v1.13.2

fix(tests): stabilize OCR extraction tests and manage GPU containers
update
2026-01-18 23:00:24 +00:00 · 2026-01-18 23:00:24 +00:00 · 2026-01-18 15:54:16 +00:00 · 2026-01-18 13:58:26 +00:00 · 2026-01-18 13:58:26 +00:00 · 2026-01-18 13:56:46 +00:00
25 changed files with 3760 additions and 1710 deletions
--- a/27
+++ b/27
@@ -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"]
--- a/2
+++ b/2
@@ -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
--- a/33
+++ b/33
@@ -0,0 +1,33 @@
 # Nanonets-OCR-s Vision Language Model
 # Based on Qwen2.5-VL-3B, fine-tuned for document OCR
 # ~8-10GB VRAM, outputs structured markdown with semantic tags
 #
 # Build: docker build -f Dockerfile_nanonets_ocr -t nanonets-ocr .
 # Run:   docker run --gpus all -p 8000:8000 -v ht-huggingface-cache:/root/.cache/huggingface nanonets-ocr
 FROM vllm/vllm-openai:latest
 LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
 LABEL description="Nanonets-OCR-s - Document OCR optimized Vision Language Model"
 LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
 # Environment configuration
 ENV MODEL_NAME="nanonets/Nanonets-OCR-s"
 ENV HOST="0.0.0.0"
 ENV PORT="8000"
 ENV MAX_MODEL_LEN="8192"
 ENV GPU_MEMORY_UTILIZATION="0.9"
 # Expose OpenAI-compatible API port
 EXPOSE 8000
 # Health check - vLLM exposes /health endpoint
 HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=5 \
    CMD curl -f http://localhost:8000/health || exit 1
 # Start vLLM server with Nanonets-OCR-s model
 CMD ["--model", "nanonets/Nanonets-OCR-s", \
     "--trust-remote-code", \
     "--max-model-len", "8192", \
     "--host", "0.0.0.0", \
     "--port", "8000"]
--- a/70
+++ b/70
@@ -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"]
--- a/57
+++ b/57
@@ -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"]
--- a/71
+++ b/71
@@ -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"]
--- a/26
+++ b/26
@@ -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"]
--- a/build-images.sh
+++ b/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 \
    .
--- a/changelog.md
+++ b/changelog.md
@@ -1,5 +1,115 @@
 # Changelog
 ## 2026-01-18 - 1.13.2 - fix(tests)
 stabilize OCR extraction tests and manage GPU containers
 - Add stopAllGpuContainers() and call it before starting GPU images to free GPU memory.
 - Remove PaddleOCR-VL image configs and associated ensure helpers from docker test helper to simplify images list.
 - Split invoice/bankstatement tests into two sequential stages: Stage 1 runs Nanonets OCR to produce markdown files, Stage 2 stops Nanonets and runs model extraction from saved markdown (avoids GPU contention).
 - Introduce temporary markdown directory handling and cleanup; add stopNanonets() and container running checks in tests.
 - Switch bank statement extraction model from qwen3:8b to gpt-oss:20b; add request timeout and improved logging/console output across tests.
 - Refactor extractWithConsensus and extraction functions to accept document identifiers, improve error messages and JSON extraction robustness.
 ## 2026-01-18 - 1.13.1 - fix(image_support_files)
 remove PaddleOCR-VL server scripts from image_support_files
 - 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
 - Add Dockerfile_paddleocr_vl_full and entrypoint script to build a GPU-enabled image with PP-DocLayoutV2 + PaddleOCR-VL and a FastAPI server
 - Introduce image_support_files/paddleocr_vl_full_server.py implementing the full pipeline API (/parse, OpenAI-compatible /v1/chat/completions) and a /formats endpoint
 - Improve image handling: decode_image supports data URLs, HTTP(S), raw base64 and file paths; add optimize_image_resolution to auto-scale images into the recommended 1080-2048px range
 - Add test helpers (test/helpers/docker.ts) to build/start/health-check Docker images and new ensurePaddleOcrVlFull workflow
 - Add comprehensive integration tests for bank statements and invoices (MiniCPM and PaddleOCR-VL variants) and update tests to ensure required containers are running before tests
 - Switch MiniCPM model references to 'minicpm-v:latest' and increase health/timeout expectations for the full pipeline
 ## 2026-01-17 - 1.5.0 - feat(paddleocr-vl)
 add PaddleOCR-VL GPU Dockerfile, pin vllm, update CPU image deps, and improve entrypoint and tests
--- a/image_support_files/minicpm45v_entrypoint.sh
+++ b/image_support_files/minicpm45v_entrypoint.sh
--- a/image_support_files/paddleocr-vl-cpu-entrypoint.sh
+++ b/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: ${PORT}"
 echo "Device: CPU (no GPU)"
 echo ""
 echo "Starting PaddleOCR-VL CPU server..."
 echo "==================================="
 exec python /app/paddleocr_vl_server.py
--- a/image_support_files/paddleocr-vl-entrypoint.sh
+++ b/image_support_files/paddleocr-vl-entrypoint.sh
@@ -1,59 +0,0 @@
 #!/bin/bash
 set -e
 echo "==================================="
 echo "PaddleOCR-VL Server"
 echo "==================================="
 # Configuration
 MODEL_NAME="${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[@]}"
--- a/image_support_files/paddleocr_vl_server.py
+++ b/image_support_files/paddleocr_vl_server.py
@@ -1,371 +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 decode_image(image_source: str) -> Image.Image:
    """Decode image from URL or base64"""
    if image_source.startswith("data:"):
        # Base64 encoded image
        header, data = image_source.split(",", 1)
        image_data = base64.b64decode(data)
        return Image.open(io.BytesIO(image_data)).convert("RGB")
    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()
        return Image.open(io.BytesIO(response.content)).convert("RGB")
    else:
        # Assume it's a file path or raw base64
        try:
            image_data = base64.b64decode(image_source)
            return Image.open(io.BytesIO(image_data)).convert("RGB")
        except:
            # Try as file path
            return Image.open(image_source).convert("RGB")
 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("/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)
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
  "name": "@host.today/ht-docker-ai",
-  "version": "1.5.0",
+  "version": "1.13.2",
  "type": "module",
  "private": false,
  "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",
--- a/readme.hints.md
+++ b/readme.hints.md
@@ -244,8 +244,97 @@ The bank statement extraction uses a dual-VLM consensus approach:
 ---
 ## Nanonets-OCR-s
 ### Overview
 Nanonets-OCR-s is a Qwen2.5-VL-3B model fine-tuned specifically for document OCR tasks. It outputs structured markdown with semantic tags.
 **Key features:**
 - Based on Qwen2.5-VL-3B (~4B parameters)
 - Fine-tuned for document OCR
 - Outputs markdown with semantic HTML tags
 - ~8-10GB VRAM (fits comfortably in 16GB)
 ### Docker Images
 | Tag | Description |
 |-----|-------------|
 | `nanonets-ocr` | GPU variant using vLLM (OpenAI-compatible API) |
 ### API Endpoints (OpenAI-compatible via vLLM)
 | Endpoint | Method | Description |
 |----------|--------|-------------|
 | `/health` | GET | Health check |
 | `/v1/models` | GET | List available models |
 | `/v1/chat/completions` | POST | OpenAI-compatible chat completions |
 ### Request/Response Format
 **POST /v1/chat/completions (OpenAI-compatible)**
 ```json
 {
  "model": "nanonets/Nanonets-OCR-s",
  "messages": [
    {
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}},
        {"type": "text", "text": "Extract the text from the above document..."}
      ]
    }
  ],
  "temperature": 0.0,
  "max_tokens": 4096
 }
 ```
 ### Nanonets OCR Prompt
 The model is designed to work with a specific prompt format:
 ```
 Extract the text from the above document as if you were reading it naturally.
 Return the tables in html format.
 Return the equations in LaTeX representation.
 If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
 Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
 Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.
 ```
 ### Performance
 - **GPU (vLLM)**: ~3-8 seconds per page
 - **VRAM usage**: ~8-10GB
 ### Two-Stage Pipeline (Nanonets + Qwen3)
 The Nanonets tests use a two-stage pipeline:
 1. **Stage 1**: Nanonets-OCR-s converts images to markdown (via vLLM on port 8000)
 2. **Stage 2**: Qwen3 8B extracts structured JSON from markdown (via Ollama on port 11434)
 **GPU Limitation**: Both vLLM and Ollama require significant GPU memory. On a single GPU system:
 - Running both simultaneously causes memory contention
 - For single GPU: Run services sequentially (stop Nanonets before Qwen3)
 - For multi-GPU: Assign each service to a different GPU
 **Sequential Execution**:
 ```bash
 # Step 1: Run Nanonets OCR (converts to markdown)
 docker start nanonets-test
 # ... perform OCR ...
 docker stop nanonets-test
 # Step 2: Run Qwen3 extraction (from markdown)
 docker start minicpm-test
 # ... extract JSON ...
 ```
 ---
 ## Related Resources
 - [Ollama Documentation](https://ollama.ai/docs)
 - [MiniCPM-V GitHub](https://github.com/OpenBMB/MiniCPM-V)
 - [Ollama API Reference](https://github.com/ollama/ollama/blob/main/docs/api.md)
 - [Nanonets-OCR-s on HuggingFace](https://huggingface.co/nanonets/Nanonets-OCR-s)
--- a/readme.md
+++ b/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.
--- a/test/helpers/docker.ts
+++ b/test/helpers/docker.ts
@@ -0,0 +1,351 @@
 import { execSync } from 'child_process';
 // Project container names (only manage these)
 const PROJECT_CONTAINERS = [
  'minicpm-test',
  'nanonets-test',
 ];
 // Image configurations
 export interface IImageConfig {
  name: string;
  dockerfile: string;
  buildContext: string;
  containerName: string;
  ports: string[];
  volumes?: string[];
  gpus?: boolean;
  healthEndpoint?: string;
  healthTimeout?: number;
 }
 export const IMAGES = {
  minicpm: {
    name: 'minicpm45v',
    dockerfile: 'Dockerfile_minicpm45v_gpu',
    buildContext: '.',
    containerName: 'minicpm-test',
    ports: ['11434:11434'],
    volumes: ['ht-ollama-models:/root/.ollama'],
    gpus: true,
    healthEndpoint: 'http://localhost:11434/api/tags',
    healthTimeout: 120000,
  } as IImageConfig,
  // Nanonets-OCR-s - Document OCR optimized VLM (Qwen2.5-VL-3B fine-tuned)
  nanonetsOcr: {
    name: 'nanonets-ocr',
    dockerfile: 'Dockerfile_nanonets_ocr',
    buildContext: '.',
    containerName: 'nanonets-test',
    ports: ['8000:8000'],
    volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
    gpus: true,
    healthEndpoint: 'http://localhost:8000/health',
    healthTimeout: 300000, // 5 minutes for model loading
  } as IImageConfig,
 };
 /**
 * Execute a shell command and return output
 */
 function exec(command: string, silent = false): string {
  try {
    return execSync(command, {
      encoding: 'utf-8',
      stdio: silent ? 'pipe' : 'inherit',
    });
  } catch (err: unknown) {
    if (silent) return '';
    throw err;
  }
 }
 /**
 * Check if a Docker image exists locally
 */
 export function imageExists(imageName: string): boolean {
  const result = exec(`docker images -q ${imageName}`, true);
  return result.trim().length > 0;
 }
 /**
 * Check if a container is running
 */
 export function isContainerRunning(containerName: string): boolean {
  const result = exec(`docker ps --filter "name=^${containerName}$" --format "{{.Names}}"`, true);
  return result.trim() === containerName;
 }
 /**
 * Check if a container exists (running or stopped)
 */
 export function containerExists(containerName: string): boolean {
  const result = exec(`docker ps -a --filter "name=^${containerName}$" --format "{{.Names}}"`, true);
  return result.trim() === containerName;
 }
 /**
 * Stop and remove a container
 */
 export function removeContainer(containerName: string): void {
  if (containerExists(containerName)) {
    console.log(`[Docker] Removing container: ${containerName}`);
    exec(`docker rm -f ${containerName}`, true);
  }
 }
 /**
 * Stop all project containers that conflict with the required one (port-based)
 */
 export function stopConflictingContainers(requiredContainer: string, requiredPort: string): void {
  // Stop project containers using the same port
  for (const container of PROJECT_CONTAINERS) {
    if (container === requiredContainer) continue;
    if (isContainerRunning(container)) {
      // Check if this container uses the same port
      const ports = exec(`docker port ${container} 2>/dev/null || true`, true);
      if (ports.includes(requiredPort.split(':')[0])) {
        console.log(`[Docker] Stopping conflicting container: ${container}`);
        exec(`docker stop ${container}`, true);
      }
    }
  }
 }
 /**
 * Stop all GPU-consuming project containers (for GPU memory management)
 * This ensures GPU memory is freed before starting a new GPU service
 */
 export function stopAllGpuContainers(exceptContainer?: string): void {
  for (const container of PROJECT_CONTAINERS) {
    if (container === exceptContainer) continue;
    if (isContainerRunning(container)) {
      console.log(`[Docker] Stopping GPU container: ${container}`);
      exec(`docker stop ${container}`, true);
      // Give the GPU a moment to free memory
    }
  }
  // Brief pause to allow GPU memory to be released
  execSync('sleep 2');
 }
 /**
 * Build a Docker image
 */
 export function buildImage(config: IImageConfig): void {
  console.log(`[Docker] Building image: ${config.name}`);
  const cmd = `docker build --load -f ${config.dockerfile} -t ${config.name} ${config.buildContext}`;
  exec(cmd);
 }
 /**
 * Start a container from an image
 */
 export function startContainer(config: IImageConfig): void {
  // Remove existing container if it exists
  removeContainer(config.containerName);
  console.log(`[Docker] Starting container: ${config.containerName}`);
  const portArgs = config.ports.map((p) => `-p ${p}`).join(' ');
  const volumeArgs = config.volumes?.map((v) => `-v ${v}`).join(' ') || '';
  const gpuArgs = config.gpus ? '--gpus all' : '';
  const cmd = `docker run -d --name ${config.containerName} ${gpuArgs} ${portArgs} ${volumeArgs} ${config.name}`;
  exec(cmd);
 }
 /**
 * Wait for a container to become healthy
 */
 export async function waitForHealth(
  endpoint: string,
  timeoutMs: number = 120000,
  intervalMs: number = 5000
 ): Promise<boolean> {
  const startTime = Date.now();
  console.log(`[Docker] Waiting for health: ${endpoint}`);
  while (Date.now() - startTime < timeoutMs) {
    try {
      const response = await fetch(endpoint, {
        method: 'GET',
        signal: AbortSignal.timeout(5000),
      });
      if (response.ok) {
        console.log(`[Docker] Service healthy!`);
        return true;
      }
    } catch {
      // Service not ready yet
    }
    const elapsed = Math.round((Date.now() - startTime) / 1000);
    console.log(`[Docker] Waiting... (${elapsed}s)`);
    await new Promise((resolve) => setTimeout(resolve, intervalMs));
  }
  console.log(`[Docker] Health check timeout after ${timeoutMs / 1000}s`);
  return false;
 }
 /**
 * Ensure a service is running and healthy
 * - Builds image if missing
 * - Stops conflicting project containers
 * - Starts container if not running
 * - Waits for health check
 */
 export async function ensureService(config: IImageConfig): Promise<boolean> {
  console.log(`\n[Docker] Ensuring service: ${config.name}`);
  // Build image if it doesn't exist
  if (!imageExists(config.name)) {
    console.log(`[Docker] Image not found, building...`);
    buildImage(config);
  }
  // For GPU services, stop ALL other GPU containers to free GPU memory
  if (config.gpus) {
    stopAllGpuContainers(config.containerName);
  }
  // Stop conflicting containers on the same port
  const mainPort = config.ports[0];
  stopConflictingContainers(config.containerName, mainPort);
  // Start container if not running
  if (!isContainerRunning(config.containerName)) {
    startContainer(config);
  } else {
    console.log(`[Docker] Container already running: ${config.containerName}`);
  }
  // Wait for health
  if (config.healthEndpoint) {
    return waitForHealth(config.healthEndpoint, config.healthTimeout);
  }
  return true;
 }
 /**
 * Ensure MiniCPM service is running (Ollama with GPU)
 */
 export async function ensureMiniCpm(): Promise<boolean> {
  return ensureService(IMAGES.minicpm);
 }
 /**
 * Check if GPU is available
 */
 export function isGpuAvailable(): boolean {
  try {
    const result = exec('nvidia-smi --query-gpu=name --format=csv,noheader 2>/dev/null', true);
    return result.trim().length > 0;
  } catch {
    return false;
  }
 }
 /**
 * 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');
 }
 /**
 * Ensure Nanonets-OCR-s service is running (via vLLM)
 * Document OCR optimized VLM based on Qwen2.5-VL-3B
 */
 export async function ensureNanonetsOcr(): Promise<boolean> {
  if (!isGpuAvailable()) {
    console.log('[Docker] WARNING: Nanonets-OCR-s requires GPU, but none detected');
  }
  return ensureService(IMAGES.nanonetsOcr);
 }
--- a/test/test.bankstatements.combined.ts
+++ b/test/test.bankstatements.combined.ts
@@ -1,535 +0,0 @@
 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';
 // Service URLs
 const OLLAMA_URL = 'http://localhost:11434';
 const PADDLEOCR_VL_URL = 'http://localhost:8000';
 // Models
 const MINICPM_MODEL = 'openbmb/minicpm-v4.5:q8_0';
 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('should connect to Ollama API', async () => {
  const response = await fetch(`${OLLAMA_URL}/api/tags`);
  expect(response.ok).toBeTrue();
  const data = await response.json();
  expect(data.models).toBeArray();
 });
 tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
  const response = await fetch(`${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}`);
  // This test passes regardless - PaddleOCR-VL is optional
  expect(true).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();
--- a/test/test.bankstatements.minicpm.ts
+++ b/test/test.bankstatements.minicpm.ts
@@ -0,0 +1,536 @@
 /**
 * Bank statement extraction using MiniCPM-V (visual extraction)
 *
 * JSON per-page approach:
 * 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';
 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 MODEL = 'openbmb/minicpm-v4.5:q8_0';
 interface ITransaction {
  date: string;
  counterparty: string;
  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
 */
 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 "${outputPattern}"`,
      { stdio: 'pipe' }
    );
    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
    const images: string[] = [];
    for (const file of files) {
      const imagePath = path.join(tempDir, file);
      const imageData = fs.readFileSync(imagePath);
      images.push(imageData.toString('base64'));
    }
    return images;
  } finally {
    fs.rmSync(tempDir, { recursive: true, force: true });
  }
 }
 /**
 * Query for JSON extraction
 */
 async function queryJson(image: string, queryId: string): Promise<string> {
  console.log(`      [${queryId}] Sending request to ${MODEL}...`);
  const startTime = Date.now();
  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    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: ${response.status}`);
  }
  const data = await response.json();
  const content = (data.message?.content || '').trim();
  console.log(`      [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
  return content;
 }
 /**
 * Sanitize JSON string - fix common issues from vision model output
 */
 function sanitizeJson(jsonStr: string): string {
  let s = jsonStr;
  // Fix +number (e.g., +93.80 -> 93.80) - JSON doesn't allow + prefix
  // 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$2"');
  // Remove control characters except newlines (which we handle above)
  s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
  return s;
 }
 /**
 * Parse JSON response into transactions
 */
 function parseJsonResponse(response: string, queryId: string): ITransaction[] {
  console.log(`      [${queryId}] Parsing response...`);
  // Try to find JSON in markdown code block
  const codeBlockMatch = response.match(/```(?:json)?\s*([\s\S]*?)```/);
  let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : response.trim();
  if (codeBlockMatch) {
    console.log(`      [${queryId}] Found JSON in code block`);
  }
  // Sanitize JSON (fix +number issue)
  jsonStr = sanitizeJson(jsonStr);
  try {
    const parsed = JSON.parse(jsonStr);
    if (Array.isArray(parsed)) {
      const txs = parsed.map(tx => ({
        date: String(tx.date || ''),
        counterparty: String(tx.counterparty || tx.description || ''),
        amount: parseAmount(tx.amount),
      }));
      console.log(`      [${queryId}] Parsed ${txs.length} transactions (direct)`);
      return txs;
    }
    console.log(`      [${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]),
      });
    }
  }
  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;
  }
  return true;
 }
 /**
 * 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...`);
    }
  }
  // Fallback: use last response
  console.log(`\n    [Page ${pageNum}] === FALLBACK (no consensus after ${MAX_ATTEMPTS} attempts) ===`);
  const fallbackId = `P${pageNum}FALLBACK`;
  const fallbackResponse = await queryJson(image, fallbackId);
  const fallback = parseJsonResponse(fallbackResponse, fallbackId);
  console.log(`    [Page ${pageNum}] ~ FALLBACK RESULT: ${fallback.length} transactions`);
  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);
  }
  console.log(`    [Vision] Total: ${allTransactions.length} transactions`);
  return allTransactions;
 }
 /**
 * Compare extracted transactions against expected
 */
 function compareTransactions(
  extracted: ITransaction[],
  expected: ITransaction[]
 ): { 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++) {
    const exp = expected[i];
    const ext = extracted[i];
    if (!ext) {
      errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
      continue;
    }
    const dateMatch = ext.date === exp.date;
    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
    if (dateMatch && amountMatch) {
      matches++;
      // Track counterparty variations (date and amount match but name differs)
      if (ext.counterparty !== exp.counterparty) {
        variations.push(
          `[${i}] "${exp.counterparty}" → "${ext.counterparty}"`
        );
      }
    } else {
      errors.push(
        `Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`
      );
    }
  }
  if (extracted.length > expected.length) {
    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
  }
  return { matches, total: expected.length, errors, variations };
 }
 /**
 * Find all test cases (PDF + JSON pairs) in .nogit/
 */
 function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
  const testDir = path.join(process.cwd(), '.nogit');
  if (!fs.existsSync(testDir)) {
    return [];
  }
  const files = fs.readdirSync(testDir);
  const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
  for (const pdf of pdfFiles) {
    const baseName = pdf.replace('.pdf', '');
    const jsonFile = `${baseName}.json`;
    if (files.includes(jsonFile)) {
      testCases.push({
        name: baseName,
        pdfPath: path.join(testDir, pdf),
        jsonPath: path.join(testDir, jsonFile),
      });
    }
  }
  return testCases.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');
  const minicpmOk = await ensureMiniCpm();
  expect(minicpmOk).toBeTrue();
  console.log('\n[Setup] All containers ready!\n');
 });
 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'))).toBeTrue();
 });
 const testCases = findTestCases();
 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: ${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: ${extracted.length} transactions`);
    const result = compareTransactions(extracted, expected);
    const perfectMatch = result.matches === result.total && extracted.length === expected.length;
    if (perfectMatch) {
      passedCount++;
      console.log(`  Result: PASS (${result.matches}/${result.total})`);
    } else {
      failedCount++;
      console.log(`  Result: FAIL (${result.matches}/${result.total})`);
      result.errors.slice(0, 10).forEach((e) => console.log(`    - ${e}`));
    }
    // Log counterparty variations (names that differ but date/amount matched)
    if (result.variations.length > 0) {
      console.log(`  Counterparty variations (${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();
--- a/test/test.bankstatements.nanonets.ts
+++ b/test/test.bankstatements.nanonets.ts
@@ -0,0 +1,585 @@
 /**
 * Bank statement extraction using Nanonets-OCR-s + GPT-OSS 20B (sequential two-stage pipeline)
 *
 * Stage 1: Nanonets-OCR-s converts ALL document pages to markdown (stop after completion)
 * Stage 2: GPT-OSS 20B extracts structured JSON from saved markdown (after Nanonets stops)
 *
 * This approach avoids GPU contention by running services sequentially.
 */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureNanonetsOcr, ensureMiniCpm, removeContainer, isContainerRunning } from './helpers/docker.js';
 const NANONETS_URL = 'http://localhost:8000/v1';
 const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
 const OLLAMA_URL = 'http://localhost:11434';
 const EXTRACTION_MODEL = 'gpt-oss:20b';
 // Temp directory for storing markdown between stages
 const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-markdown');
 interface ITransaction {
  date: string;
  counterparty: string;
  amount: number;
 }
 interface ITestCase {
  name: string;
  pdfPath: string;
  jsonPath: string;
  markdownPath?: string;
  images?: string[];
 }
 // Nanonets-specific prompt for document OCR to markdown
 const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
 Return the tables in html format.
 Return the equations in LaTeX representation.
 If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
 Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
 Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
 // JSON extraction prompt for GPT-OSS 20B
 const JSON_EXTRACTION_PROMPT = `Extract ALL transactions from this bank statement as JSON array. Each transaction: {"date": "YYYY-MM-DD", "counterparty": "NAME", "amount": -25.99}. Amount negative for debits, positive for credits. Only include actual transactions, not balances. Return ONLY JSON array, no explanation.
 STATEMENT:
 `;
 /**
 * 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 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 });
  }
 }
 /**
 * Convert a single page to markdown using Nanonets-OCR-s
 */
 async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
  const startTime = Date.now();
  const response = await fetch(`${NANONETS_URL}/chat/completions`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer dummy',
    },
    body: JSON.stringify({
      model: NANONETS_MODEL,
      messages: [{
        role: 'user',
        content: [
          { type: 'image_url', image_url: { url: `data:image/png;base64,${image}` }},
          { type: 'text', text: NANONETS_OCR_PROMPT },
        ],
      }],
      max_tokens: 4096,
      temperature: 0.0,
    }),
  });
  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
  if (!response.ok) {
    const errorText = await response.text();
    throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
  }
  const data = await response.json();
  const content = (data.choices?.[0]?.message?.content || '').trim();
  console.log(`        Page ${pageNum}: ${content.length} chars (${elapsed}s)`);
  return content;
 }
 /**
 * Convert all pages of a document to markdown
 */
 async function convertDocumentToMarkdown(images: string[], docName: string): Promise<string> {
  console.log(`      [${docName}] Converting ${images.length} page(s)...`);
  const markdownPages: string[] = [];
  for (let i = 0; i < images.length; i++) {
    const markdown = await convertPageToMarkdown(images[i], i + 1);
    markdownPages.push(`--- PAGE ${i + 1} ---\n${markdown}`);
  }
  const fullMarkdown = markdownPages.join('\n\n');
  console.log(`      [${docName}] Complete: ${fullMarkdown.length} chars total`);
  return fullMarkdown;
 }
 /**
 * Stop Nanonets container
 */
 function stopNanonets(): void {
  console.log('    [Docker] Stopping Nanonets container...');
  try {
    execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
    // Wait for GPU memory to be released
    execSync('sleep 5', { stdio: 'pipe' });
    console.log('    [Docker] Nanonets stopped');
  } catch {
    console.log('    [Docker] Nanonets was not running');
  }
 }
 /**
 * Ensure GPT-OSS 20B model is available and warmed up
 */
 async function ensureExtractionModel(): Promise<boolean> {
  try {
    const response = await fetch(`${OLLAMA_URL}/api/tags`);
    if (response.ok) {
      const data = await response.json();
      const models = data.models || [];
      if (models.some((m: { name: string }) => m.name === EXTRACTION_MODEL)) {
        console.log(`    [Ollama] Model available: ${EXTRACTION_MODEL}`);
        // Warmup: send a simple request to ensure model is loaded
        console.log(`    [Ollama] Warming up model...`);
        const warmupResponse = await fetch(`${OLLAMA_URL}/api/chat`, {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({
            model: EXTRACTION_MODEL,
            messages: [{ role: 'user', content: 'Return: [{"test": 1}]' }],
            stream: false,
          }),
          signal: AbortSignal.timeout(120000),
        });
        if (warmupResponse.ok) {
          const warmupData = await warmupResponse.json();
          console.log(`    [Ollama] Warmup complete (${warmupData.message?.content?.length || 0} chars)`);
        }
        return true;
      }
    }
  } catch {
    return false;
  }
  console.log(`    [Ollama] Pulling ${EXTRACTION_MODEL}...`);
  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ name: EXTRACTION_MODEL, stream: false }),
  });
  return pullResponse.ok;
 }
 /**
 * Extract transactions from markdown using GPT-OSS 20B (streaming)
 */
 async function extractTransactionsFromMarkdown(markdown: string, queryId: string): Promise<ITransaction[]> {
  console.log(`      [${queryId}] Sending to ${EXTRACTION_MODEL}...`);
  console.log(`      [${queryId}] Markdown length: ${markdown.length}`);
  const startTime = Date.now();
  const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
  console.log(`      [${queryId}] Prompt preview: ${fullPrompt.substring(0, 200)}...`);
  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      model: EXTRACTION_MODEL,
      messages: [{
        role: 'user',
        content: fullPrompt,
      }],
      stream: true,
    }),
    signal: AbortSignal.timeout(600000),  // 10 minute timeout
  });
  if (!response.ok) {
    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
    throw new Error(`Ollama API error: ${response.status}`);
  }
  // Stream the response and log to console
  let content = '';
  const reader = response.body!.getReader();
  const decoder = new TextDecoder();
  process.stdout.write(`      [${queryId}] `);
  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
    const chunk = decoder.decode(value, { stream: true });
    // Each line is a JSON object
    for (const line of chunk.split('\n').filter(l => l.trim())) {
      try {
        const json = JSON.parse(line);
        const token = json.message?.content || '';
        if (token) {
          process.stdout.write(token);
          content += token;
        }
      } catch {
        // Ignore parse errors for partial chunks
      }
    }
  }
  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
  console.log(`\n      [${queryId}] Done: ${content.length} chars (${elapsed}s)`);
  return parseJsonResponse(content, queryId);
 }
 /**
 * Sanitize JSON string
 */
 function sanitizeJson(jsonStr: string): string {
  let s = jsonStr;
  s = s.replace(/"amount"\s*:\s*\+/g, '"amount": ');
  s = s.replace(/:\s*\+(\d)/g, ': $1');
  s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3.$4');
  s = s.replace(/,\s*([}\]])/g, '$1');
  s = s.replace(/"([^"\\]*)\n([^"]*)"/g, '"$1 $2"');
  s = s.replace(/"([^"\\]*)\t([^"]*)"/g, '"$1 $2"');
  s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
  return s;
 }
 /**
 * Parse amount from various formats
 */
 function parseAmount(value: unknown): number {
  if (typeof value === 'number') return value;
  if (typeof value !== 'string') return 0;
  let s = value.replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
  if (s.includes(',') && s.indexOf(',') > s.lastIndexOf('.')) {
    s = s.replace(/\./g, '').replace(',', '.');
  } else {
    s = s.replace(/,/g, '');
  }
  return parseFloat(s) || 0;
 }
 /**
 * Parse JSON response into transactions
 */
 function parseJsonResponse(response: string, queryId: string): ITransaction[] {
  // Remove thinking tags if present
  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
  // Debug: show what we're working with
  console.log(`      [${queryId}] Response preview: ${cleanResponse.substring(0, 300)}...`);
  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
  let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
  jsonStr = sanitizeJson(jsonStr);
  try {
    const parsed = JSON.parse(jsonStr);
    if (Array.isArray(parsed)) {
      const txs = parsed.map(tx => ({
        date: String(tx.date || ''),
        counterparty: String(tx.counterparty || tx.description || ''),
        amount: parseAmount(tx.amount),
      }));
      console.log(`      [${queryId}] Parsed ${txs.length} transactions`);
      return txs;
    }
  } catch (e) {
    // Try to find a JSON array in the text
    const arrayMatch = jsonStr.match(/\[[\s\S]*\]/);
    if (arrayMatch) {
      console.log(`      [${queryId}] Array match found: ${arrayMatch[0].length} chars`);
      try {
        const parsed = JSON.parse(sanitizeJson(arrayMatch[0]));
        if (Array.isArray(parsed)) {
          const txs = parsed.map(tx => ({
            date: String(tx.date || ''),
            counterparty: String(tx.counterparty || tx.description || ''),
            amount: parseAmount(tx.amount),
          }));
          console.log(`      [${queryId}] Parsed ${txs.length} transactions (array match)`);
          return txs;
        }
      } catch (innerErr) {
        console.log(`      [${queryId}] Array parse error: ${(innerErr as Error).message}`);
      }
    } else {
      console.log(`      [${queryId}] No JSON array found in response`);
    }
  }
  console.log(`      [${queryId}] PARSE FAILED`);
  return [];
 }
 /**
 * Extract transactions (single pass)
 */
 async function extractTransactions(markdown: string, docName: string): Promise<ITransaction[]> {
  console.log(`    [${docName}] Extracting...`);
  const txs = await extractTransactionsFromMarkdown(markdown, docName);
  console.log(`    [${docName}] Extracted ${txs.length} transactions`);
  return txs;
 }
 /**
 * Compare transactions
 */
 function compareTransactions(
  extracted: ITransaction[],
  expected: ITransaction[]
 ): { matches: number; total: number; errors: string[] } {
  const errors: string[] = [];
  let matches = 0;
  for (let i = 0; i < expected.length; i++) {
    const exp = expected[i];
    const ext = extracted[i];
    if (!ext) {
      errors.push(`Missing tx ${i}: ${exp.date} ${exp.counterparty}`);
      continue;
    }
    const dateMatch = ext.date === exp.date;
    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
    if (dateMatch && amountMatch) {
      matches++;
    } else {
      errors.push(`Mismatch ${i}: exp ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
    }
  }
  if (extracted.length > expected.length) {
    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
  }
  return { matches, total: expected.length, errors };
 }
 /**
 * Find all test cases
 */
 function findTestCases(): ITestCase[] {
  const testDir = path.join(process.cwd(), '.nogit');
  if (!fs.existsSync(testDir)) return [];
  const files = fs.readdirSync(testDir);
  const testCases: ITestCase[] = [];
  for (const pdf of files.filter((f: string) => f.endsWith('.pdf'))) {
    const baseName = pdf.replace('.pdf', '');
    const jsonFile = `${baseName}.json`;
    if (files.includes(jsonFile)) {
      testCases.push({
        name: baseName,
        pdfPath: path.join(testDir, pdf),
        jsonPath: path.join(testDir, jsonFile),
      });
    }
  }
  return testCases.sort((a, b) => a.name.localeCompare(b.name));
 }
 // ============ TESTS ============
 const testCases = findTestCases();
 console.log(`\nFound ${testCases.length} bank statement test cases\n`);
 // Ensure temp directory exists
 if (!fs.existsSync(TEMP_MD_DIR)) {
  fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
 }
 // -------- STAGE 1: OCR with Nanonets --------
 // Check if all markdown files already exist
 function allMarkdownFilesExist(): boolean {
  for (const tc of testCases) {
    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
    if (!fs.existsSync(mdPath)) {
      return false;
    }
  }
  return true;
 }
 // Track whether we need to run Stage 1
 let stage1Needed = !allMarkdownFilesExist();
 tap.test('Stage 1: Setup Nanonets', async () => {
  console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
  if (!stage1Needed) {
    console.log('  [SKIP] All markdown files already exist, skipping Nanonets setup');
    return;
  }
  const ok = await ensureNanonetsOcr();
  expect(ok).toBeTrue();
 });
 tap.test('Stage 1: Convert all documents to markdown', async () => {
  if (!stage1Needed) {
    console.log('  [SKIP] Using existing markdown files from previous run\n');
    // Load existing markdown paths
    for (const tc of testCases) {
      tc.markdownPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
      console.log(`    Loaded: ${tc.markdownPath}`);
    }
    return;
  }
  console.log('\n  Converting all PDFs to markdown with Nanonets-OCR-s...\n');
  for (const tc of testCases) {
    console.log(`\n    === ${tc.name} ===`);
    // Convert PDF to images
    const images = convertPdfToImages(tc.pdfPath);
    console.log(`      Pages: ${images.length}`);
    // Convert to markdown
    const markdown = await convertDocumentToMarkdown(images, tc.name);
    // Save markdown to temp file
    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
    fs.writeFileSync(mdPath, markdown);
    tc.markdownPath = mdPath;
    console.log(`      Saved: ${mdPath}`);
  }
  console.log('\n  Stage 1 complete: All documents converted to markdown\n');
 });
 tap.test('Stage 1: Stop Nanonets', async () => {
  if (!stage1Needed) {
    console.log('  [SKIP] Nanonets was not started');
    return;
  }
  stopNanonets();
  // Verify it's stopped
  await new Promise(resolve => setTimeout(resolve, 3000));
  expect(isContainerRunning('nanonets-test')).toBeFalse();
 });
 // -------- STAGE 2: Extraction with GPT-OSS 20B --------
 tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
  console.log('\n========== STAGE 2: GPT-OSS 20B Extraction ==========\n');
  const ollamaOk = await ensureMiniCpm();
  expect(ollamaOk).toBeTrue();
  const extractionOk = await ensureExtractionModel();
  expect(extractionOk).toBeTrue();
 });
 let passedCount = 0;
 let failedCount = 0;
 for (const tc of testCases) {
  tap.test(`Stage 2: Extract ${tc.name}`, async () => {
    const expected: ITransaction[] = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
    console.log(`\n    === ${tc.name} ===`);
    console.log(`    Expected: ${expected.length} transactions`);
    // Load saved markdown
    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
    if (!fs.existsSync(mdPath)) {
      throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
    }
    const markdown = fs.readFileSync(mdPath, 'utf-8');
    console.log(`    Markdown: ${markdown.length} chars`);
    // Extract transactions (single pass)
    const extracted = await extractTransactions(markdown, tc.name);
    // Log results
    console.log(`    Extracted: ${extracted.length} transactions`);
    for (let i = 0; i < Math.min(extracted.length, 5); i++) {
      const tx = extracted[i];
      console.log(`      ${i + 1}. ${tx.date} | ${tx.counterparty.substring(0, 25).padEnd(25)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
    }
    if (extracted.length > 5) {
      console.log(`      ... and ${extracted.length - 5} more`);
    }
    // Compare
    const result = compareTransactions(extracted, expected);
    const pass = result.matches === result.total && extracted.length === expected.length;
    if (pass) {
      passedCount++;
      console.log(`    Result: PASS (${result.matches}/${result.total})`);
    } else {
      failedCount++;
      console.log(`    Result: FAIL (${result.matches}/${result.total})`);
      result.errors.slice(0, 5).forEach(e => console.log(`      - ${e}`));
    }
    expect(result.matches).toEqual(result.total);
    expect(extracted.length).toEqual(expected.length);
  });
 }
 tap.test('Summary', async () => {
  console.log(`\n======================================================`);
  console.log(`   Bank Statement Summary (Nanonets + GPT-OSS 20B Sequential)`);
  console.log(`======================================================`);
  console.log(`  Stage 1:  Nanonets-OCR-s (document -> markdown)`);
  console.log(`  Stage 2:  GPT-OSS 20B (markdown -> JSON)`);
  console.log(`  Passed:   ${passedCount}/${testCases.length}`);
  console.log(`  Failed:   ${failedCount}/${testCases.length}`);
  console.log(`======================================================\n`);
  // Only cleanup temp files if ALL tests passed
  if (failedCount === 0 && passedCount === testCases.length) {
    try {
      fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
      console.log(`  Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
    } catch {
      // Ignore
    }
  } else {
    console.log(`  Keeping temp directory for debugging: ${TEMP_MD_DIR}\n`);
  }
 });
 export default tap.start();
--- a/test/test.bankstatements.qwen3vl.ts
+++ b/test/test.bankstatements.qwen3vl.ts
@@ -0,0 +1,345 @@
 /**
 * Bank statement extraction using Qwen3-VL 8B Vision (Direct)
 *
 * Multi-query approach:
 * 1. First ask how many transactions on each page
 * 2. Then query each transaction individually
 * Single pass, no consensus voting.
 */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureMiniCpm } from './helpers/docker.js';
 const OLLAMA_URL = 'http://localhost:11434';
 const VISION_MODEL = 'qwen3-vl:8b';
 interface ITransaction {
  date: string;
  counterparty: string;
  amount: number;
 }
 /**
 * Convert PDF to PNG images
 */
 function convertPdfToImages(pdfPath: string): string[] {
  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
  const outputPattern = path.join(tempDir, 'page-%d.png');
  try {
    execSync(
      `convert -density 150 -quality 90 "${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();
--- a/test/test.invoices.combined.ts
+++ b/test/test.invoices.combined.ts
@@ -1,440 +0,0 @@
 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';
 const OLLAMA_URL = 'http://localhost:11434';
 const MODEL = 'openbmb/minicpm-v4.5:q8_0';
 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('should connect to Ollama API', async () => {
  const response = await fetch(`${OLLAMA_URL}/api/tags`);
  expect(response.ok).toBeTrue();
  const data = await response.json();
  expect(data.models).toBeArray();
 });
 tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
  const response = await fetch(`${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:  ${avgTimeSec.toFixed(1)}s`);
  console.log(`========================================\n`);
 });
 export default tap.start();
--- a/test/test.invoices.minicpm.ts
+++ b/test/test.invoices.minicpm.ts
@@ -0,0 +1,477 @@
 /**
 * Invoice extraction test using MiniCPM-V (visual extraction)
 *
 * Consensus approach:
 * 1. Pass 1: Fast JSON extraction
 * 2. Pass 2: Confirm with thinking enabled
 * 3. If mismatch: repeat until consensus or max attempts
 */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureMiniCpm } from './helpers/docker.js';
 const OLLAMA_URL = 'http://localhost:11434';
 const MODEL = 'openbmb/minicpm-v4.5:q8_0';
 interface IInvoice {
  invoice_number: string;
  invoice_date: string;
  vendor_name: string;
  currency: string;
  net_amount: number;
  vat_amount: number;
  total_amount: number;
 }
 /**
 * Convert PDF to PNG images using ImageMagick
 */
 function convertPdfToImages(pdfPath: string): string[] {
  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
  const outputPattern = path.join(tempDir, 'page-%d.png');
  try {
    execSync(
      `convert -density 300 -quality 95 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
      { stdio: 'pipe' }
    );
    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
    const images: string[] = [];
    for (const file of files) {
      const imagePath = path.join(tempDir, file);
      const imageData = fs.readFileSync(imagePath);
      images.push(imageData.toString('base64'));
    }
    return images;
  } finally {
    fs.rmSync(tempDir, { recursive: true, force: true });
  }
 }
 const JSON_PROMPT = `Extract invoice data from this image. Return ONLY a JSON object with these exact fields:
 {
  "invoice_number": "the invoice number (not VAT ID, not customer ID)",
  "invoice_date": "YYYY-MM-DD format",
  "vendor_name": "company that issued the invoice",
  "currency": "EUR, USD, or GBP",
  "net_amount": 0.00,
  "vat_amount": 0.00,
  "total_amount": 0.00
 }
 Return only the JSON, no explanation.`;
 /**
 * Query MiniCPM-V for JSON output (fast, no thinking)
 */
 async function queryJsonFast(images: string[]): Promise<string> {
  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      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: ${response.status}`);
  }
  const data = await response.json();
  return (data.message?.content || '').trim();
 }
 /**
 * Query MiniCPM-V for JSON output with thinking enabled (slower, more accurate)
 */
 async function queryJsonWithThinking(images: string[]): Promise<string> {
  const response = await fetch(`${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: ${response.status}`);
  }
  const data = await response.json();
  return (data.message?.content || '').trim();
 }
 /**
 * Parse amount from string (handles European format)
 */
 function parseAmount(s: string | number | undefined): number {
  if (s === undefined || s === null) return 0;
  if (typeof s === 'number') return s;
  const match = s.match(/([\d.,]+)/);
  if (!match) return 0;
  const numStr = match[1];
  // Handle European format: 1.234,56 → 1234.56
  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
    ? numStr.replace(/\./g, '').replace(',', '.')
    : numStr.replace(/,/g, '');
  return parseFloat(normalized) || 0;
 }
 /**
 * Extract invoice number from potentially verbose response
 */
 function extractInvoiceNumber(s: string | undefined): string {
  if (!s) return '';
  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
  const patterns = [
    /\b([A-Z]{2,3}\d{10,})\b/i,           // IEE2022006460244
    /\b([A-Z]\d{8,})\b/i,                  // R0014359508
    /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,     // INV-2024-001
    /\b(\d{7,})\b/,                        // 1579087430
  ];
  for (const pattern of patterns) {
    const match = clean.match(pattern);
    if (match) return match[1];
  }
  return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
 }
 /**
 * Extract date (YYYY-MM-DD) from response
 */
 function extractDate(s: string | undefined): string {
  if (!s) return '';
  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
  const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
  if (isoMatch) return isoMatch[1];
  // Try DD/MM/YYYY or DD.MM.YYYY
  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
  if (dmyMatch) {
    return `${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();
  try {
    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;
  }
 }
 /**
 * Parse JSON response into IInvoice
 */
 function parseJsonToInvoice(response: string): IInvoice | null {
  const parsed = extractJsonFromResponse(response);
  if (!parsed) return null;
  return {
    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
    invoice_date: extractDate(String(parsed.invoice_date || '')),
    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
    currency: extractCurrency(String(parsed.currency || '')),
    net_amount: parseAmount(parsed.net_amount as string | number),
    vat_amount: parseAmount(parsed.vat_amount as string | number),
    total_amount: parseAmount(parsed.total_amount as string | number),
  };
 }
 /**
 * Compare two invoices for consensus (key fields must match)
 */
 function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
  const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
  const dateMatch = a.invoice_date === b.invoice_date;
  const totalMatch = Math.abs(a.total_amount - b.total_amount) < 0.02;
  return numMatch && dateMatch && totalMatch;
 }
 /**
 * Extract invoice data using consensus approach:
 * 1. Pass 1: Fast JSON extraction
 * 2. Pass 2: Confirm with thinking enabled
 * 3. If mismatch: repeat until consensus or max 5 attempts
 */
 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',
  };
  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
  if (match) {
    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
  }
  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
  if (match) {
    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
  }
  return dateStr;
 }
 /**
 * Compare extracted invoice against expected
 */
 function compareInvoice(
  extracted: IInvoice,
  expected: IInvoice
 ): { match: boolean; errors: string[] } {
  const errors: string[] = [];
  // 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 (normalizeDate(extracted.invoice_date) !== normalizeDate(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),
      });
    }
  }
  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');
  const minicpmOk = await ensureMiniCpm();
  expect(minicpmOk).toBeTrue();
  console.log('\n[Setup] All containers ready!\n');
 });
 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'))).toBeTrue();
 });
 const testCases = findTestCases();
 console.log(`\nFound ${testCases.length} invoice test cases (MiniCPM-V)\n`);
 let passedCount = 0;
 let failedCount = 0;
 const processingTimes: number[] = [];
 for (const testCase of testCases) {
  tap.test(`should extract invoice: ${testCase.name}`, async () => {
    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
    console.log(`\n=== ${testCase.name} ===`);
    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
    const startTime = Date.now();
    const images = convertPdfToImages(testCase.pdfPath);
    console.log(`  Pages: ${images.length}`);
    const extracted = await extractInvoiceFromImages(images);
    console.log(`  Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
    const elapsedMs = Date.now() - startTime;
    processingTimes.push(elapsedMs);
    const result = compareInvoice(extracted, expected);
    if (result.match) {
      passedCount++;
      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
    } else {
      failedCount++;
      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
      result.errors.forEach((e) => console.log(`    - ${e}`));
    }
    expect(result.match).toBeTrue();
  });
 }
 tap.test('summary', async () => {
  const totalInvoices = testCases.length;
  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
  const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
  console.log(`\n========================================`);
  console.log(`   Invoice Extraction Summary (${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:   ${(totalTimeMs / 1000).toFixed(1)}s`);
  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
  console.log(`========================================\n`);
 });
 export default tap.start();
--- a/test/test.invoices.nanonets.ts
+++ b/test/test.invoices.nanonets.ts
@@ -0,0 +1,604 @@
 /**
 * Invoice extraction using Nanonets-OCR-s + Qwen3 (sequential two-stage pipeline)
 *
 * Stage 1: Nanonets-OCR-s converts ALL document pages to markdown (stop after completion)
 * Stage 2: Qwen3 extracts structured JSON from saved markdown (after Nanonets stops)
 *
 * This approach avoids GPU contention by running services sequentially.
 */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
 const NANONETS_URL = 'http://localhost:8000/v1';
 const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
 const OLLAMA_URL = 'http://localhost:11434';
 const QWEN_MODEL = 'qwen3:8b';
 // Temp directory for storing markdown between stages
 const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-markdown');
 interface IInvoice {
  invoice_number: string;
  invoice_date: string;
  vendor_name: string;
  currency: string;
  net_amount: number;
  vat_amount: number;
  total_amount: number;
 }
 interface ITestCase {
  name: string;
  pdfPath: string;
  jsonPath: string;
  markdownPath?: string;
 }
 // Nanonets-specific prompt for document OCR to markdown
 const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
 Return the tables in html format.
 Return the equations in LaTeX representation.
 If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
 Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
 Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
 // JSON extraction prompt for Qwen3
 const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
 IMPORTANT RULES:
 1. invoice_number: The unique invoice/document number (NOT VAT ID, NOT customer ID)
 2. invoice_date: Format as YYYY-MM-DD
 3. vendor_name: The company that issued the invoice
 4. currency: EUR, USD, or GBP
 5. net_amount: Amount before tax
 6. vat_amount: Tax/VAT amount
 7. total_amount: Final total (gross amount)
 Return ONLY this JSON format, no explanation:
 {
  "invoice_number": "INV-2024-001",
  "invoice_date": "2024-01-15",
  "vendor_name": "Company Name",
  "currency": "EUR",
  "net_amount": 100.00,
  "vat_amount": 19.00,
  "total_amount": 119.00
 }
 INVOICE TEXT:
 `;
 /**
 * 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 "${outputPattern}"`,
      { stdio: 'pipe' }
    );
    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
    const images: string[] = [];
    for (const file of files) {
      const imagePath = path.join(tempDir, file);
      const imageData = fs.readFileSync(imagePath);
      images.push(imageData.toString('base64'));
    }
    return images;
  } finally {
    fs.rmSync(tempDir, { recursive: true, force: true });
  }
 }
 /**
 * Convert a single page to markdown using Nanonets-OCR-s
 */
 async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
  const startTime = Date.now();
  const response = await fetch(`${NANONETS_URL}/chat/completions`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer dummy',
    },
    body: JSON.stringify({
      model: NANONETS_MODEL,
      messages: [{
        role: 'user',
        content: [
          { type: 'image_url', image_url: { url: `data:image/png;base64,${image}` }},
          { type: 'text', text: NANONETS_OCR_PROMPT },
        ],
      }],
      max_tokens: 4096,
      temperature: 0.0,
    }),
  });
  const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
  if (!response.ok) {
    const errorText = await response.text();
    throw new Error(`Nanonets API error: ${response.status} - ${errorText}`);
  }
  const data = await response.json();
  const content = (data.choices?.[0]?.message?.content || '').trim();
  console.log(`        Page ${pageNum}: ${content.length} chars (${elapsed}s)`);
  return content;
 }
 /**
 * Convert all pages of a document to markdown
 */
 async function convertDocumentToMarkdown(images: string[], docName: string): Promise<string> {
  console.log(`      [${docName}] Converting ${images.length} page(s)...`);
  const markdownPages: string[] = [];
  for (let i = 0; i < images.length; i++) {
    const markdown = await convertPageToMarkdown(images[i], i + 1);
    markdownPages.push(`--- PAGE ${i + 1} ---\n${markdown}`);
  }
  const fullMarkdown = markdownPages.join('\n\n');
  console.log(`      [${docName}] Complete: ${fullMarkdown.length} chars total`);
  return fullMarkdown;
 }
 /**
 * Stop Nanonets container
 */
 function stopNanonets(): void {
  console.log('    [Docker] Stopping Nanonets container...');
  try {
    execSync('docker stop nanonets-test 2>/dev/null || true', { stdio: 'pipe' });
    execSync('sleep 5', { stdio: 'pipe' });
    console.log('    [Docker] Nanonets stopped');
  } catch {
    console.log('    [Docker] Nanonets was not running');
  }
 }
 /**
 * Ensure Qwen3 model is available
 */
 async function ensureQwen3(): Promise<boolean> {
  try {
    const response = await fetch(`${OLLAMA_URL}/api/tags`);
    if (response.ok) {
      const data = await response.json();
      const models = data.models || [];
      if (models.some((m: { name: string }) => m.name === QWEN_MODEL)) {
        console.log(`    [Ollama] Model available: ${QWEN_MODEL}`);
        return true;
      }
    }
  } catch {
    return false;
  }
  console.log(`    [Ollama] Pulling ${QWEN_MODEL}...`);
  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ name: QWEN_MODEL, stream: false }),
  });
  return pullResponse.ok;
 }
 /**
 * Parse amount from string (handles European format)
 */
 function parseAmount(s: string | number | undefined): number {
  if (s === undefined || s === null) return 0;
  if (typeof s === 'number') return s;
  const match = s.match(/([\d.,]+)/);
  if (!match) return 0;
  const numStr = match[1];
  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
    ? numStr.replace(/\./g, '').replace(',', '.')
    : numStr.replace(/,/g, '');
  return parseFloat(normalized) || 0;
 }
 /**
 * Extract invoice number from potentially verbose response
 */
 function extractInvoiceNumber(s: string | undefined): string {
  if (!s) return '';
  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
  const patterns = [
    /\b([A-Z]{2,3}\d{10,})\b/i,
    /\b([A-Z]\d{8,})\b/i,
    /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,
    /\b(\d{7,})\b/,
  ];
  for (const pattern of patterns) {
    const match = clean.match(pattern);
    if (match) return match[1];
  }
  return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
 }
 /**
 * Extract date (YYYY-MM-DD) from response
 */
 function extractDate(s: string | undefined): string {
  if (!s) return '';
  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
  const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
  if (isoMatch) return isoMatch[1];
  const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
  if (dmyMatch) {
    return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
  }
  return clean.replace(/[^\d-]/g, '').trim();
 }
 /**
 * Extract currency
 */
 function extractCurrency(s: string | undefined): string {
  if (!s) return 'EUR';
  const upper = s.toUpperCase();
  if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
  if (upper.includes('USD') || upper.includes('$')) return 'USD';
  if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
  return 'EUR';
 }
 /**
 * Extract JSON from response
 */
 function extractJsonFromResponse(response: string): Record<string, unknown> | null {
  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
  try {
    return JSON.parse(jsonStr);
  } catch {
    const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
    if (jsonMatch) {
      try {
        return JSON.parse(jsonMatch[0]);
      } catch {
        return null;
      }
    }
    return null;
  }
 }
 /**
 * Parse JSON response into IInvoice
 */
 function parseJsonToInvoice(response: string): IInvoice | null {
  const parsed = extractJsonFromResponse(response);
  if (!parsed) return null;
  return {
    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
    invoice_date: extractDate(String(parsed.invoice_date || '')),
    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
    currency: extractCurrency(String(parsed.currency || '')),
    net_amount: parseAmount(parsed.net_amount as string | number),
    vat_amount: parseAmount(parsed.vat_amount as string | number),
    total_amount: parseAmount(parsed.total_amount as string | number),
  };
 }
 /**
 * Extract invoice from markdown using Qwen3
 */
 async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
  console.log(`      [${queryId}] Sending to ${QWEN_MODEL}...`);
  const startTime = Date.now();
  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
    body: JSON.stringify({
      model: QWEN_MODEL,
      messages: [{
        role: 'user',
        content: JSON_EXTRACTION_PROMPT + markdown,
      }],
      stream: false,
      options: {
        num_predict: 2000,
        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: ${response.status}`);
  }
  const data = await response.json();
  const content = (data.message?.content || '').trim();
  console.log(`      [${queryId}] Response: ${content.length} chars (${elapsed}s)`);
  return parseJsonToInvoice(content);
 }
 /**
 * Compare two invoices for consensus
 */
 function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
  const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
  const dateMatch = a.invoice_date === b.invoice_date;
  const totalMatch = Math.abs(a.total_amount - b.total_amount) < 0.02;
  return numMatch && dateMatch && totalMatch;
 }
 /**
 * Extract with consensus
 */
 async function extractWithConsensus(markdown: string, docName: string): Promise<IInvoice> {
  const MAX_ATTEMPTS = 3;
  for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
    console.log(`    [${docName}] Attempt ${attempt}/${MAX_ATTEMPTS}`);
    const inv1 = await extractInvoiceFromMarkdown(markdown, `${docName}-A${attempt}Q1`);
    const inv2 = await extractInvoiceFromMarkdown(markdown, `${docName}-A${attempt}Q2`);
    if (!inv1 || !inv2) {
      console.log(`    [${docName}] Parsing failed, retrying...`);
      continue;
    }
    console.log(`    [${docName}] Q1: ${inv1.invoice_number} | ${inv1.invoice_date} | ${inv1.total_amount}`);
    console.log(`    [${docName}] Q2: ${inv2.invoice_number} | ${inv2.invoice_date} | ${inv2.total_amount}`);
    if (invoicesMatch(inv1, inv2)) {
      console.log(`    [${docName}] CONSENSUS`);
      return inv2;
    }
    console.log(`    [${docName}] No consensus`);
  }
  // Fallback
  const fallback = await extractInvoiceFromMarkdown(markdown, `${docName}-FALLBACK`);
  if (fallback) {
    console.log(`    [${docName}] FALLBACK: ${fallback.invoice_number} | ${fallback.invoice_date} | ${fallback.total_amount}`);
    return fallback;
  }
  return {
    invoice_number: '',
    invoice_date: '',
    vendor_name: '',
    currency: 'EUR',
    net_amount: 0,
    vat_amount: 0,
    total_amount: 0,
  };
 }
 /**
 * Normalize date to YYYY-MM-DD
 */
 function normalizeDate(dateStr: string | null): string {
  if (!dateStr) return '';
  if (/^\d{4}-\d{2}-\d{2}$/.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',
  };
  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
  if (match) {
    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
  }
  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
  if (match) {
    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
  }
  return dateStr;
 }
 /**
 * Compare extracted invoice against expected
 */
 function compareInvoice(
  extracted: IInvoice,
  expected: IInvoice
 ): { match: boolean; errors: string[] } {
  const errors: string[] = [];
  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
  if (extNum !== expNum) {
    errors.push(`invoice_number: exp "${expected.invoice_number}", got "${extracted.invoice_number}"`);
  }
  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
    errors.push(`invoice_date: exp "${expected.invoice_date}", got "${extracted.invoice_date}"`);
  }
  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
    errors.push(`total_amount: exp ${expected.total_amount}, got ${extracted.total_amount}`);
  }
  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
    errors.push(`currency: exp "${expected.currency}", got "${extracted.currency}"`);
  }
  return { match: errors.length === 0, errors };
 }
 /**
 * Find all test cases
 */
 function findTestCases(): ITestCase[] {
  const testDir = path.join(process.cwd(), '.nogit/invoices');
  if (!fs.existsSync(testDir)) return [];
  const files = fs.readdirSync(testDir);
  const testCases: ITestCase[] = [];
  for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
    const baseName = pdf.replace('.pdf', '');
    const jsonFile = `${baseName}.json`;
    if (files.includes(jsonFile)) {
      testCases.push({
        name: baseName,
        pdfPath: path.join(testDir, pdf),
        jsonPath: path.join(testDir, jsonFile),
      });
    }
  }
  return testCases.sort((a, b) => a.name.localeCompare(b.name));
 }
 // ============ TESTS ============
 const testCases = findTestCases();
 console.log(`\nFound ${testCases.length} invoice test cases\n`);
 // Ensure temp directory exists
 if (!fs.existsSync(TEMP_MD_DIR)) {
  fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
 }
 // -------- STAGE 1: OCR with Nanonets --------
 tap.test('Stage 1: Setup Nanonets', async () => {
  console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
  const ok = await ensureNanonetsOcr();
  expect(ok).toBeTrue();
 });
 tap.test('Stage 1: Convert all invoices to markdown', async () => {
  console.log('\n  Converting all invoice PDFs to markdown with Nanonets-OCR-s...\n');
  for (const tc of testCases) {
    console.log(`\n    === ${tc.name} ===`);
    const images = convertPdfToImages(tc.pdfPath);
    console.log(`      Pages: ${images.length}`);
    const markdown = await convertDocumentToMarkdown(images, tc.name);
    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
    fs.writeFileSync(mdPath, markdown);
    tc.markdownPath = mdPath;
    console.log(`      Saved: ${mdPath}`);
  }
  console.log('\n  Stage 1 complete: All invoices converted to markdown\n');
 });
 tap.test('Stage 1: Stop Nanonets', async () => {
  stopNanonets();
  await new Promise(resolve => setTimeout(resolve, 3000));
  expect(isContainerRunning('nanonets-test')).toBeFalse();
 });
 // -------- STAGE 2: Extraction with Qwen3 --------
 tap.test('Stage 2: Setup Ollama + Qwen3', async () => {
  console.log('\n========== STAGE 2: Qwen3 Extraction ==========\n');
  const ollamaOk = await ensureMiniCpm();
  expect(ollamaOk).toBeTrue();
  const qwenOk = await ensureQwen3();
  expect(qwenOk).toBeTrue();
 });
 let passedCount = 0;
 let failedCount = 0;
 const processingTimes: number[] = [];
 for (const tc of testCases) {
  tap.test(`Stage 2: Extract ${tc.name}`, async () => {
    const expected: IInvoice = JSON.parse(fs.readFileSync(tc.jsonPath, 'utf-8'));
    console.log(`\n    === ${tc.name} ===`);
    console.log(`    Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
    const startTime = Date.now();
    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
    if (!fs.existsSync(mdPath)) {
      throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
    }
    const markdown = fs.readFileSync(mdPath, 'utf-8');
    console.log(`    Markdown: ${markdown.length} chars`);
    const extracted = await extractWithConsensus(markdown, tc.name);
    const elapsedMs = Date.now() - startTime;
    processingTimes.push(elapsedMs);
    console.log(`    Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
    const result = compareInvoice(extracted, expected);
    if (result.match) {
      passedCount++;
      console.log(`    Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
    } else {
      failedCount++;
      console.log(`    Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
      result.errors.forEach(e => console.log(`      - ${e}`));
    }
    expect(result.match).toBeTrue();
  });
 }
 tap.test('Summary', async () => {
  const totalInvoices = testCases.length;
  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
  const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
  console.log(`\n========================================`);
  console.log(`   Invoice Summary (Nanonets + Qwen3)`);
  console.log(`========================================`);
  console.log(`  Stage 1:   Nanonets-OCR-s (doc -> md)`);
  console.log(`  Stage 2:   Qwen3 8B (md -> JSON)`);
  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
  console.log(`----------------------------------------`);
  console.log(`  Total time:   ${(totalTimeMs / 1000).toFixed(1)}s`);
  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
  console.log(`========================================\n`);
  // Cleanup temp files
  try {
    fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
    console.log(`  Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
  } catch {
    // Ignore
  }
 });
 export default tap.start();
--- a/test/test.invoices.qwen3vl.ts
+++ b/test/test.invoices.qwen3vl.ts
@@ -0,0 +1,351 @@
 /**
 * Invoice extraction using Qwen3-VL 8B Vision (Direct)
 *
 * Multi-query approach: 5 parallel simple queries to avoid token exhaustion.
 * Single pass, no consensus voting.
 */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
 import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureMiniCpm } from './helpers/docker.js';
 const OLLAMA_URL = 'http://localhost:11434';
 const VISION_MODEL = 'qwen3-vl:8b';
 interface IInvoice {
  invoice_number: string;
  invoice_date: string;
  vendor_name: string;
  currency: string;
  net_amount: number;
  vat_amount: number;
  total_amount: number;
 }
 /**
 * Convert PDF to PNG images using ImageMagick
 */
 function convertPdfToImages(pdfPath: string): string[] {
  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
  const outputPattern = path.join(tempDir, 'page-%d.png');
  try {
    // 150 DPI is sufficient for invoice extraction, reduces context size
    execSync(
      `convert -density 150 -quality 90 "${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();
Author	SHA1	Message	Date
Juergen Kunz	6dbd06073b	v1.13.2 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 23:00:24 +00:00
Juergen Kunz	ae28a64902	fix(tests): stabilize OCR extraction tests and manage GPU containers	2026-01-18 23:00:24 +00:00
Juergen Kunz	09ea7440e8	update	2026-01-18 15:54:16 +00:00
Juergen Kunz	177e87d3b8	v1.13.1 Some checks failed Docker (tags) / security (push) Successful in 24s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 13:58:26 +00:00
Juergen Kunz	17ea7717eb	fix(image_support_files): remove PaddleOCR-VL server scripts from image_support_files	2026-01-18 13:58:26 +00:00
Juergen Kunz	bd5bb5d874	v1.13.0 Some checks failed Docker (tags) / security (push) Successful in 29s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 13:56:46 +00:00
Juergen Kunz	d91df70fff	feat(tests): revamp tests and remove legacy Dockerfiles: adopt JSON/consensus workflows, switch MiniCPM model, and delete deprecated Docker/test variants	2026-01-18 13:56:46 +00:00
Juergen Kunz	d6c97a9625	v1.12.0 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 57s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 11:26:38 +00:00
Juergen Kunz	76b21f1f7b	feat(tests): switch vision tests to multi-query extraction (count then per-row/field queries) and add logging/summaries	2026-01-18 11:26:38 +00:00
Juergen Kunz	4c368dfef9	v1.11.0 Some checks failed Docker (tags) / security (push) Successful in 29s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 04:50:57 +00:00
Juergen Kunz	e76768da55	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	2026-01-18 04:50:57 +00:00
Juergen Kunz	63d72a52c9	update	2026-01-18 04:28:57 +00:00
Juergen Kunz	386122c8c7	v1.10.1 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 04:17:30 +00:00
Juergen Kunz	7c8f10497e	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	2026-01-18 04:17:30 +00:00
Juergen Kunz	9f9ec0a671	v1.10.0 Some checks failed Docker (tags) / security (push) Successful in 32s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 03:35:06 +00:00
Juergen Kunz	3780105c6f	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	2026-01-18 03:35:05 +00:00
Juergen Kunz	d237ad19f4	v1.9.0 Some checks failed Docker (tags) / security (push) Successful in 33s Details Docker (tags) / test (push) Failing after 39s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 02:53:24 +00:00
Juergen Kunz	7652a2df52	feat(tests): add Ministral 3 vision tests and improve invoice extraction pipeline to use Ollama chat schema, sanitization, and multi-page support	2026-01-18 02:53:24 +00:00
Juergen Kunz	b316d98f24	v1.8.0 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 41s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-18 00:11:17 +00:00
Juergen Kunz	f0d88fcbe0	feat(paddleocr-vl): add structured HTML output and table parsing for PaddleOCR-VL, update API, tests, and README	2026-01-18 00:11:17 +00:00
Juergen Kunz	0d8a1ebac2	v1.7.1 Some checks failed Docker (tags) / security (push) Successful in 31s Details Docker (tags) / test (push) Failing after 39s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-17 23:13:47 +00:00
Juergen Kunz	5a311dca2d	fix(docker): standardize Dockerfile and entrypoint filenames; add GPU-specific Dockerfiles and update build and test references	2026-01-17 23:13:47 +00:00
Juergen Kunz	ab288380f1	v1.7.0 Some checks failed Docker (tags) / security (push) Successful in 30s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-17 21:50:09 +00:00
Juergen Kunz	30c73b24c1	feat(tests): use Qwen2.5 (Ollama) for invoice extraction tests and add helpers for model management; normalize dates and coerce numeric fields	2026-01-17 21:50:09 +00:00
Juergen Kunz	311e7a8fd4	v1.6.0 Some checks failed Docker (tags) / security (push) Successful in 32s Details Docker (tags) / test (push) Failing after 40s Details Docker (tags) / release (push) Has been skipped Details Docker (tags) / metadata (push) Has been skipped Details	2026-01-17 20:22:23 +00:00
Juergen Kunz	80e6866442	feat(paddleocr-vl): add PaddleOCR-VL full pipeline Docker image and API server, plus integration tests and docker helpers	2026-01-17 20:22:23 +00:00