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readme.md
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@@ -1,19 +1,27 @@
# @host.today/ht-docker-ai 🚀
Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration or CPU fallback—no cloud API keys required.
Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration or CPU fallback—**no cloud API keys required**.
> 🔥 **Four VLMs, one registry.** From lightweight document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
## Issue Reporting and Security
For reporting bugs, issues, or security vulnerabilities, please visit [community.foss.global/](https://community.foss.global/). This is the central community hub for all issue reporting. Developers who sign and comply with our contribution agreement and go through identification can also get a [code.foss.global/](https://code.foss.global/) account to submit Pull Requests directly.
---
## 🎯 What's Included
| Model | Parameters | Best For | API |
|-------|-----------|----------|-----|
| **MiniCPM-V 4.5** | 8B | General vision understanding, image analysis, multi-image | Ollama-compatible |
| **PaddleOCR-VL** | 0.9B | Document parsing, table extraction, OCR | OpenAI-compatible |
| Model | Parameters | Best For | API | Port |
|-------|-----------|----------|-----|------|
| **MiniCPM-V 4.5** | 8B | General vision understanding, multi-image analysis | Ollama-compatible | 11434 |
| **PaddleOCR-VL** | 0.9B | Document parsing, table extraction, structured OCR | OpenAI-compatible | 8000 |
| **Nanonets-OCR-s** | ~4B | Document OCR with semantic markdown output | OpenAI-compatible | 8000 |
| **Qwen3-VL-30B** | 30B (A3B) | Advanced visual agents, code generation from images | Ollama-compatible | 11434 |
## 📦 Available Images
---
## 📦 Quick Reference: All Available Images
```
code.foss.global/host.today/ht-docker-ai:<tag>
@@ -25,12 +33,14 @@ code.foss.global/host.today/ht-docker-ai:<tag>
| `minicpm45v-cpu` | MiniCPM-V 4.5 | CPU only (8GB+ RAM) | 11434 |
| `paddleocr-vl` / `paddleocr-vl-gpu` | PaddleOCR-VL | NVIDIA GPU | 8000 |
| `paddleocr-vl-cpu` | PaddleOCR-VL | CPU only | 8000 |
| `nanonets-ocr` | Nanonets-OCR-s | NVIDIA GPU (8-10GB VRAM) | 8000 |
| `qwen3vl` | Qwen3-VL-30B-A3B | NVIDIA GPU (~20GB VRAM) | 11434 |
---
## 🖼️ MiniCPM-V 4.5
A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across 30+ languages.
A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across **30+ languages**.
### Quick Start
@@ -95,7 +105,7 @@ curl http://localhost:11434/api/chat -d '{
## 📄 PaddleOCR-VL
A specialized 0.9B Vision-Language Model optimized for document parsing. Native support for tables, formulas, charts, and text extraction in 109 languages.
A specialized **0.9B Vision-Language Model** optimized for document parsing. Native support for tables, formulas, charts, and text extraction in **109 languages**.
### Quick Start
@@ -185,8 +195,121 @@ PaddleOCR-VL accepts images in multiple formats:
---
## 🔍 Nanonets-OCR-s
A **Qwen2.5-VL-3B** model fine-tuned specifically for document OCR. Outputs structured markdown with semantic HTML tags—perfect for preserving document structure.
### Key Features
- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, watermarks/page numbers → tagged
- 🌍 **Multilingual:** Inherits Qwen's broad language support
- ⚡ **Efficient:** ~8-10GB VRAM, runs great on consumer GPUs
- 🔌 **OpenAI-compatible:** Drop-in replacement for existing pipelines
### Quick Start
```bash
docker run -d \
--name nanonets \
--gpus all \
-p 8000:8000 \
-v hf-cache:/root/.cache/huggingface \
code.foss.global/host.today/ht-docker-ai:nanonets-ocr
```
### API Usage
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "nanonets/Nanonets-OCR-s",
"messages": [{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}},
{"type": "text", "text": "Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation."}
]
}],
"temperature": 0.0,
"max_tokens": 4096
}'
```
### Output Format
Nanonets-OCR-s returns markdown with semantic tags:
| Element | Output Format |
|---------|---------------|
| Tables | `<table>...</table>` (HTML) |
| Equations | `$...$` (LaTeX) |
| Images | `<img>description</img>` |
| Watermarks | `<watermark>OFFICIAL COPY</watermark>` |
| Page numbers | `<page_number>14</page_number>` |
### Performance
| Metric | Value |
|--------|-------|
| Speed | 3–8 seconds per page |
| VRAM | ~8-10GB |
---
## 🧠 Qwen3-VL-30B-A3B
The **most powerful** Qwen vision model—30B parameters with 3B active (MoE architecture). Handles complex visual reasoning, code generation from screenshots, and visual agent capabilities.
### Key Features
- 🚀 **256K context** (expandable to 1M tokens!)
- 🤖 **Visual agent capabilities** — can plan and execute multi-step tasks
- 💻 **Code generation from images** — screenshot → working code
- 🎯 **State-of-the-art** visual reasoning
### Quick Start
```bash
docker run -d \
--name qwen3vl \
--gpus all \
-p 11434:11434 \
-v ollama-data:/root/.ollama \
code.foss.global/host.today/ht-docker-ai:qwen3vl
```
Then pull the model (one-time, ~20GB):
```bash
docker exec qwen3vl ollama pull qwen3-vl:30b-a3b
```
### API Usage
```bash
curl http://localhost:11434/api/chat -d '{
"model": "qwen3-vl:30b-a3b",
"messages": [{
"role": "user",
"content": "Analyze this screenshot and write the code to recreate this UI",
"images": ["<base64-encoded-image>"]
}]
}'
```
### Hardware Requirements
| Requirement | Value |
|-------------|-------|
| VRAM | ~20GB (Q4_K_M quantization) |
| Context | 256K tokens default |
---
## 🐳 Docker Compose
Run multiple VLMs together for maximum flexibility:
```yaml
version: '3.8'
services:
@@ -206,7 +329,7 @@ services:
capabilities: [gpu]
restart: unless-stopped
# Document parsing / OCR
# Document parsing / OCR (table specialist)
paddleocr:
image: code.foss.global/host.today/ht-docker-ai:paddleocr-vl
ports:
@@ -222,6 +345,22 @@ services:
capabilities: [gpu]
restart: unless-stopped
# Document OCR with semantic output
nanonets:
image: code.foss.global/host.today/ht-docker-ai:nanonets-ocr
ports:
- "8001:8000"
volumes:
- hf-cache:/root/.cache/huggingface
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
restart: unless-stopped
volumes:
ollama-data:
hf-cache:
@@ -231,7 +370,7 @@ volumes:
## ⚙️ Environment Variables
### MiniCPM-V 4.5
### MiniCPM-V 4.5 & Qwen3-VL (Ollama-based)
| Variable | Default | Description |
|----------|---------|-------------|
@@ -239,13 +378,47 @@ volumes:
| `OLLAMA_HOST` | `0.0.0.0` | API bind address |
| `OLLAMA_ORIGINS` | `*` | Allowed CORS origins |
### PaddleOCR-VL
### PaddleOCR-VL & Nanonets-OCR (vLLM-based)
| Variable | Default | Description |
|----------|---------|-------------|
| `MODEL_NAME` | `PaddlePaddle/PaddleOCR-VL` | HuggingFace model ID |
| `SERVER_HOST` | `0.0.0.0` | API bind address |
| `SERVER_PORT` | `8000` | API port |
| `MODEL_NAME` | Model-specific | HuggingFace model ID |
| `HOST` | `0.0.0.0` | API bind address |
| `PORT` | `8000` | API port |
| `MAX_MODEL_LEN` | `8192` | Maximum sequence length |
| `GPU_MEMORY_UTILIZATION` | `0.9` | GPU memory usage (0-1) |
---
## 🏗️ Architecture Notes
### Dual-VLM Consensus Strategy
For production document extraction, consider using multiple models together:
1. **Pass 1:** MiniCPM-V visual extraction (images → JSON)
2. **Pass 2:** PaddleOCR-VL table recognition (images → markdown → JSON)
3. **Consensus:** If results match → Done (fast path)
4. **Pass 3+:** Additional visual passes if needed
This dual-VLM approach catches extraction errors that single models miss.
### Why Multi-Model Works
- **Different architectures:** Independent models cross-validate each other
- **Specialized strengths:** PaddleOCR-VL excels at tables; MiniCPM-V handles general vision
- **Native processing:** All VLMs see original images—no intermediate structure loss
### Model Selection Guide
| Task | Recommended Model |
|------|-------------------|
| General image understanding | MiniCPM-V 4.5 |
| Table extraction from documents | PaddleOCR-VL |
| Document OCR with structure preservation | Nanonets-OCR-s |
| Complex visual reasoning / code generation | Qwen3-VL-30B |
| Multi-image analysis | MiniCPM-V 4.5 |
| Visual agent tasks | Qwen3-VL-30B |
---
@@ -265,37 +438,16 @@ cd ht-docker-ai
---
## 🏗️ Architecture Notes
### Dual-VLM Consensus Strategy
For production document extraction, consider using both models together:
1. **Pass 1:** MiniCPM-V visual extraction (images → JSON)
2. **Pass 2:** PaddleOCR-VL table recognition (images → markdown → JSON)
3. **Consensus:** If results match → Done (fast path)
4. **Pass 3+:** Additional visual passes if needed
This dual-VLM approach catches extraction errors that single models miss.
### Why This Works
- **Different architectures:** Two independent models cross-validate each other
- **Specialized strengths:** PaddleOCR-VL excels at tables; MiniCPM-V handles general vision
- **Native processing:** Both VLMs see original images—no intermediate HTML/structure loss
---
## 🔍 Troubleshooting
### Model download hangs
```bash
docker logs -f <container-name>
```
Model downloads can take several minutes (~5GB for MiniCPM-V).
Model downloads can take several minutes (~5GB for MiniCPM-V, ~20GB for Qwen3-VL).
### Out of memory
- **GPU:** Use the CPU variant or upgrade VRAM
- **GPU:** Use a lighter model variant or upgrade VRAM
- **CPU:** Increase container memory: `--memory=16g`
### API not responding
@@ -315,6 +467,13 @@ sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```
### GPU Memory Contention (Multi-Model)
When running multiple VLMs on a single GPU:
- vLLM and Ollama both need significant GPU memory
- **Single GPU:** Run services sequentially (stop one before starting another)
- **Multi-GPU:** Assign each service to a different GPU via `CUDA_VISIBLE_DEVICES`
---
## License and Legal Information

200
test/test.bankstatements.nanonets.ts
View File

@@ -28,12 +28,19 @@ interface ITransaction {
amount: number;
}
interface IImageData {
base64: string;
width: number;
height: number;
pageNum: number;
}
interface ITestCase {
name: string;
pdfPath: string;
jsonPath: string;
markdownPath?: string;
images?: string[];
images?: IImageData[];
}
// Nanonets-specific prompt for document OCR to markdown
@@ -50,12 +57,48 @@ const JSON_EXTRACTION_PROMPT = `Extract ALL transactions from this bank statemen
STATEMENT:
`;
// Constants for smart batching
const MAX_VISUAL_TOKENS = 28000; // ~32K context minus prompt/output headroom
const PATCH_SIZE = 14; // Qwen2.5-VL uses 14x14 patches
/**
* Convert PDF to PNG images using ImageMagick
* Estimate visual tokens for an image based on dimensions
*/
function convertPdfToImages(pdfPath: string): string[] {
function estimateVisualTokens(width: number, height: number): number {
return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
}
/**
* Batch images to fit within context window
*/
function batchImages(images: IImageData[]): IImageData[][] {
const batches: IImageData[][] = [];
let currentBatch: IImageData[] = [];
let currentTokens = 0;
for (const img of images) {
const imgTokens = estimateVisualTokens(img.width, img.height);
if (currentTokens + imgTokens > MAX_VISUAL_TOKENS && currentBatch.length > 0) {
batches.push(currentBatch);
currentBatch = [img];
currentTokens = imgTokens;
} else {
currentBatch.push(img);
currentTokens += imgTokens;
}
}
if (currentBatch.length > 0) batches.push(currentBatch);
return batches;
}
/**
* Convert PDF to JPEG images using ImageMagick with dimension tracking
*/
function convertPdfToImages(pdfPath: string): IImageData[] {
const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
const outputPattern = path.join(tempDir, 'page-%d.png');
const outputPattern = path.join(tempDir, 'page-%d.jpg');
try {
execSync(
@@ -63,13 +106,24 @@ function convertPdfToImages(pdfPath: string): string[] {
{ stdio: 'pipe' }
);
const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
const images: string[] = [];
const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
const images: IImageData[] = [];
for (const file of files) {
for (let i = 0; i < files.length; i++) {
const file = files[i];
const imagePath = path.join(tempDir, file);
const imageData = fs.readFileSync(imagePath);
images.push(imageData.toString('base64'));
// Get image dimensions using identify command
const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
const [width, height] = dimensions.split(' ').map(Number);
images.push({
base64: imageData.toString('base64'),
width,
height,
pageNum: i + 1,
});
}
return images;
@@ -79,10 +133,28 @@ function convertPdfToImages(pdfPath: string): string[] {
}
/**
* Convert a single page to markdown using Nanonets-OCR-s
* Convert a batch of pages to markdown using Nanonets-OCR-s
*/
async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
const startTime = Date.now();
const pageNums = batch.map(img => img.pageNum).join(', ');
// Build content array with all images first, then the prompt
const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
for (const img of batch) {
content.push({
type: 'image_url',
image_url: { url: `data:image/jpeg;base64,${img.base64}` },
});
}
// Add prompt with page separator instruction if multiple pages
const promptText = batch.length > 1
? `${NANONETS_OCR_PROMPT}\n\nPlease clearly separate each page's content with "--- PAGE N ---" markers, where N is the page number starting from ${batch[0].pageNum}.`
: NANONETS_OCR_PROMPT;
content.push({ type: 'text', text: promptText });
const response = await fetch(`${NANONETS_URL}/chat/completions`, {
method: 'POST',
@@ -94,12 +166,9 @@ async function convertPageToMarkdown(image: string, pageNum: number): Promise<st
model: NANONETS_MODEL,
messages: [{
role: 'user',
content: [
{ type: 'image_url', image_url: { url: `data:image/png;base64,${image}` }},
{ type: 'text', text: NANONETS_OCR_PROMPT },
],
content,
}],
max_tokens: 4096,
max_tokens: 4096 * batch.length, // Scale output tokens with batch size
temperature: 0.0,
}),
});
@@ -112,25 +181,35 @@ async function convertPageToMarkdown(image: string, pageNum: number): Promise<st
}
const data = await response.json();
const content = (data.choices?.[0]?.message?.content || '').trim();
console.log(` Page ${pageNum}: ${content.length} chars (${elapsed}s)`);
return content;
let responseContent = (data.choices?.[0]?.message?.content || '').trim();
// For single-page batches, add page marker if not present
if (batch.length === 1 && !responseContent.includes('--- PAGE')) {
responseContent = `--- PAGE ${batch[0].pageNum} ---\n${responseContent}`;
}
console.log(` Pages [${pageNums}]: ${responseContent.length} chars (${elapsed}s)`);
return responseContent;
}
/**
* Convert all pages of a document to markdown
* Convert all pages of a document to markdown using smart batching
*/
async function convertDocumentToMarkdown(images: string[], docName: string): Promise<string> {
console.log(` [${docName}] Converting ${images.length} page(s)...`);
async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
const batches = batchImages(images);
console.log(` [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
const markdownPages: string[] = [];
const markdownParts: string[] = [];
for (let i = 0; i < images.length; i++) {
const markdown = await convertPageToMarkdown(images[i], i + 1);
markdownPages.push(`--- PAGE ${i + 1} ---\n${markdown}`);
for (let i = 0; i < batches.length; i++) {
const batch = batches[i];
const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
console.log(` Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
const markdown = await convertBatchToMarkdown(batch);
markdownParts.push(markdown);
}
const fullMarkdown = markdownPages.join('\n\n');
const fullMarkdown = markdownParts.join('\n\n');
console.log(` [${docName}] Complete: ${fullMarkdown.length} chars total`);
return fullMarkdown;
}
@@ -161,25 +240,6 @@ async function ensureExtractionModel(): Promise<boolean> {
const models = data.models || [];
if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
console.log(` [Ollama] Model available: ${EXTRACTION_MODEL}`);
// Warmup: send a simple request to ensure model is loaded
console.log(` [Ollama] Warming up model...`);
const warmupResponse = await fetch(`${OLLAMA_URL}/api/chat`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
model: EXTRACTION_MODEL,
messages: [{ role: 'user', content: 'Return: [{"test": 1}]' }],
stream: false,
}),
signal: AbortSignal.timeout(120000),
});
if (warmupResponse.ok) {
const warmupData = await warmupResponse.json();
console.log(` [Ollama] Warmup complete (${warmupData.message?.content?.length || 0} chars)`);
}
return true;
}
}
@@ -201,22 +261,24 @@ async function ensureExtractionModel(): Promise<boolean> {
* Extract transactions from markdown using GPT-OSS 20B (streaming)
*/
async function extractTransactionsFromMarkdown(markdown: string, queryId: string): Promise<ITransaction[]> {
console.log(` [${queryId}] Sending to ${EXTRACTION_MODEL}...`);
console.log(` [${queryId}] Markdown length: ${markdown.length}`);
const startTime = Date.now();
const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
console.log(` [${queryId}] Prompt preview: ${fullPrompt.substring(0, 200)}...`);
// Log exact prompt
console.log(`\n [${queryId}] ===== PROMPT =====`);
console.log(fullPrompt);
console.log(` [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
const response = await fetch(`${OLLAMA_URL}/api/chat`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
model: EXTRACTION_MODEL,
messages: [{
role: 'user',
content: fullPrompt,
}],
messages: [
{ role: 'user', content: 'Hi there, how are you?' },
{ role: 'assistant', content: 'Good, how can I help you today?' },
{ role: 'user', content: fullPrompt },
],
stream: true,
}),
signal: AbortSignal.timeout(600000), // 10 minute timeout
@@ -228,24 +290,45 @@ async function extractTransactionsFromMarkdown(markdown: string, queryId: string
throw new Error(`Ollama API error: ${response.status}`);
}
// Stream the response and log to console
// Stream the response
let content = '';
let thinkingContent = '';
let thinkingStarted = false;
let outputStarted = false;
const reader = response.body!.getReader();
const decoder = new TextDecoder();
process.stdout.write(` [${queryId}] `);
try {
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);
// Stream thinking tokens
const thinking = json.message?.thinking || '';
if (thinking) {
if (!thinkingStarted) {
process.stdout.write(` [${queryId}] THINKING: `);
thinkingStarted = true;
}
process.stdout.write(thinking);
thinkingContent += thinking;
}
// Stream content tokens
const token = json.message?.content || '';
if (token) {
if (!outputStarted) {
if (thinkingStarted) process.stdout.write('\n');
process.stdout.write(` [${queryId}] OUTPUT: `);
outputStarted = true;
}
process.stdout.write(token);
content += token;
}
@@ -254,9 +337,12 @@ async function extractTransactionsFromMarkdown(markdown: string, queryId: string
}
}
}
} finally {
if (thinkingStarted || outputStarted) process.stdout.write('\n');
}
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
console.log(`\n [${queryId}] Done: ${content.length} chars (${elapsed}s)`);
console.log(` [${queryId}] Done: ${thinkingContent.length} thinking chars, ${content.length} output chars (${elapsed}s)`);
return parseJsonResponse(content, queryId);
}

302
test/test.invoices.nanonets.ts
View File

@@ -1,8 +1,8 @@
/**
* Invoice extraction using Nanonets-OCR-s + Qwen3 (sequential two-stage pipeline)
* Invoice extraction using Nanonets-OCR-s + GPT-OSS 20B (sequential two-stage pipeline)
*
* 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)
* Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
*
* This approach avoids GPU contention by running services sequentially.
*/
@@ -17,7 +17,7 @@ const NANONETS_URL = 'http://localhost:8000/v1';
const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
const OLLAMA_URL = 'http://localhost:11434';
const QWEN_MODEL = 'qwen3:8b';
const EXTRACTION_MODEL = 'gpt-oss:20b';
// Temp directory for storing markdown between stages
const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-markdown');
@@ -32,6 +32,13 @@ interface IInvoice {
total_amount: number;
}
interface IImageData {
base64: string;
width: number;
height: number;
pageNum: number;
}
interface ITestCase {
name: string;
pdfPath: string;
@@ -47,7 +54,7 @@ If there is an image in the document and image caption is not present, add a sma
Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
// JSON extraction prompt for Qwen3
// JSON extraction prompt for GPT-OSS 20B
const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
IMPORTANT RULES:
@@ -73,12 +80,48 @@ Return ONLY this JSON format, no explanation:
INVOICE TEXT:
`;
// Constants for smart batching
const MAX_VISUAL_TOKENS = 28000; // ~32K context minus prompt/output headroom
const PATCH_SIZE = 14; // Qwen2.5-VL uses 14x14 patches
/**
* Convert PDF to PNG images
* Estimate visual tokens for an image based on dimensions
*/
function convertPdfToImages(pdfPath: string): string[] {
function estimateVisualTokens(width: number, height: number): number {
return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
}
/**
* Batch images to fit within context window
*/
function batchImages(images: IImageData[]): IImageData[][] {
const batches: IImageData[][] = [];
let currentBatch: IImageData[] = [];
let currentTokens = 0;
for (const img of images) {
const imgTokens = estimateVisualTokens(img.width, img.height);
if (currentTokens + imgTokens > MAX_VISUAL_TOKENS && currentBatch.length > 0) {
batches.push(currentBatch);
currentBatch = [img];
currentTokens = imgTokens;
} else {
currentBatch.push(img);
currentTokens += imgTokens;
}
}
if (currentBatch.length > 0) batches.push(currentBatch);
return batches;
}
/**
* Convert PDF to JPEG images using ImageMagick with dimension tracking
*/
function convertPdfToImages(pdfPath: string): IImageData[] {
const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
const outputPattern = path.join(tempDir, 'page-%d.png');
const outputPattern = path.join(tempDir, 'page-%d.jpg');
try {
execSync(
@@ -86,13 +129,24 @@ function convertPdfToImages(pdfPath: string): string[] {
{ stdio: 'pipe' }
);
const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
const images: string[] = [];
const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
const images: IImageData[] = [];
for (const file of files) {
for (let i = 0; i < files.length; i++) {
const file = files[i];
const imagePath = path.join(tempDir, file);
const imageData = fs.readFileSync(imagePath);
images.push(imageData.toString('base64'));
// Get image dimensions using identify command
const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
const [width, height] = dimensions.split(' ').map(Number);
images.push({
base64: imageData.toString('base64'),
width,
height,
pageNum: i + 1,
});
}
return images;
@@ -102,10 +156,28 @@ function convertPdfToImages(pdfPath: string): string[] {
}
/**
* Convert a single page to markdown using Nanonets-OCR-s
* Convert a batch of pages to markdown using Nanonets-OCR-s
*/
async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
const startTime = Date.now();
const pageNums = batch.map(img => img.pageNum).join(', ');
// Build content array with all images first, then the prompt
const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
for (const img of batch) {
content.push({
type: 'image_url',
image_url: { url: `data:image/jpeg;base64,${img.base64}` },
});
}
// Add prompt with page separator instruction if multiple pages
const promptText = batch.length > 1
? `${NANONETS_OCR_PROMPT}\n\nPlease clearly separate each page's content with "--- PAGE N ---" markers, where N is the page number starting from ${batch[0].pageNum}.`
: NANONETS_OCR_PROMPT;
content.push({ type: 'text', text: promptText });
const response = await fetch(`${NANONETS_URL}/chat/completions`, {
method: 'POST',
@@ -117,12 +189,9 @@ async function convertPageToMarkdown(image: string, pageNum: number): Promise<st
model: NANONETS_MODEL,
messages: [{
role: 'user',
content: [
{ type: 'image_url', image_url: { url: `data:image/png;base64,${image}` }},
{ type: 'text', text: NANONETS_OCR_PROMPT },
],
content,
}],
max_tokens: 4096,
max_tokens: 4096 * batch.length, // Scale output tokens with batch size
temperature: 0.0,
}),
});
@@ -135,25 +204,35 @@ async function convertPageToMarkdown(image: string, pageNum: number): Promise<st
}
const data = await response.json();
const content = (data.choices?.[0]?.message?.content || '').trim();
console.log(` Page ${pageNum}: ${content.length} chars (${elapsed}s)`);
return content;
let responseContent = (data.choices?.[0]?.message?.content || '').trim();
// For single-page batches, add page marker if not present
if (batch.length === 1 && !responseContent.includes('--- PAGE')) {
responseContent = `--- PAGE ${batch[0].pageNum} ---\n${responseContent}`;
}
console.log(` Pages [${pageNums}]: ${responseContent.length} chars (${elapsed}s)`);
return responseContent;
}
/**
* Convert all pages of a document to markdown
* Convert all pages of a document to markdown using smart batching
*/
async function convertDocumentToMarkdown(images: string[], docName: string): Promise<string> {
console.log(` [${docName}] Converting ${images.length} page(s)...`);
async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
const batches = batchImages(images);
console.log(` [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
const markdownPages: string[] = [];
const markdownParts: string[] = [];
for (let i = 0; i < images.length; i++) {
const markdown = await convertPageToMarkdown(images[i], i + 1);
markdownPages.push(`--- PAGE ${i + 1} ---\n${markdown}`);
for (let i = 0; i < batches.length; i++) {
const batch = batches[i];
const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
console.log(` Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
const markdown = await convertBatchToMarkdown(batch);
markdownParts.push(markdown);
}
const fullMarkdown = markdownPages.join('\n\n');
const fullMarkdown = markdownParts.join('\n\n');
console.log(` [${docName}] Complete: ${fullMarkdown.length} chars total`);
return fullMarkdown;
}
@@ -173,16 +252,16 @@ function stopNanonets(): void {
}
/**
* Ensure Qwen3 model is available
* Ensure GPT-OSS 20B model is available
*/
async function ensureQwen3(): Promise<boolean> {
async function ensureExtractionModel(): Promise<boolean> {
try {
const response = await fetch(`${OLLAMA_URL}/api/tags`);
if (response.ok) {
const data = await response.json();
const models = data.models || [];
if (models.some((m: { name: string }) => m.name === QWEN_MODEL)) {
console.log(` [Ollama] Model available: ${QWEN_MODEL}`);
if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
console.log(` [Ollama] Model available: ${EXTRACTION_MODEL}`);
return true;
}
}
@@ -190,11 +269,11 @@ async function ensureQwen3(): Promise<boolean> {
return false;
}
console.log(` [Ollama] Pulling ${QWEN_MODEL}...`);
console.log(` [Ollama] Pulling ${EXTRACTION_MODEL}...`);
const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ name: QWEN_MODEL, stream: false }),
body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
});
return pullResponse.ok;
@@ -303,88 +382,102 @@ function parseJsonToInvoice(response: string): IInvoice | null {
}
/**
* Extract invoice from markdown using Qwen3
* Extract invoice from markdown using GPT-OSS 20B (streaming)
*/
async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
console.log(` [${queryId}] Sending to ${QWEN_MODEL}...`);
const startTime = Date.now();
const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
// Log exact prompt
console.log(`\n [${queryId}] ===== PROMPT =====`);
console.log(fullPrompt);
console.log(` [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
const response = await fetch(`${OLLAMA_URL}/api/chat`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
body: JSON.stringify({
model: QWEN_MODEL,
messages: [{
role: 'user',
content: JSON_EXTRACTION_PROMPT + markdown,
}],
stream: false,
options: {
num_predict: 2000,
temperature: 0.1,
},
model: EXTRACTION_MODEL,
messages: [
{ role: 'user', content: 'Hi there, how are you?' },
{ role: 'assistant', content: 'Good, how can I help you today?' },
{ role: 'user', content: fullPrompt },
],
stream: true,
}),
signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
});
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
if (!response.ok) {
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
console.log(` [${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: ${content.length} chars (${elapsed}s)`);
// Stream the response
let content = '';
let thinkingContent = '';
let thinkingStarted = false;
let outputStarted = false;
const reader = response.body!.getReader();
const decoder = new TextDecoder();
try {
while (true) {
const { done, value } = await reader.read();
if (done) break;
const chunk = decoder.decode(value, { stream: true });
// Each line is a JSON object
for (const line of chunk.split('\n').filter(l => l.trim())) {
try {
const json = JSON.parse(line);
// Stream thinking tokens
const thinking = json.message?.thinking || '';
if (thinking) {
if (!thinkingStarted) {
process.stdout.write(` [${queryId}] THINKING: `);
thinkingStarted = true;
}
process.stdout.write(thinking);
thinkingContent += thinking;
}
// Stream content tokens
const token = json.message?.content || '';
if (token) {
if (!outputStarted) {
if (thinkingStarted) process.stdout.write('\n');
process.stdout.write(` [${queryId}] OUTPUT: `);
outputStarted = true;
}
process.stdout.write(token);
content += token;
}
} catch {
// Ignore parse errors for partial chunks
}
}
}
} finally {
if (thinkingStarted || outputStarted) process.stdout.write('\n');
}
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
console.log(` [${queryId}] Done: ${thinkingContent.length} thinking chars, ${content.length} output chars (${elapsed}s)`);
return parseJsonToInvoice(content);
}
/**
* Compare two invoices for consensus
* Extract invoice (single pass - GPT-OSS is more reliable)
*/
function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
const dateMatch = a.invoice_date === b.invoice_date;
const totalMatch = Math.abs(a.total_amount - b.total_amount) < 0.02;
return numMatch && dateMatch && totalMatch;
}
/**
* Extract with consensus
*/
async function extractWithConsensus(markdown: string, docName: string): Promise<IInvoice> {
const MAX_ATTEMPTS = 3;
for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
console.log(` [${docName}] Attempt ${attempt}/${MAX_ATTEMPTS}`);
const inv1 = await extractInvoiceFromMarkdown(markdown, `${docName}-A${attempt}Q1`);
const inv2 = await extractInvoiceFromMarkdown(markdown, `${docName}-A${attempt}Q2`);
if (!inv1 || !inv2) {
console.log(` [${docName}] Parsing failed, retrying...`);
continue;
}
console.log(` [${docName}] Q1: ${inv1.invoice_number} | ${inv1.invoice_date} | ${inv1.total_amount}`);
console.log(` [${docName}] Q2: ${inv2.invoice_number} | ${inv2.invoice_date} | ${inv2.total_amount}`);
if (invoicesMatch(inv1, inv2)) {
console.log(` [${docName}] CONSENSUS`);
return inv2;
}
console.log(` [${docName}] No consensus`);
}
// Fallback
const fallback = await extractInvoiceFromMarkdown(markdown, `${docName}-FALLBACK`);
if (fallback) {
console.log(` [${docName}] FALLBACK: ${fallback.invoice_number} | ${fallback.invoice_date} | ${fallback.total_amount}`);
return fallback;
}
async function extractInvoice(markdown: string, docName: string): Promise<IInvoice> {
console.log(` [${docName}] Extracting...`);
const invoice = await extractInvoiceFromMarkdown(markdown, docName);
if (!invoice) {
return {
invoice_number: '',
invoice_date: '',
@@ -395,6 +488,9 @@ async function extractWithConsensus(markdown: string, docName: string): Promise<
total_amount: 0,
};
}
console.log(` [${docName}] Extracted: ${invoice.invoice_number}`);
return invoice;
}
/**
* Normalize date to YYYY-MM-DD
@@ -520,16 +616,16 @@ tap.test('Stage 1: Stop Nanonets', async () => {
expect(isContainerRunning('nanonets-test')).toBeFalse();
});
// -------- STAGE 2: Extraction with Qwen3 --------
// -------- STAGE 2: Extraction with GPT-OSS 20B --------
tap.test('Stage 2: Setup Ollama + Qwen3', async () => {
console.log('\n========== STAGE 2: Qwen3 Extraction ==========\n');
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 qwenOk = await ensureQwen3();
expect(qwenOk).toBeTrue();
const extractionOk = await ensureExtractionModel();
expect(extractionOk).toBeTrue();
});
let passedCount = 0;
@@ -551,7 +647,7 @@ for (const tc of testCases) {
const markdown = fs.readFileSync(mdPath, 'utf-8');
console.log(` Markdown: ${markdown.length} chars`);
const extracted = await extractWithConsensus(markdown, tc.name);
const extracted = await extractInvoice(markdown, tc.name);
const elapsedMs = Date.now() - startTime;
processingTimes.push(elapsedMs);
@@ -580,10 +676,10 @@ tap.test('Summary', async () => {
const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
console.log(`\n========================================`);
console.log(` Invoice Summary (Nanonets + Qwen3)`);
console.log(` Invoice Summary (Nanonets + GPT-OSS 20B)`);
console.log(`========================================`);
console.log(` Stage 1: Nanonets-OCR-s (doc -> md)`);
console.log(` Stage 2: Qwen3 8B (md -> JSON)`);
console.log(` Stage 2: GPT-OSS 20B (md -> JSON)`);
console.log(` Passed: ${passedCount}/${totalInvoices}`);
console.log(` Failed: ${failedCount}/${totalInvoices}`);
console.log(` Accuracy: ${accuracy.toFixed(1)}%`);