feat(docker-images): add vLLM-based Nanonets-OCR2-3B image, Qwen3-VL Ollama image and refactor build/docs/tests to use new runtime/layout
This commit is contained in:
@@ -32,10 +32,10 @@ export const IMAGES = {
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healthTimeout: 120000,
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} as IImageConfig,
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// Nanonets-OCR-s - Document OCR optimized VLM (Qwen2.5-VL-3B fine-tuned)
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// Nanonets-OCR2-3B - Document OCR optimized VLM (Qwen2.5-VL-3B fine-tuned, Oct 2025)
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nanonetsOcr: {
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name: 'nanonets-ocr',
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dockerfile: 'Dockerfile_nanonets_ocr',
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dockerfile: 'Dockerfile_nanonets_vllm_gpu_VRAM10GB',
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buildContext: '.',
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containerName: 'nanonets-test',
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ports: ['8000:8000'],
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@@ -340,12 +340,12 @@ export async function ensureQwen3Vl(): Promise<boolean> {
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}
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/**
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* Ensure Nanonets-OCR-s service is running (via vLLM)
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* Document OCR optimized VLM based on Qwen2.5-VL-3B
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* Ensure Nanonets-OCR2-3B service is running (via vLLM)
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* Document OCR optimized VLM based on Qwen2.5-VL-3B (Oct 2025 release)
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*/
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export async function ensureNanonetsOcr(): Promise<boolean> {
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if (!isGpuAvailable()) {
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console.log('[Docker] WARNING: Nanonets-OCR-s requires GPU, but none detected');
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console.log('[Docker] WARNING: Nanonets-OCR2-3B requires GPU, but none detected');
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}
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return ensureService(IMAGES.nanonetsOcr);
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}
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@@ -1,7 +1,7 @@
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/**
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* Bank statement extraction using Nanonets-OCR-s + GPT-OSS 20B (sequential two-stage pipeline)
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* Bank statement extraction using Nanonets-OCR2-3B + GPT-OSS 20B (sequential two-stage pipeline)
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*
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* Stage 1: Nanonets-OCR-s converts ALL document pages to markdown (stop after completion)
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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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@@ -14,7 +14,7 @@ import * as os from 'os';
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import { ensureNanonetsOcr, ensureMiniCpm, removeContainer, 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-OCR-s';
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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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@@ -69,28 +69,11 @@ function estimateVisualTokens(width: number, height: number): number {
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}
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/**
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* Batch images to fit within context window
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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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const batches: IImageData[][] = [];
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let currentBatch: IImageData[] = [];
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let currentTokens = 0;
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for (const img of images) {
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const imgTokens = estimateVisualTokens(img.width, img.height);
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if (currentTokens + imgTokens > MAX_VISUAL_TOKENS && currentBatch.length > 0) {
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batches.push(currentBatch);
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currentBatch = [img];
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currentTokens = imgTokens;
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} else {
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currentBatch.push(img);
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currentTokens += imgTokens;
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}
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}
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if (currentBatch.length > 0) batches.push(currentBatch);
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return batches;
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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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@@ -171,6 +154,7 @@ async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
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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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436
test/test.invoices.extraction.ts
Normal file
436
test/test.invoices.extraction.ts
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@@ -0,0 +1,436 @@
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/**
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* Invoice extraction tuning - uses pre-generated markdown files
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*
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* Skips OCR stage, only runs GPT-OSS extraction on existing .debug.md files.
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* Use this to quickly iterate on extraction prompts and logic.
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*
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* Run with: tstest test/test.invoices.extraction.ts --verbose
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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 { ensureMiniCpm } from './helpers/docker.js';
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const OLLAMA_URL = 'http://localhost:11434';
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const EXTRACTION_MODEL = 'gpt-oss:20b';
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// Test these specific invoices (must have .debug.md files)
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const TEST_INVOICES = [
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'consensus_2021-09',
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'hetzner_2022-04',
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'qonto_2021-08',
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'qonto_2021-09',
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];
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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 ITestCase {
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name: string;
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markdownPath: string;
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jsonPath: string;
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}
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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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/**
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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 - minimal normalization
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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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return s.replace(/\*\*/g, '').replace(/`/g, '').trim();
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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${markdown}` },
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{ role: 'assistant', content: 'I have read the invoice document you provided. I can see all the text content. What would you like me to do with it?' },
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{ role: 'user', content: JSON_EXTRACTION_PROMPT },
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],
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stream: true,
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}),
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signal: AbortSignal.timeout(120000), // 2 min timeout
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});
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if (!response.ok) {
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const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
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console.log(` [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
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throw new Error(`Ollama API error: ${response.status}`);
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}
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// Stream the response
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let content = '';
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let thinkingContent = '';
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let thinkingStarted = false;
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let outputStarted = false;
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const reader = response.body!.getReader();
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const decoder = new TextDecoder();
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try {
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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const chunk = decoder.decode(value, { stream: true });
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for (const line of chunk.split('\n').filter(l => l.trim())) {
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try {
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const json = JSON.parse(line);
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const thinking = json.message?.thinking || '';
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if (thinking) {
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if (!thinkingStarted) {
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process.stdout.write(` [${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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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, ${content.length} output (${elapsed}s)`);
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return parseJsonToInvoice(content);
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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);
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if (match) {
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return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
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}
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match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
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if (match) {
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return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
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}
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return dateStr;
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}
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/**
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* Normalize invoice number for comparison (remove spaces, lowercase)
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*/
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function normalizeInvoiceNumber(s: string): string {
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return s.replace(/\s+/g, '').toLowerCase();
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}
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/**
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* Compare extracted invoice against expected
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*/
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function compareInvoice(
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extracted: IInvoice,
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expected: IInvoice
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): { match: boolean; errors: string[] } {
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const errors: string[] = [];
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// Invoice number - normalize spaces for comparison
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const extNum = normalizeInvoiceNumber(extracted.invoice_number || '');
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const expNum = normalizeInvoiceNumber(expected.invoice_number || '');
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if (extNum !== expNum) {
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errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
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}
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if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
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errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
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}
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if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
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errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
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}
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if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
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errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
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}
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return { match: errors.length === 0, errors };
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}
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/**
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* Find test cases with existing debug markdown
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*/
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function findTestCases(): ITestCase[] {
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const invoicesDir = path.join(process.cwd(), '.nogit/invoices');
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if (!fs.existsSync(invoicesDir)) return [];
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const testCases: ITestCase[] = [];
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for (const invoiceName of TEST_INVOICES) {
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const markdownPath = path.join(invoicesDir, `${invoiceName}.debug.md`);
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const jsonPath = path.join(invoicesDir, `${invoiceName}.json`);
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if (fs.existsSync(markdownPath) && fs.existsSync(jsonPath)) {
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testCases.push({
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name: invoiceName,
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markdownPath,
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jsonPath,
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});
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} else {
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if (!fs.existsSync(markdownPath)) {
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console.warn(`Warning: Missing markdown: ${markdownPath}`);
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}
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if (!fs.existsSync(jsonPath)) {
|
||||
console.warn(`Warning: Missing JSON: ${jsonPath}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return testCases;
|
||||
}
|
||||
|
||||
// ============ TESTS ============
|
||||
|
||||
const testCases = findTestCases();
|
||||
console.log(`\n========================================`);
|
||||
console.log(` EXTRACTION TUNING TEST`);
|
||||
console.log(` (Skips OCR, uses existing .debug.md)`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Testing ${testCases.length} invoices:`);
|
||||
for (const tc of testCases) {
|
||||
console.log(` - ${tc.name}`);
|
||||
}
|
||||
console.log(`========================================\n`);
|
||||
|
||||
tap.test('Setup Ollama + GPT-OSS 20B', async () => {
|
||||
const ollamaOk = await ensureMiniCpm();
|
||||
expect(ollamaOk).toBeTrue();
|
||||
|
||||
const extractionOk = await ensureExtractionModel();
|
||||
expect(extractionOk).toBeTrue();
|
||||
});
|
||||
|
||||
let passedCount = 0;
|
||||
let failedCount = 0;
|
||||
|
||||
for (const tc of testCases) {
|
||||
tap.test(`Extract ${tc.name}`, async () => {
|
||||
const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
|
||||
const markdown = fs.readFileSync(tc.markdownPath, 'utf-8');
|
||||
|
||||
console.log(`\n ========================================`);
|
||||
console.log(` === ${tc.name} ===`);
|
||||
console.log(` ========================================`);
|
||||
console.log(` EXPECTED: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
|
||||
console.log(` Markdown: ${markdown.length} chars`);
|
||||
|
||||
const startTime = Date.now();
|
||||
|
||||
const extracted = await extractInvoiceFromMarkdown(markdown, tc.name);
|
||||
|
||||
if (!extracted) {
|
||||
failedCount++;
|
||||
console.log(`\n Result: ✗ FAILED TO PARSE (${((Date.now() - startTime) / 1000).toFixed(1)}s)`);
|
||||
return;
|
||||
}
|
||||
|
||||
const elapsedMs = Date.now() - startTime;
|
||||
|
||||
console.log(` EXTRACTED: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
|
||||
|
||||
const result = compareInvoice(extracted, expected);
|
||||
|
||||
if (result.match) {
|
||||
passedCount++;
|
||||
console.log(`\n Result: ✓ MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
} else {
|
||||
failedCount++;
|
||||
console.log(`\n Result: ✗ MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
console.log(` ERRORS:`);
|
||||
result.errors.forEach(e => console.log(` - ${e}`));
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
tap.test('Summary', async () => {
|
||||
const totalInvoices = testCases.length;
|
||||
const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
|
||||
|
||||
console.log(`\n========================================`);
|
||||
console.log(` Extraction Tuning Summary`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Model: ${EXTRACTION_MODEL}`);
|
||||
console.log(` Passed: ${passedCount}/${totalInvoices}`);
|
||||
console.log(` Failed: ${failedCount}/${totalInvoices}`);
|
||||
console.log(` Accuracy: ${accuracy.toFixed(1)}%`);
|
||||
console.log(`========================================\n`);
|
||||
});
|
||||
|
||||
export default tap.start();
|
||||
695
test/test.invoices.failed.ts
Normal file
695
test/test.invoices.failed.ts
Normal file
@@ -0,0 +1,695 @@
|
||||
/**
|
||||
* Focused test for failed invoice extractions
|
||||
*
|
||||
* Tests only the 4 invoices that failed in the main test:
|
||||
* - consensus_2021-09: invoice_number "2021/1384" → "20211384" (slash stripped)
|
||||
* - hetzner_2022-04: model hallucinated after 281s thinking
|
||||
* - qonto_2021-08: invoice_number "08-21-INVOICE-410870" → "4108705" (prefix stripped)
|
||||
* - qonto_2021-09: invoice_number "09-21-INVOICE-4303642" → "4303642" (prefix stripped)
|
||||
*
|
||||
* Run with: tstest test/test.invoices.failed.ts --verbose
|
||||
*/
|
||||
import { tap, expect } from '@git.zone/tstest/tapbundle';
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { execSync } from 'child_process';
|
||||
import * as os from 'os';
|
||||
import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
|
||||
|
||||
const NANONETS_URL = 'http://localhost:8000/v1';
|
||||
const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
|
||||
|
||||
const OLLAMA_URL = 'http://localhost:11434';
|
||||
const EXTRACTION_MODEL = 'gpt-oss:20b';
|
||||
|
||||
// Temp directory for storing markdown between stages
|
||||
const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-failed-debug');
|
||||
|
||||
// Only test these specific invoices that failed
|
||||
const FAILED_INVOICES = [
|
||||
'consensus_2021-09',
|
||||
'hetzner_2022-04',
|
||||
'qonto_2021-08',
|
||||
'qonto_2021-09',
|
||||
];
|
||||
|
||||
interface IInvoice {
|
||||
invoice_number: string;
|
||||
invoice_date: string;
|
||||
vendor_name: string;
|
||||
currency: string;
|
||||
net_amount: number;
|
||||
vat_amount: number;
|
||||
total_amount: number;
|
||||
}
|
||||
|
||||
interface IImageData {
|
||||
base64: string;
|
||||
width: number;
|
||||
height: number;
|
||||
pageNum: number;
|
||||
}
|
||||
|
||||
interface ITestCase {
|
||||
name: string;
|
||||
pdfPath: string;
|
||||
jsonPath: string;
|
||||
markdownPath?: string;
|
||||
}
|
||||
|
||||
// Nanonets-specific prompt for document OCR to markdown
|
||||
const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
|
||||
Return the tables in html format.
|
||||
Return the equations in LaTeX representation.
|
||||
If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
|
||||
Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
|
||||
Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
|
||||
|
||||
// JSON extraction prompt for GPT-OSS 20B
|
||||
const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
|
||||
|
||||
IMPORTANT RULES:
|
||||
1. invoice_number: The unique invoice/document number (NOT VAT ID, NOT customer ID). PRESERVE ALL CHARACTERS including slashes, dashes, and prefixes.
|
||||
2. invoice_date: Format as YYYY-MM-DD
|
||||
3. vendor_name: The company that issued the invoice
|
||||
4. currency: EUR, USD, or GBP
|
||||
5. net_amount: Amount before tax
|
||||
6. vat_amount: Tax/VAT amount
|
||||
7. total_amount: Final total (gross amount)
|
||||
|
||||
Return ONLY this JSON format, no explanation:
|
||||
{
|
||||
"invoice_number": "INV-2024-001",
|
||||
"invoice_date": "2024-01-15",
|
||||
"vendor_name": "Company Name",
|
||||
"currency": "EUR",
|
||||
"net_amount": 100.00,
|
||||
"vat_amount": 19.00,
|
||||
"total_amount": 119.00
|
||||
}
|
||||
|
||||
INVOICE TEXT:
|
||||
`;
|
||||
|
||||
const PATCH_SIZE = 14;
|
||||
|
||||
/**
|
||||
* Estimate visual tokens for an image based on dimensions
|
||||
*/
|
||||
function estimateVisualTokens(width: number, height: number): number {
|
||||
return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
|
||||
}
|
||||
|
||||
/**
|
||||
* Process images one page at a time for reliability
|
||||
*/
|
||||
function batchImages(images: IImageData[]): IImageData[][] {
|
||||
return images.map(img => [img]);
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert PDF to JPEG images using ImageMagick with dimension tracking
|
||||
*/
|
||||
function convertPdfToImages(pdfPath: string): IImageData[] {
|
||||
const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
|
||||
const outputPattern = path.join(tempDir, 'page-%d.jpg');
|
||||
|
||||
try {
|
||||
execSync(
|
||||
`convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
|
||||
{ stdio: 'pipe' }
|
||||
);
|
||||
|
||||
const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
|
||||
const images: IImageData[] = [];
|
||||
|
||||
for (let i = 0; i < files.length; i++) {
|
||||
const file = files[i];
|
||||
const imagePath = path.join(tempDir, file);
|
||||
const imageData = fs.readFileSync(imagePath);
|
||||
|
||||
const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
|
||||
const [width, height] = dimensions.split(' ').map(Number);
|
||||
|
||||
images.push({
|
||||
base64: imageData.toString('base64'),
|
||||
width,
|
||||
height,
|
||||
pageNum: i + 1,
|
||||
});
|
||||
}
|
||||
|
||||
return images;
|
||||
} finally {
|
||||
fs.rmSync(tempDir, { recursive: true, force: true });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert a batch of pages to markdown using Nanonets-OCR-s
|
||||
*/
|
||||
async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
|
||||
const startTime = Date.now();
|
||||
const pageNums = batch.map(img => img.pageNum).join(', ');
|
||||
|
||||
const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
|
||||
|
||||
for (const img of batch) {
|
||||
content.push({
|
||||
type: 'image_url',
|
||||
image_url: { url: `data:image/jpeg;base64,${img.base64}` },
|
||||
});
|
||||
}
|
||||
|
||||
const promptText = batch.length > 1
|
||||
? `${NANONETS_OCR_PROMPT}\n\nPlease clearly separate each page's content with "--- PAGE N ---" markers, where N is the page number starting from ${batch[0].pageNum}.`
|
||||
: NANONETS_OCR_PROMPT;
|
||||
|
||||
content.push({ type: 'text', text: promptText });
|
||||
|
||||
const response = await fetch(`${NANONETS_URL}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': 'Bearer dummy',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: NANONETS_MODEL,
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content,
|
||||
}],
|
||||
max_tokens: 4096 * batch.length,
|
||||
temperature: 0.0,
|
||||
}),
|
||||
signal: AbortSignal.timeout(600000),
|
||||
});
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
let responseContent = (data.choices?.[0]?.message?.content || '').trim();
|
||||
|
||||
if (batch.length === 1 && !responseContent.includes('--- PAGE')) {
|
||||
responseContent = `--- PAGE ${batch[0].pageNum} ---\n${responseContent}`;
|
||||
}
|
||||
|
||||
console.log(` Pages [${pageNums}]: ${responseContent.length} chars (${elapsed}s)`);
|
||||
return responseContent;
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert all pages of a document to markdown using smart batching
|
||||
*/
|
||||
async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
|
||||
const batches = batchImages(images);
|
||||
console.log(` [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
|
||||
|
||||
const markdownParts: string[] = [];
|
||||
|
||||
for (let i = 0; i < batches.length; i++) {
|
||||
const batch = batches[i];
|
||||
const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
|
||||
console.log(` Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
|
||||
const markdown = await convertBatchToMarkdown(batch);
|
||||
markdownParts.push(markdown);
|
||||
}
|
||||
|
||||
const fullMarkdown = markdownParts.join('\n\n');
|
||||
console.log(` [${docName}] Complete: ${fullMarkdown.length} chars total`);
|
||||
return fullMarkdown;
|
||||
}
|
||||
|
||||
/**
|
||||
* Stop Nanonets container
|
||||
*/
|
||||
function stopNanonets(): void {
|
||||
console.log(' [Docker] Stopping Nanonets container...');
|
||||
try {
|
||||
execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
|
||||
execSync('sleep 5', { stdio: 'pipe' });
|
||||
console.log(' [Docker] Nanonets stopped');
|
||||
} catch {
|
||||
console.log(' [Docker] Nanonets was not running');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Ensure GPT-OSS 20B model is available
|
||||
*/
|
||||
async function ensureExtractionModel(): Promise<boolean> {
|
||||
try {
|
||||
const response = await fetch(`${OLLAMA_URL}/api/tags`);
|
||||
if (response.ok) {
|
||||
const data = await response.json();
|
||||
const models = data.models || [];
|
||||
if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
|
||||
console.log(` [Ollama] Model available: ${EXTRACTION_MODEL}`);
|
||||
return true;
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
|
||||
console.log(` [Ollama] Pulling ${EXTRACTION_MODEL}...`);
|
||||
const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
|
||||
});
|
||||
|
||||
return pullResponse.ok;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse amount from string (handles European format)
|
||||
*/
|
||||
function parseAmount(s: string | number | undefined): number {
|
||||
if (s === undefined || s === null) return 0;
|
||||
if (typeof s === 'number') return s;
|
||||
const match = s.match(/([\d.,]+)/);
|
||||
if (!match) return 0;
|
||||
const numStr = match[1];
|
||||
const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
|
||||
? numStr.replace(/\./g, '').replace(',', '.')
|
||||
: numStr.replace(/,/g, '');
|
||||
return parseFloat(normalized) || 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract invoice number - MINIMAL normalization for debugging
|
||||
*/
|
||||
function extractInvoiceNumber(s: string | undefined): string {
|
||||
if (!s) return '';
|
||||
// Only remove markdown formatting, preserve everything else
|
||||
return s.replace(/\*\*/g, '').replace(/`/g, '').trim();
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract date (YYYY-MM-DD) from response
|
||||
*/
|
||||
function extractDate(s: string | undefined): string {
|
||||
if (!s) return '';
|
||||
let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
|
||||
const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
|
||||
if (isoMatch) return isoMatch[1];
|
||||
const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
|
||||
if (dmyMatch) {
|
||||
return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
|
||||
}
|
||||
return clean.replace(/[^\d-]/g, '').trim();
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract currency
|
||||
*/
|
||||
function extractCurrency(s: string | undefined): string {
|
||||
if (!s) return 'EUR';
|
||||
const upper = s.toUpperCase();
|
||||
if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
|
||||
if (upper.includes('USD') || upper.includes('$')) return 'USD';
|
||||
if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
|
||||
return 'EUR';
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract JSON from response
|
||||
*/
|
||||
function extractJsonFromResponse(response: string): Record<string, unknown> | null {
|
||||
let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
|
||||
const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
|
||||
|
||||
try {
|
||||
return JSON.parse(jsonStr);
|
||||
} catch {
|
||||
const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
|
||||
if (jsonMatch) {
|
||||
try {
|
||||
return JSON.parse(jsonMatch[0]);
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse JSON response into IInvoice
|
||||
*/
|
||||
function parseJsonToInvoice(response: string): IInvoice | null {
|
||||
const parsed = extractJsonFromResponse(response);
|
||||
if (!parsed) return null;
|
||||
|
||||
return {
|
||||
invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
|
||||
invoice_date: extractDate(String(parsed.invoice_date || '')),
|
||||
vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
|
||||
currency: extractCurrency(String(parsed.currency || '')),
|
||||
net_amount: parseAmount(parsed.net_amount as string | number),
|
||||
vat_amount: parseAmount(parsed.vat_amount as string | number),
|
||||
total_amount: parseAmount(parsed.total_amount as string | number),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract invoice from markdown using GPT-OSS 20B (streaming)
|
||||
*/
|
||||
async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
|
||||
const startTime = Date.now();
|
||||
const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
|
||||
|
||||
// Log exact prompt
|
||||
console.log(`\n [${queryId}] ===== PROMPT =====`);
|
||||
console.log(fullPrompt);
|
||||
console.log(` [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
|
||||
|
||||
const response = await fetch(`${OLLAMA_URL}/api/chat`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: EXTRACTION_MODEL,
|
||||
messages: [
|
||||
{ role: 'user', content: 'Hi there, how are you?' },
|
||||
{ role: 'assistant', content: 'Good, how can I help you today?' },
|
||||
{ role: 'user', content: fullPrompt },
|
||||
],
|
||||
stream: true,
|
||||
}),
|
||||
signal: AbortSignal.timeout(600000),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
console.log(` [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
|
||||
throw new Error(`Ollama API error: ${response.status}`);
|
||||
}
|
||||
|
||||
// Stream the response
|
||||
let content = '';
|
||||
let thinkingContent = '';
|
||||
let thinkingStarted = false;
|
||||
let outputStarted = false;
|
||||
const reader = response.body!.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
|
||||
try {
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
const chunk = decoder.decode(value, { stream: true });
|
||||
|
||||
for (const line of chunk.split('\n').filter(l => l.trim())) {
|
||||
try {
|
||||
const json = JSON.parse(line);
|
||||
|
||||
const thinking = json.message?.thinking || '';
|
||||
if (thinking) {
|
||||
if (!thinkingStarted) {
|
||||
process.stdout.write(` [${queryId}] THINKING: `);
|
||||
thinkingStarted = true;
|
||||
}
|
||||
process.stdout.write(thinking);
|
||||
thinkingContent += thinking;
|
||||
}
|
||||
|
||||
const token = json.message?.content || '';
|
||||
if (token) {
|
||||
if (!outputStarted) {
|
||||
if (thinkingStarted) process.stdout.write('\n');
|
||||
process.stdout.write(` [${queryId}] OUTPUT: `);
|
||||
outputStarted = true;
|
||||
}
|
||||
process.stdout.write(token);
|
||||
content += token;
|
||||
}
|
||||
} catch {
|
||||
// Ignore parse errors for partial chunks
|
||||
}
|
||||
}
|
||||
}
|
||||
} finally {
|
||||
if (thinkingStarted || outputStarted) process.stdout.write('\n');
|
||||
}
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
console.log(` [${queryId}] Done: ${thinkingContent.length} thinking chars, ${content.length} output chars (${elapsed}s)`);
|
||||
|
||||
// Log raw response for debugging
|
||||
console.log(` [${queryId}] RAW RESPONSE: ${content}`);
|
||||
|
||||
return parseJsonToInvoice(content);
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract invoice (single pass)
|
||||
*/
|
||||
async function extractInvoice(markdown: string, docName: string): Promise<IInvoice> {
|
||||
console.log(` [${docName}] Extracting...`);
|
||||
const invoice = await extractInvoiceFromMarkdown(markdown, docName);
|
||||
if (!invoice) {
|
||||
return {
|
||||
invoice_number: '',
|
||||
invoice_date: '',
|
||||
vendor_name: '',
|
||||
currency: 'EUR',
|
||||
net_amount: 0,
|
||||
vat_amount: 0,
|
||||
total_amount: 0,
|
||||
};
|
||||
}
|
||||
console.log(` [${docName}] Extracted: ${JSON.stringify(invoice, null, 2)}`);
|
||||
return invoice;
|
||||
}
|
||||
|
||||
/**
|
||||
* Normalize date to YYYY-MM-DD
|
||||
*/
|
||||
function normalizeDate(dateStr: string | null): string {
|
||||
if (!dateStr) return '';
|
||||
if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
|
||||
|
||||
const monthMap: Record<string, string> = {
|
||||
JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
|
||||
JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
|
||||
};
|
||||
|
||||
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 - detailed output
|
||||
*/
|
||||
function compareInvoice(
|
||||
extracted: IInvoice,
|
||||
expected: IInvoice
|
||||
): { match: boolean; errors: string[] } {
|
||||
const errors: string[] = [];
|
||||
|
||||
// Invoice number comparison - exact match after whitespace normalization
|
||||
const extNum = extracted.invoice_number?.trim() || '';
|
||||
const expNum = expected.invoice_number?.trim() || '';
|
||||
if (extNum.toLowerCase() !== expNum.toLowerCase()) {
|
||||
errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
|
||||
}
|
||||
|
||||
if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
|
||||
errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
|
||||
}
|
||||
|
||||
if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
|
||||
errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
|
||||
}
|
||||
|
||||
if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
|
||||
errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
|
||||
}
|
||||
|
||||
return { match: errors.length === 0, errors };
|
||||
}
|
||||
|
||||
/**
|
||||
* Find test cases for failed invoices only
|
||||
*/
|
||||
function findTestCases(): ITestCase[] {
|
||||
const testDir = path.join(process.cwd(), '.nogit/invoices');
|
||||
if (!fs.existsSync(testDir)) return [];
|
||||
|
||||
const files = fs.readdirSync(testDir);
|
||||
const testCases: ITestCase[] = [];
|
||||
|
||||
for (const invoiceName of FAILED_INVOICES) {
|
||||
const pdfFile = `${invoiceName}.pdf`;
|
||||
const jsonFile = `${invoiceName}.json`;
|
||||
|
||||
if (files.includes(pdfFile) && files.includes(jsonFile)) {
|
||||
testCases.push({
|
||||
name: invoiceName,
|
||||
pdfPath: path.join(testDir, pdfFile),
|
||||
jsonPath: path.join(testDir, jsonFile),
|
||||
});
|
||||
} else {
|
||||
console.warn(`Warning: Missing files for ${invoiceName}`);
|
||||
}
|
||||
}
|
||||
|
||||
return testCases;
|
||||
}
|
||||
|
||||
// ============ TESTS ============
|
||||
|
||||
const testCases = findTestCases();
|
||||
console.log(`\n========================================`);
|
||||
console.log(` FAILED INVOICES DEBUG TEST`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Testing ${testCases.length} failed invoices:`);
|
||||
for (const tc of testCases) {
|
||||
console.log(` - ${tc.name}`);
|
||||
}
|
||||
console.log(`========================================\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 failed invoices to markdown', async () => {
|
||||
console.log('\n Converting failed 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: ${mdPath}`);
|
||||
|
||||
// Also save to .nogit for inspection
|
||||
const debugMdPath = path.join(process.cwd(), '.nogit/invoices', `${tc.name}.debug.md`);
|
||||
fs.writeFileSync(debugMdPath, markdown);
|
||||
console.log(` Debug copy: ${debugMdPath}`);
|
||||
}
|
||||
|
||||
console.log('\n Stage 1 complete: All failed 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;
|
||||
|
||||
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 ========================================`);
|
||||
console.log(` === ${tc.name} ===`);
|
||||
console.log(` ========================================`);
|
||||
console.log(` EXPECTED:`);
|
||||
console.log(` invoice_number: "${expected.invoice_number}"`);
|
||||
console.log(` invoice_date: "${expected.invoice_date}"`);
|
||||
console.log(` vendor_name: "${expected.vendor_name}"`);
|
||||
console.log(` total_amount: ${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: ${markdown.length} chars`);
|
||||
|
||||
const extracted = await extractInvoice(markdown, tc.name);
|
||||
|
||||
const elapsedMs = Date.now() - startTime;
|
||||
|
||||
console.log(`\n EXTRACTED:`);
|
||||
console.log(` invoice_number: "${extracted.invoice_number}"`);
|
||||
console.log(` invoice_date: "${extracted.invoice_date}"`);
|
||||
console.log(` vendor_name: "${extracted.vendor_name}"`);
|
||||
console.log(` total_amount: ${extracted.total_amount} ${extracted.currency}`);
|
||||
|
||||
const result = compareInvoice(extracted, expected);
|
||||
|
||||
if (result.match) {
|
||||
passedCount++;
|
||||
console.log(`\n Result: ✓ MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
} else {
|
||||
failedCount++;
|
||||
console.log(`\n Result: ✗ MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
console.log(` ERRORS:`);
|
||||
result.errors.forEach(e => console.log(` - ${e}`));
|
||||
}
|
||||
|
||||
// Don't fail the test - we're debugging
|
||||
// expect(result.match).toBeTrue();
|
||||
});
|
||||
}
|
||||
|
||||
tap.test('Summary', async () => {
|
||||
const totalInvoices = testCases.length;
|
||||
const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
|
||||
|
||||
console.log(`\n========================================`);
|
||||
console.log(` Failed Invoices Debug Summary`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Passed: ${passedCount}/${totalInvoices}`);
|
||||
console.log(` Failed: ${failedCount}/${totalInvoices}`);
|
||||
console.log(` Accuracy: ${accuracy.toFixed(1)}%`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Markdown files saved to: ${TEMP_MD_DIR}`);
|
||||
console.log(` Debug copies in: .nogit/invoices/*.debug.md`);
|
||||
console.log(`========================================\n`);
|
||||
|
||||
// Don't cleanup temp files for debugging
|
||||
console.log(` Keeping temp files for debugging.\n`);
|
||||
});
|
||||
|
||||
export default tap.start();
|
||||
@@ -1,7 +1,7 @@
|
||||
/**
|
||||
* Invoice extraction using Nanonets-OCR-s + GPT-OSS 20B (sequential two-stage pipeline)
|
||||
* Invoice extraction using Nanonets-OCR2-3B + GPT-OSS 20B (sequential two-stage pipeline)
|
||||
*
|
||||
* Stage 1: Nanonets-OCR-s converts ALL document pages to markdown (stop after completion)
|
||||
* Stage 1: Nanonets-OCR2-3B converts ALL document pages to markdown (stop after completion)
|
||||
* Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
|
||||
*
|
||||
* This approach avoids GPU contention by running services sequentially.
|
||||
@@ -14,7 +14,7 @@ import * as os from 'os';
|
||||
import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
|
||||
|
||||
const NANONETS_URL = 'http://localhost:8000/v1';
|
||||
const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
|
||||
const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
|
||||
|
||||
const OLLAMA_URL = 'http://localhost:11434';
|
||||
const EXTRACTION_MODEL = 'gpt-oss:20b';
|
||||
@@ -92,28 +92,11 @@ function estimateVisualTokens(width: number, height: number): number {
|
||||
}
|
||||
|
||||
/**
|
||||
* Batch images to fit within context window
|
||||
* Process images one page at a time for reliability
|
||||
*/
|
||||
function batchImages(images: IImageData[]): IImageData[][] {
|
||||
const batches: IImageData[][] = [];
|
||||
let currentBatch: IImageData[] = [];
|
||||
let currentTokens = 0;
|
||||
|
||||
for (const img of images) {
|
||||
const imgTokens = estimateVisualTokens(img.width, img.height);
|
||||
|
||||
if (currentTokens + imgTokens > MAX_VISUAL_TOKENS && currentBatch.length > 0) {
|
||||
batches.push(currentBatch);
|
||||
currentBatch = [img];
|
||||
currentTokens = imgTokens;
|
||||
} else {
|
||||
currentBatch.push(img);
|
||||
currentTokens += imgTokens;
|
||||
}
|
||||
}
|
||||
if (currentBatch.length > 0) batches.push(currentBatch);
|
||||
|
||||
return batches;
|
||||
// One page per batch for reliable processing
|
||||
return images.map(img => [img]);
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -194,6 +177,7 @@ async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
|
||||
max_tokens: 4096 * batch.length, // Scale output tokens with batch size
|
||||
temperature: 0.0,
|
||||
}),
|
||||
signal: AbortSignal.timeout(600000), // 10 minute timeout for OCR
|
||||
});
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
Reference in New Issue
Block a user