152 lines
4.0 KiB
TypeScript
152 lines
4.0 KiB
TypeScript
import { describe, expect, it } from 'vitest';
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import { buildModelAnalysis } from './modelAnalysisService.js';
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import { formatLocalDate } from './localTimeService.js';
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const DAY_MS = 24 * 60 * 60 * 1000;
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describe('buildModelAnalysis', () => {
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it('aggregates spend/calls data in a rolling day window', () => {
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const logs = [
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{
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createdAt: '2026-02-24T02:00:00.000Z',
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modelActual: 'gpt-4o',
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modelRequested: null,
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status: 'success',
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latencyMs: 800,
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totalTokens: 1000,
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estimatedCost: 1.25,
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},
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{
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createdAt: '2026-02-24T06:00:00.000Z',
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modelActual: null,
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modelRequested: 'gpt-4o',
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status: 'failed',
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latencyMs: 1200,
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totalTokens: 300,
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estimatedCost: 0.25,
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},
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{
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createdAt: '2026-02-23T08:00:00.000Z',
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modelActual: 'claude-3-5-sonnet',
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modelRequested: null,
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status: 'success',
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latencyMs: 500,
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totalTokens: 2000,
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estimatedCost: 2,
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},
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{
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createdAt: '2026-02-19T10:00:00.000Z',
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modelActual: 'gpt-4.1-mini',
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modelRequested: null,
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status: 'success',
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latencyMs: 200,
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totalTokens: 120,
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estimatedCost: 0.08,
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},
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];
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const now = new Date('2026-02-24T12:00:00.000Z');
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const result = buildModelAnalysis(logs, {
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now,
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days: 3,
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});
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const endDay = new Date(now.getFullYear(), now.getMonth(), now.getDate(), 0, 0, 0, 0);
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const startDay = new Date(endDay.getTime() - 2 * DAY_MS);
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const dayKeys = Array.from({ length: 3 }, (_, index) =>
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formatLocalDate(new Date(startDay.getTime() + index * DAY_MS)),
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);
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const daySet = new Set(dayKeys);
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const spendByDay = new Map<string, number>(dayKeys.map((key) => [key, 0]));
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for (const log of logs) {
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const createdAt = new Date(log.createdAt);
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const dayKey = formatLocalDate(createdAt);
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if (!daySet.has(dayKey)) continue;
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spendByDay.set(dayKey, (spendByDay.get(dayKey) ?? 0) + (log.estimatedCost ?? 0));
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}
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expect(result.window).toEqual({
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start: dayKeys[0],
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end: dayKeys[dayKeys.length - 1],
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days: 3,
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});
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expect(result.totals.calls).toBe(3);
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expect(result.totals.tokens).toBe(3300);
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expect(result.totals.spend).toBe(3.5);
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expect(result.spendTrend).toEqual(dayKeys.map((day) => ({
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day,
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spend: spendByDay.get(day) ?? 0,
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})));
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expect(result.callRanking[0]).toMatchObject({
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model: 'gpt-4o',
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calls: 2,
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successRate: 50,
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});
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});
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it('normalizes unknown model names and invalid numbers', () => {
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const logs = [
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{
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createdAt: '2026-02-24T12:00:00.000Z',
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modelActual: null,
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modelRequested: null,
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status: 'success',
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latencyMs: null,
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totalTokens: null,
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estimatedCost: null,
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},
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{
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createdAt: 'invalid-date',
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modelActual: 'gpt-4o',
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modelRequested: null,
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status: 'success',
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latencyMs: 10,
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totalTokens: 10,
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estimatedCost: 1,
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},
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];
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const result = buildModelAnalysis(logs, {
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now: new Date('2026-02-24T12:00:00.000Z'),
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days: 1,
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});
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expect(result.totals).toEqual({
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calls: 1,
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tokens: 0,
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spend: 0,
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});
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expect(result.callsDistribution[0]).toMatchObject({
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model: 'unknown',
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calls: 1,
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share: 100,
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});
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});
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it('falls back to token-based spend estimation when estimatedCost is missing', () => {
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const logs = [
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{
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createdAt: '2026-02-24T12:30:00.000Z',
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modelActual: 'gpt-4o-mini',
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modelRequested: null,
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status: 'success',
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latencyMs: 120,
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totalTokens: 1000,
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estimatedCost: null,
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},
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];
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const result = buildModelAnalysis(logs, {
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now: new Date('2026-02-24T12:00:00.000Z'),
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days: 1,
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});
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expect(result.totals.spend).toBe(0.002);
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expect(result.spendDistribution[0]).toMatchObject({
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model: 'gpt-4o-mini',
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spend: 0.002,
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calls: 1,
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});
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});
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});
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