v1.13.1

Dockerfile_minicpm45v_cpu

@@ -1,27 +0,0 @@
-# MiniCPM-V 4.5 CPU Variant
-# Vision-Language Model optimized for CPU-only inference
-FROM ollama/ollama:latest
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="MiniCPM-V 4.5 Vision-Language Model - CPU optimized (GGUF)"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration for CPU-only mode
-ENV MODEL_NAME="minicpm-v"
-ENV OLLAMA_HOST="0.0.0.0"
-ENV OLLAMA_ORIGINS="*"
-# Disable GPU usage for CPU-only variant
-ENV CUDA_VISIBLE_DEVICES=""
-
-# Copy and setup entrypoint
-COPY image_support_files/docker-entrypoint.sh /usr/local/bin/docker-entrypoint.sh
-RUN chmod +x /usr/local/bin/docker-entrypoint.sh
-
-# Expose Ollama API port
-EXPOSE 11434
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
-    CMD curl -f http://localhost:11434/api/tags || exit 1
-
-ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]

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

@@ -1,57 +0,0 @@
-# PaddleOCR-VL CPU Variant
-# Vision-Language Model for document parsing using transformers (slower, no GPU required)
-FROM python:3.11-slim-bookworm
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL 0.9B CPU - Vision-Language Model for document parsing"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration
-ENV PYTHONUNBUFFERED=1
-ENV HF_HOME=/root/.cache/huggingface
-ENV CUDA_VISIBLE_DEVICES=""
-ENV SERVER_PORT=8000
-ENV SERVER_HOST=0.0.0.0
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    libgl1-mesa-glx \
-    libglib2.0-0 \
-    libgomp1 \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install Python dependencies
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir \
-    torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cpu && \
-    pip install --no-cache-dir \
-    transformers \
-    accelerate \
-    safetensors \
-    pillow \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    httpx \
-    protobuf \
-    sentencepiece \
-    einops
-
-# 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
-RUN chmod +x /usr/local/bin/paddleocr-vl-cpu-entrypoint.sh
-
-# Expose API port
-EXPOSE 8000
-
-# Health check (longer start-period for CPU + model download)
-HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
-
-ENTRYPOINT ["/usr/local/bin/paddleocr-vl-cpu-entrypoint.sh"]

Dockerfile_paddleocr_vl_full

@@ -1,90 +0,0 @@
-# PaddleOCR-VL Full Pipeline (PP-DocLayoutV2 + PaddleOCR-VL + Structured Output)
-# Self-contained GPU image with complete document parsing pipeline
-FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL Full Pipeline - Layout Detection + VL Recognition + JSON/Markdown Output"
-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 PADDLEOCR_HOME=/root/.paddleocr
-ENV SERVER_PORT=8000
-ENV SERVER_HOST=0.0.0.0
-ENV VLM_PORT=8080
-
-# 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 \
-    libgl1-mesa-glx \
-    libglib2.0-0 \
-    libgomp1 \
-    libsm6 \
-    libxext6 \
-    libxrender1 \
-    curl \
-    git \
-    wget \
-    && 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"
-
-# Upgrade pip
-RUN pip install --no-cache-dir --upgrade pip setuptools wheel
-
-# Install PaddlePaddle GPU (CUDA 12.x)
-RUN pip install --no-cache-dir \
-    paddlepaddle-gpu==3.2.1 \
-    --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
-
-# Install PaddleOCR with doc-parser (includes PP-DocLayoutV2)
-RUN pip install --no-cache-dir \
-    "paddleocr[doc-parser]" \
-    safetensors
-
-# Install PyTorch with CUDA support
-RUN pip install --no-cache-dir \
-    torch==2.5.1 \
-    torchvision \
-    --index-url https://download.pytorch.org/whl/cu124
-
-# Install transformers for PaddleOCR-VL inference (no vLLM - use local inference)
-# PaddleOCR-VL requires transformers>=4.55.0 for use_kernel_forward_from_hub
-RUN pip install --no-cache-dir \
-    transformers>=4.55.0 \
-    accelerate \
-    hf-kernels
-
-# Install our API server dependencies
-RUN pip install --no-cache-dir \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    httpx \
-    pillow
-
-# Copy server files
-COPY image_support_files/paddleocr_vl_full_server.py /app/server.py
-COPY image_support_files/paddleocr_vl_full_entrypoint.sh /usr/local/bin/entrypoint.sh
-RUN chmod +x /usr/local/bin/entrypoint.sh
-
-# Expose ports (8000 = API, 8080 = internal VLM server)
-EXPOSE 8000
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
-
-ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]

Dockerfile_paddleocr_vl_gpu

@@ -1,71 +0,0 @@
-# PaddleOCR-VL GPU Variant (Transformers-based, not vLLM)
-# Vision-Language Model for document parsing using transformers with CUDA
-FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL 0.9B GPU - Vision-Language Model using transformers"
-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 SERVER_PORT=8000
-ENV SERVER_HOST=0.0.0.0
-
-# 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 \
-    libgl1-mesa-glx \
-    libglib2.0-0 \
-    libgomp1 \
-    curl \
-    git \
-    && 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 Python dependencies (transformers-based, not vLLM)
-RUN pip install --no-cache-dir \
-    transformers \
-    accelerate \
-    safetensors \
-    pillow \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    httpx \
-    protobuf \
-    sentencepiece \
-    einops
-
-# 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
-RUN chmod +x /usr/local/bin/paddleocr-vl-entrypoint.sh
-
-# Expose 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_qwen3vl

@@ -0,0 +1,26 @@
+# Qwen3-VL-30B-A3B Vision Language Model
+# Q4_K_M quantization (~20GB model)
+#
+# Most powerful Qwen vision model:
+# - 256K context (expandable to 1M)
+# - Visual agent capabilities
+# - Code generation from images
+#
+# Build: docker build -f Dockerfile_qwen3vl -t qwen3vl .
+# Run:   docker run --gpus all -p 11434:11434 -v ht-ollama-models:/root/.ollama qwen3vl
+
+FROM ollama/ollama:latest
+
+# Pre-pull the model during build (optional - can also pull at runtime)
+# This makes the image larger but faster to start
+# RUN ollama serve & sleep 5 && ollama pull qwen3-vl:30b-a3b && pkill ollama
+
+# Expose Ollama API port
+EXPOSE 11434
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+  CMD curl -f http://localhost:11434/api/tags || exit 1
+
+# Start Ollama server
+CMD ["serve"]

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 +1,95 @@
 # Changelog
 
+## 2026-01-18 - 1.13.1 - fix(image_support_files)
+remove PaddleOCR-VL server scripts from image_support_files
+
+- Deleted files: image_support_files/paddleocr_vl_full_server.py (approx. 636 lines) and image_support_files/paddleocr_vl_server.py (approx. 465 lines)
+- Cleanup/removal of legacy PaddleOCR-VL FastAPI server implementations — may affect users who relied on these local scripts
+
+## 2026-01-18 - 1.13.0 - feat(tests)
+revamp tests and remove legacy Dockerfiles: adopt JSON/consensus workflows, switch MiniCPM model, and delete deprecated Docker/test variants
+
+- Removed multiple Dockerfiles and related entrypoints for MiniCPM and PaddleOCR-VL (cpu/gpu/full), cleaning up legacy image recipes.
+- Pruned many older test files (combined, ministral3, paddleocr-vl, and several invoice/test variants) to consolidate the test suite.
+- Updated bank statement MiniCPM test: now uses MODEL='openbmb/minicpm-v4.5:q8_0', JSON per-page extraction prompt, consensus retry logic, expanded logging, and stricter result matching.
+- Updated invoice MiniCPM test: switched to a consensus flow (fast JSON pass + thinking pass), increased PDF conversion quality, endpoints migrated to chat-style API calls with image-in-message payloads, and improved finalization logic.
+- API usage changed from /api/generate to /api/chat with message-based payloads and embedded images — CI and local test runners will need model availability and possible pipeline adjustments.
+
+## 2026-01-18 - 1.12.0 - feat(tests)
+switch vision tests to multi-query extraction (count then per-row/field queries) and add logging/summaries
+
+- Replace streaming + consensus pipeline with multi-query approach: count rows per page, then query each transaction/field individually (batched parallel queries).
+- Introduce unified helpers (queryVision / queryField / getTransaction / countTransactions) and simplify Ollama requests (stream:false, reduced num_predict, /no_think prompts).
+- Improve parsing and normalization for amounts (European formats), invoice numbers, dates and currency extraction.
+- Adjust model checks to look for generic 'minicpm' and update test names/messages; add pass/fail counters and a summary test output.
+- Remove previous consensus voting and streaming JSON accumulation logic, and add immediate per-transaction logging and batching.
+
+## 2026-01-18 - 1.11.0 - feat(vision)
+process pages separately and make Qwen3-VL vision extraction more robust; add per-page parsing, safer JSON handling, reduced token usage, and multi-query invoice extraction
+
+- Bank statements: split extraction into extractTransactionsFromPage and sequentially process pages to avoid thinking-token exhaustion
+- Bank statements: reduced num_predict from 8000 to 4000, send single image per request, added per-page logging and non-throwing handling for empty or non-JSON responses
+- Bank statements: catch JSON.parse errors and return empty array instead of throwing
+- Invoices: introduced queryField to request single values and perform multiple simple queries (reduces model thinking usage)
+- Invoices: reduced num_predict for invoice queries from 4000 to 500 and parse amounts robustly (handles European formats like 1.234,56)
+- Invoices: normalize currency to uppercase 3-letter code, return safe defaults (empty strings / 0) instead of nulls, and parse net/vat/total with fallbacks
+- General: simplified Ollama API error messages to avoid including response body content in thrown errors
+
+## 2026-01-18 - 1.10.1 - fix(tests)
+improve Qwen3-VL invoice extraction test by switching to non-stream API, adding model availability/pull checks, simplifying response parsing, and tightening model options
+
+- Replaced streaming reader logic with direct JSON parsing of the /api/chat response
+- Added ensureQwen3Vl() to check and pull the Qwen3-VL:8b model from Ollama
+- Switched to ensureMiniCpm() to verify Ollama service is running before model checks
+- Use /no_think prompt for direct JSON output and set temperature to 0.0 and num_predict to 512
+- Removed retry loop and streaming parsing; improved error messages to include response body
+- Updated logging and test setup messages for clarity
+
+## 2026-01-18 - 1.10.0 - feat(vision)
+add Qwen3-VL vision model support with Dockerfile and tests; improve invoice OCR conversion and prompts; simplify extraction flow by removing consensus voting
+
+- Add Dockerfile_qwen3vl to provide an Ollama-based image for Qwen3-VL and expose the Ollama API on port 11434
+- Introduce test/test.invoices.qwen3vl.ts and ensureQwen3Vl() helper to pull and test qwen3-vl:8b
+- Improve PDF->PNG conversion and prompt in ministral3 tests (higher DPI, max quality, sharpen) and increase num_predict from 512 to 1024
+- Simplify extraction pipeline: remove consensus voting, log single-pass results, and simplify OCR HTML sanitization/truncation logic
+
+## 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-cpu-entrypoint.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-set -e
-
-echo "==================================="
-echo "PaddleOCR-VL Server (CPU)"
-echo "==================================="
-
-HOST="${SERVER_HOST:-0.0.0.0}"
-PORT="${SERVER_PORT:-8000}"
-
-echo "Host: ${HOST}"
-echo "Port: ${PORT}"
-echo "Device: CPU (no GPU)"
-echo ""
-
-echo "Starting PaddleOCR-VL CPU server..."
-echo "==================================="
-
-exec python /app/paddleocr_vl_server.py

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_entrypoint.sh

@@ -1,12 +0,0 @@
-#!/bin/bash
-set -e
-
-echo "Starting PaddleOCR-VL Full Pipeline Server (Transformers backend)..."
-
-# Environment
-SERVER_PORT=${SERVER_PORT:-8000}
-SERVER_HOST=${SERVER_HOST:-0.0.0.0}
-
-# Start our API server directly (no vLLM - uses local transformers inference)
-echo "Starting API server on port $SERVER_PORT..."
-exec python /app/server.py

image_support_files/paddleocr_vl_full_server.py

@@ -1,443 +0,0 @@
-#!/usr/bin/env python3
-"""
-PaddleOCR-VL Full Pipeline API Server (Transformers backend)
-
-Provides REST API for document parsing using:
-- PP-DocLayoutV2 for layout detection
-- PaddleOCR-VL (transformers) for recognition
-- Structured JSON/Markdown output
-"""
-
-import os
-import io
-import base64
-import logging
-import tempfile
-import time
-import json
-from typing import Optional, List, Union
-from pathlib import Path
-
-from fastapi import FastAPI, HTTPException, UploadFile, File, Form
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-from PIL import Image
-import torch
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
-
-# Environment configuration
-SERVER_HOST = os.environ.get('SERVER_HOST', '0.0.0.0')
-SERVER_PORT = int(os.environ.get('SERVER_PORT', '8000'))
-MODEL_NAME = "PaddlePaddle/PaddleOCR-VL"
-
-# Device configuration
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-logger.info(f"Using device: {DEVICE}")
-
-# Task prompts
-TASK_PROMPTS = {
-    "ocr": "OCR:",
-    "table": "Table Recognition:",
-    "formula": "Formula Recognition:",
-    "chart": "Chart Recognition:",
-}
-
-# Initialize FastAPI app
-app = FastAPI(
-    title="PaddleOCR-VL Full Pipeline Server",
-    description="Document parsing with PP-DocLayoutV2 + PaddleOCR-VL (transformers)",
-    version="1.0.0"
-)
-
-# Global model instances
-vl_model = None
-vl_processor = None
-layout_model = None
-
-
-def load_vl_model():
-    """Load the PaddleOCR-VL model for element recognition"""
-    global vl_model, vl_processor
-
-    if vl_model is not None:
-        return
-
-    logger.info(f"Loading PaddleOCR-VL model: {MODEL_NAME}")
-    from transformers import AutoModelForCausalLM, AutoProcessor
-
-    vl_processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
-
-    if DEVICE == "cuda":
-        vl_model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME,
-            trust_remote_code=True,
-            torch_dtype=torch.bfloat16,
-        ).to(DEVICE).eval()
-    else:
-        vl_model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME,
-            trust_remote_code=True,
-            torch_dtype=torch.float32,
-            low_cpu_mem_usage=True,
-        ).eval()
-
-    logger.info("PaddleOCR-VL model loaded successfully")
-
-
-def load_layout_model():
-    """Load the LayoutDetection model for layout detection"""
-    global layout_model
-
-    if layout_model is not None:
-        return
-
-    try:
-        logger.info("Loading LayoutDetection model (PP-DocLayout_plus-L)...")
-        from paddleocr import LayoutDetection
-
-        layout_model = LayoutDetection()
-        logger.info("LayoutDetection model loaded successfully")
-    except Exception as e:
-        logger.warning(f"Could not load LayoutDetection: {e}")
-        logger.info("Falling back to VL-only mode (no layout detection)")
-
-
-def recognize_element(image: Image.Image, task: str = "ocr") -> str:
-    """Recognize a single element using PaddleOCR-VL"""
-    load_vl_model()
-
-    prompt = TASK_PROMPTS.get(task, TASK_PROMPTS["ocr"])
-
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": prompt},
-            ]
-        }
-    ]
-
-    inputs = vl_processor.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_dict=True,
-        return_tensors="pt"
-    )
-
-    if DEVICE == "cuda":
-        inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
-
-    with torch.inference_mode():
-        outputs = vl_model.generate(
-            **inputs,
-            max_new_tokens=4096,
-            do_sample=False,
-            use_cache=True
-        )
-
-    response = vl_processor.batch_decode(outputs, skip_special_tokens=True)[0]
-
-    # Extract only the assistant's response content
-    # The response format is: "User: <prompt>\nAssistant: <content>"
-    # We want to extract just the content after "Assistant:"
-    if "Assistant:" in response:
-        parts = response.split("Assistant:")
-        if len(parts) > 1:
-            response = parts[-1].strip()
-    elif "assistant:" in response.lower():
-        # Case-insensitive fallback
-        import re
-        match = re.split(r'[Aa]ssistant:', response)
-        if len(match) > 1:
-            response = match[-1].strip()
-
-    return response
-
-
-def detect_layout(image: Image.Image) -> List[dict]:
-    """Detect layout regions in the image"""
-    load_layout_model()
-
-    if layout_model is None:
-        # No layout model - return a single region covering the whole image
-        return [{
-            "type": "text",
-            "bbox": [0, 0, image.width, image.height],
-            "score": 1.0
-        }]
-
-    # Save image to temp file
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-        image.save(tmp.name, "PNG")
-        tmp_path = tmp.name
-
-    try:
-        results = layout_model.predict(tmp_path)
-        regions = []
-
-        for res in results:
-            # LayoutDetection returns boxes in 'boxes' key
-            for box in res.get("boxes", []):
-                coord = box.get("coordinate", [0, 0, image.width, image.height])
-                # Convert numpy floats to regular floats
-                bbox = [float(c) for c in coord]
-                regions.append({
-                    "type": box.get("label", "text"),
-                    "bbox": bbox,
-                    "score": float(box.get("score", 1.0))
-                })
-
-        # Sort regions by vertical position (top to bottom)
-        regions.sort(key=lambda r: r["bbox"][1])
-
-        return regions if regions else [{
-            "type": "text",
-            "bbox": [0, 0, image.width, image.height],
-            "score": 1.0
-        }]
-
-    finally:
-        os.unlink(tmp_path)
-
-
-def process_document(image: Image.Image) -> dict:
-    """Process a document through the full pipeline"""
-    logger.info(f"Processing document: {image.size}")
-
-    # Step 1: Detect layout
-    regions = detect_layout(image)
-    logger.info(f"Detected {len(regions)} layout regions")
-
-    # Step 2: Recognize each region
-    blocks = []
-    for i, region in enumerate(regions):
-        region_type = region["type"].lower()
-        bbox = region["bbox"]
-
-        # Crop region from image
-        x1, y1, x2, y2 = [int(c) for c in bbox]
-        region_image = image.crop((x1, y1, x2, y2))
-
-        # Determine task based on region type
-        if "table" in region_type:
-            task = "table"
-        elif "formula" in region_type or "math" in region_type:
-            task = "formula"
-        elif "chart" in region_type or "figure" in region_type:
-            task = "chart"
-        else:
-            task = "ocr"
-
-        # Recognize the region
-        try:
-            content = recognize_element(region_image, task)
-            blocks.append({
-                "index": i,
-                "type": region_type,
-                "bbox": bbox,
-                "content": content,
-                "task": task
-            })
-            logger.info(f"  Region {i} ({region_type}): {len(content)} chars")
-        except Exception as e:
-            logger.error(f"  Region {i} error: {e}")
-            blocks.append({
-                "index": i,
-                "type": region_type,
-                "bbox": bbox,
-                "content": "",
-                "error": str(e)
-            })
-
-    return {"blocks": blocks, "image_size": list(image.size)}
-
-
-def result_to_markdown(result: dict) -> str:
-    """Convert result to Markdown format"""
-    lines = []
-
-    for block in result.get("blocks", []):
-        block_type = block.get("type", "text")
-        content = block.get("content", "")
-
-        if "table" in block_type.lower():
-            lines.append(f"\n{content}\n")
-        elif "formula" in block_type.lower():
-            lines.append(f"\n$$\n{content}\n$$\n")
-        else:
-            lines.append(content)
-
-    return "\n\n".join(lines)
-
-
-# Request/Response models
-class ParseRequest(BaseModel):
-    image: str  # base64 encoded image
-    output_format: Optional[str] = "json"
-
-
-class ParseResponse(BaseModel):
-    success: bool
-    format: str
-    result: Union[dict, str]
-    processing_time: float
-    error: Optional[str] = None
-
-
-def decode_image(image_source: str) -> Image.Image:
-    """Decode image from base64 or data URL"""
-    if image_source.startswith("data:"):
-        header, data = image_source.split(",", 1)
-        image_data = base64.b64decode(data)
-    else:
-        image_data = base64.b64decode(image_source)
-
-    return Image.open(io.BytesIO(image_data)).convert("RGB")
-
-
-@app.on_event("startup")
-async def startup_event():
-    """Pre-load models on startup"""
-    logger.info("Starting PaddleOCR-VL Full Pipeline Server...")
-    try:
-        load_vl_model()
-        load_layout_model()
-        logger.info("Models loaded successfully")
-    except Exception as e:
-        logger.error(f"Failed to pre-load models: {e}")
-
-
-@app.get("/health")
-async def health_check():
-    """Health check endpoint"""
-    return {
-        "status": "healthy" if vl_model is not None else "loading",
-        "service": "PaddleOCR-VL Full Pipeline (Transformers)",
-        "device": DEVICE,
-        "vl_model_loaded": vl_model is not None,
-        "layout_model_loaded": layout_model is not None
-    }
-
-
-@app.get("/formats")
-async def supported_formats():
-    """List supported output formats"""
-    return {
-        "output_formats": ["json", "markdown"],
-        "image_formats": ["PNG", "JPEG", "WebP", "BMP", "GIF", "TIFF"],
-        "capabilities": [
-            "Layout detection (PP-DocLayoutV2)",
-            "Text recognition (OCR)",
-            "Table recognition",
-            "Formula recognition (LaTeX)",
-            "Chart recognition",
-            "Multi-language support (109 languages)"
-        ]
-    }
-
-
-@app.post("/parse", response_model=ParseResponse)
-async def parse_document_endpoint(request: ParseRequest):
-    """Parse a document image and return structured output"""
-    try:
-        start_time = time.time()
-
-        image = decode_image(request.image)
-        result = process_document(image)
-
-        if request.output_format == "markdown":
-            markdown = result_to_markdown(result)
-            output = {"markdown": markdown}
-        else:
-            output = result
-
-        elapsed = time.time() - start_time
-        logger.info(f"Processing complete in {elapsed:.2f}s")
-
-        return ParseResponse(
-            success=True,
-            format=request.output_format,
-            result=output,
-            processing_time=elapsed
-        )
-
-    except Exception as e:
-        logger.error(f"Error processing document: {e}", exc_info=True)
-        return ParseResponse(
-            success=False,
-            format=request.output_format,
-            result={},
-            processing_time=0,
-            error=str(e)
-        )
-
-
-@app.post("/v1/chat/completions")
-async def chat_completions(request: dict):
-    """OpenAI-compatible chat completions endpoint"""
-    try:
-        messages = request.get("messages", [])
-        output_format = request.get("output_format", "json")
-
-        # Find user message with image
-        image = None
-        for msg in reversed(messages):
-            if msg.get("role") == "user":
-                content = msg.get("content", [])
-                if isinstance(content, list):
-                    for item in content:
-                        if item.get("type") == "image_url":
-                            url = item.get("image_url", {}).get("url", "")
-                            image = decode_image(url)
-                            break
-                break
-
-        if image is None:
-            raise HTTPException(status_code=400, detail="No image provided")
-
-        start_time = time.time()
-        result = process_document(image)
-
-        if output_format == "markdown":
-            content = result_to_markdown(result)
-        else:
-            content = json.dumps(result, ensure_ascii=False, indent=2)
-
-        elapsed = time.time() - start_time
-
-        return {
-            "id": f"chatcmpl-{int(time.time()*1000)}",
-            "object": "chat.completion",
-            "created": int(time.time()),
-            "model": "paddleocr-vl-full",
-            "choices": [{
-                "index": 0,
-                "message": {"role": "assistant", "content": content},
-                "finish_reason": "stop"
-            }],
-            "usage": {
-                "prompt_tokens": 100,
-                "completion_tokens": len(content) // 4,
-                "total_tokens": 100 + len(content) // 4
-            },
-            "processing_time": elapsed
-        }
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error in chat completions: {e}", exc_info=True)
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host=SERVER_HOST, port=SERVER_PORT)

image_support_files/paddleocr_vl_server.py

@@ -1,465 +0,0 @@
-#!/usr/bin/env python3
-"""
-PaddleOCR-VL FastAPI Server (CPU variant)
-Provides OpenAI-compatible REST API for document parsing using PaddleOCR-VL
-"""
-
-import os
-import io
-import base64
-import logging
-import time
-from typing import Optional, List, Any, Dict, Union
-
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel
-import torch
-from PIL import Image
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-)
-logger = logging.getLogger(__name__)
-
-# Environment configuration
-SERVER_HOST = os.environ.get('SERVER_HOST', '0.0.0.0')
-SERVER_PORT = int(os.environ.get('SERVER_PORT', '8000'))
-MODEL_NAME = os.environ.get('MODEL_NAME', 'PaddlePaddle/PaddleOCR-VL')
-
-# Device configuration
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-logger.info(f"Using device: {DEVICE}")
-
-# Task prompts for PaddleOCR-VL
-TASK_PROMPTS = {
-    "ocr": "OCR:",
-    "table": "Table Recognition:",
-    "formula": "Formula Recognition:",
-    "chart": "Chart Recognition:",
-}
-
-# Initialize FastAPI app
-app = FastAPI(
-    title="PaddleOCR-VL Server",
-    description="OpenAI-compatible REST API for document parsing using PaddleOCR-VL",
-    version="1.0.0"
-)
-
-# Global model instances
-model = None
-processor = None
-
-
-# Request/Response models (OpenAI-compatible)
-class ImageUrl(BaseModel):
-    url: str
-
-
-class ContentItem(BaseModel):
-    type: str
-    text: Optional[str] = None
-    image_url: Optional[ImageUrl] = None
-
-
-class Message(BaseModel):
-    role: str
-    content: Union[str, List[ContentItem]]
-
-
-class ChatCompletionRequest(BaseModel):
-    model: str = "paddleocr-vl"
-    messages: List[Message]
-    temperature: Optional[float] = 0.0
-    max_tokens: Optional[int] = 4096
-
-
-class Choice(BaseModel):
-    index: int
-    message: Message
-    finish_reason: str
-
-
-class Usage(BaseModel):
-    prompt_tokens: int
-    completion_tokens: int
-    total_tokens: int
-
-
-class ChatCompletionResponse(BaseModel):
-    id: str
-    object: str = "chat.completion"
-    created: int
-    model: str
-    choices: List[Choice]
-    usage: Usage
-
-
-class HealthResponse(BaseModel):
-    status: str
-    model: str
-    device: str
-
-
-def load_model():
-    """Load the PaddleOCR-VL model and processor"""
-    global model, processor
-
-    if model is not None:
-        return
-
-    logger.info(f"Loading PaddleOCR-VL model: {MODEL_NAME}")
-
-    from transformers import AutoModelForCausalLM, AutoProcessor
-
-    # Load processor
-    processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True)
-
-    # Load model with appropriate settings for CPU/GPU
-    if DEVICE == "cuda":
-        model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME,
-            trust_remote_code=True,
-            torch_dtype=torch.bfloat16,
-        ).to(DEVICE).eval()
-    else:
-        # CPU mode - use float32 for compatibility
-        model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME,
-            trust_remote_code=True,
-            torch_dtype=torch.float32,
-            low_cpu_mem_usage=True,
-        ).eval()
-
-    logger.info("PaddleOCR-VL model loaded successfully")
-
-
-def optimize_image_resolution(image: Image.Image, max_size: int = 2048, min_size: int = 1080) -> Image.Image:
-    """
-    Optimize image resolution for PaddleOCR-VL.
-
-    Best results are achieved with images in the 1080p-2K range.
-    - Images larger than max_size are scaled down
-    - Very small images are scaled up to min_size
-    """
-    width, height = image.size
-    max_dim = max(width, height)
-    min_dim = min(width, height)
-
-    # Scale down if too large (4K+ images often miss text)
-    if max_dim > max_size:
-        scale = max_size / max_dim
-        new_width = int(width * scale)
-        new_height = int(height * scale)
-        logger.info(f"Scaling down image from {width}x{height} to {new_width}x{new_height}")
-        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-    # Scale up if too small
-    elif max_dim < min_size and min_dim < min_size:
-        scale = min_size / max_dim
-        new_width = int(width * scale)
-        new_height = int(height * scale)
-        logger.info(f"Scaling up image from {width}x{height} to {new_width}x{new_height}")
-        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-    else:
-        logger.info(f"Image size {width}x{height} is optimal, no scaling needed")
-
-    return image
-
-
-def decode_image(image_source: str, optimize: bool = True) -> Image.Image:
-    """
-    Decode image from various sources.
-
-    Supported formats:
-    - Base64 data URL: data:image/png;base64,... or data:image/jpeg;base64,...
-    - HTTP/HTTPS URL: https://example.com/image.png
-    - Raw base64 string
-    - Local file path
-
-    Supported image types: PNG, JPEG, WebP, BMP, GIF, TIFF
-    """
-    image = None
-
-    if image_source.startswith("data:"):
-        # Base64 encoded image with MIME type header
-        # Supports: data:image/png;base64,... data:image/jpeg;base64,... etc.
-        header, data = image_source.split(",", 1)
-        image_data = base64.b64decode(data)
-        image = Image.open(io.BytesIO(image_data)).convert("RGB")
-        logger.debug(f"Decoded base64 image with header: {header}")
-    elif image_source.startswith("http://") or image_source.startswith("https://"):
-        # URL - fetch image
-        import httpx
-        response = httpx.get(image_source, timeout=30.0)
-        response.raise_for_status()
-        image = Image.open(io.BytesIO(response.content)).convert("RGB")
-        logger.debug(f"Fetched image from URL: {image_source[:50]}...")
-    else:
-        # Assume it's a file path or raw base64
-        try:
-            image_data = base64.b64decode(image_source)
-            image = Image.open(io.BytesIO(image_data)).convert("RGB")
-            logger.debug("Decoded raw base64 image")
-        except:
-            # Try as file path
-            image = Image.open(image_source).convert("RGB")
-            logger.debug(f"Loaded image from file: {image_source}")
-
-    # Optimize resolution for best OCR results
-    if optimize:
-        image = optimize_image_resolution(image)
-
-    return image
-
-
-def extract_image_and_text(content: Union[str, List[ContentItem]]) -> tuple:
-    """Extract image and text prompt from message content"""
-    if isinstance(content, str):
-        return None, content
-
-    image = None
-    text = ""
-
-    for item in content:
-        if item.type == "image_url" and item.image_url:
-            image = decode_image(item.image_url.url)
-        elif item.type == "text" and item.text:
-            text = item.text
-
-    return image, text
-
-
-def generate_response(image: Image.Image, prompt: str, max_tokens: int = 4096) -> str:
-    """Generate response using PaddleOCR-VL"""
-    load_model()
-
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image},
-                {"type": "text", "text": prompt},
-            ]
-        }
-    ]
-
-    inputs = processor.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_dict=True,
-        return_tensors="pt"
-    )
-
-    if DEVICE == "cuda":
-        inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
-
-    with torch.inference_mode():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=max_tokens,
-            do_sample=False,
-            use_cache=True
-        )
-
-    response = processor.batch_decode(outputs, skip_special_tokens=True)[0]
-
-    # Extract the assistant's response (after the prompt)
-    if "assistant" in response.lower():
-        parts = response.split("assistant")
-        if len(parts) > 1:
-            response = parts[-1].strip()
-
-    return response
-
-
-@app.on_event("startup")
-async def startup_event():
-    """Pre-load the model on startup"""
-    logger.info("Pre-loading PaddleOCR-VL model...")
-    try:
-        load_model()
-        logger.info("Model pre-loaded successfully")
-    except Exception as e:
-        logger.error(f"Failed to pre-load model: {e}")
-        # Don't fail startup - model will be loaded on first request
-
-
-@app.get("/health", response_model=HealthResponse)
-async def health_check():
-    """Health check endpoint"""
-    return HealthResponse(
-        status="healthy" if model is not None else "loading",
-        model=MODEL_NAME,
-        device=DEVICE
-    )
-
-
-@app.get("/formats")
-async def supported_formats():
-    """List supported image formats and input methods"""
-    return {
-        "image_formats": {
-            "supported": ["PNG", "JPEG", "WebP", "BMP", "GIF", "TIFF"],
-            "recommended": ["PNG", "JPEG"],
-            "mime_types": [
-                "image/png",
-                "image/jpeg",
-                "image/webp",
-                "image/bmp",
-                "image/gif",
-                "image/tiff"
-            ]
-        },
-        "input_methods": {
-            "base64_data_url": {
-                "description": "Base64 encoded image with MIME type header",
-                "example": "data:image/png;base64,iVBORw0KGgo..."
-            },
-            "http_url": {
-                "description": "Direct HTTP/HTTPS URL to image",
-                "example": "https://example.com/image.png"
-            },
-            "raw_base64": {
-                "description": "Raw base64 string without header",
-                "example": "iVBORw0KGgo..."
-            }
-        },
-        "resolution": {
-            "optimal_range": "1080p to 2K (1080-2048 pixels on longest side)",
-            "auto_scaling": True,
-            "note": "Images are automatically scaled to optimal range. 4K+ images are scaled down for better accuracy."
-        },
-        "task_prompts": TASK_PROMPTS
-    }
-
-
-@app.get("/v1/models")
-async def list_models():
-    """List available models (OpenAI-compatible)"""
-    return {
-        "object": "list",
-        "data": [
-            {
-                "id": "paddleocr-vl",
-                "object": "model",
-                "created": int(time.time()),
-                "owned_by": "paddlepaddle"
-            }
-        ]
-    }
-
-
-@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
-async def chat_completions(request: ChatCompletionRequest):
-    """
-    OpenAI-compatible chat completions endpoint for PaddleOCR-VL
-
-    Supports tasks:
-    - "OCR:" - Text recognition
-    - "Table Recognition:" - Table extraction
-    - "Formula Recognition:" - Formula extraction
-    - "Chart Recognition:" - Chart extraction
-    """
-    try:
-        # Get the last user message
-        user_message = None
-        for msg in reversed(request.messages):
-            if msg.role == "user":
-                user_message = msg
-                break
-
-        if not user_message:
-            raise HTTPException(status_code=400, detail="No user message found")
-
-        # Extract image and prompt
-        image, prompt = extract_image_and_text(user_message.content)
-
-        if image is None:
-            raise HTTPException(status_code=400, detail="No image provided in message")
-
-        # Default to OCR if no specific prompt
-        if not prompt or prompt.strip() == "":
-            prompt = "OCR:"
-
-        logger.info(f"Processing request with prompt: {prompt[:50]}...")
-
-        # Generate response
-        start_time = time.time()
-        response_text = generate_response(image, prompt, request.max_tokens or 4096)
-        elapsed = time.time() - start_time
-
-        logger.info(f"Generated response in {elapsed:.2f}s ({len(response_text)} chars)")
-
-        # Build OpenAI-compatible response
-        return ChatCompletionResponse(
-            id=f"chatcmpl-{int(time.time()*1000)}",
-            created=int(time.time()),
-            model=request.model,
-            choices=[
-                Choice(
-                    index=0,
-                    message=Message(role="assistant", content=response_text),
-                    finish_reason="stop"
-                )
-            ],
-            usage=Usage(
-                prompt_tokens=100,  # Approximate
-                completion_tokens=len(response_text) // 4,
-                total_tokens=100 + len(response_text) // 4
-            )
-        )
-
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.error(f"Error processing request: {e}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-# Legacy endpoint for compatibility with old PaddleOCR API
-class LegacyOCRRequest(BaseModel):
-    image: str
-    task: Optional[str] = "ocr"
-
-
-class LegacyOCRResponse(BaseModel):
-    success: bool
-    result: str
-    task: str
-    error: Optional[str] = None
-
-
-@app.post("/ocr", response_model=LegacyOCRResponse)
-async def legacy_ocr(request: LegacyOCRRequest):
-    """
-    Legacy OCR endpoint for backwards compatibility
-
-    Tasks: ocr, table, formula, chart
-    """
-    try:
-        image = decode_image(request.image)
-        prompt = TASK_PROMPTS.get(request.task, TASK_PROMPTS["ocr"])
-
-        result = generate_response(image, prompt)
-
-        return LegacyOCRResponse(
-            success=True,
-            result=result,
-            task=request.task
-        )
-    except Exception as e:
-        logger.error(f"Legacy OCR error: {e}")
-        return LegacyOCRResponse(
-            success=False,
-            result="",
-            task=request.task,
-            error=str(e)
-        )
-
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host=SERVER_HOST, port=SERVER_PORT)

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@host.today/ht-docker-ai",
-  "version": "1.6.0",
+  "version": "1.13.1",
   "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 +295,91 @@ 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 || [];
+      // Exact match required - don't match on prefix
+      const exists = models.some((m: { name: string }) => m.name === modelName);
+
+      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');
+}
+
+/**
+ * Ensure Qwen3-VL 8B model is available (vision-language model)
+ * Q4_K_M quantization (~5GB) - fits in 15GB VRAM with room to spare
+ */
+export async function ensureQwen3Vl(): Promise<boolean> {
+  // First ensure the Ollama service is running
+  const ollamaOk = await ensureMiniCpm();
+  if (!ollamaOk) return false;
+
+  // Then ensure Qwen3-VL 8B is pulled
+  return ensureOllamaModel('qwen3-vl:8b');
+}

test/test.bankstatements.combined.ts

@@ -1,549 +0,0 @@
-/**
- * Bank statement extraction test using MiniCPM-V (visual) + PaddleOCR-VL (table recognition)
- *
- * This is the combined/dual-VLM approach that uses both models for consensus:
- *   - MiniCPM-V for visual extraction
- *   - PaddleOCR-VL for table recognition
- */
-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 { ensurePaddleOcrVl, ensureMiniCpm } from './helpers/docker.js';
-
-// Service URLs
-const OLLAMA_URL = 'http://localhost:11434';
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-
-// Models
-const MINICPM_MODEL = 'minicpm-v:latest';
-const PADDLEOCR_VL_MODEL = 'paddleocr-vl';
-
-// Prompt for MiniCPM-V visual extraction
-const MINICPM_EXTRACT_PROMPT = `/nothink
-You are a bank statement parser. Extract EVERY transaction from the table.
-
-Read the Amount column carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point
-
-For each row output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Do not skip any rows. Return ONLY the JSON array, no explanation.`;
-
-// Prompt for PaddleOCR-VL table extraction
-const PADDLEOCR_VL_TABLE_PROMPT = `Table Recognition:`;
-
-// Post-processing prompt to convert PaddleOCR-VL output to JSON
-const PADDLEOCR_VL_CONVERT_PROMPT = `/nothink
-Convert the following bank statement table data to JSON.
-
-Read the Amount values carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point
-
-For each transaction output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Return ONLY the JSON array, no explanation.
-
-Table data:
----
-{TABLE_DATA}
----`;
-
-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 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
-      { stdio: 'pipe' }
-    );
-
-    const files = fs.readdirSync(tempDir).filter((f: string) => 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 using MiniCPM-V via Ollama
- */
-async function extractWithMiniCPM(images: string[], passLabel: string): Promise<ITransaction[]> {
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt: MINICPM_EXTRACT_PROMPT,
-    images,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
-
-  const reader = response.body?.getReader();
-  if (!reader) {
-    throw new Error('No response body');
-  }
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-  let lineBuffer = '';
-
-  console.log(`[${passLabel}] Extracting with MiniCPM-V...`);
-
-  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.response) {
-          fullText += json.response;
-          lineBuffer += json.response;
-
-          if (lineBuffer.includes('\n')) {
-            const parts = lineBuffer.split('\n');
-            for (let i = 0; i < parts.length - 1; i++) {
-              console.log(parts[i]);
-            }
-            lineBuffer = parts[parts.length - 1];
-          }
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  if (lineBuffer) {
-    console.log(lineBuffer);
-  }
-  console.log('');
-
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error('No JSON array found in response');
-  }
-
-  return JSON.parse(fullText.substring(startIdx, endIdx));
-}
-
-/**
- * Extract table using PaddleOCR-VL via OpenAI-compatible API
- */
-async function extractTableWithPaddleOCRVL(imageBase64: string): Promise<string> {
-  const payload = {
-    model: PADDLEOCR_VL_MODEL,
-    messages: [
-      {
-        role: 'user',
-        content: [
-          {
-            type: 'image_url',
-            image_url: { url: `data:image/png;base64,${imageBase64}` },
-          },
-          {
-            type: 'text',
-            text: PADDLEOCR_VL_TABLE_PROMPT,
-          },
-        ],
-      },
-    ],
-    temperature: 0.0,
-    max_tokens: 8192,
-  };
-
-  const response = await fetch(`${PADDLEOCR_VL_URL}/v1/chat/completions`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    const text = await response.text();
-    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
-  }
-
-  const data = await response.json();
-  return data.choices?.[0]?.message?.content || '';
-}
-
-/**
- * Convert PaddleOCR-VL table output to transactions using MiniCPM-V
- */
-async function convertTableToTransactions(
-  tableData: string,
-  passLabel: string
-): Promise<ITransaction[]> {
-  const prompt = PADDLEOCR_VL_CONVERT_PROMPT.replace('{TABLE_DATA}', tableData);
-
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
-
-  const reader = response.body?.getReader();
-  if (!reader) {
-    throw new Error('No response body');
-  }
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-
-  console.log(`[${passLabel}] Converting table data to JSON...`);
-
-  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.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error('No JSON array found in response');
-  }
-
-  return JSON.parse(fullText.substring(startIdx, endIdx));
-}
-
-/**
- * Extract using PaddleOCR-VL (table recognition) + conversion
- */
-async function extractWithPaddleOCRVL(
-  images: string[],
-  passLabel: string
-): Promise<ITransaction[]> {
-  console.log(`[${passLabel}] Extracting tables with PaddleOCR-VL...`);
-
-  // Extract table data from each page
-  const tableDataParts: string[] = [];
-  for (let i = 0; i < images.length; i++) {
-    console.log(`[${passLabel}] Processing page ${i + 1}/${images.length}...`);
-    const tableData = await extractTableWithPaddleOCRVL(images[i]);
-    if (tableData.trim()) {
-      tableDataParts.push(`--- Page ${i + 1} ---\n${tableData}`);
-    }
-  }
-
-  const combinedTableData = tableDataParts.join('\n\n');
-  console.log(`[${passLabel}] Got ${combinedTableData.length} chars of table data`);
-
-  // Convert to transactions
-  return convertTableToTransactions(combinedTableData, passLabel);
-}
-
-/**
- * Create a hash of transactions for comparison
- */
-function hashTransactions(transactions: ITransaction[]): string {
-  return transactions
-    .map((t) => `${t.date}|${t.amount.toFixed(2)}`)
-    .sort()
-    .join(';');
-}
-
-/**
- * Check if PaddleOCR-VL service is available
- */
-async function isPaddleOCRVLAvailable(): Promise<boolean> {
-  try {
-    const response = await fetch(`${PADDLEOCR_VL_URL}/health`, {
-      method: 'GET',
-      signal: AbortSignal.timeout(5000),
-    });
-    return response.ok;
-  } catch {
-    return false;
-  }
-}
-
-/**
- * Extract with dual-VLM consensus
- * Strategy:
- *   Pass 1 = MiniCPM-V visual extraction
- *   Pass 2 = PaddleOCR-VL table recognition (if available)
- *   Pass 3+ = MiniCPM-V visual (fallback)
- */
-async function extractWithConsensus(
-  images: string[],
-  maxPasses: number = 5
-): Promise<ITransaction[]> {
-  const results: Array<{ transactions: ITransaction[]; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
-
-  const addResult = (transactions: ITransaction[], passLabel: string): number => {
-    const hash = hashTransactions(transactions);
-    results.push({ transactions, hash });
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(
-      `[${passLabel}] Got ${transactions.length} transactions (hash: ${hash.substring(0, 20)}...)`
-    );
-    return hashCounts.get(hash)!;
-  };
-
-  // Check if PaddleOCR-VL is available
-  const paddleOCRVLAvailable = await isPaddleOCRVLAvailable();
-  if (paddleOCRVLAvailable) {
-    console.log('[Setup] PaddleOCR-VL service available - using dual-VLM consensus');
-  } else {
-    console.log('[Setup] PaddleOCR-VL not available - using MiniCPM-V only');
-  }
-
-  // Pass 1: MiniCPM-V visual extraction
-  try {
-    const pass1Result = await extractWithMiniCPM(images, 'Pass 1 MiniCPM-V');
-    addResult(pass1Result, 'Pass 1 MiniCPM-V');
-  } catch (err) {
-    console.log(`[Pass 1] Error: ${err}`);
-  }
-
-  // Pass 2: PaddleOCR-VL table recognition (if available)
-  if (paddleOCRVLAvailable) {
-    try {
-      const pass2Result = await extractWithPaddleOCRVL(images, 'Pass 2 PaddleOCR-VL');
-      const count = addResult(pass2Result, 'Pass 2 PaddleOCR-VL');
-      if (count >= 2) {
-        console.log('[Consensus] MiniCPM-V and PaddleOCR-VL extractions match!');
-        return pass2Result;
-      }
-    } catch (err) {
-      console.log(`[Pass 2 PaddleOCR-VL] Error: ${err}`);
-    }
-  }
-
-  // Pass 3+: Continue with MiniCPM-V visual passes
-  const startPass = paddleOCRVLAvailable ? 3 : 2;
-  for (let pass = startPass; pass <= maxPasses; pass++) {
-    try {
-      const transactions = await extractWithMiniCPM(images, `Pass ${pass} MiniCPM-V`);
-      const count = addResult(transactions, `Pass ${pass} MiniCPM-V`);
-
-      if (count >= 2) {
-        console.log(`[Consensus] Reached after ${pass} passes`);
-        return transactions;
-      }
-
-      console.log(`[Pass ${pass}] No consensus yet, trying again...`);
-    } catch (err) {
-      console.log(`[Pass ${pass}] Error: ${err}`);
-    }
-  }
-
-  // No consensus reached - return the most common result
-  let bestHash = '';
-  let bestCount = 0;
-  for (const [hash, count] of hashCounts) {
-    if (count > bestCount) {
-      bestCount = count;
-      bestHash = hash;
-    }
-  }
-
-  if (!bestHash) {
-    throw new Error('No valid results obtained');
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`[No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.transactions;
-}
-
-/**
- * Compare extracted transactions against expected
- */
-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 transaction ${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 at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`
-      );
-    }
-  }
-
-  if (extracted.length > expected.length) {
-    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
-  }
-
-  return { matches, total: expected.length, errors };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL is running (auto-detects GPU/CPU)
-  const paddleOk = await ensurePaddleOcrVl();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
-  const response = await fetch(`${OLLAMA_URL}/api/tags`);
-  const data = await response.json();
-  const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
-});
-
-tap.test('should check PaddleOCR-VL availability', async () => {
-  const available = await isPaddleOCRVLAvailable();
-  console.log(`PaddleOCR-VL available: ${available}`);
-  expect(available).toBeTrue();
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-for (const testCase of testCases) {
-  tap.test(`should extract transactions from ${testCase.name}`, async () => {
-    // Load expected transactions
-    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
-    console.log(`\n=== ${testCase.name} ===`);
-    console.log(`Expected: ${expected.length} transactions`);
-
-    // Convert PDF to images
-    console.log('Converting PDF to images...');
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`Converted: ${images.length} pages\n`);
-
-    // Extract with dual-VLM consensus
-    const extracted = await extractWithConsensus(images);
-    console.log(`\nFinal: ${extracted.length} transactions`);
-
-    // Compare results
-    const result = compareTransactions(extracted, expected);
-    console.log(`Accuracy: ${result.matches}/${result.total}`);
-
-    if (result.errors.length > 0) {
-      console.log('Errors:');
-      result.errors.forEach((e) => console.log(`  - ${e}`));
-    }
-
-    // Assert high accuracy
-    const accuracy = result.matches / result.total;
-    expect(accuracy).toBeGreaterThan(0.95);
-    expect(extracted.length).toEqual(expected.length);
-  });
-}
-
-export default tap.start();

test/test.bankstatements.minicpm.ts

@@ -1,8 +1,9 @@
 /**
- * Bank statement extraction test using MiniCPM-V only (visual extraction)
+ * Bank statement extraction using MiniCPM-V (visual extraction)
  *
- * This tests MiniCPM-V's ability to extract bank transactions directly from images
+ * JSON per-page approach:
- * without any OCR augmentation.
+ * 1. Ask for structured JSON of all transactions per page
+ * 2. Consensus: extract twice, compare, retry if mismatch
  */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
@@ -11,24 +12,8 @@ import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureMiniCpm } from './helpers/docker.js';
 
-// Service URL
 const OLLAMA_URL = 'http://localhost:11434';
-
+const MODEL = 'openbmb/minicpm-v4.5:q8_0';
-// Model
-const MINICPM_MODEL = 'minicpm-v:latest';
-
-// Prompt for MiniCPM-V visual extraction
-const MINICPM_EXTRACT_PROMPT = `/nothink
-You are a bank statement parser. Extract EVERY transaction from the table.
-
-Read the Amount column carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point
-
-For each row output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Do not skip any rows. Return ONLY the JSON array, no explanation.`;
 
 interface ITransaction {
   date: string;
@@ -36,6 +21,22 @@ interface ITransaction {
   amount: number;
 }
 
+const JSON_PROMPT = `Extract ALL transactions from this bank statement page as a JSON array.
+
+IMPORTANT RULES:
+1. Each transaction has: date, description/counterparty, and an amount
+2. Amount is NEGATIVE for money going OUT (debits, payments, withdrawals)
+3. Amount is POSITIVE for money coming IN (credits, deposits, refunds)
+4. Date format: YYYY-MM-DD
+5. Do NOT include: opening balance, closing balance, subtotals, headers, or summary rows
+6. Only include actual transactions with a specific date and amount
+
+Return ONLY this JSON format, no explanation:
+[
+  {"date": "2021-06-01", "counterparty": "COMPANY NAME", "amount": -25.99},
+  {"date": "2021-06-02", "counterparty": "DEPOSIT FROM", "amount": 100.00}
+]`;
+
 /**
  * Convert PDF to PNG images using ImageMagick
  */
@@ -65,149 +66,330 @@ function convertPdfToImages(pdfPath: string): string[] {
 }
 
 /**
- * Extract using MiniCPM-V via Ollama
+ * Query for JSON extraction
  */
-async function extractWithMiniCPM(images: string[], passLabel: string): Promise<ITransaction[]> {
+async function queryJson(image: string, queryId: string): Promise<string> {
-  const payload = {
+  console.log(`      [${queryId}] Sending request to ${MODEL}...`);
-    model: MINICPM_MODEL,
+  const startTime = Date.now();
-    prompt: MINICPM_EXTRACT_PROMPT,
-    images,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
 
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
+    body: JSON.stringify({
+      model: MODEL,
+      messages: [{
+        role: 'user',
+        content: JSON_PROMPT,
+        images: [image],
+      }],
+      stream: false,
+      options: {
+        num_predict: 4000,
+        temperature: 0.1,
+      },
+    }),
   });
 
+  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+
   if (!response.ok) {
+    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
     throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const reader = response.body?.getReader();
+  const data = await response.json();
-  if (!reader) {
+  const content = (data.message?.content || '').trim();
-    throw new Error('No response body');
+  console.log(`      [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
-  }
+  return content;
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-  let lineBuffer = '';
-
-  console.log(`[${passLabel}] Extracting with MiniCPM-V...`);
-
-  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.response) {
-          fullText += json.response;
-          lineBuffer += json.response;
-
-          if (lineBuffer.includes('\n')) {
-            const parts = lineBuffer.split('\n');
-            for (let i = 0; i < parts.length - 1; i++) {
-              console.log(parts[i]);
-            }
-            lineBuffer = parts[parts.length - 1];
-          }
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  if (lineBuffer) {
-    console.log(lineBuffer);
-  }
-  console.log('');
-
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error('No JSON array found in response');
-  }
-
-  return JSON.parse(fullText.substring(startIdx, endIdx));
 }
 
 /**
- * Create a hash of transactions for comparison
+ * Sanitize JSON string - fix common issues from vision model output
  */
-function hashTransactions(transactions: ITransaction[]): string {
+function sanitizeJson(jsonStr: string): string {
-  return transactions
+  let s = jsonStr;
-    .map((t) => `${t.date}|${t.amount.toFixed(2)}`)
+
-    .sort()
+  // Fix +number (e.g., +93.80 -> 93.80) - JSON doesn't allow + prefix
-    .join(';');
+  // Handle various whitespace patterns
+  s = s.replace(/"amount"\s*:\s*\+/g, '"amount": ');
+  s = s.replace(/:\s*\+(\d)/g, ': $1');
+
+  // Fix European number format with thousands separator (e.g., 1.000.00 -> 1000.00)
+  // Pattern: "amount": X.XXX.XX where X.XXX is thousands and .XX is decimal
+  s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3.$4');
+  // Also handle larger numbers like 10.000.00
+  s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3$4.$5');
+
+  // Fix trailing commas before ] or }
+  s = s.replace(/,\s*([}\]])/g, '$1');
+
+  // Fix unescaped newlines inside strings (replace with space)
+  s = s.replace(/"([^"\\]*)\n([^"]*)"/g, '"$1 $2"');
+
+  // Fix unescaped tabs inside strings
+  s = s.replace(/"([^"\\]*)\t([^"]*)"/g, '"$1 $2"');
+
+  // Fix unescaped backslashes (but not already escaped ones)
+  s = s.replace(/\\(?!["\\/bfnrtu])/g, '\\\\');
+
+  // Fix common issues with counterparty names containing special chars
+  s = s.replace(/"counterparty":\s*"([^"]*)'([^"]*)"/g, '"counterparty": "$1$2"');
+
+  // Remove control characters except newlines (which we handle above)
+  s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
+
+  return s;
 }
 
 /**
- * Extract with consensus voting using MiniCPM-V only
+ * Parse JSON response into transactions
  */
-async function extractWithConsensus(
+function parseJsonResponse(response: string, queryId: string): ITransaction[] {
-  images: string[],
+  console.log(`      [${queryId}] Parsing response...`);
-  maxPasses: number = 5
-): Promise<ITransaction[]> {
-  const results: Array<{ transactions: ITransaction[]; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
 
-  const addResult = (transactions: ITransaction[], passLabel: string): number => {
+  // Try to find JSON in markdown code block
-    const hash = hashTransactions(transactions);
+  const codeBlockMatch = response.match(/```(?:json)?\s*([\s\S]*?)```/);
-    results.push({ transactions, hash });
+  let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : response.trim();
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(
-      `[${passLabel}] Got ${transactions.length} transactions (hash: ${hash.substring(0, 20)}...)`
-    );
-    return hashCounts.get(hash)!;
-  };
 
-  console.log('[Setup] Using MiniCPM-V only');
+  if (codeBlockMatch) {
+    console.log(`      [${queryId}] Found JSON in code block`);
+  }
+
+  // Sanitize JSON (fix +number issue)
+  jsonStr = sanitizeJson(jsonStr);
 
-  for (let pass = 1; pass <= maxPasses; pass++) {
   try {
-      const transactions = await extractWithMiniCPM(images, `Pass ${pass} MiniCPM-V`);
+    const parsed = JSON.parse(jsonStr);
-      const count = addResult(transactions, `Pass ${pass} MiniCPM-V`);
+    if (Array.isArray(parsed)) {
+      const txs = parsed.map(tx => ({
+        date: String(tx.date || ''),
+        counterparty: String(tx.counterparty || tx.description || ''),
+        amount: parseAmount(tx.amount),
+      }));
+      console.log(`      [${queryId}] Parsed ${txs.length} transactions (direct)`);
+      return txs;
+    }
+    console.log(`      [${queryId}] Parsed JSON is not an array`);
+  } catch (e) {
+    const errMsg = (e as Error).message;
+    console.log(`      [${queryId}] Direct parse failed: ${errMsg}`);
+
+    // Log problematic section with context
+    const posMatch = errMsg.match(/position (\d+)/);
+    if (posMatch) {
+      const pos = parseInt(posMatch[1]);
+      const start = Math.max(0, pos - 40);
+      const end = Math.min(jsonStr.length, pos + 40);
+      const context = jsonStr.substring(start, end);
+      const marker = ' '.repeat(pos - start) + '^';
+      console.log(`      [${queryId}] Context around error position ${pos}:`);
+      console.log(`      [${queryId}] ...${context}...`);
+      console.log(`      [${queryId}]    ${marker}`);
+    }
+
+    // Try to find JSON array pattern
+    const arrayMatch = jsonStr.match(/\[[\s\S]*\]/);
+    if (arrayMatch) {
+      console.log(`      [${queryId}] Found array pattern, trying to parse...`);
+      const sanitizedArray = sanitizeJson(arrayMatch[0]);
+      try {
+        const parsed = JSON.parse(sanitizedArray);
+        if (Array.isArray(parsed)) {
+          const txs = parsed.map(tx => ({
+            date: String(tx.date || ''),
+            counterparty: String(tx.counterparty || tx.description || ''),
+            amount: parseAmount(tx.amount),
+          }));
+          console.log(`      [${queryId}] Parsed ${txs.length} transactions (array match)`);
+          return txs;
+        }
+      } catch (e2) {
+        const errMsg2 = (e2 as Error).message;
+        console.log(`      [${queryId}] Array parse failed: ${errMsg2}`);
+        const posMatch2 = errMsg2.match(/position (\d+)/);
+        if (posMatch2) {
+          const pos2 = parseInt(posMatch2[1]);
+          console.log(`      [${queryId}] Context around error: ...${sanitizedArray.substring(Math.max(0, pos2 - 30), pos2 + 30)}...`);
+        }
+
+        // Try to extract individual objects from the malformed array
+        console.log(`      [${queryId}] Attempting object-by-object extraction...`);
+        const extracted = extractTransactionsFromMalformedJson(sanitizedArray, queryId);
+        if (extracted.length > 0) {
+          console.log(`      [${queryId}] Recovered ${extracted.length} transactions via object extraction`);
+          return extracted;
+        }
+      }
+    } else {
+      console.log(`      [${queryId}] No array pattern found in response`);
+      console.log(`      [${queryId}] Raw response preview: ${response.substring(0, 200)}...`);
+    }
+  }
+
+  console.log(`      [${queryId}] PARSE FAILED - returning empty array`);
+  return [];
+}
+
+/**
+ * Extract transactions from malformed JSON by parsing objects individually
+ */
+function extractTransactionsFromMalformedJson(jsonStr: string, queryId: string): ITransaction[] {
+  const transactions: ITransaction[] = [];
+
+  // Match individual transaction objects
+  const objectPattern = /\{\s*"date"\s*:\s*"([^"]+)"\s*,\s*"counterparty"\s*:\s*"([^"]+)"\s*,\s*"amount"\s*:\s*([+-]?\d+\.?\d*)\s*\}/g;
+  let match;
+
+  while ((match = objectPattern.exec(jsonStr)) !== null) {
+    transactions.push({
+      date: match[1],
+      counterparty: match[2],
+      amount: parseFloat(match[3]),
+    });
+  }
+
+  // Also try with different field orders (amount before counterparty, etc.)
+  if (transactions.length === 0) {
+    const altPattern = /\{\s*"date"\s*:\s*"([^"]+)"[^}]*"amount"\s*:\s*([+-]?\d+\.?\d*)[^}]*\}/g;
+    while ((match = altPattern.exec(jsonStr)) !== null) {
+      // Try to extract counterparty from the match
+      const counterpartyMatch = match[0].match(/"counterparty"\s*:\s*"([^"]+)"/);
+      const descMatch = match[0].match(/"description"\s*:\s*"([^"]+)"/);
+      transactions.push({
+        date: match[1],
+        counterparty: counterpartyMatch?.[1] || descMatch?.[1] || 'UNKNOWN',
+        amount: parseFloat(match[2]),
+      });
+    }
+  }
 
-      if (count >= 2) {
-        console.log(`[Consensus] Reached after ${pass} passes`);
   return transactions;
+}
+
+/**
+ * Parse amount from various formats
+ */
+function parseAmount(value: unknown): number {
+  if (typeof value === 'number') return value;
+  if (typeof value !== 'string') return 0;
+
+  let s = value.replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
+  // European format: comma is decimal
+  if (s.includes(',') && s.indexOf(',') > s.lastIndexOf('.')) {
+    s = s.replace(/\./g, '').replace(',', '.');
+  } else {
+    s = s.replace(/,/g, '');
+  }
+  return parseFloat(s) || 0;
+}
+
+/**
+ * Compare two transaction arrays for consensus
+ */
+function transactionArraysMatch(a: ITransaction[], b: ITransaction[]): boolean {
+  if (a.length !== b.length) return false;
+
+  for (let i = 0; i < a.length; i++) {
+    const dateMatch = a[i].date === b[i].date;
+    const amountMatch = Math.abs(a[i].amount - b[i].amount) < 0.01;
+    if (!dateMatch || !amountMatch) return false;
   }
 
-      console.log(`[Pass ${pass}] No consensus yet, trying again...`);
+  return true;
-    } catch (err) {
+}
-      console.log(`[Pass ${pass}] Error: ${err}`);
+
+/**
+ * Compare two transaction arrays and log differences
+ */
+function compareAndLogDifferences(txs1: ITransaction[], txs2: ITransaction[], pageNum: number): void {
+  if (txs1.length !== txs2.length) {
+    console.log(`    [Page ${pageNum}] Length mismatch: Q1=${txs1.length}, Q2=${txs2.length}`);
+    return;
+  }
+
+  for (let i = 0; i < txs1.length; i++) {
+    const dateMatch = txs1[i].date === txs2[i].date;
+    const amountMatch = Math.abs(txs1[i].amount - txs2[i].amount) < 0.01;
+
+    if (!dateMatch || !amountMatch) {
+      console.log(`    [Page ${pageNum}] Tx ${i + 1} differs:`);
+      console.log(`      Q1: ${txs1[i].date} | ${txs1[i].amount}`);
+      console.log(`      Q2: ${txs2[i].date} | ${txs2[i].amount}`);
+    }
+  }
+}
+
+/**
+ * Extract transactions from a single page with consensus
+ */
+async function extractTransactionsFromPage(image: string, pageNum: number): Promise<ITransaction[]> {
+  const MAX_ATTEMPTS = 5;
+  console.log(`\n    ======== Page ${pageNum} ========`);
+  console.log(`    [Page ${pageNum}] Starting JSON extraction...`);
+
+  for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
+    console.log(`\n    [Page ${pageNum}] --- Attempt ${attempt}/${MAX_ATTEMPTS} ---`);
+
+    // Extract twice in parallel
+    const q1Id = `P${pageNum}A${attempt}Q1`;
+    const q2Id = `P${pageNum}A${attempt}Q2`;
+
+    const [response1, response2] = await Promise.all([
+      queryJson(image, q1Id),
+      queryJson(image, q2Id),
+    ]);
+
+    const txs1 = parseJsonResponse(response1, q1Id);
+    const txs2 = parseJsonResponse(response2, q2Id);
+
+    console.log(`    [Page ${pageNum}] Results: Q1=${txs1.length} txs, Q2=${txs2.length} txs`);
+
+    if (txs1.length > 0 && transactionArraysMatch(txs1, txs2)) {
+      console.log(`    [Page ${pageNum}] ✓ CONSENSUS REACHED: ${txs1.length} transactions`);
+      console.log(`    [Page ${pageNum}] Transactions:`);
+      for (let i = 0; i < txs1.length; i++) {
+        const tx = txs1[i];
+        console.log(`      ${(i + 1).toString().padStart(2)}. ${tx.date} | ${tx.counterparty.substring(0, 30).padEnd(30)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
+      }
+      return txs1;
+    }
+
+    console.log(`    [Page ${pageNum}] ✗ NO CONSENSUS`);
+    compareAndLogDifferences(txs1, txs2, pageNum);
+
+    if (attempt < MAX_ATTEMPTS) {
+      console.log(`    [Page ${pageNum}] Retrying...`);
     }
   }
 
-  // No consensus reached - return the most common result
+  // Fallback: use last response
-  let bestHash = '';
+  console.log(`\n    [Page ${pageNum}] === FALLBACK (no consensus after ${MAX_ATTEMPTS} attempts) ===`);
-  let bestCount = 0;
+  const fallbackId = `P${pageNum}FALLBACK`;
-  for (const [hash, count] of hashCounts) {
+  const fallbackResponse = await queryJson(image, fallbackId);
-    if (count > bestCount) {
+  const fallback = parseJsonResponse(fallbackResponse, fallbackId);
-      bestCount = count;
+  console.log(`    [Page ${pageNum}] ~ FALLBACK RESULT: ${fallback.length} transactions`);
-      bestHash = hash;
+  for (let i = 0; i < fallback.length; i++) {
+    const tx = fallback[i];
+    console.log(`      ${(i + 1).toString().padStart(2)}. ${tx.date} | ${tx.counterparty.substring(0, 30).padEnd(30)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
   }
+  return fallback;
+}
+
+/**
+ * Extract all transactions from bank statement
+ */
+async function extractTransactions(images: string[]): Promise<ITransaction[]> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with ${MODEL} (JSON consensus)`);
+
+  const allTransactions: ITransaction[] = [];
+
+  for (let i = 0; i < images.length; i++) {
+    const pageTransactions = await extractTransactionsFromPage(images[i], i + 1);
+    allTransactions.push(...pageTransactions);
   }
 
-  if (!bestHash) {
+  console.log(`    [Vision] Total: ${allTransactions.length} transactions`);
-    throw new Error('No valid results obtained');
+  return allTransactions;
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`[No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.transactions;
 }
 
 /**
@@ -216,8 +398,9 @@ async function extractWithConsensus(
 function compareTransactions(
   extracted: ITransaction[],
   expected: ITransaction[]
-): { matches: number; total: number; errors: string[] } {
+): { matches: number; total: number; errors: string[]; variations: string[] } {
   const errors: string[] = [];
+  const variations: string[] = [];
   let matches = 0;
 
   for (let i = 0; i < expected.length; i++) {
@@ -234,6 +417,12 @@ function compareTransactions(
 
     if (dateMatch && amountMatch) {
       matches++;
+      // Track counterparty variations (date and amount match but name differs)
+      if (ext.counterparty !== exp.counterparty) {
+        variations.push(
+          `[${i}] "${exp.counterparty}" → "${ext.counterparty}"`
+        );
+      }
     } else {
       errors.push(
         `Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`
@@ -245,7 +434,7 @@ function compareTransactions(
     errors.push(`Extra transactions: ${extracted.length - expected.length}`);
   }
 
-  return { matches, total: expected.length, errors };
+  return { matches, total: expected.length, errors, variations };
 }
 
 /**
@@ -273,62 +462,75 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
     }
   }
 
-  return testCases;
+  return testCases.sort((a, b) => a.name.localeCompare(b.name));
 }
 
 // Tests
 
 tap.test('setup: ensure Docker containers are running', async () => {
   console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure MiniCPM is running
   const minicpmOk = await ensureMiniCpm();
   expect(minicpmOk).toBeTrue();
-
   console.log('\n[Setup] All containers ready!\n');
 });
 
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
+tap.test('should have MiniCPM-V model loaded', async () => {
   const response = await fetch(`${OLLAMA_URL}/api/tags`);
   const data = await response.json();
   const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
+  expect(modelNames.some((name: string) => name.includes('minicpm'))).toBeTrue();
 });
 
-// Dynamic test for each PDF/JSON pair
 const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} bank statement test cases (MiniCPM-V only)\n`);
+console.log(`\nFound ${testCases.length} bank statement test cases (MiniCPM-V)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
 
 for (const testCase of testCases) {
-  tap.test(`should extract transactions from ${testCase.name}`, async () => {
+  tap.test(`should extract: ${testCase.name}`, async () => {
-    // Load expected transactions
     const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
     console.log(`\n=== ${testCase.name} ===`);
     console.log(`Expected: ${expected.length} transactions`);
 
-    // Convert PDF to images
-    console.log('Converting PDF to images...');
     const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`Converted: ${images.length} pages\n`);
+    console.log(`  Pages: ${images.length}`);
 
-    // Extract with consensus (MiniCPM-V only)
+    const extracted = await extractTransactions(images);
-    const extracted = await extractWithConsensus(images);
+    console.log(`  Extracted: ${extracted.length} transactions`);
-    console.log(`\nFinal: ${extracted.length} transactions`);
 
-    // Compare results
     const result = compareTransactions(extracted, expected);
-    console.log(`Accuracy: ${result.matches}/${result.total}`);
+    const perfectMatch = result.matches === result.total && extracted.length === expected.length;
 
-    if (result.errors.length > 0) {
+    if (perfectMatch) {
-      console.log('Errors:');
+      passedCount++;
-      result.errors.forEach((e) => console.log(`  - ${e}`));
+      console.log(`  Result: PASS (${result.matches}/${result.total})`);
+    } else {
+      failedCount++;
+      console.log(`  Result: FAIL (${result.matches}/${result.total})`);
+      result.errors.slice(0, 10).forEach((e) => console.log(`    - ${e}`));
     }
 
-    // Assert high accuracy
+    // Log counterparty variations (names that differ but date/amount matched)
-    const accuracy = result.matches / result.total;
+    if (result.variations.length > 0) {
-    expect(accuracy).toBeGreaterThan(0.95);
+      console.log(`  Counterparty variations (${result.variations.length}):`);
+      result.variations.forEach((v) => console.log(`    ${v}`));
+    }
+
+    expect(result.matches).toEqual(result.total);
     expect(extracted.length).toEqual(expected.length);
   });
 }
 
+tap.test('summary', async () => {
+  const total = testCases.length;
+  console.log(`\n======================================================`);
+  console.log(`   Bank Statement Summary (${MODEL})`);
+  console.log(`======================================================`);
+  console.log(`  Method:   JSON per-page + consensus`);
+  console.log(`  Passed:   ${passedCount}/${total}`);
+  console.log(`  Failed:   ${failedCount}/${total}`);
+  console.log(`======================================================\n`);
+});
+
 export default tap.start();

test/test.bankstatements.paddleocr-vl.ts

@@ -1,346 +0,0 @@
-/**
- * Bank statement extraction test using PaddleOCR-VL Full Pipeline
- *
- * This tests the complete PaddleOCR-VL pipeline for bank statements:
- *   1. PP-DocLayoutV2 for layout detection
- *   2. PaddleOCR-VL for recognition (tables with proper structure)
- *   3. Structured Markdown output with tables
- *   4. MiniCPM extracts transactions from structured tables
- *
- * The structured Markdown has properly formatted tables,
- * making it much easier for MiniCPM to extract transaction data.
- */
-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';
-
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-const OLLAMA_URL = 'http://localhost:11434';
-const MINICPM_MODEL = 'minicpm-v:latest';
-
-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 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
-      { stdio: 'pipe' }
-    );
-
-    const files = fs.readdirSync(tempDir).filter((f: string) => 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 });
-  }
-}
-
-/**
- * Parse document using PaddleOCR-VL Full Pipeline (returns structured Markdown)
- */
-async function parseDocument(imageBase64: string): Promise<string> {
-  const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      image: imageBase64,
-      output_format: 'markdown',
-    }),
-  });
-
-  if (!response.ok) {
-    const text = await response.text();
-    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
-  }
-
-  const data = await response.json();
-
-  if (!data.success) {
-    throw new Error(`PaddleOCR-VL error: ${data.error}`);
-  }
-
-  return data.result?.markdown || '';
-}
-
-/**
- * Extract transactions from structured Markdown using MiniCPM
- */
-async function extractTransactionsFromMarkdown(markdown: string): Promise<ITransaction[]> {
-  console.log(`    [Extract] Processing ${markdown.length} chars of Markdown`);
-
-  const prompt = `/nothink
-Convert this bank statement to a JSON array of transactions.
-
-Read the Amount values carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point, dot = thousands
-
-For each transaction output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Return ONLY the JSON array, no explanation.
-
-Document:
-${markdown}`;
-
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  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.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  // Extract JSON array from response
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error(`No JSON array found in response: ${fullText.substring(0, 200)}`);
-  }
-
-  const jsonStr = fullText.substring(startIdx, endIdx);
-  return JSON.parse(jsonStr);
-}
-
-/**
- * Extract transactions from all pages of a bank statement
- */
-async function extractAllTransactions(images: string[]): Promise<ITransaction[]> {
-  const allTransactions: ITransaction[] = [];
-
-  for (let i = 0; i < images.length; i++) {
-    console.log(`  Processing page ${i + 1}/${images.length}...`);
-
-    // Parse with full pipeline
-    const markdown = await parseDocument(images[i]);
-    console.log(`    [Parse] Got ${markdown.split('\n').length} lines of Markdown`);
-
-    // Extract transactions
-    try {
-      const transactions = await extractTransactionsFromMarkdown(markdown);
-      console.log(`    [Extracted] ${transactions.length} transactions`);
-      allTransactions.push(...transactions);
-    } catch (err) {
-      console.log(`    [Error] ${err}`);
-    }
-  }
-
-  return allTransactions;
-}
-
-/**
- * Compare transactions - find matching transaction in expected list
- */
-function findMatchingTransaction(
-  tx: ITransaction,
-  expectedList: ITransaction[]
-): ITransaction | undefined {
-  return expectedList.find((exp) => {
-    const dateMatch = tx.date === exp.date;
-    const amountMatch = Math.abs(tx.amount - exp.amount) < 0.02;
-    const counterpartyMatch =
-      tx.counterparty?.toLowerCase().includes(exp.counterparty?.toLowerCase().slice(0, 10)) ||
-      exp.counterparty?.toLowerCase().includes(tx.counterparty?.toLowerCase().slice(0, 10));
-    return dateMatch && amountMatch && counterpartyMatch;
-  });
-}
-
-/**
- * Calculate extraction accuracy
- */
-function calculateAccuracy(
-  extracted: ITransaction[],
-  expected: ITransaction[]
-): { matched: number; total: number; accuracy: number } {
-  let matched = 0;
-  const usedExpected = new Set<number>();
-
-  for (const tx of extracted) {
-    for (let i = 0; i < expected.length; i++) {
-      if (usedExpected.has(i)) continue;
-
-      const exp = expected[i];
-      const dateMatch = tx.date === exp.date;
-      const amountMatch = Math.abs(tx.amount - exp.amount) < 0.02;
-
-      if (dateMatch && amountMatch) {
-        matched++;
-        usedExpected.add(i);
-        break;
-      }
-    }
-  }
-
-  return {
-    matched,
-    total: expected.length,
-    accuracy: expected.length > 0 ? (matched / expected.length) * 100 : 0,
-  };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/bankstatements/
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit/bankstatements');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    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),
-      });
-    }
-  }
-
-  testCases.sort((a, b) => a.name.localeCompare(b.name));
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL Full Pipeline is running
-  const paddleOk = await ensurePaddleOcrVlFull();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running (for field extraction from Markdown)
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} bank statement test cases (PaddleOCR-VL Full Pipeline)\n`);
-
-const results: Array<{ name: string; accuracy: number; matched: number; total: number }> = [];
-
-for (const testCase of testCases) {
-  tap.test(`should extract bank statement: ${testCase.name}`, async () => {
-    // Load expected data
-    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
-    console.log(`\n=== ${testCase.name} ===`);
-    console.log(`Expected: ${expected.length} transactions`);
-
-    const startTime = Date.now();
-
-    // Convert PDF to images
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`  Pages: ${images.length}`);
-
-    // Extract all transactions
-    const extracted = await extractAllTransactions(images);
-
-    const endTime = Date.now();
-    const elapsedMs = endTime - startTime;
-
-    // Calculate accuracy
-    const accuracy = calculateAccuracy(extracted, expected);
-    results.push({
-      name: testCase.name,
-      accuracy: accuracy.accuracy,
-      matched: accuracy.matched,
-      total: accuracy.total,
-    });
-
-    console.log(`  Extracted: ${extracted.length} transactions`);
-    console.log(`  Matched: ${accuracy.matched}/${accuracy.total} (${accuracy.accuracy.toFixed(1)}%)`);
-    console.log(`  Time: ${(elapsedMs / 1000).toFixed(1)}s`);
-
-    // We expect at least 50% accuracy
-    expect(accuracy.accuracy).toBeGreaterThan(50);
-  });
-}
-
-tap.test('summary', async () => {
-  const totalStatements = results.length;
-  const avgAccuracy =
-    results.length > 0 ? results.reduce((a, b) => a + b.accuracy, 0) / results.length : 0;
-  const totalMatched = results.reduce((a, b) => a + b.matched, 0);
-  const totalExpected = results.reduce((a, b) => a + b.total, 0);
-
-  console.log(`\n======================================================`);
-  console.log(`  Bank Statement Extraction Summary (PaddleOCR-VL Full)`);
-  console.log(`======================================================`);
-  console.log(`  Method:      PaddleOCR-VL Full Pipeline -> MiniCPM`);
-  console.log(`  Statements:  ${totalStatements}`);
-  console.log(`  Transactions: ${totalMatched}/${totalExpected} matched`);
-  console.log(`  Avg accuracy: ${avgAccuracy.toFixed(1)}%`);
-  console.log(`======================================================\n`);
-});
-
-export default tap.start();

test/test.bankstatements.qwen3vl.ts

@@ -0,0 +1,345 @@
+/**
+ * Bank statement extraction using Qwen3-VL 8B Vision (Direct)
+ *
+ * Multi-query approach:
+ * 1. First ask how many transactions on each page
+ * 2. Then query each transaction individually
+ * Single pass, no consensus voting.
+ */
+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 { ensureMiniCpm } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const VISION_MODEL = 'qwen3-vl:8b';
+
+interface ITransaction {
+  date: string;
+  counterparty: string;
+  amount: number;
+}
+
+/**
+ * Convert PDF to PNG images
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => 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 });
+  }
+}
+
+/**
+ * Query Qwen3-VL with a simple prompt
+ */
+async function queryVision(image: string, prompt: string): Promise<string> {
+  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],
+      }],
+      stream: false,
+      options: {
+        num_predict: 500,
+        temperature: 0.1,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const data = await response.json();
+  return (data.message?.content || '').trim();
+}
+
+/**
+ * Count transactions on a page
+ */
+async function countTransactions(image: string, pageNum: number): Promise<number> {
+  const response = await queryVision(image,
+    `How many transaction rows are in this bank statement table?
+Count only the data rows (with dates like "01.01.2024" and amounts like "- 50,00 €").
+Do NOT count the header row or summary/total rows.
+Answer with just the number, for example: 7`
+  );
+
+  console.log(`    [Page ${pageNum}] Count query response: "${response}"`);
+  const match = response.match(/(\d+)/);
+  const count = match ? parseInt(match[1], 10) : 0;
+  console.log(`    [Page ${pageNum}] Parsed count: ${count}`);
+  return count;
+}
+
+/**
+ * Get a single transaction by index (logs immediately when complete)
+ */
+async function getTransaction(image: string, index: number, pageNum: number): Promise<ITransaction | null> {
+  const response = await queryVision(image,
+    `This is a bank statement. Look at transaction row #${index} in the table (counting from top, excluding headers).
+
+Extract this transaction's details:
+- Date in YYYY-MM-DD format
+- Counterparty/description name
+- Amount as number (negative for debits like "- 21,47 €" = -21.47, positive for credits like "+ 100,00 €" = 100.00)
+
+Answer in format: DATE|COUNTERPARTY|AMOUNT
+Example: 2024-01-15|Amazon|−25.99`
+  );
+
+  // Parse the response
+  const lines = response.split('\n').filter(l => l.includes('|'));
+  const line = lines[lines.length - 1] || response;
+  const parts = line.split('|').map(p => p.trim());
+
+  if (parts.length >= 3) {
+    // Parse amount - handle various formats
+    let amountStr = parts[2].replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
+    // European format: comma is decimal
+    if (amountStr.includes(',')) {
+      amountStr = amountStr.replace(/\./g, '').replace(',', '.');
+    }
+    const amount = parseFloat(amountStr) || 0;
+
+    const tx = {
+      date: parts[0],
+      counterparty: parts[1],
+      amount: amount,
+    };
+    // Log immediately as this transaction completes
+    console.log(`    [P${pageNum} Tx${index.toString().padStart(2, ' ')}] ${tx.date} | ${tx.counterparty.substring(0, 25).padEnd(25)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
+    return tx;
+  }
+
+  // Log raw response on parse failure
+  console.log(`    [P${pageNum} Tx${index.toString().padStart(2, ' ')}] PARSE FAILED: "${response.replace(/\n/g, ' ').substring(0, 60)}..."`);
+  return null;
+}
+
+/**
+ * Extract transactions from a single page using multi-query approach
+ */
+async function extractTransactionsFromPage(image: string, pageNum: number): Promise<ITransaction[]> {
+  // Step 1: Count transactions
+  const count = await countTransactions(image, pageNum);
+
+  if (count === 0) {
+    return [];
+  }
+
+  // Step 2: Query each transaction (in batches to avoid overwhelming)
+  // Each transaction logs itself as it completes
+  const transactions: ITransaction[] = [];
+  const batchSize = 5;
+
+  for (let start = 1; start <= count; start += batchSize) {
+    const end = Math.min(start + batchSize - 1, count);
+    const indices = Array.from({ length: end - start + 1 }, (_, i) => start + i);
+
+    // Query batch in parallel - each logs as it completes
+    const results = await Promise.all(
+      indices.map(i => getTransaction(image, i, pageNum))
+    );
+
+    for (const tx of results) {
+      if (tx) {
+        transactions.push(tx);
+      }
+    }
+  }
+
+  console.log(`    [Page ${pageNum}] Complete: ${transactions.length}/${count} extracted`);
+  return transactions;
+}
+
+/**
+ * Extract all transactions from bank statement
+ */
+async function extractTransactions(images: string[]): Promise<ITransaction[]> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with Qwen3-VL (multi-query)`);
+
+  const allTransactions: ITransaction[] = [];
+
+  for (let i = 0; i < images.length; i++) {
+    const pageTransactions = await extractTransactionsFromPage(images[i], i + 1);
+    allTransactions.push(...pageTransactions);
+  }
+
+  console.log(`    [Vision] Total: ${allTransactions.length} transactions`);
+  return allTransactions;
+}
+
+/**
+ * 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 transaction ${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 at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
+    }
+  }
+
+  if (extracted.length > expected.length) {
+    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
+  }
+
+  return { matches, total: expected.length, errors };
+}
+
+/**
+ * Find test cases in .nogit/
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of files.filter((f: string) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${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));
+}
+
+/**
+ * Ensure Qwen3-VL model is available
+ */
+async function ensureQwen3Vl(): 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 === VISION_MODEL)) {
+        console.log(`[Ollama] Model available: ${VISION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    return false;
+  }
+
+  console.log(`[Ollama] Pulling ${VISION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: VISION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+// Tests
+
+tap.test('setup: ensure Qwen3-VL is running', async () => {
+  console.log('\n[Setup] Checking Qwen3-VL 8B...\n');
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+  const visionOk = await ensureQwen3Vl();
+  expect(visionOk).toBeTrue();
+  console.log('\n[Setup] Ready!\n');
+});
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} bank statement test cases (Qwen3-VL)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
+
+for (const testCase of testCases) {
+  tap.test(`should extract: ${testCase.name}`, async () => {
+    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.length} transactions`);
+
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    const extracted = await extractTransactions(images);
+    console.log(`  Extracted: ${extracted.length} transactions`);
+
+    const result = compareTransactions(extracted, expected);
+    const accuracy = result.total > 0 ? result.matches / result.total : 0;
+
+    if (accuracy >= 0.95 && extracted.length === expected.length) {
+      passedCount++;
+      console.log(`  Result: PASS (${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(accuracy).toBeGreaterThan(0.95);
+    expect(extracted.length).toEqual(expected.length);
+  });
+}
+
+tap.test('summary', async () => {
+  const total = testCases.length;
+  console.log(`\n======================================================`);
+  console.log(`   Bank Statement Summary (Qwen3-VL Vision)`);
+  console.log(`======================================================`);
+  console.log(`  Method:   Multi-query (count then extract each)`);
+  console.log(`  Passed:   ${passedCount}/${total}`);
+  console.log(`  Failed:   ${failedCount}/${total}`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();

test/test.invoices.combined.ts

@@ -1,455 +0,0 @@
-/**
- * Invoice extraction test using MiniCPM-V (visual) + PaddleOCR-VL (OCR augmentation)
- *
- * This is the combined approach that uses both models for best accuracy:
- *   - MiniCPM-V for visual understanding
- *   - PaddleOCR-VL for OCR text to augment prompts
- */
-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 { ensurePaddleOcrVl, ensureMiniCpm } from './helpers/docker.js';
-
-const OLLAMA_URL = 'http://localhost:11434';
-const MODEL = 'minicpm-v:latest';
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-
-interface IInvoice {
-  invoice_number: string;
-  invoice_date: string;
-  vendor_name: string;
-  currency: string;
-  net_amount: number;
-  vat_amount: number;
-  total_amount: number;
-}
-
-/**
- * Extract OCR text from an image using PaddleOCR-VL (OpenAI-compatible API)
- */
-async function extractOcrText(imageBase64: string): Promise<string> {
-  try {
-    const response = await fetch(`${PADDLEOCR_VL_URL}/v1/chat/completions`, {
-      method: 'POST',
-      headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify({
-        model: 'paddleocr-vl',
-        messages: [{
-          role: 'user',
-          content: [
-            { type: 'image_url', image_url: { url: `data:image/png;base64,${imageBase64}` } },
-            { type: 'text', text: 'OCR:' }
-          ]
-        }],
-        temperature: 0.0,
-        max_tokens: 4096
-      }),
-    });
-
-    if (!response.ok) return '';
-
-    const data = await response.json();
-    return data.choices?.[0]?.message?.content || '';
-  } catch {
-    // PaddleOCR-VL unavailable
-  }
-  return '';
-}
-
-/**
- * Build prompt with optional OCR text
- */
-function buildPrompt(ocrText: string): string {
-  const base = `/nothink
-You are an invoice parser. Extract the following fields from this invoice:
-
-1. invoice_number: The invoice/receipt number
-2. invoice_date: Date in YYYY-MM-DD format
-3. vendor_name: Company that issued the invoice
-4. currency: EUR, USD, etc.
-5. net_amount: Amount before tax (if shown)
-6. vat_amount: Tax/VAT amount (if shown, 0 if reverse charge or no tax)
-7. total_amount: Final amount due
-
-Return ONLY valid JSON in this exact format:
-{"invoice_number":"XXX","invoice_date":"YYYY-MM-DD","vendor_name":"Company Name","currency":"EUR","net_amount":100.00,"vat_amount":19.00,"total_amount":119.00}
-
-If a field is not visible, use null for strings or 0 for numbers.
-No explanation, just the JSON object.`;
-
-  if (ocrText) {
-    // Limit OCR text to prevent context overflow
-    const maxOcrLength = 4000;
-    const truncatedOcr = ocrText.length > maxOcrLength
-      ? ocrText.substring(0, maxOcrLength) + '\n... (truncated)'
-      : ocrText;
-
-    return `${base}
-
-OCR text extracted from the invoice (use for reference):
----
-${truncatedOcr}
----
-
-Cross-reference the image with the OCR text above for accuracy.`;
-  }
-  return base;
-}
-
-/**
- * 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 });
-  }
-}
-
-/**
- * Single extraction pass
- */
-async function extractOnce(images: string[], passNum: number, ocrText: string = ''): Promise<IInvoice> {
-  const payload = {
-    model: MODEL,
-    prompt: buildPrompt(ocrText),
-    images,
-    stream: true,
-    options: {
-      num_predict: 2048,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  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.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  // Extract JSON from response
-  const startIdx = fullText.indexOf('{');
-  const endIdx = fullText.lastIndexOf('}') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error(`No JSON object found in response: ${fullText.substring(0, 200)}`);
-  }
-
-  const jsonStr = fullText.substring(startIdx, endIdx);
-  return JSON.parse(jsonStr);
-}
-
-/**
- * Create a hash of invoice for comparison (using key fields)
- */
-function hashInvoice(invoice: IInvoice): string {
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
-}
-
-/**
- * Extract with majority voting - run until 2 passes match
- * Optimization: Run Pass 1, OCR, and Pass 2 (after OCR) in parallel
- */
-async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
-  const results: Array<{ invoice: IInvoice; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
-
-  const addResult = (invoice: IInvoice, passLabel: string): number => {
-    const hash = hashInvoice(invoice);
-    results.push({ invoice, hash });
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
-    return hashCounts.get(hash)!;
-  };
-
-  // OPTIMIZATION: Run Pass 1 (no OCR) in parallel with OCR -> Pass 2 (with OCR)
-  let ocrText = '';
-  const pass1Promise = extractOnce(images, 1, '').catch((err) => ({ error: err }));
-
-  // OCR then immediately Pass 2
-  const ocrThenPass2Promise = (async () => {
-    ocrText = await extractOcrText(images[0]);
-    if (ocrText) {
-      console.log(`  [OCR] Extracted ${ocrText.split('\n').length} text lines`);
-    }
-    return extractOnce(images, 2, ocrText).catch((err) => ({ error: err }));
-  })();
-
-  // Wait for both to complete
-  const [pass1Result, pass2Result] = await Promise.all([pass1Promise, ocrThenPass2Promise]);
-
-  // Process Pass 1 result
-  if ('error' in pass1Result) {
-    console.log(`  [Pass 1] Error: ${(pass1Result as {error: unknown}).error}`);
-  } else {
-    const count = addResult(pass1Result as IInvoice, 'Pass 1');
-    if (count >= 2) {
-      console.log(`  [Consensus] Reached after parallel passes`);
-      return pass1Result as IInvoice;
-    }
-  }
-
-  // Process Pass 2 result
-  if ('error' in pass2Result) {
-    console.log(`  [Pass 2+OCR] Error: ${(pass2Result as {error: unknown}).error}`);
-  } else {
-    const count = addResult(pass2Result as IInvoice, 'Pass 2+OCR');
-    if (count >= 2) {
-      console.log(`  [Consensus] Reached after parallel passes`);
-      return pass2Result as IInvoice;
-    }
-  }
-
-  // Continue with passes 3+ using OCR text if no consensus yet
-  for (let pass = 3; pass <= maxPasses; pass++) {
-    try {
-      const invoice = await extractOnce(images, pass, ocrText);
-      const count = addResult(invoice, `Pass ${pass}+OCR`);
-
-      if (count >= 2) {
-        console.log(`  [Consensus] Reached after ${pass} passes`);
-        return invoice;
-      }
-    } catch (err) {
-      console.log(`  [Pass ${pass}] Error: ${err}`);
-    }
-  }
-
-  // No consensus reached - return the 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 ${invoiceName}`);
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.invoice;
-}
-
-/**
- * Compare extracted invoice against expected
- */
-function compareInvoice(
-  extracted: IInvoice,
-  expected: IInvoice
-): { match: boolean; errors: string[] } {
-  const errors: string[] = [];
-
-  // Compare invoice number (normalize by removing spaces and case)
-  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
-  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
-  if (extNum !== expNum) {
-    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
-  }
-
-  // Compare date
-  if (extracted.invoice_date !== expected.invoice_date) {
-    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
-  }
-
-  // Compare total amount (with tolerance)
-  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
-    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
-  }
-
-  // Compare currency
-  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
-    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
-  }
-
-  return { match: errors.length === 0, errors };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/invoices/
- * Priority invoices (like vodafone) run first for quick feedback
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit/invoices');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  // Sort with priority invoices first, then alphabetically
-  const priorityPrefixes = ['vodafone'];
-  testCases.sort((a, b) => {
-    const aPriority = priorityPrefixes.findIndex((p) => a.name.startsWith(p));
-    const bPriority = priorityPrefixes.findIndex((p) => b.name.startsWith(p));
-
-    // Both have priority - sort by priority order
-    if (aPriority >= 0 && bPriority >= 0) return aPriority - bPriority;
-    // Only a has priority - a comes first
-    if (aPriority >= 0) return -1;
-    // Only b has priority - b comes first
-    if (bPriority >= 0) return 1;
-    // Neither has priority - alphabetical
-    return a.name.localeCompare(b.name);
-  });
-
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL is running (auto-detects GPU/CPU)
-  const paddleOk = await ensurePaddleOcrVl();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
-  const response = await fetch(`${OLLAMA_URL}/api/tags`);
-  const data = await response.json();
-  const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} invoice test cases\n`);
-
-let passedCount = 0;
-let failedCount = 0;
-const processingTimes: number[] = [];
-
-for (const testCase of testCases) {
-  tap.test(`should extract invoice: ${testCase.name}`, async () => {
-    // Load expected data
-    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 startTime = Date.now();
-
-    // Convert PDF to images
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`  Pages: ${images.length}`);
-
-    // Extract with consensus voting
-    const extracted = await extractWithConsensus(images, testCase.name);
-
-    const endTime = Date.now();
-    const elapsedMs = endTime - startTime;
-    processingTimes.push(elapsedMs);
-
-    // Compare results
-    const result = compareInvoice(extracted, expected);
-
-    if (result.match) {
-      passedCount++;
-      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-    } else {
-      failedCount++;
-      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-      result.errors.forEach((e) => console.log(`    - ${e}`));
-    }
-
-    // Assert match
-    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 avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
-  const avgTimeSec = avgTimeMs / 1000;
-  const totalTimeSec = totalTimeMs / 1000;
-
-  console.log(`\n========================================`);
-  console.log(`        Invoice Extraction Summary`);
-  console.log(`========================================`);
-  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
-  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
-  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
-  console.log(`----------------------------------------`);
-  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
-  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
-  console.log(`========================================\n`);
-});
-
-export default tap.start();

test/test.invoices.minicpm.ts

@@ -1,8 +1,10 @@
 /**
- * Invoice extraction test using MiniCPM-V only (visual extraction)
+ * Invoice extraction test using MiniCPM-V (visual extraction)
  *
- * This tests MiniCPM-V's ability to extract invoice data directly from images
+ * Consensus approach:
- * without any OCR augmentation.
+ * 1. Pass 1: Fast JSON extraction
+ * 2. Pass 2: Confirm with thinking enabled
+ * 3. If mismatch: repeat until consensus or max attempts
  */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
@@ -12,7 +14,7 @@ import * as os from 'os';
 import { ensureMiniCpm } from './helpers/docker.js';
 
 const OLLAMA_URL = 'http://localhost:11434';
-const MODEL = 'minicpm-v:latest';
+const MODEL = 'openbmb/minicpm-v4.5:q8_0';
 
 interface IInvoice {
   invoice_number: string;
@@ -24,28 +26,6 @@ interface IInvoice {
   total_amount: number;
 }
 
-/**
- * Build extraction prompt (MiniCPM-V only, no OCR augmentation)
- */
-function buildPrompt(): string {
-  return `/nothink
-You are an invoice parser. Extract the following fields from this invoice:
-
-1. invoice_number: The invoice/receipt number
-2. invoice_date: Date in YYYY-MM-DD format
-3. vendor_name: Company that issued the invoice
-4. currency: EUR, USD, etc.
-5. net_amount: Amount before tax (if shown)
-6. vat_amount: Tax/VAT amount (if shown, 0 if reverse charge or no tax)
-7. total_amount: Final amount due
-
-Return ONLY valid JSON in this exact format:
-{"invoice_number":"XXX","invoice_date":"YYYY-MM-DD","vendor_name":"Company Name","currency":"EUR","net_amount":100.00,"vat_amount":19.00,"total_amount":119.00}
-
-If a field is not visible, use null for strings or 0 for numbers.
-No explanation, just the JSON object.`;
-}
-
 /**
  * Convert PDF to PNG images using ImageMagick
  */
@@ -55,7 +35,7 @@ function convertPdfToImages(pdfPath: string): string[] {
 
   try {
     execSync(
-      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      `convert -density 300 -quality 95 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
       { stdio: 'pipe' }
     );
 
@@ -74,123 +54,288 @@ function convertPdfToImages(pdfPath: string): string[] {
   }
 }
 
-/**
+const JSON_PROMPT = `Extract invoice data from this image. Return ONLY a JSON object with these exact fields:
- * Single extraction pass with MiniCPM-V
+{
- */
+  "invoice_number": "the invoice number (not VAT ID, not customer ID)",
-async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
+  "invoice_date": "YYYY-MM-DD format",
-  const payload = {
+  "vendor_name": "company that issued the invoice",
-    model: MODEL,
+  "currency": "EUR, USD, or GBP",
-    prompt: buildPrompt(),
+  "net_amount": 0.00,
-    images,
+  "vat_amount": 0.00,
-    stream: true,
+  "total_amount": 0.00
-    options: {
+}
-      num_predict: 2048,
+Return only the JSON, no explanation.`;
-      temperature: 0.1,
-    },
-  };
 
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+/**
+ * Query MiniCPM-V for JSON output (fast, no thinking)
+ */
+async function queryJsonFast(images: string[]): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
+    body: JSON.stringify({
+      model: MODEL,
+      messages: [{
+        role: 'user',
+        content: JSON_PROMPT,
+        images: images,
+      }],
+      stream: false,
+      options: {
+        num_predict: 1000,
+        temperature: 0.1,
+      },
+    }),
   });
 
   if (!response.ok) {
     throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const reader = response.body?.getReader();
+  const data = await response.json();
-  if (!reader) {
+  return (data.message?.content || '').trim();
-    throw new Error('No response body');
+}
+
+/**
+ * Query MiniCPM-V for JSON output with thinking enabled (slower, more accurate)
+ */
+async function queryJsonWithThinking(images: string[]): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: MODEL,
+      messages: [{
+        role: 'user',
+        content: `Think carefully about this invoice image, then ${JSON_PROMPT}`,
+        images: images,
+      }],
+      stream: false,
+      options: {
+        num_predict: 2000,
+        temperature: 0.1,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const decoder = new TextDecoder();
+  const data = await response.json();
-  let fullText = '';
+  return (data.message?.content || '').trim();
+}
 
-  while (true) {
+/**
-    const { done, value } = await reader.read();
+ * Parse amount from string (handles European format)
-    if (done) break;
+ */
+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];
+  // Handle European format: 1.234,56 → 1234.56
+  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+    ? numStr.replace(/\./g, '').replace(',', '.')
+    : numStr.replace(/,/g, '');
+  return parseFloat(normalized) || 0;
+}
 
-    const chunk = decoder.decode(value, { stream: true });
+/**
-    const lines = chunk.split('\n').filter((l) => l.trim());
+ * 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,           // IEE2022006460244
+    /\b([A-Z]\d{8,})\b/i,                  // R0014359508
+    /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,     // INV-2024-001
+    /\b(\d{7,})\b/,                        // 1579087430
+  ];
+  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];
+  // Try DD/MM/YYYY or DD.MM.YYYY
+  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
+  if (dmyMatch) {
+    return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
+  }
+  return clean.replace(/[^\d-]/g, '').trim();
+}
+
+/**
+ * Extract currency
+ */
+function extractCurrency(s: string | undefined): string {
+  if (!s) return 'EUR';
+  const upper = s.toUpperCase();
+  if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
+  if (upper.includes('USD') || upper.includes('$')) return 'USD';
+  if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
+  return 'EUR';
+}
+
+/**
+ * Extract JSON from response (handles markdown code blocks)
+ */
+function extractJsonFromResponse(response: string): Record<string, unknown> | null {
+  // Try to find JSON in markdown code block
+  const codeBlockMatch = response.match(/```(?:json)?\s*([\s\S]*?)```/);
+  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : response.trim();
 
-    for (const line of lines) {
   try {
-        const json = JSON.parse(line);
-        if (json.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  // Extract JSON from response
-  const startIdx = fullText.indexOf('{');
-  const endIdx = fullText.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);
     return JSON.parse(jsonStr);
+  } catch {
+    // Try to find JSON object pattern
+    const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
+    if (jsonMatch) {
+      try {
+        return JSON.parse(jsonMatch[0]);
+      } catch {
+        return null;
+      }
+    }
+    return null;
+  }
 }
 
 /**
- * Create a hash of invoice for comparison (using key fields)
+ * Parse JSON response into IInvoice
  */
-function hashInvoice(invoice: IInvoice): string {
+function parseJsonToInvoice(response: string): IInvoice | null {
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
+  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 with consensus voting using MiniCPM-V only
+ * Compare two invoices for consensus (key fields must match)
  */
-async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
+function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
-  const results: Array<{ invoice: IInvoice; hash: string }> = [];
+  const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
-  const hashCounts: Map<string, number> = new Map();
+  const dateMatch = a.invoice_date === b.invoice_date;
+  const totalMatch = Math.abs(a.total_amount - b.total_amount) < 0.02;
+  return numMatch && dateMatch && totalMatch;
+}
 
-  const addResult = (invoice: IInvoice, passLabel: string): number => {
+/**
-    const hash = hashInvoice(invoice);
+ * Extract invoice data using consensus approach:
-    results.push({ invoice, hash });
+ * 1. Pass 1: Fast JSON extraction
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+ * 2. Pass 2: Confirm with thinking enabled
-    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
+ * 3. If mismatch: repeat until consensus or max 5 attempts
-    return hashCounts.get(hash)!;
+ */
+async function extractInvoiceFromImages(images: string[]): Promise<IInvoice> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with ${MODEL} (consensus)`);
+
+  const MAX_ATTEMPTS = 5;
+  let attempt = 0;
+
+  while (attempt < MAX_ATTEMPTS) {
+    attempt++;
+    console.log(`    [Attempt ${attempt}/${MAX_ATTEMPTS}]`);
+
+    // PASS 1: Fast JSON extraction
+    console.log(`    [Pass 1] Fast extraction...`);
+    const fastResponse = await queryJsonFast(images);
+    const fastInvoice = parseJsonToInvoice(fastResponse);
+
+    if (!fastInvoice) {
+      console.log(`    [Pass 1] JSON parsing failed, retrying...`);
+      continue;
+    }
+    console.log(`    [Pass 1] Result: ${fastInvoice.invoice_number} | ${fastInvoice.invoice_date} | ${fastInvoice.total_amount} ${fastInvoice.currency}`);
+
+    // PASS 2: Confirm with thinking
+    console.log(`    [Pass 2] Thinking confirmation...`);
+    const thinkResponse = await queryJsonWithThinking(images);
+    const thinkInvoice = parseJsonToInvoice(thinkResponse);
+
+    if (!thinkInvoice) {
+      console.log(`    [Pass 2] JSON parsing failed, retrying...`);
+      continue;
+    }
+    console.log(`    [Pass 2] Result: ${thinkInvoice.invoice_number} | ${thinkInvoice.invoice_date} | ${thinkInvoice.total_amount} ${thinkInvoice.currency}`);
+
+    // Check consensus
+    if (invoicesMatch(fastInvoice, thinkInvoice)) {
+      console.log(`    [Consensus] MATCH - using result`);
+      return thinkInvoice; // Prefer thinking result
+    }
+
+    console.log(`    [Consensus] MISMATCH - repeating...`);
+    console.log(`      Fast:  ${fastInvoice.invoice_number} | ${fastInvoice.invoice_date} | ${fastInvoice.total_amount}`);
+    console.log(`      Think: ${thinkInvoice.invoice_number} | ${thinkInvoice.invoice_date} | ${thinkInvoice.total_amount}`);
+  }
+
+  // Max attempts reached - do one final thinking pass and use that
+  console.log(`    [Final] Max attempts reached, using final thinking pass`);
+  const finalResponse = await queryJsonWithThinking(images);
+  const finalInvoice = parseJsonToInvoice(finalResponse);
+
+  if (finalInvoice) {
+    console.log(`    [Final] Result: ${finalInvoice.invoice_number} | ${finalInvoice.invoice_date} | ${finalInvoice.total_amount} ${finalInvoice.currency}`);
+    return finalInvoice;
+  }
+
+  // Return empty invoice if all else fails
+  console.log(`    [Final] All parsing failed, returning empty`);
+  return {
+    invoice_number: '',
+    invoice_date: '',
+    vendor_name: '',
+    currency: 'EUR',
+    net_amount: 0,
+    vat_amount: 0,
+    total_amount: 0,
+  };
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.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',
   };
 
-  for (let pass = 1; pass <= maxPasses; pass++) {
+  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
-    try {
+  if (match) {
-      const invoice = await extractOnce(images, pass);
+    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
-      const count = addResult(invoice, `Pass ${pass}`);
-
-      if (count >= 2) {
-        console.log(`  [Consensus] Reached after ${pass} passes`);
-        return invoice;
-      }
-    } catch (err) {
-      console.log(`  [Pass ${pass}] Error: ${err}`);
-    }
   }
 
-  // No consensus reached - return the most common result
+  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
-  let bestHash = '';
+  if (match) {
-  let bestCount = 0;
+    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
-  for (const [hash, count] of hashCounts) {
-    if (count > bestCount) {
-      bestCount = count;
-      bestHash = hash;
-    }
   }
 
-  if (!bestHash) {
+  return dateStr;
-    throw new Error(`No valid results for ${invoiceName}`);
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.invoice;
 }
 
 /**
@@ -210,7 +355,7 @@ function compareInvoice(
   }
 
   // Compare date
-  if (extracted.invoice_date !== expected.invoice_date) {
+  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
     errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
   }
 
@@ -252,9 +397,7 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
     }
   }
 
-  // Sort alphabetically
   testCases.sort((a, b) => a.name.localeCompare(b.name));
-
   return testCases;
 }
 
@@ -262,24 +405,20 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
 
 tap.test('setup: ensure Docker containers are running', async () => {
   console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure MiniCPM is running
   const minicpmOk = await ensureMiniCpm();
   expect(minicpmOk).toBeTrue();
-
   console.log('\n[Setup] All containers ready!\n');
 });
 
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
+tap.test('should have MiniCPM-V model loaded', async () => {
   const response = await fetch(`${OLLAMA_URL}/api/tags`);
   const data = await response.json();
   const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
+  expect(modelNames.some((name: string) => name.includes('minicpm'))).toBeTrue();
 });
 
-// Dynamic test for each PDF/JSON pair
 const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} invoice test cases (MiniCPM-V only)\n`);
+console.log(`\nFound ${testCases.length} invoice test cases (MiniCPM-V)\n`);
 
 let passedCount = 0;
 let failedCount = 0;
@@ -287,25 +426,20 @@ const processingTimes: number[] = [];
 
 for (const testCase of testCases) {
   tap.test(`should extract invoice: ${testCase.name}`, async () => {
-    // Load expected data
     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 startTime = Date.now();
-
-    // Convert PDF to images
     const images = convertPdfToImages(testCase.pdfPath);
     console.log(`  Pages: ${images.length}`);
 
-    // Extract with consensus voting (MiniCPM-V only)
+    const extracted = await extractInvoiceFromImages(images);
-    const extracted = await extractWithConsensus(images, testCase.name);
+    console.log(`  Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
 
-    const endTime = Date.now();
+    const elapsedMs = Date.now() - startTime;
-    const elapsedMs = endTime - startTime;
     processingTimes.push(elapsedMs);
 
-    // Compare results
     const result = compareInvoice(extracted, expected);
 
     if (result.match) {
@@ -317,7 +451,6 @@ for (const testCase of testCases) {
       result.errors.forEach((e) => console.log(`    - ${e}`));
     }
 
-    // Assert match
     expect(result.match).toBeTrue();
   });
 }
@@ -326,18 +459,17 @@ 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 avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
+  const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
-  const avgTimeSec = avgTimeMs / 1000;
-  const totalTimeSec = totalTimeMs / 1000;
 
   console.log(`\n========================================`);
-  console.log(`   Invoice Extraction Summary (MiniCPM)`);
+  console.log(`   Invoice Extraction Summary (${MODEL})`);
   console.log(`========================================`);
+  console.log(`  Method:    Consensus (fast + thinking)`);
   console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
   console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
   console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
   console.log(`----------------------------------------`);
-  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
+  console.log(`  Total time:   ${(totalTimeMs / 1000).toFixed(1)}s`);
   console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
   console.log(`========================================\n`);
 });

test/test.invoices.paddleocr-vl.ts

@@ -1,393 +0,0 @@
-/**
- * Invoice extraction test using PaddleOCR-VL Full Pipeline
- *
- * This tests the complete PaddleOCR-VL pipeline:
- *   1. PP-DocLayoutV2 for layout detection
- *   2. PaddleOCR-VL for recognition
- *   3. Structured Markdown output
- *   4. MiniCPM extracts invoice fields from structured Markdown
- *
- * The structured Markdown has proper tables and formatting,
- * making it much easier for MiniCPM to extract invoice data.
- */
-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';
-
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-const OLLAMA_URL = 'http://localhost:11434';
-const MINICPM_MODEL = 'minicpm-v:latest';
-
-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 });
-  }
-}
-
-/**
- * Parse document using PaddleOCR-VL Full Pipeline (returns structured Markdown)
- */
-async function parseDocument(imageBase64: string): Promise<string> {
-  const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      image: imageBase64,
-      output_format: 'markdown',
-    }),
-  });
-
-  if (!response.ok) {
-    const text = await response.text();
-    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
-  }
-
-  const data = await response.json();
-
-  if (!data.success) {
-    throw new Error(`PaddleOCR-VL error: ${data.error}`);
-  }
-
-  return data.result?.markdown || '';
-}
-
-/**
- * Extract invoice fields from structured Markdown using MiniCPM with image context
- */
-async function extractInvoiceFromMarkdown(markdown: string, images: string[]): Promise<IInvoice> {
-  // Truncate if too long
-  const truncated = markdown.length > 8000 ? markdown.slice(0, 8000) : markdown;
-  console.log(`    [Extract] Processing ${truncated.length} chars of Markdown`);
-
-  const prompt = `/nothink
-You are an invoice parser. Extract fields from this invoice image.
-
-Required fields:
-- invoice_number: The invoice/receipt number
-- invoice_date: Date in YYYY-MM-DD format
-- vendor_name: Company that issued the invoice
-- currency: EUR, USD, etc.
-- net_amount: Amount before tax
-- vat_amount: Tax/VAT amount (0 if reverse charge)
-- total_amount: Final amount due
-
-Return ONLY a JSON object like:
-{"invoice_number":"123","invoice_date":"2022-01-28","vendor_name":"Adobe","currency":"EUR","net_amount":24.99,"vat_amount":0,"total_amount":24.99}
-
-Use null for missing strings, 0 for missing numbers. No explanation.
-
-OCR text from the invoice (for reference):
----
-${truncated}
----`;
-
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt,
-    images,  // Send the actual image to MiniCPM
-    stream: true,
-    options: {
-      num_predict: 2048,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  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.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  // Extract JSON from response
-  const startIdx = fullText.indexOf('{');
-  const endIdx = fullText.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);
-  return JSON.parse(jsonStr);
-}
-
-/**
- * Single extraction pass: Parse with PaddleOCR-VL Full, extract with MiniCPM
- */
-async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
-  // Parse document with full pipeline
-  const markdown = await parseDocument(images[0]);
-  console.log(`    [Parse] Got ${markdown.split('\n').length} lines of Markdown`);
-
-  // Extract invoice fields from Markdown with image context
-  return extractInvoiceFromMarkdown(markdown, images);
-}
-
-/**
- * Create a hash of invoice for comparison (using key fields)
- */
-function hashInvoice(invoice: IInvoice): string {
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
-}
-
-/**
- * Extract with consensus voting
- */
-async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
-  const results: Array<{ invoice: IInvoice; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
-
-  const addResult = (invoice: IInvoice, passLabel: string): number => {
-    const hash = hashInvoice(invoice);
-    results.push({ invoice, hash });
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
-    return hashCounts.get(hash)!;
-  };
-
-  for (let pass = 1; pass <= maxPasses; pass++) {
-    try {
-      const invoice = await extractOnce(images, pass);
-      const count = addResult(invoice, `Pass ${pass}`);
-
-      if (count >= 2) {
-        console.log(`  [Consensus] Reached after ${pass} passes`);
-        return invoice;
-      }
-    } catch (err) {
-      console.log(`  [Pass ${pass}] Error: ${err}`);
-    }
-  }
-
-  // No consensus reached - return the 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 ${invoiceName}`);
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.invoice;
-}
-
-/**
- * Compare extracted invoice against expected
- */
-function compareInvoice(
-  extracted: IInvoice,
-  expected: IInvoice
-): { match: boolean; errors: string[] } {
-  const errors: string[] = [];
-
-  // Compare invoice number (normalize by removing spaces and case)
-  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
-  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
-  if (extNum !== expNum) {
-    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
-  }
-
-  // Compare date
-  if (extracted.invoice_date !== expected.invoice_date) {
-    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
-  }
-
-  // Compare total amount (with tolerance)
-  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
-    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
-  }
-
-  // Compare currency
-  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
-    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
-  }
-
-  return { match: errors.length === 0, errors };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/invoices/
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit/invoices');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  // Sort alphabetically
-  testCases.sort((a, b) => a.name.localeCompare(b.name));
-
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL Full Pipeline is running
-  const paddleOk = await ensurePaddleOcrVlFull();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running (for field extraction from Markdown)
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} invoice test cases (PaddleOCR-VL Full Pipeline)\n`);
-
-let passedCount = 0;
-let failedCount = 0;
-const processingTimes: number[] = [];
-
-for (const testCase of testCases) {
-  tap.test(`should extract invoice: ${testCase.name}`, async () => {
-    // Load expected data
-    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 startTime = Date.now();
-
-    // Convert PDF to images
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`  Pages: ${images.length}`);
-
-    // Extract with consensus voting (PaddleOCR-VL Full -> MiniCPM)
-    const extracted = await extractWithConsensus(images, testCase.name);
-
-    const endTime = Date.now();
-    const elapsedMs = endTime - startTime;
-    processingTimes.push(elapsedMs);
-
-    // Compare results
-    const result = compareInvoice(extracted, expected);
-
-    if (result.match) {
-      passedCount++;
-      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-    } else {
-      failedCount++;
-      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-      result.errors.forEach((e) => console.log(`    - ${e}`));
-    }
-
-    // Assert match
-    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 avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
-  const avgTimeSec = avgTimeMs / 1000;
-  const totalTimeSec = totalTimeMs / 1000;
-
-  console.log(`\n======================================================`);
-  console.log(`   Invoice Extraction Summary (PaddleOCR-VL Full)`);
-  console.log(`======================================================`);
-  console.log(`  Method:    PaddleOCR-VL Full Pipeline -> MiniCPM`);
-  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
-  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
-  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
-  console.log(`------------------------------------------------------`);
-  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
-  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
-  console.log(`======================================================\n`);
-});
-
-export default tap.start();

test/test.invoices.qwen3vl.ts

@@ -0,0 +1,351 @@
+/**
+ * Invoice extraction using Qwen3-VL 8B Vision (Direct)
+ *
+ * Multi-query approach: 5 parallel simple queries to avoid token exhaustion.
+ * Single pass, no consensus voting.
+ */
+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 { ensureMiniCpm } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const VISION_MODEL = 'qwen3-vl: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 {
+    // 150 DPI is sufficient for invoice extraction, reduces context size
+    execSync(
+      `convert -density 150 -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 });
+  }
+}
+
+/**
+ * Query Qwen3-VL for a single field
+ * Uses simple prompts to minimize thinking tokens
+ */
+async function queryField(images: string[], question: string): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: VISION_MODEL,
+      messages: [{
+        role: 'user',
+        content: `${question} Reply with just the value, nothing else.`,
+        images: images,
+      }],
+      stream: false,
+      options: {
+        num_predict: 500,
+        temperature: 0.1,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const data = await response.json();
+  return (data.message?.content || '').trim();
+}
+
+/**
+ * Extract invoice data using multiple simple queries
+ * Each query asks for 1-2 fields to minimize thinking tokens
+ * (Qwen3's thinking mode uses all tokens on complex prompts)
+ */
+async function extractInvoiceFromImages(images: string[]): Promise<IInvoice> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with Qwen3-VL (multi-query)`);
+
+  // Query each field separately to avoid excessive thinking tokens
+  // Use explicit questions to avoid confusion between similar fields
+  // Log each result as it comes in (not waiting for all to complete)
+  const queryAndLog = async (name: string, question: string): Promise<string> => {
+    const result = await queryField(images, question);
+    console.log(`    [Query] ${name}: "${result}"`);
+    return result;
+  };
+
+  const [invoiceNum, invoiceDate, vendor, currency, totalAmount, netAmount, vatAmount] = await Promise.all([
+    queryAndLog('Invoice Number', 'What is the INVOICE NUMBER (not VAT number, not customer ID)? Look for "Invoice No", "Invoice #", "Rechnung Nr", "Facture". Just the number/code.'),
+    queryAndLog('Invoice Date  ', 'What is the INVOICE DATE (not due date, not delivery date)? The date the invoice was issued. Format: YYYY-MM-DD'),
+    queryAndLog('Vendor        ', 'What company ISSUED this invoice (the seller/vendor, not the buyer)? Look at the letterhead or "From" section.'),
+    queryAndLog('Currency      ', 'What CURRENCY is used? Look for € (EUR), $ (USD), or £ (GBP). Answer with 3-letter code: EUR, USD, or GBP'),
+    queryAndLog('Total Amount  ', 'What is the TOTAL AMOUNT INCLUDING TAX (the final amount to pay, with VAT/tax included)? Just the number, e.g. 24.99'),
+    queryAndLog('Net Amount    ', 'What is the NET AMOUNT (subtotal before VAT/tax)? Just the number, e.g. 20.99'),
+    queryAndLog('VAT Amount    ', 'What is the VAT/TAX AMOUNT? Just the number, e.g. 4.00'),
+  ]);
+
+  // Parse amount from string (handles European format)
+  const parseAmount = (s: string): number => {
+    if (!s) return 0;
+    // Extract number from the response
+    const match = s.match(/([\d.,]+)/);
+    if (!match) return 0;
+    const numStr = match[1];
+    // Handle European format: 1.234,56 → 1234.56
+    const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+      ? numStr.replace(/\./g, '').replace(',', '.')
+      : numStr.replace(/,/g, '');
+    return parseFloat(normalized) || 0;
+  };
+
+  // Extract invoice number from potentially verbose response
+  const extractInvoiceNumber = (s: string): string => {
+    let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+    // Look for common invoice number patterns
+    const patterns = [
+      /\b([A-Z]{2,3}\d{10,})\b/i,           // IEE2022006460244
+      /\b([A-Z]\d{8,})\b/i,                  // R0014359508
+      /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,     // INV-2024-001
+      /\b(\d{7,})\b/,                        // 1579087430
+    ];
+    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
+  const extractDate = (s: string): string => {
+    let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+    const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
+    if (isoMatch) return isoMatch[1];
+    return clean.replace(/[^\d-]/g, '').trim();
+  };
+
+  // Extract currency
+  const extractCurrency = (s: string): string => {
+    const upper = s.toUpperCase();
+    if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
+    if (upper.includes('USD') || upper.includes('$')) return 'USD';
+    if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
+    return 'EUR';
+  };
+
+  return {
+    invoice_number: extractInvoiceNumber(invoiceNum),
+    invoice_date: extractDate(invoiceDate),
+    vendor_name: vendor.replace(/\*\*/g, '').replace(/`/g, '').trim() || '',
+    currency: extractCurrency(currency),
+    net_amount: parseAmount(netAmount),
+    vat_amount: parseAmount(vatAmount),
+    total_amount: parseAmount(totalAmount),
+  };
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.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));
+}
+
+/**
+ * Ensure Qwen3-VL 8B model is available
+ */
+async function ensureQwen3Vl(): 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 === VISION_MODEL)) {
+        console.log(`[Ollama] Model already available: ${VISION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    console.log('[Ollama] Cannot check models');
+    return false;
+  }
+
+  console.log(`[Ollama] Pulling model: ${VISION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: VISION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+// Tests
+
+tap.test('setup: ensure Qwen3-VL is running', async () => {
+  console.log('\n[Setup] Checking Qwen3-VL 8B...\n');
+
+  // Ensure Ollama service is running
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+
+  // Ensure Qwen3-VL 8B model
+  const visionOk = await ensureQwen3Vl();
+  expect(visionOk).toBeTrue();
+
+  console.log('\n[Setup] Ready!\n');
+});
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} invoice test cases (Qwen3-VL Vision)\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 extractInvoiceFromImages(images);
+    console.log(`  Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
+    const elapsed = Date.now() - start;
+    times.push(elapsed);
+
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`  Result: MATCH (${(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 (Qwen3-VL Vision)`);
+  console.log(`======================================================`);
+  console.log(`  Method:    Multi-query (single pass)`);
+  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();