656 lines
21 KiB
TypeScript
656 lines
21 KiB
TypeScript
/**
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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-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, 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-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-markdown');
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interface ITransaction {
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date: string;
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counterparty: string;
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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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images?: IImageData[];
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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
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const JSON_EXTRACTION_PROMPT = `Extract ALL transactions from this bank statement as JSON array. Each transaction: {"date": "YYYY-MM-DD", "counterparty": "NAME", "amount": -25.99}. Amount negative for debits, positive for credits. Only include actual transactions, not balances. Return ONLY JSON array, no explanation.
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STATEMENT:
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`;
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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: ${fullMarkdown.length} chars total`);
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return fullMarkdown;
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}
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/**
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* Stop Nanonets container
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*/
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function stopNanonets(): void {
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console.log(' [Docker] Stopping Nanonets container...');
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try {
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execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
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// Wait for GPU memory to be released
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execSync('sleep 5', { stdio: 'pipe' });
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console.log(' [Docker] Nanonets stopped');
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} catch {
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console.log(' [Docker] Nanonets was not running');
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}
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}
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/**
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* Ensure GPT-OSS 20B model is available and warmed up
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*/
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async function ensureExtractionModel(): Promise<boolean> {
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try {
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const response = await fetch(`${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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* Extract transactions from markdown using GPT-OSS 20B (streaming)
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*/
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async function extractTransactionsFromMarkdown(markdown: string, queryId: string): Promise<ITransaction[]> {
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const startTime = Date.now();
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const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
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// Log exact prompt
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console.log(`\n [${queryId}] ===== PROMPT =====`);
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console.log(fullPrompt);
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console.log(` [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
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const response = await fetch(`${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: fullPrompt },
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],
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stream: true,
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}),
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signal: AbortSignal.timeout(600000), // 10 minute 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: ${response.status}`);
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}
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// Stream the response
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let content = '';
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let thinkingContent = '';
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let thinkingStarted = false;
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let outputStarted = false;
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const reader = response.body!.getReader();
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const decoder = new TextDecoder();
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try {
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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const chunk = decoder.decode(value, { stream: true });
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// Each line is a JSON object
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for (const line of chunk.split('\n').filter(l => l.trim())) {
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try {
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const json = JSON.parse(line);
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// Stream thinking tokens
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const thinking = json.message?.thinking || '';
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if (thinking) {
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if (!thinkingStarted) {
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process.stdout.write(` [${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 parseJsonResponse(content, queryId);
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}
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/**
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* Sanitize JSON string
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*/
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function sanitizeJson(jsonStr: string): string {
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let s = jsonStr;
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s = s.replace(/"amount"\s*:\s*\+/g, '"amount": ');
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s = s.replace(/:\s*\+(\d)/g, ': $1');
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s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3.$4');
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s = s.replace(/,\s*([}\]])/g, '$1');
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s = s.replace(/"([^"\\]*)\n([^"]*)"/g, '"$1 $2"');
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s = s.replace(/"([^"\\]*)\t([^"]*)"/g, '"$1 $2"');
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s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
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return s;
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}
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/**
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* Parse amount from various formats
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*/
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function parseAmount(value: unknown): number {
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if (typeof value === 'number') return value;
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if (typeof value !== 'string') return 0;
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let s = value.replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
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if (s.includes(',') && s.indexOf(',') > s.lastIndexOf('.')) {
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s = s.replace(/\./g, '').replace(',', '.');
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} else {
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s = s.replace(/,/g, '');
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}
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return parseFloat(s) || 0;
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}
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/**
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* Parse JSON response into transactions
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*/
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function parseJsonResponse(response: string, queryId: string): ITransaction[] {
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// Remove thinking tags if present
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let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
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// Debug: show what we're working with
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console.log(` [${queryId}] Response preview: ${cleanResponse.substring(0, 300)}...`);
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const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
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let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
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jsonStr = sanitizeJson(jsonStr);
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try {
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const parsed = JSON.parse(jsonStr);
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if (Array.isArray(parsed)) {
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const txs = parsed.map(tx => ({
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date: String(tx.date || ''),
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counterparty: String(tx.counterparty || tx.description || ''),
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amount: parseAmount(tx.amount),
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}));
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console.log(` [${queryId}] Parsed ${txs.length} transactions`);
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return txs;
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}
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} catch (e) {
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// Try to find a JSON array in the text
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const arrayMatch = jsonStr.match(/\[[\s\S]*\]/);
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if (arrayMatch) {
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console.log(` [${queryId}] Array match found: ${arrayMatch[0].length} chars`);
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try {
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const parsed = JSON.parse(sanitizeJson(arrayMatch[0]));
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if (Array.isArray(parsed)) {
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const txs = parsed.map(tx => ({
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date: String(tx.date || ''),
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counterparty: String(tx.counterparty || tx.description || ''),
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amount: parseAmount(tx.amount),
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}));
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console.log(` [${queryId}] Parsed ${txs.length} transactions (array match)`);
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return txs;
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}
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} catch (innerErr) {
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console.log(` [${queryId}] Array parse error: ${(innerErr as Error).message}`);
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}
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} else {
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console.log(` [${queryId}] No JSON array found in response`);
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}
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}
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console.log(` [${queryId}] PARSE FAILED`);
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return [];
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}
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/**
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* Extract transactions (single pass)
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*/
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async function extractTransactions(markdown: string, docName: string): Promise<ITransaction[]> {
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console.log(` [${docName}] Extracting...`);
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const txs = await extractTransactionsFromMarkdown(markdown, docName);
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console.log(` [${docName}] Extracted ${txs.length} transactions`);
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return txs;
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}
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/**
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* Compare transactions
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*/
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function compareTransactions(
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extracted: ITransaction[],
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expected: ITransaction[]
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): { matches: number; total: number; errors: string[] } {
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const errors: string[] = [];
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let matches = 0;
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for (let i = 0; i < expected.length; i++) {
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const exp = expected[i];
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const ext = extracted[i];
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if (!ext) {
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errors.push(`Missing tx ${i}: ${exp.date} ${exp.counterparty}`);
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continue;
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}
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const dateMatch = ext.date === exp.date;
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const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
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if (dateMatch && amountMatch) {
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matches++;
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} else {
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errors.push(`Mismatch ${i}: exp ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
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}
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}
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if (extracted.length > expected.length) {
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errors.push(`Extra transactions: ${extracted.length - expected.length}`);
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}
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return { matches, total: expected.length, errors };
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}
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/**
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* Find all test cases
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*/
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function findTestCases(): ITestCase[] {
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const testDir = path.join(process.cwd(), '.nogit');
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if (!fs.existsSync(testDir)) return [];
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const files = fs.readdirSync(testDir);
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const testCases: ITestCase[] = [];
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for (const pdf of files.filter((f: string) => f.endsWith('.pdf'))) {
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const baseName = pdf.replace('.pdf', '');
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const jsonFile = `${baseName}.json`;
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if (files.includes(jsonFile)) {
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testCases.push({
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name: baseName,
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pdfPath: path.join(testDir, pdf),
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jsonPath: path.join(testDir, jsonFile),
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});
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}
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}
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return testCases.sort((a, b) => a.name.localeCompare(b.name));
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}
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// ============ TESTS ============
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const testCases = findTestCases();
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console.log(`\nFound ${testCases.length} bank statement test cases\n`);
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// Ensure temp directory exists
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if (!fs.existsSync(TEMP_MD_DIR)) {
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fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
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}
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// -------- STAGE 1: OCR with Nanonets --------
|
||
|
||
// Check if all markdown files already exist
|
||
function allMarkdownFilesExist(): boolean {
|
||
for (const tc of testCases) {
|
||
const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
|
||
if (!fs.existsSync(mdPath)) {
|
||
return false;
|
||
}
|
||
}
|
||
return true;
|
||
}
|
||
|
||
// Track whether we need to run Stage 1
|
||
let stage1Needed = !allMarkdownFilesExist();
|
||
|
||
tap.test('Stage 1: Setup Nanonets', async () => {
|
||
console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
|
||
|
||
if (!stage1Needed) {
|
||
console.log(' [SKIP] All markdown files already exist, skipping Nanonets setup');
|
||
return;
|
||
}
|
||
|
||
const ok = await ensureNanonetsOcr();
|
||
expect(ok).toBeTrue();
|
||
});
|
||
|
||
tap.test('Stage 1: Convert all documents to markdown', async () => {
|
||
if (!stage1Needed) {
|
||
console.log(' [SKIP] Using existing markdown files from previous run\n');
|
||
// Load existing markdown paths
|
||
for (const tc of testCases) {
|
||
tc.markdownPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
|
||
console.log(` Loaded: ${tc.markdownPath}`);
|
||
}
|
||
return;
|
||
}
|
||
|
||
console.log('\n Converting all PDFs to markdown with Nanonets-OCR-s...\n');
|
||
|
||
for (const tc of testCases) {
|
||
console.log(`\n === ${tc.name} ===`);
|
||
|
||
// Convert PDF to images
|
||
const images = convertPdfToImages(tc.pdfPath);
|
||
console.log(` Pages: ${images.length}`);
|
||
|
||
// Convert to markdown
|
||
const markdown = await convertDocumentToMarkdown(images, tc.name);
|
||
|
||
// Save markdown to temp file
|
||
const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
|
||
fs.writeFileSync(mdPath, markdown);
|
||
tc.markdownPath = mdPath;
|
||
console.log(` Saved: ${mdPath}`);
|
||
}
|
||
|
||
console.log('\n Stage 1 complete: All documents converted to markdown\n');
|
||
});
|
||
|
||
tap.test('Stage 1: Stop Nanonets', async () => {
|
||
if (!stage1Needed) {
|
||
console.log(' [SKIP] Nanonets was not started');
|
||
return;
|
||
}
|
||
|
||
stopNanonets();
|
||
// Verify it's stopped
|
||
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: ITransaction[] = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
|
||
console.log(`\n === ${tc.name} ===`);
|
||
console.log(` Expected: ${expected.length} transactions`);
|
||
|
||
// Load saved markdown
|
||
const mdPath = path.join(TEMP_MD_DIR, `${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`);
|
||
|
||
// Extract transactions (single pass)
|
||
const extracted = await extractTransactions(markdown, tc.name);
|
||
|
||
// Log results
|
||
console.log(` Extracted: ${extracted.length} transactions`);
|
||
for (let i = 0; i < Math.min(extracted.length, 5); i++) {
|
||
const tx = extracted[i];
|
||
console.log(` ${i + 1}. ${tx.date} | ${tx.counterparty.substring(0, 25).padEnd(25)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
|
||
}
|
||
if (extracted.length > 5) {
|
||
console.log(` ... and ${extracted.length - 5} more`);
|
||
}
|
||
|
||
// Compare
|
||
const result = compareTransactions(extracted, expected);
|
||
const pass = result.matches === result.total && extracted.length === expected.length;
|
||
|
||
if (pass) {
|
||
passedCount++;
|
||
console.log(` Result: PASS (${result.matches}/${result.total})`);
|
||
} else {
|
||
failedCount++;
|
||
console.log(` Result: FAIL (${result.matches}/${result.total})`);
|
||
result.errors.slice(0, 5).forEach(e => console.log(` - ${e}`));
|
||
}
|
||
|
||
expect(result.matches).toEqual(result.total);
|
||
expect(extracted.length).toEqual(expected.length);
|
||
});
|
||
}
|
||
|
||
tap.test('Summary', async () => {
|
||
console.log(`\n======================================================`);
|
||
console.log(` Bank Statement Summary (Nanonets + GPT-OSS 20B Sequential)`);
|
||
console.log(`======================================================`);
|
||
console.log(` Stage 1: Nanonets-OCR-s (document -> markdown)`);
|
||
console.log(` Stage 2: GPT-OSS 20B (markdown -> JSON)`);
|
||
console.log(` Passed: ${passedCount}/${testCases.length}`);
|
||
console.log(` Failed: ${failedCount}/${testCases.length}`);
|
||
console.log(`======================================================\n`);
|
||
|
||
// Only cleanup temp files if ALL tests passed
|
||
if (failedCount === 0 && passedCount === testCases.length) {
|
||
try {
|
||
fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
|
||
console.log(` Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
|
||
} catch {
|
||
// Ignore
|
||
}
|
||
} else {
|
||
console.log(` Keeping temp directory for debugging: ${TEMP_MD_DIR}\n`);
|
||
}
|
||
});
|
||
|
||
export default tap.start();
|