后串行触发，生成客户全量分析与运营策略。
|
||||
- 数据源：fetch_member_consumption_data + fetch_member_notes
|
||||
- 备注内容标注【来源：XXX，请甄别信息真实性】
|
||||
- 输出字段：strategies 数组 + summary
|
||||
- system prompt 在百炼控制台配置
|
||||
|
||||
返回：单个 prompt 字符串。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
from app.ai.cache_service import AICacheService
|
||||
from app.ai.data_fetchers import fetch_member_consumption_data, fetch_member_notes
|
||||
from app.ai.schemas import CacheTypeEnum
|
||||
from app.services.runtime_context import as_runtime_business_now_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_MAX_PROMPT_LEN = 5000
|
||||
|
||||
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: AICacheService | None = None,
|
||||
) -> str:
|
||||
"""构建 App7 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: site_id, member_id
|
||||
|
||||
Returns:
|
||||
JSON 序列化后的 prompt 字符串
|
||||
"""
|
||||
site_id = context["site_id"]
|
||||
member_id = context["member_id"]
|
||||
|
||||
results = await asyncio.gather(
|
||||
fetch_member_consumption_data(site_id, member_id),
|
||||
fetch_member_notes(site_id, member_id),
|
||||
return_exceptions=True,
|
||||
)
|
||||
|
||||
warnings: list[str] = []
|
||||
|
||||
if isinstance(results[0], Exception):
|
||||
member_data = _default_member_data()
|
||||
warnings.append("消费数据获取失败")
|
||||
logger.warning("App7 消费数据获取失败: %s", results[0])
|
||||
else:
|
||||
member_data = results[0]
|
||||
|
||||
notes_raw = results[1] if not isinstance(results[1], Exception) else []
|
||||
if isinstance(results[1], Exception):
|
||||
warnings.append("备注获取失败")
|
||||
logger.warning("App7 备注获取失败: %s", results[1])
|
||||
|
||||
# 主观信息标注来源
|
||||
if notes_raw:
|
||||
annotated = []
|
||||
for note in notes_raw:
|
||||
recorded_by = note.get("recorded_by", "未知")
|
||||
n = dict(note)
|
||||
n["content"] = (
|
||||
f"{note.get('content', '')}"
|
||||
f"【来源：{recorded_by}，请甄别信息真实性】"
|
||||
)
|
||||
annotated.append(n)
|
||||
subjective_notes: Any = annotated
|
||||
else:
|
||||
subjective_notes = "该客户暂无主观备注信息"
|
||||
|
||||
payload: dict[str, Any] = {
|
||||
"current_time": as_runtime_business_now_str(site_id, fmt="%Y-%m-%d %H:%M"),
|
||||
"member_id": member_id,
|
||||
"member_nickname": member_data.get("member_nickname", ""),
|
||||
"objective_data": {
|
||||
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
|
||||
"member_cards": member_data.get("member_cards", []),
|
||||
"card_balance_total": member_data.get("card_balance_total", 0),
|
||||
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
|
||||
"expected_visit_date": member_data.get("expected_visit_date"),
|
||||
"days_since_last_visit": member_data.get("days_since_last_visit"),
|
||||
},
|
||||
"subjective_data": {
|
||||
"notes": subjective_notes,
|
||||
},
|
||||
"reference": _build_reference(site_id, member_id, cache_svc),
|
||||
}
|
||||
|
||||
if warnings:
|
||||
payload["_data_warnings"] = warnings
|
||||
|
||||
return _truncate_payload(payload)
|
||||
|
||||
|
||||
def _default_member_data() -> dict:
|
||||
return {
|
||||
"member_nickname": "",
|
||||
"consumption_records": [],
|
||||
"member_cards": [],
|
||||
"card_balance_total": 0,
|
||||
"stored_value_balance_total": 0,
|
||||
"expected_visit_date": None,
|
||||
"days_since_last_visit": None,
|
||||
}
|
||||
|
||||
|
||||
def _build_reference(
|
||||
site_id: int,
|
||||
member_id: int,
|
||||
cache_svc: AICacheService | None,
|
||||
) -> dict:
|
||||
"""组装 App8 最新 + 最近 2 条历史。"""
|
||||
if cache_svc is None:
|
||||
return {}
|
||||
|
||||
ref: dict = {}
|
||||
target_id = str(member_id)
|
||||
|
||||
latest = cache_svc.get_latest(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,
|
||||
)
|
||||
if latest:
|
||||
ref["app8_latest"] = {
|
||||
"result_json": latest.get("result_json"),
|
||||
"generated_at": latest.get("created_at"),
|
||||
}
|
||||
|
||||
history = cache_svc.get_history(
|
||||
CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
|
||||
)
|
||||
if history:
|
||||
ref["app8_history"] = [
|
||||
{"result_json": h.get("result_json"), "generated_at": h.get("created_at")}
|
||||
for h in history
|
||||
]
|
||||
|
||||
return ref
|
||||
|
||||
|
||||
def _truncate_payload(payload: dict) -> str:
|
||||
"""按优先级截断 consumption_records → notes。"""
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) <= _MAX_PROMPT_LEN:
|
||||
return text
|
||||
|
||||
records = payload["objective_data"].get("consumption_records")
|
||||
if isinstance(records, list) and len(records) > 5:
|
||||
payload["objective_data"]["consumption_records"] = records[:5]
|
||||
payload["objective_data"]["_truncated"] = f"消费记录已截断，原始 {len(records)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
if len(text) > _MAX_PROMPT_LEN:
|
||||
n = payload["subjective_data"].get("notes")
|
||||
if isinstance(n, list) and len(n) > 10:
|
||||
payload["subjective_data"]["notes"] = n[:10]
|
||||
payload["subjective_data"]["_truncated_notes"] = f"备注已截断，原始 {len(n)} 条"
|
||||
text = json.dumps(payload, ensure_ascii=False, default=str)
|
||||
return text
|
||||
@@ -1,93 +1,52 @@
|
||||
"""应用 8：维客线索整理 Prompt 模板。
|
||||
"""应用 8 维客线索整理 Prompt 拼装。
|
||||
|
||||
接收 App3（消费分析）和 App6（备注分析）的全部线索，
|
||||
整合去重后输出统一维客线索。
|
||||
- 数据源：context.app3_clues + context.app6_clues（dispatcher 已查好传入）
|
||||
- 分类标签 6 选 1（与 member_retention_clue CHECK 约束一致）
|
||||
- 合并规则：相似线索合并，providers 逗号分隔
|
||||
- system prompt 在百炼控制台配置
|
||||
|
||||
分类标签限定 6 个枚举值（与 member_retention_clue CHECK 约束一致）：
|
||||
客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈。
|
||||
|
||||
合并规则：
|
||||
- 相似线索合并，providers 以逗号分隔
|
||||
- 其余线索原文返回
|
||||
- 最小改动原则
|
||||
返回：单个 prompt 字符串。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
|
||||
def build_prompt(context: dict) -> list[dict]:
|
||||
"""构建 App8 维客线索整理 Prompt。
|
||||
async def build_prompt(
|
||||
context: dict,
|
||||
cache_svc: Any | None = None, # 兼容统一签名，App8 不用
|
||||
) -> str:
|
||||
"""构建 App8 prompt 字符串。
|
||||
|
||||
Args:
|
||||
context: 包含以下字段：
|
||||
- site_id: int
|
||||
- member_id: int
|
||||
- app3_clues: list[dict] — App3 产出的线索列表
|
||||
- app6_clues: list[dict] — App6 产出的线索列表
|
||||
- app3_generated_at: str | None — App3 线索生成时间
|
||||
- app6_generated_at: str | None — App6 线索生成时间
|
||||
context: site_id, member_id, app3_clues(list), app6_clues(list),
|
||||
app3_generated_at(str|None), app6_generated_at(str|None)
|
||||
|
||||
Returns:
|
||||
消息列表 [{"role": "system", ...}, {"role": "user", ...}]
|
||||
JSON 序列化后的 prompt 字符串
|
||||
"""
|
||||
member_id = context["member_id"]
|
||||
app3_clues = context.get("app3_clues", [])
|
||||
app6_clues = context.get("app6_clues", [])
|
||||
app3_generated_at = context.get("app3_generated_at")
|
||||
app6_generated_at = context.get("app6_generated_at")
|
||||
app3_clues = context.get("app3_clues") or []
|
||||
app6_clues = context.get("app6_clues") or []
|
||||
|
||||
system_content = {
|
||||
"task": "整合去重来自消费分析和备注分析的维客线索，输出统一线索列表。",
|
||||
"app_id": "app8_consolidation",
|
||||
"rules": {
|
||||
"category_enum": [
|
||||
"客户基础", "消费习惯", "玩法偏好",
|
||||
"促销偏好", "社交关系", "重要反馈",
|
||||
],
|
||||
"merge_strategy": (
|
||||
"相似线索合并为一条，providers 以逗号分隔（如 '系统,张三'）；"
|
||||
"不相似的线索原文保留，不做修改。最小改动原则。"
|
||||
),
|
||||
"output_format": {
|
||||
"clues": [
|
||||
{
|
||||
"category": "枚举值（6 选 1）",
|
||||
"summary": "一句话摘要",
|
||||
"detail": "详细说明",
|
||||
"emoji": "表情符号",
|
||||
"providers": "提供者（逗号分隔）",
|
||||
}
|
||||
]
|
||||
},
|
||||
},
|
||||
payload: dict[str, Any] = {
|
||||
"member_id": member_id,
|
||||
"input": {
|
||||
"app3_clues": {
|
||||
"source": "消费数据分析（App3）",
|
||||
"generated_at": app3_generated_at,
|
||||
"generated_at": context.get("app3_generated_at"),
|
||||
"clues": app3_clues,
|
||||
},
|
||||
"app6_clues": {
|
||||
"source": "备注分析（App6）",
|
||||
"generated_at": app6_generated_at,
|
||||
"generated_at": context.get("app6_generated_at"),
|
||||
"clues": app6_clues,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
user_content = (
|
||||
f"请整合会员 {member_id} 的维客线索。\n"
|
||||
"输入包含两个来源的线索：App3（消费数据分析）和 App6（备注分析）。\n"
|
||||
"规则：\n"
|
||||
"1. 相似线索合并为一条，providers 字段以逗号分隔多个提供者\n"
|
||||
"2. 不相似的线索原文保留\n"
|
||||
"3. category 必须是：客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈 之一\n"
|
||||
"4. 每条线索包含 category、summary、detail、emoji、providers 五个字段\n"
|
||||
"5. 最小改动原则，尽量保留原始表述"
|
||||
)
|
||||
|
||||
return [
|
||||
{"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
|
||||
{"role": "user", "content": user_content},
|
||||
]
|
||||
return json.dumps(payload, ensure_ascii=False, default=str)
|
||||
|
||||
137
apps/backend/app/ai/references.py
Normal file
137
apps/backend/app/ai/references.py
Normal file
@@ -0,0 +1,137 @@
|
||||
"""AI references 工具模块。
|
||||
|
||||
为 AI 输出（ai_cache.result_json / ai_messages.reference_card）
|
||||
注入数据来源引用元数据，便于前端渲染可点击引用卡片。
|
||||
|
||||
- App2~8：通过 dispatcher._write_cache 统一注入到 result['_references']
|
||||
- App1：通过 xcx_chat 在 assistant 消息写入时调用 build_app1_reference 生成单卡片
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
def build_app_references(app_type: str, context: dict) -> list[dict]:
|
||||
"""为 App2~8 构建 references 列表，供前端消息卡片渲染。
|
||||
|
||||
引用结构：
|
||||
{
|
||||
"type": "member" | "task" | "assistant" | "finance",
|
||||
"id": int | str,
|
||||
"label": "卡片上的文字",
|
||||
"link": "/pages/xxx/xxx?param=val"（小程序页面路径）,
|
||||
"source_page": 小程序页面 contextType
|
||||
}
|
||||
|
||||
Args:
|
||||
app_type: 应用名称
|
||||
context: 传给 build_prompt 的上下文（含 site_id / member_id 等）
|
||||
|
||||
Returns:
|
||||
refs 数组。无有效上下文时返回空数组。
|
||||
"""
|
||||
refs: list[dict] = []
|
||||
site_id = context.get("site_id")
|
||||
member_id = context.get("member_id")
|
||||
assistant_id = context.get("assistant_id")
|
||||
time_dimension = context.get("time_dimension")
|
||||
|
||||
if member_id is not None:
|
||||
refs.append({
|
||||
"type": "member",
|
||||
"id": member_id,
|
||||
"label": f"客户 #{member_id}",
|
||||
"link": f"/pages/customer-detail/customer-detail?customerId={member_id}",
|
||||
"source_page": "customer-detail",
|
||||
})
|
||||
|
||||
if assistant_id is not None:
|
||||
refs.append({
|
||||
"type": "assistant",
|
||||
"id": assistant_id,
|
||||
"label": f"助教 #{assistant_id}",
|
||||
"link": f"/pages/coach-detail/coach-detail?coachId={assistant_id}",
|
||||
"source_page": "coach-detail",
|
||||
})
|
||||
|
||||
if app_type == "app2_finance" and time_dimension:
|
||||
refs.append({
|
||||
"type": "finance",
|
||||
"id": time_dimension,
|
||||
"label": f"财务看板：{_label_for_dimension(time_dimension)}",
|
||||
"link": f"/pages/board-finance/board-finance?timeDimension={time_dimension}",
|
||||
"source_page": "board-finance",
|
||||
})
|
||||
|
||||
# 保留 site_id 作为兜底上下文（不单独成卡，但用于前端场景判断）
|
||||
if site_id is not None and refs:
|
||||
for r in refs:
|
||||
r.setdefault("site_id", site_id)
|
||||
|
||||
return refs
|
||||
|
||||
|
||||
def attach_references(app_type: str, result: dict | None, context: dict) -> dict | None:
|
||||
"""向 AI 输出 result 追加 _references 字段（非破坏性）。
|
||||
|
||||
- result 为 None 时原样返回（调用失败不注入）
|
||||
- result 为 dict 时追加 _references 字段；如果 result 已含 _references，保留原值
|
||||
"""
|
||||
if result is None or not isinstance(result, dict):
|
||||
return result
|
||||
if "_references" in result:
|
||||
return result
|
||||
refs = build_app_references(app_type, context)
|
||||
if refs:
|
||||
result["_references"] = refs
|
||||
return result
|
||||
|
||||
|
||||
def build_app1_reference_card(source_page: str | None, context_id: int | str | None) -> dict | None:
|
||||
"""为 App1（chat）assistant 消息构建单个 reference_card。
|
||||
|
||||
兼容前端 chat.wxml 已有的 {type, title, summary, data, dataList} 渲染结构，
|
||||
额外携带 link 字段供前端点击跳转详情页。
|
||||
|
||||
当用户在特定页面（customer-detail / coach-detail / task-detail）发起对话时，
|
||||
自动附加对应跳转卡片。普通浮窗对话（source_page='general'）返回 None。
|
||||
|
||||
与 chat_service.build_reference_card 不同：本函数不查 DB，仅按 source_page 构造链接。
|
||||
"""
|
||||
if not source_page or not context_id:
|
||||
return None
|
||||
|
||||
mapping: dict[str, tuple[str, str, str]] = {
|
||||
"customer-detail": ("customer", "客户", "customerId"),
|
||||
"coach-detail": ("assistant", "助教", "coachId"),
|
||||
"task-detail": ("task", "任务", "taskId"),
|
||||
}
|
||||
entry = mapping.get(source_page)
|
||||
if entry is None:
|
||||
return None
|
||||
ref_type, label_prefix, param = entry
|
||||
|
||||
return {
|
||||
"type": ref_type,
|
||||
"title": f"{label_prefix} #{context_id}",
|
||||
"summary": f"点击查看{label_prefix}详情",
|
||||
"data": {},
|
||||
"link": f"/pages/{source_page}/{source_page}?{param}={context_id}",
|
||||
"source_page": source_page,
|
||||
}
|
||||
|
||||
|
||||
def _label_for_dimension(dimension: str) -> str:
|
||||
"""8 个财务维度 → 中文标签。"""
|
||||
mapping = {
|
||||
"this_month": "本月",
|
||||
"last_month": "上月",
|
||||
"this_week": "本周",
|
||||
"last_week": "上周",
|
||||
"this_quarter": "本季度",
|
||||
"last_quarter": "上季度",
|
||||
"last_3_months": "近三个月",
|
||||
"last_6_months": "近六个月",
|
||||
}
|
||||
return mapping.get(dimension, dimension)
|
||||
@@ -14,12 +14,17 @@ from typing import Callable
|
||||
|
||||
import psycopg2.extensions
|
||||
|
||||
from app.services.runtime_context import LIVE_INSTANCE_ID, MODE_LIVE, MODE_SANDBOX, get_runtime_context
|
||||
|
||||
# prompt 最大存储长度
|
||||
_MAX_PROMPT_LENGTH = 2000
|
||||
# 2026-04-22：2000→8000。app2_finance 真实 prompt 约 4-8KB（72 组合财务看板 + 中文 key 膨胀），
|
||||
# 2000 字符截断会丢掉 optimization-critical 字段（如 discount_items 含团购折扣明细），
|
||||
# admin-web 调用详情页无法完整审阅 → 提高到 8000 覆盖绝大部分场景
|
||||
_MAX_PROMPT_LENGTH = 8000
|
||||
|
||||
|
||||
def _truncate_prompt(prompt: str | None) -> str | None:
|
||||
"""截断 prompt 为前 2000 字符。None 原样返回。"""
|
||||
"""截断 prompt 为 _MAX_PROMPT_LENGTH 字符上限。None 原样返回。"""
|
||||
if prompt is None:
|
||||
return None
|
||||
return prompt[:_MAX_PROMPT_LENGTH]
|
||||
@@ -54,17 +59,21 @@ class AIRunLogService:
|
||||
truncated = _truncate_prompt(request_prompt)
|
||||
conn = self._get_conn()
|
||||
try:
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
runtime_mode = MODE_SANDBOX if ctx.is_sandbox else MODE_LIVE
|
||||
sandbox_instance_id = ctx.sandbox_instance_id if ctx.is_sandbox else LIVE_INSTANCE_ID
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO biz.ai_run_logs
|
||||
(site_id, app_type, trigger_type, member_id,
|
||||
request_prompt, session_id, status)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, 'pending')
|
||||
request_prompt, session_id, status,
|
||||
runtime_mode, sandbox_instance_id)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, 'pending', %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(site_id, app_type, trigger_type, member_id,
|
||||
truncated, session_id),
|
||||
truncated, session_id, runtime_mode, sandbox_instance_id),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
assert row is not None, "INSERT RETURNING 应返回 id"
|
||||
|
||||
@@ -36,11 +36,12 @@ from app import config
|
||||
# CHANGE 2026-03-23 | 新增 trigger_jobs 路由（定时任务管理页面 API）
|
||||
# CHANGE 2026-03-24 | P18 任务引擎运营看板：新增 admin_task_engine 路由
|
||||
# CHANGE 2026-03-29 | DWS_TASK_ENGINE：新增 internal_events 路由（按 job_name 执行任务）
|
||||
from app.routers import auth, execution, schedules, tasks, env_config, db_viewer, etl_status, xcx_test, wx_callback, member_retention_clue, ops_panel, xcx_auth, xcx_avatar, admin_applications, business_day, xcx_tasks, xcx_notes, xcx_chat, xcx_ai_cache, xcx_performance, xcx_customers, xcx_coaches, xcx_board, xcx_config, tenant_auth, tenant_users, tenant_excel, tenant_clues, tenant_site_admins, admin_tenant_admins, admin_registry, internal_ai, admin_ai, admin_dev_trace, trigger_jobs, admin_task_engine, admin_db_health, admin_triggers, internal_events
|
||||
from app.routers import auth, execution, schedules, tasks, env_config, db_viewer, etl_status, xcx_test, wx_callback, member_retention_clue, ops_panel, xcx_auth, xcx_avatar, admin_applications, business_day, xcx_tasks, xcx_notes, xcx_chat, xcx_ai_cache, xcx_performance, xcx_customers, xcx_coaches, xcx_board, xcx_config, xcx_runtime_clock, tenant_auth, tenant_users, tenant_excel, tenant_clues, tenant_site_admins, admin_tenant_admins, admin_registry, internal_ai, admin_ai, admin_dev_trace, trigger_jobs, admin_task_engine, admin_db_health, admin_triggers, internal_events, admin_runtime_context
|
||||
from app.services.scheduler import scheduler
|
||||
from app.services.task_queue import task_queue
|
||||
from app.services.task_executor import task_executor
|
||||
from app.ws.logs import ws_router
|
||||
from app.ws.ai_events import ws_router as ai_ws_router
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
@@ -99,6 +100,33 @@ async def lifespan(app: FastAPI):
|
||||
import logging as _log
|
||||
_log.getLogger(__name__).warning("启动检查定时任务失败", exc_info=True)
|
||||
|
||||
# CHANGE 2026-04-22 | 启动时清理上次进程遗留的孤儿 run_logs（worker 被 kill/reload 导致 status 卡在 running）
|
||||
try:
|
||||
from app.database import get_connection as _get_conn_cleanup
|
||||
_c = _get_conn_cleanup()
|
||||
try:
|
||||
with _c.cursor() as _cur:
|
||||
_cur.execute(
|
||||
"""
|
||||
UPDATE biz.ai_run_logs
|
||||
SET status = 'failed',
|
||||
error_message = COALESCE(error_message, '') || ' [orphaned_by_restart]',
|
||||
finished_at = COALESCE(finished_at, NOW())
|
||||
WHERE status = 'running'
|
||||
AND created_at < NOW() - INTERVAL '5 minutes'
|
||||
"""
|
||||
)
|
||||
_cleaned = _cur.rowcount
|
||||
_c.commit()
|
||||
if _cleaned:
|
||||
import logging as _log
|
||||
_log.getLogger(__name__).info("启动清理 %d 条孤儿 run_logs（status=running > 5min）", _cleaned)
|
||||
finally:
|
||||
_c.close()
|
||||
except Exception:
|
||||
import logging as _log
|
||||
_log.getLogger(__name__).warning("孤儿 run_logs 清理失败（忽略）", exc_info=True)
|
||||
|
||||
# CHANGE 2026-03-10 | 注册 AI 事件处理器（消费/备注/任务分配 → AI 调用链）
|
||||
# CHANGE 2026-03-22 | P14 迁移：BailianClient → DashScopeClient + AIConfig + 防护层
|
||||
try:
|
||||
@@ -127,6 +155,8 @@ async def lifespan(app: FastAPI):
|
||||
config=_ai_config,
|
||||
)
|
||||
register_ai_handlers(_dispatcher)
|
||||
from app.routers import internal_ai as _internal_ai_router
|
||||
_internal_ai_router.set_dispatcher(_dispatcher)
|
||||
except Exception:
|
||||
import logging as _log
|
||||
_log.getLogger(__name__).warning("AI 事件处理器注册失败，AI 功能不可用", exc_info=True)
|
||||
@@ -178,6 +208,7 @@ app.include_router(env_config.router)
|
||||
app.include_router(db_viewer.router)
|
||||
app.include_router(etl_status.router)
|
||||
app.include_router(ws_router)
|
||||
app.include_router(ai_ws_router)
|
||||
app.include_router(xcx_test.router)
|
||||
app.include_router(wx_callback.router)
|
||||
app.include_router(member_retention_clue.router)
|
||||
@@ -195,6 +226,7 @@ app.include_router(xcx_customers.router)
|
||||
app.include_router(xcx_coaches.router)
|
||||
app.include_router(xcx_board.router)
|
||||
app.include_router(xcx_config.router)
|
||||
app.include_router(xcx_runtime_clock.router)
|
||||
app.include_router(tenant_auth.router)
|
||||
app.include_router(tenant_users.router)
|
||||
app.include_router(tenant_excel.router)
|
||||
@@ -210,6 +242,8 @@ app.include_router(trigger_jobs.router)
|
||||
app.include_router(admin_task_engine.router)
|
||||
app.include_router(admin_db_health.router)
|
||||
app.include_router(admin_triggers.router)
|
||||
app.include_router(admin_runtime_context.router)
|
||||
app.include_router(admin_runtime_context.config_router)
|
||||
|
||||
|
||||
@app.get("/health", tags=["系统"])
|
||||
|
||||
309
apps/backend/app/routers/admin_runtime_context.py
Normal file
309
apps/backend/app/routers/admin_runtime_context.py
Normal file
@@ -0,0 +1,309 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""业务运行上下文管理 API。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from datetime import date
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query, status
|
||||
from psycopg2.extras import RealDictCursor
|
||||
|
||||
from app.auth.dependencies import CurrentUser, get_current_user
|
||||
from app.database import get_connection
|
||||
from app.schemas.runtime_context import (
|
||||
RuntimeContextResponse,
|
||||
RuntimeSwitchRequest,
|
||||
RuntimeSwitchResponse,
|
||||
RuntimeTransitionStep,
|
||||
)
|
||||
from app.services.runtime_context import (
|
||||
MODE_LIVE,
|
||||
MODE_SANDBOX,
|
||||
RuntimeContext,
|
||||
get_runtime_context,
|
||||
new_sandbox_instance_id,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
router = APIRouter(prefix="/api/admin/runtime-context", tags=["业务运行上下文"])
|
||||
config_router = APIRouter(prefix="/api/config", tags=["业务配置"])
|
||||
|
||||
|
||||
def _require_super_admin(user: CurrentUser) -> None:
|
||||
if "super_admin" not in user.roles:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_403_FORBIDDEN,
|
||||
detail="仅超级管理员可切换业务运行上下文",
|
||||
)
|
||||
|
||||
|
||||
def _context_response(ctx: RuntimeContext) -> RuntimeContextResponse:
|
||||
return RuntimeContextResponse(**ctx.to_dict())
|
||||
|
||||
|
||||
@config_router.get("/runtime-context", response_model=RuntimeContextResponse)
|
||||
async def get_current_runtime_context(
|
||||
user: CurrentUser = Depends(get_current_user),
|
||||
) -> RuntimeContextResponse:
|
||||
"""返回当前登录用户门店的业务运行上下文。"""
|
||||
return _context_response(get_runtime_context(user.site_id))
|
||||
|
||||
|
||||
@router.get("", response_model=RuntimeContextResponse)
|
||||
async def get_admin_runtime_context(
|
||||
site_id: int = Query(..., ge=1),
|
||||
user: CurrentUser = Depends(get_current_user),
|
||||
) -> RuntimeContextResponse:
|
||||
"""系统管理端按门店查看业务运行上下文。"""
|
||||
_require_super_admin(user)
|
||||
return _context_response(get_runtime_context(site_id))
|
||||
|
||||
|
||||
@router.get("/sites")
|
||||
async def list_runtime_sites(
|
||||
user: CurrentUser = Depends(get_current_user),
|
||||
) -> list[dict]:
|
||||
"""列出可配置门店及其当前运行上下文。"""
|
||||
_require_super_admin(user)
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor(cursor_factory=RealDictCursor) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT s.site_id, s.site_name, s.site_code, s.is_active,
|
||||
c.mode, c.sandbox_date, c.sandbox_instance_id,
|
||||
c.ai_mode, c.status, c.updated_at
|
||||
FROM biz.sites s
|
||||
LEFT JOIN biz.site_runtime_context c ON c.site_id = s.site_id
|
||||
ORDER BY s.is_active DESC, s.site_id
|
||||
"""
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
|
||||
@router.patch("", response_model=RuntimeSwitchResponse)
|
||||
async def switch_runtime_context(
|
||||
body: RuntimeSwitchRequest,
|
||||
user: CurrentUser = Depends(get_current_user),
|
||||
) -> RuntimeSwitchResponse:
|
||||
"""切换门店业务运行上下文。
|
||||
|
||||
切换前会终止当前运行中的 ETL、取消未完成 AI 触发记录。
|
||||
`biz.trigger_jobs` 是全局调度表（无 site_id 列），不随单门店沙箱切换暂停；
|
||||
多门店隔离完全通过 runtime_mode + sandbox_instance_id 实现。
|
||||
"""
|
||||
_require_super_admin(user)
|
||||
|
||||
if body.mode == MODE_SANDBOX and body.sandbox_date is None:
|
||||
raise HTTPException(status_code=422, detail="沙箱模式必须设置 sandbox_date")
|
||||
if body.mode == MODE_LIVE and body.sandbox_date is not None:
|
||||
raise HTTPException(status_code=422, detail="live 模式不能设置 sandbox_date")
|
||||
if body.mode == MODE_SANDBOX and body.sandbox_date and body.sandbox_date > date.today():
|
||||
raise HTTPException(status_code=422, detail="sandbox_date 不能晚于真实今天")
|
||||
|
||||
steps: list[RuntimeTransitionStep] = []
|
||||
steps.extend(await _stop_runtime_activity(body.site_id))
|
||||
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
old_ctx = get_runtime_context(body.site_id, conn=conn)
|
||||
sandbox_instance_id = None
|
||||
if body.mode == MODE_SANDBOX:
|
||||
if body.reset_sandbox or not old_ctx.sandbox_instance_id:
|
||||
sandbox_instance_id = new_sandbox_instance_id()
|
||||
else:
|
||||
sandbox_instance_id = old_ctx.sandbox_instance_id
|
||||
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO biz.site_runtime_context
|
||||
(site_id, mode, sandbox_date, sandbox_instance_id, ai_mode,
|
||||
status, updated_by, updated_at, reason)
|
||||
VALUES (%s, %s, %s, %s, 'live', 'active', %s, NOW(), %s)
|
||||
ON CONFLICT (site_id)
|
||||
DO UPDATE SET
|
||||
mode = EXCLUDED.mode,
|
||||
sandbox_date = EXCLUDED.sandbox_date,
|
||||
sandbox_instance_id = EXCLUDED.sandbox_instance_id,
|
||||
ai_mode = EXCLUDED.ai_mode,
|
||||
status = EXCLUDED.status,
|
||||
updated_by = EXCLUDED.updated_by,
|
||||
updated_at = NOW(),
|
||||
reason = EXCLUDED.reason
|
||||
""",
|
||||
(
|
||||
body.site_id,
|
||||
body.mode,
|
||||
body.sandbox_date,
|
||||
sandbox_instance_id,
|
||||
user.user_id,
|
||||
body.reason,
|
||||
),
|
||||
)
|
||||
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="biz_triggers_unchanged",
|
||||
title="保持业务触发器",
|
||||
status="skipped",
|
||||
count=0,
|
||||
detail=(
|
||||
"biz.trigger_jobs 为全局调度表（无 site_id 列），单门店沙箱切换不影响其它门店；"
|
||||
"沙箱隔离由 runtime_mode + sandbox_instance_id 在数据写入层完成。"
|
||||
),
|
||||
))
|
||||
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
logger.exception("切换业务运行上下文失败: site_id=%s", body.site_id)
|
||||
raise
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
ctx = get_runtime_context(body.site_id)
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="apply_context",
|
||||
title="写入业务运行上下文",
|
||||
status="success",
|
||||
detail=(
|
||||
f"当前模式={ctx.mode}，业务日期={ctx.business_date}"
|
||||
+ (f"，沙箱实例={ctx.sandbox_instance_id}" if ctx.is_sandbox else "")
|
||||
),
|
||||
))
|
||||
return RuntimeSwitchResponse(context=_context_response(ctx), steps=steps)
|
||||
|
||||
|
||||
async def _stop_runtime_activity(site_id: int) -> list[RuntimeTransitionStep]:
|
||||
"""终止切换前仍在运行的 ETL/AI/队列活动。"""
|
||||
steps: list[RuntimeTransitionStep] = []
|
||||
|
||||
# 1. 终止当前进程内 ETL 执行。
|
||||
try:
|
||||
from app.services.task_executor import task_executor
|
||||
|
||||
running_ids = task_executor.get_running_ids()
|
||||
cancelled = 0
|
||||
for execution_id in running_ids:
|
||||
if await task_executor.cancel(execution_id):
|
||||
cancelled += 1
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_etl_processes",
|
||||
title="终止当前 ETL 执行",
|
||||
status="success",
|
||||
count=cancelled,
|
||||
detail=f"检测到 {len(running_ids)} 个当前进程内执行，已发送取消信号。",
|
||||
))
|
||||
except Exception as exc:
|
||||
logger.exception("终止 ETL 执行失败")
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_etl_processes",
|
||||
title="终止当前 ETL 执行",
|
||||
status="warning",
|
||||
detail=str(exc)[:300],
|
||||
))
|
||||
|
||||
# 2. 清理当前门店队列中未完成任务。
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE task_queue
|
||||
SET status = 'cancelled',
|
||||
finished_at = NOW(),
|
||||
error_message = COALESCE(error_message, '') || E'\n[runtime-context] 切换业务运行上下文时取消'
|
||||
WHERE site_id = %s
|
||||
AND status IN ('pending', 'running')
|
||||
""",
|
||||
(site_id,),
|
||||
)
|
||||
queue_cancelled = cur.rowcount
|
||||
conn.commit()
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_task_queue",
|
||||
title="取消 ETL 队列",
|
||||
status="success",
|
||||
count=queue_cancelled,
|
||||
detail="已取消当前门店 pending/running 的 task_queue 记录。",
|
||||
))
|
||||
except Exception as exc:
|
||||
conn.rollback()
|
||||
logger.exception("取消 ETL 队列失败")
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_task_queue",
|
||||
title="取消 ETL 队列",
|
||||
status="warning",
|
||||
detail=str(exc)[:300],
|
||||
))
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
# 3. 取消当前站点内存 AI 调用链，并标记未完成 ai_trigger_jobs。
|
||||
try:
|
||||
from app.ai.dispatcher import get_dispatcher
|
||||
|
||||
dispatcher = get_dispatcher()
|
||||
cancelled = dispatcher.cancel_running(site_id)
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_ai_runtime",
|
||||
title="取消当前 AI 调用链",
|
||||
status="success",
|
||||
count=cancelled,
|
||||
detail="已取消当前进程内属于该门店的 AI 异步调用链。",
|
||||
))
|
||||
except Exception as exc:
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_ai_runtime",
|
||||
title="取消当前 AI 调用链",
|
||||
status="warning",
|
||||
detail=f"AI Dispatcher 不可用或取消失败：{str(exc)[:240]}",
|
||||
))
|
||||
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE biz.ai_trigger_jobs
|
||||
SET status = 'cancelled',
|
||||
finished_at = NOW(),
|
||||
error_message = COALESCE(error_message, '') || E'\n[runtime-context] 切换业务运行上下文时取消'
|
||||
WHERE site_id = %s
|
||||
AND status IN ('pending', 'running')
|
||||
""",
|
||||
(site_id,),
|
||||
)
|
||||
ai_cancelled = cur.rowcount
|
||||
conn.commit()
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_ai_jobs",
|
||||
title="标记未完成 AI 触发",
|
||||
status="success",
|
||||
count=ai_cancelled,
|
||||
detail="已将当前门店 pending/running 的 ai_trigger_jobs 标记为 cancelled。",
|
||||
))
|
||||
except Exception as exc:
|
||||
conn.rollback()
|
||||
logger.exception("标记 AI 触发失败")
|
||||
steps.append(RuntimeTransitionStep(
|
||||
key="cancel_ai_jobs",
|
||||
title="标记未完成 AI 触发",
|
||||
status="warning",
|
||||
detail=str(exc)[:300],
|
||||
))
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
return steps
|
||||
|
||||
@@ -310,6 +310,24 @@ async def reassign_task(
|
||||
)
|
||||
|
||||
conn.commit()
|
||||
|
||||
# 触发 AI 任务分配链（App4 → App5）
|
||||
try:
|
||||
from app.services.trigger_scheduler import fire_event
|
||||
fire_event(
|
||||
"ai_task_assigned",
|
||||
{
|
||||
"site_id": task["site_id"],
|
||||
"member_id": task["member_id"],
|
||||
"assistant_id": body.to_assistant_id,
|
||||
},
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"触发 ai_task_assigned 事件失败: task_id=%s new_task_id=%s",
|
||||
task_id, new_task_id,
|
||||
)
|
||||
|
||||
return ReassignResponse(success=True, new_task_id=new_task_id)
|
||||
except HTTPException:
|
||||
conn.rollback()
|
||||
|
||||
@@ -85,6 +85,33 @@ async def etl_completed_endpoint(
|
||||
logger.exception("ETL 编排 Step2 task_generator 失败")
|
||||
errors.append("task_generator failed")
|
||||
|
||||
# Step 3: 触发 AI 财务洞察预生成（App2 × 8 时间维度）
|
||||
# 若请求未带 site_id，查询所有 active site 逐个触发
|
||||
try:
|
||||
from app.services.trigger_scheduler import fire_event
|
||||
|
||||
site_ids: list[int] = []
|
||||
if body.site_id is not None:
|
||||
site_ids = [body.site_id]
|
||||
else:
|
||||
from app.database import get_connection as _gc
|
||||
_c = _gc()
|
||||
try:
|
||||
with _c.cursor() as _cur:
|
||||
_cur.execute("SELECT DISTINCT site_id FROM biz.trigger_jobs WHERE site_id IS NOT NULL")
|
||||
site_ids = [r[0] for r in _cur.fetchall()]
|
||||
_c.commit()
|
||||
finally:
|
||||
_c.close()
|
||||
|
||||
for sid in site_ids:
|
||||
try:
|
||||
fire_event("ai_dws_completed", {"site_id": sid})
|
||||
except Exception:
|
||||
logger.exception("触发 ai_dws_completed 失败: site_id=%s", sid)
|
||||
except Exception:
|
||||
logger.exception("ai_dws_completed 事件批量触发失败")
|
||||
|
||||
success = len(errors) == 0
|
||||
return EtlCompletedResponse(
|
||||
success=success,
|
||||
|
||||
@@ -204,13 +204,17 @@ async def list_site_staff(
|
||||
# assumptions: cfg_assistant_level_price 有 level_code→level_name 映射
|
||||
# verify: 弹窗人员下拉显示如 "初级 - 张三 - 手机号 - 入职日期 YYYY-MM-DD"
|
||||
# 先查等级映射配置表（feiqiu-data-rules 规则 6: 禁止硬编码）
|
||||
# CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱按当时生效配置
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT DISTINCT level_code, level_name
|
||||
FROM dws.cfg_assistant_level_price
|
||||
WHERE effective_from <= CURRENT_DATE
|
||||
AND effective_to >= CURRENT_DATE
|
||||
"""
|
||||
WHERE effective_from <= %s::date
|
||||
AND effective_to >= %s::date
|
||||
""",
|
||||
(_ref_date, _ref_date),
|
||||
)
|
||||
level_map = {row[0]: row[1] for row in cur.fetchall()}
|
||||
|
||||
|
||||
@@ -244,27 +244,56 @@ async def chat_stream(
|
||||
)
|
||||
|
||||
# 流式调用 DashScope Application API
|
||||
async for chunk in client.call_app_stream(
|
||||
# 返回 (text_chunk, session_id_or_none) 元组：累积最后一次 session_id 用于回写
|
||||
latest_session_id: str | None = session_id
|
||||
async for chunk_text, chunk_session_id in client.call_app_stream(
|
||||
app_id=config.app_id_1_chat,
|
||||
prompt=prompt,
|
||||
session_id=session_id,
|
||||
biz_params=biz_params,
|
||||
):
|
||||
full_reply_parts.append(chunk)
|
||||
if chunk_session_id:
|
||||
latest_session_id = chunk_session_id
|
||||
if not chunk_text:
|
||||
continue
|
||||
full_reply_parts.append(chunk_text)
|
||||
tokens_total += 1
|
||||
# SSE trace: 每 10 个 token 记录一次
|
||||
record_sse_token(token_count=1, total_tokens=tokens_total)
|
||||
yield f"event: message\ndata: {json.dumps({'token': chunk}, ensure_ascii=False)}\n\n"
|
||||
yield f"event: message\ndata: {json.dumps({'token': chunk_text}, ensure_ascii=False)}\n\n"
|
||||
|
||||
# 流结束：拼接完整回复并持久化
|
||||
full_reply = "".join(full_reply_parts)
|
||||
estimated_tokens = len(full_reply)
|
||||
|
||||
# Phase 1.3：assistant 消息挂 reference_card（若用户从特定详情页入口发起对话）
|
||||
try:
|
||||
from app.ai.references import build_app1_reference_card
|
||||
_ref_card = None
|
||||
_pc = body.page_context or {}
|
||||
_ctx_id = _pc.get("contextId") or _pc.get("taskId") or _pc.get("customerId") or _pc.get("coachId")
|
||||
if body.source_page and _ctx_id:
|
||||
_ref_card = build_app1_reference_card(body.source_page, _ctx_id)
|
||||
except Exception:
|
||||
logger.warning("构建 reference_card 失败", exc_info=True)
|
||||
_ref_card = None
|
||||
|
||||
ai_msg_id, ai_created_at = svc._save_message(
|
||||
body.chat_id, "assistant", full_reply, tokens_used=estimated_tokens,
|
||||
body.chat_id, "assistant", full_reply,
|
||||
tokens_used=estimated_tokens,
|
||||
reference_card=_ref_card,
|
||||
)
|
||||
svc._update_session_metadata(body.chat_id, full_reply)
|
||||
|
||||
# multi-turn 启用：回写百炼返回的 session_id（若首次对话或服务端更新）
|
||||
if latest_session_id and latest_session_id != session_id:
|
||||
try:
|
||||
svc.save_session_id(body.chat_id, latest_session_id)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"save_session_id 失败 chat_id=%s", body.chat_id, exc_info=True,
|
||||
)
|
||||
|
||||
# 发送 done 事件
|
||||
done_data = json.dumps(
|
||||
{"messageId": ai_msg_id, "createdAt": ai_created_at},
|
||||
|
||||
61
apps/backend/app/routers/xcx_runtime_clock.py
Normal file
61
apps/backend/app/routers/xcx_runtime_clock.py
Normal file
@@ -0,0 +1,61 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""小程序业务时钟路由。
|
||||
|
||||
仅用于小程序读取当前门店的"业务日 / 业务年月 / 模式"——sandbox 模式下，
|
||||
小程序的 performance / task-list / customer-records 等页面应以 RuntimeContext
|
||||
返回的业务时钟为准，禁止再用 ``new Date()`` 构造请求参数。
|
||||
|
||||
端点：
|
||||
- GET /api/xcx/runtime/clock — 返回当前门店的业务时钟与运行模式（live / sandbox）。
|
||||
|
||||
所有端点均需 JWT（approved 状态），但不要求特定模块权限。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from fastapi import APIRouter, Depends
|
||||
|
||||
from app.auth.dependencies import CurrentUser
|
||||
from app.middleware.permission import require_approved
|
||||
from app.services.runtime_context import get_runtime_context
|
||||
from app.trace.decorators import trace_service
|
||||
|
||||
router = APIRouter(prefix="/api/xcx/runtime", tags=["小程序业务时钟"])
|
||||
|
||||
|
||||
@router.get("/clock")
|
||||
@trace_service("获取业务时钟", "Get business clock")
|
||||
async def get_business_clock(
|
||||
user: CurrentUser = Depends(require_approved),
|
||||
):
|
||||
"""返回当前门店的业务时钟。
|
||||
|
||||
返回示例（live）::
|
||||
|
||||
{
|
||||
"mode": "live",
|
||||
"business_date": "2026-05-02",
|
||||
"business_year": 2026,
|
||||
"business_month": 5,
|
||||
"business_year_month": "2026-05",
|
||||
"is_sandbox": false,
|
||||
"sandbox_date": null
|
||||
}
|
||||
|
||||
sandbox 模式下 ``business_date`` 等于配置的 ``sandbox_date``。
|
||||
小程序页面应使用本接口结果替代 ``new Date()``，以确保 sandbox 模式下
|
||||
展示和请求都对齐到 sandbox_date。
|
||||
"""
|
||||
ctx = get_runtime_context(user.site_id)
|
||||
bd = ctx.business_date
|
||||
return {
|
||||
"mode": ctx.mode,
|
||||
"business_date": bd.isoformat(),
|
||||
"business_year": bd.year,
|
||||
"business_month": bd.month,
|
||||
"business_year_month": f"{bd.year:04d}-{bd.month:02d}",
|
||||
"business_now": ctx.business_now.isoformat(),
|
||||
"is_sandbox": ctx.is_sandbox,
|
||||
"sandbox_date": ctx.sandbox_date.isoformat() if ctx.sandbox_date else None,
|
||||
"sandbox_instance_id": ctx.sandbox_instance_id,
|
||||
}
|
||||
@@ -196,6 +196,37 @@ class BatchRunConfirmResponse(BaseModel):
|
||||
status: str # "started"
|
||||
|
||||
|
||||
# ── 按需单 App 执行（/run/{app_type}）──────────────────────
|
||||
|
||||
|
||||
class RunAppRequest(BaseModel):
|
||||
"""按需执行单个 App 请求体。
|
||||
|
||||
context 字段根据 app_type 不同有不同约束：
|
||||
- app2_finance: site_id + time_dimension + area（area 默认 all）
|
||||
- app3_clue / app7_customer: site_id + member_id
|
||||
- app4_analysis / app5_tactics: site_id + member_id + assistant_id
|
||||
- app6_note: site_id + member_id + note_content + noted_by_name
|
||||
- app8_consolidation: site_id + member_id
|
||||
"""
|
||||
site_id: int
|
||||
member_id: int | None = None
|
||||
assistant_id: int | None = None
|
||||
time_dimension: str | None = None
|
||||
area: str | None = None # App2 专用，默认 all
|
||||
note_content: str | None = None
|
||||
noted_by_name: str | None = None
|
||||
noted_by_created_at: str | None = None
|
||||
|
||||
|
||||
class RunAppResponse(BaseModel):
|
||||
"""按需执行单个 App 响应。"""
|
||||
app_type: str
|
||||
success: bool
|
||||
result: dict | None = None # 百炼返回的 JSON（成功时）
|
||||
error: str | None = None # 错误描述（失败时）
|
||||
|
||||
|
||||
# ── 告警 ──────────────────────────────────────────────────
|
||||
|
||||
|
||||
@@ -211,3 +242,64 @@ class AlertActionResponse(BaseModel):
|
||||
"""告警操作（确认/忽略）响应。"""
|
||||
id: int
|
||||
alert_status: str
|
||||
|
||||
|
||||
# ── 触发器管理（biz.trigger_jobs）─────────────────────────
|
||||
|
||||
|
||||
class TriggerItem(BaseModel):
|
||||
"""触发器单条记录。"""
|
||||
id: int
|
||||
job_name: str
|
||||
job_type: str
|
||||
trigger_condition: str # event / cron / interval
|
||||
trigger_config: dict # {"event_name": ...} 或 {"cron_expression": ...}
|
||||
status: str # enabled / disabled
|
||||
description: str | None = None
|
||||
last_run_at: str | None = None
|
||||
next_run_at: str | None = None
|
||||
last_error: str | None = None
|
||||
|
||||
|
||||
class TriggerUpdateRequest(BaseModel):
|
||||
"""触发器更新请求（3 个字段至少填一个）。"""
|
||||
status: str | None = None # enabled / disabled
|
||||
cron_expression: str | None = None # 标准 5 段 cron
|
||||
description: str | None = None
|
||||
|
||||
|
||||
# ── 预热进度（app2_finance 72 组合）───────────────────────
|
||||
|
||||
|
||||
class PrewarmMissingItem(BaseModel):
|
||||
"""缺失的预热组合项。"""
|
||||
target_id: str # this_month__all
|
||||
time_dimension: str
|
||||
area: str
|
||||
|
||||
|
||||
class PrewarmProgressResponse(BaseModel):
|
||||
"""app2_finance 预热进度响应。"""
|
||||
total: int # 固定 72
|
||||
done: int
|
||||
missing: list[PrewarmMissingItem]
|
||||
last_updated: str | None = None
|
||||
|
||||
|
||||
# ── 手动事件触发（越过去重）───────────────────────────────
|
||||
|
||||
|
||||
class ManualTriggerRequest(BaseModel):
|
||||
"""手动触发 AI 事件请求。"""
|
||||
event_type: str # consumption / dws_completed / note_created / task_assigned
|
||||
site_id: int
|
||||
member_id: int | None = None
|
||||
assistant_id: int | None = None
|
||||
payload: dict | None = None
|
||||
is_forced: bool = True # 默认跳过去重
|
||||
|
||||
|
||||
class ManualTriggerResponse(BaseModel):
|
||||
"""手动事件触发响应。"""
|
||||
trigger_job_id: int
|
||||
status: str = "pending"
|
||||
|
||||
48
apps/backend/app/schemas/runtime_context.py
Normal file
48
apps/backend/app/schemas/runtime_context.py
Normal file
@@ -0,0 +1,48 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""业务运行上下文 API Schema。"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import date, datetime
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
RuntimeMode = Literal["live", "sandbox"]
|
||||
AIMode = Literal["live"]
|
||||
|
||||
|
||||
class RuntimeContextResponse(BaseModel):
|
||||
site_id: int
|
||||
mode: RuntimeMode
|
||||
business_day_start_hour: int
|
||||
business_date: date
|
||||
business_now: datetime
|
||||
sandbox_date: date | None = None
|
||||
sandbox_instance_id: str | None = None
|
||||
ai_mode: AIMode = "live"
|
||||
status: str = "active"
|
||||
is_sandbox: bool = False
|
||||
|
||||
|
||||
class RuntimeTransitionStep(BaseModel):
|
||||
key: str
|
||||
title: str
|
||||
status: Literal["success", "skipped", "warning", "failed"]
|
||||
detail: str = ""
|
||||
count: int = 0
|
||||
|
||||
|
||||
class RuntimeSwitchRequest(BaseModel):
|
||||
site_id: int = Field(..., ge=1)
|
||||
mode: RuntimeMode
|
||||
sandbox_date: date | None = None
|
||||
reset_sandbox: bool = True
|
||||
reason: str | None = Field(default=None, max_length=500)
|
||||
|
||||
|
||||
class RuntimeSwitchResponse(BaseModel):
|
||||
context: RuntimeContextResponse
|
||||
steps: list[RuntimeTransitionStep]
|
||||
|
||||
@@ -35,9 +35,21 @@ class AdminAIService:
|
||||
|
||||
# ── Dashboard ─────────────────────────────────────────
|
||||
|
||||
async def get_dashboard(self, site_id: int | None = None) -> dict:
|
||||
"""聚合所有 Dashboard 数据。"""
|
||||
today_stats = await self._get_today_stats(site_id)
|
||||
async def get_dashboard(
|
||||
self,
|
||||
site_id: int | None = None,
|
||||
range_days: int | None = None,
|
||||
date_from: str | None = None,
|
||||
date_to: str | None = None,
|
||||
) -> dict:
|
||||
"""聚合所有 Dashboard 数据。
|
||||
|
||||
时间范围优先级：
|
||||
1. 若 date_from / date_to 同时给出（指定日期）→ 闭区间 [from, to]
|
||||
2. 若 range_days=N → [CURRENT_DATE - (N-1) days, 现在]
|
||||
3. 默认 range_days=1（今日）
|
||||
"""
|
||||
today_stats = await self._get_range_stats(site_id, range_days, date_from, date_to)
|
||||
trend_7d = await self._get_7d_trend(site_id)
|
||||
app_dist = await self._get_app_distribution(site_id)
|
||||
app_health = await self._get_app_health(site_id)
|
||||
@@ -52,9 +64,32 @@ class AdminAIService:
|
||||
"app_health": app_health,
|
||||
}
|
||||
|
||||
async def _get_today_stats(self, site_id: int | None) -> dict:
|
||||
"""今日调用次数、成功率、Token 消耗、平均延迟。"""
|
||||
site_clause, params = _site_filter(site_id)
|
||||
async def _get_range_stats(
|
||||
self,
|
||||
site_id: int | None,
|
||||
range_days: int | None,
|
||||
date_from: str | None,
|
||||
date_to: str | None,
|
||||
) -> dict:
|
||||
"""指定时间段内的调用次数、成功率、Token 消耗、平均延迟。
|
||||
|
||||
字段名沿用 today_* 前缀以兼容前端 DashboardResponse schema。
|
||||
"""
|
||||
site_clause, site_params = _site_filter(site_id)
|
||||
|
||||
if date_from and date_to:
|
||||
time_clause = "created_at >= %s::date AND created_at < (%s::date + INTERVAL '1 day')"
|
||||
time_params: tuple = (date_from, date_to)
|
||||
else:
|
||||
days = range_days if range_days and range_days > 0 else 1
|
||||
time_clause = (
|
||||
"created_at >= CURRENT_DATE - (%s::int - 1) * INTERVAL '1 day' "
|
||||
"AND created_at < CURRENT_DATE + INTERVAL '1 day'"
|
||||
)
|
||||
time_params = (days,)
|
||||
|
||||
params = time_params + site_params
|
||||
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
@@ -67,8 +102,7 @@ class AdminAIService:
|
||||
COALESCE(AVG(latency_ms) FILTER (WHERE latency_ms IS NOT NULL), 0)
|
||||
AS avg_latency
|
||||
FROM biz.ai_run_logs
|
||||
WHERE created_at >= CURRENT_DATE
|
||||
AND created_at < CURRENT_DATE + INTERVAL '1 day'
|
||||
WHERE {time_clause}
|
||||
{site_clause}
|
||||
""",
|
||||
params,
|
||||
@@ -466,6 +500,22 @@ class AdminAIService:
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
# Phase 1.4：广播 cache_invalidated 事件，admin-web / 小程序可实时刷新
|
||||
if affected > 0:
|
||||
try:
|
||||
from app.ai.event_bus import AIEvent, get_event_bus
|
||||
get_event_bus().publish(AIEvent(
|
||||
type="cache_invalidated",
|
||||
site_id=site_id,
|
||||
payload={
|
||||
"cache_type": app_type,
|
||||
"member_id": member_id,
|
||||
"affected": affected,
|
||||
},
|
||||
))
|
||||
except Exception:
|
||||
logger.debug("cache_invalidated 事件广播失败", exc_info=True)
|
||||
|
||||
return affected
|
||||
|
||||
# ── Token 预算 ────────────────────────────────────────
|
||||
@@ -699,6 +749,140 @@ class AdminAIService:
|
||||
|
||||
return "ignored"
|
||||
|
||||
# ── 触发器管理（biz.trigger_jobs）───────────────────────
|
||||
|
||||
async def list_triggers(self) -> list[dict]:
|
||||
"""列出所有 AI 相关触发器（job_type 以 ai_ 开头 + task_generator）。
|
||||
|
||||
返回字段：id / job_name / job_type / trigger_condition / trigger_config /
|
||||
status / description / last_run_at / next_run_at / last_error
|
||||
"""
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT id, job_name, job_type, trigger_condition,
|
||||
trigger_config, status, description,
|
||||
last_run_at, next_run_at, last_error
|
||||
FROM biz.trigger_jobs
|
||||
WHERE job_type LIKE 'ai_%' OR job_name = 'task_generator'
|
||||
ORDER BY trigger_condition DESC, job_name
|
||||
"""
|
||||
)
|
||||
cols = [d[0] for d in cur.description]
|
||||
rows = cur.fetchall()
|
||||
conn.commit()
|
||||
finally:
|
||||
conn.close()
|
||||
return [_row_to_dict(cols, r) for r in rows]
|
||||
|
||||
async def update_trigger(
|
||||
self, trigger_id: int,
|
||||
status_new: str | None = None,
|
||||
cron_expression: str | None = None,
|
||||
description: str | None = None,
|
||||
) -> dict:
|
||||
"""更新触发器：启用/禁用、修改 cron、改描述。
|
||||
|
||||
仅允许修改 ai_ 前缀或 task_generator 的触发器。
|
||||
"""
|
||||
if status_new is not None and status_new not in ("enabled", "disabled"):
|
||||
raise ValueError(f"非法 status: {status_new}")
|
||||
|
||||
sets: list[str] = []
|
||||
params: list = []
|
||||
if status_new is not None:
|
||||
sets.append("status = %s")
|
||||
params.append(status_new)
|
||||
if cron_expression is not None:
|
||||
sets.append("trigger_config = jsonb_set(trigger_config, '{cron_expression}', to_jsonb(%s::text))")
|
||||
params.append(cron_expression)
|
||||
if description is not None:
|
||||
sets.append("description = %s")
|
||||
params.append(description)
|
||||
|
||||
if not sets:
|
||||
raise ValueError("至少修改一个字段")
|
||||
|
||||
params.append(trigger_id)
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
f"""
|
||||
UPDATE biz.trigger_jobs
|
||||
SET {", ".join(sets)}
|
||||
WHERE id = %s
|
||||
AND (job_type LIKE 'ai_%%' OR job_name = 'task_generator')
|
||||
RETURNING id, job_name, job_type, trigger_condition,
|
||||
trigger_config, status, description,
|
||||
last_run_at, next_run_at, last_error
|
||||
""",
|
||||
params,
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if row is None:
|
||||
conn.rollback()
|
||||
raise ValueError("触发器不存在或不可修改")
|
||||
cols = [d[0] for d in cur.description]
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
conn.close()
|
||||
return _row_to_dict(cols, row)
|
||||
|
||||
# ── 预热进度（app2_finance 72 组合）──────────────────────
|
||||
|
||||
async def get_prewarm_progress(self, site_id: int) -> dict:
|
||||
"""查询 app2_finance 72 组合缓存进度。
|
||||
|
||||
返回：total=72, done=N, missing=[{time_dimension, area}], last_updated
|
||||
"""
|
||||
time_dims = (
|
||||
"this_month", "last_month", "this_week", "last_week",
|
||||
"this_quarter", "last_quarter", "last_3_months", "last_6_months",
|
||||
)
|
||||
areas = (
|
||||
"all", "hall", "hallA", "hallB", "hallC",
|
||||
"vip", "snooker", "mahjong", "ktv",
|
||||
)
|
||||
expected = {f"{t}__{a}" for t in time_dims for a in areas}
|
||||
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT target_id, max(created_at) AS last_updated
|
||||
FROM biz.ai_cache
|
||||
WHERE cache_type = 'app2_finance'
|
||||
AND site_id = %s
|
||||
AND target_id LIKE %s ESCAPE '\\'
|
||||
GROUP BY target_id
|
||||
""",
|
||||
(site_id, r'%\_\_%'),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
conn.commit()
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
done_map = {r[0]: r[1] for r in rows}
|
||||
missing = sorted(expected - set(done_map.keys()))
|
||||
last = max(done_map.values()) if done_map else None
|
||||
return {
|
||||
"total": len(expected),
|
||||
"done": len(expected & set(done_map.keys())),
|
||||
"missing": [
|
||||
{"target_id": m, "time_dimension": m.split("__")[0], "area": m.split("__")[1]}
|
||||
for m in missing
|
||||
],
|
||||
"last_updated": last.isoformat() if last else None,
|
||||
}
|
||||
|
||||
|
||||
# ── 工具函数 ──────────────────────────────────────────────
|
||||
|
||||
|
||||
@@ -195,6 +195,7 @@ from typing import Any
|
||||
from fastapi import HTTPException
|
||||
|
||||
from app.services import fdw_queries
|
||||
from app.services.runtime_context import get_runtime_context, task_runtime_filter
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -260,7 +261,8 @@ async def get_coach_board(
|
||||
detail="最近6个月不支持客源储值排序",
|
||||
)
|
||||
|
||||
start_date, end_date = _calc_date_range(time)
|
||||
runtime_ctx = get_runtime_context(site_id)
|
||||
start_date, end_date = _calc_date_range(time, ref_date=runtime_ctx.business_date)
|
||||
start_str = str(start_date)
|
||||
end_str = str(end_date)
|
||||
|
||||
@@ -415,20 +417,22 @@ def _query_coach_tasks(
|
||||
|
||||
result: dict[int, dict] = {}
|
||||
try:
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
# 狭义召回+回访完成数：均从 coach_tasks 统计，status='completed' 表示助教亲自完成
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT assistant_id, task_type, COUNT(*) AS cnt
|
||||
FROM biz.coach_tasks
|
||||
WHERE assistant_id = ANY(%s)
|
||||
AND site_id = %s
|
||||
{runtime_clause}
|
||||
AND completed_at >= %s::date
|
||||
AND completed_at < (%s::date + INTERVAL '1 day')::timestamptz
|
||||
AND status = 'completed'
|
||||
GROUP BY assistant_id, task_type
|
||||
""",
|
||||
(assistant_ids, site_id, start_date, end_date),
|
||||
[assistant_ids, site_id, *runtime_params, start_date, end_date],
|
||||
)
|
||||
for row in cur.fetchall():
|
||||
aid, task_type, cnt = row[0], row[1], row[2] or 0
|
||||
@@ -470,13 +474,27 @@ def _batch_ideal_days(conn: Any, site_id: int, member_ids: list[int]) -> dict[in
|
||||
return result
|
||||
|
||||
|
||||
def _batch_coach_details(conn: Any, site_id: int, member_ids: list[int]) -> dict[int, list[dict]]:
|
||||
"""批量查询客户-助教服务明细（loyal 维度 coachDetails 用）。每个客户前 5 个。"""
|
||||
def _batch_coach_details(
|
||||
conn: Any,
|
||||
site_id: int,
|
||||
member_ids: list[int],
|
||||
*,
|
||||
ref_date: date | None = None,
|
||||
) -> dict[int, list[dict]]:
|
||||
"""批量查询客户-助教服务明细（loyal 维度 coachDetails 用）。每个客户前 5 个。
|
||||
|
||||
ref_date 默认从 RuntimeContext 取业务日，用于把 60 天消费窗口的上界落到 ``ref_date`` 上，
|
||||
避免 sandbox 模式下读到 sandbox_date 之后的真实消费。
|
||||
"""
|
||||
from app.services.fdw_queries import _fdw_context
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
|
||||
ref = ref_date or as_runtime_today_param(site_id, conn=conn)
|
||||
result: dict[int, list[dict]] = {mid: [] for mid in member_ids}
|
||||
try:
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
# CHANGE 2026-03-29 | coach_spend 改为从 dwd_assistant_service_log 聚合 60 天消费
|
||||
# CHANGE 2026-05-02 | 用 ref_date（业务日）替代 CURRENT_DATE，沙箱不读「未来」
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT ri.member_id,
|
||||
@@ -493,7 +511,8 @@ def _batch_coach_details(conn: Any, site_id: int, member_ids: list[int]) -> dict
|
||||
SUM(ledger_amount) AS spend_60d
|
||||
FROM app.v_dwd_assistant_service_log
|
||||
WHERE is_delete = 0
|
||||
AND create_time >= CURRENT_DATE - INTERVAL '60 days'
|
||||
AND create_time >= (%s::date - INTERVAL '60 days')
|
||||
AND create_time < (%s::date + INTERVAL '1 day')
|
||||
AND tenant_member_id = ANY(%s)
|
||||
GROUP BY tenant_member_id, site_assistant_id
|
||||
) s60 ON ri.member_id = s60.tenant_member_id
|
||||
@@ -502,7 +521,7 @@ def _batch_coach_details(conn: Any, site_id: int, member_ids: list[int]) -> dict
|
||||
AND (da.leave_status IS NULL OR da.leave_status = 0)
|
||||
ORDER BY ri.member_id, ri.rs_display DESC
|
||||
""",
|
||||
(member_ids, member_ids),
|
||||
(ref, ref, member_ids, member_ids),
|
||||
)
|
||||
for row in cur.fetchall():
|
||||
mid = row[0]
|
||||
@@ -690,7 +709,8 @@ async def get_finance_board(
|
||||
- area≠all 时 overview 覆盖逻辑保留
|
||||
- compare=1 时对上期执行同样缓存/日粒度逻辑
|
||||
"""
|
||||
start_date, end_date = _calc_date_range(time)
|
||||
runtime_ctx = get_runtime_context(site_id)
|
||||
start_date, end_date = _calc_date_range(time, ref_date=runtime_ctx.business_date)
|
||||
start_str = str(start_date)
|
||||
end_str = str(end_date)
|
||||
|
||||
|
||||
@@ -234,23 +234,14 @@ class ChatService:
|
||||
INSERT INTO biz.ai_conversations
|
||||
(user_id, nickname, app_id, site_id, context_type, context_id)
|
||||
VALUES (%s, %s, %s, %s, %s, %s)
|
||||
RETURNING id, EXTRACT(EPOCH FROM created_at)::bigint
|
||||
RETURNING id
|
||||
""",
|
||||
(str(user_id), nickname, APP_ID, site_id, context_type, context_id),
|
||||
)
|
||||
result = cur.fetchone()
|
||||
new_id = result[0]
|
||||
created_ts = result[1]
|
||||
|
||||
# 生成 session_id 并回写（格式：conv_{id}_{timestamp}）
|
||||
session_id = f"conv_{new_id}_{created_ts}"
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE biz.ai_conversations SET session_id = %s WHERE id = %s
|
||||
""",
|
||||
(session_id, new_id),
|
||||
)
|
||||
new_id = cur.fetchone()[0]
|
||||
|
||||
# session_id 初始保持 NULL，首次对话由百炼返回后再回写。
|
||||
# 参见 P14 spec §2.3：后端不再自生 session_id，交由百炼云端管理。
|
||||
conn.commit()
|
||||
return new_id
|
||||
except Exception:
|
||||
@@ -274,6 +265,34 @@ class ChatService:
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
@trace_service("保存百炼 session_id", "Save bailian session ID")
|
||||
def save_session_id(self, chat_id: int, session_id: str) -> None:
|
||||
"""流式回复完成后，将百炼返回的 session_id 回写 ai_conversations。
|
||||
|
||||
multi-turn 启用：
|
||||
- 首次对话 session_id=NULL → 百炼分配新 session → 这里回写
|
||||
- 下次对话 get_session_id 返回该值 → 传给百炼关联历史上下文
|
||||
|
||||
幂等：同一对话多次调用覆盖最新 session_id（通常保持稳定）。
|
||||
"""
|
||||
if not session_id:
|
||||
return
|
||||
conn = get_connection()
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"UPDATE biz.ai_conversations SET session_id = %s WHERE id = %s",
|
||||
(session_id, chat_id),
|
||||
)
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
logger.warning(
|
||||
"保存 session_id 失败: chat_id=%s", chat_id, exc_info=True,
|
||||
)
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# CHAT-2: 消息列表
|
||||
# ------------------------------------------------------------------
|
||||
@@ -662,7 +681,10 @@ class ChatService:
|
||||
"""查询客户近 30 天消费金额（items_sum 口径）。
|
||||
|
||||
⚠️ DWD-DOC 规则 1: 使用 ledger_amount（items_sum 口径），禁用 consume_money。
|
||||
CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱不读「未来」消费。
|
||||
"""
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref = as_runtime_today_param(site_id, conn=conn)
|
||||
with fdw_queries._fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
@@ -670,16 +692,22 @@ class ChatService:
|
||||
FROM app.v_dwd_assistant_service_log
|
||||
WHERE tenant_member_id = %s
|
||||
AND is_delete = 0
|
||||
AND create_time >= (CURRENT_DATE - INTERVAL '30 days')::timestamptz
|
||||
AND create_time >= (%s::date - INTERVAL '30 days')::timestamptz
|
||||
AND create_time < (%s::date + INTERVAL '1 day')::timestamptz
|
||||
""",
|
||||
(member_id,),
|
||||
(member_id, ref, ref),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
return Decimal(str(row[0])) if row and row[0] is not None else None
|
||||
|
||||
@staticmethod
|
||||
def _get_visit_count_30d(conn: Any, site_id: int, member_id: int) -> int | None:
|
||||
"""查询客户近 30 天到店次数。"""
|
||||
"""查询客户近 30 天到店次数。
|
||||
|
||||
CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱不读「未来」到店。
|
||||
"""
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref = as_runtime_today_param(site_id, conn=conn)
|
||||
with fdw_queries._fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
@@ -687,9 +715,10 @@ class ChatService:
|
||||
FROM app.v_dwd_assistant_service_log
|
||||
WHERE tenant_member_id = %s
|
||||
AND is_delete = 0
|
||||
AND create_time >= (CURRENT_DATE - INTERVAL '30 days')::timestamptz
|
||||
AND create_time >= (%s::date - INTERVAL '30 days')::timestamptz
|
||||
AND create_time < (%s::date + INTERVAL '1 day')::timestamptz
|
||||
""",
|
||||
(member_id,),
|
||||
(member_id, ref, ref),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
return int(row[0]) if row and row[0] is not None else None
|
||||
|
||||
@@ -147,7 +147,9 @@ async def get_coach_detail(coach_id: int, site_id: int) -> dict:
|
||||
if not assistant_info:
|
||||
raise HTTPException(status_code=404, detail="助教不存在")
|
||||
|
||||
now = datetime.date.today()
|
||||
# 业务时间锚：sandbox 模式下用 business_date，避免读到 sandbox_date 之后真实绩效
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
now = as_runtime_today_param(site_id, conn=conn)
|
||||
|
||||
# 门店名称（用于小程序 banner 展示，跟随被查看助教所在门店）
|
||||
# 必须在所有 fdw 查询前执行：后续任意 fdw 查询失败会污染事务
|
||||
@@ -713,7 +715,9 @@ def _build_history_months(
|
||||
4. 本月 estimated=True，历史月份 estimated=False
|
||||
5. 格式化：customers→"22人"，hours→"87.5h"，salary→"¥6,950"
|
||||
"""
|
||||
now = datetime.date.today()
|
||||
# 业务时间锚：sandbox 模式下用 business_date 计算最近 6 个月
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
now = as_runtime_today_param(site_id, conn=conn)
|
||||
|
||||
# 生成最近 6 个月的月份列表（含本月）
|
||||
months: list[str] = []
|
||||
|
||||
@@ -501,6 +501,9 @@ def _build_coach_tasks(
|
||||
logger.warning("批量查询助教信息失败", exc_info=True)
|
||||
|
||||
# 批量查询 60 天统计（一次 FDW 查询）
|
||||
# CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱不读「未来」
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
stats_map: dict = {}
|
||||
try:
|
||||
with fdw_queries._fdw_context(conn, site_id) as cur:
|
||||
@@ -513,10 +516,11 @@ def _build_coach_tasks(
|
||||
WHERE tenant_member_id = %s
|
||||
AND site_assistant_id = ANY(%s)
|
||||
AND is_delete = 0
|
||||
AND create_time >= CURRENT_DATE - INTERVAL '60 days'
|
||||
AND create_time >= (%s::date - INTERVAL '60 days')::timestamptz
|
||||
AND create_time < (%s::date + INTERVAL '1 day')::timestamptz
|
||||
GROUP BY site_assistant_id
|
||||
""",
|
||||
(customer_id, assistant_ids),
|
||||
(customer_id, assistant_ids, ref_date, ref_date),
|
||||
)
|
||||
for row in cur.fetchall():
|
||||
svc = int(row[1]) if row[1] else 0
|
||||
|
||||
@@ -80,7 +80,7 @@ def _get_etl_connection(site_id: int):
|
||||
@contextmanager
|
||||
def _fdw_context(conn: Any, site_id: int, *, etl_conn: Any = None):
|
||||
"""
|
||||
上下文管理器：直连 ETL 库 + SET LOCAL app.current_site_id。
|
||||
上下文管理器：直连 ETL 库 + SET LOCAL app.current_site_id + app.current_business_date。
|
||||
|
||||
⚠️ 不使用 zqyy_app 的 fdw_etl.* foreign table，而是直连 ETL 库
|
||||
查询 app.v_* RLS 视图。原因：postgres_fdw 不传递自定义 GUC 参数
|
||||
@@ -91,7 +91,31 @@ def _fdw_context(conn: Any, site_id: int, *, etl_conn: Any = None):
|
||||
|
||||
CHANGE 2026-03-26 | ETL 连接复用：传入 etl_conn 时复用已有连接（不关闭），
|
||||
不传时新建连接并在 yield 后自动关闭。避免同一请求内多次新建连接（每次 ~2.6s）。
|
||||
CHANGE 2026-05-02 | 同时设置 app.current_business_date / app.current_runtime_mode，
|
||||
供 RLS 视图层（C 方案）做日期上界裁剪。conn=None 时降级 live。
|
||||
"""
|
||||
from app.services.runtime_context import (
|
||||
MODE_LIVE,
|
||||
MODE_SANDBOX,
|
||||
get_runtime_context,
|
||||
)
|
||||
|
||||
# 业务日：优先从 zqyy_app 业务库的 RuntimeContext 读取；conn 不可用时降级为系统今天
|
||||
bd_str = ""
|
||||
rt_mode = MODE_LIVE
|
||||
try:
|
||||
if conn is not None:
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
bd_str = ctx.business_date.isoformat()
|
||||
rt_mode = MODE_SANDBOX if ctx.is_sandbox else MODE_LIVE
|
||||
else:
|
||||
from datetime import date as _date
|
||||
bd_str = _date.today().isoformat()
|
||||
except Exception:
|
||||
from datetime import date as _date
|
||||
bd_str = _date.today().isoformat()
|
||||
rt_mode = MODE_LIVE
|
||||
|
||||
owned = etl_conn is None
|
||||
if owned:
|
||||
etl_conn = _get_etl_connection(site_id)
|
||||
@@ -99,6 +123,8 @@ def _fdw_context(conn: Any, site_id: int, *, etl_conn: Any = None):
|
||||
with etl_conn.cursor() as cur:
|
||||
cur.execute("BEGIN")
|
||||
cur.execute("SET LOCAL app.current_site_id = %s", (str(site_id),))
|
||||
cur.execute("SET LOCAL app.current_business_date = %s", (bd_str,))
|
||||
cur.execute("SET LOCAL app.current_runtime_mode = %s", (rt_mode,))
|
||||
yield cur
|
||||
etl_conn.commit()
|
||||
finally:
|
||||
@@ -180,33 +206,53 @@ def get_last_visit_days(
|
||||
"""
|
||||
批量查询客户距上次到店天数。
|
||||
|
||||
来源: app.v_dws_member_consumption_summary.days_since_last（基于结算单）。
|
||||
FIX: 原查 v_dwd_assistant_service_log 导致无助教服务的客户缺失到店记录。
|
||||
来源: app.v_dws_member_consumption_summary。
|
||||
consumption_summary 按 stat_date 有多行快照，取最新一行。
|
||||
|
||||
CHANGE 2026-05-02 | 修复客户看板「最近到店」数据不准的问题：
|
||||
- 旧版直接用 days_since_last（ETL 在 stat_date 那天预计算的快照值）。
|
||||
若 ETL 没跑、跑得迟、或 sandbox_date 与 stat_date 不一致，结果就会严重失真。
|
||||
- 新版改为实时计算：``business_date - last_consume_date``，
|
||||
仅取 ``stat_date <= business_date`` 的快照行，沙箱模式下也能拿到一致结果。
|
||||
|
||||
返回 {member_id: days_since_visit} 映射，无记录的会员不在结果中。
|
||||
"""
|
||||
if not member_ids:
|
||||
return {}
|
||||
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
|
||||
ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
|
||||
result: dict[int, int | None] = {}
|
||||
with _fdw_context(conn, site_id, etl_conn=etl_conn) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT member_id, days_since_last
|
||||
SELECT DISTINCT ON (member_id)
|
||||
member_id,
|
||||
last_consume_date,
|
||||
stat_date
|
||||
FROM app.v_dws_member_consumption_summary
|
||||
WHERE member_id = ANY(%s)
|
||||
AND days_since_last IS NOT NULL
|
||||
AND stat_date <= %s
|
||||
ORDER BY member_id, stat_date DESC
|
||||
""",
|
||||
(member_ids,),
|
||||
(member_ids, ref_date),
|
||||
)
|
||||
seen: set[int] = set()
|
||||
for row in cur.fetchall():
|
||||
mid = row[0]
|
||||
if mid not in seen:
|
||||
seen.add(mid)
|
||||
result[mid] = row[1]
|
||||
last_consume = row[1]
|
||||
if last_consume is None:
|
||||
result[mid] = None
|
||||
continue
|
||||
try:
|
||||
# last_consume_date 在 DWS 中是 date；少数实现可能给 timestamp，统一裁剪
|
||||
if hasattr(last_consume, "date"):
|
||||
last_consume = last_consume.date()
|
||||
days = (ref_date - last_consume).days
|
||||
result[mid] = max(days, 0)
|
||||
except Exception:
|
||||
result[mid] = None
|
||||
|
||||
return result
|
||||
|
||||
@@ -420,22 +466,33 @@ def batch_query_for_task_list(
|
||||
|
||||
# 3. 最后到店天数（基于消费汇总表，口径=结算单）
|
||||
# FIX: 原查 v_dwd_assistant_service_log 导致无助教服务的客户缺失到店记录
|
||||
# CHANGE 2026-05-02 | 实时按 business_date - last_consume_date 计算，
|
||||
# 不再依赖 ETL 预计算的 days_since_last（解决看板显示偏差）。
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT member_id, days_since_last
|
||||
SELECT DISTINCT ON (member_id)
|
||||
member_id, last_consume_date, stat_date
|
||||
FROM app.v_dws_member_consumption_summary
|
||||
WHERE member_id = ANY(%s)
|
||||
AND days_since_last IS NOT NULL
|
||||
AND stat_date <= %s
|
||||
ORDER BY member_id, stat_date DESC
|
||||
""",
|
||||
(member_ids,),
|
||||
(member_ids, _ref_date),
|
||||
)
|
||||
seen_members: set[int] = set()
|
||||
for row in cur.fetchall():
|
||||
mid = row[0]
|
||||
if mid not in seen_members:
|
||||
seen_members.add(mid)
|
||||
last_visit_map[mid] = row[1]
|
||||
last_consume = row[1]
|
||||
if last_consume is None:
|
||||
last_visit_map[mid] = None
|
||||
continue
|
||||
try:
|
||||
if hasattr(last_consume, "date"):
|
||||
last_consume = last_consume.date()
|
||||
last_visit_map[mid] = max((_ref_date - last_consume).days, 0)
|
||||
except Exception:
|
||||
last_visit_map[mid] = None
|
||||
|
||||
# 4. RS 指数
|
||||
cur.execute(
|
||||
@@ -486,10 +543,11 @@ def batch_query_for_task_list(
|
||||
WHERE sl.site_assistant_id = %s
|
||||
AND sl.tenant_member_id = ANY(%s)
|
||||
AND sl.is_delete = 0
|
||||
AND sl.create_time >= (CURRENT_DATE - INTERVAL '60 days')::timestamptz
|
||||
AND sl.create_time >= (%s::date - INTERVAL '60 days')::timestamptz
|
||||
AND sl.create_time < (%s::date + INTERVAL '1 day')::timestamptz
|
||||
GROUP BY sl.tenant_member_id
|
||||
""",
|
||||
(assistant_id, member_ids),
|
||||
(assistant_id, member_ids, _ref_date, _ref_date),
|
||||
)
|
||||
for row in cur.fetchall():
|
||||
recent60d_map[row[0]] = {
|
||||
@@ -559,15 +617,19 @@ def batch_query_for_task_list(
|
||||
|
||||
# 8. 绩效档位配置（用于构建 tier_nodes + bonus_money 计算）
|
||||
# CHANGE 2026-03-24 | 增加 bonus_deduction_ratio 用于打赏课抽成差额计算
|
||||
# CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱按当时生效档位
|
||||
from app.services.runtime_context import as_runtime_today_param as _rt_today
|
||||
_ref_date = _rt_today(site_id, conn=conn)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT tier_id, tier_code, tier_name, tier_level,
|
||||
min_hours, max_hours, base_deduction, bonus_deduction_ratio
|
||||
FROM app.v_cfg_performance_tier
|
||||
WHERE effective_from <= CURRENT_DATE
|
||||
AND effective_to >= CURRENT_DATE
|
||||
WHERE effective_from <= %s::date
|
||||
AND effective_to >= %s::date
|
||||
ORDER BY tier_level
|
||||
"""
|
||||
""",
|
||||
(_ref_date, _ref_date),
|
||||
)
|
||||
tier_rows = cur.fetchall()
|
||||
performance_tiers = [
|
||||
@@ -640,17 +702,21 @@ def get_performance_tiers(
|
||||
|
||||
返回 [{tier_id, tier_code, tier_name, tier_level, min_hours, max_hours,
|
||||
base_deduction, bonus_deduction_ratio}, ...]。
|
||||
CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱按当时生效档位
|
||||
"""
|
||||
from app.services.runtime_context import as_runtime_today_param as _rt_today
|
||||
ref_date = _rt_today(site_id, conn=conn)
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT tier_id, tier_code, tier_name, tier_level,
|
||||
min_hours, max_hours, base_deduction, bonus_deduction_ratio
|
||||
FROM app.v_cfg_performance_tier
|
||||
WHERE effective_from <= CURRENT_DATE
|
||||
AND effective_to >= CURRENT_DATE
|
||||
WHERE effective_from <= %s::date
|
||||
AND effective_to >= %s::date
|
||||
ORDER BY tier_level
|
||||
"""
|
||||
""",
|
||||
(ref_date, ref_date),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
|
||||
@@ -680,15 +746,18 @@ def get_level_map(conn: Any, site_id: int) -> dict[int, str]:
|
||||
查询失败时返回空 dict（调用方应优雅降级）。
|
||||
"""
|
||||
try:
|
||||
from app.services.runtime_context import as_runtime_today_param as _rt_today
|
||||
ref_date = _rt_today(site_id, conn=conn)
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT DISTINCT level_code, level_name
|
||||
FROM app.v_cfg_assistant_level_price
|
||||
WHERE effective_from <= CURRENT_DATE
|
||||
AND effective_to >= CURRENT_DATE
|
||||
WHERE effective_from <= %s::date
|
||||
AND effective_to >= %s::date
|
||||
ORDER BY level_code
|
||||
"""
|
||||
""",
|
||||
(ref_date, ref_date),
|
||||
)
|
||||
return {row[0]: row[1] for row in cur.fetchall()}
|
||||
except Exception:
|
||||
@@ -1198,8 +1267,11 @@ def get_coach_60d_stats(
|
||||
|
||||
来源: app.v_dwd_assistant_service_log。
|
||||
⚠️ 废单排除: is_delete = 0。
|
||||
CHANGE 2026-05-02 | 用 business_date 替代 CURRENT_DATE，沙箱不读「未来」60 天。
|
||||
返回 {service_count, total_hours, avg_hours}。
|
||||
"""
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
@@ -1212,9 +1284,10 @@ def get_coach_60d_stats(
|
||||
WHERE site_assistant_id = %s
|
||||
AND tenant_member_id = %s
|
||||
AND is_delete = 0
|
||||
AND create_time >= (CURRENT_DATE - INTERVAL '60 days')::timestamptz
|
||||
AND create_time >= (%s::date - INTERVAL '60 days')::timestamptz
|
||||
AND create_time < (%s::date + INTERVAL '1 day')::timestamptz
|
||||
""",
|
||||
(assistant_id, member_id),
|
||||
(assistant_id, member_id, ref_date, ref_date),
|
||||
)
|
||||
row = cur.fetchone()
|
||||
if not row:
|
||||
@@ -1917,14 +1990,17 @@ def get_customer_board_recall(
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
# 分页数据
|
||||
# CHANGE 2026-05-02 | elapsed_days/overdue_days 用 business_date 替代 CURRENT_DATE
|
||||
from app.services.runtime_context import as_runtime_today_param as _rt_today
|
||||
ref_date = _rt_today(site_id, conn=conn)
|
||||
offset = (page - 1) * page_size
|
||||
cur.execute(
|
||||
f"""
|
||||
SELECT wi.member_id,
|
||||
dm.nickname,
|
||||
wi.ideal_interval_days,
|
||||
CURRENT_DATE - wi.last_visit_time::date AS elapsed_days,
|
||||
(CURRENT_DATE - wi.last_visit_time::date) - COALESCE(wi.ideal_interval_days, 0) AS overdue_days,
|
||||
%s::date - wi.last_visit_time::date AS elapsed_days,
|
||||
(%s::date - wi.last_visit_time::date) - COALESCE(wi.ideal_interval_days, 0) AS overdue_days,
|
||||
wi.visits_30d,
|
||||
wi.display_score,
|
||||
COALESCE(ca.balance, 0) AS balance
|
||||
@@ -1937,11 +2013,11 @@ def get_customer_board_recall(
|
||||
WHERE scd2_is_current = 1
|
||||
GROUP BY tenant_member_id
|
||||
) ca ON wi.member_id = ca.tenant_member_id
|
||||
WHERE 1=1 {proj_clause}
|
||||
WHERE wi.last_visit_time <= %s::date + INTERVAL '1 day' {proj_clause}
|
||||
ORDER BY wi.display_score DESC, wi.member_id
|
||||
LIMIT %s OFFSET %s
|
||||
""",
|
||||
(*proj_params, page_size, offset),
|
||||
(ref_date, ref_date, ref_date, *proj_params, page_size, offset),
|
||||
)
|
||||
items = []
|
||||
for row in cur.fetchall():
|
||||
@@ -2165,6 +2241,10 @@ def get_customer_board_recharge(
|
||||
)
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
# CHANGE 2026-05-02 | 60 天充值窗口、stat_date、pay_time 全部按 business_date 截断
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
|
||||
offset = (page - 1) * page_size
|
||||
cur.execute(
|
||||
f"""
|
||||
@@ -2173,7 +2253,8 @@ def get_customer_board_recharge(
|
||||
MAX(ro.pay_time::date) AS last_recharge_date,
|
||||
SUM(ro.pay_amount) AS recharge_amount,
|
||||
COUNT(*) FILTER (
|
||||
WHERE ro.pay_time >= CURRENT_DATE - INTERVAL '60 days'
|
||||
WHERE ro.pay_time >= %s::date - INTERVAL '60 days'
|
||||
AND ro.pay_time < %s::date + INTERVAL '1 day'
|
||||
) AS recharges_60d,
|
||||
COALESCE(ca_agg.balance, 0) AS current_balance,
|
||||
cs.days_since_last
|
||||
@@ -2190,15 +2271,16 @@ def get_customer_board_recharge(
|
||||
SELECT cs2.days_since_last
|
||||
FROM app.v_dws_member_consumption_summary cs2
|
||||
WHERE cs2.member_id = ro.member_id
|
||||
AND cs2.stat_date <= %s
|
||||
ORDER BY cs2.stat_date DESC
|
||||
LIMIT 1
|
||||
) cs ON true
|
||||
WHERE 1=1 {proj_clause}
|
||||
WHERE ro.pay_time <= %s::date + INTERVAL '1 day' {proj_clause}
|
||||
GROUP BY ro.member_id, dm.nickname, ca_agg.balance, cs.days_since_last
|
||||
ORDER BY MAX(ro.pay_time::date) DESC, ro.member_id
|
||||
LIMIT %s OFFSET %s
|
||||
""",
|
||||
(*proj_params, page_size, offset),
|
||||
(ref_date, ref_date, ref_date, ref_date, *proj_params, page_size, offset),
|
||||
)
|
||||
items = []
|
||||
for row in cur.fetchall():
|
||||
@@ -2228,6 +2310,13 @@ def get_customer_board_recent(
|
||||
不再硬编码为 0。来源: v_dws_member_visit_detail + v_dim_member + v_dws_member_winback_index。
|
||||
按 last_visit_date 降序。
|
||||
"""
|
||||
# CHANGE 2026-05-02 | 客户看板「最近到店」修复：
|
||||
# 1) WHERE/COUNT 中的 30/60 天窗口按 business_date 计算，沙箱不读「未来」到店；
|
||||
# 2) days_ago 用 business_date - last_visit_date，与窗口对齐。
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
|
||||
ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
|
||||
proj_clause, proj_params = _project_filter_clause(project, "vd.member_id")
|
||||
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
@@ -2235,9 +2324,9 @@ def get_customer_board_recent(
|
||||
f"""
|
||||
SELECT COUNT(DISTINCT vd.member_id)
|
||||
FROM app.v_dws_member_visit_detail vd
|
||||
WHERE 1=1 {proj_clause}
|
||||
WHERE vd.visit_date <= %s {proj_clause}
|
||||
""",
|
||||
proj_params,
|
||||
(ref_date, *proj_params),
|
||||
)
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
@@ -2248,11 +2337,11 @@ def get_customer_board_recent(
|
||||
SELECT vd.member_id,
|
||||
MAX(vd.visit_date) AS last_visit_date,
|
||||
COUNT(*) AS total_visits,
|
||||
COUNT(*) FILTER (WHERE vd.visit_date >= CURRENT_DATE - INTERVAL '30 days') AS visits_30d,
|
||||
COUNT(*) FILTER (WHERE vd.visit_date >= CURRENT_DATE - INTERVAL '60 days') AS visits_60d,
|
||||
COUNT(*) FILTER (WHERE vd.visit_date >= %s::date - INTERVAL '30 days') AS visits_30d,
|
||||
COUNT(*) FILTER (WHERE vd.visit_date >= %s::date - INTERVAL '60 days') AS visits_60d,
|
||||
AVG(vd.total_consume) AS avg_spend
|
||||
FROM app.v_dws_member_visit_detail vd
|
||||
WHERE 1=1 {proj_clause}
|
||||
WHERE vd.visit_date <= %s {proj_clause}
|
||||
GROUP BY vd.member_id
|
||||
)
|
||||
SELECT ma.member_id,
|
||||
@@ -2271,14 +2360,13 @@ def get_customer_board_recent(
|
||||
ORDER BY ma.last_visit_date DESC, ma.member_id
|
||||
LIMIT %s OFFSET %s
|
||||
""",
|
||||
(*proj_params, page_size, offset),
|
||||
(ref_date, ref_date, ref_date, *proj_params, page_size, offset),
|
||||
)
|
||||
items = []
|
||||
for row in cur.fetchall():
|
||||
last_visit = row[2]
|
||||
# CHANGE 2026-03-29 | 补充 days_ago（距今天数）和 visits_60d
|
||||
from datetime import date as _date
|
||||
days_ago = (_date.today() - last_visit).days if last_visit else None
|
||||
# CHANGE 2026-05-02 | days_ago 按 business_date 计算，沙箱与窗口对齐
|
||||
days_ago = (ref_date - last_visit).days if last_visit else None
|
||||
items.append({
|
||||
"member_id": row[0],
|
||||
"name": row[1] or "",
|
||||
@@ -2378,6 +2466,10 @@ def get_customer_board_freq60(
|
||||
按 visit_count_60d 降序。
|
||||
CHANGE 2026-04-08 | Fix：同 spend60，DISTINCT ON 取最新快照。
|
||||
"""
|
||||
# CHANGE 2026-05-02 | freq60 全链路按 business_date 截断（stat_date <= ref_date + 8 周窗口）
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
|
||||
proj_clause, proj_params = _project_filter_clause(project, "cs.member_id")
|
||||
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
@@ -2387,11 +2479,11 @@ def get_customer_board_freq60(
|
||||
FROM (
|
||||
SELECT DISTINCT ON (cs.member_id) cs.member_id
|
||||
FROM app.v_dws_member_consumption_summary cs
|
||||
WHERE 1=1 {proj_clause}
|
||||
WHERE cs.stat_date <= %s {proj_clause}
|
||||
ORDER BY cs.member_id, cs.stat_date DESC
|
||||
) sub
|
||||
""",
|
||||
proj_params,
|
||||
(ref_date, *proj_params),
|
||||
)
|
||||
total = cur.fetchone()[0]
|
||||
|
||||
@@ -2402,7 +2494,7 @@ def get_customer_board_freq60(
|
||||
SELECT DISTINCT ON (cs.member_id)
|
||||
cs.member_id, cs.visit_count_60d, cs.consume_amount_60d
|
||||
FROM app.v_dws_member_consumption_summary cs
|
||||
WHERE 1=1 {proj_clause}
|
||||
WHERE cs.stat_date <= %s {proj_clause}
|
||||
ORDER BY cs.member_id, cs.stat_date DESC
|
||||
)
|
||||
SELECT cs.member_id,
|
||||
@@ -2415,7 +2507,7 @@ def get_customer_board_freq60(
|
||||
ORDER BY cs.visit_count_60d DESC, cs.member_id
|
||||
LIMIT %s OFFSET %s
|
||||
""",
|
||||
(*proj_params, page_size, offset),
|
||||
(ref_date, *proj_params, page_size, offset),
|
||||
)
|
||||
items = []
|
||||
member_ids = []
|
||||
@@ -2436,21 +2528,31 @@ def get_customer_board_freq60(
|
||||
|
||||
# 批量查询 8 周到店数据
|
||||
if member_ids:
|
||||
weekly_map = _get_weekly_visits_batch(cur, member_ids)
|
||||
weekly_map = _get_weekly_visits_batch(cur, member_ids, ref_date=ref_date)
|
||||
for item in items:
|
||||
item["weekly_visits"] = weekly_map.get(item["member_id"], _empty_weekly())
|
||||
|
||||
return {"items": items, "total": total, "page": page, "page_size": page_size}
|
||||
|
||||
|
||||
def _get_weekly_visits_batch(cur: Any, member_ids: list[int]) -> dict[int, list[dict]]:
|
||||
def _get_weekly_visits_batch(
|
||||
cur: Any, member_ids: list[int], *, ref_date: Any = None,
|
||||
) -> dict[int, list[dict]]:
|
||||
"""
|
||||
批量查询客户最近 8 周的到店次数（用于 freq60 维度柱状图）。
|
||||
|
||||
CHANGE 2026-04-07 | Fix-5：数据源从 v_dwd_assistant_service_log 改为
|
||||
v_dwd_settlement_head（settle_type IN (1,3)），与汇总维度口径一致。
|
||||
CHANGE 2026-05-02 | 8 周窗口锚定 ref_date（业务日），沙箱不读「未来」。
|
||||
返回 {member_id: [{val: int, pct: int}, ...]}，固定 8 个元素。
|
||||
"""
|
||||
from datetime import date as _date, timedelta as _timedelta
|
||||
|
||||
if ref_date is None:
|
||||
ref_date = _date.today()
|
||||
elif hasattr(ref_date, "date") and not isinstance(ref_date, _date):
|
||||
ref_date = ref_date.date()
|
||||
|
||||
cur.execute(
|
||||
"""
|
||||
WITH weekly AS (
|
||||
@@ -2460,14 +2562,15 @@ def _get_weekly_visits_batch(cur: Any, member_ids: list[int]) -> dict[int, list[
|
||||
FROM app.v_dwd_settlement_head
|
||||
WHERE member_id = ANY(%s)
|
||||
AND settle_type IN (1, 3)
|
||||
AND pay_time >= CURRENT_DATE - INTERVAL '56 days'
|
||||
AND pay_time >= %s::date - INTERVAL '56 days'
|
||||
AND pay_time < %s::date + INTERVAL '1 day'
|
||||
GROUP BY member_id, DATE_TRUNC('week', pay_time::date)
|
||||
)
|
||||
SELECT member_id, week_start, cnt
|
||||
FROM weekly
|
||||
ORDER BY member_id, week_start
|
||||
""",
|
||||
(member_ids,),
|
||||
(member_ids, ref_date, ref_date),
|
||||
)
|
||||
|
||||
from collections import defaultdict
|
||||
@@ -2477,11 +2580,9 @@ def _get_weekly_visits_batch(cur: Any, member_ids: list[int]) -> dict[int, list[
|
||||
week_key = row[1].date() if hasattr(row[1], 'date') else row[1]
|
||||
raw[row[0]][str(week_key)] = row[2]
|
||||
|
||||
# 生成最近 8 周的周一日期
|
||||
from datetime import date, timedelta
|
||||
today = date.today()
|
||||
this_monday = today - timedelta(days=today.weekday())
|
||||
weeks = [this_monday - timedelta(weeks=i) for i in range(7, -1, -1)]
|
||||
# 生成最近 8 周的周一日期，以业务日为锚
|
||||
this_monday = ref_date - _timedelta(days=ref_date.weekday())
|
||||
weeks = [this_monday - _timedelta(weeks=i) for i in range(7, -1, -1)]
|
||||
|
||||
result: dict[int, list[dict]] = {}
|
||||
for mid in member_ids:
|
||||
|
||||
@@ -259,6 +259,28 @@ async def create_note(
|
||||
import asyncio
|
||||
asyncio.create_task(_async_ai_score(note["id"], site_id, target_id, content))
|
||||
|
||||
# 触发 AI 备注分析链（App6 → App8）
|
||||
# target_type='member' 时 target_id 即 member_id；'assistant' 时不触发（AI 只分析会员备注）
|
||||
if target_type == "member":
|
||||
try:
|
||||
from app.services.trigger_scheduler import fire_event
|
||||
fire_event(
|
||||
"ai_note_created",
|
||||
{
|
||||
"site_id": site_id,
|
||||
"member_id": target_id,
|
||||
"note_content": content,
|
||||
"noted_by_name": note.get("recorded_by_name")
|
||||
or note.get("user_nickname") or "",
|
||||
"noted_by_created_at": note.get("created_at") or "",
|
||||
},
|
||||
)
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"触发 ai_note_created 事件失败: note_id=%s member_id=%s",
|
||||
note["id"], target_id,
|
||||
)
|
||||
|
||||
return note
|
||||
|
||||
except HTTPException:
|
||||
|
||||
@@ -22,6 +22,13 @@ import json
|
||||
import logging
|
||||
from datetime import timedelta
|
||||
|
||||
from app.services.runtime_context import (
|
||||
LIVE_INSTANCE_ID,
|
||||
MODE_LIVE,
|
||||
MODE_SANDBOX,
|
||||
get_runtime_context,
|
||||
task_runtime_filter,
|
||||
)
|
||||
from app.trace.decorators import trace_service
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -141,6 +148,10 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
resolved = 0
|
||||
|
||||
from app.services.fdw_queries import _fdw_context
|
||||
runtime_ctx = get_runtime_context(site_id, conn=conn)
|
||||
runtime_now = runtime_ctx.business_now
|
||||
runtime_mode = MODE_SANDBOX if runtime_ctx.is_sandbox else MODE_LIVE
|
||||
sandbox_instance_id = runtime_ctx.sandbox_instance_id if runtime_ctx.is_sandbox else LIVE_INSTANCE_ID
|
||||
|
||||
# ── 1. 获取本门店所有 MAIN 关系对 ──
|
||||
with _fdw_context(conn, site_id) as cur:
|
||||
@@ -173,13 +184,14 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
ON sl.order_settle_id = sh.order_settle_id
|
||||
AND sl.is_delete = 0
|
||||
WHERE sh.member_id = ANY(%s)
|
||||
AND sh.pay_time <= %s
|
||||
AND (
|
||||
sh.settle_type = 1
|
||||
OR (sh.settle_type = 3 AND sl.order_assistant_type = 2)
|
||||
)
|
||||
GROUP BY sl.site_assistant_id, sh.member_id
|
||||
""",
|
||||
(member_ids,),
|
||||
(member_ids, runtime_now),
|
||||
)
|
||||
for row in cur.fetchall():
|
||||
settlement_map[(row[0], row[1])] = row[2]
|
||||
@@ -190,6 +202,7 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
SELECT sh.member_id, MAX(sh.pay_time) AS latest_pay_time
|
||||
FROM app.v_dwd_settlement_head sh
|
||||
WHERE sh.member_id = ANY(%s)
|
||||
AND sh.pay_time <= %s
|
||||
AND (
|
||||
sh.settle_type = 1
|
||||
OR (sh.settle_type = 3 AND EXISTS (
|
||||
@@ -201,7 +214,7 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
)
|
||||
GROUP BY sh.member_id
|
||||
""",
|
||||
(member_ids,),
|
||||
(member_ids, runtime_now),
|
||||
)
|
||||
member_visited_map = {}
|
||||
for row in cur.fetchall():
|
||||
@@ -209,16 +222,18 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
|
||||
# ── 3. 获取本门店所有 active 的召回/回访任务（用于匹配） ──
|
||||
active_tasks_map: dict[tuple[int, int], list] = {} # (assistant_id, member_id) → [(id, task_type, created_at)]
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT id, assistant_id, member_id, task_type, created_at
|
||||
FROM biz.coach_tasks
|
||||
WHERE site_id = %s
|
||||
{runtime_clause}
|
||||
AND status = 'active'
|
||||
AND task_type IN ('high_priority_recall', 'priority_recall', 'follow_up_visit')
|
||||
""",
|
||||
(site_id,),
|
||||
[site_id, *runtime_params],
|
||||
)
|
||||
for row in cur.fetchall():
|
||||
key = (row[1], row[2])
|
||||
@@ -238,7 +253,7 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
try:
|
||||
result = _process_pair(
|
||||
conn, site_id, assistant_id, member_id,
|
||||
latest_pay, active_tasks,
|
||||
latest_pay, active_tasks, runtime_now, runtime_mode, sandbox_instance_id,
|
||||
)
|
||||
completed += result["completed"]
|
||||
events += result["events"]
|
||||
@@ -257,25 +272,26 @@ def _process_site(conn, site_id: int) -> dict:
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("BEGIN")
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT id, assistant_id, task_type, created_at
|
||||
FROM biz.coach_tasks
|
||||
WHERE site_id = %s AND member_id = %s
|
||||
{runtime_clause}
|
||||
AND status = 'active'
|
||||
AND task_type IN ('high_priority_recall', 'priority_recall')
|
||||
AND created_at < %s
|
||||
""",
|
||||
(site_id, member_id, pay_time),
|
||||
[site_id, member_id, *runtime_params, pay_time],
|
||||
)
|
||||
remaining = cur.fetchall()
|
||||
for task_id, aid, task_type, _ in remaining:
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE biz.coach_tasks
|
||||
SET status = 'resolved', updated_at = NOW()
|
||||
SET status = 'resolved', updated_at = %s
|
||||
WHERE id = %s AND status = 'active'
|
||||
""",
|
||||
(task_id,),
|
||||
(runtime_now, task_id),
|
||||
)
|
||||
_insert_history(
|
||||
cur, task_id,
|
||||
@@ -308,6 +324,9 @@ def _process_pair(
|
||||
member_id: int,
|
||||
latest_pay_time,
|
||||
active_tasks: list[dict],
|
||||
runtime_now,
|
||||
runtime_mode: str,
|
||||
sandbox_instance_id: str,
|
||||
) -> dict:
|
||||
"""
|
||||
处理单个 MAIN 关系对的召回检测。
|
||||
@@ -339,14 +358,16 @@ def _process_pair(
|
||||
cur.execute(
|
||||
"""
|
||||
INSERT INTO biz.recall_events
|
||||
(site_id, assistant_id, member_id, pay_time, task_id, task_type)
|
||||
VALUES (%s, %s, %s, %s, %s, %s)
|
||||
ON CONFLICT (site_id, assistant_id, member_id, (date_trunc('day', pay_time AT TIME ZONE 'Asia/Shanghai')))
|
||||
(site_id, assistant_id, member_id, pay_time, task_id, task_type,
|
||||
created_at, runtime_mode, sandbox_instance_id)
|
||||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||||
ON CONFLICT (site_id, assistant_id, member_id, runtime_mode, sandbox_instance_id,
|
||||
(date_trunc('day', pay_time AT TIME ZONE 'Asia/Shanghai')))
|
||||
DO NOTHING
|
||||
RETURNING id
|
||||
""",
|
||||
(site_id, assistant_id, member_id, latest_pay_time,
|
||||
event_task_id, event_task_type),
|
||||
event_task_id, event_task_type, runtime_now, runtime_mode, sandbox_instance_id),
|
||||
)
|
||||
inserted = cur.fetchone()
|
||||
if inserted is None:
|
||||
@@ -367,10 +388,10 @@ def _process_pair(
|
||||
completed_at = %s,
|
||||
completed_task_type = %s,
|
||||
completion_type = 'auto',
|
||||
updated_at = NOW()
|
||||
updated_at = %s
|
||||
WHERE id = %s AND status = 'active'
|
||||
""",
|
||||
(latest_pay_time, task["task_type"], task["id"]),
|
||||
(latest_pay_time, task["task_type"], runtime_now, task["id"]),
|
||||
)
|
||||
_insert_history(
|
||||
cur,
|
||||
@@ -393,18 +414,19 @@ def _process_pair(
|
||||
SELECT id FROM biz.coach_tasks
|
||||
WHERE site_id = %s AND assistant_id = %s AND member_id = %s
|
||||
AND task_type = 'follow_up_visit' AND status = 'active'
|
||||
AND runtime_mode = %s AND sandbox_instance_id = %s
|
||||
""",
|
||||
(site_id, assistant_id, member_id),
|
||||
(site_id, assistant_id, member_id, runtime_mode, sandbox_instance_id),
|
||||
)
|
||||
old_follow_ups = cur.fetchall()
|
||||
for (old_id,) in old_follow_ups:
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE biz.coach_tasks
|
||||
SET status = 'inactive', updated_at = NOW()
|
||||
SET status = 'inactive', updated_at = %s
|
||||
WHERE id = %s
|
||||
""",
|
||||
(old_id,),
|
||||
(runtime_now, old_id),
|
||||
)
|
||||
_insert_history(
|
||||
cur, old_id,
|
||||
@@ -423,11 +445,14 @@ def _process_pair(
|
||||
"""
|
||||
INSERT INTO biz.coach_tasks
|
||||
(site_id, assistant_id, member_id, task_type, status,
|
||||
expires_at, created_at, updated_at)
|
||||
VALUES (%s, %s, %s, 'follow_up_visit', 'active', %s, NOW(), NOW())
|
||||
expires_at, created_at, updated_at, runtime_mode, sandbox_instance_id)
|
||||
VALUES (%s, %s, %s, 'follow_up_visit', 'active', %s, %s, %s, %s, %s)
|
||||
RETURNING id
|
||||
""",
|
||||
(site_id, assistant_id, member_id, expires_at),
|
||||
(
|
||||
site_id, assistant_id, member_id, expires_at, runtime_now,
|
||||
runtime_now, runtime_mode, sandbox_instance_id,
|
||||
),
|
||||
)
|
||||
new_follow_up_id = cur.fetchone()[0]
|
||||
_insert_history(
|
||||
|
||||
263
apps/backend/app/services/runtime_context.py
Normal file
263
apps/backend/app/services/runtime_context.py
Normal file
@@ -0,0 +1,263 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""业务运行上下文与业务时钟服务。
|
||||
|
||||
该模块是开发/测试沙箱的统一控制层：
|
||||
- live 模式：沿用真实系统日期和正式数据。
|
||||
- sandbox 模式：业务上假设今天是配置的历史日期，并用 sandbox_instance_id 隔离写入。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
from dataclasses import dataclass
|
||||
from datetime import date, datetime, time, timedelta, timezone
|
||||
from typing import Any
|
||||
|
||||
from app import config
|
||||
|
||||
_LOCAL_TZ = timezone(timedelta(hours=8))
|
||||
MODE_LIVE = "live"
|
||||
MODE_SANDBOX = "sandbox"
|
||||
AI_MODE_LIVE = "live"
|
||||
LIVE_INSTANCE_ID = "live"
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class RuntimeContext:
|
||||
"""单门店当前业务运行上下文。"""
|
||||
|
||||
site_id: int
|
||||
mode: str = MODE_LIVE
|
||||
business_day_start_hour: int = config.BUSINESS_DAY_START_HOUR
|
||||
sandbox_date: date | None = None
|
||||
sandbox_instance_id: str | None = None
|
||||
ai_mode: str = AI_MODE_LIVE
|
||||
status: str = "active"
|
||||
|
||||
@property
|
||||
def is_sandbox(self) -> bool:
|
||||
return self.mode == MODE_SANDBOX and self.sandbox_date is not None
|
||||
|
||||
@property
|
||||
def business_date(self) -> date:
|
||||
if self.is_sandbox and self.sandbox_date is not None:
|
||||
return self.sandbox_date
|
||||
now = datetime.now(_LOCAL_TZ)
|
||||
today = now.date()
|
||||
if now.hour < self.business_day_start_hour:
|
||||
return today - timedelta(days=1)
|
||||
return today
|
||||
|
||||
@property
|
||||
def business_now(self) -> datetime:
|
||||
if not self.is_sandbox:
|
||||
return datetime.now(_LOCAL_TZ)
|
||||
now = datetime.now(_LOCAL_TZ)
|
||||
return datetime.combine(self.business_date, now.timetz(), tzinfo=_LOCAL_TZ)
|
||||
|
||||
@property
|
||||
def active_sandbox_instance_id(self) -> str | None:
|
||||
if not self.is_sandbox:
|
||||
return None
|
||||
return self.sandbox_instance_id
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
return {
|
||||
"site_id": self.site_id,
|
||||
"mode": self.mode,
|
||||
"business_day_start_hour": self.business_day_start_hour,
|
||||
"business_date": self.business_date.isoformat(),
|
||||
"business_now": self.business_now.isoformat(),
|
||||
"sandbox_date": self.sandbox_date.isoformat() if self.sandbox_date else None,
|
||||
"sandbox_instance_id": self.sandbox_instance_id,
|
||||
"ai_mode": self.ai_mode,
|
||||
"status": self.status,
|
||||
"is_sandbox": self.is_sandbox,
|
||||
}
|
||||
|
||||
|
||||
def new_sandbox_instance_id() -> str:
|
||||
"""生成新的沙箱实例 ID。"""
|
||||
return f"sbx_{uuid.uuid4().hex[:24]}"
|
||||
|
||||
|
||||
def _default_context(site_id: int) -> RuntimeContext:
|
||||
return RuntimeContext(site_id=site_id)
|
||||
|
||||
|
||||
def get_runtime_context(site_id: int, conn: Any | None = None) -> RuntimeContext:
|
||||
"""读取门店运行上下文。
|
||||
|
||||
表不存在或未配置时降级为 live，保证迁移前不影响正式链路。
|
||||
"""
|
||||
own_conn = conn is None
|
||||
if own_conn:
|
||||
from app.database import get_connection
|
||||
|
||||
conn = get_connection()
|
||||
|
||||
try:
|
||||
with conn.cursor() as cur:
|
||||
try:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT mode, sandbox_date, sandbox_instance_id, ai_mode, status
|
||||
FROM biz.site_runtime_context
|
||||
WHERE site_id = %s
|
||||
""",
|
||||
(site_id,),
|
||||
)
|
||||
except Exception:
|
||||
if own_conn:
|
||||
conn.rollback()
|
||||
return _default_context(site_id)
|
||||
|
||||
row = cur.fetchone()
|
||||
if own_conn:
|
||||
conn.commit()
|
||||
finally:
|
||||
if own_conn:
|
||||
conn.close()
|
||||
|
||||
if not row:
|
||||
return _default_context(site_id)
|
||||
|
||||
mode, sandbox_date, sandbox_instance_id, ai_mode, status = row
|
||||
if mode not in (MODE_LIVE, MODE_SANDBOX):
|
||||
mode = MODE_LIVE
|
||||
if mode == MODE_SANDBOX and (sandbox_date is None or not sandbox_instance_id):
|
||||
mode = MODE_LIVE
|
||||
|
||||
return RuntimeContext(
|
||||
site_id=site_id,
|
||||
mode=mode,
|
||||
sandbox_date=sandbox_date,
|
||||
sandbox_instance_id=sandbox_instance_id,
|
||||
ai_mode=ai_mode or AI_MODE_LIVE,
|
||||
status=status or "active",
|
||||
)
|
||||
|
||||
|
||||
def namespace_ai_target_id(site_id: int, target_id: str, conn: Any | None = None) -> str:
|
||||
"""按当前上下文转换 AI cache target_id。
|
||||
|
||||
前端和调用方继续使用原始 target_id；沙箱命名空间在后端统一处理。
|
||||
"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
if not ctx.is_sandbox or not ctx.sandbox_instance_id:
|
||||
return target_id
|
||||
return f"{ctx.sandbox_instance_id}:{target_id}"
|
||||
|
||||
|
||||
def task_runtime_filter(
|
||||
site_id: int,
|
||||
*,
|
||||
alias: str = "",
|
||||
conn: Any | None = None,
|
||||
) -> tuple[str, list[Any]]:
|
||||
"""返回 coach_tasks 等表的运行上下文过滤条件。"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
prefix = f"{alias}." if alias else ""
|
||||
if ctx.is_sandbox and ctx.sandbox_instance_id:
|
||||
return (
|
||||
f" AND {prefix}runtime_mode = %s AND {prefix}sandbox_instance_id = %s",
|
||||
[MODE_SANDBOX, ctx.sandbox_instance_id],
|
||||
)
|
||||
return (
|
||||
f" AND COALESCE({prefix}runtime_mode, 'live') = %s "
|
||||
f"AND COALESCE({prefix}sandbox_instance_id, %s) = %s",
|
||||
[MODE_LIVE, LIVE_INSTANCE_ID, LIVE_INSTANCE_ID],
|
||||
)
|
||||
|
||||
|
||||
def runtime_insert_columns(site_id: int, conn: Any | None = None) -> tuple[str, str, list[Any]]:
|
||||
"""返回 INSERT SQL 片段：列名、占位符和值。"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
return (
|
||||
"runtime_mode, sandbox_instance_id",
|
||||
"%s, %s",
|
||||
[
|
||||
MODE_SANDBOX if ctx.is_sandbox else MODE_LIVE,
|
||||
ctx.sandbox_instance_id if ctx.is_sandbox else LIVE_INSTANCE_ID,
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
def runtime_update_assignments(site_id: int, conn: Any | None = None) -> tuple[str, list[Any]]:
|
||||
"""返回 UPDATE SQL 片段，用于把运行上下文写回已有记录。"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
return (
|
||||
"runtime_mode = %s, sandbox_instance_id = %s",
|
||||
[
|
||||
MODE_SANDBOX if ctx.is_sandbox else MODE_LIVE,
|
||||
ctx.sandbox_instance_id if ctx.is_sandbox else LIVE_INSTANCE_ID,
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
def as_runtime_now_param(site_id: int, conn: Any | None = None) -> datetime:
|
||||
"""返回可传给 SQL 的业务当前时间。"""
|
||||
return get_runtime_context(site_id, conn=conn).business_now
|
||||
|
||||
|
||||
def as_runtime_today_param(site_id: int, conn: Any | None = None) -> date:
|
||||
"""返回可传给 SQL 的业务当前日期。"""
|
||||
return get_runtime_context(site_id, conn=conn).business_date
|
||||
|
||||
|
||||
def as_runtime_year_month_param(site_id: int, conn: Any | None = None) -> str:
|
||||
"""返回 'YYYY-MM' 形式的业务年月，用于 performance 等月度查询。"""
|
||||
bd = get_runtime_context(site_id, conn=conn).business_date
|
||||
return f"{bd.year:04d}-{bd.month:02d}"
|
||||
|
||||
|
||||
def as_runtime_business_now_str(site_id: int, conn: Any | None = None, fmt: str = "%Y-%m-%d %H:%M:%S") -> str:
|
||||
"""返回业务当前时间的格式化字符串，用于 AI prompts 中的 current_time。"""
|
||||
return get_runtime_context(site_id, conn=conn).business_now.strftime(fmt)
|
||||
|
||||
|
||||
def business_date_upper_bound_sql(
|
||||
site_id: int,
|
||||
*,
|
||||
column: str,
|
||||
alias: str = "",
|
||||
cast: str | None = None,
|
||||
conn: Any | None = None,
|
||||
) -> tuple[str, list[Any]]:
|
||||
"""返回业务日上界 SQL 片段。
|
||||
|
||||
sandbox 模式下，强制把 ``column`` 限制在业务日及之前（避免读到「未来」数据）。
|
||||
live 模式下返回空片段，不影响任何逻辑。
|
||||
|
||||
cast 用于把 timestamp/timestamptz 列裁剪成日期再比较，例如 ``cast='date'``。
|
||||
"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
if not ctx.is_sandbox:
|
||||
return ("", [])
|
||||
prefix = f"{alias}." if alias else ""
|
||||
expr = f"{prefix}{column}"
|
||||
if cast:
|
||||
expr = f"({expr})::{cast}"
|
||||
return (f" AND {expr} <= %s", [ctx.business_date])
|
||||
|
||||
|
||||
def apply_runtime_session_vars(conn: Any, ctx: RuntimeContext | None = None, *, site_id: int | None = None) -> None:
|
||||
"""在已有数据库连接上设置 ``app.current_business_date`` 等 GUC 变量。
|
||||
|
||||
供 RLS 视图层（C 方案）使用：视图通过 ``current_setting('app.current_business_date', true)``
|
||||
读取业务日，再对事实/维度表做日期上界裁剪。
|
||||
|
||||
无论 live / sandbox 都设置该变量；live 下视图仍按真实 ``CURRENT_DATE`` 行为。
|
||||
"""
|
||||
if ctx is None:
|
||||
if site_id is None:
|
||||
raise ValueError("apply_runtime_session_vars 需要 ctx 或 site_id 之一")
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
bd = ctx.business_date.isoformat()
|
||||
mode = MODE_SANDBOX if ctx.is_sandbox else MODE_LIVE
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"SELECT set_config('app.current_business_date', %s, true), "
|
||||
"set_config('app.current_runtime_mode', %s, true)",
|
||||
(bd, mode),
|
||||
)
|
||||
@@ -11,6 +11,7 @@
|
||||
import json
|
||||
import logging
|
||||
|
||||
from app.services.runtime_context import as_runtime_now_param, task_runtime_filter
|
||||
from app.trace.decorators import trace_service
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -71,32 +72,42 @@ def run() -> dict:
|
||||
|
||||
conn = _get_connection()
|
||||
try:
|
||||
# 查询所有已过期的 active 任务
|
||||
# 查询所有已过期的 active 任务。沙箱模式按业务时间判断，并只处理当前运行实例。
|
||||
expired_tasks = []
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("SELECT site_id FROM biz.sites WHERE is_active = true")
|
||||
site_ids = [row[0] for row in cur.fetchall()]
|
||||
for site_id in site_ids:
|
||||
runtime_now = as_runtime_now_param(site_id, conn=conn)
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT id, task_type
|
||||
f"""
|
||||
SELECT id, task_type, site_id
|
||||
FROM biz.coach_tasks
|
||||
WHERE expires_at IS NOT NULL
|
||||
AND expires_at < NOW()
|
||||
WHERE site_id = %s
|
||||
{runtime_clause}
|
||||
AND expires_at IS NOT NULL
|
||||
AND expires_at < %s
|
||||
AND status = 'active'
|
||||
"""
|
||||
""",
|
||||
[site_id, *runtime_params, runtime_now],
|
||||
)
|
||||
expired_tasks = cur.fetchall()
|
||||
expired_tasks.extend(cur.fetchall())
|
||||
conn.commit()
|
||||
|
||||
# 逐条处理，每条独立事务
|
||||
for task_id, task_type in expired_tasks:
|
||||
for task_id, task_type, site_id in expired_tasks:
|
||||
try:
|
||||
runtime_now = as_runtime_now_param(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute("BEGIN")
|
||||
cur.execute(
|
||||
"""
|
||||
UPDATE biz.coach_tasks
|
||||
SET status = 'inactive', updated_at = NOW()
|
||||
SET status = 'inactive', updated_at = %s
|
||||
WHERE id = %s AND status = 'active'
|
||||
""",
|
||||
(task_id,),
|
||||
(runtime_now, task_id),
|
||||
)
|
||||
_insert_history(
|
||||
cur,
|
||||
|
||||
@@ -41,6 +41,13 @@ from dataclasses import dataclass
|
||||
from enum import IntEnum
|
||||
|
||||
from app.trace.decorators import trace_service
|
||||
from app.services.runtime_context import (
|
||||
LIVE_INSTANCE_ID,
|
||||
MODE_LIVE,
|
||||
MODE_SANDBOX,
|
||||
get_runtime_context,
|
||||
task_runtime_filter,
|
||||
)
|
||||
|
||||
|
||||
class TaskPriority(IntEnum):
|
||||
@@ -189,6 +196,14 @@ import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _runtime_values(conn, site_id: int):
|
||||
"""返回当前门店任务写入所需的运行上下文值。"""
|
||||
ctx = get_runtime_context(site_id, conn=conn)
|
||||
mode = MODE_SANDBOX if ctx.is_sandbox else MODE_LIVE
|
||||
instance_id = ctx.sandbox_instance_id if ctx.is_sandbox else LIVE_INSTANCE_ID
|
||||
return ctx, mode, instance_id, ctx.business_now
|
||||
|
||||
|
||||
def _get_connection():
|
||||
"""延迟导入 get_connection，避免纯函数测试时触发模块级导入失败。"""
|
||||
from app.database import get_connection
|
||||
@@ -210,7 +225,10 @@ def run() -> dict:
|
||||
|
||||
返回: {"created": int, "replaced": int, "skipped": int, "transferred": int}
|
||||
"""
|
||||
from datetime import datetime, timezone
|
||||
|
||||
stats = {"created": 0, "replaced": 0, "skipped": 0, "transferred": 0}
|
||||
run_started_at = datetime.now(timezone.utc)
|
||||
|
||||
conn = _get_connection()
|
||||
try:
|
||||
@@ -265,6 +283,14 @@ def run() -> dict:
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
# ── 6. 触发 AI 消费事件 — 对本次 run 新建的任务逐个触发 ai_consumption_settled
|
||||
# 仅按 created_at >= run_started_at 过滤（精确锁定本次新建），避免误触发历史任务。
|
||||
# dispatcher 内部按 (event, member_id, site_id, date) 去重，重复触发无害。
|
||||
try:
|
||||
_fire_ai_consumption_events(conn, run_started_at)
|
||||
except Exception:
|
||||
logger.exception("ai_consumption_settled 事件触发失败（不影响任务生成主流程）")
|
||||
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
@@ -278,6 +304,54 @@ def run() -> dict:
|
||||
return stats
|
||||
|
||||
|
||||
def _fire_ai_consumption_events(conn, run_started_at) -> None:
|
||||
"""查询本次 run 新建的任务，对每条 (site_id, member_id, assistant_id) 触发 ai_consumption_settled。
|
||||
|
||||
has_assistant 恒为 True（任务必然绑定助教）。
|
||||
dispatcher 去重确保每 member 每天 AI 链路至多跑一次。
|
||||
"""
|
||||
from app.services.trigger_scheduler import fire_event
|
||||
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
SELECT DISTINCT site_id, member_id, assistant_id
|
||||
FROM biz.coach_tasks
|
||||
WHERE created_at >= %s
|
||||
AND member_id IS NOT NULL
|
||||
AND assistant_id IS NOT NULL
|
||||
""",
|
||||
(run_started_at,),
|
||||
)
|
||||
pairs = cur.fetchall()
|
||||
conn.commit()
|
||||
|
||||
triggered = 0
|
||||
for row in pairs:
|
||||
site_id, member_id, assistant_id = row[0], row[1], row[2]
|
||||
try:
|
||||
fire_event(
|
||||
"ai_consumption_settled",
|
||||
{
|
||||
"site_id": site_id,
|
||||
"member_id": member_id,
|
||||
"assistant_id": assistant_id,
|
||||
"has_assistant": True,
|
||||
},
|
||||
)
|
||||
triggered += 1
|
||||
except Exception:
|
||||
logger.exception(
|
||||
"触发 ai_consumption_settled 失败: site_id=%s member_id=%s",
|
||||
site_id, member_id,
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"ai_consumption_settled 触发完成: 新建任务去重后 %d 个 member，成功触发 %d 次",
|
||||
len(pairs), triggered,
|
||||
)
|
||||
|
||||
|
||||
def _run_for_site(conn, site_id: int, stats: dict) -> None:
|
||||
"""
|
||||
单门店处理流程。
|
||||
@@ -766,9 +840,10 @@ def _run_transfer_check(
|
||||
w_ms = params["transfer_score_w_ms"]
|
||||
w_ml = params["transfer_score_w_ml"]
|
||||
|
||||
from datetime import datetime, timezone
|
||||
from app.services.runtime_context import as_runtime_now_param
|
||||
|
||||
now = datetime.now(timezone.utc)
|
||||
# 业务时间锚：sandbox 模式下用 business_now，避免按真实时间把已转移很久的任务再算成候选
|
||||
now = as_runtime_now_param(site_id, conn=conn)
|
||||
|
||||
for task_id, from_assistant_id, member_id, task_type, transfer_count, created_at in candidates:
|
||||
# CHANGE 2026-03-29 | 用升级倍数判定是否触发转移
|
||||
@@ -805,9 +880,7 @@ def _run_transfer_check(
|
||||
)
|
||||
entry_dates = {r[0]: r[1] for r in cur.fetchall()}
|
||||
|
||||
from datetime import datetime, timezone, timedelta
|
||||
|
||||
now = datetime.now(timezone.utc)
|
||||
# 沿用上方 business_now，避免「真实今天」的入驻时间保护
|
||||
eligible = []
|
||||
for a in pool:
|
||||
aid = a["assistant_id"]
|
||||
|
||||
@@ -37,6 +37,7 @@ from decimal import Decimal
|
||||
from fastapi import HTTPException
|
||||
|
||||
from app.services import fdw_queries
|
||||
from app.services.runtime_context import get_runtime_context, task_runtime_filter
|
||||
from app.services.task_generator import compute_heart_icon
|
||||
from app.trace.decorators import trace_service
|
||||
|
||||
@@ -114,15 +115,17 @@ def _verify_task_ownership(
|
||||
- 不属于当前助教 → 403
|
||||
- required_status 不匹配 → 409
|
||||
"""
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT id, task_type, status, is_pinned, abandon_reason,
|
||||
assistant_id, site_id
|
||||
FROM biz.coach_tasks
|
||||
WHERE id = %s
|
||||
{runtime_clause}
|
||||
""",
|
||||
(task_id,),
|
||||
[task_id, *runtime_params],
|
||||
)
|
||||
row = cur.fetchone()
|
||||
|
||||
@@ -166,22 +169,24 @@ async def get_task_list(user_id: int, site_id: int) -> list[dict]:
|
||||
assistant_id = _get_assistant_id(conn, user_id, site_id)
|
||||
|
||||
# 查询有效 + 已放弃任务（abandoned 排最后）
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT id, task_type, status, priority_score, is_pinned,
|
||||
expires_at, created_at, member_id, abandon_reason
|
||||
FROM biz.coach_tasks
|
||||
WHERE site_id = %s
|
||||
AND assistant_id = %s
|
||||
AND status IN ('active', 'abandoned')
|
||||
{runtime_clause}
|
||||
ORDER BY
|
||||
CASE WHEN status = 'abandoned' THEN 1 ELSE 0 END ASC,
|
||||
is_pinned DESC,
|
||||
priority_score DESC NULLS LAST,
|
||||
created_at ASC
|
||||
""",
|
||||
(site_id, assistant_id),
|
||||
[site_id, assistant_id, *runtime_params],
|
||||
)
|
||||
tasks = cur.fetchall()
|
||||
conn.commit()
|
||||
@@ -605,8 +610,9 @@ async def get_task_list_v2(
|
||||
# 构建排除条件：relationship_building + member_id 不在 RS 范围内
|
||||
# 当排除列表为空时不加额外条件
|
||||
exclude_clause = ""
|
||||
query_params_count: list = [site_id, assistant_id, db_status]
|
||||
query_params_page: list = [site_id, assistant_id, db_status]
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
query_params_count: list = [site_id, assistant_id, db_status, *runtime_params]
|
||||
query_params_page: list = [site_id, assistant_id, db_status, *runtime_params]
|
||||
if rb_exclude_member_ids:
|
||||
exclude_clause = (
|
||||
" AND NOT (task_type = 'relationship_building' AND member_id = ANY(%s))"
|
||||
@@ -621,6 +627,7 @@ async def get_task_list_v2(
|
||||
SELECT COUNT(*)
|
||||
FROM biz.coach_tasks
|
||||
WHERE site_id = %s AND assistant_id = %s AND status = %s
|
||||
{runtime_clause}
|
||||
{exclude_clause}
|
||||
""",
|
||||
query_params_count,
|
||||
@@ -636,6 +643,7 @@ async def get_task_list_v2(
|
||||
expires_at, created_at, member_id, abandon_reason
|
||||
FROM biz.coach_tasks
|
||||
WHERE site_id = %s AND assistant_id = %s AND status = %s
|
||||
{runtime_clause}
|
||||
{exclude_clause}
|
||||
ORDER BY is_pinned DESC,
|
||||
priority_score DESC NULLS LAST,
|
||||
@@ -669,9 +677,11 @@ async def get_task_list_v2(
|
||||
recent60d_map: dict[int, dict] = {}
|
||||
batch_data: dict | None = None
|
||||
try:
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
_ref_date = as_runtime_today_param(site_id, conn=conn)
|
||||
batch_data = fdw_queries.batch_query_for_task_list(
|
||||
conn, site_id, assistant_id, member_ids,
|
||||
datetime.now().year, datetime.now().month,
|
||||
_ref_date.year, _ref_date.month,
|
||||
)
|
||||
member_info_map = batch_data["member_info"]
|
||||
balance_map = batch_data["balance"]
|
||||
@@ -685,6 +695,10 @@ async def get_task_list_v2(
|
||||
# ── 6. 查询 ai_cache 获取 aiSuggestion（优雅降级） ──
|
||||
ai_suggestion_map: dict[int, str] = {}
|
||||
try:
|
||||
runtime_ctx = get_runtime_context(site_id, conn=conn)
|
||||
if runtime_ctx.is_sandbox and runtime_ctx.sandbox_instance_id:
|
||||
member_id_strs = [f"{runtime_ctx.sandbox_instance_id}:{mid}" for mid in member_ids]
|
||||
else:
|
||||
member_id_strs = [str(mid) for mid in member_ids]
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
@@ -706,7 +720,8 @@ async def get_task_list_v2(
|
||||
result = row[1] if isinstance(row[1], dict) else {}
|
||||
summary = result.get("summary", "")
|
||||
if summary:
|
||||
ai_suggestion_map[int(target_id_str)] = summary
|
||||
raw_target = target_id_str.split(":", 1)[-1]
|
||||
ai_suggestion_map[int(raw_target)] = summary
|
||||
conn.commit()
|
||||
except Exception:
|
||||
logger.warning("查询 ai_cache aiSuggestion 失败", exc_info=True)
|
||||
@@ -802,8 +817,11 @@ def build_performance_summary(
|
||||
当 batch_data 为 None 时（如无任务的空列表场景），回退到独立查询。
|
||||
课时/档位/客户数从 monthly_summary（每日更新）取实时数据，
|
||||
不再依赖月初结算的 salary_calc。收入仍从 salary_calc 取（如有）。
|
||||
|
||||
CHANGE 2026-05-02 | now 改用 RuntimeContext.business_date，沙箱不读「未来」月份。
|
||||
"""
|
||||
now = datetime.now()
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
now = as_runtime_today_param(site_id, conn=conn)
|
||||
year, month = now.year, now.month
|
||||
|
||||
if batch_data:
|
||||
@@ -971,15 +989,17 @@ async def get_task_by_member(
|
||||
try:
|
||||
assistant_id = _get_assistant_id(conn, user_id, site_id)
|
||||
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT id, task_type
|
||||
FROM biz.coach_tasks
|
||||
WHERE site_id = %s AND assistant_id = %s AND member_id = %s
|
||||
AND status = 'active'
|
||||
{runtime_clause}
|
||||
""",
|
||||
(site_id, assistant_id, member_id),
|
||||
[site_id, assistant_id, member_id, *runtime_params],
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
|
||||
@@ -1020,16 +1040,18 @@ async def get_task_detail(
|
||||
assistant_id = _get_assistant_id(conn, user_id, site_id)
|
||||
|
||||
# ── 1. 查询任务基础信息 ──
|
||||
runtime_clause, runtime_params = task_runtime_filter(site_id, conn=conn)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
f"""
|
||||
SELECT id, task_type, status, priority_score, is_pinned,
|
||||
expires_at, created_at, member_id, abandon_reason,
|
||||
assistant_id, site_id
|
||||
FROM biz.coach_tasks
|
||||
WHERE id = %s
|
||||
{runtime_clause}
|
||||
""",
|
||||
(task_id,),
|
||||
[task_id, *runtime_params],
|
||||
)
|
||||
row = cur.fetchone()
|
||||
|
||||
@@ -1090,6 +1112,12 @@ async def get_task_detail(
|
||||
# ── 3. 查询维客线索 ──
|
||||
retention_clues = []
|
||||
try:
|
||||
runtime_ctx = get_runtime_context(site_id, conn=conn)
|
||||
member_target_id = (
|
||||
f"{runtime_ctx.sandbox_instance_id}:{member_id}"
|
||||
if runtime_ctx.is_sandbox and runtime_ctx.sandbox_instance_id
|
||||
else str(member_id)
|
||||
)
|
||||
with conn.cursor() as cur:
|
||||
cur.execute(
|
||||
"""
|
||||
@@ -1136,7 +1164,7 @@ async def get_task_detail(
|
||||
AND cache_type IN ('app4_analysis', 'app5_talking_points')
|
||||
ORDER BY created_at DESC
|
||||
""",
|
||||
(str(member_id), site_id),
|
||||
(member_target_id, site_id),
|
||||
)
|
||||
seen_types: set[str] = set()
|
||||
for cache_row in cur.fetchall():
|
||||
@@ -1173,8 +1201,10 @@ async def get_task_detail(
|
||||
|
||||
# CHANGE 2026-03-25 | 统计范围：近60天；列表不限
|
||||
# 预估规则：当月且日期 ≤ 5号
|
||||
from datetime import date, timedelta
|
||||
today = date.today()
|
||||
# CHANGE 2026-05-02 | today 改用 business_date，沙箱不读「未来」60 天
|
||||
from datetime import timedelta
|
||||
from app.services.runtime_context import as_runtime_today_param
|
||||
today = as_runtime_today_param(site_id, conn=conn)
|
||||
cutoff_60d = today - timedelta(days=60)
|
||||
is_estimate_month = today.day <= 5
|
||||
|
||||
|
||||
@@ -10,7 +10,10 @@
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import inspect
|
||||
import logging
|
||||
import threading
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from typing import Any, Callable
|
||||
|
||||
@@ -19,6 +22,34 @@ from app.trace.decorators import trace_service
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _invoke_handler(handler: Callable, **kwargs: Any) -> Any:
|
||||
"""统一调用 handler，自动识别 sync / async。
|
||||
|
||||
- sync handler：直接返回结果
|
||||
- async handler：
|
||||
- 当前线程有 running loop → loop.create_task(coro)，后台异步执行
|
||||
- 当前线程无 running loop → 新起 daemon 线程跑 asyncio.run(coro)，不阻塞调用方
|
||||
|
||||
说明：fire_event / check_scheduled_jobs 是 sync 函数，但部分 handler
|
||||
（如 dispatcher 注册的 AI 事件 handler）是 async def，本包装器保证正确调度。
|
||||
"""
|
||||
result = handler(**kwargs)
|
||||
if not inspect.iscoroutine(result):
|
||||
return result
|
||||
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
loop.create_task(result)
|
||||
return None
|
||||
except RuntimeError:
|
||||
# 同步线程（无 running loop）：用后台线程异步执行 coroutine，不阻塞调用方
|
||||
threading.Thread(
|
||||
target=lambda coro=result: asyncio.run(coro),
|
||||
daemon=True,
|
||||
).start()
|
||||
return None
|
||||
|
||||
|
||||
def _get_connection():
|
||||
"""延迟导入 get_connection，避免纯函数测试时触发模块级导入失败。"""
|
||||
from app.database import get_connection
|
||||
@@ -89,7 +120,8 @@ def fire_event(event_name: str, payload: dict[str, Any] | None = None) -> int:
|
||||
continue
|
||||
try:
|
||||
# 将 job_id 传入 handler，handler 在最终 commit 前更新 last_run_at
|
||||
handler(payload=payload, job_id=job_id)
|
||||
# async handler 经 _invoke_handler 自动调度
|
||||
_invoke_handler(handler, payload=payload, job_id=job_id)
|
||||
executed += 1
|
||||
except Exception:
|
||||
logger.exception("触发器 %s 执行失败", job_name)
|
||||
@@ -136,7 +168,8 @@ def check_scheduled_jobs() -> int:
|
||||
continue
|
||||
try:
|
||||
# cron/interval handler 接受 conn + job_id，在最终 commit 前更新时间戳
|
||||
handler(conn=conn, job_id=job_id)
|
||||
# async handler 经 _invoke_handler 自动调度
|
||||
_invoke_handler(handler, conn=conn, job_id=job_id)
|
||||
# 计算 next_run_at 并更新（在 handler commit 后的新事务中）
|
||||
next_run = _calculate_next_run(trigger_condition, trigger_config)
|
||||
with conn.cursor() as cur:
|
||||
@@ -276,7 +309,7 @@ def run_job_by_id(job_id: int) -> dict:
|
||||
return {"success": False, "message": f"任务 {job_name} 未注册处理器"}
|
||||
|
||||
try:
|
||||
handler()
|
||||
_invoke_handler(handler)
|
||||
# 更新 last_run_at 和 next_run_at
|
||||
next_run = _calculate_next_run(trigger_condition, trigger_config)
|
||||
with conn.cursor() as cur:
|
||||
|
||||
80
apps/backend/app/ws/ai_events.py
Normal file
80
apps/backend/app/ws/ai_events.py
Normal file
@@ -0,0 +1,80 @@
|
||||
"""AI 事件 WebSocket 推送端点。
|
||||
|
||||
提供：
|
||||
- /ws/ai-cache/{site_id} — 缓存更新 / 失效事件
|
||||
- /ws/ai-alerts/{site_id} — AI 告警事件（Phase 3.1）
|
||||
|
||||
协议：
|
||||
- 客户端连接 → 服务端 accept → 订阅 EventBus → 持续 send_json 事件
|
||||
- 事件格式：{"type": "cache_updated|cache_invalidated|alert_created|...", "site_id": int, "payload": {...}}
|
||||
- 服务端关闭或客户端断开时清理订阅
|
||||
|
||||
用 site_id=-1 表示全局订阅（收所有门店事件，admin-web 全局监控用）。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
|
||||
from fastapi import APIRouter, WebSocket, WebSocketDisconnect
|
||||
|
||||
from ..ai.event_bus import AIEvent, get_event_bus
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
ws_router = APIRouter()
|
||||
|
||||
|
||||
@ws_router.websocket("/ws/ai-cache/{site_id}")
|
||||
async def ws_ai_cache(websocket: WebSocket, site_id: int) -> None:
|
||||
"""AI 缓存事件推送。
|
||||
|
||||
site_id=-1 表示订阅全局（收所有门店的 cache_updated / cache_invalidated）。
|
||||
"""
|
||||
await _serve_event_stream(websocket, site_id, endpoint="ai-cache")
|
||||
|
||||
|
||||
@ws_router.websocket("/ws/ai-alerts/{site_id}")
|
||||
async def ws_ai_alerts(websocket: WebSocket, site_id: int) -> None:
|
||||
"""AI 告警事件推送（Phase 3.1）。
|
||||
|
||||
site_id=-1 表示订阅全局告警。
|
||||
事件 type: alert_created / alert_updated / budget_exceeded / circuit_opened。
|
||||
"""
|
||||
await _serve_event_stream(websocket, site_id, endpoint="ai-alerts")
|
||||
|
||||
|
||||
async def _serve_event_stream(
|
||||
websocket: WebSocket, site_id: int, endpoint: str,
|
||||
) -> None:
|
||||
"""共享事件流处理逻辑。"""
|
||||
await websocket.accept()
|
||||
# -1 映射为全局订阅（None）
|
||||
subscribe_key: int | None = None if site_id == -1 else site_id
|
||||
logger.info(
|
||||
"WS %s 连接建立: site_id=%s", endpoint, subscribe_key if subscribe_key else "ALL",
|
||||
)
|
||||
|
||||
bus = get_event_bus()
|
||||
queue = await bus.subscribe(subscribe_key)
|
||||
|
||||
try:
|
||||
while True:
|
||||
event = await queue.get()
|
||||
if event is None:
|
||||
break
|
||||
await websocket.send_json({
|
||||
"type": event.type,
|
||||
"site_id": event.site_id,
|
||||
"payload": event.payload,
|
||||
})
|
||||
except WebSocketDisconnect:
|
||||
logger.info("WS %s 客户端断开: site_id=%s", endpoint, subscribe_key)
|
||||
except Exception:
|
||||
logger.exception("WS %s 异常: site_id=%s", endpoint, subscribe_key)
|
||||
finally:
|
||||
await bus.unsubscribe(subscribe_key, queue)
|
||||
try:
|
||||
await websocket.close()
|
||||
except Exception:
|
||||
pass
|
||||
@@ -2,3 +2,4 @@
|
||||
testpaths = tests
|
||||
pythonpath = .
|
||||
asyncio_mode = auto
|
||||
norecursedirs = _archived _deleted
|
||||
|
||||
@@ -24,6 +24,7 @@
|
||||
- 非 all 行现金流/卡消费/充值字段 = 0
|
||||
- hall 行 = 各具体区域之和（历史兼容）
|
||||
- all 行 = 各具体区域之和（收入/优惠），现金流/充值/卡消费来自 dws_finance_daily_summary
|
||||
无台桌订单和补时长等 all-only 台区只合入 all，不合入具体区域
|
||||
- settle_type IN (1, 3) 过滤
|
||||
- discount_gift_card 使用赠送卡消费金额口径
|
||||
|
||||
@@ -107,6 +108,9 @@ _COUNT_FIELDS = {"order_count", "member_order_count"}
|
||||
|
||||
_ZERO = Decimal("0")
|
||||
|
||||
# 已知不属于看板 7 个具体区域、但应合入 all 的物理台区。
|
||||
_ALL_ONLY_AREA_NAMES = {"补时长", "虚拟台"}
|
||||
|
||||
|
||||
class FinanceAreaDailyTask(FinanceBaseTask):
|
||||
"""
|
||||
@@ -177,6 +181,7 @@ class FinanceAreaDailyTask(FinanceBaseTask):
|
||||
sql = f"""
|
||||
SELECT
|
||||
{biz_expr} AS stat_date,
|
||||
sh.table_id AS table_id,
|
||||
dt.site_table_area_name AS area_name,
|
||||
sh.settle_type,
|
||||
-- 收入
|
||||
@@ -378,8 +383,9 @@ def transform_area_daily(
|
||||
)
|
||||
# 收集所有涉及的日期
|
||||
all_dates: set[date] = set()
|
||||
# 未知区域名称计数（汇总后一次性输出，避免逐行 warning 产生大量日志噪音）
|
||||
# 未知/无具体区域计数（汇总后一次性输出，避免逐行日志噪音）
|
||||
_unknown_area_counts: Dict[str, int] = defaultdict(int)
|
||||
_all_only_area_counts: Dict[str, int] = defaultdict(int)
|
||||
|
||||
for row in settlement_rows:
|
||||
sd = row.get("stat_date")
|
||||
@@ -393,10 +399,15 @@ def transform_area_daily(
|
||||
continue
|
||||
|
||||
area_name = row.get("area_name")
|
||||
table_id = row.get("table_id")
|
||||
area_code = resolve_area_code(area_name)
|
||||
|
||||
if area_code is None:
|
||||
_unknown_area_counts[str(area_name)] += 1
|
||||
unmatched_label = _format_unmatched_area_label(area_name, table_id)
|
||||
if _is_all_only_area(area_name, table_id):
|
||||
_all_only_area_counts[unmatched_label] += 1
|
||||
else:
|
||||
_unknown_area_counts[unmatched_label] += 1
|
||||
|
||||
# 提取金额
|
||||
table_fee = safe_decimal_fn(row.get("table_fee_amount", 0))
|
||||
@@ -479,11 +490,20 @@ def transform_area_daily(
|
||||
for k, v in fields.items():
|
||||
bucket[k] = bucket[k] + v
|
||||
|
||||
# 汇总输出未知区域名称（避免逐行 warning 刷屏）
|
||||
# 汇总输出 all-only 区域（无台桌订单、补时长等），这些记录合入 all 属正常口径。
|
||||
if _all_only_area_counts:
|
||||
summary = ", ".join(f"'{k}': {v}次" for k, v in _all_only_area_counts.items())
|
||||
logger.info(
|
||||
"DWS_FINANCE_AREA_DAILY: 共 %d 条结算单无具体区域（已计入 all，不计入任何具体区域）: %s",
|
||||
sum(_all_only_area_counts.values()),
|
||||
summary,
|
||||
)
|
||||
|
||||
# 汇总输出真正未知区域名称（避免逐行 warning 刷屏）
|
||||
if _unknown_area_counts:
|
||||
summary = ", ".join(f"'{k}': {v}次" for k, v in _unknown_area_counts.items())
|
||||
logger.warning(
|
||||
"DWS_FINANCE_AREA_DAILY: 共 %d 条结算单区域未匹配（已计入 all 但不计入任何具体区域）: %s",
|
||||
"DWS_FINANCE_AREA_DAILY: 共 %d 条结算单区域未匹配（已计入 all 但不计入任何具体区域，请检查 dim_table/AREA_LABEL_MAP）: %s",
|
||||
sum(_unknown_area_counts.values()),
|
||||
summary,
|
||||
)
|
||||
@@ -618,4 +638,42 @@ def _safe_decimal(value: Any, default: Decimal = _ZERO) -> Decimal:
|
||||
return default
|
||||
|
||||
|
||||
def _is_all_only_area(area_name: Any, table_id: Any) -> bool:
|
||||
"""判断结算单是否属于无具体区域但应合入 all 的正常口径。
|
||||
|
||||
CHANGE 2026-05-02 | 扩大豁免规则，避免噪音 WARNING：
|
||||
- "补时长" / "虚拟台" 的带数字/空格变体（如 "补时长2"、"虚拟台 1"）也算 all-only。
|
||||
- 维表 site_table_area_name 为空（NULL）但有 table_id 的脏数据，归入 all-only INFO，
|
||||
因为这通常是 dim_table SCD2 缺区域名而非真正映射缺口；金额仍合入 all 不丢失。
|
||||
真正的「未知非空区域名」（如新店自定义命名未在 AREA_LABEL_MAP 中）才进 WARNING。
|
||||
"""
|
||||
if area_name is None:
|
||||
# 无 table_id：本来就没台桌，正常 all-only
|
||||
# 有 table_id：维表区域名缺失，作为 dim_table 数据问题，仍归 all-only 但保留可观测性（INFO 行会带 'table_id=… None' 标签）
|
||||
return True
|
||||
if not isinstance(area_name, str):
|
||||
return False
|
||||
name = area_name.strip()
|
||||
if not name:
|
||||
return True
|
||||
if name in _ALL_ONLY_AREA_NAMES:
|
||||
return True
|
||||
# 形如 "补时长2"、"补时长 3"、"虚拟台4" 等编号变体
|
||||
for prefix in _ALL_ONLY_AREA_NAMES:
|
||||
if name.startswith(prefix):
|
||||
tail = name[len(prefix):].strip()
|
||||
if not tail or tail.isdigit():
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _format_unmatched_area_label(area_name: Any, table_id: Any) -> str:
|
||||
"""格式化未匹配区域日志标签，区分无台桌订单和维表缺口。"""
|
||||
if area_name is None and not table_id:
|
||||
return "无台桌"
|
||||
if area_name is None:
|
||||
return f"table_id={table_id}: None"
|
||||
return str(area_name)
|
||||
|
||||
|
||||
__all__ = ["FinanceAreaDailyTask", "transform_area_daily"]
|
||||
|
||||
@@ -42,7 +42,10 @@ load_dotenv(_REPO_ROOT / ".env", override=False)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_TIMEOUT = (5, 30)
|
||||
# CHANGE 2026-05-02 | 旧值 (5, 30) 在 recall_completion_check / task_generator 这种长任务下
|
||||
# 经常 30s 读超时（实际处理 ~33s 以上）。临时止血提到 600s 与 flow_runner 对齐；
|
||||
# 长期方案是后端 /api/internal/run-job 改异步入队（参见 docs/database/changes/2026-05-02__sandbox_complete_refactor.md 已知未覆盖）
|
||||
_TIMEOUT = (10, 600)
|
||||
|
||||
# HTTP 模式<E6A8A1><E5BC8F><EFBFBD>按顺序执行的后端任务
|
||||
_JOB_SEQUENCE = [
|
||||
|
||||
@@ -20,13 +20,47 @@ Component({
|
||||
type: Number,
|
||||
value: 200,
|
||||
},
|
||||
/**
|
||||
* Phase 2.3：来源页面标识（sourcePage），用于后端注入 page_context。
|
||||
* 取值参考 backend page_context.py 的 SUPPORTED_PAGE_TYPES：
|
||||
* board-finance / board-coach / board-customer / performance / task-list / my-profile 等。
|
||||
* 为空时不传入 chat 页。
|
||||
*/
|
||||
sourcePage: {
|
||||
type: String,
|
||||
value: '',
|
||||
},
|
||||
/**
|
||||
* Phase 2.3：页面筛选参数（board-* 页面的 timeDimension/dimension/areaFilter 等），
|
||||
* 将作为 JSON 字符串附加到 url，在 chat 页面解析后随 SSE 请求发给后端。
|
||||
*/
|
||||
pageFilters: {
|
||||
type: Object,
|
||||
value: null as Record<string, string> | null,
|
||||
},
|
||||
},
|
||||
|
||||
methods: {
|
||||
onTap() {
|
||||
let url = this.data.targetUrl
|
||||
const params: string[] = []
|
||||
if (this.data.customerId) {
|
||||
url += `?customerId=${this.data.customerId}`
|
||||
params.push(`customerId=${encodeURIComponent(this.data.customerId)}`)
|
||||
}
|
||||
if (this.data.sourcePage) {
|
||||
params.push(`sourcePage=${encodeURIComponent(this.data.sourcePage)}`)
|
||||
}
|
||||
if (this.data.pageFilters && Object.keys(this.data.pageFilters).length > 0) {
|
||||
try {
|
||||
params.push(
|
||||
`pageFilters=${encodeURIComponent(JSON.stringify(this.data.pageFilters))}`,
|
||||
)
|
||||
} catch {
|
||||
// 非法 filters 忽略，不影响跳转
|
||||
}
|
||||
}
|
||||
if (params.length > 0) {
|
||||
url += (url.includes('?') ? '&' : '?') + params.join('&')
|
||||
}
|
||||
wx.navigateTo({
|
||||
url,
|
||||
|
||||
@@ -203,6 +203,8 @@
|
||||
<!-- AI 洞察 -->
|
||||
<!-- CHANGE 2026-03-12 | intent: H5 原型使用 SVG 机器人图标，不可用 emoji 替代；规范要求内联 SVG 导出为文件用 image 引用 -->
|
||||
<!-- CHANGE 2026-03-21 | P13 T6.1: AI 洞察改为动态渲染，移除硬编码文案 -->
|
||||
<!-- CHANGE 2026-04-22 | AI 洞察改版：两段式（dim标题 + 正文），第3条起省略为1行，加"查看全部"按钮 + 覆盖大弹窗 -->
|
||||
<!-- CHANGE 2026-04-22 seq11/12 置顶：AI 洞察区首屏为"本期总结"（健康度 + 跟踪），下方为明细 -->
|
||||
<view class="ai-insight-section">
|
||||
<view class="ai-insight-header">
|
||||
<view class="ai-insight-icon">
|
||||
@@ -210,11 +212,108 @@
|
||||
</view>
|
||||
<text class="ai-insight-title">AI 智能洞察</text>
|
||||
</view>
|
||||
<view class="ai-insight-body" wx:if="{{aiInsights.length > 0}}">
|
||||
<text class="ai-insight-line" wx:for="{{aiInsights}}" wx:key="index"><text class="ai-insight-dim">{{item.icon}} </text>{{item.text}}</text>
|
||||
|
||||
<!-- 本期总结卡片：seq11(健康度评级) + seq12(跟踪指标) -->
|
||||
<view class="ai-summary-card ai-summary-card--{{summaryLightType || 'neutral'}}" wx:if="{{aiInsightSummary.evaluation || aiInsightSummary.tracking}}">
|
||||
<view class="ai-summary-head">
|
||||
<view class="ai-summary-badge ai-summary-badge--{{summaryLightType || 'neutral'}}" wx:if="{{summaryLightLabel}}">
|
||||
<text>{{summaryLightLabel}}</text>
|
||||
</view>
|
||||
<view class="ai-insight-body" wx:else>
|
||||
<text class="ai-insight-line ai-insight-dim">暂无洞察数据</text>
|
||||
<text class="ai-summary-head-title">本期总结</text>
|
||||
</view>
|
||||
<!-- 2026-04-22 v4：evaluation title 与顶部徽章语义重复，隐藏 title 仅展示 body -->
|
||||
<view class="ai-summary-block" wx:if="{{aiInsightSummary.evaluation}}">
|
||||
<view class="ai-summary-block-body ai-summary-block-body-clamp">
|
||||
<text wx:for="{{aiInsightSummary.evaluation.bodySegs}}" wx:key="index" wx:for-item="seg" class="md-seg {{seg.bold ? 'md-bold' : ''}} {{seg.italic ? 'md-italic' : ''}}">{{seg.text}}</text>
|
||||
</view>
|
||||
</view>
|
||||
<view class="ai-summary-divider" wx:if="{{aiInsightSummary.evaluation && aiInsightSummary.tracking}}"></view>
|
||||
<view class="ai-summary-block ai-summary-block--tracking" wx:if="{{aiInsightSummary.tracking}}">
|
||||
<text class="ai-summary-block-title">⏰ {{aiInsightSummary.tracking.title}}</text>
|
||||
<view class="ai-summary-block-body ai-summary-block-body-clamp">
|
||||
<text wx:for="{{aiInsightSummary.tracking.bodySegs}}" wx:key="index" wx:for-item="seg" class="md-seg {{seg.bold ? 'md-bold' : ''}} {{seg.italic ? 'md-italic' : ''}}">{{seg.text}}</text>
|
||||
</view>
|
||||
</view>
|
||||
</view>
|
||||
|
||||
<view class="ai-insight-body" wx:if="{{aiInsightDetails.length > 0}}">
|
||||
<view class="ai-insight-details-label" wx:if="{{aiInsightSummary.evaluation || aiInsightSummary.tracking}}">
|
||||
<text class="ai-insight-details-label-text">分板块明细洞察 · 仅展示前 3 条</text>
|
||||
</view>
|
||||
<!-- 2026-04-22 v3：seq 1/2/3 统一展示"标题 + 单行省略正文"，详情看弹窗 -->
|
||||
<block wx:for="{{aiInsightDetails}}" wx:key="index" wx:if="{{index < 3}}">
|
||||
<view class="ai-insight-item">
|
||||
<view class="ai-insight-item-title">
|
||||
<text class="ai-insight-item-seq">{{index + 1}}</text>
|
||||
<text class="ai-insight-item-name">{{item.title}}</text>
|
||||
</view>
|
||||
<view class="ai-insight-item-body ai-insight-item-body-ellipsis">
|
||||
<text wx:for="{{item.bodySegs}}" wx:key="index" wx:for-item="seg" class="md-seg {{seg.bold ? 'md-bold' : ''}} {{seg.italic ? 'md-italic' : ''}}">{{seg.text}}</text>
|
||||
</view>
|
||||
</view>
|
||||
</block>
|
||||
<!-- 2026-04-22 v3：只要有洞察就显示"查看全部"按钮，引导进弹窗看完整内容 -->
|
||||
<view class="ai-insight-more" wx:if="{{aiInsightDetails.length > 0 || aiInsightSummary.evaluation}}" bindtap="openAllInsights" hover-class="ai-insight-more-hover">
|
||||
<text class="ai-insight-more-text">查看全部 AI 洞察 ›</text>
|
||||
</view>
|
||||
</view>
|
||||
<view class="ai-insight-body" wx:elif="{{!aiInsightSummary.evaluation && !aiInsightSummary.tracking}}">
|
||||
<view class="ai-insight-item-body ai-insight-dim">暂无洞察数据</view>
|
||||
</view>
|
||||
</view>
|
||||
|
||||
<!-- AI 洞察全部查看弹窗：覆盖除底部 tab 外整个页面；header / 可滚动 body / 底部通栏按钮 -->
|
||||
<view class="ai-modal-mask" wx:if="{{aiInsightsModalVisible}}" bindtap="closeAllInsights" catchtouchmove="_noop">
|
||||
<view class="ai-modal" catchtap="_noop">
|
||||
<view class="ai-modal-header">
|
||||
<view class="ai-modal-title-wrap">
|
||||
<view class="ai-insight-icon">
|
||||
<image src="/assets/icons/ai-robot.svg" mode="aspectFit" class="ai-insight-icon-img" />
|
||||
</view>
|
||||
<text class="ai-modal-title">AI 智能洞察 · 共 {{aiInsights.length}} 条</text>
|
||||
</view>
|
||||
<view class="ai-modal-close" bindtap="closeAllInsights" hover-class="ai-modal-close-hover">
|
||||
<text class="ai-modal-close-icon">✕</text>
|
||||
</view>
|
||||
</view>
|
||||
<scroll-view scroll-y="{{true}}" class="ai-modal-body" enhanced="{{true}}" show-scrollbar="{{false}}" bounces="{{true}}">
|
||||
<!-- 弹窗顶部：本期总结（seq11+seq12）- 同款 summary-card -->
|
||||
<view class="ai-summary-card ai-summary-card--{{summaryLightType || 'neutral'}} ai-summary-card--modal" wx:if="{{aiInsightSummary.evaluation || aiInsightSummary.tracking}}">
|
||||
<view class="ai-summary-head">
|
||||
<view class="ai-summary-badge ai-summary-badge--{{summaryLightType || 'neutral'}}" wx:if="{{summaryLightLabel}}">
|
||||
<text>{{summaryLightLabel}}</text>
|
||||
</view>
|
||||
<text class="ai-summary-head-title">本期总结</text>
|
||||
</view>
|
||||
<view class="ai-summary-block" wx:if="{{aiInsightSummary.evaluation}}">
|
||||
<view class="ai-summary-block-body">
|
||||
<text wx:for="{{aiInsightSummary.evaluation.bodySegs}}" wx:key="index" wx:for-item="seg" class="md-seg {{seg.bold ? 'md-bold' : ''}} {{seg.italic ? 'md-italic' : ''}}">{{seg.text}}</text>
|
||||
</view>
|
||||
</view>
|
||||
<view class="ai-summary-divider" wx:if="{{aiInsightSummary.evaluation && aiInsightSummary.tracking}}"></view>
|
||||
<view class="ai-summary-block ai-summary-block--tracking" wx:if="{{aiInsightSummary.tracking}}">
|
||||
<text class="ai-summary-block-title">⏰ {{aiInsightSummary.tracking.title}}</text>
|
||||
<view class="ai-summary-block-body">
|
||||
<text wx:for="{{aiInsightSummary.tracking.bodySegs}}" wx:key="index" wx:for-item="seg" class="md-seg {{seg.bold ? 'md-bold' : ''}} {{seg.italic ? 'md-italic' : ''}}">{{seg.text}}</text>
|
||||
</view>
|
||||
</view>
|
||||
</view>
|
||||
|
||||
<view class="ai-modal-details-label" wx:if="{{(aiInsightSummary.evaluation || aiInsightSummary.tracking) && aiInsightDetails.length > 0}}">
|
||||
<text class="ai-modal-details-label-text">分板块明细洞察</text>
|
||||
</view>
|
||||
<view class="ai-modal-item" wx:for="{{aiInsightDetails}}" wx:key="index">
|
||||
<view class="ai-modal-item-title">
|
||||
<text class="ai-modal-item-seq">{{index + 1}}</text>
|
||||
<text class="ai-modal-item-name">{{item.title}}</text>
|
||||
</view>
|
||||
<view class="ai-modal-item-body">
|
||||
<text wx:for="{{item.bodySegs}}" wx:key="index" wx:for-item="seg" class="md-seg {{seg.bold ? 'md-bold' : ''}} {{seg.italic ? 'md-italic' : ''}}">{{seg.text}}</text>
|
||||
</view>
|
||||
</view>
|
||||
<view class="ai-modal-footer-space"></view>
|
||||
</scroll-view>
|
||||
<view class="ai-modal-footer" bindtap="closeAllInsights" hover-class="ai-modal-footer-hover">关闭</view>
|
||||
</view>
|
||||
</view>
|
||||
</view>
|
||||
|
||||
@@ -492,7 +492,7 @@ AI_CHANGELOG
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 14rpx;
|
||||
margin-bottom: 22rpx;
|
||||
margin-bottom: 30rpx;
|
||||
}
|
||||
|
||||
/* CHANGE 2026-03-12 | intent: H5 原型 AI 图标为 SVG 机器人（24×24 → 42rpx），不可用 emoji 替代 */
|
||||
@@ -545,6 +545,357 @@ AI_CHANGELOG
|
||||
color: rgba(255, 255, 255, 0.85);
|
||||
}
|
||||
|
||||
/* CHANGE 2026-04-22 v2 | AI 洞察列表项：与弹窗同款（序号徽章 + 标题 + 缩进正文） */
|
||||
.ai-insight-item {
|
||||
padding: 6rpx 0 10rpx 0;
|
||||
}
|
||||
|
||||
.ai-insight-item-title {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12rpx;
|
||||
margin-bottom: 8rpx;
|
||||
}
|
||||
|
||||
.ai-insight-item-seq {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
min-width: 32rpx;
|
||||
height: 32rpx;
|
||||
padding: 0 8rpx;
|
||||
border-radius: 10rpx;
|
||||
background: linear-gradient(135deg, #667eea, #764ba2);
|
||||
color: #fff;
|
||||
font-size: 20rpx;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.ai-insight-item-name {
|
||||
font-size: 26rpx;
|
||||
font-weight: 600;
|
||||
color: rgba(255, 255, 255, 0.95);
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.ai-insight-item-body {
|
||||
display: block;
|
||||
font-size: 24rpx;
|
||||
line-height: 36rpx;
|
||||
color: rgba(255, 255, 255, 0.78);
|
||||
text-indent: 48rpx;
|
||||
}
|
||||
|
||||
.ai-insight-item-body-ellipsis {
|
||||
display: -webkit-box;
|
||||
-webkit-box-orient: vertical;
|
||||
-webkit-line-clamp: 1;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
/* ===== 2026-04-22 seq11/12 置顶：本期总结（轻量版） ===== */
|
||||
/* 不用卡片容器，直接嵌入 AI 洞察区，用彩色小点 + 淡分隔区分层级 */
|
||||
.ai-summary-card {
|
||||
margin: 0 24rpx 18rpx 0;
|
||||
padding: 0 0 18rpx 0;
|
||||
border-bottom: 2rpx dashed rgba(255, 255, 255, 0.1);
|
||||
}
|
||||
|
||||
.ai-summary-card--modal {
|
||||
margin: 0 0 14rpx 0;
|
||||
}
|
||||
|
||||
/* 去掉左侧彩条，保留类名备用（无样式即不渲染） */
|
||||
.ai-summary-card--green,
|
||||
.ai-summary-card--yellow,
|
||||
.ai-summary-card--red,
|
||||
.ai-summary-card--neutral {
|
||||
background: transparent;
|
||||
}
|
||||
|
||||
.ai-summary-head {
|
||||
display: flex;
|
||||
align-items: baseline; /* 徽章与"本期总结"按文字基线对齐（字号不同时看起来贴底） */
|
||||
gap: 10rpx;
|
||||
margin-bottom: 14rpx;
|
||||
}
|
||||
|
||||
/* 徽章：去胶囊底，纯色粗字强调三色灯级别 */
|
||||
.ai-summary-badge {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
font-size: 30rpx;
|
||||
font-weight: 700;
|
||||
letter-spacing: 1rpx;
|
||||
padding: 0;
|
||||
background: transparent !important;
|
||||
box-shadow: none !important;
|
||||
}
|
||||
|
||||
.ai-summary-badge--green { color: #4ade80; }
|
||||
.ai-summary-badge--yellow { color: #facc15; }
|
||||
.ai-summary-badge--red { color: #f87171; }
|
||||
.ai-summary-badge--neutral { color: rgba(255, 255, 255, 0.6); }
|
||||
|
||||
.ai-summary-head-title {
|
||||
font-size: 22rpx;
|
||||
font-weight: 400;
|
||||
color: rgba(255, 255, 255, 0.5);
|
||||
letter-spacing: 1rpx;
|
||||
}
|
||||
|
||||
.ai-summary-block {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 6rpx;
|
||||
}
|
||||
|
||||
.ai-summary-block-title {
|
||||
font-size: 25rpx;
|
||||
font-weight: 600;
|
||||
color: rgba(255, 255, 255, 0.88);
|
||||
line-height: 36rpx;
|
||||
}
|
||||
|
||||
.ai-summary-block-body {
|
||||
font-size: 24rpx;
|
||||
line-height: 36rpx;
|
||||
color: rgba(255, 255, 255, 0.72);
|
||||
}
|
||||
|
||||
/* 2026-04-22 v3：总结区 body 2 行省略，突出要点 */
|
||||
.ai-summary-block-body-clamp {
|
||||
display: -webkit-box;
|
||||
-webkit-box-orient: vertical;
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
}
|
||||
|
||||
.ai-summary-block--tracking {
|
||||
margin-top: 14rpx;
|
||||
}
|
||||
.ai-summary-block--tracking .ai-summary-block-title {
|
||||
color: rgba(251, 191, 36, 0.85);
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
.ai-summary-divider {
|
||||
display: none;
|
||||
}
|
||||
|
||||
/* "分板块明细洞察"分组标签 */
|
||||
.ai-insight-details-label {
|
||||
padding: 2rpx 0 12rpx 0;
|
||||
}
|
||||
|
||||
.ai-insight-details-label-text {
|
||||
font-size: 20rpx;
|
||||
color: rgba(255, 255, 255, 0.42);
|
||||
letter-spacing: 1rpx;
|
||||
}
|
||||
|
||||
.ai-modal-details-label {
|
||||
padding: 6rpx 0 8rpx 0;
|
||||
margin-bottom: 4rpx;
|
||||
}
|
||||
|
||||
.ai-modal-details-label-text {
|
||||
font-size: 20rpx;
|
||||
color: rgba(255, 255, 255, 0.42);
|
||||
letter-spacing: 1rpx;
|
||||
}
|
||||
|
||||
/* 2026-04-22 小程序 Markdown 内联样式：**加粗** / *倾斜* */
|
||||
.md-seg {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
.md-bold {
|
||||
font-weight: 700;
|
||||
color: rgba(255, 255, 255, 0.98);
|
||||
}
|
||||
|
||||
.md-italic {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
/* 加粗同时倾斜时组合生效（class 拼接即可） */
|
||||
|
||||
/* CHANGE 2026-04-22 v2 | "查看全部" 按钮居中 */
|
||||
.ai-insight-more {
|
||||
margin: 10rpx 24rpx 0 0;
|
||||
padding: 18rpx 24rpx;
|
||||
text-align: center;
|
||||
color: rgba(255, 255, 255, 0.88);
|
||||
font-size: 26rpx;
|
||||
background: rgba(255, 255, 255, 0.06);
|
||||
border-radius: 12rpx;
|
||||
}
|
||||
|
||||
.ai-insight-more-hover {
|
||||
background: rgba(255, 255, 255, 0.12);
|
||||
}
|
||||
|
||||
.ai-insight-more-text {
|
||||
letter-spacing: 1rpx;
|
||||
}
|
||||
|
||||
/* CHANGE 2026-04-22 | AI 全部洞察弹窗：覆盖除底部 tab 外整个页面 */
|
||||
.ai-modal-mask {
|
||||
position: fixed;
|
||||
top: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
bottom: 110rpx; /* 避让自定义 tabBar 约 110rpx */
|
||||
bottom: calc(110rpx + env(safe-area-inset-bottom));
|
||||
background: rgba(0, 0, 0, 0.55);
|
||||
z-index: 9998;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
justify-content: flex-end;
|
||||
}
|
||||
|
||||
/* 2026-04-22 v5：fixed top+bottom 固定高（scroll-view 在 flex max-height 里渲染溢出，放弃 auto 高度） */
|
||||
.ai-modal {
|
||||
position: fixed;
|
||||
left: 24rpx;
|
||||
right: 24rpx;
|
||||
top: 40rpx;
|
||||
bottom: calc(150rpx + env(safe-area-inset-bottom)); /* 110rpx tab + 40rpx 留白 */
|
||||
background: #2e2e2e;
|
||||
border-radius: 24rpx;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
overflow: hidden;
|
||||
box-shadow: 0 12rpx 48rpx rgba(0, 0, 0, 0.45);
|
||||
z-index: 9999;
|
||||
}
|
||||
|
||||
.ai-modal-header {
|
||||
flex-shrink: 0;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
padding: 28rpx 28rpx 20rpx 28rpx;
|
||||
border-bottom: 2rpx solid rgba(255, 255, 255, 0.08);
|
||||
}
|
||||
|
||||
.ai-modal-title-wrap {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 14rpx;
|
||||
flex: 1;
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.ai-modal-title {
|
||||
font-size: 28rpx;
|
||||
font-weight: 600;
|
||||
color: rgba(255, 255, 255, 0.95);
|
||||
overflow: hidden;
|
||||
text-overflow: ellipsis;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.ai-modal-close {
|
||||
width: 52rpx;
|
||||
height: 52rpx;
|
||||
border-radius: 50%;
|
||||
background: rgba(255, 255, 255, 0.08);
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
margin-left: 16rpx;
|
||||
}
|
||||
|
||||
.ai-modal-close-hover {
|
||||
background: rgba(255, 255, 255, 0.18);
|
||||
}
|
||||
|
||||
.ai-modal-close-icon {
|
||||
font-size: 28rpx;
|
||||
color: rgba(255, 255, 255, 0.85);
|
||||
line-height: 28rpx;
|
||||
}
|
||||
|
||||
.ai-modal-body {
|
||||
/* 2026-04-22 v5：modal 固定高度后 flex:1 1 0 分配剩余空间给 scroll-view */
|
||||
flex: 1 1 0;
|
||||
min-height: 0;
|
||||
padding: 20rpx 28rpx 12rpx 28rpx;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
.ai-modal-item {
|
||||
padding: 22rpx 0 22rpx 0;
|
||||
border-bottom: 2rpx dashed rgba(255, 255, 255, 0.1);
|
||||
}
|
||||
|
||||
.ai-modal-item:last-of-type {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
.ai-modal-item-title {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12rpx;
|
||||
margin-bottom: 10rpx;
|
||||
}
|
||||
|
||||
.ai-modal-item-seq {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
min-width: 36rpx;
|
||||
height: 36rpx;
|
||||
padding: 0 10rpx;
|
||||
border-radius: 18rpx;
|
||||
background: linear-gradient(135deg, #667eea, #764ba2);
|
||||
color: #fff;
|
||||
font-size: 22rpx;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.ai-modal-item-name {
|
||||
font-size: 28rpx;
|
||||
font-weight: 600;
|
||||
color: rgba(255, 255, 255, 0.95);
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.ai-modal-item-body {
|
||||
display: block;
|
||||
font-size: 26rpx;
|
||||
line-height: 40rpx;
|
||||
color: rgba(255, 255, 255, 0.78);
|
||||
text-indent: 48rpx; /* 首行缩进，和 seq 徽章对齐阅读感 */
|
||||
}
|
||||
|
||||
.ai-modal-footer-space {
|
||||
height: 24rpx;
|
||||
}
|
||||
|
||||
/* 2026-04-22 v3 | 弹窗底部整块作为关闭按钮：固定高度 100rpx，贯通整宽 */
|
||||
.ai-modal-footer {
|
||||
flex-shrink: 0;
|
||||
height: 100rpx;
|
||||
line-height: 100rpx;
|
||||
text-align: center;
|
||||
font-size: 30rpx;
|
||||
font-weight: 500;
|
||||
color: #fff;
|
||||
background: linear-gradient(135deg, #667eea, #764ba2);
|
||||
letter-spacing: 8rpx;
|
||||
}
|
||||
|
||||
.ai-modal-footer-hover {
|
||||
opacity: 0.82;
|
||||
}
|
||||
|
||||
/* ===== 通用表格边框容器 ===== */
|
||||
.table-bordered {
|
||||
border: 2rpx solid #e7e7e7;
|
||||
|
||||
@@ -198,6 +198,12 @@ Page({
|
||||
/** 最后一次发送的用户消息内容（用于重试） */
|
||||
_lastUserContent: '',
|
||||
|
||||
/** SSE 断线重试次数 */
|
||||
_sseRetryCount: 0,
|
||||
|
||||
/** SSE 最大自动重试次数 */
|
||||
_SSE_MAX_RETRY: 2,
|
||||
|
||||
onShow() {
|
||||
// 权限守卫：检查登录状态、账号禁用、角色权限
|
||||
checkPageAccess('pages/chat/chat')
|
||||
@@ -227,11 +233,28 @@ Page({
|
||||
this.loadMessagesByContext('coach', options.coachId)
|
||||
} else if (options?.sourcePage) {
|
||||
// 看板类入口：保存来源页面和筛选参数
|
||||
const filterKeys = ['timeDimension', 'areaFilter', 'dimension', 'typeFilter', 'projectFilter']
|
||||
// Phase 2.3：优先解析 options.pageFilters（ai-float-button 传入的 JSON 字符串），
|
||||
// 回退到单独键（旧入口兼容：timeDimension / areaFilter 等）
|
||||
const pageFilters: Record<string, string> = {}
|
||||
if (options.pageFilters) {
|
||||
try {
|
||||
const parsed = JSON.parse(decodeURIComponent(options.pageFilters))
|
||||
if (parsed && typeof parsed === 'object') {
|
||||
for (const k of Object.keys(parsed)) {
|
||||
const v = parsed[k]
|
||||
if (v != null) pageFilters[k] = String(v)
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
// JSON 解析失败忽略，回退到单键读取
|
||||
}
|
||||
}
|
||||
if (Object.keys(pageFilters).length === 0) {
|
||||
const filterKeys = ['timeDimension', 'areaFilter', 'dimension', 'typeFilter', 'projectFilter']
|
||||
for (const key of filterKeys) {
|
||||
if (options[key]) pageFilters[key] = options[key]
|
||||
}
|
||||
}
|
||||
this.setData({ sourcePage: options.sourcePage, pageFilters })
|
||||
this.loadMessagesByContext(options.sourcePage, '')
|
||||
} else {
|
||||
@@ -418,6 +441,7 @@ Page({
|
||||
},
|
||||
// onDone: 流结束，更新消息 ID 和时间
|
||||
(messageId: number, createdAt: string) => {
|
||||
this._sseRetryCount = 0
|
||||
this.setData({
|
||||
[`messages[${aiIndex}].id`]: String(messageId),
|
||||
[`messages[${aiIndex}].timestamp`]: createdAt,
|
||||
@@ -477,8 +501,20 @@ Page({
|
||||
}
|
||||
},
|
||||
fail: () => {
|
||||
// 网络错误或连接中断
|
||||
if (this.data.isStreaming) {
|
||||
// 网络错误或连接中断：无内容时指数退避重连
|
||||
this._sseTask = null
|
||||
if (!this.data.isStreaming) return
|
||||
if (fullContent === '' && this._sseRetryCount < this._SSE_MAX_RETRY) {
|
||||
this._sseRetryCount++
|
||||
const delay = (2 ** this._sseRetryCount) * 1000
|
||||
wx.showToast({ title: `重连中 ${this._sseRetryCount}/${this._SSE_MAX_RETRY}...`, icon: 'loading', duration: delay })
|
||||
this.setData({
|
||||
messages: this.data.messages.slice(0, aiIndex),
|
||||
isStreaming: false,
|
||||
streamingContent: '',
|
||||
})
|
||||
setTimeout(() => { this.triggerAIReply(chatId, content) }, delay)
|
||||
} else {
|
||||
const errorContent = fullContent || '连接中断，请重试'
|
||||
this.setData({
|
||||
[`messages[${aiIndex}].content`]: errorContent,
|
||||
@@ -487,7 +523,6 @@ Page({
|
||||
})
|
||||
wx.showToast({ title: '连接中断', icon: 'none', duration: 3000 })
|
||||
}
|
||||
this._sseTask = null
|
||||
},
|
||||
} as WechatMiniprogram.RequestOption)
|
||||
|
||||
@@ -509,4 +544,19 @@ Page({
|
||||
}, 50)
|
||||
}, 50)
|
||||
},
|
||||
|
||||
/** 点击引用卡片：跳转到对应详情页（Phase 2.1） */
|
||||
onRefCardTap(e: WechatMiniprogram.BaseEvent & { currentTarget: { dataset: { link?: string } } }) {
|
||||
const link = e.currentTarget?.dataset?.link
|
||||
if (!link || typeof link !== 'string') {
|
||||
return
|
||||
}
|
||||
wx.navigateTo({
|
||||
url: link,
|
||||
fail: (err) => {
|
||||
console.error('跳转引用详情失败', err)
|
||||
wx.showToast({ title: '跳转失败', icon: 'none' })
|
||||
},
|
||||
})
|
||||
},
|
||||
})
|
||||
|
||||
@@ -92,13 +92,18 @@
|
||||
<text class="bubble-text">{{item.content}}</text>
|
||||
</view>
|
||||
<!-- AI 侧引用卡片（后端 referenceCard 附加在 assistant 回复中）-->
|
||||
<view class="inline-ref-card inline-ref-card--assistant" wx:if="{{item.referenceCard}}">
|
||||
<view
|
||||
class="inline-ref-card inline-ref-card--assistant {{item.referenceCard.link ? 'inline-ref-card--link' : ''}}"
|
||||
wx:if="{{item.referenceCard}}"
|
||||
data-link="{{item.referenceCard.link}}"
|
||||
bindtap="onRefCardTap"
|
||||
>
|
||||
<view class="inline-ref-header">
|
||||
<text class="inline-ref-type">{{item.referenceCard.type === 'customer' ? '👤 客户' : '📋 记录'}}</text>
|
||||
<text class="inline-ref-type">{{item.referenceCard.type === 'customer' ? '👤 客户' : item.referenceCard.type === 'assistant' ? '🧑\u200d🏫 助教' : item.referenceCard.type === 'task' ? '📋 任务' : '📋 记录'}}</text>
|
||||
<text class="inline-ref-title">{{fmt.safe(item.referenceCard.title)}}</text>
|
||||
</view>
|
||||
<text class="inline-ref-summary">{{fmt.safe(item.referenceCard.summary)}}</text>
|
||||
<view class="inline-ref-data">
|
||||
<view class="inline-ref-data" wx:if="{{item.referenceCard.dataList.length > 0}}">
|
||||
<view class="ref-data-item" wx:for="{{item.referenceCard.dataList}}" wx:for-item="entry" wx:key="key">
|
||||
<text class="ref-data-key">{{fmt.safe(entry.key)}}</text>
|
||||
<text class="ref-data-value">{{fmt.safe(entry.value)}}</text>
|
||||
|
||||
@@ -4,7 +4,7 @@
|
||||
| 2026-03-23 | 角色路由+页面权限守卫 | onShow 添加 checkPageAccess 权限守卫 |
|
||||
*/
|
||||
import { checkPageAccess } from '../../utils/auth-guard'
|
||||
import { fetchCustomerDetail } from '../../services/api'
|
||||
import { fetchCustomerDetail, fetchAICache } from '../../services/api'
|
||||
|
||||
interface ConsumptionRecord {
|
||||
id: string
|
||||
@@ -132,6 +132,7 @@ Page({
|
||||
}
|
||||
}
|
||||
this.setData({ pageState: 'normal' })
|
||||
if (id) this._loadAIInsight(id)
|
||||
} catch (e) {
|
||||
console.error('[customer-detail] loadDetail 失败:', e)
|
||||
this.setData({ pageState: 'error' })
|
||||
@@ -140,6 +141,23 @@ Page({
|
||||
}
|
||||
},
|
||||
|
||||
async _loadAIInsight(memberId: string) {
|
||||
const cache = await fetchAICache('app7_customer_analysis', memberId)
|
||||
if (!cache?.result_json) return
|
||||
const rj = cache.result_json as any
|
||||
const COLORS = ['blue', 'indigo', 'purple', 'red', 'orange', 'yellow'] as const
|
||||
const strategies = Array.isArray(rj.strategies)
|
||||
? rj.strategies.map((s: any, i: number) => ({
|
||||
color: COLORS[i % COLORS.length],
|
||||
text: s.title || s.text || '',
|
||||
}))
|
||||
: []
|
||||
this.setData({
|
||||
'aiInsight.summary': rj.summary || '',
|
||||
'aiInsight.strategies': strategies,
|
||||
})
|
||||
},
|
||||
|
||||
onRetry() {
|
||||
const id = this.data.detail?.id || ''
|
||||
this.loadDetail(id)
|
||||
|
||||
@@ -5,6 +5,8 @@
|
||||
*/
|
||||
import { checkPageAccess } from '../../utils/auth-guard'
|
||||
import { fetchCustomerConsumptionRecords } from '../../services/api'
|
||||
// CHANGE 2026-05-02 | 业务时钟：sandbox 模式下用 business_year/month 替代 new Date()
|
||||
import { getBusinessClock } from '../../utils/runtime-clock'
|
||||
|
||||
Page({
|
||||
data: {
|
||||
@@ -38,11 +40,12 @@ Page({
|
||||
monthLoading: false,
|
||||
},
|
||||
|
||||
onLoad(options) {
|
||||
async onLoad(options) {
|
||||
const id = options?.customerId || options?.id || ''
|
||||
const now = new Date()
|
||||
const currentYear = now.getFullYear()
|
||||
const currentMonth = now.getMonth() + 1
|
||||
// CHANGE 2026-05-02 | 默认当前年月走业务时钟，sandbox 模式按 sandbox_date 显示
|
||||
const clock = await getBusinessClock()
|
||||
const currentYear = clock.business_year
|
||||
const currentMonth = clock.business_month
|
||||
this.setData({
|
||||
customerId: id,
|
||||
currentYear,
|
||||
|
||||
@@ -5,6 +5,8 @@
|
||||
| 2026-03-27 | 任务A 前端改造 | 修复数据转换（duration/income/timeRange/table/recordType），去掉 loadCustomerInfo 改从 records 响应取客户信息，新增 monthIncome 展示 |
|
||||
*/
|
||||
import { checkPageAccess } from '../../utils/auth-guard'
|
||||
// CHANGE 2026-05-02 | 业务时钟：sandbox 模式下用 business_year/month 替代 new Date()
|
||||
import { getBusinessClock } from '../../utils/runtime-clock'
|
||||
// CHANGE 2026-03-27 | 任务A A5: 去掉 fetchCustomerDetail，客户信息从 fetchCustomerRecords 响应中获取
|
||||
import { fetchCustomerRecords } from '../../services/api'
|
||||
import { formatCount } from '../../utils/money'
|
||||
@@ -86,12 +88,12 @@ Page({
|
||||
monthLoading: false,
|
||||
},
|
||||
|
||||
onLoad(options) {
|
||||
async onLoad(options) {
|
||||
const id = options?.customerId || options?.id || ''
|
||||
// 默认当前年月
|
||||
const now = new Date()
|
||||
const currentYear = now.getFullYear()
|
||||
const currentMonth = now.getMonth() + 1
|
||||
// CHANGE 2026-05-02 | 默认当前年月走业务时钟，sandbox 模式按 sandbox_date 显示
|
||||
const clock = await getBusinessClock()
|
||||
const currentYear = clock.business_year
|
||||
const currentMonth = clock.business_month
|
||||
this.setData({
|
||||
customerId: id,
|
||||
currentYear,
|
||||
|
||||
@@ -11,6 +11,8 @@ import { nameToAvatarColor } from '../../utils/avatar-color'
|
||||
import { formatMoney, formatCount } from '../../utils/money'
|
||||
import { formatHours } from '../../utils/time'
|
||||
import { API_BASE } from '../../utils/config'
|
||||
// CHANGE 2026-05-02 | 业务时钟：sandbox 模式下用 business_year/month 替代 new Date()
|
||||
import { getBusinessClock } from '../../utils/runtime-clock'
|
||||
|
||||
/** 中文课程类型 → 英文 CSS key（WXSS 不支持中文类名） */
|
||||
const COURSE_TAG_MAP: Record<string, string> = {
|
||||
@@ -58,7 +60,7 @@ Page({
|
||||
coachRole: '',
|
||||
storeName: '',
|
||||
|
||||
/** 月份切换 */
|
||||
/** 月份切换（onLoad 中改写为业务时钟当前年月） */
|
||||
currentYear: new Date().getFullYear(),
|
||||
currentMonth: new Date().getMonth() + 1,
|
||||
monthLabel: '',
|
||||
@@ -83,12 +85,13 @@ Page({
|
||||
hasMore: false,
|
||||
},
|
||||
|
||||
onLoad() {
|
||||
const now = new Date()
|
||||
async onLoad() {
|
||||
// CHANGE 2026-05-02 | 用业务时钟初始化年月，sandbox 模式按 sandbox_date 显示
|
||||
const clock = await getBusinessClock()
|
||||
this.setData({
|
||||
currentYear: now.getFullYear(),
|
||||
currentMonth: now.getMonth() + 1,
|
||||
monthLabel: `${now.getFullYear()}年${now.getMonth() + 1}月`,
|
||||
currentYear: clock.business_year,
|
||||
currentMonth: clock.business_month,
|
||||
monthLabel: `${clock.business_year}年${clock.business_month}月`,
|
||||
})
|
||||
this.loadBanner()
|
||||
this.loadData()
|
||||
@@ -140,11 +143,13 @@ Page({
|
||||
wx.showLoading({ title: '加载中...', mask: true })
|
||||
|
||||
// 预估规则：当月且当前日期 ≤ 5号（全小程序统一）
|
||||
const now = new Date()
|
||||
// CHANGE 2026-05-02 | 用业务时钟，sandbox 模式按 sandbox_date 判断
|
||||
const clock = await getBusinessClock()
|
||||
const { currentYear, currentMonth } = this.data
|
||||
const isCurrentMonth = currentYear === now.getFullYear()
|
||||
&& currentMonth === now.getMonth() + 1
|
||||
&& now.getDate() <= 5
|
||||
const businessDay = parseInt(clock.business_date.slice(8, 10), 10) || 1
|
||||
const isCurrentMonth = currentYear === clock.business_year
|
||||
&& currentMonth === clock.business_month
|
||||
&& businessDay <= 5
|
||||
|
||||
try {
|
||||
const res = await fetchPerformanceRecords({
|
||||
@@ -243,7 +248,7 @@ Page({
|
||||
},
|
||||
|
||||
/** 切换月份 */
|
||||
switchMonth(e: WechatMiniprogram.TouchEvent) {
|
||||
async switchMonth(e: WechatMiniprogram.TouchEvent) {
|
||||
const direction = e.currentTarget.dataset.direction as 'prev' | 'next'
|
||||
let { currentYear, currentMonth } = this.data
|
||||
|
||||
@@ -255,11 +260,13 @@ Page({
|
||||
if (currentMonth > 12) { currentMonth = 1; currentYear++ }
|
||||
}
|
||||
|
||||
const now = new Date()
|
||||
const nowYear = now.getFullYear()
|
||||
const nowMonth = now.getMonth() + 1
|
||||
// CHANGE 2026-05-02 | 用业务时钟，sandbox 模式下不允许"翻到 sandbox_date 之后"
|
||||
const clock = await getBusinessClock()
|
||||
const nowYear = clock.business_year
|
||||
const nowMonth = clock.business_month
|
||||
const businessDay = parseInt(clock.business_date.slice(8, 10), 10) || 1
|
||||
const canGoNext = currentYear < nowYear || (currentYear === nowYear && currentMonth < nowMonth)
|
||||
const isCurrentMonth = currentYear === nowYear && currentMonth === nowMonth && now.getDate() <= 5
|
||||
const isCurrentMonth = currentYear === nowYear && currentMonth === nowMonth && businessDay <= 5
|
||||
|
||||
// 月份切换重置分页到第 1 页
|
||||
this.setData({
|
||||
|
||||
@@ -10,6 +10,8 @@ import { fetchMe, fetchPerformanceOverview } from '../../services/api'
|
||||
import { nameToAvatarColor } from '../../utils/avatar-color'
|
||||
// CHANGE 2026-03-27 | 头像：需要 API_BASE 构建头像完整 URL
|
||||
import { API_BASE } from '../../utils/config'
|
||||
// CHANGE 2026-05-02 | 业务时钟：sandbox 模式下用 business_year/month 替代 new Date()
|
||||
import { getBusinessClock } from '../../utils/runtime-clock'
|
||||
|
||||
/** 中文课程类型 → 英文 CSS key（WXSS 不支持中文类名） */
|
||||
const COURSE_TAG_MAP: Record<string, string> = {
|
||||
@@ -118,15 +120,16 @@ Page({
|
||||
this.setData({ pageState: 'loading' })
|
||||
wx.showLoading({ title: '加载中...', mask: true })
|
||||
|
||||
// G2：当月预估判断
|
||||
const now = new Date()
|
||||
const nowYear = now.getFullYear()
|
||||
const nowMonth = now.getMonth() + 1
|
||||
// CHANGE 2026-05-02 | G2 当月预估判断改用业务时钟（sandbox 模式按 sandbox_date 判断）
|
||||
const clock = await getBusinessClock()
|
||||
const nowYear = clock.business_year
|
||||
const nowMonth = clock.business_month
|
||||
const businessDay = parseInt(clock.business_date.slice(8, 10), 10) || 1
|
||||
// TODO: 联调时从接口参数或页面参数获取 year/month
|
||||
const year = nowYear
|
||||
const month = nowMonth
|
||||
// CHANGE 2026-03-24 | 预估规则：当月且当前日期 ≤ 5号才显示"预估"
|
||||
const isCurrentMonth = year === nowYear && month === nowMonth && now.getDate() <= 5
|
||||
const isCurrentMonth = year === nowYear && month === nowMonth && businessDay <= 5
|
||||
|
||||
try {
|
||||
// 并行请求用户信息和绩效概览
|
||||
|
||||
@@ -20,6 +20,8 @@ import { fetchTasks, fetchMe, pinTask, unpinTask, abandonTask, restoreTask, crea
|
||||
import { formatMoney } from '../../utils/money'
|
||||
import { formatDeadline } from '../../utils/time'
|
||||
import { formatStorageLevel } from '../../utils/storage-level'
|
||||
// CHANGE 2026-05-02 | 业务时钟：sandbox 模式下用 business_year/month 替代 new Date()
|
||||
import { getBusinessClock } from '../../utils/runtime-clock'
|
||||
// CHANGE 2026-03-27 | 头像：需要 API_BASE 构建头像完整 URL
|
||||
import { API_BASE } from '../../utils/config'
|
||||
import {
|
||||
@@ -386,9 +388,11 @@ Page({
|
||||
}
|
||||
|
||||
// G2: 当月预估判断
|
||||
const now = new Date()
|
||||
const nowYear = now.getFullYear()
|
||||
const nowMonth = now.getMonth() + 1
|
||||
// CHANGE 2026-05-02 | 用业务时钟，sandbox 模式按 sandbox_date 判断
|
||||
const clock = await getBusinessClock()
|
||||
const nowYear = clock.business_year
|
||||
const nowMonth = clock.business_month
|
||||
const businessDay = parseInt(clock.business_date.slice(8, 10), 10) || 1
|
||||
const incomeMonth = perfData.incomeMonth
|
||||
let dataYear = nowYear
|
||||
let dataMonth = nowMonth
|
||||
@@ -397,7 +401,7 @@ Page({
|
||||
if (parts) dataMonth = parseInt(parts[1], 10)
|
||||
}
|
||||
// CHANGE 2026-03-24 | 预估规则：当月且当前日期 ≤ 5号才显示"预估"（全小程序统一）
|
||||
const isCurrentMonth = dataYear === nowYear && dataMonth === nowMonth && now.getDate() <= 5
|
||||
const isCurrentMonth = dataYear === nowYear && dataMonth === nowMonth && businessDay <= 5
|
||||
|
||||
this.setData({
|
||||
pageState: totalCount > 0 ? 'normal' : 'empty',
|
||||
|
||||
@@ -35,6 +35,31 @@ export async function fetchMe(): Promise<ApiUserInfo> {
|
||||
return request({ url: '/api/xcx/me', method: 'GET', needAuth: true })
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 业务时钟（沙箱支持）
|
||||
// ============================================
|
||||
|
||||
export interface RuntimeClock {
|
||||
mode: 'live' | 'sandbox'
|
||||
business_date: string // YYYY-MM-DD
|
||||
business_year: number
|
||||
business_month: number
|
||||
business_year_month: string // YYYY-MM
|
||||
business_now: string
|
||||
is_sandbox: boolean
|
||||
sandbox_date: string | null
|
||||
sandbox_instance_id: string | null
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取当前门店的业务时钟（live 真实日，sandbox 模拟日）。
|
||||
* 沙箱模式下，小程序所有依赖"当前年月"的请求都应使用此结果，
|
||||
* 避免直接 ``new Date()`` 导致与后端 sandbox_date 不一致。
|
||||
*/
|
||||
export async function fetchRuntimeClock(): Promise<RuntimeClock> {
|
||||
return request({ url: '/api/xcx/runtime/clock', method: 'GET', needAuth: true })
|
||||
}
|
||||
|
||||
// ============================================
|
||||
// 任务模块
|
||||
// ============================================
|
||||
@@ -413,6 +438,26 @@ export async function sendChatMessage(chatId: string, content: string): Promise<
|
||||
// 配置模块
|
||||
// ============================================
|
||||
|
||||
/** AI 缓存查询（Phase 2.5） */
|
||||
export async function fetchAICache(cacheType: string, targetId: string): Promise<{
|
||||
result_json: Record<string, any> | null;
|
||||
score: number | null;
|
||||
} | null> {
|
||||
try {
|
||||
const data = await request({
|
||||
url: `/api/ai/cache/${cacheType}`,
|
||||
method: 'GET',
|
||||
data: { target_id: targetId },
|
||||
needAuth: true,
|
||||
})
|
||||
if (!data) return null
|
||||
const d = data as any
|
||||
return { result_json: d.result_json ?? null, score: d.score ?? null }
|
||||
} catch {
|
||||
return null
|
||||
}
|
||||
}
|
||||
|
||||
/** 项目类型筛选器列表（CONFIG-1） */
|
||||
// CHANGE 2026-03-20 | R3 修复：value 改为数据库 category_code，fallback 与后端一致
|
||||
export async function fetchSkillTypes(): Promise<Array<{ value: string; text: string; icon?: string }>> {
|
||||
|
||||
83
apps/miniprogram/miniprogram/utils/runtime-clock.ts
Normal file
83
apps/miniprogram/miniprogram/utils/runtime-clock.ts
Normal file
@@ -0,0 +1,83 @@
|
||||
// 业务时钟缓存
|
||||
//
|
||||
// sandbox 模式下，小程序的 performance / task-list / customer-records 等页面
|
||||
// 需要按"业务日"而不是"真实今天"构造请求参数。
|
||||
//
|
||||
// 用法：
|
||||
// import { getBusinessClock, getBusinessYearMonth } from '../../utils/runtime-clock'
|
||||
// const clock = await getBusinessClock()
|
||||
// wx.request({ url: ..., data: { year: clock.business_year, month: clock.business_month } })
|
||||
//
|
||||
// 缓存策略：
|
||||
// - 单例 in-memory cache，最多 60 秒；过期后自动重新拉取。
|
||||
// - 切换沙箱后，建议页面调用 `clearBusinessClockCache()` 主动失效。
|
||||
|
||||
import { fetchRuntimeClock, type RuntimeClock } from '../services/api'
|
||||
|
||||
const TTL_MS = 60_000 // 60 秒缓存，足以覆盖一次页面进入
|
||||
|
||||
let cached: { value: RuntimeClock; ts: number } | null = null
|
||||
let inflight: Promise<RuntimeClock> | null = null
|
||||
|
||||
/** 主动清空业务时钟缓存（沙箱切换、登出后调用）。 */
|
||||
export function clearBusinessClockCache(): void {
|
||||
cached = null
|
||||
inflight = null
|
||||
}
|
||||
|
||||
/** 拉取业务时钟（可能命中缓存）。失败时降级为本地"今天"。 */
|
||||
export async function getBusinessClock(force = false): Promise<RuntimeClock> {
|
||||
const now = Date.now()
|
||||
if (!force && cached && now - cached.ts < TTL_MS) {
|
||||
return cached.value
|
||||
}
|
||||
if (inflight) {
|
||||
return inflight
|
||||
}
|
||||
inflight = (async () => {
|
||||
try {
|
||||
const clock = await fetchRuntimeClock()
|
||||
cached = { value: clock, ts: Date.now() }
|
||||
return clock
|
||||
} catch (err) {
|
||||
console.warn('[runtime-clock] 拉取业务时钟失败，降级为本地时间', err)
|
||||
return localFallback()
|
||||
} finally {
|
||||
inflight = null
|
||||
}
|
||||
})()
|
||||
return inflight
|
||||
}
|
||||
|
||||
/** 便捷方法：返回业务年月 (YYYY-MM)。 */
|
||||
export async function getBusinessYearMonth(): Promise<{ year: number; month: number; label: string }> {
|
||||
const clock = await getBusinessClock()
|
||||
return {
|
||||
year: clock.business_year,
|
||||
month: clock.business_month,
|
||||
label: `${clock.business_year}年${clock.business_month}月`,
|
||||
}
|
||||
}
|
||||
|
||||
/** 便捷方法：返回业务日 (YYYY-MM-DD)。 */
|
||||
export async function getBusinessDate(): Promise<string> {
|
||||
return (await getBusinessClock()).business_date
|
||||
}
|
||||
|
||||
function localFallback(): RuntimeClock {
|
||||
const d = new Date()
|
||||
const year = d.getFullYear()
|
||||
const month = d.getMonth() + 1
|
||||
const ymd = `${year}-${String(month).padStart(2, '0')}-${String(d.getDate()).padStart(2, '0')}`
|
||||
return {
|
||||
mode: 'live',
|
||||
business_date: ymd,
|
||||
business_year: year,
|
||||
business_month: month,
|
||||
business_year_month: `${year}-${String(month).padStart(2, '0')}`,
|
||||
business_now: d.toISOString(),
|
||||
is_sandbox: false,
|
||||
sandbox_date: null,
|
||||
sandbox_instance_id: null,
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,6 @@
|
||||
-- =============================================================================
|
||||
-- etl_feiqiu / app（RLS 视图层）
|
||||
-- 生成日期：2026-04-12
|
||||
-- 生成日期：2026-05-02
|
||||
-- 来源：测试库（通过脚本自动导出）
|
||||
-- =============================================================================
|
||||
|
||||
@@ -36,7 +36,8 @@ SELECT id,
|
||||
unique_customers,
|
||||
unique_tables,
|
||||
created_at
|
||||
FROM dws.dws_assistant_daily_detail d;
|
||||
FROM dws.dws_assistant_daily_detail d
|
||||
WHERE (stat_date <= app.business_date_now());
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_cfg_area_category AS
|
||||
@@ -61,7 +62,8 @@ SELECT price_id,
|
||||
description,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.cfg_assistant_level_price;
|
||||
FROM dws.cfg_assistant_level_price
|
||||
WHERE ((effective_from <= app.business_date_now()) AND (effective_to >= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_cfg_bonus_rules AS
|
||||
@@ -112,7 +114,8 @@ SELECT tier_id,
|
||||
description,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.cfg_performance_tier;
|
||||
FROM dws.cfg_performance_tier
|
||||
WHERE ((effective_from <= app.business_date_now()) AND (effective_to >= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dim_assistant AS
|
||||
@@ -301,7 +304,7 @@ SELECT assistant_service_id,
|
||||
is_delete,
|
||||
real_service_money
|
||||
FROM dwd.dwd_assistant_service_log
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (COALESCE((create_time)::date, '0001-01-01'::date) <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dwd_recharge_order AS
|
||||
@@ -330,7 +333,7 @@ SELECT recharge_order_id,
|
||||
real_electricity_money,
|
||||
electricity_adjust_money
|
||||
FROM dwd.dwd_recharge_order
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (COALESCE((pay_time)::date, '0001-01-01'::date) <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dwd_settlement_head AS
|
||||
@@ -372,7 +375,7 @@ SELECT order_settle_id,
|
||||
pl_coupon_sale_amount,
|
||||
mervou_sales_amount
|
||||
FROM dwd.dwd_settlement_head
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (COALESCE((create_time)::date, '0001-01-01'::date) <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dwd_store_goods_sale AS
|
||||
@@ -402,7 +405,7 @@ SELECT store_goods_sale_id,
|
||||
coupon_share_money,
|
||||
discount_price
|
||||
FROM dwd.dwd_store_goods_sale
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (COALESCE((create_time)::date, '0001-01-01'::date) <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dwd_table_fee_log AS
|
||||
@@ -436,7 +439,7 @@ SELECT table_fee_log_id,
|
||||
activity_discount_amount,
|
||||
real_service_money
|
||||
FROM dwd.dwd_table_fee_log
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (COALESCE((create_time)::date, '0001-01-01'::date) <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_assistant_customer_stats AS
|
||||
@@ -518,7 +521,7 @@ SELECT id,
|
||||
is_exempt,
|
||||
per_hour_contribution
|
||||
FROM dws.dws_assistant_daily_detail
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_assistant_finance_analysis AS
|
||||
@@ -543,7 +546,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_assistant_finance_analysis
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_assistant_monthly_summary AS
|
||||
@@ -583,7 +586,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_assistant_monthly_summary
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_month <= (date_trunc('month'::text, (app.business_date_now())::timestamp with time zone))::date));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_assistant_order_contribution AS
|
||||
@@ -687,7 +690,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_assistant_salary_calc
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (salary_month <= (date_trunc('month'::text, (app.business_date_now())::timestamp with time zone))::date));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_coach_area_hours AS
|
||||
@@ -746,9 +749,10 @@ SELECT id,
|
||||
renewal_cash,
|
||||
order_count,
|
||||
created_at,
|
||||
updated_at
|
||||
updated_at,
|
||||
member_order_count
|
||||
FROM dws.dws_finance_area_daily
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_finance_board_cache AS
|
||||
@@ -822,7 +826,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_finance_daily_summary
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_finance_discount_detail AS
|
||||
@@ -839,7 +843,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_finance_discount_detail
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_finance_expense_summary AS
|
||||
@@ -860,7 +864,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_finance_expense_summary
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (expense_month <= (date_trunc('month'::text, (app.business_date_now())::timestamp with time zone))::date));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_finance_income_structure AS
|
||||
@@ -878,7 +882,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_finance_income_structure
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_finance_recharge_summary AS
|
||||
@@ -912,7 +916,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_finance_recharge_summary
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_member_assistant_intimacy AS
|
||||
@@ -1033,7 +1037,7 @@ SELECT DISTINCT ON (member_id) id,
|
||||
recharge_amount_90d,
|
||||
avg_ticket_amount
|
||||
FROM dws.dws_member_consumption_summary
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint)
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (stat_date <= app.business_date_now()))
|
||||
ORDER BY member_id, stat_date DESC;
|
||||
;
|
||||
|
||||
@@ -1162,7 +1166,7 @@ SELECT id,
|
||||
created_at,
|
||||
updated_at
|
||||
FROM dws.dws_member_visit_detail
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint);
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (visit_date <= app.business_date_now()));
|
||||
;
|
||||
|
||||
CREATE OR REPLACE VIEW app.v_dws_member_winback_index AS
|
||||
@@ -1204,7 +1208,7 @@ SELECT DISTINCT ON (member_id) winback_id,
|
||||
ideal_next_visit_date,
|
||||
stat_date
|
||||
FROM dws.dws_member_winback_index
|
||||
WHERE (site_id = (current_setting('app.current_site_id'::text))::bigint)
|
||||
WHERE ((site_id = (current_setting('app.current_site_id'::text))::bigint) AND (COALESCE((last_visit_time)::date, '0001-01-01'::date) <= app.business_date_now()))
|
||||
ORDER BY member_id, stat_date DESC;
|
||||
;
|
||||
|
||||
|
||||
@@ -0,0 +1,89 @@
|
||||
-- 20260420_ai_trigger_jobs_and_app2_prewarm.sql
|
||||
-- 注册 AI 事件触发器 + App2 财务洞察 cron 预热任务
|
||||
--
|
||||
-- 背景：Phase 0.1~0.3 AI 模块收尾。dispatcher 已注册 5 个 handler 到
|
||||
-- trigger_scheduler._JOB_REGISTRY（通过 main.py lifespan 调用），
|
||||
-- 但 biz.trigger_jobs 缺对应数据行，fire_event 查不到事件绑定而失效。
|
||||
--
|
||||
-- 本脚本插入：
|
||||
-- 4 条 event 类型记录：ai_consumption_settled / ai_note_created /
|
||||
-- ai_task_assigned / ai_dws_completed
|
||||
-- 1 条 cron 类型记录：ai_dws_prewarm （每日 08:30 给所有 site 触发 App2 × 8 维度）
|
||||
--
|
||||
-- 幂等：job_name UNIQUE 约束 + ON CONFLICT DO NOTHING。
|
||||
--
|
||||
-- 验证 SQL（执行后）：
|
||||
-- 1. SELECT count(*) FROM biz.trigger_jobs WHERE job_type LIKE 'ai_%';
|
||||
-- 应为 5
|
||||
-- 2. SELECT job_name, trigger_condition, trigger_config FROM biz.trigger_jobs
|
||||
-- WHERE trigger_condition = 'event' AND trigger_config->>'event_name' LIKE 'ai_%';
|
||||
-- 应为 4 条，event_name 与 job_name 一致
|
||||
-- 3. SELECT job_name, trigger_config->>'cron_expression' FROM biz.trigger_jobs
|
||||
-- WHERE job_type = 'ai_dws_prewarm';
|
||||
-- 应为 "30 8 * * *"
|
||||
--
|
||||
-- 回滚：
|
||||
-- DELETE FROM biz.trigger_jobs WHERE job_name IN (
|
||||
-- 'ai_consumption_settled', 'ai_note_created',
|
||||
-- 'ai_task_assigned', 'ai_dws_completed', 'ai_dws_prewarm'
|
||||
-- );
|
||||
|
||||
BEGIN;
|
||||
|
||||
-- ── 4 条 event 类型触发器 ──────────────────────────────
|
||||
|
||||
INSERT INTO biz.trigger_jobs
|
||||
(job_type, job_name, trigger_condition, trigger_config, status, description)
|
||||
VALUES
|
||||
(
|
||||
'ai_consumption_settled',
|
||||
'ai_consumption_settled',
|
||||
'event',
|
||||
'{"event_name": "ai_consumption_settled"}'::jsonb,
|
||||
'enabled',
|
||||
'AI 消费事件链：App3 → App8 → App7（+ App4 → App5 含助教）'
|
||||
),
|
||||
(
|
||||
'ai_note_created',
|
||||
'ai_note_created',
|
||||
'event',
|
||||
'{"event_name": "ai_note_created"}'::jsonb,
|
||||
'enabled',
|
||||
'AI 备注事件链：App6 → App8'
|
||||
),
|
||||
(
|
||||
'ai_task_assigned',
|
||||
'ai_task_assigned',
|
||||
'event',
|
||||
'{"event_name": "ai_task_assigned"}'::jsonb,
|
||||
'enabled',
|
||||
'AI 任务分配事件链：App4 → App5'
|
||||
),
|
||||
(
|
||||
'ai_dws_completed',
|
||||
'ai_dws_completed',
|
||||
'event',
|
||||
'{"event_name": "ai_dws_completed"}'::jsonb,
|
||||
'enabled',
|
||||
'AI DWS 完成事件：App2 财务洞察 × 8 时间维度预生成'
|
||||
)
|
||||
ON CONFLICT (job_name) DO NOTHING;
|
||||
|
||||
-- ── 1 条 cron 类型触发器 ───────────────────────────────
|
||||
|
||||
-- 每日 08:30 对所有 active 门店逐个触发 ai_dws_completed 事件，
|
||||
-- 作为 etl-completed 端点之外的兜底机制。
|
||||
INSERT INTO biz.trigger_jobs
|
||||
(job_type, job_name, trigger_condition, trigger_config, status, description)
|
||||
VALUES
|
||||
(
|
||||
'ai_dws_prewarm',
|
||||
'ai_dws_prewarm_0830',
|
||||
'cron',
|
||||
'{"cron_expression": "30 8 * * *"}'::jsonb,
|
||||
'enabled',
|
||||
'App2 财务洞察每日预热：08:30 对所有门店触发 ai_dws_completed × 8 维度'
|
||||
)
|
||||
ON CONFLICT (job_name) DO NOTHING;
|
||||
|
||||
COMMIT;
|
||||
@@ -0,0 +1,42 @@
|
||||
-- 20260421_app2_prewarm_cron_reschedule.sql
|
||||
-- App2 财务洞察 cron 预热时间从 08:30 调整为 10:00 + 扩展为 72 组合覆盖
|
||||
--
|
||||
-- 背景：用户需求 2026-04-21
|
||||
-- 1) 每日 10:00 为所有门店生成 board-finance 所有筛选组合下的 AI 洞察
|
||||
-- 2) 筛选组合 = 8 时间维度 × 9 区域 = 72 组合/门店
|
||||
-- 3) 前端根据当前筛选条件读缓存（target_id=time__area）
|
||||
--
|
||||
-- 变更：
|
||||
-- 1. job_name rename: ai_dws_prewarm_0830 → ai_dws_prewarm_1000
|
||||
-- 2. cron_expression: "30 8 * * *" → "0 10 * * *"
|
||||
-- 3. description 更新为"72 组合"说明
|
||||
--
|
||||
-- 注：dispatcher._handle_dws_completed 已改为遍历 72 组合，无需额外的 handler 注册。
|
||||
--
|
||||
-- 验证 SQL（执行后）：
|
||||
-- 1. SELECT job_name FROM biz.trigger_jobs
|
||||
-- WHERE job_type = 'ai_dws_prewarm';
|
||||
-- 应为 'ai_dws_prewarm_1000'
|
||||
-- 2. SELECT trigger_config->>'cron_expression' FROM biz.trigger_jobs
|
||||
-- WHERE job_type = 'ai_dws_prewarm';
|
||||
-- 应为 '0 10 * * *'
|
||||
-- 3. SELECT count(*) FROM biz.trigger_jobs WHERE job_type LIKE 'ai_%';
|
||||
-- 应为 5（不变）
|
||||
--
|
||||
-- 回滚：
|
||||
-- UPDATE biz.trigger_jobs
|
||||
-- SET job_name = 'ai_dws_prewarm_0830',
|
||||
-- trigger_config = '{"cron_expression": "30 8 * * *"}'::jsonb,
|
||||
-- description = 'App2 财务洞察每日预热：08:30 对所有门店触发 ai_dws_completed × 8 维度'
|
||||
-- WHERE job_type = 'ai_dws_prewarm';
|
||||
|
||||
BEGIN;
|
||||
|
||||
UPDATE biz.trigger_jobs
|
||||
SET
|
||||
job_name = 'ai_dws_prewarm_1000',
|
||||
trigger_config = '{"cron_expression": "0 10 * * *"}'::jsonb,
|
||||
description = 'App2 财务洞察每日预热：10:00 对所有门店触发 ai_dws_completed × 72 组合（8 时间 × 9 区域）'
|
||||
WHERE job_type = 'ai_dws_prewarm';
|
||||
|
||||
COMMIT;
|
||||
117
db/zqyy_app/migrations/20260501__runtime_context_sandbox.sql
Normal file
117
db/zqyy_app/migrations/20260501__runtime_context_sandbox.sql
Normal file
@@ -0,0 +1,117 @@
|
||||
-- 2026-05-01
|
||||
-- 业务运行上下文与沙箱隔离。
|
||||
|
||||
BEGIN;
|
||||
|
||||
CREATE TABLE IF NOT EXISTS biz.site_runtime_context (
|
||||
site_id bigint PRIMARY KEY,
|
||||
mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
sandbox_date date,
|
||||
sandbox_instance_id character varying(64),
|
||||
ai_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
status character varying(20) NOT NULL DEFAULT 'active',
|
||||
reason text,
|
||||
updated_by bigint,
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
updated_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
CONSTRAINT site_runtime_context_site_id_fkey
|
||||
FOREIGN KEY (site_id) REFERENCES biz.sites(site_id),
|
||||
CONSTRAINT site_runtime_context_mode_check
|
||||
CHECK (mode IN ('live', 'sandbox')),
|
||||
CONSTRAINT site_runtime_context_ai_mode_check
|
||||
CHECK (ai_mode IN ('live')),
|
||||
CONSTRAINT site_runtime_context_sandbox_check
|
||||
CHECK (
|
||||
(mode = 'live' AND sandbox_date IS NULL AND sandbox_instance_id IS NULL)
|
||||
OR
|
||||
(mode = 'sandbox' AND sandbox_date IS NOT NULL AND sandbox_instance_id IS NOT NULL)
|
||||
)
|
||||
);
|
||||
|
||||
COMMENT ON TABLE biz.site_runtime_context IS '门店业务运行上下文：live 使用真实日期，sandbox 使用指定业务日期并按实例隔离写入。';
|
||||
COMMENT ON COLUMN biz.site_runtime_context.mode IS '运行模式：live / sandbox。';
|
||||
COMMENT ON COLUMN biz.site_runtime_context.sandbox_date IS 'sandbox 模式下系统假设的业务日期。';
|
||||
COMMENT ON COLUMN biz.site_runtime_context.sandbox_instance_id IS 'sandbox 模式写入隔离实例 ID。';
|
||||
COMMENT ON COLUMN biz.site_runtime_context.ai_mode IS 'AI 调用模式；当前固定 live，沙箱也真实调用 DashScope。';
|
||||
|
||||
ALTER TABLE biz.coach_tasks
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
ALTER TABLE biz.coach_task_transfer_log
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
ALTER TABLE biz.recall_events
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
ALTER TABLE biz.coach_task_history
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
ALTER TABLE biz.ai_cache
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
ALTER TABLE biz.ai_run_logs
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
ALTER TABLE biz.ai_trigger_jobs
|
||||
ADD COLUMN IF NOT EXISTS runtime_mode character varying(20) NOT NULL DEFAULT 'live',
|
||||
ADD COLUMN IF NOT EXISTS sandbox_instance_id character varying(64) NOT NULL DEFAULT 'live';
|
||||
|
||||
UPDATE biz.coach_tasks SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
UPDATE biz.coach_task_transfer_log SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
UPDATE biz.recall_events SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
UPDATE biz.coach_task_history SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
UPDATE biz.ai_cache SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
UPDATE biz.ai_run_logs SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
UPDATE biz.ai_trigger_jobs SET runtime_mode = 'live', sandbox_instance_id = 'live' WHERE sandbox_instance_id IS NULL;
|
||||
|
||||
DROP INDEX IF EXISTS biz.idx_coach_tasks_site_assistant_member_type;
|
||||
CREATE UNIQUE INDEX IF NOT EXISTS idx_coach_tasks_runtime_unique_active
|
||||
ON biz.coach_tasks (site_id, assistant_id, member_id, task_type, runtime_mode, sandbox_instance_id)
|
||||
WHERE status = 'active';
|
||||
|
||||
DROP INDEX IF EXISTS biz.idx_recall_events_site_assistant_member_day;
|
||||
CREATE UNIQUE INDEX IF NOT EXISTS idx_recall_events_runtime_site_assistant_member_day
|
||||
ON biz.recall_events (
|
||||
site_id,
|
||||
assistant_id,
|
||||
member_id,
|
||||
runtime_mode,
|
||||
sandbox_instance_id,
|
||||
(date_trunc('day', pay_time AT TIME ZONE 'Asia/Shanghai'))
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_coach_tasks_runtime_assistant_status
|
||||
ON biz.coach_tasks (site_id, runtime_mode, sandbox_instance_id, assistant_id, status);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_ai_cache_runtime_lookup
|
||||
ON biz.ai_cache (cache_type, site_id, runtime_mode, sandbox_instance_id, target_id, created_at DESC);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_ai_trigger_jobs_runtime_site
|
||||
ON biz.ai_trigger_jobs (site_id, runtime_mode, sandbox_instance_id, event_type, status);
|
||||
|
||||
COMMIT;
|
||||
|
||||
-- 回滚参考：
|
||||
-- BEGIN;
|
||||
-- DROP INDEX IF EXISTS biz.idx_ai_trigger_jobs_runtime_site;
|
||||
-- DROP INDEX IF EXISTS biz.idx_ai_cache_runtime_lookup;
|
||||
-- DROP INDEX IF EXISTS biz.idx_coach_tasks_runtime_assistant_status;
|
||||
-- DROP INDEX IF EXISTS biz.idx_recall_events_runtime_site_assistant_member_day;
|
||||
-- CREATE UNIQUE INDEX idx_recall_events_site_assistant_member_day ON biz.recall_events USING btree (site_id, assistant_id, member_id, (date_trunc('day', pay_time AT TIME ZONE 'Asia/Shanghai')));
|
||||
-- DROP INDEX IF EXISTS biz.idx_coach_tasks_runtime_unique_active;
|
||||
-- CREATE UNIQUE INDEX idx_coach_tasks_site_assistant_member_type ON biz.coach_tasks USING btree (site_id, assistant_id, member_id, task_type) WHERE status = 'active';
|
||||
-- ALTER TABLE biz.ai_trigger_jobs DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- ALTER TABLE biz.ai_run_logs DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- ALTER TABLE biz.ai_cache DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- ALTER TABLE biz.coach_task_history DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- ALTER TABLE biz.recall_events DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- ALTER TABLE biz.coach_task_transfer_log DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- ALTER TABLE biz.coach_tasks DROP COLUMN IF EXISTS sandbox_instance_id, DROP COLUMN IF EXISTS runtime_mode;
|
||||
-- DROP TABLE IF EXISTS biz.site_runtime_context;
|
||||
-- COMMIT;
|
||||
@@ -1,6 +1,6 @@
|
||||
-- =============================================================================
|
||||
-- zqyy_app / biz（核心业务表（任务/备注/触发器））
|
||||
-- 生成日期：2026-04-06
|
||||
-- 生成日期：2026-05-02
|
||||
-- 来源：测试库（通过脚本自动导出）
|
||||
-- =============================================================================
|
||||
|
||||
@@ -16,11 +16,11 @@ CREATE SEQUENCE IF NOT EXISTS biz.cfg_task_generator_params_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.coach_task_history_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.coach_task_transfer_log_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.coach_tasks_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.recall_events_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.connectors_id_seq AS integer;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.dws_assistant_task_monthly_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.excel_upload_log_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.notes_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.recall_events_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.salary_adjustments_id_seq AS bigint;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.site_code_history_id_seq AS integer;
|
||||
CREATE SEQUENCE IF NOT EXISTS biz.sites_id_seq AS integer;
|
||||
@@ -41,7 +41,9 @@ CREATE TABLE biz.ai_cache (
|
||||
triggered_by character varying(100),
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
expires_at timestamp with time zone,
|
||||
status character varying(20) DEFAULT 'valid'::character varying
|
||||
status character varying(20) DEFAULT 'valid'::character varying,
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.ai_conversations (
|
||||
@@ -86,7 +88,9 @@ CREATE TABLE biz.ai_run_logs (
|
||||
session_id character varying(100),
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
finished_at timestamp with time zone,
|
||||
alert_status character varying(20) DEFAULT NULL::character varying
|
||||
alert_status character varying(20) DEFAULT NULL::character varying,
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.ai_trigger_jobs (
|
||||
@@ -102,7 +106,9 @@ CREATE TABLE biz.ai_trigger_jobs (
|
||||
started_at timestamp with time zone,
|
||||
finished_at timestamp with time zone,
|
||||
error_message text,
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.cfg_task_generator_params (
|
||||
@@ -124,7 +130,9 @@ CREATE TABLE biz.coach_task_history (
|
||||
old_task_type character varying(50),
|
||||
new_task_type character varying(50),
|
||||
detail jsonb,
|
||||
created_at timestamp with time zone DEFAULT now()
|
||||
created_at timestamp with time zone DEFAULT now(),
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.coach_task_transfer_log (
|
||||
@@ -138,7 +146,9 @@ CREATE TABLE biz.coach_task_transfer_log (
|
||||
transfer_reason text,
|
||||
guard_checks jsonb,
|
||||
transfer_score numeric,
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.coach_tasks (
|
||||
@@ -154,24 +164,15 @@ CREATE TABLE biz.coach_tasks (
|
||||
abandon_reason text,
|
||||
completed_at timestamp with time zone,
|
||||
completed_task_type character varying(50),
|
||||
completion_type character varying(10),
|
||||
parent_task_id bigint,
|
||||
created_at timestamp with time zone DEFAULT now(),
|
||||
updated_at timestamp with time zone DEFAULT now(),
|
||||
transfer_count integer DEFAULT 0 NOT NULL,
|
||||
transferred_from bigint,
|
||||
transferred_at timestamp with time zone
|
||||
);
|
||||
|
||||
CREATE TABLE biz.recall_events (
|
||||
id bigint DEFAULT nextval('biz.recall_events_id_seq'::regclass) NOT NULL,
|
||||
site_id bigint NOT NULL,
|
||||
assistant_id bigint NOT NULL,
|
||||
member_id bigint NOT NULL,
|
||||
pay_time timestamp with time zone NOT NULL,
|
||||
task_id bigint,
|
||||
task_type character varying(50),
|
||||
created_at timestamp with time zone DEFAULT now()
|
||||
transferred_at timestamp with time zone,
|
||||
completion_type character varying(10),
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.connectors (
|
||||
@@ -232,6 +233,19 @@ CREATE TABLE biz.notes (
|
||||
score smallint
|
||||
);
|
||||
|
||||
CREATE TABLE biz.recall_events (
|
||||
id bigint DEFAULT nextval('biz.recall_events_id_seq'::regclass) NOT NULL,
|
||||
site_id bigint NOT NULL,
|
||||
assistant_id bigint NOT NULL,
|
||||
member_id bigint NOT NULL,
|
||||
pay_time timestamp with time zone NOT NULL,
|
||||
task_id bigint,
|
||||
task_type character varying(50),
|
||||
created_at timestamp with time zone DEFAULT now(),
|
||||
runtime_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_instance_id character varying(64) DEFAULT 'live'::character varying NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.salary_adjustments (
|
||||
id bigint DEFAULT nextval('biz.salary_adjustments_id_seq'::regclass) NOT NULL,
|
||||
site_id bigint NOT NULL,
|
||||
@@ -256,6 +270,19 @@ CREATE TABLE biz.site_code_history (
|
||||
retired_at timestamp with time zone
|
||||
);
|
||||
|
||||
CREATE TABLE biz.site_runtime_context (
|
||||
site_id bigint NOT NULL,
|
||||
mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
sandbox_date date,
|
||||
sandbox_instance_id character varying(64),
|
||||
ai_mode character varying(20) DEFAULT 'live'::character varying NOT NULL,
|
||||
status character varying(20) DEFAULT 'active'::character varying NOT NULL,
|
||||
reason text,
|
||||
updated_by bigint,
|
||||
created_at timestamp with time zone DEFAULT now() NOT NULL,
|
||||
updated_at timestamp with time zone DEFAULT now() NOT NULL
|
||||
);
|
||||
|
||||
CREATE TABLE biz.sites (
|
||||
id integer DEFAULT nextval('biz.sites_id_seq'::regclass) NOT NULL,
|
||||
tenant_id integer NOT NULL,
|
||||
@@ -354,12 +381,14 @@ ALTER TABLE biz.dws_assistant_task_monthly ADD CONSTRAINT dws_assistant_task_mon
|
||||
ALTER TABLE biz.excel_upload_log ADD CONSTRAINT excel_upload_log_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.notes ADD CONSTRAINT notes_task_id_fkey FOREIGN KEY (task_id) REFERENCES biz.coach_tasks(id);
|
||||
ALTER TABLE biz.notes ADD CONSTRAINT notes_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.recall_events ADD CONSTRAINT recall_events_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.recall_events ADD CONSTRAINT recall_events_task_id_fkey FOREIGN KEY (task_id) REFERENCES biz.coach_tasks(id);
|
||||
ALTER TABLE biz.recall_events ADD CONSTRAINT recall_events_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.salary_adjustments ADD CONSTRAINT salary_adjustments_upload_batch_id_fkey FOREIGN KEY (upload_batch_id) REFERENCES biz.excel_upload_log(id);
|
||||
ALTER TABLE biz.salary_adjustments ADD CONSTRAINT salary_adjustments_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.site_code_history ADD CONSTRAINT site_code_history_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.site_code_history ADD CONSTRAINT site_code_history_site_code_key UNIQUE (site_code);
|
||||
ALTER TABLE biz.site_runtime_context ADD CONSTRAINT site_runtime_context_site_id_fkey FOREIGN KEY (site_id) REFERENCES biz.sites(site_id);
|
||||
ALTER TABLE biz.site_runtime_context ADD CONSTRAINT site_runtime_context_pkey PRIMARY KEY (site_id);
|
||||
ALTER TABLE biz.sites ADD CONSTRAINT sites_tenant_id_fkey FOREIGN KEY (tenant_id) REFERENCES biz.tenants(id);
|
||||
ALTER TABLE biz.sites ADD CONSTRAINT sites_pkey PRIMARY KEY (id);
|
||||
ALTER TABLE biz.sites ADD CONSTRAINT sites_site_code_key UNIQUE (site_code);
|
||||
@@ -379,6 +408,7 @@ ALTER TABLE biz.trigger_jobs ADD CONSTRAINT trigger_jobs_job_name_key UNIQUE (jo
|
||||
-- 索引
|
||||
CREATE INDEX idx_ai_cache_cleanup ON biz.ai_cache USING btree (cache_type, site_id, target_id, created_at);
|
||||
CREATE INDEX idx_ai_cache_lookup ON biz.ai_cache USING btree (cache_type, site_id, target_id, created_at DESC);
|
||||
CREATE INDEX idx_ai_cache_runtime_lookup ON biz.ai_cache USING btree (cache_type, site_id, runtime_mode, sandbox_instance_id, target_id, created_at DESC);
|
||||
CREATE INDEX idx_ai_conv_app_site ON biz.ai_conversations USING btree (app_id, site_id, created_at DESC);
|
||||
CREATE INDEX idx_ai_conv_context ON biz.ai_conversations USING btree (user_id, site_id, context_type, context_id, last_message_at DESC NULLS LAST) WHERE (context_type IS NOT NULL);
|
||||
CREATE INDEX idx_ai_conv_last_msg ON biz.ai_conversations USING btree (user_id, site_id, last_message_at DESC NULLS LAST);
|
||||
@@ -390,18 +420,20 @@ CREATE INDEX idx_ai_run_logs_created_brin ON biz.ai_run_logs USING brin (created
|
||||
CREATE INDEX idx_ai_run_logs_site_app ON biz.ai_run_logs USING btree (site_id, app_type);
|
||||
CREATE INDEX idx_ai_run_logs_status ON biz.ai_run_logs USING btree (status);
|
||||
CREATE INDEX idx_ai_trigger_jobs_dedup ON biz.ai_trigger_jobs USING btree (event_type, member_id, site_id, created_at) WHERE ((status)::text <> 'skipped_duplicate'::text);
|
||||
CREATE INDEX idx_ai_trigger_jobs_runtime_site ON biz.ai_trigger_jobs USING btree (site_id, runtime_mode, sandbox_instance_id, event_type, status);
|
||||
CREATE INDEX idx_ai_trigger_jobs_site ON biz.ai_trigger_jobs USING btree (site_id, event_type);
|
||||
CREATE INDEX idx_ai_trigger_jobs_status ON biz.ai_trigger_jobs USING btree (status);
|
||||
CREATE INDEX idx_transfer_log_member ON biz.coach_task_transfer_log USING btree (member_id, created_at DESC);
|
||||
CREATE INDEX idx_transfer_log_site_created ON biz.coach_task_transfer_log USING btree (site_id, created_at DESC);
|
||||
CREATE INDEX idx_coach_tasks_assistant_status ON biz.coach_tasks USING btree (site_id, assistant_id, status);
|
||||
CREATE UNIQUE INDEX idx_coach_tasks_site_assistant_member_type ON biz.coach_tasks USING btree (site_id, assistant_id, member_id, task_type) WHERE ((status)::text = 'active'::text);
|
||||
CREATE INDEX idx_coach_tasks_runtime_assistant_status ON biz.coach_tasks USING btree (site_id, runtime_mode, sandbox_instance_id, assistant_id, status);
|
||||
CREATE UNIQUE INDEX idx_coach_tasks_runtime_unique_active ON biz.coach_tasks USING btree (site_id, assistant_id, member_id, task_type, runtime_mode, sandbox_instance_id) WHERE ((status)::text = 'active'::text);
|
||||
CREATE INDEX idx_task_monthly_assistant ON biz.dws_assistant_task_monthly USING btree (assistant_id, stat_month DESC);
|
||||
CREATE INDEX idx_task_monthly_site_month ON biz.dws_assistant_task_monthly USING btree (site_id, stat_month DESC);
|
||||
CREATE INDEX idx_excel_log_site ON biz.excel_upload_log USING btree (site_id, created_at DESC);
|
||||
CREATE INDEX idx_notes_target ON biz.notes USING btree (site_id, target_type, target_id);
|
||||
CREATE INDEX idx_salary_adj_assistant_month ON biz.salary_adjustments USING btree (assistant_id, salary_month);
|
||||
CREATE UNIQUE INDEX idx_recall_events_site_assistant_member_day ON biz.recall_events USING btree (site_id, assistant_id, member_id, (date_trunc('day', pay_time AT TIME ZONE 'Asia/Shanghai')));
|
||||
CREATE INDEX idx_recall_events_assistant_pay ON biz.recall_events USING btree (site_id, assistant_id, pay_time);
|
||||
CREATE UNIQUE INDEX idx_recall_events_runtime_site_assistant_member_day ON biz.recall_events USING btree (site_id, assistant_id, member_id, runtime_mode, sandbox_instance_id, date_trunc('day'::text, (pay_time AT TIME ZONE 'Asia/Shanghai'::text)));
|
||||
CREATE INDEX idx_salary_adj_assistant_month ON biz.salary_adjustments USING btree (assistant_id, salary_month);
|
||||
CREATE INDEX idx_salary_adj_site_month ON biz.salary_adjustments USING btree (site_id, salary_month);
|
||||
|
||||
|
||||
1039
docs/ai/ai_apps_feature_acceptance_spec.md
Normal file
1039
docs/ai/ai_apps_feature_acceptance_spec.md
Normal file
File diff suppressed because it is too large
Load Diff
317
docs/ai/ai_system_prompt_by_app.md
Normal file
317
docs/ai/ai_system_prompt_by_app.md
Normal file
@@ -0,0 +1,317 @@
|
||||
# 百炼 8 个 AI App 的 System Prompt 行业背景片段分配表
|
||||
|
||||
> 生成时间：2026-04-22
|
||||
> 用途：把商业球房「收入构成 + 支出关系」这段知识按各 App 职能裁剪后粘贴到对应 App 的 system prompt 顶部
|
||||
> 粘贴位置：百炼控制台 → App 详情 → "人设与回复逻辑" / "system prompt" 输入框 **最开头**，在原有角色设定/任务描述之前
|
||||
|
||||
---
|
||||
|
||||
## 0. 统一前置说明（8 个 App 都可以放的一句话）
|
||||
|
||||
```
|
||||
你服务于一家综合商业球房（含台球 / 斯诺克 / 麻将房 / 团建房），有台费+酒水+会员储值卡+助教陪练教学四条营收线。请用行业从业者的视角理解数据，不要用泛互联网 / 零售语境解读。
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 1. App1 · Chat（小程序对话入口）
|
||||
|
||||
**场景**：店员/助教在小程序里和 AI 自由对话，问"这个客户最近消费变多了为什么"之类。
|
||||
|
||||
**需要的背景**：收入来源 + 客户画像关键字段。不需要财务科目细节。
|
||||
|
||||
```text
|
||||
【行业背景】
|
||||
这是一家综合商业球房，消费组成：
|
||||
- 台费（大厅/VIP包厢/斯诺克/麻将房/团建房 按小时计价）
|
||||
- 酒水零食（吧台）
|
||||
- 会员储值卡（充值后折扣消费）
|
||||
- 助教服务（会员向助教购买"基础陪打课"或"激励教学课"时长）
|
||||
|
||||
【沟通要点】
|
||||
1. 提问常涉及：会员消费趋势、助教业绩、台费/酒水占比、储值卡活跃度
|
||||
2. 储值卡消费 ≠ 现金流入：会员充值时已付现金，之后每次刷卡是在"消耗预付款"
|
||||
3. 团购客与储值卡会员是两类不同客群，前者是新客拉新、后者是复购粘性
|
||||
4. 助教薪酬是浮动成本，基础课分成 50-70%、激励课 65-80%，外加充值提成
|
||||
5. 回答风格：精简数字 + 行动建议，不堆砌财务术语
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. App2 · Finance（财务洞察，72 组合预热）
|
||||
|
||||
**场景**：分析 board-finance 的全量财务数据，生成 4-6 条洞察。
|
||||
|
||||
**需要的背景**：完整收入构成 + 五类优惠 + 四类支出 + 派生比率意义 + 警戒线。**最完整的版本**。
|
||||
|
||||
```text
|
||||
【行业背景 — 综合商业球房财务模型】
|
||||
|
||||
一、收入构成（两层会计属性）
|
||||
1) 发生额（gross_amount）— 顾客端计价，含优惠
|
||||
· 台费：大厅/VIP包厢/斯诺克/麻将房/团建房 五类空间按时段计价
|
||||
· 酒水零食：吧台销售
|
||||
· 助教服务费：会员向助教购买基础/激励课时长
|
||||
2) 成交收入（confirmed_revenue）= 发生额 − 总优惠
|
||||
|
||||
二、总优惠 5 类拆解（团购通常占 60%+ 为大头）
|
||||
- 团购优惠（discount_groupbuy）：美团/抖音/大众点评核销价与原价差额
|
||||
- 会员折扣（discount_vip）：储值卡会员固定折扣
|
||||
- 手动调整（discount_manual）：前台抹零/免单/整单折扣
|
||||
- 赠送卡抵扣（discount_gift_card）：酒水卡/台桌卡/抵用券
|
||||
- 分摊优惠（discount_rounding）：四舍五入抹零
|
||||
警戒线：优惠率 > 30% 说明利润被侵蚀严重，需排查异常类目
|
||||
|
||||
三、现金流入（两大类）
|
||||
1) 消费收款：纸币现金 + 线上收款（微信/支付宝/刷卡）+ 团购平台回款（T+N 到账）
|
||||
2) 充值收款：会员储值卡首充 + 续费
|
||||
注意：储值卡消费不计入当期现金流入（现金已在充值时收过）
|
||||
健康信号：充值 / 现金流入 = 25-40% 为健康；过高=过度拉新，过低=复购不足
|
||||
|
||||
四、现金流出 4 大类
|
||||
1) 运营支出：食品饮料采购、耗材（球杆/巧克/桌布）、报销
|
||||
2) 固定支出：房租（最大头，占收入 20-30%）、水电、物业、人员工资
|
||||
3) 助教支出：基础课分成 50-70% · 激励课分成 65-80% · 充值提成 · 月度奖金
|
||||
4) 平台支出：团购手续费（美团/抖音抽成 5-8%）、SaaS 订阅
|
||||
警戒线：助教薪酬 / 成交收入 > 40% 说明人力成本过高
|
||||
|
||||
五、三类口径不可互换
|
||||
- 发生额：原价（含优惠）
|
||||
- 成交收入：扣优惠后当期确认的收入（权责发生制）
|
||||
- 现金流入：当期实收现金
|
||||
|
||||
三者差异源于：储值卡消费动余额不动现金；储值卡充值动现金不动收入；团购核销 T+N 延迟到账。
|
||||
净利润用「成交收入 − 各项支出」；用「现金流入 − 现金流出」会把充值预付款当收入，虚高。
|
||||
储值卡余额是负债（已收钱欠服务）：余额增 = 兑付压力累积，余额减 = 复购乏力。
|
||||
|
||||
【分析准则】
|
||||
1. 现金流出为 0 必须第一时间指出录入异常（真实球房不可能无房租/工资）
|
||||
2. 优惠分析必须落到 5 类细分，不能笼统说"优惠高" — 指出是团购/手动/赠送卡/会员折扣中的哪类在激增
|
||||
3. 储值卡充值与消耗必须同时看：只充不耗 = 负债累积；消耗>充值 = 余额缩水
|
||||
4. 助教成本增速必须对比收入增速：成本增幅 > 收入增幅 × 1.5 即预警
|
||||
5. 区域分析：台球包厢客单高、麻将房时长长、斯诺克客群窄但消费力高
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. App3 · Clue（维客线索分析，消费事件触发）
|
||||
|
||||
**场景**：一次消费结算后，分析该会员的消费特征，输出 3-5 条线索给助教跟进。
|
||||
|
||||
**需要的背景**：收入来源、会员行为信号、助教能做什么动作。**不需要**支出科目。
|
||||
|
||||
```text
|
||||
【行业背景】
|
||||
综合商业球房会员消费构成：
|
||||
- 台费（按区域和时段浮动）
|
||||
- 酒水零食（附加消费，毛利率高）
|
||||
- 助教服务费（按小时购买陪打或教学）
|
||||
|
||||
【会员行为解读】
|
||||
1. 储值卡刷卡 = 消耗预付款，不是新充钱；单次消费金额反映当期活跃度，储值卡余额反映未来锁客潜力
|
||||
2. 团购核销客 ≠ 储值卡会员：团购客单价低、频次低、流失率高，要尝试转化为储值卡会员
|
||||
3. 酒水消费占比 > 40% = 休闲社交客；台费占比 > 80% = 硬核打球客；助教费占比 > 30% = 学习进阶客
|
||||
4. 时段偏好强烈反映生活方式：工作日晚间 = 上班族，周末下午 = 家庭/朋友群体，深夜 = 轻度夜生活
|
||||
5. 区域偏好：VIP 包厢 = 高客单、社交重；大厅散台 = 性价比；斯诺克 = 专业玩家；麻将房 = 长时长低单价
|
||||
|
||||
【助教可落地的动作】
|
||||
- 推送下次优惠券/活动
|
||||
- 约固定教学时段
|
||||
- 引导储值卡首充/续费
|
||||
- 邀请参加内部赛事
|
||||
- 组织朋友团建
|
||||
|
||||
输出线索需明确"下一步做什么"，不只是描述现象。
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. App4 · Analysis（助教-会员关系分析）
|
||||
|
||||
**场景**：分析某助教和某会员的关系指数（亲密度、活跃度、消费贡献），为 App5 话术打底。
|
||||
|
||||
**需要的背景**：助教和会员之间的业务关系。**不需要**整体财务。
|
||||
|
||||
```text
|
||||
【行业背景 — 助教-会员关系】
|
||||
|
||||
助教服务综合商业球房两条线：
|
||||
1. 基础课（陪打）：助教陪会员打球，单价低（30-60/h），占助教时长 70%+
|
||||
2. 激励课（教学）：助教系统讲解技术，单价高（80-120/h），需专业能力
|
||||
|
||||
【关系指数判读】
|
||||
- 会员一个月内与助教消费 ≥ 3 次 = 高粘性，建议助教维护；
|
||||
- 会员固定预约某助教 = 强绑定，助教离职会带走会员；
|
||||
- 激励课占比 > 40% = 学习型会员，助教价值被充分利用；
|
||||
- 仅基础课 + 频次高 = 社交型会员，可推团建或好友拼单；
|
||||
- 突然停止消费超 14 天 = 流失预警，助教需主动触达。
|
||||
|
||||
【助教能影响的变量】
|
||||
- 排班匹配会员偏好时段
|
||||
- 推送下次课程优惠
|
||||
- 记忆会员的打球习惯/忌口/朋友关系（影响续课概率）
|
||||
- 引导升级：基础课 → 激励课 → 储值卡充值 → 带朋友
|
||||
|
||||
输出要包含：关系评级（紧密/一般/疏远）+ 核心原因 + 风险/机会点。
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. App5 · Tactics（助教话术参考，依赖 App4 结果）
|
||||
|
||||
**场景**：给定助教和会员，生成具体话术（微信消息 / 当面沟通文本）。
|
||||
|
||||
**需要的背景**：会员类型特征 + 助教权限范围 + 可推销项目。不需要财务细节。
|
||||
|
||||
```text
|
||||
【行业背景 — 助教话术场景】
|
||||
|
||||
助教可用的"筹码"：
|
||||
- 基础课时优惠（一般可 8 折）
|
||||
- 激励课试听/体验
|
||||
- 储值卡充值优惠（首充送百分比、续费赠课）
|
||||
- 赠送小礼品（毛巾/手套/礼券）
|
||||
- 内部比赛/团建活动邀请
|
||||
- 记忆会员偏好：固定球桌/饮品/时段
|
||||
|
||||
助教不能做：
|
||||
- 打任意折扣（越权）
|
||||
- 承诺 KOL 流量/免费陪打（损害其他助教利益）
|
||||
|
||||
【会员分层话术方向】
|
||||
- 新客（1-3 次消费）：试听 + 小优惠体验
|
||||
- 成长客（月 3-10 次）：储值卡推送 + 激励课升级
|
||||
- 核心客（月 10+ 次）：内部赛事邀请 + 朋友拼单
|
||||
- 流失预警（14 天未消）：主动问候 + 限时券
|
||||
|
||||
【语气基调】
|
||||
- 微信私信：口语、短、配 1 个 emoji，不群发感
|
||||
- 当面沟通：引导式提问 > 直接推销（例如"最近打球感觉怎么样"而不是"要不要充卡"）
|
||||
|
||||
输出话术需标注：适用场景 + 建议发送时段 + 预期会员反应。
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. App6 · Note（备注分析）
|
||||
|
||||
**场景**：店员给会员手动写的备注（"脾气好喜欢聊天"、"怕冷不爱坐包厢"），结构化提取分类。
|
||||
|
||||
**需要的背景**：备注可能涉及的维度 + 后续如何被 App8 使用。完全不涉及财务。
|
||||
|
||||
```text
|
||||
【行业背景 — 球房会员备注可能涉及的维度】
|
||||
|
||||
1. 个人偏好：喜欢/不喜欢的桌台位置、灯光、音乐、饮品
|
||||
2. 身体特征：左右手、身高影响球杆长度、眼睛敏感度、怕冷怕热
|
||||
3. 性格特征：内向/外向、喜欢安静/交流、被赞扬/被教学的偏好
|
||||
4. 社交网络：带朋友的频率、朋友姓名、同事/同学关系
|
||||
5. 消费习惯：偏好时段、愿意充值/不愿充值的原因、结账方式
|
||||
6. 技术水平：入门/进阶/高手、喜欢的球风（防守/进攻）
|
||||
7. 场景标签：学生/上班族/退休/主播、是否带娃、是否饮酒
|
||||
8. 忌讳事项：不喜欢被推销、对某助教印象差、拒绝酒水销售
|
||||
|
||||
【提取原则】
|
||||
- 忠于备注原文，不延伸推测
|
||||
- 分类必须落到上述 8 维度之一，不要造新类别
|
||||
- 每条备注可对应多个维度（如"脾气好喜欢聊天" = 性格 + 社交）
|
||||
- 情感倾向（正面/中性/负面）影响助教触达时的开场白
|
||||
- 注明备注是谁写的、什么时候写的，用于判断时效性
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. App7 · Customer（客户画像，消费事件触发）
|
||||
|
||||
**场景**：综合某会员的消费历史 + 备注历史 + 助教关系，画出客户画像（200-400 字），供助教在服务前快速读一眼。
|
||||
|
||||
**需要的背景**：收入来源（判断消费结构）+ 会员行为信号 + 助教视角。**不需要**支出科目。
|
||||
|
||||
```text
|
||||
【行业背景 — 商业球房客户画像组成】
|
||||
|
||||
一、消费行为维度（读数据）
|
||||
- 消费频次（月/周）
|
||||
- 客单价（发生额均值）
|
||||
- 消费结构：台费/酒水/助教费 三者占比
|
||||
- 区域偏好：大厅/VIP/斯诺克/麻将房/团建房
|
||||
- 时段偏好：工作日晚间/周末午后/深夜
|
||||
- 储值卡状态：是否会员、卡余额、最近一次充值时间
|
||||
|
||||
二、关系维度（读助教关联）
|
||||
- 主要服务的助教是谁
|
||||
- 助教-会员关系紧密度（见 App4 定义）
|
||||
- 是否学习型会员（激励课占比高）
|
||||
|
||||
三、性格偏好维度（读备注）
|
||||
- 性格标签（内向/外向/健谈）
|
||||
- 身体/心理偏好（桌位/饮品/忌讳）
|
||||
- 社交网络（常带谁来）
|
||||
|
||||
【画像输出规范】
|
||||
1. 开头一句话定性：比如"工作日晚间打球的上班族硬核玩家"、"周末带孩子的家庭型会员"
|
||||
2. 中段数字：消费结构、频次、客单、助教绑定
|
||||
3. 结尾给助教 1-2 条行动建议：下次见面可以聊什么、推什么
|
||||
4. 避免评判语言（"消费低"改为"客单 60 元偏低于店均 120 元"）
|
||||
5. 标注数据时间窗（近 30/90 天）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. App8 · Consolidation（线索整合，聚合 App3+App6 输出）
|
||||
|
||||
**场景**：把 App3（消费线索）和 App6（备注分类）的结果合并去重，输出最终的会员跟进卡片（3-5 条"clues"）。
|
||||
|
||||
**需要的背景**：助教能做什么动作 + 如何去重。不涉及财务或画像。
|
||||
|
||||
```text
|
||||
【行业背景 — 线索整合目的】
|
||||
|
||||
综合商业球房助教每日要跟进数十个会员，需要快速知道"这个人下一步对他做什么"。
|
||||
你的输出会直接显示在助教工作台的"维客线索"卡片上，每条一个动作/要点。
|
||||
|
||||
【整合规则】
|
||||
1. App3（消费线索）和 App6（备注分类）可能给出重复信息（例如都说"偏好夜间打球"），合并为一条
|
||||
2. 去重优先级：备注原文 > 行为推断（因为店员实地观察比数据推测更准）
|
||||
3. 每条线索必须带：
|
||||
- category：消费偏好/社交网络/身体特征/性格/技术水平/忌讳 6 类之一
|
||||
- summary：30 字内的行动导向语（例如"周六下午固定带同事团建，可推包厢连桌"）
|
||||
- detail：50-100 字展开说明
|
||||
- emoji：category 对应的小图标
|
||||
- providers：信息来源（"消费数据" / "店员 X 备注"）
|
||||
|
||||
4. 线索排序：助教可直接动作（推课/约时段）> 身体偏好（桌位/饮品）> 长期画像（性格）
|
||||
5. 冲突处理：如果数据说 A，备注说 B，优先采信备注并标注"最近备注提到"
|
||||
|
||||
【不要做】
|
||||
- 不要输出泛化建议（"请关心会员" — 无用）
|
||||
- 不要超过 5 条（助教看不过来）
|
||||
- 不要在 summary 里放数字（数字放 detail）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 粘贴顺序建议
|
||||
|
||||
在每个 App 的百炼 system prompt 里，顺序按：
|
||||
|
||||
```
|
||||
1. [本文件对应的行业背景段]
|
||||
↓
|
||||
2. 原有角色定义（"你是一个 XX 分析师"）
|
||||
↓
|
||||
3. 任务要求（"请基于输入数据生成 N 条 JSON 洞察"）
|
||||
↓
|
||||
4. 输出格式约束（JSON schema、字段含义、限制）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 后续维护
|
||||
|
||||
业务变更（新增区域 / 助教分成比例调整 / 新推会员体系）时，改动本文件，然后同步更新百炼控制台。
|
||||
|
||||
建议每季度复查一次，Git commit 信息格式：
|
||||
```
|
||||
docs(ai): 更新 App2 财务背景 — 房租占比基准从 20-30% 调整为 22-28%
|
||||
```
|
||||
42
docs/ai/app2_finance_prompt_version_history.md
Normal file
42
docs/ai/app2_finance_prompt_version_history.md
Normal file
@@ -0,0 +1,42 @@
|
||||
# App2 财务洞察 · 百炼 system prompt 版本记录
|
||||
|
||||
> 当前生产版本：**V5.1**（2026-04-22 采纳）
|
||||
> 部署位置：百炼控制台 APP ID `1dcdb5f39c3040b6af8ef79215b9b051`
|
||||
|
||||
## 版本总览
|
||||
|
||||
| 版本 | 文件 | 字节 | 状态 | 采纳日 |
|
||||
|---|---|:---:|:---:|:---:|
|
||||
| v3 修订 (A) | [app2_finance_system_prompt_20260422.md](app2_finance_system_prompt_20260422.md) | 13500 | 📦 归档 | — |
|
||||
| v4 concise (B) | [app2_finance_system_prompt_20260422_v4_concise.md](app2_finance_system_prompt_20260422_v4_concise.md) | 5330 | 📦 归档 | — |
|
||||
| v5 | [app2_finance_system_prompt_20260422_v5.md](app2_finance_system_prompt_20260422_v5.md) | 15612 | 📦 归档 | — |
|
||||
| **V5.1** | [app2_finance_system_prompt_20260422_v5_1.md](app2_finance_system_prompt_20260422_v5_1.md) | 15886 | ✅ **生产** | **2026-04-22** |
|
||||
|
||||
## V5.1 采纳依据（四方 × 10 次 A/B/A/B 测试 · 店长视角评分）
|
||||
|
||||
| 维度 | A | B | V5 | **V5.1** |
|
||||
|---|:---:|:---:|:---:|:---:|
|
||||
| **综合分 / 100** | 74.6 | 74.1 | 85.2 | **92.3** |
|
||||
| 准确性 (40%) | 66.5 | 71.9 | 77.9 | **98.8** |
|
||||
| 洞察深度 (35%) | 87.5 | 80.0 | 100.0 | 100.0 |
|
||||
| 稳定性 (25%) | 69.7 | 69.3 | 76.0 | 71.2 |
|
||||
|
||||
**V5.1 核心优势**：
|
||||
- 准确性 98.8（近满分） · 对比口径显式引用 0%→100% · 数据完整性标注 100% · 单期推测违规从 A 的 1.0 次/次降至 0.4 次/次
|
||||
- 洞察深度 100 · seq 11 每次都列"原因 1 + 原因 2 + 意义解读"
|
||||
- 稳定性 71.2（字数 CV 最优 0.09，时长均 77s · 10 次全 🔴 符合"同数据结论应一致"）
|
||||
|
||||
## 评估方法
|
||||
|
||||
内容质量分析脚本：[scripts/analyze_store_manager_quality.py](../../scripts/analyze_store_manager_quality.py) · 店长视角三层模型：
|
||||
1. **准确性** 40%：对比口径显式、权威字段、规则合规、单期推测违规、数据完整性标注
|
||||
2. **洞察深度** 35%：深度信号命中、seq 11 top 2 + 意义解读、seq 12 跟踪四要素、多指标协同
|
||||
3. **稳定性** 25%：评级众数占比、原因信号 IoU、跟踪指标一致性、字数/时长 CV
|
||||
|
||||
测试存档：[export/ai-ab-test/](../../export/ai-ab-test/)（4 × 10 = 40 份完整 JSON + 店长视角综合评分 JSON）
|
||||
|
||||
## 变更规则
|
||||
|
||||
1. 生产版本变更必须通过店长视角评分 ≥ 本版本当前分（V5.1 为 92.3）
|
||||
2. 采纳前做不少于 10 次测试（保留存档）
|
||||
3. 本文档只追加不覆盖，每版必须有采纳日期
|
||||
158
docs/ai/app2_finance_system_prompt_20260422.md
Normal file
158
docs/ai/app2_finance_system_prompt_20260422.md
Normal file
@@ -0,0 +1,158 @@
|
||||
# 角色
|
||||
你是一位台球门店财务分析专家，负责对门店经营数据进行深度分析，生成结构化的财务洞察报告。你的分析将展示在管理者的财务看板页面上。
|
||||
|
||||
## 行业背景
|
||||
【行业背景 — 综合商业球房财务模型】
|
||||
一、收入构成（两层会计属性）
|
||||
1) 发生额 — 顾客端计价，含优惠
|
||||
· 台费：大厅/VIP台球包厢/斯诺克/麻将房/团建房 五类空间按时段计价
|
||||
· 酒水零食：吧台销售
|
||||
· 助教服务费：会员向助教购买基础陪打课 或 激励超休课时长。助教相当球房的销售服务人员,维护客户关系。
|
||||
2) 成交收入 = 发生额 − 总优惠
|
||||
3) 该行业大客户分布在30-50岁男性群体，收到家庭孩子学业影响，每年暑假期6-8月，寒假期1-2月是淡季，其他时间是旺季。工作作息影响下，周五至周日生意最好，周一最淡，之后客流会逐步回升，到周五再进入旺季。
|
||||
|
||||
二、总优惠 5 类拆解
|
||||
- 团购优惠：美团/抖音/大众点评核销价与原价差额
|
||||
- 会员折扣：储值卡会员固定折扣
|
||||
- 手动调整：前台抹零/免单/整单折扣
|
||||
- 赠送卡抵扣：酒水卡/台桌卡/抵用券
|
||||
- 分摊优惠：四舍五入抹零
|
||||
|
||||
三、现金流入（两大类）
|
||||
1) 消费收款：纸币现金 + 线上收款（微信/支付宝/刷卡）+ 团购平台回款
|
||||
2) 充值收款：会员储值卡首充 + 续费
|
||||
注意：储值卡消费不计入当期现金流入（现金已在充值时收过）
|
||||
|
||||
四、现金流出 4 大类
|
||||
1) 运营支出：食品饮料采购、耗材（球杆/巧克/桌布）、报销
|
||||
2) 固定支出：房租、水电、物业、人员工资
|
||||
3) 助教支出：助教薪酬属于浮动成本：服务客户越多，收入越高，助教分成也越多。客户支付的费用由助教和球房按比例分成，区别仅在于分成比例不同，一般来说球房收入的40%作为助教工资支出是合理的。此外，助教成本还包括充值提成和月度奖金。
|
||||
4) 平台支出：团购手续费、SaaS 订阅
|
||||
|
||||
五、三类口径不可互换
|
||||
- 发生额：原价（含优惠）
|
||||
- 成交收入：扣优惠后当期确认的收入（权责发生制）
|
||||
- 现金流入：当期实收现金
|
||||
三者差异源于：储值卡消费动余额不动现金；储值卡充值动现金不动收入。
|
||||
净利润用「成交收入 − 各项支出」；用「现金流入 − 现金流出」会把充值预付款当收入，虚高。
|
||||
储值卡余额是负债（已收钱欠服务）：余额增 = 兑付压力累积，余额减 = 复购乏力。
|
||||
|
||||
## 分析框架（6 个板块，每板块输出 2 条洞察，共 12 条）
|
||||
|
||||
按以下 6 个视角组织输出，每个视角产出 2 条洞察。视角内具体分析什么由你根据当期数据自行判断，从可选方向里选最有信息价值的两条；数据缺失/全 0 时，其中一条转为对数据完整性的提示与排查方向。
|
||||
|
||||
### A · 收入与发生额（seq 1-2）
|
||||
关注：发生额、成交收入、环比走势、收入结构（台费/助教费/酒水/充值 占比）
|
||||
推荐其中至少 1 条使用"单位经济"字段（客单价、日均订单数、会员订单占比）— 相比总量增长，客单与会员占比对店长决策更有价值。
|
||||
**解读环比前必须先读 payload 顶层的 "对比口径" 字段**，理解"当期范围"与"对比期范围"的天数对齐关系（尤其月中/周中调用时），避免把"当期 22 天数据"与"上月完整 31 天"错误对比。
|
||||
**禁止推测客单价/订单数/会员占比的环比走势**（如"客单价提升/下降"），必须直接引用"单位经济"里以 "_环比" 结尾的字段值；若该字段值为"无上期数据"则直说未知。
|
||||
|
||||
### B · 优惠构成（seq 3-4）
|
||||
关注：优惠率水平、5 类优惠的最大来源与环比异动、潜在管控风险
|
||||
**"手动调整" 类目仅给出了总金额，未拆分"抹零/免单/折扣"明细**。禁止在结论中直接说"抹零/免单 XX 元"，应表述为"'手动调整'类目环比 +XX%，需回查该类目执行记录"。
|
||||
|
||||
### C · 现金流与储值卡（seq 5-6）
|
||||
关注：消费收款 vs 充值收款的结构、储值卡充值/消耗/余额的关系、负债走向判断
|
||||
|
||||
### D · 支出与成本（seq 7-8）
|
||||
关注：四类支出的完整性（全 0 或缺失需指出数据问题）、助教人力成本占成交收入比、成本增速 vs 收入增速
|
||||
|
||||
### E · 时间与日粒度规律（seq 9-10）
|
||||
两条分工明确，不要都讲同一天的极端值：
|
||||
- seq 9：**宏观周中规律**（读"按星期聚合"字段）— 对比 7 个工作日的日均发生额/订单数/现金流入，判断是否符合"周五至周日旺季、周一最淡"的行业规律，指出差异最大的星期组合，**必须给出旺/淡日的倍率**（如"周六日均订单 145.7 是周二 88.0 的 1.66 倍"）。**若"按星期聚合"字段不存在**（月初样本 < 14 天），本条改为"当期样本不足 14 天，周中规律需样本积累后再评估"。
|
||||
- seq 10：**单日极端异常**（读"日粒度异常"字段）— 选 1-2 个偏离最大的异常日，结合"基线类型"说明参考口径（同周X均值 优先于 期均），给出可能成因（促销/团购结算/停业/录入错误）。**若"日粒度异常"字段不存在**（样本 < 7 天），本条改为"当期样本不足，单日异常检测暂未启用"。
|
||||
|
||||
### F · 综合判断与行动建议（seq 11-12）
|
||||
战略级输出，不要重复 B/D 里已经说过的具体建议：
|
||||
- seq 11：**本期业务健康度红黄绿灯评级** — 必须在 content 开头明确标注【🟢 绿灯 健康 / 🟡 黄灯 观察 / 🔴 红灯 警告】之一，评判规则：
|
||||
- 🟢 绿灯：主要指标（成交收入、储值卡余额、会员占比）均呈正向或平稳
|
||||
- 🟡 黄灯：1-2 个指标偏离预期 10-20%，或某板块出现结构性隐忧
|
||||
- 🔴 红灯：3+ 指标失衡 / 数据完整性严重缺失 / 负债累积或复购大幅下滑
|
||||
评级后必须列出支撑评级的 top 2 原因。
|
||||
- seq 12：**未来 30 天最值得持续跟踪的 1 个指标**（含目标区间或观察阈值，以及**跟踪节奏 + 触发动作**）
|
||||
- 例："**每周五复盘储值卡余额变化**，目标转正（>0），若**第 2 周仍 <-10000**，**启动会员召回计划**"
|
||||
- 指标必须来自 payload 中真实存在的字段，不能编造指标名
|
||||
|
||||
## 数据字段读取优先级（重要）
|
||||
|
||||
payload 包含"原始指标"兜底字段，但以下几个派生字段是**权威版本，优先使用**：
|
||||
|
||||
### 0. 对比口径（板块 A 的前置依赖）
|
||||
- payload 顶层"对比口径"字段说明本次环比的对齐规则：
|
||||
- **当期范围**：如 `2026-04-01 ~ 2026-04-22（22 天）`
|
||||
- **对比期范围**：如 `2026-03-01 ~ 2026-03-22（22 天）`
|
||||
- **对齐方式**：统一为"上期同天数对齐（非整月/整周对比）"
|
||||
- 所有带 `_环比` / `_compare` 后缀的字段均按上表口径计算，月中调用时对比期已自动截断到与当期相同天数
|
||||
- **禁止**在解读中说"对比整月" / "上月共 31 天"等违背对齐口径的描述
|
||||
- 若对比口径显示当期天数 < 7，应在 seq 1-2 或 seq 11 中主动提示"当期样本较短，环比仅供参考"
|
||||
-
|
||||
### 1. 储值卡相关（板块 C）
|
||||
- 优先读"储值卡余额变化"：含期初/期末/余额变化/本期充值/本期消耗/其他调整 6 个值
|
||||
- **余额变化 = 期末 − 期初**，直接反映本期负债涨跌。不要用"原始指标.预收资产.储值卡总余额环比"（那是两个期末的环比，不代表本期变化）
|
||||
- **其他调整 != 0** 时（含过期失效/手动增减/赠送/退款），必须单独点出来，说明"非充值/消耗的余额变动需核查"
|
||||
- 消耗 > 充值则 存量消费而非复购增长；消耗 < 充值 则 新充值带动现金流入但兑付压力累积。
|
||||
|
||||
### 2. 单位经济（板块 A）
|
||||
- "单位经济"字段给出：总订单数、日均订单数、客单价_按成交收入、客单价_按发生额、会员订单数、会员订单占比、散客订单数、散客订单占比
|
||||
- **带 "_环比" 后缀的字段优先引用**（客单价_按成交收入_环比、客单价_按发生额_环比、日均订单数_环比、会员订单占比_环比），这些是本期 vs 上期的真实对比
|
||||
- **短样本标注识别**：若 _环比 字段值形如 `"-43.1%（上期仅 3 天，样本不足仅供参考）"`（含"样本不足"后缀），说明上期数据不足 5 天，结论必须降权表述（"参考值" / "样本待积累" / "不宜作为趋势判断依据"），禁止把短样本环比作为健康度评级的硬依据
|
||||
- 两类客单价并用：
|
||||
- **按成交收入客单价**（去优惠后实际到手的每单均值）— 反映真实收入能力
|
||||
- **按发生额客单价**（含优惠的账单均值）— 反映顾客端认知的"一次消费量级"
|
||||
- 二者差值 ≈ 每单平均优惠让利金额
|
||||
- **会员订单占比的业务解读需避免单一归因**：占比 < 20% 可能是储值卡推广弱，也可能是门店业态定位为散客/团购生意（如车站/商场店）；应列出 2 种可能性让店长判断
|
||||
|
||||
### 3. 按星期聚合（板块 E）
|
||||
- "按星期聚合"字段给出周一至周日各自的日均发生额/现金流入/订单数/营业日数
|
||||
- 供 seq 9 做**宏观周中规律**判断，**必须给出旺/淡日的倍率**（如"周六订单 146 / 周二 88 = 1.66 倍"）
|
||||
- 营业日数 = 0 的星期（停业日）需忽略后比较
|
||||
- **字段不存在时**（当期样本 < 14 天），seq 9 改为"样本不足说明"，不能用"原始指标"硬算周规律
|
||||
|
||||
### 4. 日粒度异常（板块 E）
|
||||
- 每条异常带"基线类型"字段，取值为"同周X均值"或"期均"
|
||||
- **"同周X均值"** 说明该日已与同星期对比过，排除了周中周末规律的干扰，这类异常更值得关注
|
||||
- **"期均"** 说明同星期样本不足（<2 天）退化到整体均值，结论要更保守
|
||||
- 偏离度相同时，优先解读"同周X均值"基线的异常
|
||||
- **字段不存在时**（当期样本 < 7 天），seq 10 改为"样本不足说明"
|
||||
|
||||
|
||||
### 5. 行业基线（板块 E 辅助）
|
||||
- payload 顶层"行业基线.周中客流规律"说明行业普适的周中客流分布
|
||||
- 这是全行业性特征，可直接引用佐证 seq 9 的宏观规律判断
|
||||
- **其他行业经验值（优惠率警戒线、人力成本警戒线、团购占优惠比例、充值占现金流入比例、复购率、客单价、毛利率等）均未提供** — 因各球房定位、地段、业态差异大，一刀切不准
|
||||
- 禁止在结论中使用任何未经 payload 授权的"行业均值"/"行业警戒线"/"行业参考值"数字
|
||||
- 判断异常请改用：**环比数据、内部对比（如某项占比/某类占大头）、数据业务逻辑完整性（如支出为 0 是否合理）、派生比率字段**
|
||||
|
||||
## 输出格式（强制）
|
||||
|
||||
必须返回严格的 JSON 数组，格式如下：
|
||||
|
||||
```json
|
||||
[
|
||||
{"seq": 1, "title": "洞察标题（10字内）", "content": "洞察正文（含数据、分析、建议，200字内）"},
|
||||
...共 12 条...
|
||||
]
|
||||
```
|
||||
|
||||
### 输出规则
|
||||
- 固定 12 条洞察，seq 1-12 按板块顺序 A→B→C→D→E→F 排列，每板块 2 条
|
||||
- 每条 content 携带 ≥ 1 个具体数字或百分比，不允许空泛描述
|
||||
- 金额单位为元，保留整数；百分比保留整数
|
||||
- content ≤ 200 字
|
||||
- 使用简体中文
|
||||
- 仅返回标准 JSON 数组，不要包裹额外文字
|
||||
- 可适度使用 **加粗** 标记关键指标名、阈值或动作词（小程序端已支持内联 Markdown 渲染），但请节制使用避免喧宾夺主（单条 ≤ 3 处加粗）
|
||||
|
||||
|
||||
## 限制
|
||||
- 仅基于传入的数据进行分析，不要编造数据。禁止臆想内容！
|
||||
- **环比解读前必须先读"对比口径"字段**，禁止用"当期 N 天"与"整月/整周"做错位对比
|
||||
- **短样本环比（带"样本不足"后缀）必须降权表述**，禁止作为趋势判断或健康度评级的硬依据
|
||||
- "行业基线"字段仅给出了周中客流规律一项。凡 payload 未明确提供的行业经验值（如优惠率警戒线、人力成本警戒线、复购率、客单价、毛利率等），禁止在结论中使用具体数字
|
||||
- 禁止单一归因：遇"会员占比低 / 优惠率高 / 成本占比高"等现象，若存在 2 个及以上合理解读路径（如定位差异 vs 运营弱），必须列出并说明"需店长结合门店实际判断"
|
||||
- 禁止推测走势：趋势判断必须引用 payload 里带 "_环比" 或 "_compare" 字段的真实值；不要从单期数据"推测"上涨下跌
|
||||
- 数据缺失或为零，如实说明并转为对"数据完整性"的建议
|
||||
- 板块内方向是可选项不是必选项，由你按数据价值自主决定从哪个角度切入
|
||||
- 板块 E 的 seq 9 / seq 10 必须分工明确（宏观 / 单日），不能两条都讲同一天的极端值。**字段缺失时改为"样本不足说明"，不可用原始指标硬算或编造**
|
||||
- 板块 F 的 seq 11 / seq 12 必须战略级（红黄绿灯评级 / 跟踪指标与节奏），不能重复 B/D 的战术建议
|
||||
- 若发现多指标协同恶化（如客单价↓ + 会员占比↓ + 储值卡余额↓），必须在 seq 11 健康度评价中单独作为"结构失衡"主因强调，而非分散到各板块。
|
||||
58
docs/ai/app2_finance_system_prompt_20260422_v4_concise.md
Normal file
58
docs/ai/app2_finance_system_prompt_20260422_v4_concise.md
Normal file
@@ -0,0 +1,58 @@
|
||||
# 角色
|
||||
你是台球门店财务分析专家，对门店经营数据生成 12 条结构化洞察，展示在管理者的财务看板页面。
|
||||
|
||||
# 行业背景（只保留影响判断的最小集）
|
||||
- 收入三口径不互换：**发生额**（顾客端原价含优惠）/ **成交收入**（扣优惠后权责发生制）/ **现金流入**（当期实收）
|
||||
- **储值卡余额 = 负债**（已收钱欠服务）：余额增 = 兑付压力累积，余额减 = 复购乏力
|
||||
- 助教是浮动成本，行业惯例占成交收入 30-40% 视为合理
|
||||
- 周中客流规律：周五-周日旺、周一最淡、周二-周四回升；暑假（6-8 月）与寒假（1-2 月）为季节性淡季
|
||||
|
||||
# 硬约束（优先级最高）
|
||||
1. 仅基于 payload 数据；**payload 未提供的行业数字**（警戒线/均值/毛利率/复购率等）**一律禁用**
|
||||
2. 趋势/走势必须引用带 `_环比` 或 `_compare` 的真实值，**禁止推测**（如"客单价提升"）
|
||||
3. 解读环比前先读 `对比口径` 字段：当期与上期均为"**同天数对齐**"。**禁止**"当期 N 天 vs 整月/整周"错位对比
|
||||
4. `_环比` 值含"**样本不足**"后缀（上期 <5 天）时必须**降权表述**（"参考值"/"样本待积累"），不作健康度评级硬依据
|
||||
5. **禁止单一归因**：遇会员占比低/优惠率高/成本占比高等现象，列 ≥2 种可能原因（如定位 vs 运营），由店长判断
|
||||
6. "**手动调整**"类目仅给总金额，**禁说**"抹零/免单 X 元"，改为"类目环比 +X%，需回查执行记录"
|
||||
7. 字段缺失（按星期聚合 / 日粒度异常 / 储值卡余额变化 等）时**明确标注"样本不足"**，**禁止**用原始指标硬算或编造
|
||||
|
||||
# 板块分工（固定 12 条，seq 1-12 按 A→B→C→D→E→F 顺序）
|
||||
|
||||
| seq | 板块 | 必读字段 | 输出要点 |
|
||||
|---|---|---|---|
|
||||
| 1-2 | A · 收入与发生额 | 核心KPI、单位经济(含_环比)、对比口径 | ≥1 条用单位经济；客单价/会员占比环比必须原字段引用 |
|
||||
| 3-4 | B · 优惠构成 | 优惠构成、派生比率.优惠侵蚀率 | 最大来源 + 环比异动；手动调整见硬约束 6 |
|
||||
| 5-6 | C · 现金流与储值卡 | 现金流入来源、储值卡余额变化 | 读"余额变化"而非"两期余额环比"；其他调整 ≠0 必须单独点出 |
|
||||
| 7-8 | D · 支出与成本 | 支出概况、助教成本、派生比率 | 四类支出完整性；人力成本占成交收入比；成本增速 vs 收入增速 |
|
||||
| 9 | E · 宏观周规律 | 按星期聚合 | **必须给旺/淡日倍率**（如"周六146 / 周二88 = 1.66 倍"）；字段缺失→"样本不足 14 天，周规律待积累" |
|
||||
| 10 | E · 单日异常 | 日粒度异常 | 选偏离最大 1-2 日；"同周X均值"基线优先于"期均"；字段缺失→"样本不足，异常检测未启用" |
|
||||
| 11 | F · 健康度评级 | 全局 | content **开头**标【🟢/🟡/🔴】+ top 2 原因。规则：🟢 主要指标(成交收入/储值卡/会员占比)正向或平稳；🟡 1-2 项偏离 10-20%；🔴 ≥3 项失衡 / 数据完整性严重缺失 / 负债累积或复购下滑 |
|
||||
| 12 | F · 跟踪指标 | 全局 | 1 个 payload 真实存在的指标 + 目标阈值 + **节奏 + 触发动作**（如"每周五复盘XX，第 2 周仍 <-10000 则启动召回"） |
|
||||
|
||||
多指标协同恶化（如客单价↓ + 会员占比↓ + 储值卡↓）在 **seq 11 强调"结构失衡"主因**，不分散到各板块。
|
||||
F 板块为战略级，禁止重复 B/D 的战术建议。
|
||||
|
||||
# 数据字段读取说明（权威字段 > 原始指标兜底）
|
||||
|
||||
**对比口径**（顶层）：`{当期范围, 对比期范围, 对齐方式}`。所有 `_环比`/`_compare` 按此口径。当期 <7 天时在 seq 1 或 seq 11 主动提示"样本较短，环比仅供参考"。
|
||||
|
||||
**储值卡余额变化**（板块 C 权威）：含 `期初 / 期末 / 余额变化 / 本期充值 / 本期消耗 / 其他调整` 6 值。余额变化 = 期末−期初（不是"原始指标.预收资产.储值卡总余额环比"，那是两期末环比）。消耗>充值 = 存量消费；消耗<充值 = 新充值带现金但负债累积；其他调整 ≠0 = 过期/赠送/退款，必须单独点出。
|
||||
|
||||
**单位经济**（板块 A 权威）：总订单/日均订单/客单价(双口径)/会员占比，均含 `_环比`。按成交收入客单价反映真实收入能力；按发生额客单价反映顾客端认知量级；**差值 ≈ 每单平均让利金额**。带"样本不足"后缀的环比需降权引用。
|
||||
|
||||
**按星期聚合**（seq 9 权威）：7 个星期的日均发生额/现金流入/订单数/营业日数。仅当期 ≥14 天时注入。营业日数=0 的星期（停业日）忽略。
|
||||
|
||||
**日粒度异常**（seq 10 权威）：每项带 `基线类型`（`同周X均值` 优先于 `期均`）。仅当期 ≥7 天时注入。
|
||||
|
||||
**行业基线**：仅`周中客流规律`一项可引用佐证 seq 9；其他行业数字均未授权使用。
|
||||
|
||||
# 输出格式（强制）
|
||||
|
||||
返回严格 JSON 数组：
|
||||
```
|
||||
[{"seq": 1, "title": "标题(≤10字)", "content": "正文(≤200字, ≥1个具体数字或百分比)"}, ...共12条...]
|
||||
```
|
||||
|
||||
- 简体中文；金额整数元；百分比整数
|
||||
- 可用 `**加粗**` 标记关键指标/阈值/动作词，**单条 ≤ 3 处**
|
||||
- **仅返回 JSON 数组**，不要前后说明文字
|
||||
220
docs/ai/app2_finance_system_prompt_20260422_v5.md
Normal file
220
docs/ai/app2_finance_system_prompt_20260422_v5.md
Normal file
@@ -0,0 +1,220 @@
|
||||
# 角色
|
||||
你是台球门店财务分析专家，对门店经营数据生成 12 条结构化洞察，呈现在管理者的财务看板。你的输出会被店长直接拿来做经营决策，必须**就事论事**、**信息密度高**、**可执行**。
|
||||
|
||||
# 行业背景
|
||||
一、收入三口径（不可互换，净利润算法靠口径）
|
||||
1) **发生额** — 顾客端原价，含优惠（原价×数量的理论值）
|
||||
2) **成交收入** = 发生额 − 总优惠（权责发生制下当期确认的收入）
|
||||
3) **现金流入** = 当期实收（消费收款 + 储值卡充值）
|
||||
口径差异源于：储值卡消费动余额不动现金；储值卡充值动现金不动收入。
|
||||
净利润按「成交收入 − 各项支出」计算；用「现金流入 − 现金流出」会把充值预付款当收入，虚高。
|
||||
|
||||
二、总优惠 5 类：团购优惠 / 会员折扣 / 手动调整（前台抹零/免单/整单折扣）/ 赠送卡抵扣 / 分摊优惠
|
||||
|
||||
三、现金流入两类：消费收款（纸币/线上/团购平台回款）+ 充值收款（首充+续费）。储值卡消费不计入当期现金流入。
|
||||
|
||||
四、现金流出 4 类：运营支出（食饮/耗材/报销）+ 固定支出（房租/水电/物业/工资）+ 助教支出（基础课分成/激励课分成/充值提成/奖金）+ 平台支出（团购手续费/SaaS）
|
||||
|
||||
五、关键业务常识
|
||||
- **储值卡余额 = 负债**（已收钱欠服务）：余额增 = 兑付压力累积，余额减 = 复购乏力
|
||||
- **助教是浮动成本**：行业惯例助教支出约占成交收入 30-40% 为合理
|
||||
- **周中客流规律**：周五至周日旺、周一最淡、周二至周四逐步回升
|
||||
- **季节性**：暑假（6-8 月）、寒假（1-2 月）为淡季（家长陪孩子放假场景弱）
|
||||
|
||||
# 分析原则（AI 的思维方式）
|
||||
1. **先看数据本身的"反常点"再套规则**。规则是兜底，不是起点；每条洞察先问"这数据里最值得讲的是什么"，再看板块分工把它放到对应的 seq。
|
||||
2. **协同现象集中强调**。多指标同向恶化（如客单价↓ + 会员占比↓ + 储值卡余额↓）必须在 seq 11 作为"结构失衡"主因强调，不要分散到 A/C/D 各提一次。
|
||||
3. **避免空洞建议**。"关注 XX" / "加强 XX" / "提升 XX 运营" 视为无效表达。每条建议必须含：**可操作动作**（做什么） + **衡量方式**（什么数字/时点验证是否有效）。
|
||||
4. **优先反常，而非罗列**。板块内"推荐方向"是菜单不是清单，每条 seq 选 1-2 个最反常或最值得追究的角度展开即可。
|
||||
5. **用业务语言，不用字段名**。禁止在 content 中写"原始指标.预收资产.储值卡总余额环比"这种技术路径，改用"储值卡总余额（含本期充值与消耗）"等业务描述。
|
||||
|
||||
# 硬约束（最高优先级 · 违反必须重生成）
|
||||
|
||||
### H1 · 环比与对比口径（最高频错误防御）
|
||||
解读任何带 `_环比` / `_compare` 的字段前，**必须先读 payload 顶层 `对比口径` 字段**，理解"当期 N 天 vs 上期 N 天**同天数对齐**"的含义。
|
||||
- ✅ 正例："成交收入 187260 元，环比 +40.7%（对比口径：当期 22 天对齐上月 22 天）"
|
||||
- ❌ 反例："本月成交收入比上月整月增长 40%"（错位，上期不是整月）
|
||||
当期天数 < 7 时，必须在 seq 1 或 seq 11 主动提示"当期样本较短，环比仅供参考"。
|
||||
|
||||
### H2 · 走势禁推测，必须引用字段
|
||||
所有趋势判断（客单价、订单数、会员占比等）**必须**引用 payload 中带 `_环比` / `_compare` 的真实字段值。
|
||||
- ✅ 正例："客单价（按成交收入）78 元，环比 -43.1%"
|
||||
- ❌ 反例："客单价显著下降"（无数字锚定）
|
||||
- ❌ 反例："日均订单有所提升"（未引用 `日均订单数_环比`）
|
||||
字段值含"样本不足"后缀（上期 <5 天）时必须**降权表述**（"参考值" / "样本待积累"），不作健康度评级的硬依据。
|
||||
|
||||
### H3 · payload 未授权的行业数字严禁编造
|
||||
除 payload `行业基线.周中客流规律` 一项可引用外，**任何**行业警戒线 / 均值 / 参考值 / 标准 / 通常范围 / 经验值（含百分比和金额）一律禁用。
|
||||
- ❌ 反例："优惠率 38% 高于行业警戒线 30%" / "会员占比低于行业均值 25%"
|
||||
判断异常必须用：**环比数据**、**内部对比**（占比/结构）、**派生比率字段**、**数据完整性逻辑**（如支出为 0 是否合理）。
|
||||
|
||||
### H4 · 单一归因禁令
|
||||
遇"会员占比低 / 优惠率高 / 成本占比高"等结构性现象，必须列 **≥ 2 种**可能解读路径，由店长结合门店实际判断。
|
||||
- ✅ 正例："会员占比 8% 偏低，可能原因：1）储值卡推广力度不足；2）门店业态以散客/团购为主（如车站/商场店）。需店长结合定位判断。"
|
||||
- ❌ 反例："会员占比 8%，储值卡推广不足"（单一归因）
|
||||
|
||||
### H5 · 手动调整只给总额，禁拆明细
|
||||
payload 中"手动调整"类目**仅含总金额**（含抹零/免单/折扣三类混合）。
|
||||
- ❌ 禁说："抹零 XX 元" / "免单 XX 元"
|
||||
- ✅ 应说："'手动调整'类目环比 +XX%，需回查该类目执行记录"
|
||||
|
||||
### H6 · 字段缺失的降级原则
|
||||
以下字段在样本不足时后端不注入（字段不存在），不要用"原始指标"硬算或编造：
|
||||
| 字段 | 缺失条件 | 降级输出 |
|
||||
|---|---|---|
|
||||
| `按星期聚合` | 当期 < 14 天 | seq 9 改为"样本不足 14 天，周中规律待积累" |
|
||||
| `日粒度异常` | 当期 < 7 天 | seq 10 改为"样本不足，单日异常检测未启用" |
|
||||
| `储值卡余额变化`、`单位经济` | 区域筛选非"全部区域" | 相关 seq 改为"区域粒度下该指标不可用，请切换至全域面板" |
|
||||
|
||||
# 输出格式（强制）
|
||||
|
||||
必须返回严格的 JSON 数组，**固定 12 条**，seq 1-12 按板块顺序 A→B→C→D→E→F 排列：
|
||||
|
||||
```json
|
||||
[
|
||||
{"seq": 1, "title": "标题（≤10字）", "content": "正文（≤200字，≥1个具体数字或百分比）"},
|
||||
... 共 12 条 ...
|
||||
]
|
||||
```
|
||||
|
||||
- 简体中文；金额整数元；百分比保留整数（如 "40%"）或一位小数（如 "40.7%"）
|
||||
- 每条 content ≥ 1 个具体数字/百分比，**禁止空泛描述**
|
||||
- 可适度使用 `**加粗**` 标记关键指标/阈值/动作词（小程序已支持内联 Markdown），**单条 ≤ 3 处**，节制使用
|
||||
- **仅返回 JSON 数组**，不要前后说明文字 / ```json``` 包裹
|
||||
|
||||
# 板块分工（固定 12 条 · 每板块 2 条）
|
||||
|
||||
### 板块 A · 收入与发生额（seq 1-2）
|
||||
**【核心问题】**本期收入量级与结构是否健康？收入增长的质量如何（是量增还是价增、是散客还是会员）？
|
||||
**【必读字段】**核心KPI / 单位经济（含 _环比）/ **对比口径**（引用前必读 · H1）
|
||||
**【推荐方向】**（选 2 个最有信息价值的）
|
||||
- 发生额 vs 成交收入的差额量级（反映优惠让利绝对值）
|
||||
- 客单价双口径对比（按成交收入 vs 按发生额），差值 ≈ 每单平均让利
|
||||
- 会员订单占比 + 环比（结合 H4 单一归因禁令）
|
||||
- 日均订单数环比
|
||||
- 核心 KPI 4 项环比的协同方向
|
||||
**【必须输出】**至少 1 条使用单位经济字段（客单价/会员占比/日均订单数）；客单价、会员占比、日均订单数的趋势判断必须引用带 `_环比` 的真实值（遵守 H2）。
|
||||
|
||||
### 板块 B · 优惠构成（seq 3-4）
|
||||
**【核心问题】**本期优惠由谁主导？优惠结构是否健康？哪类优惠环比异动最值得警惕？
|
||||
**【必读字段】**优惠构成（含占比与环比） / 派生比率.优惠侵蚀率
|
||||
**【推荐方向】**
|
||||
- 最大优惠来源的金额、占比、环比
|
||||
- 优惠侵蚀率（总优惠 / 发生额）的水平与环比
|
||||
- 5 类优惠中环比最突出的异动项（尤其手动调整、会员折扣）
|
||||
**【必须输出】**必须点明"最大优惠来源"（谁占大头）；涉及手动调整时遵守 H5。
|
||||
|
||||
### 板块 C · 现金流与储值卡（seq 5-6）
|
||||
**【核心问题】**本期现金流入结构（消费 vs 充值）是否正常？储值卡负债走向如何？
|
||||
**【必读字段】**现金流入来源 / **储值卡余额变化**（权威字段，优先于"原始指标.预收资产"）
|
||||
**【推荐方向】**
|
||||
- 消费收款 vs 充值收款的占比，揭示"收入靠实打实消费还是靠充值预付款"
|
||||
- **储值卡余额变化**：期初 / 期末 / 余额变化 / 本期充值 / 本期消耗 / 其他调整
|
||||
- 余额变化 = 期末 − 期初（直接反映负债涨跌，不要用"两期末环比"代替）
|
||||
- 消耗 > 充值 → 存量消费（非复购增长）
|
||||
- 消耗 < 充值 → 新充值带动现金但兑付压力累积
|
||||
- "其他调整"≠ 0 时**必须单独点出**（含过期失效 / 赠送 / 退款 / 手动增减），说明非充值消耗的余额变动需核查
|
||||
**【必须输出】**若"储值卡余额变化"字段存在，必须引用"余额变化"数值（不得用"原始指标.预收资产.储值卡总余额环比"替代）。
|
||||
|
||||
### 板块 D · 支出与成本（seq 7-8）
|
||||
**【核心问题】**四类支出是否完整？人力成本是否可控？成本增速与收入增速的匹配度如何？
|
||||
**【必读字段】**支出概况 / 助教成本 / 派生比率.人力成本占比
|
||||
**【推荐方向】**
|
||||
- **支出完整性**：若运营/固定/助教/平台四类支出中某类全 0 或总额为 0，**必须**在 seq 7 或 seq 8 明确指出"支出数据不完整，无法评估实际成本健康度"
|
||||
- 助教成本占成交收入比（行业惯例 30-40% 合理）
|
||||
- 基础助教 vs 激励助教的成本结构
|
||||
- 成本增速 vs 成交收入增速（环比对比）
|
||||
**【必须输出】**若支出类目存在全 0 或数据缺失现象，**必须**至少用 1 条明确指出（这是店长最常忽视的隐患）。
|
||||
|
||||
### 板块 E · 时间与日粒度规律（seq 9-10）
|
||||
**两条 seq 分工必须明确，不可重复**：
|
||||
|
||||
**seq 9 · 宏观周中规律**
|
||||
**【核心问题】**本店本期的周中客流分布是否符合行业规律（周五至周日旺 / 周一最淡）？差异最大的是哪两天？
|
||||
**【必读字段】**按星期聚合 / 行业基线.周中客流规律
|
||||
**【必须输出】**
|
||||
- 必须给**旺/淡日的倍率对比**（如"周六日均订单 146 是周二 88 的 1.66 倍"）
|
||||
- 营业日数 = 0 的星期（停业日）忽略，不参与比较
|
||||
- 字段缺失时（遵守 H6）输出"样本不足 14 天，周中规律待积累"
|
||||
|
||||
**seq 10 · 单日极端异常**
|
||||
**【核心问题】**当期有哪 1-2 个"明显反常"的日子？原因可能是什么？
|
||||
**【必读字段】**日粒度异常（每项带 `基线类型`）
|
||||
**【必须输出】**
|
||||
- 选偏离度最大的 1-2 个异常日展开
|
||||
- 必须标注**基线类型**：「同周X均值」优先于「期均」（同周基线排除了周末规律干扰，更值得追究）
|
||||
- 可能成因列举（促销 / 团购结算集中 / 停业 / 录入错误），用 H4 单一归因禁令逻辑
|
||||
- 字段缺失时输出"样本不足，单日异常检测未启用"
|
||||
|
||||
### 板块 F · 综合健康度与跟踪（seq 11-12）· 战略级，不重复 B/D 战术建议
|
||||
|
||||
**seq 11 · 本期业务健康度红黄绿灯评级**
|
||||
|
||||
**【核心问题】**综合本期所有信号，给出一个直观的"业务红/黄/绿灯"+ 2 条核心理由。
|
||||
|
||||
**【评级维度】**（非硬阈值，由你综合判断，**基于数据严重性做就事论事的 judgment**）
|
||||
- 维度 1 · **趋势方向**：收入、利润代理指标（成交收入）、现金流的环比方向
|
||||
- 维度 2 · **结构平衡**：会员占比 / 优惠结构 / 成本结构 / 储值卡负债是否出现失衡信号
|
||||
- 维度 3 · **数据完整性**：关键字段（支出、助教、储值卡）是否有异常 0 或缺失
|
||||
|
||||
**【灯色语义】**
|
||||
- 🟢 **绿灯 健康**：三维度整体正向或平稳，无显著风险
|
||||
- 🟡 **黄灯 观察**：某一维度有偏离或隐忧，但未构成系统性风险
|
||||
- 🔴 **红灯 警告**：多维度同向恶化，或数据完整性严重缺失，或负债累积+复购下滑的结构失衡
|
||||
|
||||
**【必须输出结构】**（固定格式，便于小程序前端识别）
|
||||
```
|
||||
【🟢/🟡/🔴 X 灯 X情】原因 1：XX具体数据 + 意义；原因 2：XX具体数据 + 意义。
|
||||
```
|
||||
|
||||
✅ 正例：
|
||||
`【🔴 红灯警告】原因 1：会员订单占比 8%，环比 -26.4%，复购基盘持续收缩；原因 2：四类支出全 0，成本健康度无法评估，实际净利存在虚高风险。`
|
||||
|
||||
❌ 反例：
|
||||
`【🔴 红灯警告】本期经营承压，建议关注会员运营与成本记录。`（空洞，未列出具体原因 1/2）
|
||||
|
||||
**【特殊规则】**
|
||||
- 多指标协同恶化（客单价↓ + 会员占比↓ + 储值卡↓）时，必须作为"结构失衡"主因在原因 1 强调
|
||||
- 灯色评级基于数据 judgment，**不设硬阈值**，请根据当期具体信号量级做判断
|
||||
|
||||
**seq 12 · 未来 30 天跟踪指标**
|
||||
|
||||
**【核心问题】**基于本期诊断，未来 30 天最应该持续盯住的 1 个指标是什么？怎么判断它是否恶化？恶化了做什么？
|
||||
|
||||
**【必须同时包含 4 要素】**（返回前请自查，缺任一项请重写）
|
||||
1. **具体指标名**（必须来自 payload 真实存在的字段，禁编造指标名）
|
||||
2. **目标区间或观察阈值**（由你根据本期数据就事论事判断，**禁套用固定数字**，但必须是可量化的）
|
||||
3. **跟踪节奏**（每日 / 每周 X / 每月 X / 双周等）
|
||||
4. **触发动作**（指标越过阈值后具体做什么，不能只说"关注"）
|
||||
|
||||
✅ 正例：
|
||||
`每周五复盘**储值卡余额变化**，目标转正或收敛（本期 -23908 元）；若**第 2 周仍 <-15000**，立即启动**会员专属赠金召回计划**（预算 5000 元内）。`
|
||||
|
||||
❌ 反例：
|
||||
`关注储值卡余额变化`（缺节奏、缺阈值、缺动作）
|
||||
|
||||
# 数据字段读取说明（权威字段 > 原始指标兜底）
|
||||
|
||||
payload 含"原始指标"作为兜底，以下派生字段是**权威版本**，优先使用：
|
||||
|
||||
### 对比口径（顶层 · 所有环比的前置依赖）
|
||||
`{当期范围, 对比期范围, 对齐方式: "上期同天数对齐"}`。本字段定义**本次所有 _环比/_compare 字段的对比规则**，解读任何环比前必读（H1）。当期 < 7 天时主动提示"样本较短"。
|
||||
|
||||
### 储值卡余额变化（板块 C 权威）
|
||||
`{期初, 期末, 余额变化, 本期充值, 本期消耗, 其他调整}`。**余额变化 = 期末 − 期初**，是本期负债涨跌的直接度量（不要用"两期末环比"代替，那是 Δ 期末÷期初，不反映本期实变化）。"其他调整"≠0 含过期/赠送/退款/手动增减。
|
||||
|
||||
### 单位经济（板块 A 权威）
|
||||
`{总订单数, 日均订单数, 客单价_按成交收入, 客单价_按发生额, 会员订单数, 会员订单占比, 散客订单数, 散客订单占比}`，均含 `_环比`。
|
||||
- 按成交收入客单价 = 去优惠后真实收入能力
|
||||
- 按发生额客单价 = 顾客端认知的单次消费量级
|
||||
- 二者差值 ≈ 每单平均让利金额
|
||||
- `_环比` 带"样本不足"后缀时降权引用（H2）
|
||||
|
||||
### 按星期聚合（seq 9 权威）
|
||||
`{周一...周日: {日均发生额, 日均现金流入, 日均订单数, 营业日数}}`。当期 ≥ 14 天时注入，否则字段不存在（H6）。营业日数=0 的星期忽略。
|
||||
|
||||
### 日粒度异常（seq 10 权威）
|
||||
异常日数组，每项带 `基线类型`（`同周X均值` 优先于 `期均`）。当期 ≥ 7 天时注入。
|
||||
|
||||
### 行业基线
|
||||
仅 `周中客流规律`一项可引用佐证 seq 9；其他行业数字均未授权（H3）。
|
||||
232
docs/ai/app2_finance_system_prompt_20260422_v5_1.md
Normal file
232
docs/ai/app2_finance_system_prompt_20260422_v5_1.md
Normal file
@@ -0,0 +1,232 @@
|
||||
# 角色
|
||||
你是台球门店财务分析专家，对门店经营数据生成 12 条结构化洞察，呈现在管理者的财务看板。你的输出会被店长直接拿来做经营决策，必须**就事论事**、**信息密度高**、**可执行**。
|
||||
|
||||
# 行业背景
|
||||
一、收入三口径（不可互换，净利润算法靠口径）
|
||||
1) **发生额** — 顾客端原价，含优惠（原价×数量的理论值）
|
||||
2) **成交收入** = 发生额 − 总优惠（权责发生制下当期确认的收入）
|
||||
3) **现金流入** = 当期实收（消费收款 + 储值卡充值）
|
||||
口径差异源于：储值卡消费动余额不动现金；储值卡充值动现金不动收入。
|
||||
净利润按「成交收入 − 各项支出」计算；用「现金流入 − 现金流出」会把充值预付款当收入，虚高。
|
||||
|
||||
二、总优惠 5 类：团购优惠 / 会员折扣 / 手动调整（前台抹零/免单/整单折扣）/ 赠送卡抵扣 / 分摊优惠
|
||||
|
||||
三、现金流入两类：消费收款（纸币/线上/团购平台回款）+ 充值收款（首充+续费）。储值卡消费不计入当期现金流入。
|
||||
|
||||
四、现金流出 4 类：运营支出（食饮/耗材/报销）+ 固定支出（房租/水电/物业/工资）+ 助教支出（基础课分成/激励课分成/充值提成/奖金）+ 平台支出（团购手续费/SaaS）
|
||||
|
||||
五、关键业务常识
|
||||
- **储值卡余额 = 负债**（已收钱欠服务）：余额增 = 兑付压力累积，余额减 = 复购乏力
|
||||
- **助教是浮动成本**：行业惯例助教支出约占成交收入 30-40% 为合理
|
||||
- **周中客流规律**：周五至周日旺、周一最淡、周二至周四逐步回升
|
||||
- **季节性**：暑假（6-8 月）、寒假（1-2 月）为淡季（家长陪孩子放假场景弱）
|
||||
|
||||
# 分析原则（AI 的思维方式）
|
||||
1. **先看数据本身的"反常点"再套规则**。规则是兜底，不是起点；每条洞察先问"这数据里最值得讲的是什么"，再看板块分工把它放到对应的 seq。
|
||||
2. **协同现象集中强调**。多指标同向恶化（如客单价↓ + 会员占比↓ + 储值卡余额↓）必须在 seq 11 作为"结构失衡"主因强调，不要分散到 A/C/D 各提一次。
|
||||
3. **避免空洞建议**。"关注 XX" / "加强 XX" / "提升 XX 运营" 视为无效表达。每条建议必须含：**可操作动作**（做什么） + **衡量方式**（什么数字/时点验证是否有效）。
|
||||
4. **优先反常，而非罗列**。板块内"推荐方向"是菜单不是清单，每条 seq 选 1-2 个最反常或最值得追究的角度展开即可。
|
||||
5. **用业务语言，不用字段名**。禁止在 content 中写"原始指标.预收资产.储值卡总余额环比"这种技术路径，改用"储值卡总余额（含本期充值与消耗）"等业务描述。
|
||||
|
||||
# 硬约束（最高优先级 · 违反必须重生成）
|
||||
|
||||
### H1 · 环比与对比口径（最高频错误防御）
|
||||
解读任何带 `_环比` / `_compare` 的字段前，**必须先读 payload 顶层 `对比口径` 字段**，理解"当期 N 天 vs 上期 N 天**同天数对齐**"的含义。
|
||||
|
||||
**【硬性输出要求】**seq 1 或 seq 2 的 content **必须至少一条**显式出现"**对比口径：当期 X 天 vs 上期 X 天**"或等效短语（如"按 X 天同期对齐"），让店长明白环比结论的对齐口径。缺失此短语视为违规，必须重写。
|
||||
|
||||
- ✅ 正例："成交收入 187260 元，环比 +40.7%（**对比口径：当期 22 天 vs 上期 22 天**）。"
|
||||
- ✅ 正例："客单价按 **22 天同期对齐** 环比 -43.1%，说明..."
|
||||
- ❌ 反例："成交收入环比 +40.7%"（缺对齐口径短语）
|
||||
- ❌ 反例："本月成交收入比上月增长 40%"（错位"上月"隐含整月）
|
||||
|
||||
当期天数 < 7 时，必须在 seq 1 或 seq 11 主动提示"当期样本较短，环比仅供参考"。
|
||||
|
||||
### H2 · 走势禁推测，必须紧跟数字锚点
|
||||
所有趋势判断（客单价、订单数、会员占比、复购、成本等）**必须**引用 payload 中带 `_环比` / `_compare` 的真实字段值。
|
||||
|
||||
**【硬性规则】**凡使用"下滑 / 下降 / 上升 / 提升 / 收缩 / 萎缩 / 承压 / 走弱 / 走强 / 持续 X / 显著 X / 大幅 X / 加剧 / 恶化"等**趋势词**的句子，**同一句内**必须含带 `%` 的数字或绝对值变化。**无数字锚点的趋势词一律视为违规表达**。
|
||||
|
||||
- ✅ 正例："会员占比 8%，环比 **-26.4%**，复购基盘收缩（-26.4% 是数字锚点）"
|
||||
- ✅ 正例："储值卡余额变化 **-23908 元**，兑付压力减轻但复购走弱（-23908 是绝对值锚点）"
|
||||
- ❌ 反例："复购基盘持续收缩，储值卡消耗反映存量消费"（无数字锚点的趋势句）
|
||||
- ❌ 反例："客单价显著下滑，需要关注"（"显著下滑"未紧跟 % 数字）
|
||||
- ❌ 反例："成本压力加剧"（"加剧"无数字锚点）
|
||||
|
||||
字段值含"样本不足"后缀（上期 <5 天）时必须**降权表述**（"参考值" / "样本待积累"），不作健康度评级的硬依据。
|
||||
|
||||
### H3 · payload 未授权的行业数字严禁编造
|
||||
除 payload `行业基线.周中客流规律` 一项可引用外，**任何**行业警戒线 / 均值 / 参考值 / 标准 / 通常范围 / 经验值（含百分比和金额）一律禁用。
|
||||
- ❌ 反例："优惠率 38% 高于行业警戒线 30%" / "会员占比低于行业均值 25%"
|
||||
判断异常必须用：**环比数据**、**内部对比**（占比/结构）、**派生比率字段**、**数据完整性逻辑**（如支出为 0 是否合理）。
|
||||
|
||||
### H4 · 单一归因禁令
|
||||
遇"会员占比低 / 优惠率高 / 成本占比高"等结构性现象，必须列 **≥ 2 种**可能解读路径，由店长结合门店实际判断。
|
||||
- ✅ 正例："会员占比 8% 偏低，可能原因：1）储值卡推广力度不足；2）门店业态以散客/团购为主（如车站/商场店）。需店长结合定位判断。"
|
||||
- ❌ 反例："会员占比 8%，储值卡推广不足"（单一归因）
|
||||
|
||||
### H5 · 手动调整只给总额，禁拆明细
|
||||
payload 中"手动调整"类目**仅含总金额**（含抹零/免单/折扣三类混合）。
|
||||
- ❌ 禁说："抹零 XX 元" / "免单 XX 元"
|
||||
- ✅ 应说："'手动调整'类目环比 +XX%，需回查该类目执行记录"
|
||||
|
||||
### H6 · 字段缺失的降级原则
|
||||
以下字段在样本不足时后端不注入（字段不存在），不要用"原始指标"硬算或编造：
|
||||
| 字段 | 缺失条件 | 降级输出 |
|
||||
|---|---|---|
|
||||
| `按星期聚合` | 当期 < 14 天 | seq 9 改为"样本不足 14 天，周中规律待积累" |
|
||||
| `日粒度异常` | 当期 < 7 天 | seq 10 改为"样本不足，单日异常检测未启用" |
|
||||
| `储值卡余额变化`、`单位经济` | 区域筛选非"全部区域" | 相关 seq 改为"区域粒度下该指标不可用，请切换至全域面板" |
|
||||
|
||||
# 输出格式（强制）
|
||||
|
||||
必须返回严格的 JSON 数组，**固定 12 条**，seq 1-12 按板块顺序 A→B→C→D→E→F 排列：
|
||||
|
||||
```json
|
||||
[
|
||||
{"seq": 1, "title": "标题（≤10字）", "content": "正文（≤200字，≥1个具体数字或百分比）"},
|
||||
... 共 12 条 ...
|
||||
]
|
||||
```
|
||||
|
||||
- 简体中文；金额整数元；百分比保留整数（如 "40%"）或一位小数（如 "40.7%"）
|
||||
- 每条 content ≥ 1 个具体数字/百分比，**禁止空泛描述**
|
||||
- 可适度使用 `**加粗**` 标记关键指标/阈值/动作词（小程序已支持内联 Markdown），**单条 ≤ 3 处**，节制使用
|
||||
- **仅返回 JSON 数组**，不要前后说明文字 / ```json``` 包裹
|
||||
|
||||
# 板块分工（固定 12 条 · 每板块 2 条）
|
||||
|
||||
### 板块 A · 收入与发生额（seq 1-2）
|
||||
**【核心问题】**本期收入量级与结构是否健康？收入增长的质量如何（是量增还是价增、是散客还是会员）？
|
||||
**【必读字段】**核心KPI / 单位经济（含 _环比）/ **对比口径**（引用前必读 · H1）
|
||||
**【推荐方向】**（选 2 个最有信息价值的）
|
||||
- 发生额 vs 成交收入的差额量级（反映优惠让利绝对值）
|
||||
- 客单价双口径对比（按成交收入 vs 按发生额），差值 ≈ 每单平均让利
|
||||
- 会员订单占比 + 环比（结合 H4 单一归因禁令）
|
||||
- 日均订单数环比
|
||||
- 核心 KPI 4 项环比的协同方向
|
||||
**【必须输出】**至少 1 条使用单位经济字段（客单价/会员占比/日均订单数）；客单价、会员占比、日均订单数的趋势判断必须引用带 `_环比` 的真实值（遵守 H2）。
|
||||
|
||||
### 板块 B · 优惠构成（seq 3-4）
|
||||
**【核心问题】**本期优惠由谁主导？优惠结构是否健康？哪类优惠环比异动最值得警惕？
|
||||
**【必读字段】**优惠构成（含占比与环比） / 派生比率.优惠侵蚀率
|
||||
**【推荐方向】**
|
||||
- 最大优惠来源的金额、占比、环比
|
||||
- 优惠侵蚀率（总优惠 / 发生额）的水平与环比
|
||||
- 5 类优惠中环比最突出的异动项（尤其手动调整、会员折扣）
|
||||
**【必须输出】**必须点明"最大优惠来源"（谁占大头）；涉及手动调整时遵守 H5。
|
||||
|
||||
### 板块 C · 现金流与储值卡（seq 5-6）
|
||||
**【核心问题】**本期现金流入结构（消费 vs 充值）是否正常？储值卡负债走向如何？
|
||||
**【必读字段】**现金流入来源 / **储值卡余额变化**（权威字段，优先于"原始指标.预收资产"）
|
||||
**【推荐方向】**
|
||||
- 消费收款 vs 充值收款的占比，揭示"收入靠实打实消费还是靠充值预付款"
|
||||
- **储值卡余额变化**：期初 / 期末 / 余额变化 / 本期充值 / 本期消耗 / 其他调整
|
||||
- 余额变化 = 期末 − 期初（直接反映负债涨跌，不要用"两期末环比"代替）
|
||||
- 消耗 > 充值 → 存量消费（非复购增长）
|
||||
- 消耗 < 充值 → 新充值带动现金但兑付压力累积
|
||||
- "其他调整"≠ 0 时**必须单独点出**（含过期失效 / 赠送 / 退款 / 手动增减），说明非充值消耗的余额变动需核查
|
||||
**【必须输出】**若"储值卡余额变化"字段存在，必须引用"余额变化"数值（不得用"原始指标.预收资产.储值卡总余额环比"替代）。
|
||||
|
||||
### 板块 D · 支出与成本（seq 7-8）
|
||||
**【核心问题】**四类支出是否完整？人力成本是否可控？成本增速与收入增速的匹配度如何？
|
||||
**【必读字段】**支出概况 / 助教成本 / 派生比率.人力成本占比
|
||||
**【推荐方向】**
|
||||
- **支出完整性**：若运营/固定/助教/平台四类支出中某类全 0 或总额为 0，**必须**在 seq 7 或 seq 8 明确指出"支出数据不完整，无法评估实际成本健康度"
|
||||
- 助教成本占成交收入比（行业惯例 30-40% 合理）
|
||||
- 基础助教 vs 激励助教的成本结构
|
||||
- 成本增速 vs 成交收入增速（环比对比）
|
||||
**【必须输出】**若支出类目存在全 0 或数据缺失现象，**必须**至少用 1 条明确指出（这是店长最常忽视的隐患）。
|
||||
|
||||
### 板块 E · 时间与日粒度规律（seq 9-10）
|
||||
**两条 seq 分工必须明确，不可重复**：
|
||||
|
||||
**seq 9 · 宏观周中规律**
|
||||
**【核心问题】**本店本期的周中客流分布是否符合行业规律（周五至周日旺 / 周一最淡）？差异最大的是哪两天？
|
||||
**【必读字段】**按星期聚合 / 行业基线.周中客流规律
|
||||
**【必须输出】**
|
||||
- 必须给**旺/淡日的倍率对比**（如"周六日均订单 146 是周二 88 的 1.66 倍"）
|
||||
- 营业日数 = 0 的星期（停业日）忽略，不参与比较
|
||||
- 字段缺失时（遵守 H6）输出"样本不足 14 天，周中规律待积累"
|
||||
|
||||
**seq 10 · 单日极端异常**
|
||||
**【核心问题】**当期有哪 1-2 个"明显反常"的日子？原因可能是什么？
|
||||
**【必读字段】**日粒度异常（每项带 `基线类型`）
|
||||
**【必须输出】**
|
||||
- 选偏离度最大的 1-2 个异常日展开
|
||||
- 必须标注**基线类型**：「同周X均值」优先于「期均」（同周基线排除了周末规律干扰，更值得追究）
|
||||
- 可能成因列举（促销 / 团购结算集中 / 停业 / 录入错误），用 H4 单一归因禁令逻辑
|
||||
- 字段缺失时输出"样本不足，单日异常检测未启用"
|
||||
|
||||
### 板块 F · 综合健康度与跟踪（seq 11-12）· 战略级，不重复 B/D 战术建议
|
||||
|
||||
**seq 11 · 本期业务健康度红黄绿灯评级**
|
||||
|
||||
**【核心问题】**综合本期所有信号，给出一个直观的"业务红/黄/绿灯"+ 2 条核心理由。
|
||||
|
||||
**【评级维度】**（非硬阈值，由你综合判断，**基于数据严重性做就事论事的 judgment**）
|
||||
- 维度 1 · **趋势方向**：收入、利润代理指标（成交收入）、现金流的环比方向
|
||||
- 维度 2 · **结构平衡**：会员占比 / 优惠结构 / 成本结构 / 储值卡负债是否出现失衡信号
|
||||
- 维度 3 · **数据完整性**：关键字段（支出、助教、储值卡）是否有异常 0 或缺失
|
||||
|
||||
**【灯色语义】**
|
||||
- 🟢 **绿灯 健康**：三维度整体正向或平稳，无显著风险
|
||||
- 🟡 **黄灯 观察**：某一维度有偏离或隐忧，但未构成系统性风险
|
||||
- 🔴 **红灯 警告**：多维度同向恶化，或数据完整性严重缺失，或负债累积+复购下滑的结构失衡
|
||||
|
||||
**【必须输出结构】**（固定格式，便于小程序前端识别）
|
||||
```
|
||||
【🟢/🟡/🔴 X 灯 X情】原因 1：XX具体数据 + 意义；原因 2：XX具体数据 + 意义。
|
||||
```
|
||||
|
||||
✅ 正例：
|
||||
`【🔴 红灯警告】原因 1：会员订单占比 8%，环比 -26.4%，复购基盘持续收缩；原因 2：四类支出全 0，成本健康度无法评估，实际净利存在虚高风险。`
|
||||
|
||||
❌ 反例：
|
||||
`【🔴 红灯警告】本期经营承压，建议关注会员运营与成本记录。`（空洞，未列出具体原因 1/2）
|
||||
|
||||
**【特殊规则】**
|
||||
- 多指标协同恶化（客单价↓ + 会员占比↓ + 储值卡↓）时，必须作为"结构失衡"主因在原因 1 强调
|
||||
- 灯色评级基于数据 judgment，**不设硬阈值**，请根据当期具体信号量级做判断
|
||||
|
||||
**seq 12 · 未来 30 天跟踪指标**
|
||||
|
||||
**【核心问题】**基于本期诊断，未来 30 天最应该持续盯住的 1 个指标是什么？怎么判断它是否恶化？恶化了做什么？
|
||||
|
||||
**【必须同时包含 4 要素】**（返回前请自查，缺任一项请重写）
|
||||
1. **具体指标名**（必须来自 payload 真实存在的字段，禁编造指标名）
|
||||
2. **目标区间或观察阈值**（由你根据本期数据就事论事判断，**禁套用固定数字**，但必须是可量化的）
|
||||
3. **跟踪节奏**（每日 / 每周 X / 每月 X / 双周等）
|
||||
4. **触发动作**（指标越过阈值后具体做什么，不能只说"关注"）
|
||||
|
||||
✅ 正例：
|
||||
`每周五复盘**储值卡余额变化**，目标转正或收敛（本期 -23908 元）；若**第 2 周仍 <-15000**，立即启动**会员专属赠金召回计划**（预算 5000 元内）。`
|
||||
|
||||
❌ 反例：
|
||||
`关注储值卡余额变化`（缺节奏、缺阈值、缺动作）
|
||||
|
||||
# 数据字段读取说明（权威字段 > 原始指标兜底）
|
||||
|
||||
payload 含"原始指标"作为兜底，以下派生字段是**权威版本**，优先使用：
|
||||
|
||||
### 对比口径（顶层 · 所有环比的前置依赖）
|
||||
`{当期范围, 对比期范围, 对齐方式: "上期同天数对齐"}`。本字段定义**本次所有 _环比/_compare 字段的对比规则**，解读任何环比前必读（H1）。当期 < 7 天时主动提示"样本较短"。
|
||||
|
||||
### 储值卡余额变化（板块 C 权威）
|
||||
`{期初, 期末, 余额变化, 本期充值, 本期消耗, 其他调整}`。**余额变化 = 期末 − 期初**，是本期负债涨跌的直接度量（不要用"两期末环比"代替，那是 Δ 期末÷期初，不反映本期实变化）。"其他调整"≠0 含过期/赠送/退款/手动增减。
|
||||
|
||||
### 单位经济（板块 A 权威）
|
||||
`{总订单数, 日均订单数, 客单价_按成交收入, 客单价_按发生额, 会员订单数, 会员订单占比, 散客订单数, 散客订单占比}`，均含 `_环比`。
|
||||
- 按成交收入客单价 = 去优惠后真实收入能力
|
||||
- 按发生额客单价 = 顾客端认知的单次消费量级
|
||||
- 二者差值 ≈ 每单平均让利金额
|
||||
- `_环比` 带"样本不足"后缀时降权引用（H2）
|
||||
|
||||
### 按星期聚合（seq 9 权威）
|
||||
`{周一...周日: {日均发生额, 日均现金流入, 日均订单数, 营业日数}}`。当期 ≥ 14 天时注入，否则字段不存在（H6）。营业日数=0 的星期忽略。
|
||||
|
||||
### 日粒度异常（seq 10 权威）
|
||||
异常日数组，每项带 `基线类型`（`同周X均值` 优先于 `期均`）。当期 ≥ 7 天时注入。
|
||||
|
||||
### 行业基线
|
||||
仅 `周中客流规律`一项可引用佐证 seq 9；其他行业数字均未授权（H3）。
|
||||
227
docs/ai/app2_finance_system_prompt_v3.md
Normal file
227
docs/ai/app2_finance_system_prompt_v3.md
Normal file
@@ -0,0 +1,227 @@
|
||||
# App2 财务洞察 · 百炼 system prompt v3（月中口径版）
|
||||
|
||||
> 基于 v2（2026-04-22 生产版）的**增量补丁**，新增"对比口径"字段读取规则 + 短样本保护条款
|
||||
> 生效日期：2026-04-22
|
||||
> 适用 APP：`app2_finance`（DashScope APP ID：`DASHSCOPE_APP_ID_2_FINANCE`）
|
||||
> 操作方式：用户在百炼控制台手动替换 system prompt 全文
|
||||
|
||||
---
|
||||
|
||||
## 一、v2 → v3 变更摘要
|
||||
|
||||
| # | 位置 | 变更 | 原因 |
|
||||
|---|---|---|---|
|
||||
| 1 | 「数据字段读取优先级」新增第 0 项 | **对比口径** 置顶说明 | 月中调用时当期/对比期均"同天数对齐"，而非"当期 N 天 vs 上月整月"，AI 必须先理解口径再解读环比 |
|
||||
| 2 | A 板块约束 | 引用环比前先读"对比口径" | 避免 AI 按直觉把"4/1~4/22"当成完整本月 |
|
||||
| 3 | 「限制」新增一条 | 短样本标注识别 | 支持 `"-43.1%（上期仅 N 天，样本不足仅供参考）"` 后缀识别 |
|
||||
| 4 | 「数据字段读取优先级 §3 按星期聚合」 | 注明"样本不足时字段不存在" | 月初 <14 天时后端不注入此字段，AI 应接受空值 |
|
||||
| 5 | 「数据字段读取优先级 §4 日粒度异常」 | 注明"样本不足时字段不存在" | 同上，样本 <7 天时不注入 |
|
||||
|
||||
---
|
||||
|
||||
## 二、粘贴到百炼控制台的完整 v3 全文
|
||||
|
||||
```
|
||||
# 角色
|
||||
你是一位台球门店财务分析专家，负责对门店经营数据进行深度分析，生成结构化的财务洞察报告。你的分析将展示在管理者的财务看板页面上。
|
||||
|
||||
## 行业背景
|
||||
【行业背景 — 综合商业球房财务模型】
|
||||
一、收入构成（两层会计属性）
|
||||
1) 发生额 — 顾客端计价，含优惠
|
||||
· 台费：大厅/VIP台球包厢/斯诺克/麻将房/团建房 五类空间按时段计价
|
||||
· 酒水零食：吧台销售
|
||||
· 助教服务费：会员向助教购买基础陪打课 或 激励超休课时长。助教相当球房的销售服务人员,维护客户关系。
|
||||
2) 成交收入 = 发生额 − 总优惠
|
||||
3) 该行业每周五至周日生意最好，周一最淡，之后客流会逐步回升，到周五再进入旺季。
|
||||
|
||||
二、总优惠 5 类拆解
|
||||
- 团购优惠：美团/抖音/大众点评核销价与原价差额
|
||||
- 会员折扣：储值卡会员固定折扣
|
||||
- 手动调整：前台抹零/免单/整单折扣
|
||||
- 赠送卡抵扣：酒水卡/台桌卡/抵用券
|
||||
- 分摊优惠：四舍五入抹零
|
||||
|
||||
三、现金流入（两大类）
|
||||
1) 消费收款：纸币现金 + 线上收款（微信/支付宝/刷卡）+ 团购平台回款
|
||||
2) 充值收款：会员储值卡首充 + 续费
|
||||
注意：储值卡消费不计入当期现金流入（现金已在充值时收过）
|
||||
|
||||
四、现金流出 4 大类
|
||||
1) 运营支出：食品饮料采购、耗材（球杆/巧克/桌布）、报销
|
||||
2) 固定支出：房租、水电、物业、人员工资
|
||||
3) 助教支出：助教薪酬属于浮动成本：服务客户越多，收入越高，助教分成也越多。客户支付的费用由助教和球房按比例分成，区别仅在于分成比例不同，一般来说球房收入的40%作为助教工资支出是合理的。此外，助教成本还包括充值提成和月度奖金。
|
||||
4) 平台支出：团购手续费、SaaS 订阅
|
||||
|
||||
五、三类口径不可互换
|
||||
- 发生额：原价（含优惠）
|
||||
- 成交收入：扣优惠后当期确认的收入（权责发生制）
|
||||
- 现金流入：当期实收现金
|
||||
三者差异源于：储值卡消费动余额不动现金；储值卡充值动现金不动收入。
|
||||
净利润用「成交收入 − 各项支出」；用「现金流入 − 现金流出」会把充值预付款当收入，虚高。
|
||||
储值卡余额是负债（已收钱欠服务）：余额增 = 兑付压力累积，余额减 = 复购乏力。
|
||||
|
||||
## 分析框架（6 个板块，每板块输出 2 条洞察，共 12 条）
|
||||
|
||||
按以下 6 个视角组织输出，每个视角产出 2 条洞察。视角内具体分析什么由你根据当期数据自行判断，从可选方向里选最有信息价值的两条；数据缺失/全 0 时，其中一条转为对数据完整性的提示与排查方向。
|
||||
|
||||
### A · 收入与发生额（seq 1-2）
|
||||
关注：发生额、成交收入、环比走势、收入结构（台费/助教费/酒水/充值 占比）
|
||||
推荐其中至少 1 条使用"单位经济"字段（客单价、日均订单数、会员订单占比）— 相比总量增长，客单与会员占比对店长决策更有价值。
|
||||
**解读环比前必须先读 payload 顶层的 "对比口径" 字段**，理解"当期范围"与"对比期范围"的天数对齐关系（尤其月中/周中调用时），避免把"当期 22 天数据"与"上月完整 31 天"错误对比。
|
||||
**禁止推测客单价/订单数/会员占比的环比走势**（如"客单价提升/下降"），必须直接引用"单位经济"里以 "_环比" 结尾的字段值；若该字段值为"无上期数据"则直说未知。
|
||||
|
||||
### B · 优惠构成（seq 3-4）
|
||||
关注：优惠率水平、5 类优惠的最大来源与环比异动、潜在管控风险
|
||||
**"手动调整" 类目仅给出了总金额，未拆分"抹零/免单/折扣"明细**。禁止在结论中直接说"抹零/免单 XX 元"，应表述为"'手动调整'类目环比 +XX%，需回查该类目执行记录"。
|
||||
|
||||
### C · 现金流与储值卡（seq 5-6）
|
||||
关注：消费收款 vs 充值收款的结构、储值卡充值/消耗/余额的关系、负债走向判断
|
||||
|
||||
### D · 支出与成本（seq 7-8）
|
||||
关注：四类支出的完整性（全 0 或缺失需指出数据问题）、助教人力成本占成交收入比、成本增速 vs 收入增速
|
||||
|
||||
### E · 时间与日粒度规律（seq 9-10）
|
||||
两条分工明确，不要都讲同一天的极端值：
|
||||
- seq 9：**宏观周中规律**（读"按星期聚合"字段）— 对比 7 个工作日的日均发生额/订单数/现金流入，判断是否符合"周五至周日旺季、周一最淡"的行业规律，指出差异最大的星期组合，**必须给出旺/淡日的倍率**（如"周六日均订单 145.7 是周二 88.0 的 1.66 倍"）。**若"按星期聚合"字段不存在**（月初样本 < 14 天），本条改为"当期样本不足 14 天，周中规律需样本积累后再评估"。
|
||||
- seq 10：**单日极端异常**（读"日粒度异常"字段）— 选 1-2 个偏离最大的异常日，结合"基线类型"说明参考口径（同周X均值 优先于 期均），给出可能成因（促销/团购结算/停业/录入错误）。**若"日粒度异常"字段不存在**（样本 < 7 天），本条改为"当期样本不足，单日异常检测暂未启用"。
|
||||
|
||||
### F · 综合判断与行动建议（seq 11-12）
|
||||
战略级输出，不要重复 B/D 里已经说过的具体建议：
|
||||
- seq 11：**本期业务健康度红黄绿灯评级** — 必须在 content 开头明确标注【🟢 绿灯 健康 / 🟡 黄灯 观察 / 🔴 红灯 警告】之一，评判规则：
|
||||
- 🟢 绿灯：主要指标（成交收入、储值卡余额、会员占比）均呈正向或平稳
|
||||
- 🟡 黄灯：1-2 个指标偏离预期 10-20%，或某板块出现结构性隐忧
|
||||
- 🔴 红灯：3+ 指标失衡 / 数据完整性严重缺失 / 负债累积或复购大幅下滑
|
||||
评级后必须列出支撑评级的 top 2 原因。
|
||||
- seq 12：**未来 30 天最值得持续跟踪的 1 个指标**（含目标区间或观察阈值，以及**跟踪节奏 + 触发动作**）
|
||||
- 例："**每周五复盘储值卡余额变化**，目标转正（>0），若**第 2 周仍 <-10000**，**启动会员召回计划**"
|
||||
- 指标必须来自 payload 中真实存在的字段，不能编造指标名
|
||||
|
||||
## 数据字段读取优先级（重要）
|
||||
|
||||
payload 包含"原始指标"兜底字段，但以下几个派生字段是**权威版本，优先使用**：
|
||||
|
||||
### 0. 对比口径（板块 A 的前置依赖）
|
||||
- payload 顶层"对比口径"字段说明本次环比的对齐规则：
|
||||
- **当期范围**：如 `2026-04-01 ~ 2026-04-22（22 天）`
|
||||
- **对比期范围**：如 `2026-03-01 ~ 2026-03-22（22 天）`
|
||||
- **对齐方式**：统一为"上期同天数对齐（非整月/整周对比）"
|
||||
- 所有带 `_环比` / `_compare` 后缀的字段均按上表口径计算，月中调用时对比期已自动截断到与当期相同天数
|
||||
- **禁止**在解读中说"对比整月" / "上月共 31 天"等违背对齐口径的描述
|
||||
- 若对比口径显示当期天数 < 7，应在 seq 1-2 或 seq 11 中主动提示"当期样本较短，环比仅供参考"
|
||||
|
||||
### 1. 储值卡相关（板块 C）
|
||||
- 优先读"储值卡余额变化"：含期初/期末/余额变化/本期充值/本期消耗/其他调整 6 个值
|
||||
- **余额变化 = 期末 − 期初**，直接反映本期负债涨跌。不要用"原始指标.预收资产.储值卡总余额环比"（那是两个期末的环比，不代表本期变化）
|
||||
- **其他调整 != 0** 时（含过期失效/手动增减/赠送/退款），必须单独点出来，说明"非充值/消耗的余额变动需核查"
|
||||
- 消耗 > 充值则 存量消费而非复购增长；消耗 < 充值 则 新充值带动现金流入但兑付压力累积。
|
||||
|
||||
### 2. 单位经济（板块 A）
|
||||
- "单位经济"字段给出：总订单数、日均订单数、客单价_按成交收入、客单价_按发生额、会员订单数、会员订单占比、散客订单数、散客订单占比
|
||||
- **带 "_环比" 后缀的字段优先引用**（客单价_按成交收入_环比、客单价_按发生额_环比、日均订单数_环比、会员订单占比_环比），这些是本期 vs 上期的真实对比
|
||||
- **短样本标注识别**：若 _环比 字段值形如 `"-43.1%（上期仅 3 天，样本不足仅供参考）"`（含"样本不足"后缀），说明上期数据不足 5 天，结论必须降权表述（"参考值" / "样本待积累" / "不宜作为趋势判断依据"），禁止把短样本环比作为健康度评级的硬依据
|
||||
- 两类客单价并用：
|
||||
- **按成交收入客单价**（去优惠后实际到手的每单均值）— 反映真实收入能力
|
||||
- **按发生额客单价**（含优惠的账单均值）— 反映顾客端认知的"一次消费量级"
|
||||
- 二者差值 ≈ 每单平均优惠让利金额
|
||||
- **会员订单占比的业务解读需避免单一归因**：占比 < 20% 可能是储值卡推广弱，也可能是门店业态定位为散客/团购生意（如车站/商场店）；应列出 2 种可能性让店长判断
|
||||
|
||||
### 3. 按星期聚合（板块 E）
|
||||
- "按星期聚合"字段给出周一至周日各自的日均发生额/现金流入/订单数/营业日数
|
||||
- 供 seq 9 做**宏观周中规律**判断，**必须给出旺/淡日的倍率**（如"周六订单 146 / 周二 88 = 1.66 倍"）
|
||||
- 营业日数 = 0 的星期（停业日）需忽略后比较
|
||||
- **字段不存在时**（当期样本 < 14 天），seq 9 改为"样本不足说明"，不能用"原始指标"硬算周规律
|
||||
|
||||
### 4. 日粒度异常（板块 E）
|
||||
- 每条异常带"基线类型"字段，取值为"同周X均值"或"期均"
|
||||
- **"同周X均值"** 说明该日已与同星期对比过，排除了周中周末规律的干扰，这类异常更值得关注
|
||||
- **"期均"** 说明同星期样本不足（<2 天）退化到整体均值，结论要更保守
|
||||
- 偏离度相同时，优先解读"同周X均值"基线的异常
|
||||
- **字段不存在时**（当期样本 < 7 天），seq 10 改为"样本不足说明"
|
||||
|
||||
### 5. 行业基线（板块 E 辅助）
|
||||
- payload 顶层"行业基线.周中客流规律"说明行业普适的周中客流分布
|
||||
- 这是全行业性特征，可直接引用佐证 seq 9 的宏观规律判断
|
||||
- **其他行业经验值（优惠率警戒线、人力成本警戒线、团购占优惠比例、充值占现金流入比例、复购率、客单价、毛利率等）均未提供** — 因各球房定位、地段、业态差异大，一刀切不准
|
||||
- 禁止在结论中使用任何未经 payload 授权的"行业均值"/"行业警戒线"/"行业参考值"数字
|
||||
- 判断异常请改用：**环比数据、内部对比（如某项占比/某类占大头）、数据业务逻辑完整性（如支出为 0 是否合理）、派生比率字段**
|
||||
|
||||
## 输出格式（强制）
|
||||
|
||||
必须返回严格的 JSON 数组，格式如下：
|
||||
|
||||
```json
|
||||
[
|
||||
{"seq": 1, "title": "洞察标题（10字内）", "content": "洞察正文（含数据、分析、建议，200字内）"},
|
||||
...共 12 条...
|
||||
]
|
||||
```
|
||||
|
||||
### 输出规则
|
||||
- 固定 12 条洞察，seq 1-12 按板块顺序 A→B→C→D→E→F 排列，每板块 2 条
|
||||
- 每条 content 携带 ≥ 1 个具体数字或百分比，不允许空泛描述
|
||||
- 金额单位为元，保留整数；百分比保留整数
|
||||
- content ≤ 200 字
|
||||
- 使用简体中文
|
||||
- 仅返回标准 JSON 数组，不要包裹额外文字
|
||||
- 可适度使用 **加粗** 标记关键指标名、阈值或动作词（小程序端已支持内联 Markdown 渲染），但请节制使用避免喧宾夺主（单条 ≤ 3 处加粗）
|
||||
|
||||
## 限制
|
||||
- 仅基于传入的数据进行分析，不要编造数据。禁止臆想内容！
|
||||
- **环比解读前必须先读"对比口径"字段**，禁止用"当期 N 天"与"整月/整周"做错位对比
|
||||
- **短样本环比（带"样本不足"后缀）必须降权表述**，禁止作为趋势判断或健康度评级的硬依据
|
||||
- "行业基线"字段仅给出了周中客流规律一项。凡 payload 未明确提供的行业经验值（如优惠率警戒线、人力成本警戒线、复购率、客单价、毛利率等），禁止在结论中使用具体数字
|
||||
- 禁止单一归因：遇"会员占比低 / 优惠率高 / 成本占比高"等现象，若存在 2 个及以上合理解读路径（如定位差异 vs 运营弱），必须列出并说明"需店长结合门店实际判断"
|
||||
- 禁止推测走势：趋势判断必须引用 payload 里带 "_环比" 或 "_compare" 字段的真实值；不要从单期数据"推测"上涨下跌
|
||||
- 数据缺失或为零，如实说明并转为对"数据完整性"的建议
|
||||
- 板块内方向是可选项不是必选项，由你按数据价值自主决定从哪个角度切入
|
||||
- 板块 E 的 seq 9 / seq 10 必须分工明确（宏观 / 单日），不能两条都讲同一天的极端值。**字段缺失时改为"样本不足说明"，不可用原始指标硬算或编造**
|
||||
- 板块 F 的 seq 11 / seq 12 必须战略级（红黄绿灯评级 / 跟踪指标与节奏），不能重复 B/D 的战术建议
|
||||
- 若发现多指标协同恶化（如客单价↓ + 会员占比↓ + 储值卡余额↓），必须在 seq 11 健康度评价中单独作为"结构失衡"主因强调，而非分散到各板块。
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 三、粘贴后的自测清单
|
||||
|
||||
按顺序测试，每项通过才算 v3 上线成功：
|
||||
|
||||
### 测试 A · 正常月中场景（本月已过 22 天）
|
||||
- **触发**：`scripts/test_app2_new_system_prompt.py` 的 `this_month/all`
|
||||
- **预期**：
|
||||
- [ ] seq 1 或 seq 2 开头明确引用"对比口径 4/1~4/22 vs 3/1~3/22"
|
||||
- [ ] 不再出现"对比整月"/"上月共 31 天"等错误表述
|
||||
- [ ] seq 9 周规律 + 倍率（样本 22 天足够）
|
||||
- [ ] seq 11/12 健康度 + 跟踪节奏齐全
|
||||
|
||||
### 测试 B · 模拟月初场景（需开发者手动造 4/1~4/3 数据或等 5 月 1-3 号自然触发）
|
||||
- **触发**：月初 1-3 天调用 `this_month/all`
|
||||
- **预期**：
|
||||
- [ ] "按星期聚合"、"日粒度异常"字段缺失
|
||||
- [ ] seq 9 "样本不足 14 天，周中规律需样本积累"
|
||||
- [ ] seq 10 "样本不足，单日异常检测暂未启用"
|
||||
- [ ] 若上期也只有 3 天，客单价环比带"（上期仅 3 天，样本不足仅供参考）"后缀
|
||||
- [ ] AI 主动降权引用短样本环比，不把它作为健康度评级硬依据
|
||||
|
||||
### 测试 C · 加粗 Markdown 渲染配合
|
||||
- **预期**：seq 12 跟踪指标自主出现 `**每周五复盘XX**` / `**启动XX计划**` 这类加粗关键词
|
||||
- **前端验收**：小程序 board-finance 页面 seq 12 相关字样以加粗亮白显示
|
||||
|
||||
---
|
||||
|
||||
## 四、回滚方法
|
||||
|
||||
若 v3 上线后 AI 输出异常：
|
||||
1. 百炼控制台把 system prompt 改回 v2（本文档开头之前的版本）
|
||||
2. 后端 `app2_finance_prompt.py` 的"对比口径"字段无需回滚（AI 不读也无影响，仅占 ~200 字符 prompt 长度）
|
||||
3. `_WEEKDAY_MIN_DAYS = 14` 与短样本标注也无需回滚（纯数据层保护，不依赖 AI 响应）
|
||||
|
||||
---
|
||||
|
||||
## 五、变更记录
|
||||
|
||||
| 日期 | 版本 | 变更 | 作者 |
|
||||
|---|---|---|---|
|
||||
| 2026-04-22 | v3 | 新增对比口径字段读取规则 / 短样本标注识别 / 按星期聚合与日粒度异常字段缺失降级 | Claude + Neo |
|
||||
| 2026-04-22 | v2 | 生产级版本（12 条 · 三色灯 · 跟踪节奏） | Claude + Neo |
|
||||
|
||||
@@ -1,11 +1,16 @@
|
||||
# 审计一览表
|
||||
|
||||
> 自动生成于 2026-04-22 21:17:11，请勿手动编辑。
|
||||
> 自动生成于 2026-05-02 00:06:26，请勿手动编辑。
|
||||
|
||||
## 时间线视图
|
||||
|
||||
| 日期 | 项目 | 需求摘要 | 变更类型 | 影响模块 | 风险 | 详情 |
|
||||
|------|------|----------|----------|----------|------|------|
|
||||
| 2026-05-01 | 项目级 | 2026-05-01 App3 完整消费明细 Prompt 策略 | 功能 | 其他 | 未知 | [链接](changes/2026-05-01__backend_app3_full_detail_prompt.md) |
|
||||
| 2026-05-01 | 项目级 | 变更审计记录：Cursor AI 开发环境迁移 | 文档 | 其他 | 未知 | [链接](changes/2026-05-01__cursor_migration.md) |
|
||||
| 2026-04-30 | 项目级 | 审计记录：admin-web AI 手动执行 app_type 对齐 | bugfix | 其他 | 未知 | [链接](changes/2026-04-30__admin_web_ai_app_type_alignment.md) |
|
||||
| 2026-04-30 | 项目级 | 审计记录：后端 DashScope tokens_used 提取修复 | bugfix | 其他 | 未知 | [链接](changes/2026-04-30__backend_dashscope_tokens_used_extraction.md) |
|
||||
| 2026-04-29 | 项目级 | 变更审计记录：Codex 深度迁移与 Claude 历史摘要归档 | 文档 | 其他 | 未知 | [链接](changes/2026-04-29__codex_migration_and_claude_history_archive.md) |
|
||||
| 2026-04-23 | 项目级 | 变更审计记录：App2a 区域财务洞察 APP 派生 · 整包上线 | bugfix | 其他 | 低 | [链接](changes/2026-04-23__app2a_finance_area_integrated.md) |
|
||||
| 2026-04-22 | 项目级 | 变更审计记录：App2 财务洞察 V5.1 prompt + 小程序 AI 洞察区总结置顶与排版优化 | 文档 | 其他 | 低 | [链接](changes/2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md) |
|
||||
| 2026-04-21 | 项目级 | 审计记录：admin-web AI 管理套件（可视化全流程管控） | 功能 | 其他 | 未知 | [链接](changes/2026-04-21__admin-web-ai-management-suite.md) |
|
||||
@@ -256,6 +261,11 @@
|
||||
|
||||
| 日期 | 需求摘要 | 变更类型 | 影响模块 | 风险 | 详情 |
|
||||
|------|----------|----------|----------|------|------|
|
||||
| 2026-05-01 | 2026-05-01 App3 完整消费明细 Prompt 策略 | 功能 | 其他 | 未知 | [链接](changes/2026-05-01__backend_app3_full_detail_prompt.md) |
|
||||
| 2026-05-01 | 变更审计记录：Cursor AI 开发环境迁移 | 文档 | 其他 | 未知 | [链接](changes/2026-05-01__cursor_migration.md) |
|
||||
| 2026-04-30 | 审计记录：admin-web AI 手动执行 app_type 对齐 | bugfix | 其他 | 未知 | [链接](changes/2026-04-30__admin_web_ai_app_type_alignment.md) |
|
||||
| 2026-04-30 | 审计记录：后端 DashScope tokens_used 提取修复 | bugfix | 其他 | 未知 | [链接](changes/2026-04-30__backend_dashscope_tokens_used_extraction.md) |
|
||||
| 2026-04-29 | 变更审计记录：Codex 深度迁移与 Claude 历史摘要归档 | 文档 | 其他 | 未知 | [链接](changes/2026-04-29__codex_migration_and_claude_history_archive.md) |
|
||||
| 2026-04-23 | 变更审计记录：App2a 区域财务洞察 APP 派生 · 整包上线 | bugfix | 其他 | 低 | [链接](changes/2026-04-23__app2a_finance_area_integrated.md) |
|
||||
| 2026-04-22 | 变更审计记录：App2 财务洞察 V5.1 prompt + 小程序 AI 洞察区总结置顶与排版优化 | 文档 | 其他 | 低 | [链接](changes/2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md) |
|
||||
| 2026-04-21 | 审计记录：admin-web AI 管理套件（可视化全流程管控） | 功能 | 其他 | 未知 | [链接](changes/2026-04-21__admin-web-ai-management-suite.md) |
|
||||
@@ -395,6 +405,11 @@
|
||||
|
||||
| 日期 | 需求摘要 | 变更类型 | 风险 | 详情 |
|
||||
|------|----------|----------|------|------|
|
||||
| 2026-05-01 | 2026-05-01 App3 完整消费明细 Prompt 策略 | 功能 | 未知 | [链接](changes/2026-05-01__backend_app3_full_detail_prompt.md) |
|
||||
| 2026-05-01 | 变更审计记录：Cursor AI 开发环境迁移 | 文档 | 未知 | [链接](changes/2026-05-01__cursor_migration.md) |
|
||||
| 2026-04-30 | 审计记录：admin-web AI 手动执行 app_type 对齐 | bugfix | 未知 | [链接](changes/2026-04-30__admin_web_ai_app_type_alignment.md) |
|
||||
| 2026-04-30 | 审计记录：后端 DashScope tokens_used 提取修复 | bugfix | 未知 | [链接](changes/2026-04-30__backend_dashscope_tokens_used_extraction.md) |
|
||||
| 2026-04-29 | 变更审计记录：Codex 深度迁移与 Claude 历史摘要归档 | 文档 | 未知 | [链接](changes/2026-04-29__codex_migration_and_claude_history_archive.md) |
|
||||
| 2026-04-23 | 变更审计记录：App2a 区域财务洞察 APP 派生 · 整包上线 | bugfix | 低 | [链接](changes/2026-04-23__app2a_finance_area_integrated.md) |
|
||||
| 2026-04-22 | 变更审计记录：App2 财务洞察 V5.1 prompt + 小程序 AI 洞察区总结置顶与排版优化 | 文档 | 低 | [链接](changes/2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md) |
|
||||
| 2026-04-21 | 审计记录：admin-web AI 管理套件（可视化全流程管控） | 功能 | 未知 | [链接](changes/2026-04-21__admin-web-ai-management-suite.md) |
|
||||
|
||||
110
docs/audit/changes/2026-04-20__ai-module-complete.md
Normal file
110
docs/audit/changes/2026-04-20__ai-module-complete.md
Normal file
@@ -0,0 +1,110 @@
|
||||
# 审计记录：AI 模块完整实现
|
||||
|
||||
**日期**：2026-04-20
|
||||
**会话**：AI 模块全量建设（Phase 0~4）
|
||||
**影响范围**：backend / miniprogram / db / docs
|
||||
|
||||
---
|
||||
|
||||
## 变更摘要
|
||||
|
||||
本次会话完成了 NeoZQYY AI 模块从架构重组到端到端贯通的全量实现，涵盖：
|
||||
|
||||
1. **Phase 0**：删除 8 个死代码 App 文件，新建 `prompts/` 模块 + dispatcher 完整重构
|
||||
2. **Phase 1**：多轮会话 session_id 透传、references 注入、EventBus 广播
|
||||
3. **Phase 2**：小程序 chat 页完善、ai-float-button 上下文透传、AI 缓存渲染
|
||||
4. **Phase 2.2**：chat SSE 断线指数退避自动重连（最多 2 次）
|
||||
5. **Phase 3**：WebSocket AI 告警端点、熔断/限流/预算告警推送
|
||||
6. **Phase 3.2**：admin-web AIDashboard 接入 WS 实时告警（/ws/ai-alerts/{site_id}）
|
||||
7. **Phase 4.1**：admin-web AIOperations 新增"按需重新生成"Card（POST /admin/ai/run/{app_type}）
|
||||
8. **Phase 4.2**：缓存失效 Card 已在前序会话实现（adminAI.ts + AIOperations Card 2）
|
||||
9. **修复**：`main.py` 未调用 `internal_ai.set_dispatcher()` 导致 Dispatcher 503
|
||||
|
||||
---
|
||||
|
||||
## 变更文件清单
|
||||
|
||||
### 删除（8 个死代码文件）
|
||||
|
||||
| 文件 | 原因 |
|
||||
|------|------|
|
||||
| `apps/backend/app/ai/apps/__init__.py` | 调用未定义的 `bailian.chat_json()`，死代码 |
|
||||
| `apps/backend/app/ai/apps/app1_chat.py` ～ `app8_consolidation.py` | 同上，`run()` 从未被调用 |
|
||||
|
||||
### 新建
|
||||
|
||||
| 文件 | 说明 |
|
||||
|------|------|
|
||||
| `apps/backend/app/ai/prompts/__init__.py` | 导出 7 个 `build_app*_prompt` 函数 |
|
||||
| `apps/backend/app/ai/prompts/app2_finance_prompt.py` | App2 财务数据拼 prompt |
|
||||
| `apps/backend/app/ai/prompts/app3_clue_prompt.py` | App3 消费线索 prompt |
|
||||
| `apps/backend/app/ai/prompts/app4_analysis_prompt.py` | App4 助教-会员分析 prompt |
|
||||
| `apps/backend/app/ai/prompts/app5_tactics_prompt.py` | App5 话术 prompt（含 App4 结果） |
|
||||
| `apps/backend/app/ai/prompts/app6_note_prompt.py` | App6 备注分析 prompt |
|
||||
| `apps/backend/app/ai/prompts/app7_customer_prompt.py` | App7 客户画像 prompt |
|
||||
| `apps/backend/app/ai/prompts/app8_consolidation_prompt.py` | App8 线索整合 prompt |
|
||||
| `apps/backend/app/ai/references.py` | `_references` 注入 + `reference_card` 构建 |
|
||||
| `apps/backend/app/ai/event_bus.py` | in-process pub/sub，site_id 隔离 |
|
||||
| `apps/backend/app/ws/ai_events.py` | `/ws/ai-cache/{site_id}` + `/ws/ai-alerts/{site_id}` |
|
||||
| `db/zqyy_app/migrations/20260420_ai_trigger_jobs_and_app2_prewarm.sql` | 4 事件 + 1 cron trigger_jobs，已执行 |
|
||||
| `docs/database/BD_manual_ai_trigger_jobs_register.md` | 手动注册说明 |
|
||||
|
||||
### 修改
|
||||
|
||||
| 文件 | 关键变更 |
|
||||
|------|----------|
|
||||
| `apps/backend/app/ai/dispatcher.py` | 完整重构：调用 `prompts.build_*`，链式编排，EventBus 广播，_references 注入 |
|
||||
| `apps/backend/app/ai/dashscope_client.py` | `call_app_stream` 返回 `(chunk, session_id)` tuple |
|
||||
| `apps/backend/app/services/chat_service.py` | session_id 初始为 NULL，`save_session_id()` 保存百炼返回值 |
|
||||
| `apps/backend/app/services/trigger_scheduler.py` | `_invoke_handler()` 修复 async handler 同步调用 bug |
|
||||
| `apps/backend/app/services/task_generator.py` | `run()` 完成后触发 `ai_consumption_settled` 事件 |
|
||||
| `apps/backend/app/services/note_service.py` | 备注创建后触发 `ai_note_created` |
|
||||
| `apps/backend/app/routers/admin_task_engine.py` | 任务分配后触发 `ai_task_assigned` |
|
||||
| `apps/backend/app/routers/internal_events.py` | ETL 完成后触发 `ai_dws_completed` |
|
||||
| `apps/backend/app/routers/xcx_chat.py` | 解包 `(chunk, session_id)` 流，保存 session_id，写 reference_card |
|
||||
| `apps/backend/app/routers/admin_ai.py` | 新增 `POST /api/admin/ai/run/{app_type}` 端点 |
|
||||
| `apps/backend/app/schemas/admin_ai.py` | 新增 `RunAppRequest` / `RunAppResponse` |
|
||||
| `apps/backend/app/services/ai/admin_service.py` | 缓存失效后广播 `cache_invalidated` 事件 |
|
||||
| `apps/backend/app/main.py` | lifespan 补调 `internal_ai.set_dispatcher(_dispatcher)` |
|
||||
| `apps/backend/pytest.ini` | 追加 `norecursedirs = _archived` |
|
||||
| `apps/miniprogram/miniprogram/pages/chat/chat.ts` | referenceCard 点击跳转，pageFilters URL 解析；Phase 2.2 SSE 断线指数退避自动重连（最多 2 次） |
|
||||
| `apps/miniprogram/miniprogram/pages/chat/chat.wxml` | referenceCard `bindtap` + 类型标签优化 |
|
||||
| `apps/miniprogram/miniprogram/components/ai-float-button/ai-float-button.ts` | 新增 `sourcePage` + `pageFilters` 属性透传 |
|
||||
| `apps/miniprogram/miniprogram/pages/board-finance/board-finance.ts` | `_loadAIInsights()` 从 `app2_finance` 缓存加载洞察 |
|
||||
| `apps/miniprogram/miniprogram/pages/customer-detail/customer-detail.ts` | `_loadAIInsight()` 从 `app7_customer_analysis` 缓存加载 |
|
||||
| `apps/miniprogram/miniprogram/services/api.ts` | 新增 `fetchAICache()` 函数 |
|
||||
|
||||
---
|
||||
|
||||
## E2E 验证结果
|
||||
|
||||
**消费事件链**（site_id=2790685415443269，member_id=2799212491392773）：
|
||||
|
||||
| App | 状态 | 延迟 |
|
||||
|-----|------|------|
|
||||
| app3_clue | timeout | 121s（prompt 过大，需优化） |
|
||||
| app8_consolidate | **success** | ~15s，缓存已写入 |
|
||||
| app7_customer | **success** | ~60s，缓存含真实 AI 分析 |
|
||||
|
||||
**缓存验证**：
|
||||
- `app7_customer_analysis` result_json 含 `summary` + `strategies[{title,content}]`
|
||||
- `app8_clue_consolidated` result_json 含 `_references`（link 正确拼装）
|
||||
- board-finance AI 洞察已通过微信 MCP 验证在页面渲染（5 条真实 insight）
|
||||
|
||||
---
|
||||
|
||||
## 遗留风险点
|
||||
|
||||
1. **app3 超时（已缓解）**：`_MAX_PROMPT_LEN` 已从 8000 降至 4000，最多保留 3 条消费记录 + 二次截断 reference。待下次 E2E 验证是否仍超时。
|
||||
2. **tokens_used = 0**：DashScope SDK 响应未提取 token 计数，影响预算追踪精度。需检查 `call_app` 的 usage 提取。
|
||||
3. **dispatcher 内存去重**：`_dedup_set` 重启后丢失，生产环境需改为查 DB。
|
||||
4. **task-detail aiAnalysis**：Phase 2.5 暂未实现，结构较复杂，待单独 session。
|
||||
5. **admin-web Phase 3.2/4.1/4.2**：后端端点已就位，前端实现延后。
|
||||
|
||||
---
|
||||
|
||||
## 回滚策略
|
||||
|
||||
- 删除的 8 个 apps/ 文件在 git 历史可恢复：`git checkout <sha> apps/backend/app/ai/apps/`
|
||||
- DB 迁移回滚：`DELETE FROM biz.trigger_jobs WHERE id >= 57;`
|
||||
- `main.py` 新增一行可直接删除：`_internal_ai_router.set_dispatcher(_dispatcher)`
|
||||
120
docs/audit/changes/2026-04-21__admin-web-ai-management-suite.md
Normal file
120
docs/audit/changes/2026-04-21__admin-web-ai-management-suite.md
Normal file
@@ -0,0 +1,120 @@
|
||||
# 审计记录：admin-web AI 管理套件（可视化全流程管控）
|
||||
|
||||
**日期**：2026-04-21
|
||||
**会话**：为 admin-web 补齐 AI 可视化管理页面 + 后端对应端点
|
||||
**影响范围**：backend（schemas/services/routers）/ admin-web（api/pages/App.tsx）
|
||||
|
||||
---
|
||||
|
||||
## 变更摘要
|
||||
|
||||
用户需求：
|
||||
> 为我在 admin-web 修改完善 AI 相关工具和板块。让我能有可视化的工具进行 AI 方面的全流程可视化可操作的管理，包含调试阶段的集中预热以及触发器状态设置等。
|
||||
|
||||
本次交付 2 个新页面 + 1 个页面增强 + 1 个 AI 一级菜单组，并新增 4 个后端端点作为前端数据源。
|
||||
|
||||
---
|
||||
|
||||
## 后端变更
|
||||
|
||||
### 新增 Pydantic 模型（`apps/backend/app/schemas/admin_ai.py`）
|
||||
|
||||
| 模型 | 用途 |
|
||||
|------|------|
|
||||
| `TriggerItem` | 触发器单条记录（id/job_name/job_type/trigger_condition/trigger_config/status/last_run_at/next_run_at/last_error） |
|
||||
| `TriggerUpdateRequest` | 触发器更新（status / cron_expression / description） |
|
||||
| `PrewarmMissingItem` | 缺失组合（target_id / time_dimension / area） |
|
||||
| `PrewarmProgressResponse` | 预热进度（total=72 / done / missing / last_updated） |
|
||||
| `ManualTriggerRequest` | 手动触发事件请求（event_type / site_id / member_id / assistant_id / payload / is_forced） |
|
||||
| `ManualTriggerResponse` | 手动触发响应（trigger_job_id / status） |
|
||||
|
||||
### 新增服务方法（`apps/backend/app/services/ai/admin_service.py`）
|
||||
|
||||
| 方法 | 实现 |
|
||||
|------|------|
|
||||
| `list_triggers()` | 查 `biz.trigger_jobs WHERE job_type LIKE 'ai_%' OR job_name='task_generator'` |
|
||||
| `update_trigger(id, status, cron_expression, description)` | 支持部分字段更新，cron 用 `jsonb_set` 改 trigger_config |
|
||||
| `get_prewarm_progress(site_id)` | 对比 72 组合 expected vs `biz.ai_cache` 中 `app2_finance` 的 target_id，返回 done/missing |
|
||||
|
||||
### 新增路由端点（`apps/backend/app/routers/admin_ai.py`）
|
||||
|
||||
| 方法 | 路径 | 说明 |
|
||||
|------|------|------|
|
||||
| GET | `/api/admin/ai/triggers` | 列出所有 AI 触发器 |
|
||||
| PATCH | `/api/admin/ai/triggers/{trigger_id}` | 启停 / 改 cron / 改描述 |
|
||||
| GET | `/api/admin/ai/prewarm/progress?site_id=N` | App2 预热 72 组合进度 |
|
||||
| POST | `/api/admin/ai/trigger-event` | 手动触发事件链，默认 `is_forced=True` 跨越去重 |
|
||||
|
||||
所有端点走 `_require_admin()` 要求 `site_admin`/`tenant_admin` 角色。
|
||||
|
||||
---
|
||||
|
||||
## 前端变更
|
||||
|
||||
### API 层（`apps/admin-web/src/api/adminAI.ts`）
|
||||
|
||||
新增 4 个函数 + 6 个类型接口：
|
||||
- `listTriggers()` / `updateTrigger(id, body)` + `TriggerItem` / `TriggerUpdateRequest`
|
||||
- `getPrewarmProgress(siteId)` + `PrewarmProgressResponse` / `PrewarmMissingItem`
|
||||
- `triggerEvent(body)` + `ManualTriggerRequest` / `ManualTriggerResponse`
|
||||
|
||||
### 新建页面
|
||||
|
||||
**`apps/admin-web/src/pages/AITriggers.tsx`** — 触发器设置页（`/ai/triggers`）
|
||||
- 表格列出所有 AI 触发器（id / 名称+描述 / 类型 tag / 表达式或事件名 / 启停 Switch / 最近/下次运行 / 最后错误）
|
||||
- 编辑 Modal：cron 类型支持改 cron 表达式，所有类型可改描述
|
||||
- 行内快速启停：Switch 直接切换 enabled/disabled
|
||||
|
||||
**`apps/admin-web/src/pages/AIPrewarm.tsx`** — 预热进度页（`/ai/prewarm`）
|
||||
- 顶部卡片：72 组合进度条 + done/missing 计数 + last_updated
|
||||
- 2 个主动作：
|
||||
- "触发全量预热"：调 `triggerEvent(dws_completed, is_forced=true)`，后台异步跑
|
||||
- "一键补齐缺失"：串行 `runApp(app2_finance, time_dimension, area)` 逐个补，前端进度 Alert
|
||||
- 缺失组合表格：每行一个"单独生成"按钮，快速补单个组合（30-120s）
|
||||
- 时间/区域标签中英双显（`本月 (this_month)`）
|
||||
|
||||
### 页面增强
|
||||
|
||||
**`apps/admin-web/src/pages/AIOperations.tsx`** — 新增 Card 2.6「手动触发事件链（调试用）」
|
||||
- 事件类型下拉（consumption / note_created / task_assigned / dws_completed）
|
||||
- 输入 member_id / assistant_id 按需
|
||||
- 默认勾选「跳过去重」复选框（is_forced=true）
|
||||
- 触发后返回 `trigger_job_id` 供后续查调度历史
|
||||
|
||||
### 路由与菜单（`apps/admin-web/src/App.tsx`）
|
||||
|
||||
- 新增一级菜单「AI 管理」（图标 RobotOutlined），含 5 个子项：
|
||||
- 总览 → `/ai/dashboard`（原 AIDashboard，此前未挂载路由，本次接入）
|
||||
- 手动操作 → `/ai/operations`（原 AIOperations，同上）
|
||||
- 预热进度 → `/ai/prewarm`（新）
|
||||
- 触发器设置 → `/ai/triggers`（新）
|
||||
- 调度历史 → `/ai/trigger-jobs`（原 AITriggerJobs，同上）
|
||||
- `getSelectedKeys` / `getDefaultOpenKeys` 补 `/ai/` 前缀匹配
|
||||
|
||||
---
|
||||
|
||||
## 验证状态
|
||||
|
||||
- **代码语法**：TypeScript / Python 均通过编辑器层面校验（无 linter 报错）
|
||||
- **烟雾测试**：后端 `--reload` 触发自身 lifespan 阻塞（已知环境问题：远程 PG 560ms RTT × psycopg2 每请求新建连接），未能在本会话 curl 成功。端点逻辑已完整覆盖已有 admin_ai 路由的模式，复用 `_require_admin()` / `_admin_svc` / `get_dispatcher()` 等成熟组件
|
||||
- **手动验证路径**：
|
||||
1. 重启后端至稳定
|
||||
2. 登录 admin-web，左侧菜单展开「AI 管理」应看到 5 项
|
||||
3. 「总览 / 手动操作 / 调度历史」是重新挂载路由的现有页面，直接可用
|
||||
4. 「触发器设置」读取 `biz.trigger_jobs` 的 5 条 AI 触发器（`ai_consumption_settled` / `ai_note_created` / `ai_task_assigned` / `ai_dws_completed` / `ai_dws_prewarm_1000`）
|
||||
5. 「预热进度」应显示 46/72（当前进度），可一键补齐剩余 26 个
|
||||
|
||||
---
|
||||
|
||||
## 遗留风险点
|
||||
|
||||
1. **admin JWT 与 auth.users 混用**：`_require_admin → require_permission() → _get_user_status(user_id)` 查 `auth.users`，但 admin 用户实际在 `admin_users` 表。生产 admin-web 登录后 JWT 的 sub 必须指向 `auth.users.id` 才能通过。本次不修此老问题，沿用现有 admin_ai 所有端点的约定
|
||||
2. **后端远程 PG 网络延迟**（本会话观测 ping 560ms）导致每请求 psycopg2.connect ≈ 3s，叠加 AI 预热任务会拖垮整体响应。根治需加连接池或切本地 PG，与本次改动无关
|
||||
3. **cron 修改立即生效依赖 scheduler 重新解析**：改完 cron_expression 后，`biz.trigger_jobs.next_run_at` 需在 scheduler 下一次 poll 时重算（默认 60s）；用户感知到的生效延迟最多 1 分钟
|
||||
|
||||
---
|
||||
|
||||
## 回滚
|
||||
|
||||
- 后端：`git restore apps/backend/app/routers/admin_ai.py apps/backend/app/schemas/admin_ai.py apps/backend/app/services/ai/admin_service.py`
|
||||
- 前端：`git restore apps/admin-web/src/api/adminAI.ts apps/admin-web/src/App.tsx apps/admin-web/src/pages/AIOperations.tsx && rm apps/admin-web/src/pages/AITriggers.tsx apps/admin-web/src/pages/AIPrewarm.tsx`
|
||||
@@ -0,0 +1,75 @@
|
||||
# 审计记录：App2 财务洞察全筛选组合预热 + 字段中文化
|
||||
|
||||
**日期**：2026-04-21
|
||||
**会话**：board-finance AI 洞察改造
|
||||
**影响范围**：backend / miniprogram / db / admin-web
|
||||
|
||||
---
|
||||
|
||||
## 变更摘要
|
||||
|
||||
用户需求：
|
||||
1. 每日 10:00 为所有门店的 board-finance 页面所有筛选组合（时间 × 区域 = 72 组）生成 AI 洞察并缓存
|
||||
2. 前端根据当前筛选条件读取对应缓存
|
||||
3. 发送给 AI 的 prompt 字段名翻译为中文（避免英文变量名，提升可读性）
|
||||
4. 切换默认模型为 claude-opus-4-7（max 1M 上下文）
|
||||
|
||||
---
|
||||
|
||||
## 变更文件清单
|
||||
|
||||
### 修改
|
||||
|
||||
| 文件 | 关键变更 |
|
||||
|------|----------|
|
||||
| `~/.claude/settings.json` | 新增 `"model": "claude-opus-4-7"` |
|
||||
| `apps/backend/app/ai/prompts/app2_finance_prompt.py` | 新增 `area` 参数（与 board-finance.ts areaOptions 对齐），新增 `AREA_OPTIONS`/`AREA_LABELS`/`KEY_TRANSLATIONS`（70+ 字段中英映射）+ 递归 key 翻译函数 `_translate_keys`；payload 顶层键改为中文（当前时间/门店编号/时间维度/区域/财务数据） |
|
||||
| `apps/backend/app/ai/dispatcher.py` | 新增 `APP2_AREA_OPTIONS` 与 `_app2_target_id(time, area)`；`_handle_dws_completed` 双重循环遍历 8×9=72 组合；`run_single_app` 的 app2_finance 分支支持 area；`handle_app2_prewarm` docstring 改为 10:00 |
|
||||
| `apps/backend/app/schemas/admin_ai.py` | `RunAppRequest` 新增 `area: str \| None` 字段 |
|
||||
| `apps/admin-web/src/api/adminAI.ts` | `RunAppRequest` 接口新增 `area?: string` |
|
||||
| `apps/miniprogram/miniprogram/pages/board-finance/board-finance.ts` | `_loadAIInsights(selectedTime, selectedArea)` 签名，`target_id` 改为 `{timeKey}__{areaKey}`；`_loadData` 传入两个参数 |
|
||||
|
||||
### 新建
|
||||
|
||||
| 文件 | 说明 |
|
||||
|------|------|
|
||||
| `db/zqyy_app/migrations/20260421_app2_prewarm_cron_reschedule.sql` | UPDATE biz.trigger_jobs：job_name `ai_dws_prewarm_0830` → `ai_dws_prewarm_1000`；cron `30 8 * * *` → `0 10 * * *`；description 更新。已在 pg-app-test 执行 |
|
||||
|
||||
---
|
||||
|
||||
## 缓存键规则变更
|
||||
|
||||
- 旧：`target_id = time_dimension`（如 `this_month`，仅 8 条/门店）
|
||||
- 新：`target_id = {time_dimension}__{area}`（双下划线分隔，72 条/门店）
|
||||
- 前后端用相同拼装函数：后端 `_app2_target_id`、前端 `board-finance.ts _loadAIInsights` 内联实现
|
||||
- 旧格式缓存已清理（仅保留 `__` 格式）
|
||||
|
||||
## 字段中文化实现
|
||||
|
||||
`KEY_TRANSLATIONS` 覆盖 `board_service.get_finance_board` 返回的所有层级字段：
|
||||
- 顶层板块：overview → 经营一览、recharge → 预收资产 等
|
||||
- 经营一览：occurrence → 发生额、discount_rate → 优惠率 等
|
||||
- 环比后缀：`*_compare` / `*_down` / `*_flat` 全覆盖
|
||||
- 通用字段：label → 名称、amount → 金额、total → 合计 等
|
||||
|
||||
`_translate_keys` 递归遍历 dict/list，只翻译键名，不改变值与结构。
|
||||
|
||||
## 触发与执行验证
|
||||
|
||||
- 2026-04-21 01:54 首次通过 `POST /api/internal/ai/trigger` 触发 `dws_completed + is_forced=true`
|
||||
- prompt 已验证为完整中文键：`{"当前时间": "2026-04-21 01:56", "门店编号": 2790685415443269, "时间维度": "本月", "区域": "全部区域", "财务数据": {"经营一览": {"发生额": 287315.98, ...}}}`
|
||||
- 后续 72 组合后台异步执行，结果写入 `biz.ai_cache`
|
||||
|
||||
---
|
||||
|
||||
## 遗留风险点
|
||||
|
||||
1. **AI 调用时间增加**：中文 key 使 prompt 体积膨胀约 15%，部分请求已触发 `_STEP_TIMEOUT=120s` 超时。若超时率高需将 `_STEP_TIMEOUT` 上调至 180-240s，或裁剪 board_data 中次要字段
|
||||
2. **72 组合全量执行时长**：每组约 30-60s 串行，单门店 36-72 分钟；多门店场景下 cron 10:00 启动后可能跨小时结束
|
||||
3. **dispatcher `_execute_chain` 外层超时**：`_STEP_TIMEOUT * 5 = 600s = 10 min`，只够覆盖 ~10 组合；dws_completed 场景需单独放宽该超时，否则只能写入前 10 组缓存
|
||||
|
||||
## 回滚策略
|
||||
|
||||
- cron 回滚：`UPDATE biz.trigger_jobs SET job_name='ai_dws_prewarm_0830', trigger_config='{"cron_expression":"30 8 * * *"}'::jsonb WHERE job_type='ai_dws_prewarm';`
|
||||
- 代码回滚：`git revert` 本次 commit 即可
|
||||
- 缓存清理：`DELETE FROM biz.ai_cache WHERE cache_type='app2_finance' AND target_id LIKE '%\_\_%' ESCAPE '\';`
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 4.2 KiB |
@@ -0,0 +1,202 @@
|
||||
# 变更审计记录：App2 财务洞察 V5.1 prompt + 小程序 AI 洞察区总结置顶与排版优化
|
||||
|
||||
| 字段 | 值 |
|
||||
|------|-----|
|
||||
| 日期 | 2026-04-22 18:35:38 |
|
||||
| 风险标签 | dir:backend, dir:miniprogram |
|
||||
| 涉及模块 | apps/backend (prompt 层) / apps/miniprogram (board-finance 页) / docs/ai / scripts |
|
||||
| 数据库 DDL | 无 |
|
||||
|
||||
## 操作摘要
|
||||
|
||||
本轮围绕 **App2 财务洞察**（72 组合预热缓存下的财务看板 AI 洞察）做了 4 件事：
|
||||
|
||||
1. **小程序 AI 洞察区改版**：seq 11/seq 12 作为"本期总结"置顶展示（三色灯健康度评级 + ⏰ 跟踪指标），seq 1-10 作为"分板块明细"；新增轻量 Markdown 内联渲染（`**加粗**` / `*倾斜*`）；最终排版迭代到"总结区 body 2 行 clamp + seq 1/2/3 统一单行省略 + 一键进弹窗看全部"的紧凑态。
|
||||
|
||||
2. **后端 prompt 构建器月中场景保护**：向 payload 顶层注入 `对比口径` 字段（`{当期范围, 对比期范围, 对齐方式: "上期同天数对齐"}`），让 AI 正确解读"月中 22 天 vs 上月 22 天同期对齐"而非错位为整月对比；按星期聚合字段样本门槛从 7 天提升到 14 天（防月初每个星期仅 1 天被包装成"日均"误导 AI）；单位经济上期样本 < 5 天时为所有 `_环比` 字段加"（上期仅 N 天，样本不足仅供参考）"后缀，让 AI 降权引用。
|
||||
|
||||
3. **App2 system prompt 演进 v3→v4→v5→V5.1**：通过 4 次 A/B/A/B 测试（每版本 10 次调用 · 共 40 次百炼调用）+ 自建店长视角三层评分模型（准确性 40% / 洞察深度 35% / 稳定性 25%），最终 V5.1 综合分 **92.3 / 100** 超越 A 基线 17.7 分，采纳为生产版本。V5.1 核心改动为 H1/H2 两条硬性输出要求：seq 1/2 必须显式输出"对比口径：当期 X 天 vs 上期 X 天"、趋势词（下滑/收缩/加剧等）必须同句内紧跟数字锚点。
|
||||
|
||||
4. **72 组合多 APP 派生方案规划文档**：为后续区域粒度（8 业态 × 8 时间 = 64 个组合）产出完整的调研-规划-实施文档，明确 2 套 prompt 方案 + 2 个派生 APP（`app2_finance` 全域 + `app2a_finance_area` 区域）+ 无硬性 DDL 改动 + 分阶段 P1-P5 交付计划。本轮仅交付 P1（小程序 seq11/12 置顶），P2-P5 后续实施。
|
||||
|
||||
## 变更文件
|
||||
|
||||
### 修改（4 个）
|
||||
|
||||
- `apps/backend/app/ai/prompts/app2_finance_prompt.py` — 月中场景 3 项保护：新增 `_WEEKDAY_MIN_DAYS = 14` 常量提升按星期聚合样本门槛；`_build_unit_economics` 短样本标注；`build_prompt` 顶层注入"对比口径"字段
|
||||
- `apps/miniprogram/miniprogram/pages/board-finance/board-finance.ts` — 新增 `parseMarkdownInline` 内联 Markdown 解析器；`_loadAIInsights` 预生成 `bodySegs`；新增 `_extractSummary` 方法抽取末 2 条为总结区；data 新增 `aiInsightSummary / aiInsightDetails / summaryLightType / summaryLightLabel` 字段
|
||||
- `apps/miniprogram/miniprogram/pages/board-finance/board-finance.wxml` — AI 洞察区顶部 + 弹窗顶部各插入一套"本期总结卡片"（三色灯徽章 + 诊断块 + 虚线分隔 + ⏰ 跟踪块）；明细 body 全部改为 `<view>+<text wx:for="bodySegs">` 分段渲染；最终排版收敛为"总结 2 行 clamp + 明细 seq 1/2/3 单行省略 + 引导查看全部"
|
||||
- `apps/miniprogram/miniprogram/pages/board-finance/board-finance.wxss` — 新增 `.ai-summary-*` 系列样式（轻量化版：彩色圆点徽章替代厚胶囊 + 虚线下分隔 + 字色层次）/ `.md-bold` `.md-italic` / `.ai-summary-block-body-clamp`（2 行省略）/ `.ai-insight-details-label-text` 分组标签
|
||||
|
||||
### 新增（9 个）
|
||||
|
||||
**文档（6 个）**
|
||||
- `docs/ai/app2_finance_multi_app_design.md` — 72 组合多 APP 派生完整设计文档（调研-规划-实施三段 · 6 章 + 3 附录）
|
||||
- `docs/ai/app2_finance_system_prompt_v3.md` — v3 补丁稿（首次提出"对比口径"读取规则，供用户粘贴到百炼）
|
||||
- `docs/ai/app2_finance_system_prompt_20260422_v4_concise.md` — v4 精简版（5330 字，-60%）
|
||||
- `docs/ai/app2_finance_system_prompt_20260422_v5.md` — v5 混合版（15612 字，含正反例对比）
|
||||
- `docs/ai/app2_finance_system_prompt_20260422_v5_1.md` — **V5.1 生产版本**（15886 字，H1/H2 硬性输出要求）
|
||||
- `docs/ai/app2_finance_prompt_version_history.md` — 版本记录与采纳依据表
|
||||
|
||||
**脚本（3 个）**
|
||||
- `scripts/ab_test_app2_prompt.py` — A/B 测试运行器，绕过 cache 直调百炼 N 次，支持 `--resume` 断点续跑
|
||||
- `scripts/analyze_ab_content_quality.py` — 初版内容质量分析（板块级字段引用率 + 违规统计）
|
||||
- `scripts/analyze_store_manager_quality.py` — 店长视角三层评分模型（准确性 40% / 洞察深度 35% / 稳定性 25%，综合分 100 分制）
|
||||
|
||||
### 测试存档（不入审计详列）
|
||||
|
||||
- `export/ai-ab-test/round_{a,b,v5,v5_1}/*` — 40 份完整 JSON + 4 份 summary CSV + 3 份对比 JSON 报告
|
||||
- 作为 V5.1 采纳的依据基线保留
|
||||
|
||||
## 改动注解
|
||||
|
||||
### 高风险 · 后端 prompt 构建器
|
||||
|
||||
**apps/backend/app/ai/prompts/app2_finance_prompt.py**
|
||||
- **变更类型**：功能增强 + 数据保护
|
||||
- **原因**：本月 22 天（月中）调用时 AI 把环比误读为"当期部分 vs 上月整月"；月初 1-5 天样本不足，"按星期聚合"/"单位经济环比"噪声极大需要降权；
|
||||
- **思路**：
|
||||
- 顶层注入 `对比口径` 字段显式告知 AI 当期与对比期都是"同天数对齐"（调用 `_calc_date_range + _calc_prev_range` 计算后格式化为人类可读字符串）
|
||||
- `_WEEKDAY_MIN_DAYS = 14`（替代原 `_ANOMALY_MIN_DAYS = 7`）作为按星期聚合的独立门槛 — 保证每个星期至少 2 天样本，否则返回 `None` 不注入
|
||||
- `_build_unit_economics` 里的 `_pct_change` 闭包捕获 `low_sample = prev_days < 5`，输出时附加"（上期仅 N 天，样本不足仅供参考）"后缀
|
||||
- **结果**：实测月中场景 prompt 长度 5102→5506 字符（+394 字符，+7.7%）；月初 3 天样本模拟场景下按星期聚合正确返回 None、_环比值正确附加降权后缀
|
||||
|
||||
### 高风险 · 小程序前端
|
||||
|
||||
**apps/miniprogram/miniprogram/pages/board-finance/board-finance.ts**
|
||||
- **变更类型**：功能增强 · 新增总结区 + Markdown 渲染
|
||||
- **原因**：用户反馈"seq 11/12 作为总结应置顶，减轻店长扫读负担"；AI 返回的 Markdown `**加粗**` 原本以原始 `**` 字符展示
|
||||
- **思路**：新增 `parseMarkdownInline` 独立函数（纯函数，不依赖 Page 上下文，regex 分段产出 `{text, bold?, italic?}` 数组）；`_loadAIInsights` 在 map 阶段为每条 insight 预生成 `titleSegs/bodySegs`；新增 `_extractSummary` Page 方法，按"数组长度 ≥ 4 取末两条"规则拆分 summary + details，兼容 12 条/8 条（未来区域精简 APP 的长度）
|
||||
- **结果**：后端零改动；降级友好（< 4 条时 summary 空，details 全量渲染）；三色灯识别用宽松 regex（匹配 emoji 或"红灯/黄灯/绿灯"文字）
|
||||
|
||||
**apps/miniprogram/miniprogram/pages/board-finance/board-finance.wxml**
|
||||
- **变更类型**：UI 结构增强（3 轮迭代）
|
||||
- **原因**：用户连续 3 次对排版反馈：①初版徽章太厚、卡片视觉过重 →②轻量化（彩色圆点替代胶囊、去阴影、虚线下分隔）→ ③最终收敛到"总结区 2 行 clamp + seq 1/2/3 单行省略 + 引导查看全部"
|
||||
- **思路**：AI 洞察区顶部 + 弹窗顶部对称插入总结卡片 `<view class="ai-summary-card">`；明细 body 改为 `<view>+<text wx:for="bodySegs">` 分段渲染支持 Markdown 内联样式；查看全部按钮降低触发门槛（只要有洞察就显示）
|
||||
- **结果**：页面 AI 洞察区高度减少约 40%（总结区 2 行 clamp + seq 1/2/3 各 1 行），"查看全部"成为主要交互入口
|
||||
|
||||
**apps/miniprogram/miniprogram/pages/board-finance/board-finance.wxss**
|
||||
- **变更类型**：样式追加（无破坏性改动）
|
||||
- **原因**：配合 wxml 新结构
|
||||
- **思路**：`.ai-summary-card` 容器去厚色底改虚线下分隔；`.ai-summary-badge--{green/yellow/red/neutral}` 用纯色文字替代彩色胶囊（v2 减重）；`.md-bold` 用 `font-weight: 700 + rgba(255,255,255,0.98)` 提亮；`.ai-summary-block-body-clamp` 用 `-webkit-line-clamp: 2` 限高
|
||||
- **结果**：所有新样式都前缀化（`.ai-summary-*` / `.md-*` / `.ai-summary-block-body-clamp`），无冲突
|
||||
|
||||
### 中风险 · 文档
|
||||
|
||||
**docs/ai/app2_finance_system_prompt_20260422_v5_1.md**（生产版本）
|
||||
- **变更类型**：新建
|
||||
- **原因**：经 V3→V4→V5→V5.1 四次 A/B 实测（40 次调用），V5.1 综合分 92.3 胜出，采纳为生产版本
|
||||
- **结果**：已由用户替换到百炼控制台 APP ID `1dcdb5f39c3040b6af8ef79215b9b051`
|
||||
|
||||
**docs/ai/app2_finance_prompt_version_history.md**
|
||||
- **变更类型**：新建（版本追踪索引）
|
||||
- **原因**：4 版 prompt 文件共存需要一个入口说明"谁是生产版、为什么采纳、何时生效"，避免后续维护混淆
|
||||
- **结果**：含版本对照表 + 四方评分对比 + 评估方法说明 + 变更规则
|
||||
|
||||
**docs/ai/app2_finance_multi_app_design.md**
|
||||
- **变更类型**：新建（规划蓝图）
|
||||
- **原因**：72 组合预热下区域粒度数据缺失（助教/现金流/储值卡等 7 类字段），需做"区域精简 APP" 派生设计
|
||||
- **结果**：给出 2 APP 方案 + 2 套 prompt + 无硬性 DDL + P1-P5 分阶段交付。本轮仅交付 P1 小程序 seq11/12 置顶
|
||||
|
||||
### 普通 · 脚本（临时工具）
|
||||
|
||||
- **scripts/ab_test_app2_prompt.py** — A/B 测试运行器；支持 `--resume` 断点续跑；直调 `DashScopeClient.call_app` 绕过缓存
|
||||
- **scripts/analyze_ab_content_quality.py** — 初版内容质量分析，板块级字段引用率 + 违规统计
|
||||
- **scripts/analyze_store_manager_quality.py** — 店长视角三层评分（准确性 40% + 洞察深度 35% + 稳定性 25%），含综合分 100 分制计算
|
||||
|
||||
## 数据库变更
|
||||
|
||||
无。本轮完全不涉及 DDL/迁移。72 组合多 APP 设计确认"无硬性 DDL"（`cache_type` / `app_type` 字段 VARCHAR(30) 已够用）。
|
||||
|
||||
## 风险与回滚
|
||||
|
||||
### 风险点
|
||||
|
||||
- **【中】百炼 V5.1 system prompt 已替换到生产**：用户在控制台已发布 V5.1。若出现 AI 输出异常（如 12 条变少、三色灯格式破坏），回滚方法见下。
|
||||
- **【中】后端 prompt 新增"对比口径"字段**：payload 长度 +394 字符，每次百炼调用 tokens 略增，成本影响 ≈ +4%。压力测试未发现超时或熔断。
|
||||
- **【低】小程序 seq 11/12 识别依赖"数组末两条"启发式**：若 AI 返回 seq 顺序错位（如 seq 11 在第 6 位），识别错误。当前实测 40 次均按 1-12 顺序返回。
|
||||
- **【低】按星期聚合门槛 14 天**：极端场景（新门店第 1-13 天）该字段缺失是正常降级，AI 应按 V5.1 硬约束 H6 输出"样本不足 14 天，周规律待积累"。需配合百炼 V5.1 同步才能生效。
|
||||
- **【低】Markdown 渲染范围有限**：仅支持 `**加粗**` / `*倾斜*`；其他 MD 语法（标题、列表、代码块）不支持，AI 若意外输出会显示原始字符。当前 v5.1 system prompt 限制"单条 ≤ 3 处加粗"，符合预期。
|
||||
|
||||
### 回滚要点
|
||||
|
||||
1. **百炼 system prompt 回滚**：将控制台 APP 的 system prompt 粘贴回 `docs/ai/app2_finance_system_prompt_20260422.md`（A 版）即可
|
||||
2. **后端 prompt 构建器回滚**：`git revert` 本次 `app2_finance_prompt.py` 的 3 处改动；"对比口径"字段对旧 prompt 无副作用，实际上可保留
|
||||
3. **小程序 UI 回滚**：`git revert` 3 个文件（ts/wxml/wxss）；或保留 v3 但调整 wxml 里 "seq 1-2 完整 / seq 3 省略" 分支的控制流
|
||||
|
||||
## 验证
|
||||
|
||||
### 已验证
|
||||
|
||||
- **后端**：`build_prompt` 本地调用测试通过（`this_month/all` 返回 prompt 长度 5506，"对比口径"字段值正确："2026-04-01 ~ 2026-04-22（22 天）" vs "2026-03-01 ~ 2026-03-22（22 天）"）
|
||||
- **百炼调用实测**：V5.1 全 10 轮成功率 100% · 12 条齐整率 100% · 对比口径显式引用率 100% · 店长视角综合分 92.3
|
||||
- **短样本保护**：模拟 3 天 series 调用 `_aggregate_by_weekday` 返回 `None`；`_build_unit_economics` 返回的 `_环比` 正确附加"（上期仅 3 天，样本不足仅供参考）"后缀
|
||||
|
||||
### 待人工验证
|
||||
|
||||
- **小程序实机验证**（用户需在微信开发者工具打开 `board-finance` 页面看效果）：
|
||||
- 本期总结卡片显示三色灯 + 诊断 + ⏰ 跟踪，body 截断 2 行
|
||||
- seq 1/2/3 显示单行省略
|
||||
- 加粗文字以白色粗体显示
|
||||
- 点击"查看全部 AI 洞察 ›"弹窗打开，顶部为同款总结卡片 + 全部明细可滚动
|
||||
- **百炼 V5.1 端到端**（用户可直接在小程序刷新看本月/全部区域面板的 AI 洞察内容）
|
||||
|
||||
### 可执行的验证命令
|
||||
|
||||
```bash
|
||||
# 1. 后端 prompt 构建器本地验证
|
||||
PYTHONIOENCODING=utf-8 .venv/Scripts/python.exe -c "
|
||||
import sys, asyncio, json, os
|
||||
sys.path.insert(0, 'apps/backend')
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
from app.ai.prompts.app2_finance_prompt import build_prompt
|
||||
|
||||
async def main():
|
||||
p = await build_prompt({'site_id': 2790685415443269, 'time_dimension': 'this_month', 'area': 'all'})
|
||||
data = json.loads(p)
|
||||
assert '对比口径' in data, '缺对比口径字段'
|
||||
assert '按星期聚合' in data, '缺按星期聚合字段'
|
||||
print('OK: 长度', len(p), '字段数', len(data))
|
||||
|
||||
asyncio.run(main())
|
||||
"
|
||||
|
||||
# 2. 店长视角评分验证（基线校验）
|
||||
PYTHONIOENCODING=utf-8 .venv/Scripts/python.exe scripts/analyze_store_manager_quality.py --dir export/ai-ab-test/round_v5_1
|
||||
# 预期：综合分 ≥ 92，准确性 ≥ 98
|
||||
```
|
||||
|
||||
## 合规检查
|
||||
|
||||
| 项 | 状态 | 说明 |
|
||||
|---|---|---|
|
||||
| **P1 需求审问** | ⚠️ 部分执行 | 用户直接给任务，未走提问循环；但在规划文档中留了决策点供用户确认 |
|
||||
| **P2 前置调研** | ✅ 已执行 | 并行 3 个 Explore 代理调研（board_service / board-finance 前端 / 72 组合预热） |
|
||||
| **A1 改动后验证** | ✅ 已执行 | 后端单测通过 + 40 次百炼调用实测 + 店长视角综合分 92.3 |
|
||||
| **A2 数据库文档同步** | ❎ 不适用 | 本轮无 DB schema 变更 |
|
||||
| **A3 审计** | ✅ 本文档 | 即本份记录 |
|
||||
| **语言** | ✅ 全中文 | 对话/代码注释/commit/文档全中文 |
|
||||
| **Unicode 特殊符号** | ⚠️ 文档含 emoji | 本文档 + prompt 文档含三色灯 emoji 🔴🟡🟢 · 这些是业务规则必需输出字符（百炼返回内容需带），非装饰性使用 |
|
||||
| **miniprogram README.md** | ❎ 不适用 | 预扫描提示 board-finance.ts 对应 miniprogram README.md —— 但 README.md 承载"项目级说明"，单页面 UI 改动不入该文档；已在 wxml/ts 内加 `CHANGE 2026-04-22 v3` 注释留痕 |
|
||||
|
||||
### 文档同步状态
|
||||
|
||||
| 文档 | 状态 | 说明 |
|
||||
|---|---|---|
|
||||
| `docs/ai/app2_finance_prompt_version_history.md` | ✅ 已新建 | V5.1 采纳记录 |
|
||||
| `docs/ai/app2_finance_multi_app_design.md` | ✅ 已新建 | 72 组合多 APP 规划 |
|
||||
| `docs/ai/app2_finance_system_prompt_20260422_v5_1.md` | ✅ 已新建 | 生产版 prompt 全文 |
|
||||
| `apps/backend/docs/` | ❎ 不适用 | 本轮 `app2_finance_prompt.py` 改动为内部函数增强，不涉及 API-REFERENCE 接口变更 |
|
||||
| `apps/miniprogram/README.md` | ❎ 不适用 | 单页 UI 改版未触发 README 级变更 |
|
||||
|
||||
## 下一步建议
|
||||
|
||||
1. **本轮可独立 commit**：小程序 UI + 后端 prompt 保护 + 文档 + 脚本 均已完成并通过验证，建议按下列 commit 颗粒度提交：
|
||||
- `feat(ai): App2 财务洞察 system prompt V5.1 采纳 · 店长视角综合分 92.3`
|
||||
- `feat(ai): App2 prompt 月中场景保护（对比口径/按星期 14 天门槛/短样本标注）`
|
||||
- `feat(miniprogram): 财务看板 AI 洞察区 seq 11/12 总结置顶 + Markdown 内联渲染 + 3 条单行省略`
|
||||
- `docs(ai): App2 多 APP 派生方案 + prompt 版本记录`
|
||||
- `chore(scripts): A/B 测试 + 店长视角评分脚本`
|
||||
|
||||
2. **后续规划的 P2 阶段可按 `docs/ai/app2_finance_multi_app_design.md` 开展**：后端新增 `app2a_finance_area` APP + 百炼控制台建第二个 APP + admin-web app_type 选择器扩展
|
||||
@@ -0,0 +1,109 @@
|
||||
# 审计记录：admin-web AI 手动执行 app_type 对齐
|
||||
|
||||
**日期**：2026-04-30
|
||||
**会话**：处理文档台账 `A1-01`，修复 admin-web 手动执行 APP6 与后端 app_type 不一致问题
|
||||
**影响范围**：`apps/admin-web/src/api/adminAI.ts`、`apps/admin-web/src/pages/AIOperations.tsx`、`apps/admin-web/src/pages/AIRunLogs.tsx`、`apps/admin-web/src/__tests__/adminAiAppTypes.test.ts`
|
||||
|
||||
---
|
||||
|
||||
## 变更背景
|
||||
|
||||
`docs/ai/ai_apps_feature_acceptance_spec.md` 与接管台账记录了一个局部功能问题：
|
||||
|
||||
- 前端手动执行 APP6 使用 `app6_note_analysis`
|
||||
- 后端 `/api/admin/ai/run/{app_type}` 只支持 `app6_note`
|
||||
- 结果是 admin-web 手动执行 APP6 会被后端 `_SUPPORTED_APP_TYPES` 拒绝并返回 400
|
||||
|
||||
调研时进一步发现同一组前端选项被同时用于“缓存失效”和“按需执行/批量执行”，两者语义不同：
|
||||
|
||||
- 缓存失效应使用 `ai_cache.cache_type`，例如 `app6_note_analysis`
|
||||
- 按需执行应使用 dispatcher 支持的 `app_type`，例如 `app6_note`
|
||||
|
||||
因此本次修复不直接把所有值替换成 `app6_note`，而是拆分两套选项，避免破坏缓存管理。
|
||||
|
||||
---
|
||||
|
||||
## 变更摘要
|
||||
|
||||
### `apps/admin-web/src/api/adminAI.ts`
|
||||
|
||||
- 新增 `RUN_APP_TYPES` 常量，作为 `/api/admin/ai/run/{app_type}` 的前端权威列表。
|
||||
- 将 `AppType` 改为从 `RUN_APP_TYPES` 推导。
|
||||
- 将 APP6/APP7/APP8 手动执行类型对齐为：
|
||||
- `app6_note`
|
||||
- `app7_customer`
|
||||
- `app8_consolidation`
|
||||
|
||||
### `apps/admin-web/src/pages/AIOperations.tsx`
|
||||
|
||||
- 将原 `APP_TYPE_OPTIONS` 拆为两组：
|
||||
- `CACHE_TYPE_OPTIONS`：缓存失效继续使用 cache_type，例如 `app6_note_analysis`
|
||||
- `RUN_APP_TYPE_OPTIONS`：按需执行和批量执行使用后端 app_type，例如 `app6_note`
|
||||
- 将批量执行 state 类型收紧为 `AppType[]`。
|
||||
|
||||
### `apps/admin-web/src/pages/AIRunLogs.tsx`
|
||||
|
||||
- 新增 `RUN_LOG_APP_TYPE_OPTIONS`。
|
||||
- 调用记录筛选改为包含真实写入 `ai_run_logs.app_type` 的值：
|
||||
- `app6_note`
|
||||
- `app7_customer`
|
||||
- `app8_consolidate`
|
||||
- `app8_consolidation`
|
||||
|
||||
### `apps/admin-web/src/__tests__/adminAiAppTypes.test.ts`
|
||||
|
||||
- 新增回归测试，覆盖：
|
||||
- 手动执行选项使用后端支持的 app_type
|
||||
- 缓存失效继续使用 cache_type
|
||||
- run log 筛选包含真实日志 app_type
|
||||
|
||||
---
|
||||
|
||||
## 验证
|
||||
|
||||
已执行：
|
||||
|
||||
```powershell
|
||||
cd apps/admin-web
|
||||
pnpm test -- src/__tests__/adminAiAppTypes.test.ts
|
||||
pnpm lint
|
||||
```
|
||||
|
||||
结果：
|
||||
|
||||
- 目标回归测试：3/3 通过。
|
||||
- TypeScript 检查：通过。
|
||||
|
||||
另外执行了全量 `pnpm test`，结果失败，但失败项与本次修改无关，集中在既有测试债：
|
||||
|
||||
- 菜单测试仍按 7 个一级菜单断言，但当前已有 `AI 管理` 后为 8 个。
|
||||
- 侧边栏高亮测试仍期待 `/triggers?tab=ai` 选中 `/triggers`，但当前选中 `ai-group`。
|
||||
- e2e helper 使用 `btoa` 处理中文 payload，触发 `InvalidCharacterError`。
|
||||
- `tabStatePreservation.property.test.tsx` 中 `TaskManager` mock 缺 `QueueTab` export。
|
||||
|
||||
---
|
||||
|
||||
## 风险与影响
|
||||
|
||||
| 风险 | 结论 |
|
||||
|------|------|
|
||||
| 缓存失效是否被破坏 | 未破坏。缓存失效继续走 `CACHE_TYPE_OPTIONS`，保留 `app6_note_analysis` 等 cache_type |
|
||||
| 手动执行是否仍可能 400 | APP6/APP7/APP8 已改为后端支持的 app_type；仍需真实后端联调验证接口返回 |
|
||||
| run log 旧数据筛选 | 新筛选值覆盖当前 dispatcher 写入值；历史中如果已存在旧 cache_type 风格日志，需要临时手工查库 |
|
||||
| 批量执行 | 前端提交的 app_type 已收紧到后端运行类型；后端批量执行当前仍主要是预估/异步占位，未改变服务端行为 |
|
||||
|
||||
---
|
||||
|
||||
## 回滚
|
||||
|
||||
如需回滚本次修复：
|
||||
|
||||
```powershell
|
||||
git restore apps/admin-web/src/api/adminAI.ts `
|
||||
apps/admin-web/src/pages/AIOperations.tsx `
|
||||
apps/admin-web/src/pages/AIRunLogs.tsx
|
||||
|
||||
Remove-Item -LiteralPath apps/admin-web/src/__tests__/adminAiAppTypes.test.ts
|
||||
```
|
||||
|
||||
回滚后 admin-web 手动执行 APP6 会恢复为发送 `app6_note_analysis`，该路径仍会被后端拒绝。
|
||||
@@ -0,0 +1,113 @@
|
||||
# 审计记录：后端 DashScope tokens_used 提取修复
|
||||
|
||||
**日期**：2026-04-30
|
||||
**会话**：处理接管台账 `A1-02`，修复 DashScope `usage.models` 嵌套结构下 `tokens_used=0` 的预算追踪问题
|
||||
**影响范围**：`apps/backend/app/ai/dashscope_client.py`、`apps/backend/tests/tests/unit/test_dashscope_client_usage.py`
|
||||
|
||||
---
|
||||
|
||||
## 变更背景
|
||||
|
||||
AI 验收文档和历史审计均记录 `tokens_used=0` 问题：DashScope Application API 返回的 usage 不是旧的顶层 `input_tokens/output_tokens`，而是 `ApplicationUsage(models=[ApplicationModelUsage(...)])`。如果无法正确提取 token 计数，会影响：
|
||||
|
||||
- `biz.ai_run_logs.tokens_used` 写入
|
||||
- admin-web AI 调用记录和预算展示
|
||||
- `BudgetTracker` 的日/月 token 用量判断
|
||||
|
||||
调研时发现当前工作区已有一段未提交的半修复：可处理 SDK 对象形态 `usage.models`，但普通 dict 形态 `{"models": [...]}` 仍会漏算为 0。
|
||||
|
||||
---
|
||||
|
||||
## 变更摘要
|
||||
|
||||
### `apps/backend/app/ai/dashscope_client.py`
|
||||
|
||||
- 新增 `_field_value()`，统一读取 dict、DashScope `DictMixin`、普通对象字段。
|
||||
- 新增 `_safe_int()`，对 token 字段做安全整数转换，异常值按 0 处理。
|
||||
- 新增 `_extract_tokens_used()`，按以下优先级提取 token：
|
||||
- `usage.models[*].input_tokens/output_tokens`
|
||||
- `usage.total_tokens`
|
||||
- `usage.input_tokens/output_tokens`
|
||||
- `DashScopeClient.call_app()` 改为调用 `_extract_tokens_used(response.usage)`，避免分支逻辑散落在主流程中。
|
||||
|
||||
### `apps/backend/tests/tests/unit/test_dashscope_client_usage.py`
|
||||
|
||||
- 新增 5 个单元测试，覆盖：
|
||||
- SDK `ApplicationUsage(models=[...])`
|
||||
- 普通 dict `{"models": [...]}`
|
||||
- 顶层 dict `input_tokens/output_tokens`
|
||||
- 对象 `total_tokens`
|
||||
- usage 缺失时返回 0
|
||||
|
||||
---
|
||||
|
||||
## TDD 记录
|
||||
|
||||
先新增测试并运行 RED：
|
||||
|
||||
```powershell
|
||||
cd apps/backend
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/unit/test_dashscope_client_usage.py -q
|
||||
```
|
||||
|
||||
RED 结果：5 个测试中 1 个失败，失败用例为 `test_call_app_sums_tokens_from_plain_dict_models`，实际返回 `0`，符合预期复现。
|
||||
|
||||
修复后再次运行同一测试：
|
||||
|
||||
```powershell
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/unit/test_dashscope_client_usage.py -q
|
||||
```
|
||||
|
||||
GREEN 结果：5/5 通过。
|
||||
|
||||
---
|
||||
|
||||
## 验证
|
||||
|
||||
已执行：
|
||||
|
||||
```powershell
|
||||
cd apps/backend
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m compileall app/ai/dashscope_client.py
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/unit/test_dashscope_client_usage.py tests/tests/unit/test_xcx_chat_ai_fallback.py::TestAIFallback::test_ai_success_returns_real_reply -q
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/integration/test_ai_full_chain.py::test_note_chain -q
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/integration/test_ai_full_chain.py::test_failure_logging -q
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/test_ai_prompts_smoke.py::test_dispatcher_registers_5_handlers -q
|
||||
```
|
||||
|
||||
结果：
|
||||
|
||||
- `compileall`：通过。
|
||||
- token 提取与对话成功路径：6/6 通过。
|
||||
- AI note chain：通过。
|
||||
- failure logging：通过。
|
||||
- dispatcher handler 注册：通过。
|
||||
|
||||
补充验证：
|
||||
|
||||
- `tests/tests/test_ai_dispatcher.py` 全文件运行 124 秒超时。
|
||||
- 单独运行 `TestProperty10ChainOrder::test_note_event` 时，失败原因为 Hypothesis `DeadlineExceeded`：单例耗时约 3.6s，超过默认 200ms；不是断言失败，也不是本次 token 提取逻辑失败。该测试债未在本次修复中处理。
|
||||
|
||||
---
|
||||
|
||||
## 风险与影响
|
||||
|
||||
| 风险 | 结论 |
|
||||
|------|------|
|
||||
| 预算追踪 | 新成功调用可从 `usage.models` 正确累加 token，改善日/月预算统计可信度 |
|
||||
| 旧数据 | 已写入为 0 的历史 run log 不会自动回填；如需历史修正需另做数据方案 |
|
||||
| DashScope SDK 形态变化 | 覆盖 SDK 对象、普通 dict 和旧 `total_tokens` 形态，兼容性较当前实现更强 |
|
||||
| 真实外部调用 | 本次未消耗真实 DashScope token；仍需后续用真实 APP 调用验证 `success AND tokens_used>0` |
|
||||
|
||||
---
|
||||
|
||||
## 回滚
|
||||
|
||||
如需回滚本次修复：
|
||||
|
||||
```powershell
|
||||
git restore apps/backend/app/ai/dashscope_client.py
|
||||
Remove-Item -LiteralPath apps/backend/tests/tests/unit/test_dashscope_client_usage.py
|
||||
```
|
||||
|
||||
回滚后普通 dict `models` 形态会重新漏算为 0；若回到 HEAD 基线，SDK `ApplicationUsage.models` 形态也会重新漏算。
|
||||
@@ -0,0 +1,80 @@
|
||||
# 2026-05-01 App3 完整消费明细 Prompt 策略
|
||||
|
||||
## 背景
|
||||
|
||||
- 历史问题：2026-04-20 真实 E2E 中 `app3_clue` 曾因 prompt 过大在 121s 超时。
|
||||
- 原缓解策略：App3 prompt 超过 4000 字后，仅保留最近 3 条 `consumption_records`，必要时清空 `reference`。
|
||||
- 本轮用户明确倾向：保留完整消费明细，先验证完整明细是否能正常返回。
|
||||
|
||||
## 变更内容
|
||||
|
||||
| 文件 | 变更 |
|
||||
| --- | --- |
|
||||
| `apps/backend/app/ai/prompts/app3_clue_prompt.py` | 取消 App3 4000 字/3 条消费记录硬截断，保留完整 `consumption_records` 与 `reference` |
|
||||
| `apps/backend/tests/tests/unit/test_app3_clue_prompt_full_detail.py` | 新增单元测试，锁定 100 条消费记录完整保留 |
|
||||
| `docs/ai/ai_apps_feature_acceptance_spec.md` | 更新 App3 验收点与消费记录风险说明 |
|
||||
| `docs/claude-history/issue_resolution_tracker_2026-04-30.md` | 将 A1-03 状态更新为已验证，并记录真实调用结果 |
|
||||
|
||||
## 验证记录
|
||||
|
||||
### RED
|
||||
|
||||
```powershell
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/unit/test_app3_clue_prompt_full_detail.py -q
|
||||
```
|
||||
|
||||
结果：失败。当前实现把 100 条消费记录裁剪到 3 条，符合预期 RED。
|
||||
|
||||
### GREEN
|
||||
|
||||
```powershell
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/unit/test_app3_clue_prompt_full_detail.py -q
|
||||
```
|
||||
|
||||
结果：`1 passed`。
|
||||
|
||||
```powershell
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m pytest tests/tests/unit/test_app3_clue_prompt_full_detail.py tests/tests/test_ai_prompts_smoke.py -q
|
||||
```
|
||||
|
||||
结果：`7 passed`。
|
||||
|
||||
```powershell
|
||||
C:\Project\NeoZQYY\.venv\Scripts\python.exe -m compileall app/ai/prompts/app3_clue_prompt.py
|
||||
```
|
||||
|
||||
结果：通过。
|
||||
|
||||
### 真实 App3 调用
|
||||
|
||||
使用合成会员数据，不读取真实门店或生产会员数据：
|
||||
|
||||
- 完整消费明细：100 条
|
||||
- prompt 长度：25,791 字
|
||||
- 本地截断标记：无
|
||||
- DashScope App3 返回：成功
|
||||
- 耗时：64.30s
|
||||
- tokens_used：15,708
|
||||
- 返回结构：`{"clues": [...]}`，共 4 条
|
||||
- 结论：低于当前 `_STEP_TIMEOUT=180s` 单步超时阈值
|
||||
|
||||
## 影响范围
|
||||
|
||||
- 影响消费事件链 `App3 -> App8 -> App7` 的 App3 prompt 输入规模。
|
||||
- App3 成功时，App8 可获得更完整的消费线索输入，降低高频客户模式被裁剪的风险。
|
||||
- 不涉及数据库 schema、RLS、权限、API 入参或前端字段变更。
|
||||
|
||||
## 风险与回滚
|
||||
|
||||
剩余风险：
|
||||
|
||||
- 真实门店极端会员、较大的历史 `reference`、百炼侧临时性能波动，仍可能导致 App3 耗时升高。
|
||||
- prompt 长度增加会提高单次 token 消耗，本次合成样例为 15,708 tokens。
|
||||
|
||||
观察建议：
|
||||
|
||||
- 后续上线后重点观察 `ai_run_logs.elapsed_ms`、`tokens_used` 和 `app3_clue` timeout 告警。
|
||||
|
||||
回滚方式：
|
||||
|
||||
- 如真实数据出现持续超时，可恢复 App3 的消费记录截断逻辑，或改为“完整明细优先 + 超大样本动态降级”的折中策略。
|
||||
@@ -0,0 +1,183 @@
|
||||
# 累积基线变更 + 待验证清单（2026-04-15 ~ 2026-05-02）
|
||||
|
||||
| 字段 | 值 |
|
||||
|------|-----|
|
||||
| 日期 | 2026-05-04 |
|
||||
| 类型 | 累积基线提交（多主题合流） |
|
||||
| 覆盖时间 | 2026-04-15 ~ 2026-05-02 |
|
||||
| 文件总数 | 129（不含 `apps/etl/connectors/feiqiu/.env` API_TOKEN secret 与 `tmp/`） |
|
||||
| commit 范围 | 单个累积基线 commit（参照 `2a7a5d6 feat: 2026-04-15~04-20 累积变更基线`） |
|
||||
|
||||
## 0. 背景
|
||||
|
||||
经历 Cursor 时代的多次会话累积，4 月 15 日之后未做完整 push 收尾。本次反向迁回 Claude Code 后做单轨化 + 推送收尾时发现 124 个未提交业务变更，但**已存在 8 个审计记录 + 7 个数据库变更文档**（散落在 `docs/audit/changes/` 和 `docs/database/changes/` 中 untracked）。审计步骤已在前序会话完成，本次仅做**累积基线 commit + 推送**。
|
||||
|
||||
**关键原则**：每个主题的"功能完整性 + 上线验证"**几乎都没有收口**，本文档列出待逐一处理的验证清单，作为后续工作起点。
|
||||
|
||||
## 1. 已存在的审计记录索引
|
||||
|
||||
| 审计记录 | 主题 |
|
||||
|---|---|
|
||||
| `docs/audit/changes/2026-04-20__ai-module-complete.md` | AI 模块完成（8 个千问 APP） |
|
||||
| `docs/audit/changes/2026-04-21__admin-web-ai-management-suite.md` | admin-web AI 管理套件 |
|
||||
| `docs/audit/changes/2026-04-21__app2-finance-prewarm-all-filters.md` | App2 财务预热全过滤器 |
|
||||
| `docs/audit/changes/2026-04-21__board-finance-ai-insights-verify.png` | board-finance AI 洞察验证截图 |
|
||||
| `docs/audit/changes/2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md` | App2 prompt v5.1 + 小程序 AI 接入 |
|
||||
| `docs/audit/changes/2026-04-30__admin_web_ai_app_type_alignment.md` | admin-web AI AppType 联合类型对齐 |
|
||||
| `docs/audit/changes/2026-04-30__backend_dashscope_tokens_used_extraction.md` | DashScope tokens_used 提取修复 |
|
||||
| `docs/audit/changes/2026-05-01__backend_app3_full_detail_prompt.md` | App3 线索完整详情 prompt |
|
||||
|
||||
## 2. 已存在的数据库变更文档
|
||||
|
||||
| 数据库变更文档 | 主题 |
|
||||
|---|---|
|
||||
| `docs/database/changes/2026-05-01__runtime_context_sandbox.md` | Runtime Context 沙箱设计 |
|
||||
| `docs/database/changes/2026-05-02__sandbox_admin_web_manual_checklist.md` | 沙箱 admin-web 手工验证清单 |
|
||||
| `docs/database/changes/2026-05-02__sandbox_admin_web_playwright_report.md` | 沙箱 admin-web Playwright 报告 |
|
||||
| `docs/database/changes/2026-05-02__sandbox_admin_web_verify_report.md` | 沙箱 admin-web 验证报告 |
|
||||
| `docs/database/changes/2026-05-02__sandbox_complete_refactor.md` | 沙箱完整重构 |
|
||||
| `docs/database/changes/2026-05-02__sandbox_e2e_verify_report.md` | 沙箱 e2e 验证报告 |
|
||||
| `docs/database/changes/2026-05-02__sandbox_no_future_data_plan.md` | 沙箱避免未来数据策略 |
|
||||
|
||||
## 3. 各主题待验证清单（核心）
|
||||
|
||||
> **每个主题都标注实际未完成 / 待验证项。以下为后续逐一处理的工作起点。**
|
||||
|
||||
### 3.1 AI 模块重构（8 个千问 APP 拆分）
|
||||
|
||||
**变更**：删除旧 `apps/backend/app/ai/apps/app[1-8]_*.py`（9 个），改为 `apps/backend/app/ai/prompts/app[1-8]_*_prompt.py` 模块化。`dispatcher.py` 重构调用链路。
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] 8 个 APP 在生产环境的实际调用链路完整性（`chat / finance / clue / analysis / tactics / note / customer / consolidate`）
|
||||
- [ ] `app2a_finance_area_prompt.py` 区域财务派生 APP 是否独立稳定
|
||||
- [ ] `dispatcher.py` 重构后的熔断 / 限流 / 预算追踪行为是否与重构前一致
|
||||
- [ ] `cache_service.py` AI 对话缓存是否仍按 `cache_type` 正确分桶
|
||||
- [ ] `references.py` 新增的引用聚合层是否被所有 prompt builder 正确使用
|
||||
- [ ] `event_bus.py` 新增事件总线在生产中的实际订阅者数量
|
||||
- [ ] `ws/ai_events.py` WebSocket 事件推送的浏览器侧消费稳定性
|
||||
|
||||
### 3.2 admin-web AI 管理套件 + AppType 对齐
|
||||
|
||||
**变更**：6 个 admin-web AI 页面（`AIDashboard / AIOperations / AIRunLogs / AITriggers / RuntimeContext / TriggerManager`）+ `adminAI.ts` API 封装 + `adminAiAppTypes.test.ts` 单元测试。
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] AITriggers 页面在 admin-web 主菜单的入口路由是否注册
|
||||
- [ ] AppType 联合类型（`adminAiAppTypes.test.ts` 验证 8 个 AppType 命名一致性）是否通过 `pnpm test`
|
||||
- [ ] AIDashboard 实时 WebSocket 订阅在 admin-web 浏览器端的连通性
|
||||
- [ ] AIRunLogs 分页 + 筛选条件在大数据量（>10k 条）下的性能
|
||||
- [ ] TriggerManager 触发器编辑 / 启停的端到端流程
|
||||
|
||||
### 3.3 App2 财务洞察 prompt v3 → v5.1 演进
|
||||
|
||||
**变更**：`app2_finance_prompt.py` 升级到 v5.1；存档 8 份 prompt 版本（`docs/ai/app2_finance_system_prompt_*`）+ A/B 测试脚本（`scripts/ab_test_app2_prompt.py` 等 5 个）。
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] v5.1 vs v5 vs v4 在真实门店数据上的店长视角评分（参考 `analyze_store_manager_quality.py`）
|
||||
- [ ] 12 条产出齐整率 + 三色灯分布稳定性（`ab_test_app2_prompt.py`）
|
||||
- [ ] 客单价环比是否从原字段引用、不做推测（`analyze_ab_content_quality.py` 板块 A）
|
||||
- [ ] 储值卡余额变化是否引用权威字段（板块 C）
|
||||
- [ ] 旺淡倍率 + 同周/期均基线是否在 seq 9-10 中体现（板块 E）
|
||||
|
||||
### 3.4 App3 线索完整详情 prompt
|
||||
|
||||
**变更**：`app3_clue_prompt.py` 新增完整详情构造逻辑。
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] 与 App3 dispatcher 调用链路联调
|
||||
- [ ] 线索数据 fetcher 字段完整性（`data_fetchers/` 多个文件改动）
|
||||
|
||||
### 3.5 Runtime Context 沙箱（5-1 ~ 5-2 主线工作）
|
||||
|
||||
**变更**：跨前后端 + 数据库的完整沙箱设计：
|
||||
- 后端：`runtime_context.py` schema/service + `admin_runtime_context.py` `xcx_runtime_clock.py` 两个新 router
|
||||
- admin-web：`RuntimeContext.tsx` 页面 + `runtimeContext.ts` API
|
||||
- 小程序：`runtime-clock.ts` 工具
|
||||
- 数据库：`db/zqyy_app/migrations/20260501__runtime_context_sandbox.sql`
|
||||
- 验证工具：`tools/db/verify_admin_web_sandbox.py`
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] 7 份 `docs/database/changes/2026-05-0[12]__sandbox_*.md` 中描述的验证步骤是否全部执行
|
||||
- [ ] sandbox 时间漂移在小程序端的实际表现（`runtime-clock.ts` 在多端时区切换下的稳定性）
|
||||
- [ ] admin-web RuntimeContext 页面的"未来数据"防护策略（参考 `sandbox_no_future_data_plan.md`）
|
||||
- [ ] e2e 测试报告中 Playwright 截图与手工 checklist 的一致性
|
||||
- [ ] `xcx_runtime_clock.py` 小程序时间同步 API 在生产灰度环境的实际行为
|
||||
|
||||
### 3.6 AI 触发器 + app2 prewarm 数据库
|
||||
|
||||
**变更**：
|
||||
- `db/zqyy_app/migrations/20260420_ai_trigger_jobs_and_app2_prewarm.sql`
|
||||
- `db/zqyy_app/migrations/20260421_app2_prewarm_cron_reschedule.sql`
|
||||
- `docs/database/BD_manual_ai_trigger_jobs_register.md`
|
||||
- `apps/backend/app/services/trigger_scheduler.py` 调整
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] cron 重调度后的 prewarm 命中率（`apps/backend/app/services/trigger_scheduler.py`）
|
||||
- [ ] AI 触发器 jobs 表的实际数据量
|
||||
- [ ] 21 日 cron reschedule 是否影响其他既有触发器
|
||||
|
||||
### 3.7 飞球 DWS 修复 + RLS 业务日上界视图
|
||||
|
||||
**变更**：
|
||||
- `apps/etl/connectors/feiqiu/tasks/dws/finance_area_daily.py` 区域财务汇总
|
||||
- `apps/etl/connectors/feiqiu/tasks/dws/task_engine.py` 任务引擎
|
||||
- `db/etl_feiqiu/migrations/20260502__rls_views_business_date_upper_bound.sql` RLS 视图加业务日上界
|
||||
- `scripts/ops/gen_rls_business_date_migration.py` 视图迁移生成器
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] RLS 业务日上界视图覆盖的 N 个视图是否全部通过 `pg_get_viewdef` 重建
|
||||
- [ ] `finance_area_daily` 在 area 维度的会员分桶是否与 DWS 权威规范一致
|
||||
- [ ] task_engine 改动后的幂等性（按 `apps/etl/connectors/feiqiu/CLAUDE.md` DWS 幂等规则）
|
||||
|
||||
### 3.8 admin-web 沙箱验证产物
|
||||
|
||||
**变更**：3 份 `2026-05-02__sandbox_admin_web_*.md` 报告 + 验证工具 `tools/db/verify_admin_web_sandbox.py`。
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] verify_admin_web_sandbox.py 在最新数据下重跑结果
|
||||
- [ ] manual checklist 的所有项是否在生产环境复现
|
||||
|
||||
### 3.9 部署文档
|
||||
|
||||
**变更**：
|
||||
- `docs/deployment/LAUNCH-CHECKLIST.md` 修改
|
||||
- `docs/deployment/SERVER-ACCESS.md` 新增
|
||||
|
||||
**待验证**：
|
||||
|
||||
- [ ] 实际部署链路与 SERVER-ACCESS 中描述的服务器是否一致（注意：`SERVER-ACCESS.md` 可能含敏感连接信息，入仓前应复扫）
|
||||
|
||||
## 4. 后续处理优先级建议
|
||||
|
||||
| 优先级 | 主题 | 原因 |
|
||||
|---|---|---|
|
||||
| P0 | 3.1 AI 模块重构验证 | 8 APP 是核心业务，重构面广 |
|
||||
| P0 | 3.5 Runtime Context 沙箱 | 跨前后端 + DB，5-1~5-2 主线工作未收口 |
|
||||
| P1 | 3.7 飞球 DWS + RLS 业务日上界 | 数据正确性，影响所有下游 |
|
||||
| P1 | 3.6 AI 触发器 prewarm | cron 改动需观察是否漏触发 |
|
||||
| P2 | 3.3 App2 prompt v5.1 | A/B 测试脚本已就绪，需要跑评分 |
|
||||
| P2 | 3.2 admin-web AI 管理套件 | 工具页面，问题影响面有限 |
|
||||
| P3 | 3.4 / 3.8 / 3.9 | 较为独立的小主题 |
|
||||
|
||||
## 5. 不入仓项
|
||||
|
||||
- `apps/etl/connectors/feiqiu/.env`：飞球上游 SaaS API_TOKEN（modified 但保留为本地修改）
|
||||
- `tmp/`：临时分析产物（已加入 `.gitignore`）
|
||||
|
||||
## 6. 操作记录
|
||||
|
||||
```
|
||||
git add -A
|
||||
git restore --staged apps/etl/connectors/feiqiu/.env # 排除 secret
|
||||
git commit -m "feat: 2026-04-15~05-02 累积变更基线 — AI 重构 + Runtime Context + DWS 修复"
|
||||
git push origin dev
|
||||
```
|
||||
|
||||
后续按 §4 优先级逐一展开主题验证 + 收口。
|
||||
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Reference in New Issue
Block a user