v1.14.0

Dockerfile_nanonets_vllm_gpu_VRAM10GB

@@ -0,0 +1,34 @@
+# Nanonets-OCR2-3B Vision Language Model
+# Based on Qwen2.5-VL-3B, fine-tuned for document OCR (Oct 2025 release)
+# Improvements over OCR-s: better semantic tagging, LaTeX equations, flowcharts
+# ~12-16GB VRAM with 30K context, outputs structured markdown with semantic tags
+#
+# Build: docker build -f Dockerfile_nanonets_vllm_gpu_VRAM10GB -t nanonets-ocr .
+# Run:   docker run --gpus all -p 8000:8000 -v ht-huggingface-cache:/root/.cache/huggingface nanonets-ocr
+
+FROM vllm/vllm-openai:latest
+
+LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
+LABEL description="Nanonets-OCR2-3B - Document OCR optimized Vision Language Model"
+LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
+
+# Environment configuration
+ENV MODEL_NAME="nanonets/Nanonets-OCR2-3B"
+ENV HOST="0.0.0.0"
+ENV PORT="8000"
+ENV MAX_MODEL_LEN="30000"
+ENV GPU_MEMORY_UTILIZATION="0.9"
+
+# Expose OpenAI-compatible API port
+EXPOSE 8000
+
+# Health check - vLLM exposes /health endpoint
+HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=5 \
+    CMD curl -f http://localhost:8000/health || exit 1
+
+# Start vLLM server with Nanonets-OCR2-3B model
+CMD ["--model", "nanonets/Nanonets-OCR2-3B", \
+     "--trust-remote-code", \
+     "--max-model-len", "30000", \
+     "--host", "0.0.0.0", \
+     "--port", "8000"]

build-images.sh

@@ -13,46 +13,38 @@ NC='\033[0m' # No Color
 
 echo -e "${BLUE}Building ht-docker-ai images...${NC}"
 
-# Build GPU variant
+# Build MiniCPM-V 4.5 GPU variant
 echo -e "${GREEN}Building MiniCPM-V 4.5 GPU variant...${NC}"
 docker build \
-    -f Dockerfile_minicpm45v_gpu \
+    -f Dockerfile_minicpm45v_ollama_gpu_VRAM9GB \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-gpu \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:latest \
     .
 
-# Build CPU variant
-echo -e "${GREEN}Building MiniCPM-V 4.5 CPU variant...${NC}"
+# Build Qwen3-VL GPU variant
+echo -e "${GREEN}Building Qwen3-VL-30B-A3B GPU variant...${NC}"
 docker build \
-    -f Dockerfile_minicpm45v_cpu \
-    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-cpu \
+    -f Dockerfile_qwen3vl_ollama_gpu_VRAM20GB \
+    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:qwen3vl \
     .
 
-# Build PaddleOCR-VL GPU variant
-echo -e "${GREEN}Building PaddleOCR-VL GPU variant...${NC}"
+# Build Nanonets-OCR GPU variant
+echo -e "${GREEN}Building Nanonets-OCR-s GPU variant...${NC}"
 docker build \
-    -f Dockerfile_paddleocr_vl_gpu \
-    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl \
-    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl-gpu \
-    .
-
-# Build PaddleOCR-VL CPU variant
-echo -e "${GREEN}Building PaddleOCR-VL CPU variant...${NC}"
-docker build \
-    -f Dockerfile_paddleocr_vl_cpu \
-    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl-cpu \
+    -f Dockerfile_nanonets_vllm_gpu_VRAM10GB \
+    -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:nanonets-ocr \
     .
 
 echo -e "${GREEN}All images built successfully!${NC}"
 echo ""
 echo "Available images:"
-echo "  MiniCPM-V 4.5:"
-echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v (GPU)"
-echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-cpu (CPU)"
-echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:latest (GPU)"
+echo "  MiniCPM-V 4.5 (Ollama, ~9GB VRAM):"
+echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v"
+echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:latest"
 echo ""
-echo "  PaddleOCR-VL (Vision-Language Model):"
-echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl (GPU/vLLM)"
-echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl-gpu (GPU/vLLM)"
-echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl-cpu (CPU)"
+echo "  Qwen3-VL-30B-A3B (Ollama, ~20GB VRAM):"
+echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:qwen3vl"
+echo ""
+echo "  Nanonets-OCR-s (vLLM, ~10GB VRAM):"
+echo "  - ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:nanonets-ocr"

changelog.md

@@ -1,5 +1,26 @@
 # Changelog
 
+## 2026-01-19 - 1.14.0 - feat(docker-images)
+add vLLM-based Nanonets-OCR2-3B image, Qwen3-VL Ollama image and refactor build/docs/tests to use new runtime/layout
+
+- Add new Dockerfiles for Nanonets (Dockerfile_nanonets_vllm_gpu_VRAM10GB), Qwen3 (Dockerfile_qwen3vl_ollama_gpu_VRAM20GB) and a clarified MiniCPM Ollama variant (Dockerfile_minicpm45v_ollama_gpu_VRAM9GB); remove older, redundant Dockerfiles.
+- Update build-images.sh to build the new image tags (minicpm45v, qwen3vl, nanonets-ocr) and adjust messaging/targets accordingly.
+- Documentation overhaul: readme.md and readme.hints.md updated to reflect vLLM vs Ollama runtimes, corrected ports/VRAM estimates, volume recommendations, and API endpoint details.
+- Tests updated to target the new model ID (nanonets/Nanonets-OCR2-3B), to process one page per batch, and to include a 10-minute AbortSignal timeout for OCR requests.
+- Added focused extraction test suites (test/test.invoices.extraction.ts and test/test.invoices.failed.ts) for faster iteration and debugging of invoice extraction.
+- Bump devDependencies: @git.zone/tsrun -> ^2.0.1 and @git.zone/tstest -> ^3.1.5.
+- Misc: test helper references and docker compose/test port mapping fixed (nanonets uses 8000), and various README sections cleaned and reorganized.
+
+## 2026-01-18 - 1.13.2 - fix(tests)
+stabilize OCR extraction tests and manage GPU containers
+
+- Add stopAllGpuContainers() and call it before starting GPU images to free GPU memory.
+- Remove PaddleOCR-VL image configs and associated ensure helpers from docker test helper to simplify images list.
+- 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).
+- Introduce temporary markdown directory handling and cleanup; add stopNanonets() and container running checks in tests.
+- Switch bank statement extraction model from qwen3:8b to gpt-oss:20b; add request timeout and improved logging/console output across tests.
+- Refactor extractWithConsensus and extraction functions to accept document identifiers, improve error messages and JSON extraction robustness.
+
 ## 2026-01-18 - 1.13.1 - fix(image_support_files)
 remove PaddleOCR-VL server scripts from image_support_files
 

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@host.today/ht-docker-ai",
-  "version": "1.13.1",
+  "version": "1.14.0",
   "type": "module",
   "private": false,
   "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",
@@ -13,8 +13,8 @@
     "test": "tstest test/ --verbose"
   },
   "devDependencies": {
-    "@git.zone/tsrun": "^1.3.3",
-    "@git.zone/tstest": "^1.0.90"
+    "@git.zone/tsrun": "^2.0.1",
+    "@git.zone/tstest": "^3.1.5"
   },
   "repository": {
     "type": "git",

readme.hints.md

@@ -2,12 +2,18 @@
 
 ## Architecture
 
-This project uses **Ollama** as the runtime framework for serving AI models. This provides:
+This project uses **Ollama** and **vLLM** as runtime frameworks for serving AI models:
 
+### Ollama-based Images (MiniCPM-V, Qwen3-VL)
 - Automatic model download and caching
 - Unified REST API (compatible with OpenAI format)
 - Built-in quantization support
-- GPU/CPU auto-detection
+- GPU auto-detection
+
+### vLLM-based Images (Nanonets-OCR)
+- High-performance inference server
+- OpenAI-compatible API
+- Optimized for VLM workloads
 
 ## Model Details
 
@@ -24,18 +30,24 @@ This project uses **Ollama** as the runtime framework for serving AI models. Thi
 |------|---------------|
 | Full precision (bf16) | 18GB |
 | int4 quantized | 9GB |
-| GGUF (CPU) | 8GB RAM |
 
 ## Container Startup Flow
 
+### Ollama-based containers
 1. `docker-entrypoint.sh` starts Ollama server in background
 2. Waits for server to be ready
 3. Checks if model already exists in volume
 4. Pulls model if not present
 5. Keeps container running
 
+### vLLM-based containers
+1. vLLM server starts with model auto-download
+2. Health check endpoint available at `/health`
+3. OpenAI-compatible API at `/v1/chat/completions`
+
 ## Volume Persistence
 
+### Ollama volumes
 Mount `/root/.ollama` to persist downloaded models:
 
 ```bash
@@ -44,9 +56,16 @@ Mount `/root/.ollama` to persist downloaded models:
 
 Without this volume, the model will be re-downloaded on each container start (~5GB download).
 
+### vLLM/HuggingFace volumes
+Mount `/root/.cache/huggingface` for model caching:
+
+```bash
+-v hf-cache:/root/.cache/huggingface
+```
+
 ## API Endpoints
 
-All endpoints follow the Ollama API specification:
+### Ollama API (MiniCPM-V, Qwen3-VL)
 
 | Endpoint | Method | Description |
 |----------|--------|-------------|
@@ -56,113 +75,137 @@ All endpoints follow the Ollama API specification:
 | `/api/pull` | POST | Pull a model |
 | `/api/show` | POST | Show model info |
 
-## GPU Detection
+### vLLM API (Nanonets-OCR)
 
-The GPU variant uses Ollama's automatic GPU detection. For CPU-only mode, we set:
-
-```dockerfile
-ENV CUDA_VISIBLE_DEVICES=""
-```
-
-This forces Ollama to use CPU inference even if GPU is available.
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Health check |
+| `/v1/models` | GET | List available models |
+| `/v1/chat/completions` | POST | OpenAI-compatible chat completions |
 
 ## Health Checks
 
-Both variants include Docker health checks:
+All containers include Docker health checks:
 
 ```dockerfile
 HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
     CMD curl -f http://localhost:11434/api/tags || exit 1
 ```
 
-CPU variant has longer `start-period` (120s) due to slower startup.
+---
 
-## PaddleOCR-VL (Recommended)
+## Nanonets-OCR-s
 
 ### Overview
 
-PaddleOCR-VL is a 0.9B parameter Vision-Language Model specifically optimized for document parsing. It replaces the older PP-Structure approach with native VLM understanding.
+Nanonets-OCR-s is a Qwen2.5-VL-3B model fine-tuned specifically for document OCR tasks. It outputs structured markdown with semantic tags.
 
-**Key advantages over PP-Structure:**
-- Native table understanding (no HTML parsing needed)
-- 109 language support
-- Better handling of complex multi-row tables
-- Structured Markdown/JSON output
+**Key features:**
+- Based on Qwen2.5-VL-3B (~4B parameters)
+- Fine-tuned for document OCR
+- Outputs markdown with semantic HTML tags
+- ~10GB VRAM
 
 ### Docker Images
 
 | Tag | Description |
 |-----|-------------|
-| `paddleocr-vl` | GPU variant using vLLM (recommended) |
-| `paddleocr-vl-cpu` | CPU variant using transformers |
+| `nanonets-ocr` | GPU variant using vLLM (OpenAI-compatible API) |
 
-### API Endpoints (OpenAI-compatible)
+### API Endpoints (OpenAI-compatible via vLLM)
 
 | Endpoint | Method | Description |
 |----------|--------|-------------|
-| `/health` | GET | Health check with model info |
+| `/health` | GET | Health check |
 | `/v1/models` | GET | List available models |
 | `/v1/chat/completions` | POST | OpenAI-compatible chat completions |
-| `/ocr` | POST | Legacy OCR endpoint |
 
 ### Request/Response Format
 
 **POST /v1/chat/completions (OpenAI-compatible)**
 ```json
 {
-  "model": "paddleocr-vl",
+  "model": "nanonets/Nanonets-OCR-s",
   "messages": [
     {
       "role": "user",
       "content": [
         {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}},
-        {"type": "text", "text": "Table Recognition:"}
+        {"type": "text", "text": "Extract the text from the above document..."}
       ]
     }
   ],
   "temperature": 0.0,
-  "max_tokens": 8192
+  "max_tokens": 4096
 }
 ```
 
-**Task Prompts:**
-- `"OCR:"` - Text recognition
-- `"Table Recognition:"` - Table extraction (returns markdown)
-- `"Formula Recognition:"` - Formula extraction
-- `"Chart Recognition:"` - Chart extraction
+### Nanonets OCR Prompt
 
-**Response**
-```json
-{
-  "id": "chatcmpl-...",
-  "object": "chat.completion",
-  "choices": [
-    {
-      "index": 0,
-      "message": {
-        "role": "assistant",
-        "content": "| Date | Description | Amount |\n|---|---|---|\n| 2021-06-01 | GITLAB INC | -119.96 |"
-      },
-      "finish_reason": "stop"
-    }
-  ]
-}
+The model is designed to work with a specific prompt format:
+```
+Extract the text from the above document as if you were reading it naturally.
+Return the tables in html format.
+Return the equations in LaTeX representation.
+If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
+Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
+Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.
 ```
-
-### Environment Variables
-
-| Variable | Default | Description |
-|----------|---------|-------------|
-| `MODEL_NAME` | `PaddlePaddle/PaddleOCR-VL` | Model to load |
-| `HOST` | `0.0.0.0` | Server host |
-| `PORT` | `8000` | Server port |
-| `MAX_BATCHED_TOKENS` | `16384` | vLLM max batch tokens |
-| `GPU_MEMORY_UTILIZATION` | `0.9` | GPU memory usage (0-1) |
 
 ### Performance
 
-- **GPU (vLLM)**: ~2-5 seconds per page
-- **CPU**: ~30-60 seconds per page
+- **GPU (vLLM)**: ~3-8 seconds per page
+- **VRAM usage**: ~10GB
+
+### Two-Stage Pipeline (Nanonets + Qwen3)
+
+The Nanonets tests use a two-stage pipeline:
+1. **Stage 1**: Nanonets-OCR-s converts images to markdown (via vLLM on port 8000)
+2. **Stage 2**: Qwen3 8B extracts structured JSON from markdown (via Ollama on port 11434)
+
+**GPU Limitation**: Both vLLM and Ollama require significant GPU memory. On a single GPU system:
+- Running both simultaneously causes memory contention
+- For single GPU: Run services sequentially (stop Nanonets before Qwen3)
+- For multi-GPU: Assign each service to a different GPU
+
+**Sequential Execution**:
+```bash
+# Step 1: Run Nanonets OCR (converts to markdown)
+docker start nanonets-test
+# ... perform OCR ...
+docker stop nanonets-test
+
+# Step 2: Run Qwen3 extraction (from markdown)
+docker start minicpm-test
+# ... extract JSON ...
+```
+
+---
+
+## Multi-Pass Extraction Strategy
+
+The bank statement extraction uses a dual-VLM consensus approach:
+
+### Architecture: Dual-VLM Consensus
+
+| VLM | Model | Purpose |
+|-----|-------|---------|
+| **MiniCPM-V 4.5** | 8B params | Primary visual extraction |
+| **Nanonets-OCR-s** | ~4B params | Document OCR with semantic output |
+
+### Extraction Strategy
+
+1. **Pass 1**: MiniCPM-V visual extraction (images → JSON)
+2. **Pass 2**: Nanonets-OCR semantic extraction (images → markdown → JSON)
+3. **Consensus**: If Pass 1 == Pass 2 → Done (fast path)
+4. **Pass 3+**: MiniCPM-V visual if no consensus
+
+### Why Dual-VLM Works
+
+- **Different architectures**: Two independent models cross-check each other
+- **Specialized strengths**: Nanonets-OCR-s optimized for document structure, MiniCPM-V for general vision
+- **No structure loss**: Both VLMs see the original images directly
+- **Fast consensus**: Most documents complete in 2 passes when VLMs agree
 
 ---
 
@@ -170,7 +213,7 @@ PaddleOCR-VL is a 0.9B parameter Vision-Language Model specifically optimized fo
 
 To add a new model variant:
 
-1. Create `Dockerfile_<modelname>`
+1. Create `Dockerfile_<modelname>_<runtime>_<hardware>_VRAM<size>`
 2. Set `MODEL_NAME` environment variable
 3. Update `build-images.sh` with new build target
 4. Add documentation to `readme.md`
@@ -188,8 +231,8 @@ The model download is ~5GB and may take several minutes.
 
 ### Out of memory
 
-- GPU: Use int4 quantized version or add more VRAM
-- CPU: Increase container memory limit: `--memory=16g`
+- GPU: Use a lighter model variant or upgrade VRAM
+- Add more GPU memory: Consider multi-GPU setup
 
 ### API not responding
 
@@ -207,41 +250,6 @@ npmci docker build
 npmci docker push code.foss.global
 ```
 
-## Multi-Pass Extraction Strategy
-
-The bank statement extraction uses a dual-VLM consensus approach:
-
-### Architecture: Dual-VLM Consensus
-
-| VLM | Model | Purpose |
-|-----|-------|---------|
-| **MiniCPM-V 4.5** | 8B params | Primary visual extraction |
-| **PaddleOCR-VL** | 0.9B params | Table-specialized extraction |
-
-### Extraction Strategy
-
-1. **Pass 1**: MiniCPM-V visual extraction (images → JSON)
-2. **Pass 2**: PaddleOCR-VL table recognition (images → markdown → JSON)
-3. **Consensus**: If Pass 1 == Pass 2 → Done (fast path)
-4. **Pass 3+**: MiniCPM-V visual if no consensus
-
-### Why Dual-VLM Works
-
-- **Different architectures**: Two independent models cross-check each other
-- **Specialized strengths**: PaddleOCR-VL optimized for tables, MiniCPM-V for general vision
-- **No structure loss**: Both VLMs see the original images directly
-- **Fast consensus**: Most documents complete in 2 passes when VLMs agree
-
-### Comparison vs Old PP-Structure Approach
-
-| Approach | Bank Statement Result | Issue |
-|----------|----------------------|-------|
-| MiniCPM-V Visual | 28 transactions ✓ | - |
-| PP-Structure HTML + Visual | 13 transactions ✗ | HTML merged rows incorrectly |
-| PaddleOCR-VL Table | 28 transactions ✓ | Native table understanding |
-
-**Key insight**: PP-Structure's HTML output loses structure for complex tables. PaddleOCR-VL's native VLM approach maintains table integrity.
-
 ---
 
 ## Related Resources
@@ -249,3 +257,4 @@ The bank statement extraction uses a dual-VLM consensus approach:
 - [Ollama Documentation](https://ollama.ai/docs)
 - [MiniCPM-V GitHub](https://github.com/OpenBMB/MiniCPM-V)
 - [Ollama API Reference](https://github.com/ollama/ollama/blob/main/docs/api.md)
+- [Nanonets-OCR-s on HuggingFace](https://huggingface.co/nanonets/Nanonets-OCR-s)

readme.md

@@ -1,40 +1,45 @@
 # @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—**no cloud API keys required**.
+
+> 🔥 **Three 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 | VRAM |
+|-------|-----------|----------|-----|------|------|
+| **MiniCPM-V 4.5** | 8B | General vision understanding, multi-image analysis | Ollama-compatible | 11434 | ~9GB |
+| **Nanonets-OCR-s** | ~4B | Document OCR with semantic markdown output | OpenAI-compatible | 8000 | ~10GB |
+| **Qwen3-VL-30B** | 30B (A3B) | Advanced visual agents, code generation from images | Ollama-compatible | 11434 | ~20GB |
 
-## 📦 Available Images
+---
+
+## 📦 Quick Reference: All Available Images
 
 ```
 code.foss.global/host.today/ht-docker-ai:<tag>
 ```
 
-| Tag | Model | Hardware | Port |
-|-----|-------|----------|------|
-| `minicpm45v` / `latest` | MiniCPM-V 4.5 | NVIDIA GPU (9-18GB VRAM) | 11434 |
-| `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 |
+| Tag | Model | Runtime | Port | VRAM |
+|-----|-------|---------|------|------|
+| `minicpm45v` / `latest` | MiniCPM-V 4.5 | Ollama | 11434 | ~9GB |
+| `nanonets-ocr` | Nanonets-OCR-s | vLLM | 8000 | ~10GB |
+| `qwen3vl` | Qwen3-VL-30B-A3B | Ollama | 11434 | ~20GB |
 
 ---
 
 ## 🖼️ 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
 
-**GPU (Recommended):**
 ```bash
 docker run -d \
   --name minicpm \
@@ -44,15 +49,6 @@ docker run -d \
   code.foss.global/host.today/ht-docker-ai:minicpm45v
 ```
 
-**CPU Only:**
-```bash
-docker run -d \
-  --name minicpm \
-  -p 11434:11434 \
-  -v ollama-data:/root/.ollama \
-  code.foss.global/host.today/ht-docker-ai:minicpm45v-cpu
-```
-
 > 💡 **Pro tip:** Mount the volume to persist downloaded models (~5GB). Without it, models re-download on every container start.
 
 ### API Examples
@@ -85,108 +81,128 @@ curl http://localhost:11434/api/chat -d '{
 
 ### Hardware Requirements
 
-| Variant | VRAM/RAM | Notes |
-|---------|----------|-------|
-| GPU (int4 quantized) | 9GB VRAM | Recommended for most use cases |
-| GPU (full precision) | 18GB VRAM | Maximum quality |
-| CPU (GGUF) | 8GB+ RAM | Slower but accessible |
+| Mode | VRAM Required |
+|------|---------------|
+| int4 quantized | 9GB |
+| Full precision (bf16) | 18GB |
 
 ---
 
-## 📄 PaddleOCR-VL
+## 🔍 Nanonets-OCR-s
 
-A specialized 0.9B Vision-Language Model optimized for document parsing. Native support for tables, formulas, charts, and text extraction in 109 languages.
+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:** ~10GB VRAM, runs great on consumer GPUs
+- 🔌 **OpenAI-compatible:** Drop-in replacement for existing pipelines
 
 ### Quick Start
 
-**GPU:**
 ```bash
 docker run -d \
-  --name paddleocr \
+  --name nanonets \
   --gpus all \
   -p 8000:8000 \
   -v hf-cache:/root/.cache/huggingface \
-  code.foss.global/host.today/ht-docker-ai:paddleocr-vl
+  code.foss.global/host.today/ht-docker-ai:nanonets-ocr
 ```
 
-**CPU:**
-```bash
-docker run -d \
-  --name paddleocr \
-  -p 8000:8000 \
-  -v hf-cache:/root/.cache/huggingface \
-  code.foss.global/host.today/ht-docker-ai:paddleocr-vl-cpu
-```
-
-### OpenAI-Compatible API
-
-PaddleOCR-VL exposes a fully OpenAI-compatible `/v1/chat/completions` endpoint:
+### API Usage
 
 ```bash
 curl http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-    "model": "paddleocr-vl",
+    "model": "nanonets/Nanonets-OCR-s",
     "messages": [{
       "role": "user",
       "content": [
         {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}},
-        {"type": "text", "text": "Table Recognition:"}
+        {"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."}
       ]
     }],
-    "max_tokens": 8192
+    "temperature": 0.0,
+    "max_tokens": 4096
   }'
 ```
 
-### Task Prompts
+### Output Format
 
-| Prompt | Output | Use Case |
-|--------|--------|----------|
-| `OCR:` | Plain text | General text extraction |
-| `Table Recognition:` | Markdown table | Invoices, bank statements, spreadsheets |
-| `Formula Recognition:` | LaTeX | Math equations, scientific notation |
-| `Chart Recognition:` | Description | Graphs and visualizations |
+Nanonets-OCR-s returns markdown with semantic tags:
 
-### API Endpoints
-
-| Endpoint | Method | Description |
-|----------|--------|-------------|
-| `/health` | GET | Health check with model/device info |
-| `/formats` | GET | Supported image formats and input methods |
-| `/v1/models` | GET | List available models |
-| `/v1/chat/completions` | POST | OpenAI-compatible chat completions |
-| `/ocr` | POST | Legacy OCR endpoint |
-
-### Image Input Methods
-
-PaddleOCR-VL accepts images in multiple formats:
-
-```javascript
-// Base64 data URL
-"data:image/png;base64,iVBORw0KGgo..."
-
-// HTTP URL
-"https://example.com/document.png"
-
-// Raw base64
-"iVBORw0KGgo..."
-```
-
-**Supported formats:** PNG, JPEG, WebP, BMP, GIF, TIFF
-
-**Optimal resolution:** 1080p–2K. Images are automatically scaled for best results.
+| 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
 
-| Mode | Speed per Page |
-|------|----------------|
-| GPU (CUDA) | 2–5 seconds |
-| CPU | 30–60 seconds |
+| Metric | Value |
+|--------|-------|
+| Speed | 3–8 seconds per page |
+| VRAM | ~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:
@@ -206,9 +222,9 @@ services:
               capabilities: [gpu]
     restart: unless-stopped
 
-  # Document parsing / OCR
-  paddleocr:
-    image: code.foss.global/host.today/ht-docker-ai:paddleocr-vl
+  # Document OCR with semantic output
+  nanonets:
+    image: code.foss.global/host.today/ht-docker-ai:nanonets-ocr
     ports:
       - "8000:8000"
     volumes:
@@ -231,7 +247,7 @@ volumes:
 
 ## ⚙️ Environment Variables
 
-### MiniCPM-V 4.5
+### MiniCPM-V 4.5 & Qwen3-VL (Ollama-based)
 
 | Variable | Default | Description |
 |----------|---------|-------------|
@@ -239,13 +255,46 @@ volumes:
 | `OLLAMA_HOST` | `0.0.0.0` | API bind address |
 | `OLLAMA_ORIGINS` | `*` | Allowed CORS origins |
 
-### 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` | `nanonets/Nanonets-OCR-s` | 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:** Nanonets-OCR semantic extraction (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:** Nanonets-OCR-s excels at document structure; 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 |
+| 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 +314,16 @@ 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 +343,13 @@ 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

test/helpers/docker.ts

@@ -2,11 +2,8 @@ import { execSync } from 'child_process';
 
 // Project container names (only manage these)
 const PROJECT_CONTAINERS = [
-  'paddleocr-vl-test',
-  'paddleocr-vl-gpu-test',
-  'paddleocr-vl-cpu-test',
-  'paddleocr-vl-full-test',
   'minicpm-test',
+  'nanonets-test',
 ];
 
 // Image configurations
@@ -23,30 +20,6 @@ export interface IImageConfig {
 }
 
 export const IMAGES = {
-  paddleocrVlGpu: {
-    name: 'paddleocr-vl-gpu',
-    dockerfile: 'Dockerfile_paddleocr_vl_gpu',
-    buildContext: '.',
-    containerName: 'paddleocr-vl-test',
-    ports: ['8000:8000'],
-    volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
-    gpus: true,
-    healthEndpoint: 'http://localhost:8000/health',
-    healthTimeout: 300000, // 5 minutes for model loading
-  } as IImageConfig,
-
-  paddleocrVlCpu: {
-    name: 'paddleocr-vl-cpu',
-    dockerfile: 'Dockerfile_paddleocr_vl_cpu',
-    buildContext: '.',
-    containerName: 'paddleocr-vl-test',
-    ports: ['8000:8000'],
-    volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
-    gpus: false,
-    healthEndpoint: 'http://localhost:8000/health',
-    healthTimeout: 300000,
-  } as IImageConfig,
-
   minicpm: {
     name: 'minicpm45v',
     dockerfile: 'Dockerfile_minicpm45v_gpu',
@@ -59,20 +32,17 @@ export const IMAGES = {
     healthTimeout: 120000,
   } as IImageConfig,
 
-  // Full PaddleOCR-VL pipeline with PP-DocLayoutV2 + structured JSON output
-  paddleocrVlFull: {
-    name: 'paddleocr-vl-full',
-    dockerfile: 'Dockerfile_paddleocr_vl_full',
+  // Nanonets-OCR2-3B - Document OCR optimized VLM (Qwen2.5-VL-3B fine-tuned, Oct 2025)
+  nanonetsOcr: {
+    name: 'nanonets-ocr',
+    dockerfile: 'Dockerfile_nanonets_vllm_gpu_VRAM10GB',
     buildContext: '.',
-    containerName: 'paddleocr-vl-full-test',
+    containerName: 'nanonets-test',
     ports: ['8000:8000'],
-    volumes: [
-      'ht-huggingface-cache:/root/.cache/huggingface',
-      'ht-paddleocr-cache:/root/.paddleocr',
-    ],
+    volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
     gpus: true,
     healthEndpoint: 'http://localhost:8000/health',
-    healthTimeout: 600000, // 10 minutes for model loading (vLLM + PP-DocLayoutV2)
+    healthTimeout: 300000, // 5 minutes for model loading
   } as IImageConfig,
 };
 
@@ -126,7 +96,7 @@ export function removeContainer(containerName: string): void {
 }
 
 /**
- * Stop all project containers that conflict with the required one
+ * Stop all project containers that conflict with the required one (port-based)
  */
 export function stopConflictingContainers(requiredContainer: string, requiredPort: string): void {
   // Stop project containers using the same port
@@ -144,6 +114,24 @@ export function stopConflictingContainers(requiredContainer: string, requiredPor
   }
 }
 
+/**
+ * Stop all GPU-consuming project containers (for GPU memory management)
+ * This ensures GPU memory is freed before starting a new GPU service
+ */
+export function stopAllGpuContainers(exceptContainer?: string): void {
+  for (const container of PROJECT_CONTAINERS) {
+    if (container === exceptContainer) continue;
+
+    if (isContainerRunning(container)) {
+      console.log(`[Docker] Stopping GPU container: ${container}`);
+      exec(`docker stop ${container}`, true);
+      // Give the GPU a moment to free memory
+    }
+  }
+  // Brief pause to allow GPU memory to be released
+  execSync('sleep 2');
+}
+
 /**
  * Build a Docker image
  */
@@ -220,6 +208,11 @@ export async function ensureService(config: IImageConfig): Promise<boolean> {
     buildImage(config);
   }
 
+  // For GPU services, stop ALL other GPU containers to free GPU memory
+  if (config.gpus) {
+    stopAllGpuContainers(config.containerName);
+  }
+
   // Stop conflicting containers on the same port
   const mainPort = config.ports[0];
   stopConflictingContainers(config.containerName, mainPort);
@@ -240,21 +233,7 @@ export async function ensureService(config: IImageConfig): Promise<boolean> {
 }
 
 /**
- * Ensure PaddleOCR-VL GPU service is running
- */
-export async function ensurePaddleOcrVlGpu(): Promise<boolean> {
-  return ensureService(IMAGES.paddleocrVlGpu);
-}
-
-/**
- * Ensure PaddleOCR-VL CPU service is running
- */
-export async function ensurePaddleOcrVlCpu(): Promise<boolean> {
-  return ensureService(IMAGES.paddleocrVlCpu);
-}
-
-/**
- * Ensure MiniCPM service is running
+ * Ensure MiniCPM service is running (Ollama with GPU)
  */
 export async function ensureMiniCpm(): Promise<boolean> {
   return ensureService(IMAGES.minicpm);
@@ -272,30 +251,6 @@ export function isGpuAvailable(): boolean {
   }
 }
 
-/**
- * Ensure PaddleOCR-VL service (auto-detect GPU/CPU)
- */
-export async function ensurePaddleOcrVl(): Promise<boolean> {
-  if (isGpuAvailable()) {
-    console.log('[Docker] GPU detected, using GPU image');
-    return ensurePaddleOcrVlGpu();
-  } else {
-    console.log('[Docker] No GPU detected, using CPU image');
-    return ensurePaddleOcrVlCpu();
-  }
-}
-
-/**
- * Ensure PaddleOCR-VL Full Pipeline service (PP-DocLayoutV2 + structured output)
- * This is the recommended service for production use - outputs structured JSON/Markdown
- */
-export async function ensurePaddleOcrVlFull(): Promise<boolean> {
-  if (!isGpuAvailable()) {
-    console.log('[Docker] WARNING: Full pipeline requires GPU, but none detected');
-  }
-  return ensureService(IMAGES.paddleocrVlFull);
-}
-
 /**
  * Ensure an Ollama model is pulled and available
  * Uses the MiniCPM container (which runs Ollama) to pull the model
@@ -383,3 +338,14 @@ export async function ensureQwen3Vl(): Promise<boolean> {
   // Then ensure Qwen3-VL 8B is pulled
   return ensureOllamaModel('qwen3-vl:8b');
 }
+
+/**
+ * Ensure Nanonets-OCR2-3B service is running (via vLLM)
+ * Document OCR optimized VLM based on Qwen2.5-VL-3B (Oct 2025 release)
+ */
+export async function ensureNanonetsOcr(): Promise<boolean> {
+  if (!isGpuAvailable()) {
+    console.log('[Docker] WARNING: Nanonets-OCR2-3B requires GPU, but none detected');
+  }
+  return ensureService(IMAGES.nanonetsOcr);
+}

test/test.bankstatements.nanonets.ts

@@ -0,0 +1,655 @@
+/**
+ * Bank statement extraction using Nanonets-OCR2-3B + GPT-OSS 20B (sequential two-stage pipeline)
+ *
+ * Stage 1: Nanonets-OCR2-3B converts ALL document pages to markdown (stop after completion)
+ * Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
+ *
+ * This approach avoids GPU contention by running services sequentially.
+ */
+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, removeContainer, isContainerRunning } from './helpers/docker.js';
+
+const NANONETS_URL = 'http://localhost:8000/v1';
+const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const EXTRACTION_MODEL = 'gpt-oss:20b';
+
+// Temp directory for storing markdown between stages
+const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-markdown');
+
+interface ITransaction {
+  date: string;
+  counterparty: string;
+  amount: number;
+}
+
+interface IImageData {
+  base64: string;
+  width: number;
+  height: number;
+  pageNum: number;
+}
+
+interface ITestCase {
+  name: string;
+  pdfPath: string;
+  jsonPath: string;
+  markdownPath?: string;
+  images?: IImageData[];
+}
+
+// Nanonets-specific prompt for document OCR to markdown
+const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
+Return the tables in html format.
+Return the equations in LaTeX representation.
+If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
+Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
+Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
+
+// JSON extraction prompt for GPT-OSS 20B
+const JSON_EXTRACTION_PROMPT = `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.
+
+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
+
+/**
+ * Estimate visual tokens for an image based on dimensions
+ */
+function estimateVisualTokens(width: number, height: number): number {
+  return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
+}
+
+/**
+ * Process images one page at a time for reliability
+ */
+function batchImages(images: IImageData[]): IImageData[][] {
+  // One page per batch for reliable processing
+  return images.map(img => [img]);
+}
+
+/**
+ * Convert PDF to JPEG images using ImageMagick with dimension tracking
+ */
+function convertPdfToImages(pdfPath: string): IImageData[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.jpg');
+
+  try {
+    execSync(
+      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
+    const images: IImageData[] = [];
+
+    for (let i = 0; i < files.length; i++) {
+      const file = files[i];
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+
+      // Get image dimensions using identify command
+      const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
+      const [width, height] = dimensions.split(' ').map(Number);
+
+      images.push({
+        base64: imageData.toString('base64'),
+        width,
+        height,
+        pageNum: i + 1,
+      });
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Convert a batch of pages to markdown using Nanonets-OCR-s
+ */
+async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
+  const startTime = Date.now();
+  const pageNums = batch.map(img => img.pageNum).join(', ');
+
+  // Build content array with all images first, then the prompt
+  const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
+
+  for (const img of batch) {
+    content.push({
+      type: 'image_url',
+      image_url: { url: `data:image/jpeg;base64,${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',
+    headers: {
+      'Content-Type': 'application/json',
+      'Authorization': 'Bearer dummy',
+    },
+    body: JSON.stringify({
+      model: NANONETS_MODEL,
+      messages: [{
+        role: 'user',
+        content,
+      }],
+      max_tokens: 4096 * batch.length,  // Scale output tokens with batch size
+      temperature: 0.0,
+    }),
+    signal: AbortSignal.timeout(600000),  // 10 minute timeout for OCR
+  });
+
+  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
+  }
+
+  const data = await response.json();
+  let responseContent = (data.choices?.[0]?.message?.content || '').trim();
+
+  // 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 using smart batching
+ */
+async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
+  const batches = batchImages(images);
+  console.log(`      [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
+
+  const markdownParts: string[] = [];
+
+  for (let i = 0; i < batches.length; i++) {
+    const batch = batches[i];
+    const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
+    console.log(`        Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
+    const markdown = await convertBatchToMarkdown(batch);
+    markdownParts.push(markdown);
+  }
+
+  const fullMarkdown = markdownParts.join('\n\n');
+  console.log(`      [${docName}] Complete: ${fullMarkdown.length} chars total`);
+  return fullMarkdown;
+}
+
+/**
+ * Stop Nanonets container
+ */
+function stopNanonets(): void {
+  console.log('    [Docker] Stopping Nanonets container...');
+  try {
+    execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
+    // Wait for GPU memory to be released
+    execSync('sleep 5', { stdio: 'pipe' });
+    console.log('    [Docker] Nanonets stopped');
+  } catch {
+    console.log('    [Docker] Nanonets was not running');
+  }
+}
+
+/**
+ * Ensure GPT-OSS 20B model is available and warmed up
+ */
+async function ensureExtractionModel(): Promise<boolean> {
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
+        console.log(`    [Ollama] Model available: ${EXTRACTION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    return false;
+  }
+
+  console.log(`    [Ollama] Pulling ${EXTRACTION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+/**
+ * Extract transactions from markdown using GPT-OSS 20B (streaming)
+ */
+async function extractTransactionsFromMarkdown(markdown: string, queryId: string): Promise<ITransaction[]> {
+  const startTime = Date.now();
+  const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
+
+  // Log exact prompt
+  console.log(`\n      [${queryId}] ===== PROMPT =====`);
+  console.log(fullPrompt);
+  console.log(`      [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: EXTRACTION_MODEL,
+      messages: [
+        { role: 'user', content: 'Hi there, how are you?' },
+        { role: 'assistant', content: 'Good, how can I help you today?' },
+        { role: 'user', content: fullPrompt },
+      ],
+      stream: true,
+    }),
+    signal: AbortSignal.timeout(600000),  // 10 minute timeout
+  });
+
+  if (!response.ok) {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  // Stream the response
+  let content = '';
+  let thinkingContent = '';
+  let thinkingStarted = false;
+  let outputStarted = false;
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+
+      // Each line is a JSON object
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+
+          // Stream thinking tokens
+          const thinking = json.message?.thinking || '';
+          if (thinking) {
+            if (!thinkingStarted) {
+              process.stdout.write(`      [${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 parseJsonResponse(content, queryId);
+}
+
+/**
+ * Sanitize JSON string
+ */
+function sanitizeJson(jsonStr: string): string {
+  let s = jsonStr;
+  s = s.replace(/"amount"\s*:\s*\+/g, '"amount": ');
+  s = s.replace(/:\s*\+(\d)/g, ': $1');
+  s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3.$4');
+  s = s.replace(/,\s*([}\]])/g, '$1');
+  s = s.replace(/"([^"\\]*)\n([^"]*)"/g, '"$1 $2"');
+  s = s.replace(/"([^"\\]*)\t([^"]*)"/g, '"$1 $2"');
+  s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
+  return s;
+}
+
+/**
+ * Parse amount from various formats
+ */
+function parseAmount(value: unknown): number {
+  if (typeof value === 'number') return value;
+  if (typeof value !== 'string') return 0;
+
+  let s = value.replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
+  if (s.includes(',') && s.indexOf(',') > s.lastIndexOf('.')) {
+    s = s.replace(/\./g, '').replace(',', '.');
+  } else {
+    s = s.replace(/,/g, '');
+  }
+  return parseFloat(s) || 0;
+}
+
+/**
+ * Parse JSON response into transactions
+ */
+function parseJsonResponse(response: string, queryId: string): ITransaction[] {
+  // Remove thinking tags if present
+  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
+
+  // Debug: show what we're working with
+  console.log(`      [${queryId}] Response preview: ${cleanResponse.substring(0, 300)}...`);
+
+  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
+  let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
+  jsonStr = sanitizeJson(jsonStr);
+
+  try {
+    const parsed = JSON.parse(jsonStr);
+    if (Array.isArray(parsed)) {
+      const txs = parsed.map(tx => ({
+        date: String(tx.date || ''),
+        counterparty: String(tx.counterparty || tx.description || ''),
+        amount: parseAmount(tx.amount),
+      }));
+      console.log(`      [${queryId}] Parsed ${txs.length} transactions`);
+      return txs;
+    }
+  } catch (e) {
+    // Try to find a JSON array in the text
+    const arrayMatch = jsonStr.match(/\[[\s\S]*\]/);
+    if (arrayMatch) {
+      console.log(`      [${queryId}] Array match found: ${arrayMatch[0].length} chars`);
+      try {
+        const parsed = JSON.parse(sanitizeJson(arrayMatch[0]));
+        if (Array.isArray(parsed)) {
+          const txs = parsed.map(tx => ({
+            date: String(tx.date || ''),
+            counterparty: String(tx.counterparty || tx.description || ''),
+            amount: parseAmount(tx.amount),
+          }));
+          console.log(`      [${queryId}] Parsed ${txs.length} transactions (array match)`);
+          return txs;
+        }
+      } catch (innerErr) {
+        console.log(`      [${queryId}] Array parse error: ${(innerErr as Error).message}`);
+      }
+    } else {
+      console.log(`      [${queryId}] No JSON array found in response`);
+    }
+  }
+
+  console.log(`      [${queryId}] PARSE FAILED`);
+  return [];
+}
+
+/**
+ * Extract transactions (single pass)
+ */
+async function extractTransactions(markdown: string, docName: string): Promise<ITransaction[]> {
+  console.log(`    [${docName}] Extracting...`);
+  const txs = await extractTransactionsFromMarkdown(markdown, docName);
+  console.log(`    [${docName}] Extracted ${txs.length} transactions`);
+  return txs;
+}
+
+/**
+ * Compare transactions
+ */
+function compareTransactions(
+  extracted: ITransaction[],
+  expected: ITransaction[]
+): { matches: number; total: number; errors: string[] } {
+  const errors: string[] = [];
+  let matches = 0;
+
+  for (let i = 0; i < expected.length; i++) {
+    const exp = expected[i];
+    const ext = extracted[i];
+
+    if (!ext) {
+      errors.push(`Missing tx ${i}: ${exp.date} ${exp.counterparty}`);
+      continue;
+    }
+
+    const dateMatch = ext.date === exp.date;
+    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
+
+    if (dateMatch && amountMatch) {
+      matches++;
+    } else {
+      errors.push(`Mismatch ${i}: exp ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
+    }
+  }
+
+  if (extracted.length > expected.length) {
+    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
+  }
+
+  return { matches, total: expected.length, errors };
+}
+
+/**
+ * Find all test cases
+ */
+function findTestCases(): ITestCase[] {
+  const testDir = path.join(process.cwd(), '.nogit');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: ITestCase[] = [];
+
+  for (const pdf of files.filter((f: string) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  return testCases.sort((a, b) => a.name.localeCompare(b.name));
+}
+
+// ============ TESTS ============
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} bank statement test cases\n`);
+
+// Ensure temp directory exists
+if (!fs.existsSync(TEMP_MD_DIR)) {
+  fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
+}
+
+// -------- STAGE 1: OCR with Nanonets --------
+
+// Check if all markdown files already exist
+function allMarkdownFilesExist(): boolean {
+  for (const tc of testCases) {
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    if (!fs.existsSync(mdPath)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+// Track whether we need to run Stage 1
+let stage1Needed = !allMarkdownFilesExist();
+
+tap.test('Stage 1: Setup Nanonets', async () => {
+  console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
+
+  if (!stage1Needed) {
+    console.log('  [SKIP] All markdown files already exist, skipping Nanonets setup');
+    return;
+  }
+
+  const ok = await ensureNanonetsOcr();
+  expect(ok).toBeTrue();
+});
+
+tap.test('Stage 1: Convert all documents to markdown', async () => {
+  if (!stage1Needed) {
+    console.log('  [SKIP] Using existing markdown files from previous run\n');
+    // Load existing markdown paths
+    for (const tc of testCases) {
+      tc.markdownPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+      console.log(`    Loaded: ${tc.markdownPath}`);
+    }
+    return;
+  }
+
+  console.log('\n  Converting all PDFs to markdown with Nanonets-OCR-s...\n');
+
+  for (const tc of testCases) {
+    console.log(`\n    === ${tc.name} ===`);
+
+    // Convert PDF to images
+    const images = convertPdfToImages(tc.pdfPath);
+    console.log(`      Pages: ${images.length}`);
+
+    // Convert to markdown
+    const markdown = await convertDocumentToMarkdown(images, tc.name);
+
+    // Save markdown to temp file
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    fs.writeFileSync(mdPath, markdown);
+    tc.markdownPath = mdPath;
+    console.log(`      Saved: ${mdPath}`);
+  }
+
+  console.log('\n  Stage 1 complete: All documents converted to markdown\n');
+});
+
+tap.test('Stage 1: Stop Nanonets', async () => {
+  if (!stage1Needed) {
+    console.log('  [SKIP] Nanonets was not started');
+    return;
+  }
+
+  stopNanonets();
+  // Verify it's stopped
+  await new Promise(resolve => setTimeout(resolve, 3000));
+  expect(isContainerRunning('nanonets-test')).toBeFalse();
+});
+
+// -------- STAGE 2: Extraction with GPT-OSS 20B --------
+
+tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
+  console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');
+
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+
+  const extractionOk = await ensureExtractionModel();
+  expect(extractionOk).toBeTrue();
+});
+
+let passedCount = 0;
+let failedCount = 0;
+
+for (const tc of testCases) {
+  tap.test(`Stage 2: Extract ${tc.name}`, async () => {
+    const expected: ITransaction[] = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
+    console.log(`\n    === ${tc.name} ===`);
+    console.log(`    Expected: ${expected.length} transactions`);
+
+    // Load saved markdown
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    if (!fs.existsSync(mdPath)) {
+      throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
+    }
+    const markdown = fs.readFileSync(mdPath, 'utf-8');
+    console.log(`    Markdown: ${markdown.length} chars`);
+
+    // Extract transactions (single pass)
+    const extracted = await extractTransactions(markdown, tc.name);
+
+    // Log results
+    console.log(`    Extracted: ${extracted.length} transactions`);
+    for (let i = 0; i < Math.min(extracted.length, 5); i++) {
+      const tx = extracted[i];
+      console.log(`      ${i + 1}. ${tx.date} | ${tx.counterparty.substring(0, 25).padEnd(25)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
+    }
+    if (extracted.length > 5) {
+      console.log(`      ... and ${extracted.length - 5} more`);
+    }
+
+    // Compare
+    const result = compareTransactions(extracted, expected);
+    const pass = result.matches === result.total && extracted.length === expected.length;
+
+    if (pass) {
+      passedCount++;
+      console.log(`    Result: PASS (${result.matches}/${result.total})`);
+    } else {
+      failedCount++;
+      console.log(`    Result: FAIL (${result.matches}/${result.total})`);
+      result.errors.slice(0, 5).forEach(e => console.log(`      - ${e}`));
+    }
+
+    expect(result.matches).toEqual(result.total);
+    expect(extracted.length).toEqual(expected.length);
+  });
+}
+
+tap.test('Summary', async () => {
+  console.log(`\n======================================================`);
+  console.log(`   Bank Statement Summary (Nanonets + GPT-OSS 20B Sequential)`);
+  console.log(`======================================================`);
+  console.log(`  Stage 1:  Nanonets-OCR-s (document -> markdown)`);
+  console.log(`  Stage 2:  GPT-OSS 20B (markdown -> JSON)`);
+  console.log(`  Passed:   ${passedCount}/${testCases.length}`);
+  console.log(`  Failed:   ${failedCount}/${testCases.length}`);
+  console.log(`======================================================\n`);
+
+  // Only cleanup temp files if ALL tests passed
+  if (failedCount === 0 && passedCount === testCases.length) {
+    try {
+      fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
+      console.log(`  Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
+    } catch {
+      // Ignore
+    }
+  } else {
+    console.log(`  Keeping temp directory for debugging: ${TEMP_MD_DIR}\n`);
+  }
+});
+
+export default tap.start();

test/test.invoices.extraction.ts

@@ -0,0 +1,436 @@
+/**
+ * Invoice extraction tuning - uses pre-generated markdown files
+ *
+ * Skips OCR stage, only runs GPT-OSS extraction on existing .debug.md files.
+ * Use this to quickly iterate on extraction prompts and logic.
+ *
+ * Run with: tstest test/test.invoices.extraction.ts --verbose
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { ensureMiniCpm } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const EXTRACTION_MODEL = 'gpt-oss:20b';
+
+// Test these specific invoices (must have .debug.md files)
+const TEST_INVOICES = [
+  'consensus_2021-09',
+  'hetzner_2022-04',
+  'qonto_2021-08',
+  'qonto_2021-09',
+];
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+interface ITestCase {
+  name: string;
+  markdownPath: string;
+  jsonPath: string;
+}
+
+// JSON extraction prompt for GPT-OSS 20B (sent AFTER the invoice text is provided)
+const JSON_EXTRACTION_PROMPT = `Extract key fields from the invoice. Return ONLY valid JSON.
+
+WHERE TO FIND DATA:
+- invoice_number, invoice_date, vendor_name: Look in the HEADER section at the TOP of PAGE 1 (near "Invoice no.", "Invoice date:", "Rechnungsnummer")
+- net_amount, vat_amount, total_amount: Look in the SUMMARY section at the BOTTOM (look for "Total", "Amount due", "Gesamtbetrag")
+
+RULES:
+1. invoice_number: Extract ONLY the value (e.g., "R0015632540"), NOT the label "Invoice no."
+2. invoice_date: Convert to YYYY-MM-DD format (e.g., "14/04/2022" → "2022-04-14")
+3. vendor_name: The company issuing the invoice
+4. currency: EUR, USD, or GBP
+5. net_amount: Total before tax
+6. vat_amount: Tax amount
+7. total_amount: Final total with tax
+
+JSON only:
+{"invoice_number":"X","invoice_date":"YYYY-MM-DD","vendor_name":"X","currency":"EUR","net_amount":0,"vat_amount":0,"total_amount":0}`;
+
+/**
+ * Ensure GPT-OSS 20B model is available
+ */
+async function ensureExtractionModel(): Promise<boolean> {
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
+        console.log(`    [Ollama] Model available: ${EXTRACTION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    return false;
+  }
+
+  console.log(`    [Ollama] Pulling ${EXTRACTION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+/**
+ * Parse amount from string (handles European format)
+ */
+function parseAmount(s: string | number | undefined): number {
+  if (s === undefined || s === null) return 0;
+  if (typeof s === 'number') return s;
+  const match = s.match(/([\d.,]+)/);
+  if (!match) return 0;
+  const numStr = match[1];
+  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+    ? numStr.replace(/\./g, '').replace(',', '.')
+    : numStr.replace(/,/g, '');
+  return parseFloat(normalized) || 0;
+}
+
+/**
+ * Extract invoice number - minimal normalization
+ */
+function extractInvoiceNumber(s: string | undefined): string {
+  if (!s) return '';
+  return s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+}
+
+/**
+ * Extract date (YYYY-MM-DD) from response
+ */
+function extractDate(s: string | undefined): string {
+  if (!s) return '';
+  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+  const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
+  if (isoMatch) return isoMatch[1];
+  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
+  if (dmyMatch) {
+    return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
+  }
+  return clean.replace(/[^\d-]/g, '').trim();
+}
+
+/**
+ * Extract currency
+ */
+function extractCurrency(s: string | undefined): string {
+  if (!s) return 'EUR';
+  const upper = s.toUpperCase();
+  if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
+  if (upper.includes('USD') || upper.includes('$')) return 'USD';
+  if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
+  return 'EUR';
+}
+
+/**
+ * Extract JSON from response
+ */
+function extractJsonFromResponse(response: string): Record<string, unknown> | null {
+  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
+  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
+  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
+
+  try {
+    return JSON.parse(jsonStr);
+  } catch {
+    const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
+    if (jsonMatch) {
+      try {
+        return JSON.parse(jsonMatch[0]);
+      } catch {
+        return null;
+      }
+    }
+    return null;
+  }
+}
+
+/**
+ * Parse JSON response into IInvoice
+ */
+function parseJsonToInvoice(response: string): IInvoice | null {
+  const parsed = extractJsonFromResponse(response);
+  if (!parsed) return null;
+
+  return {
+    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
+    invoice_date: extractDate(String(parsed.invoice_date || '')),
+    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
+    currency: extractCurrency(String(parsed.currency || '')),
+    net_amount: parseAmount(parsed.net_amount as string | number),
+    vat_amount: parseAmount(parsed.vat_amount as string | number),
+    total_amount: parseAmount(parsed.total_amount as string | number),
+  };
+}
+
+/**
+ * Extract invoice from markdown using GPT-OSS 20B (streaming)
+ */
+async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
+  const startTime = Date.now();
+
+  console.log(`      [${queryId}] Invoice: ${markdown.length} chars, Prompt: ${JSON_EXTRACTION_PROMPT.length} chars`);
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: EXTRACTION_MODEL,
+      messages: [
+        { role: 'user', content: 'Hi there, how are you?' },
+        { role: 'assistant', content: 'Good, how can I help you today?' },
+        { role: 'user', content: `Here is an invoice document:\n\n${markdown}` },
+        { role: 'assistant', content: 'I have read the invoice document you provided. I can see all the text content. What would you like me to do with it?' },
+        { role: 'user', content: JSON_EXTRACTION_PROMPT },
+      ],
+      stream: true,
+    }),
+    signal: AbortSignal.timeout(120000), // 2 min timeout
+  });
+
+  if (!response.ok) {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  // Stream the response
+  let content = '';
+  let thinkingContent = '';
+  let thinkingStarted = false;
+  let outputStarted = false;
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+
+          const thinking = json.message?.thinking || '';
+          if (thinking) {
+            if (!thinkingStarted) {
+              process.stdout.write(`      [${queryId}] THINKING: `);
+              thinkingStarted = true;
+            }
+            process.stdout.write(thinking);
+            thinkingContent += thinking;
+          }
+
+          const token = json.message?.content || '';
+          if (token) {
+            if (!outputStarted) {
+              if (thinkingStarted) process.stdout.write('\n');
+              process.stdout.write(`      [${queryId}] OUTPUT: `);
+              outputStarted = true;
+            }
+            process.stdout.write(token);
+            content += token;
+          }
+        } catch {
+          // Ignore parse errors for partial chunks
+        }
+      }
+    }
+  } finally {
+    if (thinkingStarted || outputStarted) process.stdout.write('\n');
+  }
+
+  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+  console.log(`      [${queryId}] Done: ${thinkingContent.length} thinking, ${content.length} output (${elapsed}s)`);
+
+  return parseJsonToInvoice(content);
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
+
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
+  };
+
+  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
+  if (match) {
+    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
+  }
+
+  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
+  if (match) {
+    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
+  }
+
+  return dateStr;
+}
+
+/**
+ * Normalize invoice number for comparison (remove spaces, lowercase)
+ */
+function normalizeInvoiceNumber(s: string): string {
+  return s.replace(/\s+/g, '').toLowerCase();
+}
+
+/**
+ * Compare extracted invoice against expected
+ */
+function compareInvoice(
+  extracted: IInvoice,
+  expected: IInvoice
+): { match: boolean; errors: string[] } {
+  const errors: string[] = [];
+
+  // Invoice number - normalize spaces for comparison
+  const extNum = normalizeInvoiceNumber(extracted.invoice_number || '');
+  const expNum = normalizeInvoiceNumber(expected.invoice_number || '');
+  if (extNum !== expNum) {
+    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
+  }
+
+  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
+    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
+  }
+
+  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
+    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
+  }
+
+  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
+    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find test cases with existing debug markdown
+ */
+function findTestCases(): ITestCase[] {
+  const invoicesDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(invoicesDir)) return [];
+
+  const testCases: ITestCase[] = [];
+
+  for (const invoiceName of TEST_INVOICES) {
+    const markdownPath = path.join(invoicesDir, `${invoiceName}.debug.md`);
+    const jsonPath = path.join(invoicesDir, `${invoiceName}.json`);
+
+    if (fs.existsSync(markdownPath) && fs.existsSync(jsonPath)) {
+      testCases.push({
+        name: invoiceName,
+        markdownPath,
+        jsonPath,
+      });
+    } else {
+      if (!fs.existsSync(markdownPath)) {
+        console.warn(`Warning: Missing markdown: ${markdownPath}`);
+      }
+      if (!fs.existsSync(jsonPath)) {
+        console.warn(`Warning: Missing JSON: ${jsonPath}`);
+      }
+    }
+  }
+
+  return testCases;
+}
+
+// ============ TESTS ============
+
+const testCases = findTestCases();
+console.log(`\n========================================`);
+console.log(`  EXTRACTION TUNING TEST`);
+console.log(`  (Skips OCR, uses existing .debug.md)`);
+console.log(`========================================`);
+console.log(`  Testing ${testCases.length} invoices:`);
+for (const tc of testCases) {
+  console.log(`    - ${tc.name}`);
+}
+console.log(`========================================\n`);
+
+tap.test('Setup Ollama + GPT-OSS 20B', async () => {
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+
+  const extractionOk = await ensureExtractionModel();
+  expect(extractionOk).toBeTrue();
+});
+
+let passedCount = 0;
+let failedCount = 0;
+
+for (const tc of testCases) {
+  tap.test(`Extract ${tc.name}`, async () => {
+    const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
+    const markdown = fs.readFileSync(tc.markdownPath, 'utf-8');
+
+    console.log(`\n    ========================================`);
+    console.log(`    === ${tc.name} ===`);
+    console.log(`    ========================================`);
+    console.log(`    EXPECTED: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
+    console.log(`    Markdown: ${markdown.length} chars`);
+
+    const startTime = Date.now();
+
+    const extracted = await extractInvoiceFromMarkdown(markdown, tc.name);
+
+    if (!extracted) {
+      failedCount++;
+      console.log(`\n    Result: ✗ FAILED TO PARSE (${((Date.now() - startTime) / 1000).toFixed(1)}s)`);
+      return;
+    }
+
+    const elapsedMs = Date.now() - startTime;
+
+    console.log(`    EXTRACTED: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
+
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`\n    Result: ✓ MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`\n    Result: ✗ MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+      console.log(`    ERRORS:`);
+      result.errors.forEach(e => console.log(`      - ${e}`));
+    }
+  });
+}
+
+tap.test('Summary', async () => {
+  const totalInvoices = testCases.length;
+  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
+
+  console.log(`\n========================================`);
+  console.log(`   Extraction Tuning Summary`);
+  console.log(`========================================`);
+  console.log(`  Model:     ${EXTRACTION_MODEL}`);
+  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
+  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`========================================\n`);
+});
+
+export default tap.start();

test/test.invoices.failed.ts

@@ -0,0 +1,695 @@
+/**
+ * Focused test for failed invoice extractions
+ *
+ * Tests only the 4 invoices that failed in the main test:
+ * - consensus_2021-09: invoice_number "2021/1384" → "20211384" (slash stripped)
+ * - hetzner_2022-04: model hallucinated after 281s thinking
+ * - qonto_2021-08: invoice_number "08-21-INVOICE-410870" → "4108705" (prefix stripped)
+ * - qonto_2021-09: invoice_number "09-21-INVOICE-4303642" → "4303642" (prefix stripped)
+ *
+ * Run with: tstest test/test.invoices.failed.ts --verbose
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
+
+const NANONETS_URL = 'http://localhost:8000/v1';
+const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const EXTRACTION_MODEL = 'gpt-oss:20b';
+
+// Temp directory for storing markdown between stages
+const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-failed-debug');
+
+// Only test these specific invoices that failed
+const FAILED_INVOICES = [
+  'consensus_2021-09',
+  'hetzner_2022-04',
+  'qonto_2021-08',
+  'qonto_2021-09',
+];
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+interface IImageData {
+  base64: string;
+  width: number;
+  height: number;
+  pageNum: number;
+}
+
+interface ITestCase {
+  name: string;
+  pdfPath: string;
+  jsonPath: string;
+  markdownPath?: string;
+}
+
+// Nanonets-specific prompt for document OCR to markdown
+const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
+Return the tables in html format.
+Return the equations in LaTeX representation.
+If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
+Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
+Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
+
+// JSON extraction prompt for GPT-OSS 20B
+const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
+
+IMPORTANT RULES:
+1. invoice_number: The unique invoice/document number (NOT VAT ID, NOT customer ID). PRESERVE ALL CHARACTERS including slashes, dashes, and prefixes.
+2. invoice_date: Format as YYYY-MM-DD
+3. vendor_name: The company that issued the invoice
+4. currency: EUR, USD, or GBP
+5. net_amount: Amount before tax
+6. vat_amount: Tax/VAT amount
+7. total_amount: Final total (gross amount)
+
+Return ONLY this JSON format, no explanation:
+{
+  "invoice_number": "INV-2024-001",
+  "invoice_date": "2024-01-15",
+  "vendor_name": "Company Name",
+  "currency": "EUR",
+  "net_amount": 100.00,
+  "vat_amount": 19.00,
+  "total_amount": 119.00
+}
+
+INVOICE TEXT:
+`;
+
+const PATCH_SIZE = 14;
+
+/**
+ * Estimate visual tokens for an image based on dimensions
+ */
+function estimateVisualTokens(width: number, height: number): number {
+  return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
+}
+
+/**
+ * Process images one page at a time for reliability
+ */
+function batchImages(images: IImageData[]): IImageData[][] {
+  return images.map(img => [img]);
+}
+
+/**
+ * Convert PDF to JPEG images using ImageMagick with dimension tracking
+ */
+function convertPdfToImages(pdfPath: string): IImageData[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.jpg');
+
+  try {
+    execSync(
+      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
+    const images: IImageData[] = [];
+
+    for (let i = 0; i < files.length; i++) {
+      const file = files[i];
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+
+      const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
+      const [width, height] = dimensions.split(' ').map(Number);
+
+      images.push({
+        base64: imageData.toString('base64'),
+        width,
+        height,
+        pageNum: i + 1,
+      });
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Convert a batch of pages to markdown using Nanonets-OCR-s
+ */
+async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
+  const startTime = Date.now();
+  const pageNums = batch.map(img => img.pageNum).join(', ');
+
+  const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
+
+  for (const img of batch) {
+    content.push({
+      type: 'image_url',
+      image_url: { url: `data:image/jpeg;base64,${img.base64}` },
+    });
+  }
+
+  const promptText = batch.length > 1
+    ? `${NANONETS_OCR_PROMPT}\n\nPlease clearly separate each page's content with "--- PAGE N ---" markers, where N is the page number starting from ${batch[0].pageNum}.`
+    : NANONETS_OCR_PROMPT;
+
+  content.push({ type: 'text', text: promptText });
+
+  const response = await fetch(`${NANONETS_URL}/chat/completions`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+      'Authorization': 'Bearer dummy',
+    },
+    body: JSON.stringify({
+      model: NANONETS_MODEL,
+      messages: [{
+        role: 'user',
+        content,
+      }],
+      max_tokens: 4096 * batch.length,
+      temperature: 0.0,
+    }),
+    signal: AbortSignal.timeout(600000),
+  });
+
+  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
+  }
+
+  const data = await response.json();
+  let responseContent = (data.choices?.[0]?.message?.content || '').trim();
+
+  if (batch.length === 1 && !responseContent.includes('--- PAGE')) {
+    responseContent = `--- PAGE ${batch[0].pageNum} ---\n${responseContent}`;
+  }
+
+  console.log(`        Pages [${pageNums}]: ${responseContent.length} chars (${elapsed}s)`);
+  return responseContent;
+}
+
+/**
+ * Convert all pages of a document to markdown using smart batching
+ */
+async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
+  const batches = batchImages(images);
+  console.log(`      [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
+
+  const markdownParts: string[] = [];
+
+  for (let i = 0; i < batches.length; i++) {
+    const batch = batches[i];
+    const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
+    console.log(`        Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
+    const markdown = await convertBatchToMarkdown(batch);
+    markdownParts.push(markdown);
+  }
+
+  const fullMarkdown = markdownParts.join('\n\n');
+  console.log(`      [${docName}] Complete: ${fullMarkdown.length} chars total`);
+  return fullMarkdown;
+}
+
+/**
+ * Stop Nanonets container
+ */
+function stopNanonets(): void {
+  console.log('    [Docker] Stopping Nanonets container...');
+  try {
+    execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
+    execSync('sleep 5', { stdio: 'pipe' });
+    console.log('    [Docker] Nanonets stopped');
+  } catch {
+    console.log('    [Docker] Nanonets was not running');
+  }
+}
+
+/**
+ * Ensure GPT-OSS 20B model is available
+ */
+async function ensureExtractionModel(): Promise<boolean> {
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
+        console.log(`    [Ollama] Model available: ${EXTRACTION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    return false;
+  }
+
+  console.log(`    [Ollama] Pulling ${EXTRACTION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+/**
+ * Parse amount from string (handles European format)
+ */
+function parseAmount(s: string | number | undefined): number {
+  if (s === undefined || s === null) return 0;
+  if (typeof s === 'number') return s;
+  const match = s.match(/([\d.,]+)/);
+  if (!match) return 0;
+  const numStr = match[1];
+  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+    ? numStr.replace(/\./g, '').replace(',', '.')
+    : numStr.replace(/,/g, '');
+  return parseFloat(normalized) || 0;
+}
+
+/**
+ * Extract invoice number - MINIMAL normalization for debugging
+ */
+function extractInvoiceNumber(s: string | undefined): string {
+  if (!s) return '';
+  // Only remove markdown formatting, preserve everything else
+  return s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+}
+
+/**
+ * Extract date (YYYY-MM-DD) from response
+ */
+function extractDate(s: string | undefined): string {
+  if (!s) return '';
+  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+  const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
+  if (isoMatch) return isoMatch[1];
+  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
+  if (dmyMatch) {
+    return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
+  }
+  return clean.replace(/[^\d-]/g, '').trim();
+}
+
+/**
+ * Extract currency
+ */
+function extractCurrency(s: string | undefined): string {
+  if (!s) return 'EUR';
+  const upper = s.toUpperCase();
+  if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
+  if (upper.includes('USD') || upper.includes('$')) return 'USD';
+  if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
+  return 'EUR';
+}
+
+/**
+ * Extract JSON from response
+ */
+function extractJsonFromResponse(response: string): Record<string, unknown> | null {
+  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
+  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
+  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
+
+  try {
+    return JSON.parse(jsonStr);
+  } catch {
+    const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
+    if (jsonMatch) {
+      try {
+        return JSON.parse(jsonMatch[0]);
+      } catch {
+        return null;
+      }
+    }
+    return null;
+  }
+}
+
+/**
+ * Parse JSON response into IInvoice
+ */
+function parseJsonToInvoice(response: string): IInvoice | null {
+  const parsed = extractJsonFromResponse(response);
+  if (!parsed) return null;
+
+  return {
+    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
+    invoice_date: extractDate(String(parsed.invoice_date || '')),
+    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
+    currency: extractCurrency(String(parsed.currency || '')),
+    net_amount: parseAmount(parsed.net_amount as string | number),
+    vat_amount: parseAmount(parsed.vat_amount as string | number),
+    total_amount: parseAmount(parsed.total_amount as string | number),
+  };
+}
+
+/**
+ * Extract invoice from markdown using GPT-OSS 20B (streaming)
+ */
+async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
+  const startTime = Date.now();
+  const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
+
+  // Log exact prompt
+  console.log(`\n      [${queryId}] ===== PROMPT =====`);
+  console.log(fullPrompt);
+  console.log(`      [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: EXTRACTION_MODEL,
+      messages: [
+        { role: 'user', content: 'Hi there, how are you?' },
+        { role: 'assistant', content: 'Good, how can I help you today?' },
+        { role: 'user', content: fullPrompt },
+      ],
+      stream: true,
+    }),
+    signal: AbortSignal.timeout(600000),
+  });
+
+  if (!response.ok) {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  // Stream the response
+  let content = '';
+  let thinkingContent = '';
+  let thinkingStarted = false;
+  let outputStarted = false;
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+
+          const thinking = json.message?.thinking || '';
+          if (thinking) {
+            if (!thinkingStarted) {
+              process.stdout.write(`      [${queryId}] THINKING: `);
+              thinkingStarted = true;
+            }
+            process.stdout.write(thinking);
+            thinkingContent += thinking;
+          }
+
+          const token = json.message?.content || '';
+          if (token) {
+            if (!outputStarted) {
+              if (thinkingStarted) process.stdout.write('\n');
+              process.stdout.write(`      [${queryId}] OUTPUT: `);
+              outputStarted = true;
+            }
+            process.stdout.write(token);
+            content += token;
+          }
+        } catch {
+          // Ignore parse errors for partial chunks
+        }
+      }
+    }
+  } finally {
+    if (thinkingStarted || outputStarted) process.stdout.write('\n');
+  }
+
+  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+  console.log(`      [${queryId}] Done: ${thinkingContent.length} thinking chars, ${content.length} output chars (${elapsed}s)`);
+
+  // Log raw response for debugging
+  console.log(`      [${queryId}] RAW RESPONSE: ${content}`);
+
+  return parseJsonToInvoice(content);
+}
+
+/**
+ * Extract invoice (single pass)
+ */
+async function extractInvoice(markdown: string, docName: string): Promise<IInvoice> {
+  console.log(`    [${docName}] Extracting...`);
+  const invoice = await extractInvoiceFromMarkdown(markdown, docName);
+  if (!invoice) {
+    return {
+      invoice_number: '',
+      invoice_date: '',
+      vendor_name: '',
+      currency: 'EUR',
+      net_amount: 0,
+      vat_amount: 0,
+      total_amount: 0,
+    };
+  }
+  console.log(`    [${docName}] Extracted: ${JSON.stringify(invoice, null, 2)}`);
+  return invoice;
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
+
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
+  };
+
+  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
+  if (match) {
+    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
+  }
+
+  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
+  if (match) {
+    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
+  }
+
+  return dateStr;
+}
+
+/**
+ * Compare extracted invoice against expected - detailed output
+ */
+function compareInvoice(
+  extracted: IInvoice,
+  expected: IInvoice
+): { match: boolean; errors: string[] } {
+  const errors: string[] = [];
+
+  // Invoice number comparison - exact match after whitespace normalization
+  const extNum = extracted.invoice_number?.trim() || '';
+  const expNum = expected.invoice_number?.trim() || '';
+  if (extNum.toLowerCase() !== expNum.toLowerCase()) {
+    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
+  }
+
+  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
+    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
+  }
+
+  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
+    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
+  }
+
+  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
+    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find test cases for failed invoices only
+ */
+function findTestCases(): ITestCase[] {
+  const testDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: ITestCase[] = [];
+
+  for (const invoiceName of FAILED_INVOICES) {
+    const pdfFile = `${invoiceName}.pdf`;
+    const jsonFile = `${invoiceName}.json`;
+
+    if (files.includes(pdfFile) && files.includes(jsonFile)) {
+      testCases.push({
+        name: invoiceName,
+        pdfPath: path.join(testDir, pdfFile),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    } else {
+      console.warn(`Warning: Missing files for ${invoiceName}`);
+    }
+  }
+
+  return testCases;
+}
+
+// ============ TESTS ============
+
+const testCases = findTestCases();
+console.log(`\n========================================`);
+console.log(`  FAILED INVOICES DEBUG TEST`);
+console.log(`========================================`);
+console.log(`  Testing ${testCases.length} failed invoices:`);
+for (const tc of testCases) {
+  console.log(`    - ${tc.name}`);
+}
+console.log(`========================================\n`);
+
+// Ensure temp directory exists
+if (!fs.existsSync(TEMP_MD_DIR)) {
+  fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
+}
+
+// -------- STAGE 1: OCR with Nanonets --------
+
+tap.test('Stage 1: Setup Nanonets', async () => {
+  console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
+  const ok = await ensureNanonetsOcr();
+  expect(ok).toBeTrue();
+});
+
+tap.test('Stage 1: Convert failed invoices to markdown', async () => {
+  console.log('\n  Converting failed invoice PDFs to markdown with Nanonets-OCR-s...\n');
+
+  for (const tc of testCases) {
+    console.log(`\n    === ${tc.name} ===`);
+
+    const images = convertPdfToImages(tc.pdfPath);
+    console.log(`      Pages: ${images.length}`);
+
+    const markdown = await convertDocumentToMarkdown(images, tc.name);
+
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    fs.writeFileSync(mdPath, markdown);
+    tc.markdownPath = mdPath;
+    console.log(`      Saved: ${mdPath}`);
+
+    // Also save to .nogit for inspection
+    const debugMdPath = path.join(process.cwd(), '.nogit/invoices', `${tc.name}.debug.md`);
+    fs.writeFileSync(debugMdPath, markdown);
+    console.log(`      Debug copy: ${debugMdPath}`);
+  }
+
+  console.log('\n  Stage 1 complete: All failed invoices converted to markdown\n');
+});
+
+tap.test('Stage 1: Stop Nanonets', async () => {
+  stopNanonets();
+  await new Promise(resolve => setTimeout(resolve, 3000));
+  expect(isContainerRunning('nanonets-test')).toBeFalse();
+});
+
+// -------- STAGE 2: Extraction with GPT-OSS 20B --------
+
+tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
+  console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');
+
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+
+  const extractionOk = await ensureExtractionModel();
+  expect(extractionOk).toBeTrue();
+});
+
+let passedCount = 0;
+let failedCount = 0;
+
+for (const tc of testCases) {
+  tap.test(`Stage 2: Extract ${tc.name}`, async () => {
+    const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
+    console.log(`\n    ========================================`);
+    console.log(`    === ${tc.name} ===`);
+    console.log(`    ========================================`);
+    console.log(`    EXPECTED:`);
+    console.log(`      invoice_number: "${expected.invoice_number}"`);
+    console.log(`      invoice_date:   "${expected.invoice_date}"`);
+    console.log(`      vendor_name:    "${expected.vendor_name}"`);
+    console.log(`      total_amount:   ${expected.total_amount} ${expected.currency}`);
+
+    const startTime = Date.now();
+
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    if (!fs.existsSync(mdPath)) {
+      throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
+    }
+    const markdown = fs.readFileSync(mdPath, 'utf-8');
+    console.log(`    Markdown: ${markdown.length} chars`);
+
+    const extracted = await extractInvoice(markdown, tc.name);
+
+    const elapsedMs = Date.now() - startTime;
+
+    console.log(`\n    EXTRACTED:`);
+    console.log(`      invoice_number: "${extracted.invoice_number}"`);
+    console.log(`      invoice_date:   "${extracted.invoice_date}"`);
+    console.log(`      vendor_name:    "${extracted.vendor_name}"`);
+    console.log(`      total_amount:   ${extracted.total_amount} ${extracted.currency}`);
+
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`\n    Result: ✓ MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`\n    Result: ✗ MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+      console.log(`    ERRORS:`);
+      result.errors.forEach(e => console.log(`      - ${e}`));
+    }
+
+    // Don't fail the test - we're debugging
+    // expect(result.match).toBeTrue();
+  });
+}
+
+tap.test('Summary', async () => {
+  const totalInvoices = testCases.length;
+  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
+
+  console.log(`\n========================================`);
+  console.log(`   Failed Invoices Debug Summary`);
+  console.log(`========================================`);
+  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
+  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`========================================`);
+  console.log(`  Markdown files saved to: ${TEMP_MD_DIR}`);
+  console.log(`  Debug copies in: .nogit/invoices/*.debug.md`);
+  console.log(`========================================\n`);
+
+  // Don't cleanup temp files for debugging
+  console.log(`  Keeping temp files for debugging.\n`);
+});
+
+export default tap.start();

test/test.invoices.nanonets.ts

@@ -0,0 +1,684 @@
+/**
+ * Invoice extraction using Nanonets-OCR2-3B + GPT-OSS 20B (sequential two-stage pipeline)
+ *
+ * Stage 1: Nanonets-OCR2-3B converts ALL document pages to markdown (stop after completion)
+ * Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
+ *
+ * This approach avoids GPU contention by running services sequentially.
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
+
+const NANONETS_URL = 'http://localhost:8000/v1';
+const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const EXTRACTION_MODEL = 'gpt-oss:20b';
+
+// Temp directory for storing markdown between stages
+const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-markdown');
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+interface IImageData {
+  base64: string;
+  width: number;
+  height: number;
+  pageNum: number;
+}
+
+interface ITestCase {
+  name: string;
+  pdfPath: string;
+  jsonPath: string;
+  markdownPath?: string;
+}
+
+// Nanonets-specific prompt for document OCR to markdown
+const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
+Return the tables in html format.
+Return the equations in LaTeX representation.
+If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
+Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
+Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
+
+// JSON extraction prompt for GPT-OSS 20B
+const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
+
+IMPORTANT RULES:
+1. invoice_number: The unique invoice/document number (NOT VAT ID, NOT customer ID)
+2. invoice_date: Format as YYYY-MM-DD
+3. vendor_name: The company that issued the invoice
+4. currency: EUR, USD, or GBP
+5. net_amount: Amount before tax
+6. vat_amount: Tax/VAT amount
+7. total_amount: Final total (gross amount)
+
+Return ONLY this JSON format, no explanation:
+{
+  "invoice_number": "INV-2024-001",
+  "invoice_date": "2024-01-15",
+  "vendor_name": "Company Name",
+  "currency": "EUR",
+  "net_amount": 100.00,
+  "vat_amount": 19.00,
+  "total_amount": 119.00
+}
+
+INVOICE TEXT:
+`;
+
+// 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
+
+/**
+ * Estimate visual tokens for an image based on dimensions
+ */
+function estimateVisualTokens(width: number, height: number): number {
+  return Math.ceil((width * height) / (PATCH_SIZE * PATCH_SIZE));
+}
+
+/**
+ * Process images one page at a time for reliability
+ */
+function batchImages(images: IImageData[]): IImageData[][] {
+  // One page per batch for reliable processing
+  return images.map(img => [img]);
+}
+
+/**
+ * Convert PDF to JPEG images using ImageMagick with dimension tracking
+ */
+function convertPdfToImages(pdfPath: string): IImageData[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.jpg');
+
+  try {
+    execSync(
+      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.jpg')).sort();
+    const images: IImageData[] = [];
+
+    for (let i = 0; i < files.length; i++) {
+      const file = files[i];
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+
+      // Get image dimensions using identify command
+      const dimensions = execSync(`identify -format "%w %h" "${imagePath}"`, { encoding: 'utf-8' }).trim();
+      const [width, height] = dimensions.split(' ').map(Number);
+
+      images.push({
+        base64: imageData.toString('base64'),
+        width,
+        height,
+        pageNum: i + 1,
+      });
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Convert a batch of pages to markdown using Nanonets-OCR-s
+ */
+async function convertBatchToMarkdown(batch: IImageData[]): Promise<string> {
+  const startTime = Date.now();
+  const pageNums = batch.map(img => img.pageNum).join(', ');
+
+  // Build content array with all images first, then the prompt
+  const content: Array<{ type: string; image_url?: { url: string }; text?: string }> = [];
+
+  for (const img of batch) {
+    content.push({
+      type: 'image_url',
+      image_url: { url: `data:image/jpeg;base64,${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',
+    headers: {
+      'Content-Type': 'application/json',
+      'Authorization': 'Bearer dummy',
+    },
+    body: JSON.stringify({
+      model: NANONETS_MODEL,
+      messages: [{
+        role: 'user',
+        content,
+      }],
+      max_tokens: 4096 * batch.length,  // Scale output tokens with batch size
+      temperature: 0.0,
+    }),
+    signal: AbortSignal.timeout(600000),  // 10 minute timeout for OCR
+  });
+
+  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
+  }
+
+  const data = await response.json();
+  let responseContent = (data.choices?.[0]?.message?.content || '').trim();
+
+  // 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 using smart batching
+ */
+async function convertDocumentToMarkdown(images: IImageData[], docName: string): Promise<string> {
+  const batches = batchImages(images);
+  console.log(`      [${docName}] Processing ${images.length} page(s) in ${batches.length} batch(es)...`);
+
+  const markdownParts: string[] = [];
+
+  for (let i = 0; i < batches.length; i++) {
+    const batch = batches[i];
+    const batchTokens = batch.reduce((sum, img) => sum + estimateVisualTokens(img.width, img.height), 0);
+    console.log(`        Batch ${i + 1}: ${batch.length} page(s), ~${batchTokens} tokens`);
+    const markdown = await convertBatchToMarkdown(batch);
+    markdownParts.push(markdown);
+  }
+
+  const fullMarkdown = markdownParts.join('\n\n');
+  console.log(`      [${docName}] Complete: ${fullMarkdown.length} chars total`);
+  return fullMarkdown;
+}
+
+/**
+ * Stop Nanonets container
+ */
+function stopNanonets(): void {
+  console.log('    [Docker] Stopping Nanonets container...');
+  try {
+    execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
+    execSync('sleep 5', { stdio: 'pipe' });
+    console.log('    [Docker] Nanonets stopped');
+  } catch {
+    console.log('    [Docker] Nanonets was not running');
+  }
+}
+
+/**
+ * Ensure GPT-OSS 20B model is available
+ */
+async function ensureExtractionModel(): Promise<boolean> {
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
+        console.log(`    [Ollama] Model available: ${EXTRACTION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    return false;
+  }
+
+  console.log(`    [Ollama] Pulling ${EXTRACTION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+/**
+ * Parse amount from string (handles European format)
+ */
+function parseAmount(s: string | number | undefined): number {
+  if (s === undefined || s === null) return 0;
+  if (typeof s === 'number') return s;
+  const match = s.match(/([\d.,]+)/);
+  if (!match) return 0;
+  const numStr = match[1];
+  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+    ? numStr.replace(/\./g, '').replace(',', '.')
+    : numStr.replace(/,/g, '');
+  return parseFloat(normalized) || 0;
+}
+
+/**
+ * Extract invoice number from potentially verbose response
+ */
+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,
+    /\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);
+    if (match) return match[1];
+  }
+  return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
+}
+
+/**
+ * Extract date (YYYY-MM-DD) from response
+ */
+function extractDate(s: string | undefined): string {
+  if (!s) return '';
+  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+  const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
+  if (isoMatch) return isoMatch[1];
+  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
+  if (dmyMatch) {
+    return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
+  }
+  return clean.replace(/[^\d-]/g, '').trim();
+}
+
+/**
+ * Extract currency
+ */
+function extractCurrency(s: string | undefined): string {
+  if (!s) return 'EUR';
+  const upper = s.toUpperCase();
+  if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
+  if (upper.includes('USD') || upper.includes('$')) return 'USD';
+  if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
+  return 'EUR';
+}
+
+/**
+ * Extract JSON from response
+ */
+function extractJsonFromResponse(response: string): Record<string, unknown> | null {
+  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
+  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
+  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
+
+  try {
+    return JSON.parse(jsonStr);
+  } catch {
+    const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
+    if (jsonMatch) {
+      try {
+        return JSON.parse(jsonMatch[0]);
+      } catch {
+        return null;
+      }
+    }
+    return null;
+  }
+}
+
+/**
+ * Parse JSON response into IInvoice
+ */
+function parseJsonToInvoice(response: string): IInvoice | null {
+  const parsed = extractJsonFromResponse(response);
+  if (!parsed) return null;
+
+  return {
+    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
+    invoice_date: extractDate(String(parsed.invoice_date || '')),
+    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
+    currency: extractCurrency(String(parsed.currency || '')),
+    net_amount: parseAmount(parsed.net_amount as string | number),
+    vat_amount: parseAmount(parsed.vat_amount as string | number),
+    total_amount: parseAmount(parsed.total_amount as string | number),
+  };
+}
+
+/**
+ * Extract invoice from markdown using GPT-OSS 20B (streaming)
+ */
+async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
+  const startTime = Date.now();
+  const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
+
+  // Log exact prompt
+  console.log(`\n      [${queryId}] ===== PROMPT =====`);
+  console.log(fullPrompt);
+  console.log(`      [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: EXTRACTION_MODEL,
+      messages: [
+        { role: 'user', content: 'Hi there, how are you?' },
+        { role: 'assistant', content: 'Good, how can I help you today?' },
+        { role: 'user', content: fullPrompt },
+      ],
+      stream: true,
+    }),
+    signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
+  });
+
+  if (!response.ok) {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  // Stream the response
+  let content = '';
+  let thinkingContent = '';
+  let thinkingStarted = false;
+  let outputStarted = false;
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+
+      // Each line is a JSON object
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+
+          // Stream thinking tokens
+          const thinking = json.message?.thinking || '';
+          if (thinking) {
+            if (!thinkingStarted) {
+              process.stdout.write(`      [${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);
+}
+
+/**
+ * Extract invoice (single pass - GPT-OSS is more reliable)
+ */
+async function extractInvoice(markdown: string, docName: string): Promise<IInvoice> {
+  console.log(`    [${docName}] Extracting...`);
+  const invoice = await extractInvoiceFromMarkdown(markdown, docName);
+  if (!invoice) {
+    return {
+      invoice_number: '',
+      invoice_date: '',
+      vendor_name: '',
+      currency: 'EUR',
+      net_amount: 0,
+      vat_amount: 0,
+      total_amount: 0,
+    };
+  }
+  console.log(`    [${docName}] Extracted: ${invoice.invoice_number}`);
+  return invoice;
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
+
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
+  };
+
+  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
+  if (match) {
+    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
+  }
+
+  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
+  if (match) {
+    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
+  }
+
+  return dateStr;
+}
+
+/**
+ * Compare extracted invoice against expected
+ */
+function compareInvoice(
+  extracted: IInvoice,
+  expected: IInvoice
+): { match: boolean; errors: string[] } {
+  const errors: string[] = [];
+
+  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  if (extNum !== expNum) {
+    errors.push(`invoice_number: exp "${expected.invoice_number}", got "${extracted.invoice_number}"`);
+  }
+
+  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
+    errors.push(`invoice_date: exp "${expected.invoice_date}", got "${extracted.invoice_date}"`);
+  }
+
+  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
+    errors.push(`total_amount: exp ${expected.total_amount}, got ${extracted.total_amount}`);
+  }
+
+  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
+    errors.push(`currency: exp "${expected.currency}", got "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find all test cases
+ */
+function findTestCases(): ITestCase[] {
+  const testDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: ITestCase[] = [];
+
+  for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  return testCases.sort((a, b) => a.name.localeCompare(b.name));
+}
+
+// ============ TESTS ============
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} invoice test cases\n`);
+
+// Ensure temp directory exists
+if (!fs.existsSync(TEMP_MD_DIR)) {
+  fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
+}
+
+// -------- STAGE 1: OCR with Nanonets --------
+
+tap.test('Stage 1: Setup Nanonets', async () => {
+  console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
+  const ok = await ensureNanonetsOcr();
+  expect(ok).toBeTrue();
+});
+
+tap.test('Stage 1: Convert all invoices to markdown', async () => {
+  console.log('\n  Converting all invoice PDFs to markdown with Nanonets-OCR-s...\n');
+
+  for (const tc of testCases) {
+    console.log(`\n    === ${tc.name} ===`);
+
+    const images = convertPdfToImages(tc.pdfPath);
+    console.log(`      Pages: ${images.length}`);
+
+    const markdown = await convertDocumentToMarkdown(images, tc.name);
+
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    fs.writeFileSync(mdPath, markdown);
+    tc.markdownPath = mdPath;
+    console.log(`      Saved: ${mdPath}`);
+  }
+
+  console.log('\n  Stage 1 complete: All invoices converted to markdown\n');
+});
+
+tap.test('Stage 1: Stop Nanonets', async () => {
+  stopNanonets();
+  await new Promise(resolve => setTimeout(resolve, 3000));
+  expect(isContainerRunning('nanonets-test')).toBeFalse();
+});
+
+// -------- STAGE 2: Extraction with GPT-OSS 20B --------
+
+tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
+  console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');
+
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+
+  const extractionOk = await ensureExtractionModel();
+  expect(extractionOk).toBeTrue();
+});
+
+let passedCount = 0;
+let failedCount = 0;
+const processingTimes: number[] = [];
+
+for (const tc of testCases) {
+  tap.test(`Stage 2: Extract ${tc.name}`, async () => {
+    const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
+    console.log(`\n    === ${tc.name} ===`);
+    console.log(`    Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
+
+    const startTime = Date.now();
+
+    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    if (!fs.existsSync(mdPath)) {
+      throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
+    }
+    const markdown = fs.readFileSync(mdPath, 'utf-8');
+    console.log(`    Markdown: ${markdown.length} chars`);
+
+    const extracted = await extractInvoice(markdown, tc.name);
+
+    const elapsedMs = Date.now() - startTime;
+    processingTimes.push(elapsedMs);
+
+    console.log(`    Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
+
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`    Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`    Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
+      result.errors.forEach(e => console.log(`      - ${e}`));
+    }
+
+    expect(result.match).toBeTrue();
+  });
+}
+
+tap.test('Summary', async () => {
+  const totalInvoices = testCases.length;
+  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
+  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
+  const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
+
+  console.log(`\n========================================`);
+  console.log(`   Invoice Summary (Nanonets + GPT-OSS 20B)`);
+  console.log(`========================================`);
+  console.log(`  Stage 1:   Nanonets-OCR-s (doc -> md)`);
+  console.log(`  Stage 2:   GPT-OSS 20B (md -> JSON)`);
+  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
+  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`----------------------------------------`);
+  console.log(`  Total time:   ${(totalTimeMs / 1000).toFixed(1)}s`);
+  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
+  console.log(`========================================\n`);
+
+  // Cleanup temp files
+  try {
+    fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
+    console.log(`  Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
+  } catch {
+    // Ignore
+  }
+});
+
+export default tap.start();