fix(tests): stabilize OCR extraction tests and manage GPU containers
This commit is contained in:
10
changelog.md
10
changelog.md
@@ -1,5 +1,15 @@
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# Changelog
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## 2026-01-18 - 1.13.2 - fix(tests)
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stabilize OCR extraction tests and manage GPU containers
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- Add stopAllGpuContainers() and call it before starting GPU images to free GPU memory.
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- Remove PaddleOCR-VL image configs and associated ensure helpers from docker test helper to simplify images list.
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- Split invoice/bankstatement tests into two sequential stages: Stage 1 runs Nanonets OCR to produce markdown files, Stage 2 stops Nanonets and runs model extraction from saved markdown (avoids GPU contention).
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- Introduce temporary markdown directory handling and cleanup; add stopNanonets() and container running checks in tests.
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- Switch bank statement extraction model from qwen3:8b to gpt-oss:20b; add request timeout and improved logging/console output across tests.
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- Refactor extractWithConsensus and extraction functions to accept document identifiers, improve error messages and JSON extraction robustness.
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## 2026-01-18 - 1.13.1 - fix(image_support_files)
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remove PaddleOCR-VL server scripts from image_support_files
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@@ -2,10 +2,6 @@ import { execSync } from 'child_process';
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// Project container names (only manage these)
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const PROJECT_CONTAINERS = [
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'paddleocr-vl-test',
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'paddleocr-vl-gpu-test',
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'paddleocr-vl-cpu-test',
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'paddleocr-vl-full-test',
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'minicpm-test',
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'nanonets-test',
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];
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@@ -24,30 +20,6 @@ export interface IImageConfig {
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}
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export const IMAGES = {
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paddleocrVlGpu: {
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name: 'paddleocr-vl-gpu',
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dockerfile: 'Dockerfile_paddleocr_vl_gpu',
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buildContext: '.',
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containerName: 'paddleocr-vl-test',
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ports: ['8000:8000'],
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volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
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gpus: true,
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healthEndpoint: 'http://localhost:8000/health',
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healthTimeout: 300000, // 5 minutes for model loading
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} as IImageConfig,
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paddleocrVlCpu: {
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name: 'paddleocr-vl-cpu',
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dockerfile: 'Dockerfile_paddleocr_vl_cpu',
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buildContext: '.',
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containerName: 'paddleocr-vl-test',
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ports: ['8000:8000'],
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volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
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gpus: false,
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healthEndpoint: 'http://localhost:8000/health',
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healthTimeout: 300000,
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} as IImageConfig,
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minicpm: {
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name: 'minicpm45v',
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dockerfile: 'Dockerfile_minicpm45v_gpu',
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@@ -60,22 +32,6 @@ export const IMAGES = {
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healthTimeout: 120000,
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} as IImageConfig,
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// Full PaddleOCR-VL pipeline with PP-DocLayoutV2 + structured JSON output
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paddleocrVlFull: {
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name: 'paddleocr-vl-full',
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dockerfile: 'Dockerfile_paddleocr_vl_full',
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buildContext: '.',
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containerName: 'paddleocr-vl-full-test',
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ports: ['8000:8000'],
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volumes: [
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'ht-huggingface-cache:/root/.cache/huggingface',
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'ht-paddleocr-cache:/root/.paddleocr',
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],
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gpus: true,
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healthEndpoint: 'http://localhost:8000/health',
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healthTimeout: 600000, // 10 minutes for model loading (vLLM + PP-DocLayoutV2)
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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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nanonetsOcr: {
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name: 'nanonets-ocr',
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@@ -140,7 +96,7 @@ export function removeContainer(containerName: string): void {
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}
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/**
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* Stop all project containers that conflict with the required one
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* Stop all project containers that conflict with the required one (port-based)
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*/
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export function stopConflictingContainers(requiredContainer: string, requiredPort: string): void {
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// Stop project containers using the same port
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@@ -158,6 +114,24 @@ export function stopConflictingContainers(requiredContainer: string, requiredPor
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}
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}
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/**
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* Stop all GPU-consuming project containers (for GPU memory management)
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* This ensures GPU memory is freed before starting a new GPU service
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*/
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export function stopAllGpuContainers(exceptContainer?: string): void {
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for (const container of PROJECT_CONTAINERS) {
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if (container === exceptContainer) continue;
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if (isContainerRunning(container)) {
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console.log(`[Docker] Stopping GPU container: ${container}`);
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exec(`docker stop ${container}`, true);
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// Give the GPU a moment to free memory
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}
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}
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// Brief pause to allow GPU memory to be released
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execSync('sleep 2');
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}
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/**
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* Build a Docker image
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*/
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@@ -234,6 +208,11 @@ export async function ensureService(config: IImageConfig): Promise<boolean> {
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buildImage(config);
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}
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// For GPU services, stop ALL other GPU containers to free GPU memory
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if (config.gpus) {
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stopAllGpuContainers(config.containerName);
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}
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// Stop conflicting containers on the same port
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const mainPort = config.ports[0];
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stopConflictingContainers(config.containerName, mainPort);
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@@ -254,21 +233,7 @@ export async function ensureService(config: IImageConfig): Promise<boolean> {
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}
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/**
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* Ensure PaddleOCR-VL GPU service is running
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*/
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export async function ensurePaddleOcrVlGpu(): Promise<boolean> {
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return ensureService(IMAGES.paddleocrVlGpu);
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}
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/**
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* Ensure PaddleOCR-VL CPU service is running
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*/
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export async function ensurePaddleOcrVlCpu(): Promise<boolean> {
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return ensureService(IMAGES.paddleocrVlCpu);
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}
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/**
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* Ensure MiniCPM service is running
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* Ensure MiniCPM service is running (Ollama with GPU)
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*/
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export async function ensureMiniCpm(): Promise<boolean> {
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return ensureService(IMAGES.minicpm);
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@@ -286,30 +251,6 @@ export function isGpuAvailable(): boolean {
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}
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}
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/**
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* Ensure PaddleOCR-VL service (auto-detect GPU/CPU)
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*/
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export async function ensurePaddleOcrVl(): Promise<boolean> {
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if (isGpuAvailable()) {
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console.log('[Docker] GPU detected, using GPU image');
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return ensurePaddleOcrVlGpu();
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} else {
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console.log('[Docker] No GPU detected, using CPU image');
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return ensurePaddleOcrVlCpu();
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}
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}
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/**
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* Ensure PaddleOCR-VL Full Pipeline service (PP-DocLayoutV2 + structured output)
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* This is the recommended service for production use - outputs structured JSON/Markdown
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*/
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export async function ensurePaddleOcrVlFull(): Promise<boolean> {
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if (!isGpuAvailable()) {
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console.log('[Docker] WARNING: Full pipeline requires GPU, but none detected');
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}
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return ensureService(IMAGES.paddleocrVlFull);
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}
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/**
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* Ensure an Ollama model is pulled and available
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* Uses the MiniCPM container (which runs Ollama) to pull the model
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@@ -1,25 +1,26 @@
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/**
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* Bank statement extraction using Nanonets-OCR-s + Qwen3 (two-stage pipeline)
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* Bank statement extraction using Nanonets-OCR-s + GPT-OSS 20B (sequential two-stage pipeline)
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*
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* Stage 1: Nanonets-OCR-s converts document pages to markdown (its strength)
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* Stage 2: Qwen3 extracts structured JSON from the combined markdown
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* Stage 1: Nanonets-OCR-s 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 leverages each model's strengths:
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* - Nanonets: Document OCR with semantic tags
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* - Qwen3: Text understanding and JSON extraction
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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 } from './helpers/docker.js';
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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 OLLAMA_URL = 'http://localhost:11434';
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const QWEN_MODEL = 'qwen3:8b';
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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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@@ -27,6 +28,14 @@ interface ITransaction {
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amount: 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?: string[];
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}
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// Nanonets-specific prompt for document OCR to markdown
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const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
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Return the tables in html format.
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@@ -35,24 +44,10 @@ If there is an image in the document and image caption is not present, add a sma
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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 Qwen3
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const JSON_EXTRACTION_PROMPT = `You are a financial data extractor. Below is a bank statement converted to text/markdown. Extract ALL transactions from it as a JSON array.
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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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IMPORTANT RULES:
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1. Each transaction has: date, description/counterparty, and an amount
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2. Amount is NEGATIVE for money going OUT (debits, payments, withdrawals)
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3. Amount is POSITIVE for money coming IN (credits, deposits, refunds)
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4. Date format: YYYY-MM-DD
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5. Do NOT include: opening balance, closing balance, subtotals, headers, or summary rows
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6. Only include actual transactions with a specific date and amount
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Return ONLY this JSON format, no explanation:
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[
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{"date": "2021-06-01", "counterparty": "COMPANY NAME", "amount": -25.99},
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{"date": "2021-06-02", "counterparty": "DEPOSIT FROM", "amount": 100.00}
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]
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BANK STATEMENT TEXT:
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STATEMENT:
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`;
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/**
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@@ -63,7 +58,6 @@ function convertPdfToImages(pdfPath: string): string[] {
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const outputPattern = path.join(tempDir, 'page-%d.png');
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try {
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// Use 150 DPI to keep images within model's context length
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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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@@ -85,10 +79,9 @@ function convertPdfToImages(pdfPath: string): string[] {
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}
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/**
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* Stage 1: Convert a single page to markdown using Nanonets-OCR-s
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* Convert a single page to markdown using Nanonets-OCR-s
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*/
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async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
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console.log(` [Nanonets] Converting page ${pageNum} to markdown...`);
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const startTime = Date.now();
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const response = await fetch(`${NANONETS_URL}/chat/completions`, {
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@@ -115,21 +108,20 @@ async function convertPageToMarkdown(image: string, pageNum: number): Promise<st
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if (!response.ok) {
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const errorText = await response.text();
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console.log(` [Nanonets] ERROR page ${pageNum}: ${response.status} - ${errorText}`);
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throw new Error(`Nanonets API error: ${response.status}`);
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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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const content = (data.choices?.[0]?.message?.content || '').trim();
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console.log(` [Nanonets] Page ${pageNum} converted (${elapsed}s, ${content.length} chars)`);
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console.log(` Page ${pageNum}: ${content.length} chars (${elapsed}s)`);
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return content;
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}
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/**
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* Stage 1: Convert all pages to markdown using Nanonets-OCR-s
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* Convert all pages of a document to markdown
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*/
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async function convertDocumentToMarkdown(images: string[]): Promise<string> {
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console.log(` [Stage 1] Converting ${images.length} page(s) to markdown with Nanonets-OCR-s...`);
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async function convertDocumentToMarkdown(images: string[], docName: string): Promise<string> {
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console.log(` [${docName}] Converting ${images.length} page(s)...`);
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const markdownPages: string[] = [];
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@@ -139,21 +131,55 @@ async function convertDocumentToMarkdown(images: string[]): Promise<string> {
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}
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const fullMarkdown = markdownPages.join('\n\n');
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console.log(` [Stage 1] Complete: ${fullMarkdown.length} chars total`);
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console.log(` [${docName}] Complete: ${fullMarkdown.length} chars total`);
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return fullMarkdown;
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}
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/**
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* Ensure Qwen3 model is available
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* Stop Nanonets container
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*/
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async function ensureQwen3(): Promise<boolean> {
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function stopNanonets(): void {
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console.log(' [Docker] Stopping Nanonets container...');
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try {
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execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
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// Wait for GPU memory to be released
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execSync('sleep 5', { stdio: 'pipe' });
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console.log(' [Docker] Nanonets stopped');
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} catch {
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console.log(' [Docker] Nanonets was not running');
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}
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}
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/**
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* Ensure GPT-OSS 20B model is available and warmed up
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*/
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async function ensureExtractionModel(): Promise<boolean> {
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try {
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const response = await fetch(`${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 === QWEN_MODEL)) {
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console.log(` [Ollama] Model available: ${QWEN_MODEL}`);
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if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
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console.log(` [Ollama] Model available: ${EXTRACTION_MODEL}`);
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// Warmup: send a simple request to ensure model is loaded
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console.log(` [Ollama] Warming up model...`);
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const warmupResponse = await fetch(`${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: [{ role: 'user', content: 'Return: [{"test": 1}]' }],
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stream: false,
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}),
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signal: AbortSignal.timeout(120000),
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});
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if (warmupResponse.ok) {
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const warmupData = await warmupResponse.json();
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console.log(` [Ollama] Warmup complete (${warmupData.message?.content?.length || 0} chars)`);
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}
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return true;
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}
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}
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@@ -161,77 +187,92 @@ async function ensureQwen3(): Promise<boolean> {
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return false;
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}
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console.log(` [Ollama] Pulling ${QWEN_MODEL}...`);
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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: QWEN_MODEL, stream: false }),
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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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* Stage 2: Extract transactions from markdown using Qwen3
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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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console.log(` [${queryId}] Sending markdown to ${QWEN_MODEL}...`);
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console.log(` [${queryId}] Sending to ${EXTRACTION_MODEL}...`);
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console.log(` [${queryId}] Markdown length: ${markdown.length}`);
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const startTime = Date.now();
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const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
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console.log(` [${queryId}] Prompt preview: ${fullPrompt.substring(0, 200)}...`);
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const response = await fetch(`${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: QWEN_MODEL,
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model: EXTRACTION_MODEL,
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messages: [{
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role: 'user',
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content: JSON_EXTRACTION_PROMPT + markdown,
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content: fullPrompt,
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}],
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stream: false,
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options: {
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num_predict: 8000,
|
||||
temperature: 0.1,
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},
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||||
stream: true,
|
||||
}),
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signal: AbortSignal.timeout(600000), // 10 minute timeout
|
||||
});
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
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: ${response.status}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const content = (data.message?.content || '').trim();
|
||||
console.log(` [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
|
||||
// Stream the response and log to console
|
||||
let content = '';
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||||
const reader = response.body!.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
|
||||
process.stdout.write(` [${queryId}] `);
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
const chunk = decoder.decode(value, { stream: true });
|
||||
// Each line is a JSON object
|
||||
for (const line of chunk.split('\n').filter(l => l.trim())) {
|
||||
try {
|
||||
const json = JSON.parse(line);
|
||||
const token = json.message?.content || '';
|
||||
if (token) {
|
||||
process.stdout.write(token);
|
||||
content += token;
|
||||
}
|
||||
} catch {
|
||||
// Ignore parse errors for partial chunks
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
console.log(`\n [${queryId}] Done: ${content.length} chars (${elapsed}s)`);
|
||||
|
||||
return parseJsonResponse(content, queryId);
|
||||
}
|
||||
|
||||
/**
|
||||
* Sanitize JSON string - fix common issues from LLM output
|
||||
* Sanitize JSON string
|
||||
*/
|
||||
function sanitizeJson(jsonStr: string): string {
|
||||
let s = jsonStr;
|
||||
|
||||
// Fix +number (e.g., +93.80 -> 93.80) - JSON doesn't allow + prefix
|
||||
s = s.replace(/"amount"\s*:\s*\+/g, '"amount": ');
|
||||
s = s.replace(/:\s*\+(\d)/g, ': $1');
|
||||
|
||||
// Fix European number format with thousands separator
|
||||
s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3.$4');
|
||||
|
||||
// Fix trailing commas before ] or }
|
||||
s = s.replace(/,\s*([}\]])/g, '$1');
|
||||
|
||||
// Fix unescaped newlines/tabs inside strings
|
||||
s = s.replace(/"([^"\\]*)\n([^"]*)"/g, '"$1 $2"');
|
||||
s = s.replace(/"([^"\\]*)\t([^"]*)"/g, '"$1 $2"');
|
||||
|
||||
// Remove control characters
|
||||
s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
|
||||
|
||||
return s;
|
||||
}
|
||||
|
||||
@@ -243,7 +284,6 @@ function parseAmount(value: unknown): number {
|
||||
if (typeof value !== 'string') return 0;
|
||||
|
||||
let s = value.replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
|
||||
// European format: comma is decimal
|
||||
if (s.includes(',') && s.indexOf(',') > s.lastIndexOf('.')) {
|
||||
s = s.replace(/\./g, '').replace(',', '.');
|
||||
} else {
|
||||
@@ -256,16 +296,14 @@ function parseAmount(value: unknown): number {
|
||||
* Parse JSON response into transactions
|
||||
*/
|
||||
function parseJsonResponse(response: string, queryId: string): ITransaction[] {
|
||||
console.log(` [${queryId}] Parsing response...`);
|
||||
|
||||
// Remove thinking tags if present (Qwen3 may include <think>...</think>)
|
||||
// Remove thinking tags if present
|
||||
let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
|
||||
|
||||
// Try to find JSON in markdown code block
|
||||
// Debug: show what we're working with
|
||||
console.log(` [${queryId}] Response preview: ${cleanResponse.substring(0, 300)}...`);
|
||||
|
||||
const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
|
||||
|
||||
// Sanitize JSON
|
||||
jsonStr = sanitizeJson(jsonStr);
|
||||
|
||||
try {
|
||||
@@ -280,11 +318,10 @@ function parseJsonResponse(response: string, queryId: string): ITransaction[] {
|
||||
return txs;
|
||||
}
|
||||
} catch (e) {
|
||||
console.log(` [${queryId}] Direct parse failed: ${(e as Error).message}`);
|
||||
|
||||
// Try to find JSON array pattern
|
||||
// Try to find a JSON array in the text
|
||||
const arrayMatch = jsonStr.match(/\[[\s\S]*\]/);
|
||||
if (arrayMatch) {
|
||||
console.log(` [${queryId}] Array match found: ${arrayMatch[0].length} chars`);
|
||||
try {
|
||||
const parsed = JSON.parse(sanitizeJson(arrayMatch[0]));
|
||||
if (Array.isArray(parsed)) {
|
||||
@@ -296,93 +333,36 @@ function parseJsonResponse(response: string, queryId: string): ITransaction[] {
|
||||
console.log(` [${queryId}] Parsed ${txs.length} transactions (array match)`);
|
||||
return txs;
|
||||
}
|
||||
} catch (e2) {
|
||||
console.log(` [${queryId}] Array parse failed: ${(e2 as Error).message}`);
|
||||
} catch (innerErr) {
|
||||
console.log(` [${queryId}] Array parse error: ${(innerErr as Error).message}`);
|
||||
}
|
||||
} else {
|
||||
console.log(` [${queryId}] No JSON array found in response`);
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` [${queryId}] PARSE FAILED - returning empty array`);
|
||||
console.log(` [${queryId}] PARSE FAILED`);
|
||||
return [];
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare two transaction arrays for consensus
|
||||
* Extract transactions (single pass)
|
||||
*/
|
||||
function transactionArraysMatch(a: ITransaction[], b: ITransaction[]): boolean {
|
||||
if (a.length !== b.length) return false;
|
||||
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
const dateMatch = a[i].date === b[i].date;
|
||||
const amountMatch = Math.abs(a[i].amount - b[i].amount) < 0.01;
|
||||
if (!dateMatch || !amountMatch) return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
async function extractTransactions(markdown: string, docName: string): Promise<ITransaction[]> {
|
||||
console.log(` [${docName}] Extracting...`);
|
||||
const txs = await extractTransactionsFromMarkdown(markdown, docName);
|
||||
console.log(` [${docName}] Extracted ${txs.length} transactions`);
|
||||
return txs;
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 2: Extract transactions using Qwen3 with consensus
|
||||
*/
|
||||
async function extractWithConsensus(markdown: string): Promise<ITransaction[]> {
|
||||
const MAX_ATTEMPTS = 3;
|
||||
console.log(` [Stage 2] Extracting transactions with ${QWEN_MODEL} (consensus)...`);
|
||||
|
||||
for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
|
||||
console.log(`\n [Stage 2] --- Attempt ${attempt}/${MAX_ATTEMPTS} ---`);
|
||||
|
||||
// Extract twice in parallel
|
||||
const [txs1, txs2] = await Promise.all([
|
||||
extractTransactionsFromMarkdown(markdown, `A${attempt}Q1`),
|
||||
extractTransactionsFromMarkdown(markdown, `A${attempt}Q2`),
|
||||
]);
|
||||
|
||||
console.log(` [Stage 2] Results: Q1=${txs1.length} txs, Q2=${txs2.length} txs`);
|
||||
|
||||
if (txs1.length > 0 && transactionArraysMatch(txs1, txs2)) {
|
||||
console.log(` [Stage 2] CONSENSUS REACHED: ${txs1.length} transactions`);
|
||||
return txs1;
|
||||
}
|
||||
|
||||
console.log(` [Stage 2] NO CONSENSUS`);
|
||||
}
|
||||
|
||||
// Fallback: use last response
|
||||
console.log(`\n [Stage 2] === FALLBACK ===`);
|
||||
const fallback = await extractTransactionsFromMarkdown(markdown, 'FALLBACK');
|
||||
console.log(` [Stage 2] ~ FALLBACK RESULT: ${fallback.length} transactions`);
|
||||
return fallback;
|
||||
}
|
||||
|
||||
/**
|
||||
* Full pipeline: PDF -> Images -> Markdown -> JSON
|
||||
*/
|
||||
async function extractTransactions(images: string[]): Promise<ITransaction[]> {
|
||||
// Stage 1: Convert to markdown
|
||||
const markdown = await convertDocumentToMarkdown(images);
|
||||
|
||||
// Stage 2: Extract transactions with consensus
|
||||
const transactions = await extractWithConsensus(markdown);
|
||||
|
||||
// Log all transactions
|
||||
console.log(`\n [Result] Extracted ${transactions.length} transactions:`);
|
||||
for (let i = 0; i < transactions.length; i++) {
|
||||
const tx = transactions[i];
|
||||
console.log(` ${(i + 1).toString().padStart(2)}. ${tx.date} | ${tx.counterparty.substring(0, 30).padEnd(30)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
|
||||
}
|
||||
|
||||
return transactions;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare extracted transactions against expected
|
||||
* Compare transactions
|
||||
*/
|
||||
function compareTransactions(
|
||||
extracted: ITransaction[],
|
||||
expected: ITransaction[]
|
||||
): { matches: number; total: number; errors: string[]; variations: string[] } {
|
||||
): { matches: number; total: number; errors: string[] } {
|
||||
const errors: string[] = [];
|
||||
const variations: string[] = [];
|
||||
let matches = 0;
|
||||
|
||||
for (let i = 0; i < expected.length; i++) {
|
||||
@@ -390,7 +370,7 @@ function compareTransactions(
|
||||
const ext = extracted[i];
|
||||
|
||||
if (!ext) {
|
||||
errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
|
||||
errors.push(`Missing tx ${i}: ${exp.date} ${exp.counterparty}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
@@ -399,11 +379,8 @@ function compareTransactions(
|
||||
|
||||
if (dateMatch && amountMatch) {
|
||||
matches++;
|
||||
if (ext.counterparty !== exp.counterparty) {
|
||||
variations.push(`[${i}] "${exp.counterparty}" -> "${ext.counterparty}"`);
|
||||
}
|
||||
} else {
|
||||
errors.push(`Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
|
||||
errors.push(`Mismatch ${i}: exp ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -411,23 +388,20 @@ function compareTransactions(
|
||||
errors.push(`Extra transactions: ${extracted.length - expected.length}`);
|
||||
}
|
||||
|
||||
return { matches, total: expected.length, errors, variations };
|
||||
return { matches, total: expected.length, errors };
|
||||
}
|
||||
|
||||
/**
|
||||
* Find all test cases (PDF + JSON pairs) in .nogit/
|
||||
* Find all test cases
|
||||
*/
|
||||
function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
|
||||
function findTestCases(): ITestCase[] {
|
||||
const testDir = path.join(process.cwd(), '.nogit');
|
||||
if (!fs.existsSync(testDir)) {
|
||||
return [];
|
||||
}
|
||||
if (!fs.existsSync(testDir)) return [];
|
||||
|
||||
const files = fs.readdirSync(testDir);
|
||||
const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
|
||||
const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
|
||||
const testCases: ITestCase[] = [];
|
||||
|
||||
for (const pdf of pdfFiles) {
|
||||
for (const pdf of files.filter((f: string) => f.endsWith('.pdf'))) {
|
||||
const baseName = pdf.replace('.pdf', '');
|
||||
const jsonFile = `${baseName}.json`;
|
||||
if (files.includes(jsonFile)) {
|
||||
@@ -442,72 +416,142 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
|
||||
return testCases.sort((a, b) => a.name.localeCompare(b.name));
|
||||
}
|
||||
|
||||
// Tests
|
||||
// ============ TESTS ============
|
||||
|
||||
tap.test('setup: ensure containers are running', async () => {
|
||||
console.log('\n[Setup] Checking Docker containers...\n');
|
||||
const testCases = findTestCases();
|
||||
console.log(`\nFound ${testCases.length} bank statement test cases\n`);
|
||||
|
||||
// Nanonets for OCR
|
||||
const nanonetsOk = await ensureNanonetsOcr();
|
||||
expect(nanonetsOk).toBeTrue();
|
||||
// Ensure temp directory exists
|
||||
if (!fs.existsSync(TEMP_MD_DIR)) {
|
||||
fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
|
||||
}
|
||||
|
||||
// -------- STAGE 1: OCR with Nanonets --------
|
||||
|
||||
// Check if all markdown files already exist
|
||||
function allMarkdownFilesExist(): boolean {
|
||||
for (const tc of testCases) {
|
||||
const mdPath = path.join(TEMP_MD_DIR, `${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: ${mdPath}`);
|
||||
}
|
||||
|
||||
console.log('\n Stage 1 complete: All documents converted to markdown\n');
|
||||
});
|
||||
|
||||
tap.test('Stage 1: Stop Nanonets', async () => {
|
||||
if (!stage1Needed) {
|
||||
console.log(' [SKIP] Nanonets was not started');
|
||||
return;
|
||||
}
|
||||
|
||||
stopNanonets();
|
||||
// Verify it's stopped
|
||||
await new Promise(resolve => setTimeout(resolve, 3000));
|
||||
expect(isContainerRunning('nanonets-test')).toBeFalse();
|
||||
});
|
||||
|
||||
// -------- STAGE 2: Extraction with GPT-OSS 20B --------
|
||||
|
||||
tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
|
||||
console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');
|
||||
|
||||
// Ollama for Qwen3
|
||||
const ollamaOk = await ensureMiniCpm();
|
||||
expect(ollamaOk).toBeTrue();
|
||||
|
||||
// Qwen3 model
|
||||
const qwenOk = await ensureQwen3();
|
||||
expect(qwenOk).toBeTrue();
|
||||
|
||||
console.log('\n[Setup] All containers ready!\n');
|
||||
const extractionOk = await ensureExtractionModel();
|
||||
expect(extractionOk).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('should have models available', async () => {
|
||||
// Check Nanonets
|
||||
const nanonetsResp = await fetch(`${NANONETS_URL}/models`);
|
||||
expect(nanonetsResp.ok).toBeTrue();
|
||||
|
||||
// Check Qwen3
|
||||
const ollamaResp = await fetch(`${OLLAMA_URL}/api/tags`);
|
||||
expect(ollamaResp.ok).toBeTrue();
|
||||
const data = await ollamaResp.json();
|
||||
const modelNames = data.models.map((m: { name: string }) => m.name);
|
||||
expect(modelNames.some((name: string) => name.includes('qwen3'))).toBeTrue();
|
||||
});
|
||||
|
||||
const testCases = findTestCases();
|
||||
console.log(`\nFound ${testCases.length} bank statement test cases (Nanonets + Qwen3)\n`);
|
||||
|
||||
let passedCount = 0;
|
||||
let failedCount = 0;
|
||||
|
||||
for (const testCase of testCases) {
|
||||
tap.test(`should extract: ${testCase.name}`, async () => {
|
||||
const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
|
||||
console.log(`\n=== ${testCase.name} ===`);
|
||||
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`);
|
||||
|
||||
const images = convertPdfToImages(testCase.pdfPath);
|
||||
console.log(` Pages: ${images.length}`);
|
||||
// Load saved markdown
|
||||
const mdPath = path.join(TEMP_MD_DIR, `${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`);
|
||||
|
||||
const extracted = await extractTransactions(images);
|
||||
// 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 ${extracted.length - 5} more`);
|
||||
}
|
||||
|
||||
// Compare
|
||||
const result = compareTransactions(extracted, expected);
|
||||
const perfectMatch = result.matches === result.total && extracted.length === expected.length;
|
||||
const pass = result.matches === result.total && extracted.length === expected.length;
|
||||
|
||||
if (perfectMatch) {
|
||||
if (pass) {
|
||||
passedCount++;
|
||||
console.log(` Result: PASS (${result.matches}/${result.total})`);
|
||||
} else {
|
||||
failedCount++;
|
||||
console.log(` Result: FAIL (${result.matches}/${result.total})`);
|
||||
result.errors.slice(0, 10).forEach((e) => console.log(` - ${e}`));
|
||||
}
|
||||
|
||||
if (result.variations.length > 0) {
|
||||
console.log(` Counterparty variations (${result.variations.length}):`);
|
||||
result.variations.slice(0, 5).forEach((v) => console.log(` ${v}`));
|
||||
result.errors.slice(0, 5).forEach(e => console.log(` - ${e}`));
|
||||
}
|
||||
|
||||
expect(result.matches).toEqual(result.total);
|
||||
@@ -515,16 +559,27 @@ for (const testCase of testCases) {
|
||||
});
|
||||
}
|
||||
|
||||
tap.test('summary', async () => {
|
||||
const total = testCases.length;
|
||||
tap.test('Summary', async () => {
|
||||
console.log(`\n======================================================`);
|
||||
console.log(` Bank Statement Summary (Nanonets + Qwen3 Pipeline)`);
|
||||
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: Qwen3 8B (markdown -> JSON)`);
|
||||
console.log(` Passed: ${passedCount}/${total}`);
|
||||
console.log(` Failed: ${failedCount}/${total}`);
|
||||
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();
|
||||
|
||||
@@ -1,19 +1,17 @@
|
||||
/**
|
||||
* Invoice extraction using Nanonets-OCR-s + Qwen3 (two-stage pipeline)
|
||||
* Invoice extraction using Nanonets-OCR-s + Qwen3 (sequential two-stage pipeline)
|
||||
*
|
||||
* Stage 1: Nanonets-OCR-s converts document pages to markdown (its strength)
|
||||
* Stage 2: Qwen3 extracts structured JSON from the combined markdown
|
||||
* Stage 1: Nanonets-OCR-s converts ALL document pages to markdown (stop after completion)
|
||||
* Stage 2: Qwen3 extracts structured JSON from saved markdown (after Nanonets stops)
|
||||
*
|
||||
* This leverages each model's strengths:
|
||||
* - Nanonets: Document OCR with semantic tags
|
||||
* - Qwen3: Text understanding and JSON extraction
|
||||
* This approach avoids GPU contention by running services sequentially.
|
||||
*/
|
||||
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 } from './helpers/docker.js';
|
||||
import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
|
||||
|
||||
const NANONETS_URL = 'http://localhost:8000/v1';
|
||||
const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
|
||||
@@ -21,6 +19,9 @@ const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
|
||||
const OLLAMA_URL = 'http://localhost:11434';
|
||||
const QWEN_MODEL = 'qwen3:8b';
|
||||
|
||||
// Temp directory for storing markdown between stages
|
||||
const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-markdown');
|
||||
|
||||
interface IInvoice {
|
||||
invoice_number: string;
|
||||
invoice_date: string;
|
||||
@@ -31,6 +32,13 @@ interface IInvoice {
|
||||
total_amount: 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.
|
||||
@@ -66,14 +74,13 @@ INVOICE TEXT:
|
||||
`;
|
||||
|
||||
/**
|
||||
* Convert PDF to PNG images using ImageMagick
|
||||
* Convert PDF to PNG images
|
||||
*/
|
||||
function convertPdfToImages(pdfPath: string): string[] {
|
||||
const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
|
||||
const outputPattern = path.join(tempDir, 'page-%d.png');
|
||||
|
||||
try {
|
||||
// Use 150 DPI to keep images within model's context length
|
||||
execSync(
|
||||
`convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
|
||||
{ stdio: 'pipe' }
|
||||
@@ -95,10 +102,9 @@ function convertPdfToImages(pdfPath: string): string[] {
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 1: Convert a single page to markdown using Nanonets-OCR-s
|
||||
* Convert a single page to markdown using Nanonets-OCR-s
|
||||
*/
|
||||
async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
|
||||
console.log(` [Nanonets] Converting page ${pageNum} to markdown...`);
|
||||
const startTime = Date.now();
|
||||
|
||||
const response = await fetch(`${NANONETS_URL}/chat/completions`, {
|
||||
@@ -125,21 +131,20 @@ async function convertPageToMarkdown(image: string, pageNum: number): Promise<st
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.log(` [Nanonets] ERROR page ${pageNum}: ${response.status} - ${errorText}`);
|
||||
throw new Error(`Nanonets API error: ${response.status}`);
|
||||
throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const content = (data.choices?.[0]?.message?.content || '').trim();
|
||||
console.log(` [Nanonets] Page ${pageNum} converted (${elapsed}s, ${content.length} chars)`);
|
||||
console.log(` Page ${pageNum}: ${content.length} chars (${elapsed}s)`);
|
||||
return content;
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 1: Convert all pages to markdown using Nanonets-OCR-s
|
||||
* Convert all pages of a document to markdown
|
||||
*/
|
||||
async function convertDocumentToMarkdown(images: string[]): Promise<string> {
|
||||
console.log(` [Stage 1] Converting ${images.length} page(s) to markdown with Nanonets-OCR-s...`);
|
||||
async function convertDocumentToMarkdown(images: string[], docName: string): Promise<string> {
|
||||
console.log(` [${docName}] Converting ${images.length} page(s)...`);
|
||||
|
||||
const markdownPages: string[] = [];
|
||||
|
||||
@@ -149,10 +154,24 @@ async function convertDocumentToMarkdown(images: string[]): Promise<string> {
|
||||
}
|
||||
|
||||
const fullMarkdown = markdownPages.join('\n\n');
|
||||
console.log(` [Stage 1] Complete: ${fullMarkdown.length} chars total`);
|
||||
console.log(` [${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 Qwen3 model is available
|
||||
*/
|
||||
@@ -190,7 +209,6 @@ function parseAmount(s: string | number | undefined): number {
|
||||
const match = s.match(/([\d.,]+)/);
|
||||
if (!match) return 0;
|
||||
const numStr = match[1];
|
||||
// Handle European format: 1.234,56 -> 1234.56
|
||||
const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
|
||||
? numStr.replace(/\./g, '').replace(',', '.')
|
||||
: numStr.replace(/,/g, '');
|
||||
@@ -204,10 +222,10 @@ function extractInvoiceNumber(s: string | undefined): string {
|
||||
if (!s) return '';
|
||||
let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
|
||||
const patterns = [
|
||||
/\b([A-Z]{2,3}\d{10,})\b/i, // IEE2022006460244
|
||||
/\b([A-Z]\d{8,})\b/i, // R0014359508
|
||||
/\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i, // INV-2024-001
|
||||
/\b(\d{7,})\b/, // 1579087430
|
||||
/\b([A-Z]{2,3}\d{10,})\b/i,
|
||||
/\b([A-Z]\d{8,})\b/i,
|
||||
/\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,
|
||||
/\b(\d{7,})\b/,
|
||||
];
|
||||
for (const pattern of patterns) {
|
||||
const match = clean.match(pattern);
|
||||
@@ -224,7 +242,6 @@ function extractDate(s: string | undefined): string {
|
||||
let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
|
||||
const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
|
||||
if (isoMatch) return isoMatch[1];
|
||||
// Try DD/MM/YYYY or DD.MM.YYYY
|
||||
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')}`;
|
||||
@@ -245,20 +262,16 @@ function extractCurrency(s: string | undefined): string {
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract JSON from response (handles markdown code blocks)
|
||||
* Extract JSON from response
|
||||
*/
|
||||
function extractJsonFromResponse(response: string): Record<string, unknown> | null {
|
||||
// Remove thinking tags if present (Qwen3 may include <think>...</think>)
|
||||
let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
|
||||
|
||||
// Try to find JSON in markdown code block
|
||||
const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
|
||||
|
||||
try {
|
||||
return JSON.parse(jsonStr);
|
||||
} catch {
|
||||
// Try to find JSON object pattern
|
||||
const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
|
||||
if (jsonMatch) {
|
||||
try {
|
||||
@@ -290,15 +303,16 @@ function parseJsonToInvoice(response: string): IInvoice | null {
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 2: Extract invoice from markdown using Qwen3
|
||||
* Extract invoice from markdown using Qwen3
|
||||
*/
|
||||
async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
|
||||
console.log(` [${queryId}] Sending markdown to ${QWEN_MODEL}...`);
|
||||
console.log(` [${queryId}] Sending to ${QWEN_MODEL}...`);
|
||||
const startTime = Date.now();
|
||||
|
||||
const response = await fetch(`${OLLAMA_URL}/api/chat`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
|
||||
body: JSON.stringify({
|
||||
model: QWEN_MODEL,
|
||||
messages: [{
|
||||
@@ -322,13 +336,13 @@ async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Pr
|
||||
|
||||
const data = await response.json();
|
||||
const content = (data.message?.content || '').trim();
|
||||
console.log(` [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
|
||||
console.log(` [${queryId}] Response: ${content.length} chars (${elapsed}s)`);
|
||||
|
||||
return parseJsonToInvoice(content);
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare two invoices for consensus (key fields must match)
|
||||
* Compare two invoices for consensus
|
||||
*/
|
||||
function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
|
||||
const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
|
||||
@@ -338,45 +352,39 @@ function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 2: Extract invoice using Qwen3 with consensus
|
||||
* Extract with consensus
|
||||
*/
|
||||
async function extractWithConsensus(markdown: string): Promise<IInvoice> {
|
||||
async function extractWithConsensus(markdown: string, docName: string): Promise<IInvoice> {
|
||||
const MAX_ATTEMPTS = 3;
|
||||
console.log(` [Stage 2] Extracting invoice with ${QWEN_MODEL} (consensus)...`);
|
||||
|
||||
for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
|
||||
console.log(`\n [Stage 2] --- Attempt ${attempt}/${MAX_ATTEMPTS} ---`);
|
||||
console.log(` [${docName}] Attempt ${attempt}/${MAX_ATTEMPTS}`);
|
||||
|
||||
// Extract twice
|
||||
const inv1 = await extractInvoiceFromMarkdown(markdown, `A${attempt}Q1`);
|
||||
const inv2 = await extractInvoiceFromMarkdown(markdown, `A${attempt}Q2`);
|
||||
const inv1 = await extractInvoiceFromMarkdown(markdown, `${docName}-A${attempt}Q1`);
|
||||
const inv2 = await extractInvoiceFromMarkdown(markdown, `${docName}-A${attempt}Q2`);
|
||||
|
||||
if (!inv1 || !inv2) {
|
||||
console.log(` [Stage 2] Parsing failed, retrying...`);
|
||||
console.log(` [${docName}] Parsing failed, retrying...`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(` [Stage 2] Q1: ${inv1.invoice_number} | ${inv1.invoice_date} | ${inv1.total_amount} ${inv1.currency}`);
|
||||
console.log(` [Stage 2] Q2: ${inv2.invoice_number} | ${inv2.invoice_date} | ${inv2.total_amount} ${inv2.currency}`);
|
||||
console.log(` [${docName}] Q1: ${inv1.invoice_number} | ${inv1.invoice_date} | ${inv1.total_amount}`);
|
||||
console.log(` [${docName}] Q2: ${inv2.invoice_number} | ${inv2.invoice_date} | ${inv2.total_amount}`);
|
||||
|
||||
if (invoicesMatch(inv1, inv2)) {
|
||||
console.log(` [Stage 2] CONSENSUS REACHED`);
|
||||
console.log(` [${docName}] CONSENSUS`);
|
||||
return inv2;
|
||||
}
|
||||
|
||||
console.log(` [Stage 2] NO CONSENSUS`);
|
||||
console.log(` [${docName}] No consensus`);
|
||||
}
|
||||
|
||||
// Fallback: use last response
|
||||
console.log(`\n [Stage 2] === FALLBACK ===`);
|
||||
const fallback = await extractInvoiceFromMarkdown(markdown, 'FALLBACK');
|
||||
|
||||
// Fallback
|
||||
const fallback = await extractInvoiceFromMarkdown(markdown, `${docName}-FALLBACK`);
|
||||
if (fallback) {
|
||||
console.log(` [Stage 2] ~ FALLBACK: ${fallback.invoice_number} | ${fallback.invoice_date} | ${fallback.total_amount}`);
|
||||
console.log(` [${docName}] FALLBACK: ${fallback.invoice_number} | ${fallback.invoice_date} | ${fallback.total_amount}`);
|
||||
return fallback;
|
||||
}
|
||||
|
||||
// Return empty invoice if all else fails
|
||||
return {
|
||||
invoice_number: '',
|
||||
invoice_date: '',
|
||||
@@ -388,19 +396,6 @@ async function extractWithConsensus(markdown: string): Promise<IInvoice> {
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Full pipeline: PDF -> Images -> Markdown -> JSON
|
||||
*/
|
||||
async function extractInvoice(images: string[]): Promise<IInvoice> {
|
||||
// Stage 1: Convert to markdown
|
||||
const markdown = await convertDocumentToMarkdown(images);
|
||||
|
||||
// Stage 2: Extract invoice with consensus
|
||||
const invoice = await extractWithConsensus(markdown);
|
||||
|
||||
return invoice;
|
||||
}
|
||||
|
||||
/**
|
||||
* Normalize date to YYYY-MM-DD
|
||||
*/
|
||||
@@ -435,45 +430,38 @@ function compareInvoice(
|
||||
): { match: boolean; errors: string[] } {
|
||||
const errors: string[] = [];
|
||||
|
||||
// Compare invoice number (normalize by removing spaces and case)
|
||||
const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
|
||||
const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
|
||||
if (extNum !== expNum) {
|
||||
errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
|
||||
errors.push(`invoice_number: exp "${expected.invoice_number}", got "${extracted.invoice_number}"`);
|
||||
}
|
||||
|
||||
// Compare date
|
||||
if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
|
||||
errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
|
||||
errors.push(`invoice_date: exp "${expected.invoice_date}", got "${extracted.invoice_date}"`);
|
||||
}
|
||||
|
||||
// Compare total amount (with tolerance)
|
||||
if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
|
||||
errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
|
||||
errors.push(`total_amount: exp ${expected.total_amount}, got ${extracted.total_amount}`);
|
||||
}
|
||||
|
||||
// Compare currency
|
||||
if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
|
||||
errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
|
||||
errors.push(`currency: exp "${expected.currency}", got "${extracted.currency}"`);
|
||||
}
|
||||
|
||||
return { match: errors.length === 0, errors };
|
||||
}
|
||||
|
||||
/**
|
||||
* Find all test cases (PDF + JSON pairs) in .nogit/invoices/
|
||||
* Find all test cases
|
||||
*/
|
||||
function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
|
||||
function findTestCases(): ITestCase[] {
|
||||
const testDir = path.join(process.cwd(), '.nogit/invoices');
|
||||
if (!fs.existsSync(testDir)) {
|
||||
return [];
|
||||
}
|
||||
if (!fs.existsSync(testDir)) return [];
|
||||
|
||||
const files = fs.readdirSync(testDir);
|
||||
const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
|
||||
const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
|
||||
const testCases: ITestCase[] = [];
|
||||
|
||||
for (const pdf of pdfFiles) {
|
||||
for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
|
||||
const baseName = pdf.replace('.pdf', '');
|
||||
const jsonFile = `${baseName}.json`;
|
||||
if (files.includes(jsonFile)) {
|
||||
@@ -485,66 +473,91 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
|
||||
}
|
||||
}
|
||||
|
||||
testCases.sort((a, b) => a.name.localeCompare(b.name));
|
||||
return testCases;
|
||||
return testCases.sort((a, b) => a.name.localeCompare(b.name));
|
||||
}
|
||||
|
||||
// Tests
|
||||
// ============ TESTS ============
|
||||
|
||||
tap.test('setup: ensure containers are running', async () => {
|
||||
console.log('\n[Setup] Checking Docker containers...\n');
|
||||
const testCases = findTestCases();
|
||||
console.log(`\nFound ${testCases.length} invoice test cases\n`);
|
||||
|
||||
// Nanonets for OCR
|
||||
const nanonetsOk = await ensureNanonetsOcr();
|
||||
expect(nanonetsOk).toBeTrue();
|
||||
// Ensure temp directory exists
|
||||
if (!fs.existsSync(TEMP_MD_DIR)) {
|
||||
fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
|
||||
}
|
||||
|
||||
// -------- STAGE 1: OCR with Nanonets --------
|
||||
|
||||
tap.test('Stage 1: Setup Nanonets', async () => {
|
||||
console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
|
||||
const ok = await ensureNanonetsOcr();
|
||||
expect(ok).toBeTrue();
|
||||
});
|
||||
|
||||
tap.test('Stage 1: Convert all invoices to markdown', async () => {
|
||||
console.log('\n Converting all invoice PDFs to markdown with Nanonets-OCR-s...\n');
|
||||
|
||||
for (const tc of testCases) {
|
||||
console.log(`\n === ${tc.name} ===`);
|
||||
|
||||
const images = convertPdfToImages(tc.pdfPath);
|
||||
console.log(` Pages: ${images.length}`);
|
||||
|
||||
const markdown = await convertDocumentToMarkdown(images, tc.name);
|
||||
|
||||
const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
|
||||
fs.writeFileSync(mdPath, markdown);
|
||||
tc.markdownPath = mdPath;
|
||||
console.log(` Saved: ${mdPath}`);
|
||||
}
|
||||
|
||||
console.log('\n Stage 1 complete: All invoices converted to markdown\n');
|
||||
});
|
||||
|
||||
tap.test('Stage 1: Stop Nanonets', async () => {
|
||||
stopNanonets();
|
||||
await new Promise(resolve => setTimeout(resolve, 3000));
|
||||
expect(isContainerRunning('nanonets-test')).toBeFalse();
|
||||
});
|
||||
|
||||
// -------- STAGE 2: Extraction with Qwen3 --------
|
||||
|
||||
tap.test('Stage 2: Setup Ollama + Qwen3', async () => {
|
||||
console.log('\n========== STAGE 2: Qwen3 Extraction ==========\n');
|
||||
|
||||
// Ollama for Qwen3
|
||||
const ollamaOk = await ensureMiniCpm();
|
||||
expect(ollamaOk).toBeTrue();
|
||||
|
||||
// Qwen3 model
|
||||
const qwenOk = await ensureQwen3();
|
||||
expect(qwenOk).toBeTrue();
|
||||
|
||||
console.log('\n[Setup] All containers ready!\n');
|
||||
});
|
||||
|
||||
tap.test('should have models available', async () => {
|
||||
// Check Nanonets
|
||||
const nanonetsResp = await fetch(`${NANONETS_URL}/models`);
|
||||
expect(nanonetsResp.ok).toBeTrue();
|
||||
|
||||
// Check Qwen3
|
||||
const ollamaResp = await fetch(`${OLLAMA_URL}/api/tags`);
|
||||
expect(ollamaResp.ok).toBeTrue();
|
||||
const data = await ollamaResp.json();
|
||||
const modelNames = data.models.map((m: { name: string }) => m.name);
|
||||
expect(modelNames.some((name: string) => name.includes('qwen3'))).toBeTrue();
|
||||
});
|
||||
|
||||
const testCases = findTestCases();
|
||||
console.log(`\nFound ${testCases.length} invoice test cases (Nanonets + Qwen3)\n`);
|
||||
|
||||
let passedCount = 0;
|
||||
let failedCount = 0;
|
||||
const processingTimes: number[] = [];
|
||||
|
||||
for (const testCase of testCases) {
|
||||
tap.test(`should extract invoice: ${testCase.name}`, async () => {
|
||||
const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
|
||||
console.log(`\n=== ${testCase.name} ===`);
|
||||
for (const tc of testCases) {
|
||||
tap.test(`Stage 2: Extract ${tc.name}`, async () => {
|
||||
const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
|
||||
console.log(`\n === ${tc.name} ===`);
|
||||
console.log(` Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
|
||||
|
||||
const startTime = Date.now();
|
||||
const images = convertPdfToImages(testCase.pdfPath);
|
||||
console.log(` Pages: ${images.length}`);
|
||||
|
||||
const extracted = await extractInvoice(images);
|
||||
console.log(` Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
|
||||
const mdPath = path.join(TEMP_MD_DIR, `${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 extractWithConsensus(markdown, tc.name);
|
||||
|
||||
const elapsedMs = Date.now() - startTime;
|
||||
processingTimes.push(elapsedMs);
|
||||
|
||||
console.log(` Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
|
||||
|
||||
const result = compareInvoice(extracted, expected);
|
||||
|
||||
if (result.match) {
|
||||
@@ -553,22 +566,21 @@ for (const testCase of testCases) {
|
||||
} else {
|
||||
failedCount++;
|
||||
console.log(` Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
result.errors.forEach((e) => console.log(` - ${e}`));
|
||||
result.errors.forEach(e => console.log(` - ${e}`));
|
||||
}
|
||||
|
||||
expect(result.match).toBeTrue();
|
||||
});
|
||||
}
|
||||
|
||||
tap.test('summary', async () => {
|
||||
tap.test('Summary', async () => {
|
||||
const totalInvoices = testCases.length;
|
||||
const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
|
||||
const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
|
||||
const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
|
||||
|
||||
console.log(`\n========================================`);
|
||||
console.log(` Invoice Extraction Summary`);
|
||||
console.log(` (Nanonets + Qwen3 Pipeline)`);
|
||||
console.log(` Invoice Summary (Nanonets + Qwen3)`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Stage 1: Nanonets-OCR-s (doc -> md)`);
|
||||
console.log(` Stage 2: Qwen3 8B (md -> JSON)`);
|
||||
@@ -579,6 +591,14 @@ tap.test('summary', async () => {
|
||||
console.log(` Total time: ${(totalTimeMs / 1000).toFixed(1)}s`);
|
||||
console.log(` Avg per inv: ${avgTimeSec.toFixed(1)}s`);
|
||||
console.log(`========================================\n`);
|
||||
|
||||
// Cleanup temp files
|
||||
try {
|
||||
fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
|
||||
console.log(` Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
|
||||
} catch {
|
||||
// Ignore
|
||||
}
|
||||
});
|
||||
|
||||
export default tap.start();
|
||||
|
||||
Reference in New Issue
Block a user