wehub-resource-sync
108 lines
3.3 KiB
TypeScript
108 lines
3.3 KiB
TypeScript
/**
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* Query data tool implementation — returns mock financial data.
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*
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* TypeScript equivalent of showcase/shared/python/tools/query_data.py.
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* In the future this could read a CSV, but for TS backend simplicity
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* we use generated mock data matching the Python fallback format.
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*/
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export interface DataRow {
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date: string;
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category: string;
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subcategory: string;
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amount: string;
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type: string;
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notes: string;
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}
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// Seeded random for deterministic mock data
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function seededRandom(seed: number): () => number {
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let s = seed;
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return () => {
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s = (s * 1103515245 + 12345) & 0x7fffffff;
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return s / 0x7fffffff;
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};
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}
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function generateMockData(): DataRow[] {
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const rand = seededRandom(42);
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const categories: Array<{
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category: string;
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subcategory: string;
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type: string;
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}> = [
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{
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category: "Revenue",
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subcategory: "Enterprise Subscriptions",
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type: "income",
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},
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{ category: "Revenue", subcategory: "Pro Tier Upgrades", type: "income" },
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{ category: "Revenue", subcategory: "API Usage Overages", type: "income" },
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{ category: "Revenue", subcategory: "Consulting Services", type: "income" },
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{ category: "Revenue", subcategory: "Marketplace Sales", type: "income" },
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{
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category: "Expenses",
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subcategory: "Engineering Salaries",
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type: "expense",
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},
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{ category: "Expenses", subcategory: "Product Team", type: "expense" },
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{
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category: "Expenses",
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subcategory: "AWS Infrastructure",
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type: "expense",
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},
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{ category: "Expenses", subcategory: "Marketing", type: "expense" },
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{ category: "Expenses", subcategory: "Customer Success", type: "expense" },
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{ category: "Expenses", subcategory: "AI Model Costs", type: "expense" },
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];
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const notes: Record<string, string> = {
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"Enterprise Subscriptions": "3 new enterprise customers",
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"Pro Tier Upgrades": "31 upgrades + reduced churn",
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"API Usage Overages": "Heavy usage from top-10 accounts",
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"Consulting Services": "2 implementation projects",
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"Marketplace Sales": "Partner integrations revenue",
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"Engineering Salaries": "7 engineers + 2 contractors",
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"Product Team": "PM + designers + QA",
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"AWS Infrastructure": "Compute + storage + bandwidth",
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Marketing: "Paid ads + content + events",
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"Customer Success": "3 CSMs + tooling",
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"AI Model Costs": "OpenAI + Anthropic API spend",
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};
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const rows: DataRow[] = [];
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const months = ["01", "02", "03", "04", "05", "06"];
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for (const month of months) {
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for (const cat of categories) {
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const baseAmount =
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cat.type === "income"
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? 15000 + Math.floor(rand() * 35000)
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: 8000 + Math.floor(rand() * 40000);
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const day = String(1 + Math.floor(rand() * 28)).padStart(2, "0");
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rows.push({
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date: `2026-${month}-${day}`,
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category: cat.category,
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subcategory: cat.subcategory,
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amount: String(baseAmount),
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type: cat.type,
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notes: notes[cat.subcategory] ?? "",
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});
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}
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}
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return rows;
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}
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const MOCK_DATA: DataRow[] = generateMockData();
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/**
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* Query the database. Takes natural language.
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*
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* Always call before showing a chart or graph. Returns the full
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* dataset as a list of row objects.
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*/
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export function queryDataImpl(_query: string): DataRow[] {
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return MOCK_DATA;
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}
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