v1.9.0

Dockerfile_minicpm45v_cpu

@@ -14,7 +14,7 @@ ENV OLLAMA_ORIGINS="*"
 ENV CUDA_VISIBLE_DEVICES=""
 
 # Copy and setup entrypoint
-COPY image_support_files/docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
+COPY image_support_files/minicpm45v_entrypoint.sh /usr/local/bin/docker-entrypoint.sh
 RUN chmod +x /usr/local/bin/docker-entrypoint.sh
 
 # Expose Ollama API port

Dockerfile_minicpm45v_gpu

@@ -12,7 +12,7 @@ ENV OLLAMA_HOST="0.0.0.0"
 ENV OLLAMA_ORIGINS="*"
 
 # Copy and setup entrypoint
-COPY image_support_files/docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
+COPY image_support_files/minicpm45v_entrypoint.sh /usr/local/bin/docker-entrypoint.sh
 RUN chmod +x /usr/local/bin/docker-entrypoint.sh
 
 # Expose Ollama API port

Dockerfile_paddleocr_vl

@@ -1,70 +0,0 @@
-# PaddleOCR-VL GPU Variant
-# Vision-Language Model for document parsing using vLLM
-FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL 0.9B - Vision-Language Model for document parsing"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV HF_HOME=/root/.cache/huggingface
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.11 \
-    python3.11-venv \
-    python3.11-dev \
-    python3-pip \
-    git \
-    curl \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/* \
-    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
-
-# Create and activate virtual environment
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install PyTorch with CUDA support
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir \
-    torch==2.5.1 \
-    torchvision \
-    --index-url https://download.pytorch.org/whl/cu124
-
-# Install vLLM 0.11.1 (first stable release with PaddleOCR-VL support)
-RUN pip install --no-cache-dir \
-    vllm==0.11.1 \
-    --extra-index-url https://download.pytorch.org/whl/cu124
-
-# Install additional dependencies
-RUN pip install --no-cache-dir \
-    transformers \
-    accelerate \
-    safetensors \
-    pillow \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    openai \
-    httpx
-
-# Copy entrypoint script
-COPY image_support_files/paddleocr-vl-entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
-RUN chmod +x /usr/local/bin/paddleocr-vl-entrypoint.sh
-
-# Expose vLLM API port
-EXPOSE 8000
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=300s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
-
-ENTRYPOINT ["/usr/local/bin/paddleocr-vl-entrypoint.sh"]

Dockerfile_paddleocr_vl_cpu

@@ -44,7 +44,7 @@ RUN pip install --no-cache-dir --upgrade pip && \
 
 # Copy server files
 COPY image_support_files/paddleocr_vl_server.py /app/paddleocr_vl_server.py
-COPY image_support_files/paddleocr-vl-cpu-entrypoint.sh /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh
+COPY image_support_files/paddleocr_vl_entrypoint.sh /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh
 RUN chmod +x /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh
 
 # Expose API port

Dockerfile_paddleocr_vl_gpu

@@ -58,7 +58,7 @@ RUN pip install --no-cache-dir \
 
 # Copy server files (same as CPU variant - it auto-detects CUDA)
 COPY image_support_files/paddleocr_vl_server.py /app/paddleocr_vl_server.py
-COPY image_support_files/paddleocr-vl-cpu-entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
+COPY image_support_files/paddleocr_vl_entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
 RUN chmod +x /usr/local/bin/paddleocr-vl-entrypoint.sh
 
 # Expose API port

build-images.sh

@@ -16,7 +16,7 @@ echo -e "${BLUE}Building ht-docker-ai images...${NC}"
 # Build GPU variant
 echo -e "${GREEN}Building MiniCPM-V 4.5 GPU variant...${NC}"
 docker build \
-    -f Dockerfile_minicpm45v \
+    -f Dockerfile_minicpm45v_gpu \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-gpu \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:latest \
@@ -29,10 +29,10 @@ docker build \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:minicpm45v-cpu \
     .
 
-# Build PaddleOCR-VL GPU variant (vLLM)
+# Build PaddleOCR-VL GPU variant
-echo -e "${GREEN}Building PaddleOCR-VL GPU variant (vLLM)...${NC}"
+echo -e "${GREEN}Building PaddleOCR-VL GPU variant...${NC}"
 docker build \
-    -f Dockerfile_paddleocr_vl \
+    -f Dockerfile_paddleocr_vl_gpu \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl \
     -t ${REGISTRY}/${NAMESPACE}/${IMAGE_NAME}:paddleocr-vl-gpu \
     .

changelog.md

@@ -1,5 +1,42 @@
 # Changelog
 
+## 2026-01-18 - 1.9.0 - feat(tests)
+add Ministral 3 vision tests and improve invoice extraction pipeline to use Ollama chat schema, sanitization, and multi-page support
+
+- Add new vision-based test suites for Ministral 3: test/test.invoices.ministral3.ts and test/test.bankstatements.ministral3.ts (model ministral-3:8b).
+- Introduce ensureMinistral3() helper to start/check Ollama/MiniCPM model in test/helpers/docker.ts.
+- Switch invoice extraction to use Ollama /api/chat with a JSON schema (format) and streaming support (reads message.content).
+- Improve HTML handling: sanitizeHtml() to remove OCR artifacts, concatenate multi-page HTML with page markers, and increase truncation limits.
+- Enhance response parsing: strip Markdown code fences, robustly locate JSON object boundaries, and provide clearer JSON parse errors.
+- Add PDF->PNG conversion (ImageMagick) and direct image-based extraction flow for vision model tests.
+
+## 2026-01-18 - 1.8.0 - feat(paddleocr-vl)
+add structured HTML output and table parsing for PaddleOCR-VL, update API, tests, and README
+
+- Add result_to_html(), parse_markdown_table(), and parse_paddleocr_table() to emit semantic HTML and convert OCR/markdown tables to proper <table> elements
+- Enhance result_to_markdown() with positional/type hints (header/footer/title/table/figure) to improve downstream LLM processing
+- Expose 'html' in supported formats and handle output_format='html' in parse endpoints and CLI flow
+- Update tests to request HTML output and extract invoice fields from structured HTML (test/test.invoices.paddleocr-vl.ts)
+- Refresh README with usage, new images/tags, architecture notes, and troubleshooting for the updated pipeline
+
+## 2026-01-17 - 1.7.1 - fix(docker)
+standardize Dockerfile and entrypoint filenames; add GPU-specific Dockerfiles and update build and test references
+
+- Added Dockerfile_minicpm45v_gpu and image_support_files/minicpm45v_entrypoint.sh; removed the old Dockerfile_minicpm45v and docker-entrypoint.sh
+- Renamed and simplified PaddleOCR entrypoint to image_support_files/paddleocr_vl_entrypoint.sh and updated CPU/GPU Dockerfile references
+- Updated build-images.sh to use *_gpu Dockerfiles and clarified PaddleOCR GPU build log
+- Updated test/helpers/docker.ts to point to Dockerfile_minicpm45v_gpu so tests build the GPU variant
+
+## 2026-01-17 - 1.7.0 - feat(tests)
+use Qwen2.5 (Ollama) for invoice extraction tests and add helpers for model management; normalize dates and coerce numeric fields
+
+- Added ensureOllamaModel and ensureQwen25 test helpers to pull/check Ollama models via localhost:11434
+- Updated invoices test to use qwen2.5:7b instead of MiniCPM and removed image payload from the text-only extraction step
+- Increased Markdown truncate limit from 8000 to 12000 and reduced model num_predict from 2048 to 512
+- Rewrote extraction prompt to require strict JSON output and added post-processing to parse/convert numeric fields
+- Added normalizeDate and improved compareInvoice to normalize dates and handle numeric formatting/tolerance
+- Updated test setup to ensure Qwen2.5 is available and adjusted logging/messages to reflect the Qwen2.5-based workflow
+
 ## 2026-01-17 - 1.6.0 - feat(paddleocr-vl)
 add PaddleOCR-VL full pipeline Docker image and API server, plus integration tests and docker helpers
 

image_support_files/paddleocr-vl-entrypoint.sh

@@ -1,59 +0,0 @@
-#!/bin/bash
-set -e
-
-echo "==================================="
-echo "PaddleOCR-VL Server"
-echo "==================================="
-
-# Configuration
-MODEL_NAME="${MODEL_NAME:-PaddlePaddle/PaddleOCR-VL}"
-HOST="${HOST:-0.0.0.0}"
-PORT="${PORT:-8000}"
-MAX_BATCHED_TOKENS="${MAX_BATCHED_TOKENS:-16384}"
-GPU_MEMORY_UTILIZATION="${GPU_MEMORY_UTILIZATION:-0.9}"
-MAX_MODEL_LEN="${MAX_MODEL_LEN:-8192}"
-ENFORCE_EAGER="${ENFORCE_EAGER:-false}"
-
-echo "Model: ${MODEL_NAME}"
-echo "Host: ${HOST}"
-echo "Port: ${PORT}"
-echo "Max batched tokens: ${MAX_BATCHED_TOKENS}"
-echo "GPU memory utilization: ${GPU_MEMORY_UTILIZATION}"
-echo "Max model length: ${MAX_MODEL_LEN}"
-echo "Enforce eager: ${ENFORCE_EAGER}"
-echo ""
-
-# Check GPU availability
-if command -v nvidia-smi &> /dev/null; then
-    echo "GPU Information:"
-    nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv
-    echo ""
-else
-    echo "WARNING: nvidia-smi not found. GPU may not be available."
-fi
-
-echo "Starting vLLM server..."
-echo "==================================="
-
-# Build vLLM command
-VLLM_ARGS=(
-    serve "${MODEL_NAME}"
-    --trust-remote-code
-    --host "${HOST}"
-    --port "${PORT}"
-    --max-num-batched-tokens "${MAX_BATCHED_TOKENS}"
-    --gpu-memory-utilization "${GPU_MEMORY_UTILIZATION}"
-    --max-model-len "${MAX_MODEL_LEN}"
-    --no-enable-prefix-caching
-    --mm-processor-cache-gb 0
-    --served-model-name "paddleocr-vl"
-    --limit-mm-per-prompt '{"image": 1}'
-)
-
-# Add enforce-eager if enabled (disables CUDA graphs, saves memory)
-if [ "${ENFORCE_EAGER}" = "true" ]; then
-    VLLM_ARGS+=(--enforce-eager)
-fi
-
-# Start vLLM server with PaddleOCR-VL
-exec vllm "${VLLM_ARGS[@]}"

image_support_files/paddleocr_vl_full_server.py

@@ -10,6 +10,7 @@ Provides REST API for document parsing using:
 
 import os
 import io
+import re
 import base64
 import logging
 import tempfile
@@ -261,23 +262,210 @@ def process_document(image: Image.Image) -> dict:
 
 
 def result_to_markdown(result: dict) -> str:
-    """Convert result to Markdown format"""
+    """Convert result to Markdown format with structural hints for LLM processing.
+
+    Adds positional and type-based formatting to help downstream LLMs
+    understand document structure:
+    - Tables are marked with **[TABLE]** prefix
+    - Header zone content (top 15%) is bolded
+    - Footer zone content (bottom 15%) is separated with horizontal rule
+    - Titles are formatted as # headers
+    - Figures/charts are marked with *[Figure: ...]*
+    """
     lines = []
+    image_height = result.get("image_size", [0, 1000])[1]
 
     for block in result.get("blocks", []):
-        block_type = block.get("type", "text")
+        block_type = block.get("type", "text").lower()
-        content = block.get("content", "")
+        content = block.get("content", "").strip()
+        bbox = block.get("bbox", [])
 
-        if "table" in block_type.lower():
+        if not content:
-            lines.append(f"\n{content}\n")
+            continue
-        elif "formula" in block_type.lower():
+
+        # Determine position zone (top 15%, middle, bottom 15%)
+        y_pos = bbox[1] if bbox and len(bbox) > 1 else 0
+        y_end = bbox[3] if bbox and len(bbox) > 3 else y_pos
+        is_header_zone = y_pos < image_height * 0.15
+        is_footer_zone = y_end > image_height * 0.85
+
+        # Format based on type and position
+        if "table" in block_type:
+            lines.append(f"\n**[TABLE]**\n{content}\n")
+        elif "title" in block_type:
+            lines.append(f"# {content}")
+        elif "formula" in block_type or "math" in block_type:
             lines.append(f"\n$$\n{content}\n$$\n")
+        elif "figure" in block_type or "chart" in block_type:
+            lines.append(f"*[Figure: {content}]*")
+        elif is_header_zone:
+            lines.append(f"**{content}**")
+        elif is_footer_zone:
+            lines.append(f"---\n{content}")
         else:
             lines.append(content)
 
     return "\n\n".join(lines)
 
 
+def parse_markdown_table(content: str) -> str:
+    """Convert table content to HTML table.
+
+    Handles:
+    - PaddleOCR-VL format: <fcel>cell<lcel>cell<nl> (detected by <fcel> tags)
+    - Pipe-delimited tables: | Header | Header |
+    - Separator rows: |---|---|
+    - Returns HTML <table> structure
+    """
+    content_stripped = content.strip()
+
+    # Check for PaddleOCR-VL table format (<fcel>, <lcel>, <ecel>, <nl>)
+    if '<fcel>' in content_stripped or '<nl>' in content_stripped:
+        return parse_paddleocr_table(content_stripped)
+
+    lines = content_stripped.split('\n')
+    if not lines:
+        return f'<pre>{content}</pre>'
+
+    # Check if it looks like a markdown table
+    if not any('|' in line for line in lines):
+        return f'<pre>{content}</pre>'
+
+    html_rows = []
+    is_header = True
+
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith('|') == False and '|' not in line:
+            continue
+
+        # Skip separator rows (|---|---|)
+        if re.match(r'^[\|\s\-:]+$', line):
+            is_header = False
+            continue
+
+        # Parse cells
+        cells = [c.strip() for c in line.split('|')]
+        cells = [c for c in cells if c]  # Remove empty from edges
+
+        if is_header:
+            row = '<tr>' + ''.join(f'<th>{c}</th>' for c in cells) + '</tr>'
+            html_rows.append(f'<thead>{row}</thead>')
+            is_header = False
+        else:
+            row = '<tr>' + ''.join(f'<td>{c}</td>' for c in cells) + '</tr>'
+            html_rows.append(row)
+
+    if html_rows:
+        # Wrap body rows in tbody
+        header = html_rows[0] if '<thead>' in html_rows[0] else ''
+        body_rows = [r for r in html_rows if '<thead>' not in r]
+        body = f'<tbody>{"".join(body_rows)}</tbody>' if body_rows else ''
+        return f'<table>{header}{body}</table>'
+
+    return f'<pre>{content}</pre>'
+
+
+def parse_paddleocr_table(content: str) -> str:
+    """Convert PaddleOCR-VL table format to HTML table.
+
+    PaddleOCR-VL uses:
+    - <fcel> = first cell in a row
+    - <lcel> = subsequent cells
+    - <ecel> = empty cell
+    - <nl> = row separator (newline)
+
+    Example input:
+    <fcel>Header1<lcel>Header2<nl><fcel>Value1<lcel>Value2<nl>
+    """
+    # Split into rows by <nl>
+    rows_raw = re.split(r'<nl>', content)
+    html_rows = []
+    is_first_row = True
+
+    for row_content in rows_raw:
+        row_content = row_content.strip()
+        if not row_content:
+            continue
+
+        # Extract cells: split by <fcel>, <lcel>, or <ecel>
+        # Each cell is the text between these markers
+        cells = []
+
+        # Pattern to match cell markers and capture content
+        # Content is everything between markers
+        parts = re.split(r'<fcel>|<lcel>|<ecel>', row_content)
+        for part in parts:
+            part = part.strip()
+            if part:
+                cells.append(part)
+
+        if not cells:
+            continue
+
+        # First row is header
+        if is_first_row:
+            row_html = '<tr>' + ''.join(f'<th>{c}</th>' for c in cells) + '</tr>'
+            html_rows.append(f'<thead>{row_html}</thead>')
+            is_first_row = False
+        else:
+            row_html = '<tr>' + ''.join(f'<td>{c}</td>' for c in cells) + '</tr>'
+            html_rows.append(row_html)
+
+    if html_rows:
+        header = html_rows[0] if '<thead>' in html_rows[0] else ''
+        body_rows = [r for r in html_rows if '<thead>' not in r]
+        body = f'<tbody>{"".join(body_rows)}</tbody>' if body_rows else ''
+        return f'<table>{header}{body}</table>'
+
+    return f'<pre>{content}</pre>'
+
+
+def result_to_html(result: dict) -> str:
+    """Convert result to semantic HTML for optimal LLM processing.
+
+    Uses semantic HTML5 tags with position metadata as data-* attributes.
+    Markdown tables are converted to proper HTML <table> tags for
+    unambiguous parsing by downstream LLMs.
+    """
+    parts = []
+    image_height = result.get("image_size", [0, 1000])[1]
+
+    parts.append('<!DOCTYPE html><html><body>')
+
+    for block in result.get("blocks", []):
+        block_type = block.get("type", "text").lower()
+        content = block.get("content", "").strip()
+        bbox = block.get("bbox", [])
+
+        if not content:
+            continue
+
+        # Position metadata
+        y_pos = bbox[1] / image_height if bbox and len(bbox) > 1 else 0
+        data_attrs = f'data-type="{block_type}" data-y="{y_pos:.2f}"'
+
+        # Format based on type
+        if "table" in block_type:
+            table_html = parse_markdown_table(content)
+            parts.append(f'<section {data_attrs} class="table-region">{table_html}</section>')
+        elif "title" in block_type:
+            parts.append(f'<h1 {data_attrs}>{content}</h1>')
+        elif "formula" in block_type or "math" in block_type:
+            parts.append(f'<div {data_attrs} class="formula"><code>{content}</code></div>')
+        elif "figure" in block_type or "chart" in block_type:
+            parts.append(f'<figure {data_attrs}><figcaption>{content}</figcaption></figure>')
+        elif y_pos < 0.15:
+            parts.append(f'<header {data_attrs}><strong>{content}</strong></header>')
+        elif y_pos > 0.85:
+            parts.append(f'<footer {data_attrs}>{content}</footer>')
+        else:
+            parts.append(f'<p {data_attrs}>{content}</p>')
+
+    parts.append('</body></html>')
+    return '\n'.join(parts)
+
+
 # Request/Response models
 class ParseRequest(BaseModel):
     image: str  # base64 encoded image
@@ -331,7 +519,7 @@ async def health_check():
 async def supported_formats():
     """List supported output formats"""
     return {
-        "output_formats": ["json", "markdown"],
+        "output_formats": ["json", "markdown", "html"],
         "image_formats": ["PNG", "JPEG", "WebP", "BMP", "GIF", "TIFF"],
         "capabilities": [
             "Layout detection (PP-DocLayoutV2)",
@@ -356,6 +544,9 @@ async def parse_document_endpoint(request: ParseRequest):
         if request.output_format == "markdown":
             markdown = result_to_markdown(result)
             output = {"markdown": markdown}
+        elif request.output_format == "html":
+            html = result_to_html(result)
+            output = {"html": html}
         else:
             output = result
 
@@ -408,6 +599,8 @@ async def chat_completions(request: dict):
 
         if output_format == "markdown":
             content = result_to_markdown(result)
+        elif output_format == "html":
+            content = result_to_html(result)
         else:
             content = json.dumps(result, ensure_ascii=False, indent=2)
 

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@host.today/ht-docker-ai",
-  "version": "1.6.0",
+  "version": "1.9.0",
   "type": "module",
   "private": false,
   "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",

readme.md

@@ -1,23 +1,40 @@
-# @host.today/ht-docker-ai
+# @host.today/ht-docker-ai 🚀
 
-Docker images for AI vision-language models, starting with MiniCPM-V 4.5.
+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.
 
-## Overview
+## Issue Reporting and Security
 
-This project provides ready-to-use Docker containers for running state-of-the-art AI vision-language models. Built on Ollama for simplified model management and a consistent REST API.
+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.
 
-## Available Images
+## 🎯 What's Included
 
-| Tag | Description | Requirements |
+| Model | Parameters | Best For | API |
-|-----|-------------|--------------|
+|-------|-----------|----------|-----|
-| `minicpm45v` | MiniCPM-V 4.5 with GPU support | NVIDIA GPU, 9-18GB VRAM |
+| **MiniCPM-V 4.5** | 8B | General vision understanding, image analysis, multi-image | Ollama-compatible |
-| `minicpm45v-cpu` | MiniCPM-V 4.5 CPU-only | 8GB+ RAM |
+| **PaddleOCR-VL** | 0.9B | Document parsing, table extraction, OCR | OpenAI-compatible |
-| `latest` | Alias for `minicpm45v` | NVIDIA GPU |
 
-## Quick Start
+## 📦 Available Images
 
-### GPU (Recommended)
+```
+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 |
+
+---
+
+## 🖼️ 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.
+
+### Quick Start
+
+**GPU (Recommended):**
 ```bash
 docker run -d \
   --name minicpm \
@@ -27,8 +44,7 @@ docker run -d \
   code.foss.global/host.today/ht-docker-ai:minicpm45v
 ```
 
-### CPU Only
+**CPU Only:**
-
 ```bash
 docker run -d \
   --name minicpm \
@@ -37,18 +53,16 @@ docker run -d \
   code.foss.global/host.today/ht-docker-ai:minicpm45v-cpu
 ```
 
-## API Usage
+> 💡 **Pro tip:** Mount the volume to persist downloaded models (~5GB). Without it, models re-download on every container start.
 
-The container exposes the Ollama API on port 11434.
+### API Examples
-
-### List Available Models
 
+**List models:**
 ```bash
 curl http://localhost:11434/api/tags
 ```
 
-### Generate Text from Image
+**Analyze an image:**
-
 ```bash
 curl http://localhost:11434/api/generate -d '{
   "model": "minicpm-v",
@@ -57,60 +71,128 @@ curl http://localhost:11434/api/generate -d '{
 }'
 ```
 
-### Chat with Vision
+**Chat with vision:**
-
 ```bash
 curl http://localhost:11434/api/chat -d '{
   "model": "minicpm-v",
-  "messages": [
+  "messages": [{
-    {
     "role": "user",
     "content": "Describe this image in detail",
     "images": ["<base64-encoded-image>"]
-    }
+  }]
-  ]
 }'
 ```
 
-## Environment Variables
+### Hardware Requirements
 
-| Variable | Default | Description |
+| Variant | VRAM/RAM | Notes |
-|----------|---------|-------------|
+|---------|----------|-------|
-| `MODEL_NAME` | `minicpm-v` | Model to pull on startup |
+| GPU (int4 quantized) | 9GB VRAM | Recommended for most use cases |
-| `OLLAMA_HOST` | `0.0.0.0` | Host address for API |
+| GPU (full precision) | 18GB VRAM | Maximum quality |
-| `OLLAMA_ORIGINS` | `*` | Allowed CORS origins |
+| CPU (GGUF) | 8GB+ RAM | Slower but accessible |
 
-## Hardware Requirements
+---
 
-### GPU Variant (`minicpm45v`)
+## 📄 PaddleOCR-VL
 
-- NVIDIA GPU with CUDA support
+A specialized 0.9B Vision-Language Model optimized for document parsing. Native support for tables, formulas, charts, and text extraction in 109 languages.
-- Minimum 9GB VRAM (int4 quantized)
-- Recommended 18GB VRAM (full precision)
-- NVIDIA Container Toolkit installed
 
-### CPU Variant (`minicpm45v-cpu`)
+### Quick Start
 
-- Minimum 8GB RAM
+**GPU:**
-- Recommended 16GB+ RAM for better performance
+```bash
-- No GPU required
+docker run -d \
+  --name paddleocr \
+  --gpus all \
+  -p 8000:8000 \
+  -v hf-cache:/root/.cache/huggingface \
+  code.foss.global/host.today/ht-docker-ai:paddleocr-vl
+```
 
-## Model Information
+**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
+```
 
-**MiniCPM-V 4.5** is a GPT-4o level multimodal large language model developed by OpenBMB.
+### OpenAI-Compatible API
 
-- **Parameters**: 8B (Qwen3-8B + SigLIP2-400M)
+PaddleOCR-VL exposes a fully OpenAI-compatible `/v1/chat/completions` endpoint:
-- **Capabilities**: Image understanding, OCR, multi-image analysis
-- **Languages**: 30+ languages including English, Chinese, French, Spanish
 
-## Docker Compose Example
+```bash
+curl http://localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "paddleocr-vl",
+    "messages": [{
+      "role": "user",
+      "content": [
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}},
+        {"type": "text", "text": "Table Recognition:"}
+      ]
+    }],
+    "max_tokens": 8192
+  }'
+```
+
+### Task Prompts
+
+| 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 |
+
+### 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.
+
+### Performance
+
+| Mode | Speed per Page |
+|------|----------------|
+| GPU (CUDA) | 2–5 seconds |
+| CPU | 30–60 seconds |
+
+---
+
+## 🐳 Docker Compose
 
 ```yaml
 version: '3.8'
 services:
+  # General vision tasks
   minicpm:
     image: code.foss.global/host.today/ht-docker-ai:minicpm45v
-    container_name: minicpm
     ports:
       - "11434:11434"
     volumes:
@@ -124,11 +206,50 @@ services:
               capabilities: [gpu]
     restart: unless-stopped
 
+  # Document parsing / OCR
+  paddleocr:
+    image: code.foss.global/host.today/ht-docker-ai:paddleocr-vl
+    ports:
+      - "8000:8000"
+    volumes:
+      - hf-cache:/root/.cache/huggingface
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    restart: unless-stopped
+
 volumes:
   ollama-data:
+  hf-cache:
 ```
 
-## Building Locally
+---
+
+## ⚙️ Environment Variables
+
+### MiniCPM-V 4.5
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MODEL_NAME` | `minicpm-v` | Ollama model to pull on startup |
+| `OLLAMA_HOST` | `0.0.0.0` | API bind address |
+| `OLLAMA_ORIGINS` | `*` | Allowed CORS origins |
+
+### PaddleOCR-VL
+
+| 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 |
+
+---
+
+## 🔧 Building from Source
 
 ```bash
 # Clone the repository
@@ -142,6 +263,77 @@ cd ht-docker-ai
 ./test-images.sh
 ```
 
-## License
+---
 
-MIT - Task Venture Capital GmbH
+## 🏗️ 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).
+
+### Out of memory
+- **GPU:** Use the CPU variant or upgrade VRAM
+- **CPU:** Increase container memory: `--memory=16g`
+
+### API not responding
+1. Check container health: `docker ps`
+2. Review logs: `docker logs <container>`
+3. Verify port: `curl localhost:11434/api/tags` or `curl localhost:8000/health`
+
+### Enable NVIDIA GPU support on host
+```bash
+# Install NVIDIA Container Toolkit
+curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
+sudo nvidia-ctk runtime configure --runtime=docker
+sudo systemctl restart docker
+```
+
+---
+
+## License and Legal Information
+
+This repository contains open-source code licensed under the MIT License. A copy of the license can be found in the [LICENSE](./LICENSE) file.
+
+**Please note:** The MIT License does not grant permission to use the trade names, trademarks, service marks, or product names of the project, except as required for reasonable and customary use in describing the origin of the work and reproducing the content of the NOTICE file.
+
+### Trademarks
+
+This project is owned and maintained by Task Venture Capital GmbH. The names and logos associated with Task Venture Capital GmbH and any related products or services are trademarks of Task Venture Capital GmbH or third parties, and are not included within the scope of the MIT license granted herein.
+
+Use of these trademarks must comply with Task Venture Capital GmbH's Trademark Guidelines or the guidelines of the respective third-party owners, and any usage must be approved in writing. Third-party trademarks used herein are the property of their respective owners and used only in a descriptive manner, e.g. for an implementation of an API or similar.
+
+### Company Information
+
+Task Venture Capital GmbH
+Registered at District Court Bremen HRB 35230 HB, Germany
+
+For any legal inquiries or further information, please contact us via email at hello@task.vc.
+
+By using this repository, you acknowledge that you have read this section, agree to comply with its terms, and understand that the licensing of the code does not imply endorsement by Task Venture Capital GmbH of any derivative works.

test/helpers/docker.ts

@@ -49,7 +49,7 @@ export const IMAGES = {
 
   minicpm: {
     name: 'minicpm45v',
-    dockerfile: 'Dockerfile_minicpm45v',
+    dockerfile: 'Dockerfile_minicpm45v_gpu',
     buildContext: '.',
     containerName: 'minicpm-test',
     ports: ['11434:11434'],
@@ -295,3 +295,79 @@ export async function ensurePaddleOcrVlFull(): Promise<boolean> {
   }
   return ensureService(IMAGES.paddleocrVlFull);
 }
+
+/**
+ * Ensure an Ollama model is pulled and available
+ * Uses the MiniCPM container (which runs Ollama) to pull the model
+ */
+export async function ensureOllamaModel(modelName: string): Promise<boolean> {
+  const OLLAMA_URL = 'http://localhost:11434';
+
+  console.log(`\n[Ollama] Ensuring model: ${modelName}`);
+
+  // Check if model exists
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      const exists = models.some((m: { name: string }) =>
+        m.name === modelName || m.name.startsWith(modelName.split(':')[0])
+      );
+
+      if (exists) {
+        console.log(`[Ollama] Model already available: ${modelName}`);
+        return true;
+      }
+    }
+  } catch {
+    console.log(`[Ollama] Cannot check models, Ollama may not be running`);
+    return false;
+  }
+
+  // Pull the model
+  console.log(`[Ollama] Pulling model: ${modelName} (this may take a while)...`);
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/pull`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ name: modelName, stream: false }),
+    });
+
+    if (response.ok) {
+      console.log(`[Ollama] Model pulled successfully: ${modelName}`);
+      return true;
+    } else {
+      console.log(`[Ollama] Failed to pull model: ${response.status}`);
+      return false;
+    }
+  } catch (err) {
+    console.log(`[Ollama] Error pulling model: ${err}`);
+    return false;
+  }
+}
+
+/**
+ * Ensure Qwen2.5 7B model is available (for text-only JSON extraction)
+ */
+export async function ensureQwen25(): Promise<boolean> {
+  // First ensure the Ollama service (MiniCPM container) is running
+  const ollamaOk = await ensureMiniCpm();
+  if (!ollamaOk) return false;
+
+  // Then ensure the Qwen2.5 model is pulled
+  return ensureOllamaModel('qwen2.5:7b');
+}
+
+/**
+ * Ensure Ministral 3 8B model is available (for structured JSON extraction)
+ * Ministral 3 has native JSON output support and OCR-style document extraction
+ */
+export async function ensureMinistral3(): Promise<boolean> {
+  // First ensure the Ollama service (MiniCPM container) is running
+  const ollamaOk = await ensureMiniCpm();
+  if (!ollamaOk) return false;
+
+  // Then ensure the Ministral 3 8B model is pulled
+  return ensureOllamaModel('ministral-3:8b');
+}

test/test.bankstatements.ministral3.ts

@@ -0,0 +1,348 @@
+/**
+ * Bank Statement extraction using Ministral 3 Vision (Direct)
+ *
+ * NO OCR pipeline needed - Ministral 3 has built-in vision encoder:
+ *   1. Convert PDF to images
+ *   2. Send images directly to Ministral 3 via Ollama
+ *   3. Extract transactions as structured JSON
+ */
+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 { ensureMinistral3 } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const VISION_MODEL = 'ministral-3:8b';
+
+interface ITransaction {
+  date: string;
+  counterparty: string;
+  amount: number;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Extract transactions from a single page image using Ministral 3 Vision
+ */
+async function extractTransactionsFromPage(image: string, pageNum: number): Promise<ITransaction[]> {
+  console.log(`    [Vision] Processing page ${pageNum}`);
+
+  // JSON schema for array of transactions
+  const transactionSchema = {
+    type: 'array',
+    items: {
+      type: 'object',
+      properties: {
+        date: { type: 'string', description: 'Transaction date in YYYY-MM-DD format' },
+        counterparty: { type: 'string', description: 'Name of the other party' },
+        amount: { type: 'number', description: 'Amount (negative for debits, positive for credits)' },
+      },
+      required: ['date', 'counterparty', 'amount'],
+    },
+  };
+
+  const prompt = `Extract ALL bank transactions from this bank statement page.
+
+For each transaction, extract:
+- date: Transaction date in YYYY-MM-DD format
+- counterparty: The name/description of the other party (merchant, payee, etc.)
+- amount: The amount as a number (NEGATIVE for debits/expenses, POSITIVE for credits/income)
+
+Return a JSON array of transactions. If no transactions visible, return empty array [].
+Example: [{"date":"2021-06-01","counterparty":"AMAZON","amount":-50.00}]`;
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: VISION_MODEL,
+      messages: [
+        {
+          role: 'user',
+          content: prompt,
+          images: [image],
+        },
+      ],
+      format: transactionSchema,
+      stream: true,
+      options: {
+        num_predict: 4096,  // Bank statements can have many transactions
+        temperature: 0.0,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const reader = response.body?.getReader();
+  if (!reader) {
+    throw new Error('No response body');
+  }
+
+  const decoder = new TextDecoder();
+  let fullText = '';
+
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) break;
+
+    const chunk = decoder.decode(value, { stream: true });
+    const lines = chunk.split('\n').filter((l) => l.trim());
+
+    for (const line of lines) {
+      try {
+        const json = JSON.parse(line);
+        if (json.message?.content) {
+          fullText += json.message.content;
+        }
+      } catch {
+        // Skip invalid JSON lines
+      }
+    }
+  }
+
+  // Parse JSON response
+  let jsonStr = fullText.trim();
+
+  if (jsonStr.startsWith('```json')) jsonStr = jsonStr.slice(7);
+  else if (jsonStr.startsWith('```')) jsonStr = jsonStr.slice(3);
+  if (jsonStr.endsWith('```')) jsonStr = jsonStr.slice(0, -3);
+  jsonStr = jsonStr.trim();
+
+  // Find array boundaries
+  const startIdx = jsonStr.indexOf('[');
+  const endIdx = jsonStr.lastIndexOf(']') + 1;
+
+  if (startIdx < 0 || endIdx <= startIdx) {
+    console.log(`    [Page ${pageNum}] No transactions found`);
+    return [];
+  }
+
+  try {
+    const parsed = JSON.parse(jsonStr.substring(startIdx, endIdx));
+    console.log(`    [Page ${pageNum}] Found ${parsed.length} transactions`);
+    return parsed.map((t: { date?: string; counterparty?: string; amount?: number }) => ({
+      date: t.date || '',
+      counterparty: t.counterparty || '',
+      amount: parseFloat(String(t.amount)) || 0,
+    }));
+  } catch (e) {
+    console.log(`    [Page ${pageNum}] Parse error: ${e}`);
+    return [];
+  }
+}
+
+/**
+ * Extract all transactions from all pages
+ */
+async function extractAllTransactions(images: string[]): Promise<ITransaction[]> {
+  const allTransactions: ITransaction[] = [];
+
+  for (let i = 0; i < images.length; i++) {
+    const pageTransactions = await extractTransactionsFromPage(images[i], i + 1);
+    allTransactions.push(...pageTransactions);
+  }
+
+  return allTransactions;
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
+
+  // Handle DD/MM/YYYY or DD.MM.YYYY
+  const 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 transactions vs expected
+ */
+function compareTransactions(
+  extracted: ITransaction[],
+  expected: ITransaction[]
+): { matchRate: number; matched: number; missed: number; extra: number; errors: string[] } {
+  const errors: string[] = [];
+  let matched = 0;
+
+  // Normalize all dates
+  const normalizedExtracted = extracted.map((t) => ({
+    ...t,
+    date: normalizeDate(t.date),
+    counterparty: t.counterparty.toUpperCase().trim(),
+  }));
+
+  const normalizedExpected = expected.map((t) => ({
+    ...t,
+    date: normalizeDate(t.date),
+    counterparty: t.counterparty.toUpperCase().trim(),
+  }));
+
+  // Try to match each expected transaction
+  const matchedIndices = new Set<number>();
+
+  for (const exp of normalizedExpected) {
+    let found = false;
+
+    for (let i = 0; i < normalizedExtracted.length; i++) {
+      if (matchedIndices.has(i)) continue;
+
+      const ext = normalizedExtracted[i];
+
+      // Match by date + amount (counterparty names can vary)
+      if (ext.date === exp.date && Math.abs(ext.amount - exp.amount) < 0.02) {
+        matched++;
+        matchedIndices.add(i);
+        found = true;
+        break;
+      }
+    }
+
+    if (!found) {
+      errors.push(`Missing: ${exp.date} | ${exp.counterparty} | ${exp.amount}`);
+    }
+  }
+
+  const missed = expected.length - matched;
+  const extra = extracted.length - matched;
+  const matchRate = expected.length > 0 ? (matched / expected.length) * 100 : 0;
+
+  return { matchRate, matched, missed, extra, errors };
+}
+
+/**
+ * Find test cases (PDF + JSON pairs in .nogit/)
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      // Skip invoice files - only bank statements
+      if (!baseName.includes('invoice')) {
+        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
+
+tap.test('setup: ensure Ministral 3 is running', async () => {
+  console.log('\n[Setup] Checking Ministral 3...\n');
+  const ok = await ensureMinistral3();
+  expect(ok).toBeTrue();
+  console.log('\n[Setup] Ready!\n');
+});
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} bank statement test cases (Ministral 3 Vision)\n`);
+
+let totalMatched = 0;
+let totalExpected = 0;
+const times: number[] = [];
+
+for (const testCase of testCases) {
+  tap.test(`should extract bank statement: ${testCase.name}`, async () => {
+    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.length} transactions`);
+
+    const start = Date.now();
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    const extracted = await extractAllTransactions(images);
+    const elapsed = Date.now() - start;
+    times.push(elapsed);
+
+    console.log(`  Extracted: ${extracted.length} transactions`);
+
+    const result = compareTransactions(extracted, expected);
+    totalMatched += result.matched;
+    totalExpected += expected.length;
+
+    console.log(`  Match rate: ${result.matchRate.toFixed(1)}% (${result.matched}/${expected.length})`);
+    console.log(`  Missed: ${result.missed}, Extra: ${result.extra}`);
+    console.log(`  Time: ${(elapsed / 1000).toFixed(1)}s`);
+
+    if (result.errors.length > 0 && result.errors.length <= 5) {
+      result.errors.forEach((e) => console.log(`    - ${e}`));
+    } else if (result.errors.length > 5) {
+      console.log(`    (${result.errors.length} missing transactions)`);
+    }
+
+    // Consider it a pass if we match at least 70% of transactions
+    expect(result.matchRate).toBeGreaterThan(70);
+  });
+}
+
+tap.test('summary', async () => {
+  const overallMatchRate = totalExpected > 0 ? (totalMatched / totalExpected) * 100 : 0;
+  const totalTime = times.reduce((a, b) => a + b, 0) / 1000;
+  const avgTime = times.length > 0 ? totalTime / times.length : 0;
+
+  console.log(`\n======================================================`);
+  console.log(`   Bank Statement Extraction Summary (Ministral 3)`);
+  console.log(`======================================================`);
+  console.log(`  Method:      Ministral 3 8B Vision (Direct)`);
+  console.log(`  Statements:  ${testCases.length}`);
+  console.log(`  Matched:     ${totalMatched}/${totalExpected} transactions`);
+  console.log(`  Match rate:  ${overallMatchRate.toFixed(1)}%`);
+  console.log(`------------------------------------------------------`);
+  console.log(`  Total time:  ${totalTime.toFixed(1)}s`);
+  console.log(`  Avg per stmt: ${avgTime.toFixed(1)}s`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();

test/test.invoices.ministral3.ts

@@ -0,0 +1,355 @@
+/**
+ * Invoice extraction using Ministral 3 Vision (Direct)
+ *
+ * NO PaddleOCR needed - Ministral 3 has built-in vision encoder:
+ *   1. Convert PDF to images
+ *   2. Send images directly to Ministral 3 via Ollama
+ *   3. Extract structured JSON with native schema support
+ *
+ * This is the simplest possible pipeline.
+ */
+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 { ensureMinistral3 } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const VISION_MODEL = 'ministral-3:8b';
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Extract invoice data directly from images using Ministral 3 Vision
+ */
+async function extractInvoiceFromImages(images: string[]): Promise<IInvoice> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with Ministral 3`);
+
+  // JSON schema for structured output
+  const invoiceSchema = {
+    type: 'object',
+    properties: {
+      invoice_number: { type: 'string' },
+      invoice_date: { type: 'string' },
+      vendor_name: { type: 'string' },
+      currency: { type: 'string' },
+      net_amount: { type: 'number' },
+      vat_amount: { type: 'number' },
+      total_amount: { type: 'number' },
+    },
+    required: ['invoice_number', 'invoice_date', 'vendor_name', 'currency', 'net_amount', 'vat_amount', 'total_amount'],
+  };
+
+  const prompt = `Extract invoice data from this document image(s).
+
+Find and return:
+- invoice_number: The invoice number/ID (look for "Invoice No", "Invoice #", "Rechnung Nr")
+- invoice_date: The invoice date in YYYY-MM-DD format
+- vendor_name: The company issuing the invoice (in letterhead)
+- currency: EUR, USD, or GBP
+- total_amount: The FINAL total amount due
+- net_amount: Amount before VAT/tax
+- vat_amount: VAT/tax amount
+
+Return ONLY valid JSON.`;
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: VISION_MODEL,
+      messages: [
+        {
+          role: 'user',
+          content: prompt,
+          images: images,  // Send all page images
+        },
+      ],
+      format: invoiceSchema,
+      stream: true,
+      options: {
+        num_predict: 512,
+        temperature: 0.0,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const reader = response.body?.getReader();
+  if (!reader) {
+    throw new Error('No response body');
+  }
+
+  const decoder = new TextDecoder();
+  let fullText = '';
+
+  while (true) {
+    const { done, value } = await reader.read();
+    if (done) break;
+
+    const chunk = decoder.decode(value, { stream: true });
+    const lines = chunk.split('\n').filter((l) => l.trim());
+
+    for (const line of lines) {
+      try {
+        const json = JSON.parse(line);
+        if (json.message?.content) {
+          fullText += json.message.content;
+        }
+      } catch {
+        // Skip invalid JSON lines
+      }
+    }
+  }
+
+  // Parse JSON response
+  let jsonStr = fullText.trim();
+
+  if (jsonStr.startsWith('```json')) jsonStr = jsonStr.slice(7);
+  else if (jsonStr.startsWith('```')) jsonStr = jsonStr.slice(3);
+  if (jsonStr.endsWith('```')) jsonStr = jsonStr.slice(0, -3);
+  jsonStr = jsonStr.trim();
+
+  const startIdx = jsonStr.indexOf('{');
+  const endIdx = jsonStr.lastIndexOf('}') + 1;
+
+  if (startIdx < 0 || endIdx <= startIdx) {
+    throw new Error(`No JSON found: ${fullText.substring(0, 200)}`);
+  }
+
+  const parsed = JSON.parse(jsonStr.substring(startIdx, endIdx));
+
+  return {
+    invoice_number: parsed.invoice_number || null,
+    invoice_date: parsed.invoice_date || null,
+    vendor_name: parsed.vendor_name || null,
+    currency: parsed.currency || 'EUR',
+    net_amount: parseFloat(parsed.net_amount) || 0,
+    vat_amount: parseFloat(parsed.vat_amount) || 0,
+    total_amount: parseFloat(parsed.total_amount) || 0,
+  };
+}
+
+/**
+ * Extract with consensus voting (2 agreeing passes)
+ */
+async function extractWithConsensus(images: string[], name: string, maxPasses: number = 3): Promise<IInvoice> {
+  const results: Array<{ invoice: IInvoice; hash: string }> = [];
+  const hashCounts: Map<string, number> = new Map();
+
+  for (let pass = 1; pass <= maxPasses; pass++) {
+    try {
+      const invoice = await extractInvoiceFromImages(images);
+      const hash = `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount?.toFixed(2)}`;
+      results.push({ invoice, hash });
+      hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+
+      console.log(`  [Pass ${pass}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
+
+      if (hashCounts.get(hash)! >= 2) {
+        console.log(`  [Consensus] Reached after ${pass} passes`);
+        return invoice;
+      }
+    } catch (err) {
+      console.log(`  [Pass ${pass}] Error: ${err}`);
+    }
+  }
+
+  // Return most common result
+  let bestHash = '';
+  let bestCount = 0;
+  for (const [hash, count] of hashCounts) {
+    if (count > bestCount) {
+      bestCount = count;
+      bestHash = hash;
+    }
+  }
+
+  if (!bestHash) throw new Error(`No valid results for ${name}`);
+
+  console.log(`  [No consensus] Using best result (${bestCount}/${maxPasses})`);
+  return results.find((r) => r.hash === bestHash)!.invoice;
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.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 vs 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: 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 "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find test cases
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${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
+
+tap.test('setup: ensure Ministral 3 is running', async () => {
+  console.log('\n[Setup] Checking Ministral 3...\n');
+  const ok = await ensureMinistral3();
+  expect(ok).toBeTrue();
+  console.log('\n[Setup] Ready!\n');
+});
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} invoice test cases (Ministral 3 Vision Direct)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
+const times: number[] = [];
+
+for (const testCase of testCases) {
+  tap.test(`should extract invoice: ${testCase.name}`, async () => {
+    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
+
+    const start = Date.now();
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    const extracted = await extractWithConsensus(images, testCase.name);
+    const elapsed = Date.now() - start;
+    times.push(elapsed);
+
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`  Result: MATCH (${(elapsed / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`  Result: MISMATCH (${(elapsed / 1000).toFixed(1)}s)`);
+      result.errors.forEach((e) => console.log(`    - ${e}`));
+    }
+
+    expect(result.match).toBeTrue();
+  });
+}
+
+tap.test('summary', async () => {
+  const total = testCases.length;
+  const accuracy = total > 0 ? (passedCount / total) * 100 : 0;
+  const totalTime = times.reduce((a, b) => a + b, 0) / 1000;
+  const avgTime = times.length > 0 ? totalTime / times.length : 0;
+
+  console.log(`\n======================================================`);
+  console.log(`   Invoice Extraction Summary (Ministral 3 Vision)`);
+  console.log(`======================================================`);
+  console.log(`  Method:    Ministral 3 8B Vision (Direct)`);
+  console.log(`  Passed:    ${passedCount}/${total}`);
+  console.log(`  Failed:    ${failedCount}/${total}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`------------------------------------------------------`);
+  console.log(`  Total time:   ${totalTime.toFixed(1)}s`);
+  console.log(`  Avg per inv:  ${avgTime.toFixed(1)}s`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();

test/test.invoices.paddleocr-vl.ts

@@ -4,22 +4,25 @@
  * This tests the complete PaddleOCR-VL pipeline:
  *   1. PP-DocLayoutV2 for layout detection
  *   2. PaddleOCR-VL for recognition
- *   3. Structured Markdown output
+ *   3. Structured HTML output (semantic tags with proper tables)
- *   4. MiniCPM extracts invoice fields from structured Markdown
+ *   4. Qwen2.5 extracts invoice fields from structured HTML
  *
- * The structured Markdown has proper tables and formatting,
+ * HTML output is used instead of Markdown because:
- * making it much easier for MiniCPM to extract invoice data.
+ * - <table> tags are unambiguous (no parser variations)
+ * - LLMs are heavily trained on web/HTML data
+ * - Semantic tags (header, footer, section) provide clear structure
  */
 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 { ensurePaddleOcrVlFull, ensureMiniCpm } from './helpers/docker.js';
+import { ensurePaddleOcrVlFull, ensureQwen25 } from './helpers/docker.js';
 
 const PADDLEOCR_VL_URL = 'http://localhost:8000';
 const OLLAMA_URL = 'http://localhost:11434';
-const MINICPM_MODEL = 'minicpm-v:latest';
+// Use Qwen2.5 for text-only JSON extraction (not MiniCPM which is vision-focused)
+const TEXT_MODEL = 'qwen2.5:7b';
 
 interface IInvoice {
   invoice_number: string;
@@ -60,7 +63,7 @@ function convertPdfToImages(pdfPath: string): string[] {
 }
 
 /**
- * Parse document using PaddleOCR-VL Full Pipeline (returns structured Markdown)
+ * Parse document using PaddleOCR-VL Full Pipeline (returns structured HTML)
  */
 async function parseDocument(imageBase64: string): Promise<string> {
   const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
@@ -68,7 +71,7 @@ async function parseDocument(imageBase64: string): Promise<string> {
     headers: { 'Content-Type': 'application/json' },
     body: JSON.stringify({
       image: imageBase64,
-      output_format: 'markdown',
+      output_format: 'html',
     }),
   });
 
@@ -83,54 +86,75 @@ async function parseDocument(imageBase64: string): Promise<string> {
     throw new Error(`PaddleOCR-VL error: ${data.error}`);
   }
 
-  return data.result?.markdown || '';
+  return data.result?.html || '';
 }
 
 /**
- * Extract invoice fields from structured Markdown using MiniCPM with image context
+ * Sanitize HTML to remove OCR artifacts that confuse the LLM
+ * Minimal cleaning - only remove truly problematic patterns
  */
-async function extractInvoiceFromMarkdown(markdown: string, images: string[]): Promise<IInvoice> {
+function sanitizeHtml(html: string): string {
-  // Truncate if too long
+  // Remove excessively repeated characters (OCR glitches)
-  const truncated = markdown.length > 8000 ? markdown.slice(0, 8000) : markdown;
+  let sanitized = html.replace(/(\d)\1{20,}/g, '$1...');
-  console.log(`    [Extract] Processing ${truncated.length} chars of Markdown`);
+  // Remove extremely long strings (corrupted data)
+  sanitized = sanitized.replace(/\b[A-Za-z0-9]{50,}\b/g, '[OCR_ARTIFACT]');
+  return sanitized;
+}
 
-  const prompt = `/nothink
+/**
-You are an invoice parser. Extract fields from this invoice image.
+ * Extract invoice fields using simple direct prompt
+ * The OCR output has clearly labeled fields - just ask the LLM to read them
+ */
+async function extractInvoiceFromHtml(html: string): Promise<IInvoice> {
+  const sanitized = sanitizeHtml(html);
+  const truncated = sanitized.length > 32000 ? sanitized.slice(0, 32000) : sanitized;
+  console.log(`    [Extract] ${truncated.length} chars of HTML`);
 
-Required fields:
+  // JSON schema for structured output
-- invoice_number: The invoice/receipt number
+  const invoiceSchema = {
-- invoice_date: Date in YYYY-MM-DD format
+    type: 'object',
-- vendor_name: Company that issued the invoice
+    properties: {
-- currency: EUR, USD, etc.
+      invoice_number: { type: 'string' },
-- net_amount: Amount before tax
+      invoice_date: { type: 'string' },
-- vat_amount: Tax/VAT amount (0 if reverse charge)
+      vendor_name: { type: 'string' },
-- total_amount: Final amount due
+      currency: { type: 'string' },
-
+      net_amount: { type: 'number' },
-Return ONLY a JSON object like:
+      vat_amount: { type: 'number' },
-{"invoice_number":"123","invoice_date":"2022-01-28","vendor_name":"Adobe","currency":"EUR","net_amount":24.99,"vat_amount":0,"total_amount":24.99}
+      total_amount: { type: 'number' },
-
-Use null for missing strings, 0 for missing numbers. No explanation.
-
-OCR text from the invoice (for reference):
----
-${truncated}
----`;
-
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt,
-    images,  // Send the actual image to MiniCPM
-    stream: true,
-    options: {
-      num_predict: 2048,
-      temperature: 0.1,
     },
+    required: ['invoice_number', 'invoice_date', 'vendor_name', 'currency', 'net_amount', 'vat_amount', 'total_amount'],
   };
 
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+  // Simple, direct prompt - the OCR output already has labeled fields
+  const systemPrompt = `You read invoice HTML and extract labeled fields. Return JSON only.`;
+
+  const userPrompt = `Extract from this invoice HTML:
+- invoice_number: Find "Invoice no.", "Invoice #", "Invoice", "Rechnung", "Document No" and extract the value
+- invoice_date: Find "Invoice date", "Date", "Datum" and convert to YYYY-MM-DD format
+- vendor_name: The company name issuing the invoice (in header/letterhead)
+- currency: EUR, USD, or GBP (look for € $ £ symbols or text)
+- total_amount: Find "Total", "Grand Total", "Amount Due", "Gesamtbetrag" - the FINAL total amount
+- net_amount: Amount before VAT/tax (Subtotal, Net)
+- vat_amount: VAT/tax amount
+
+HTML:
+${truncated}
+
+Return ONLY valid JSON: {"invoice_number":"...", "invoice_date":"YYYY-MM-DD", "vendor_name":"...", "currency":"EUR", "net_amount":0, "vat_amount":0, "total_amount":0}`;
+
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
+    body: JSON.stringify({
+      model: TEXT_MODEL,
+      messages: [
+        { role: 'system', content: systemPrompt },
+        { role: 'user', content: userPrompt },
+      ],
+      format: invoiceSchema,
+      stream: true,
+      options: { num_predict: 512, temperature: 0.0 },
+    }),
   });
 
   if (!response.ok) {
@@ -155,7 +179,9 @@ ${truncated}
     for (const line of lines) {
       try {
         const json = JSON.parse(line);
-        if (json.response) {
+        if (json.message?.content) {
+          fullText += json.message.content;
+        } else if (json.response) {
           fullText += json.response;
         }
       } catch {
@@ -165,34 +191,78 @@ ${truncated}
   }
 
   // Extract JSON from response
-  const startIdx = fullText.indexOf('{');
+  let jsonStr = fullText.trim();
-  const endIdx = fullText.lastIndexOf('}') + 1;
+
+  // Remove markdown code block if present
+  if (jsonStr.startsWith('```json')) {
+    jsonStr = jsonStr.slice(7);
+  } else if (jsonStr.startsWith('```')) {
+    jsonStr = jsonStr.slice(3);
+  }
+  if (jsonStr.endsWith('```')) {
+    jsonStr = jsonStr.slice(0, -3);
+  }
+  jsonStr = jsonStr.trim();
+
+  // Find JSON object boundaries
+  const startIdx = jsonStr.indexOf('{');
+  const endIdx = jsonStr.lastIndexOf('}') + 1;
 
   if (startIdx < 0 || endIdx <= startIdx) {
     throw new Error(`No JSON object found in response: ${fullText.substring(0, 200)}`);
   }
 
-  const jsonStr = fullText.substring(startIdx, endIdx);
+  jsonStr = jsonStr.substring(startIdx, endIdx);
-  return JSON.parse(jsonStr);
+
+  let parsed;
+  try {
+    parsed = JSON.parse(jsonStr);
+  } catch (e) {
+    throw new Error(`Invalid JSON: ${jsonStr.substring(0, 200)}`);
+  }
+
+  // Normalize response to expected format
+  return {
+    invoice_number: parsed.invoice_number || null,
+    invoice_date: parsed.invoice_date || null,
+    vendor_name: parsed.vendor_name || null,
+    currency: parsed.currency || 'EUR',
+    net_amount: parseFloat(parsed.net_amount) || 0,
+    vat_amount: parseFloat(parsed.vat_amount) || 0,
+    total_amount: parseFloat(parsed.total_amount) || 0,
+  };
 }
 
 /**
- * Single extraction pass: Parse with PaddleOCR-VL Full, extract with MiniCPM
+ * Single extraction pass: Parse with PaddleOCR-VL Full, extract with Qwen2.5 (text-only)
+ * Processes ALL pages and concatenates HTML for multi-page invoice support
  */
 async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
-  // Parse document with full pipeline
+  // Parse ALL pages and concatenate HTML with page markers
-  const markdown = await parseDocument(images[0]);
+  const htmlParts: string[] = [];
-  console.log(`    [Parse] Got ${markdown.split('\n').length} lines of Markdown`);
 
-  // Extract invoice fields from Markdown with image context
+  for (let i = 0; i < images.length; i++) {
-  return extractInvoiceFromMarkdown(markdown, images);
+    const pageHtml = await parseDocument(images[i]);
+    // Add page marker for context
+    htmlParts.push(`<!-- Page ${i + 1} -->\n${pageHtml}`);
+  }
+
+  const fullHtml = htmlParts.join('\n\n');
+  console.log(`    [Parse] Got ${fullHtml.split('\n').length} lines from ${images.length} page(s)`);
+
+  // Extract invoice fields from HTML using text-only model (no images)
+  return extractInvoiceFromHtml(fullHtml);
 }
 
 /**
  * Create a hash of invoice for comparison (using key fields)
  */
 function hashInvoice(invoice: IInvoice): string {
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
+  // Ensure total_amount is a number
+  const amount = typeof invoice.total_amount === 'number'
+    ? invoice.total_amount.toFixed(2)
+    : String(invoice.total_amount || 0);
+  return `${invoice.invoice_number}|${invoice.invoice_date}|${amount}`;
 }
 
 /**
@@ -243,6 +313,43 @@ async function extractWithConsensus(images: string[], invoiceName: string, maxPa
   return best.invoice;
 }
 
+/**
+ * Normalize date to YYYY-MM-DD format
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+
+  // Already in correct format
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) {
+    return dateStr;
+  }
+
+  // Handle DD-MMM-YYYY format (e.g., "28-JUN-2022")
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
+  };
+
+  const match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
+  if (match) {
+    const day = match[1].padStart(2, '0');
+    const month = monthMap[match[2].toUpperCase()] || '01';
+    const year = match[3];
+    return `${year}-${month}-${day}`;
+  }
+
+  // Handle DD/MM/YYYY or DD.MM.YYYY
+  const match2 = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
+  if (match2) {
+    const day = match2[1].padStart(2, '0');
+    const month = match2[2].padStart(2, '0');
+    const year = match2[3];
+    return `${year}-${month}-${day}`;
+  }
+
+  return dateStr;
+}
+
 /**
  * Compare extracted invoice against expected
  */
@@ -259,8 +366,10 @@ function compareInvoice(
     errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
   }
 
-  // Compare date
+  // Compare date (normalize format first)
-  if (extracted.invoice_date !== expected.invoice_date) {
+  const extDate = normalizeDate(extracted.invoice_date);
+  const expDate = normalizeDate(expected.invoice_date);
+  if (extDate !== expDate) {
     errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
   }
 
@@ -317,9 +426,9 @@ tap.test('setup: ensure Docker containers are running', async () => {
   const paddleOk = await ensurePaddleOcrVlFull();
   expect(paddleOk).toBeTrue();
 
-  // Ensure MiniCPM is running (for field extraction from Markdown)
+  // Ensure Qwen2.5 is available (for text-only JSON extraction)
-  const minicpmOk = await ensureMiniCpm();
+  const qwenOk = await ensureQwen25();
-  expect(minicpmOk).toBeTrue();
+  expect(qwenOk).toBeTrue();
 
   console.log('\n[Setup] All containers ready!\n');
 });
@@ -380,7 +489,7 @@ tap.test('summary', async () => {
   console.log(`\n======================================================`);
   console.log(`   Invoice Extraction Summary (PaddleOCR-VL Full)`);
   console.log(`======================================================`);
-  console.log(`  Method:    PaddleOCR-VL Full Pipeline -> MiniCPM`);
+  console.log(`  Method:    PaddleOCR-VL Full Pipeline (HTML) -> Qwen2.5 (text-only)`);
   console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
   console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
   console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);