680 lines
22 KiB
TypeScript
680 lines
22 KiB
TypeScript
/**
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* Invoice extraction using Nanonets-OCR2-3B + GPT-OSS 20B (sequential two-stage pipeline)
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*
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* Stage 1: Nanonets-OCR2-3B converts ALL document pages to markdown (stop after completion)
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* Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
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*
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* This approach avoids GPU contention by running services sequentially.
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*/
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import { tap, expect } from '@git.zone/tstest/tapbundle';
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import * as fs from 'fs';
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import * as path from 'path';
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import { execSync } from 'child_process';
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import * as os from 'os';
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import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
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const NANONETS_URL = 'http://localhost:8000/v1';
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const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
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const OLLAMA_URL = 'http://localhost:11434';
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const EXTRACTION_MODEL = 'gpt-oss:20b';
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// Temp directory for storing markdown between stages
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const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-markdown');
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interface IInvoice {
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invoice_number: string;
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invoice_date: string;
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vendor_name: string;
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currency: string;
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net_amount: number;
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vat_amount: number;
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total_amount: number;
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}
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interface IImageData {
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base64: string;
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width: number;
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height: number;
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pageNum: number;
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}
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interface ITestCase {
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name: string;
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pdfPath: string;
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jsonPath: string;
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markdownPath?: string;
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}
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// Nanonets-specific prompt for document OCR to markdown
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const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
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Return the tables in html format.
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Return the equations in LaTeX representation.
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If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
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Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
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Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
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// JSON extraction prompt for GPT-OSS 20B (sent AFTER the invoice text is provided)
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const JSON_EXTRACTION_PROMPT = `Extract key fields from the invoice. Return ONLY valid JSON.
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WHERE TO FIND DATA:
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- invoice_number, invoice_date, vendor_name: Look in the HEADER section at the TOP of PAGE 1 (near "Invoice no.", "Invoice date:", "Rechnungsnummer")
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- net_amount, vat_amount, total_amount: Look in the SUMMARY section at the BOTTOM (look for "Total", "Amount due", "Gesamtbetrag")
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RULES:
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1. invoice_number: Extract ONLY the value (e.g., "R0015632540"), NOT the label "Invoice no."
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2. invoice_date: Convert to YYYY-MM-DD format (e.g., "14/04/2022" → "2022-04-14")
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3. vendor_name: The company issuing the invoice
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4. currency: EUR, USD, or GBP
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5. net_amount: Total before tax
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6. vat_amount: Tax amount
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7. total_amount: Final total with tax
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JSON only:
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{"invoice_number":"X","invoice_date":"YYYY-MM-DD","vendor_name":"X","currency":"EUR","net_amount":0,"vat_amount":0,"total_amount":0}`;
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// Constants for smart batching
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const MAX_VISUAL_TOKENS = 28000; // ~32K context minus prompt/output headroom
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const PATCH_SIZE = 14; // Qwen2.5-VL uses 14x14 patches
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/**
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* Estimate visual tokens for an image based on dimensions
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*/
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function estimateVisualTokens(width: number, height: number): number {
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return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
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}
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/**
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* Process images one page at a time for reliability
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*/
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function batchImages(images: IImageData[]): IImageData[][] {
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// One page per batch for reliable processing
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return images.map(img => [img]);
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}
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/**
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* Convert PDF to JPEG images using ImageMagick with dimension tracking
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*/
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function convertPdfToImages(pdfPath: string): IImageData[] {
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const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
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const outputPattern = path.join(tempDir, 'page-%d.jpg');
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try {
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execSync(
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`convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
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{ stdio: 'pipe' }
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);
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const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
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const images: IImageData[] = [];
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for (let i = 0; i < files.length; i++) {
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const file = files[i];
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const imagePath = path.join(tempDir, file);
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const imageData = fs.readFileSync(imagePath);
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// Get image dimensions using identify command
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const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
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const [width, height] = dimensions.split(' ').map(Number);
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images.push({
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base64: imageData.toString('base64'),
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width,
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height,
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pageNum: i + 1,
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});
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}
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return images;
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} finally {
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fs.rmSync(tempDir, { recursive: true, force: true });
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}
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}
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/**
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* Convert a batch of pages to markdown using Nanonets-OCR-s
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*/
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async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
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const startTime = Date.now();
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const pageNums = batch.map(img => img.pageNum).join(', ');
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// Build content array with all images first, then the prompt
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const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
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for (const img of batch) {
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content.push({
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type: 'image_url',
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image_url: { url: `data:image/jpeg;base64,${img.base64}` },
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});
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}
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// Add prompt with page separator instruction if multiple pages
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const promptText = batch.length > 1
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? `${NANONETS_OCR_PROMPT}\n\nPlease clearly separate each page's content with "--- PAGE N ---" markers, where N is the page number starting from ${batch[0].pageNum}.`
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: NANONETS_OCR_PROMPT;
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content.push({ type: 'text', text: promptText });
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const response = await fetch(`${NANONETS_URL}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': 'Bearer dummy',
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},
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body: JSON.stringify({
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model: NANONETS_MODEL,
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messages: [{
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role: 'user',
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content,
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}],
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max_tokens: 4096 * batch.length, // Scale output tokens with batch size
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temperature: 0.0,
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}),
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signal: AbortSignal.timeout(600000), // 10 minute timeout for OCR
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});
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const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
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if (!response.ok) {
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const errorText = await response.text();
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throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
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}
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const data = await response.json();
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let responseContent = (data.choices?.[0]?.message?.content || '').trim();
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// For single-page batches, add page marker if not present
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if (batch.length === 1 && !responseContent.includes('--- PAGE')) {
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responseContent = `--- PAGE ${batch[0].pageNum} ---\n${responseContent}`;
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}
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console.log(` Pages [${pageNums}]: ${responseContent.length} chars (${elapsed}s)`);
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return responseContent;
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}
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/**
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* Convert all pages of a document to markdown using smart batching
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*/
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async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
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const batches = batchImages(images);
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console.log(` [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
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const markdownParts: string[] = [];
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for (let i = 0; i < batches.length; i++) {
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const batch = batches[i];
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const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
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console.log(` Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
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const markdown = await convertBatchToMarkdown(batch);
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markdownParts.push(markdown);
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}
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const fullMarkdown = markdownParts.join('\n\n');
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console.log(` [${docName}] Complete: ${fullMarkdown.length} chars total`);
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return fullMarkdown;
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}
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/**
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* Stop Nanonets container
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*/
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function stopNanonets(): void {
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console.log(' [Docker] Stopping Nanonets container...');
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try {
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execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
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execSync('sleep 5', { stdio: 'pipe' });
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console.log(' [Docker] Nanonets stopped');
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} catch {
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console.log(' [Docker] Nanonets was not running');
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}
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}
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/**
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* Ensure GPT-OSS 20B model is available
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*/
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async function ensureExtractionModel(): Promise<boolean> {
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try {
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const response = await fetch(`${OLLAMA_URL}/api/tags`);
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if (response.ok) {
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const data = await response.json();
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const models = data.models || [];
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if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
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console.log(` [Ollama] Model available: ${EXTRACTION_MODEL}`);
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return true;
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}
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}
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} catch {
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return false;
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}
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console.log(` [Ollama] Pulling ${EXTRACTION_MODEL}...`);
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const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
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});
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return pullResponse.ok;
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}
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/**
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* Parse amount from string (handles European format)
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*/
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function parseAmount(s: string | number | undefined): number {
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if (s === undefined || s === null) return 0;
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if (typeof s === 'number') return s;
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const match = s.match(/([\d.,]+)/);
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if (!match) return 0;
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const numStr = match[1];
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const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
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? numStr.replace(/\./g, '').replace(',', '.')
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: numStr.replace(/,/g, '');
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return parseFloat(normalized) || 0;
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}
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/**
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* Extract invoice number from potentially verbose response
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*/
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function extractInvoiceNumber(s: string | undefined): string {
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if (!s) return '';
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let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
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const patterns = [
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/\b([A-Z]{2,3}\d{10,})\b/i,
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/\b([A-Z]\d{8,})\b/i,
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/\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,
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/\b(\d{7,})\b/,
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];
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for (const pattern of patterns) {
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const match = clean.match(pattern);
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if (match) return match[1];
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}
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return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
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}
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/**
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* Extract date (YYYY-MM-DD) from response
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*/
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function extractDate(s: string | undefined): string {
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if (!s) return '';
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let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
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const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
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if (isoMatch) return isoMatch[1];
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const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
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if (dmyMatch) {
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return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
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}
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return clean.replace(/[^\d-]/g, '').trim();
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}
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/**
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* Extract currency
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*/
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function extractCurrency(s: string | undefined): string {
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if (!s) return 'EUR';
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const upper = s.toUpperCase();
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if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
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if (upper.includes('USD') || upper.includes('$')) return 'USD';
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if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
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return 'EUR';
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}
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/**
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* Extract JSON from response
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*/
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function extractJsonFromResponse(response: string): Record<string, unknown> | null {
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let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
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const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
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const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
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try {
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return JSON.parse(jsonStr);
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} catch {
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const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
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if (jsonMatch) {
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try {
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return JSON.parse(jsonMatch[0]);
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} catch {
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return null;
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}
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}
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return null;
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}
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}
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/**
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* Parse JSON response into IInvoice
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*/
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function parseJsonToInvoice(response: string): IInvoice | null {
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const parsed = extractJsonFromResponse(response);
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if (!parsed) return null;
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return {
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invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
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invoice_date: extractDate(String(parsed.invoice_date || '')),
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vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
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currency: extractCurrency(String(parsed.currency || '')),
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net_amount: parseAmount(parsed.net_amount as string | number),
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vat_amount: parseAmount(parsed.vat_amount as string | number),
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total_amount: parseAmount(parsed.total_amount as string | number),
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};
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}
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/**
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* Extract invoice from markdown using GPT-OSS 20B (streaming)
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*/
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async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
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const startTime = Date.now();
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console.log(` [${queryId}] Invoice: ${markdown.length} chars, Prompt: ${JSON_EXTRACTION_PROMPT.length} chars`);
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const response = await fetch(`${OLLAMA_URL}/api/chat`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model: EXTRACTION_MODEL,
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messages: [
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{ role: 'user', content: 'Hi there, how are you?' },
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{ role: 'assistant', content: 'Good, how can I help you today?' },
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{ role: 'user', content: `Here is an invoice document:\n\n${markdown}` },
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{ role: 'assistant', content: 'I have read the invoice document you provided. I can see all the text content. What would you like me to do with it?' },
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{ role: 'user', content: JSON_EXTRACTION_PROMPT },
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],
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stream: true,
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options: {
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num_ctx: 32768, // Larger context for long invoices + thinking
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temperature: 0, // Deterministic for JSON extraction
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},
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}),
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signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
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});
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if (!response.ok) {
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const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
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console.log(` [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
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throw new Error(`Ollama API error: ${response.status}`);
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}
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// Stream the response
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let content = '';
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let thinkingContent = '';
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let thinkingStarted = false;
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let outputStarted = false;
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const reader = response.body!.getReader();
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const decoder = new TextDecoder();
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try {
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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const chunk = decoder.decode(value, { stream: true });
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// Each line is a JSON object
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for (const line of chunk.split('\n').filter(l => l.trim())) {
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try {
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const json = JSON.parse(line);
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// Stream thinking tokens
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const thinking = json.message?.thinking || '';
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if (thinking) {
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if (!thinkingStarted) {
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process.stdout.write(` [${queryId}] THINKING: `);
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thinkingStarted = true;
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}
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process.stdout.write(thinking);
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thinkingContent += thinking;
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}
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// Stream content tokens
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const token = json.message?.content || '';
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if (token) {
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if (!outputStarted) {
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if (thinkingStarted) process.stdout.write('\n');
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process.stdout.write(` [${queryId}] OUTPUT: `);
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outputStarted = true;
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}
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process.stdout.write(token);
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content += token;
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}
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} catch {
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// Ignore parse errors for partial chunks
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}
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}
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}
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} finally {
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if (thinkingStarted || outputStarted) process.stdout.write('\n');
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}
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const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
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console.log(` [${queryId}] Done: ${thinkingContent.length} thinking chars, ${content.length} output chars (${elapsed}s)`);
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return parseJsonToInvoice(content);
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}
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/**
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* Extract invoice (single pass - GPT-OSS is more reliable)
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*/
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async function extractInvoice(markdown: string, docName: string): Promise<IInvoice> {
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console.log(` [${docName}] Extracting...`);
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const invoice = await extractInvoiceFromMarkdown(markdown, docName);
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if (!invoice) {
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return {
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invoice_number: '',
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invoice_date: '',
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vendor_name: '',
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currency: 'EUR',
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net_amount: 0,
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vat_amount: 0,
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total_amount: 0,
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};
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}
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console.log(` [${docName}] Extracted: ${invoice.invoice_number}`);
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return invoice;
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}
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/**
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* Normalize date to YYYY-MM-DD
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*/
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function normalizeDate(dateStr: string | null): string {
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if (!dateStr) return '';
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if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
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const monthMap: Record<string, string> = {
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JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
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JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
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};
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|
|
|
let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
|
|
if (match) {
|
|
return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
|
|
}
|
|
|
|
match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
|
|
if (match) {
|
|
return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
|
|
}
|
|
|
|
return dateStr;
|
|
}
|
|
|
|
/**
|
|
* Compare extracted invoice against expected
|
|
*/
|
|
function compareInvoice(
|
|
extracted: IInvoice,
|
|
expected: IInvoice
|
|
): { match: boolean; errors: string[] } {
|
|
const errors: string[] = [];
|
|
|
|
const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
|
|
const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
|
|
if (extNum !== expNum) {
|
|
errors.push(`invoice_number: exp "${expected.invoice_number}", got "${extracted.invoice_number}"`);
|
|
}
|
|
|
|
if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
|
|
errors.push(`invoice_date: exp "${expected.invoice_date}", got "${extracted.invoice_date}"`);
|
|
}
|
|
|
|
if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
|
|
errors.push(`total_amount: exp ${expected.total_amount}, got ${extracted.total_amount}`);
|
|
}
|
|
|
|
if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
|
|
errors.push(`currency: exp "${expected.currency}", got "${extracted.currency}"`);
|
|
}
|
|
|
|
return { match: errors.length === 0, errors };
|
|
}
|
|
|
|
/**
|
|
* Find all test cases
|
|
*/
|
|
function findTestCases(): ITestCase[] {
|
|
const testDir = path.join(process.cwd(), '.nogit/invoices');
|
|
if (!fs.existsSync(testDir)) return [];
|
|
|
|
const files = fs.readdirSync(testDir);
|
|
const testCases: ITestCase[] = [];
|
|
|
|
for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
|
|
const baseName = pdf.replace('.pdf', '');
|
|
const jsonFile = `${baseName}.json`;
|
|
if (files.includes(jsonFile)) {
|
|
testCases.push({
|
|
name: baseName,
|
|
pdfPath: path.join(testDir, pdf),
|
|
jsonPath: path.join(testDir, jsonFile),
|
|
});
|
|
}
|
|
}
|
|
|
|
return testCases.sort((a, b) => a.name.localeCompare(b.name));
|
|
}
|
|
|
|
// ============ TESTS ============
|
|
|
|
const testCases = findTestCases();
|
|
console.log(`\nFound ${testCases.length} invoice test cases\n`);
|
|
|
|
// Ensure temp directory exists
|
|
if (!fs.existsSync(TEMP_MD_DIR)) {
|
|
fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
|
|
}
|
|
|
|
// -------- STAGE 1: OCR with Nanonets --------
|
|
|
|
tap.test('Stage 1: Setup Nanonets', async () => {
|
|
console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
|
|
const ok = await ensureNanonetsOcr();
|
|
expect(ok).toBeTrue();
|
|
});
|
|
|
|
tap.test('Stage 1: Convert all invoices to markdown', async () => {
|
|
console.log('\n Converting all invoice PDFs to markdown with Nanonets-OCR-s...\n');
|
|
|
|
for (const tc of testCases) {
|
|
console.log(`\n === ${tc.name} ===`);
|
|
|
|
const images = convertPdfToImages(tc.pdfPath);
|
|
console.log(` Pages: ${images.length}`);
|
|
|
|
const markdown = await convertDocumentToMarkdown(images, tc.name);
|
|
|
|
const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
|
|
fs.writeFileSync(mdPath, markdown);
|
|
tc.markdownPath = mdPath;
|
|
console.log(` Saved: ${mdPath}`);
|
|
}
|
|
|
|
console.log('\n Stage 1 complete: All invoices converted to markdown\n');
|
|
});
|
|
|
|
tap.test('Stage 1: Stop Nanonets', async () => {
|
|
stopNanonets();
|
|
await new Promise(resolve => setTimeout(resolve, 3000));
|
|
expect(isContainerRunning('nanonets-test')).toBeFalse();
|
|
});
|
|
|
|
// -------- STAGE 2: Extraction with GPT-OSS 20B --------
|
|
|
|
tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
|
|
console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');
|
|
|
|
const ollamaOk = await ensureMiniCpm();
|
|
expect(ollamaOk).toBeTrue();
|
|
|
|
const extractionOk = await ensureExtractionModel();
|
|
expect(extractionOk).toBeTrue();
|
|
});
|
|
|
|
let passedCount = 0;
|
|
let failedCount = 0;
|
|
const processingTimes: number[] = [];
|
|
|
|
for (const tc of testCases) {
|
|
tap.test(`Stage 2: Extract ${tc.name}`, async () => {
|
|
const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
|
|
console.log(`\n === ${tc.name} ===`);
|
|
console.log(` Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
|
|
|
|
const startTime = Date.now();
|
|
|
|
const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
|
|
if (!fs.existsSync(mdPath)) {
|
|
throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
|
|
}
|
|
const markdown = fs.readFileSync(mdPath, 'utf-8');
|
|
console.log(` Markdown: ${markdown.length} chars`);
|
|
|
|
const extracted = await extractInvoice(markdown, tc.name);
|
|
|
|
const elapsedMs = Date.now() - startTime;
|
|
processingTimes.push(elapsedMs);
|
|
|
|
console.log(` Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
|
|
|
|
const result = compareInvoice(extracted, expected);
|
|
|
|
if (result.match) {
|
|
passedCount++;
|
|
console.log(` Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
|
} else {
|
|
failedCount++;
|
|
console.log(` Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
|
result.errors.forEach(e => console.log(` - ${e}`));
|
|
}
|
|
|
|
expect(result.match).toBeTrue();
|
|
});
|
|
}
|
|
|
|
tap.test('Summary', async () => {
|
|
const totalInvoices = testCases.length;
|
|
const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
|
|
const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
|
|
const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
|
|
|
|
console.log(`\n========================================`);
|
|
console.log(` Invoice Summary (Nanonets + GPT-OSS 20B)`);
|
|
console.log(`========================================`);
|
|
console.log(` Stage 1: Nanonets-OCR-s (doc -> md)`);
|
|
console.log(` Stage 2: GPT-OSS 20B (md -> JSON)`);
|
|
console.log(` Passed: ${passedCount}/${totalInvoices}`);
|
|
console.log(` Failed: ${failedCount}/${totalInvoices}`);
|
|
console.log(` Accuracy: ${accuracy.toFixed(1)}%`);
|
|
console.log(`----------------------------------------`);
|
|
console.log(` Total time: ${(totalTimeMs / 1000).toFixed(1)}s`);
|
|
console.log(` Avg per inv: ${avgTimeSec.toFixed(1)}s`);
|
|
console.log(`========================================\n`);
|
|
|
|
// Cleanup temp files
|
|
try {
|
|
fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
|
|
console.log(` Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
|
|
} catch {
|
|
// Ignore
|
|
}
|
|
});
|
|
|
|
export default tap.start();
|