v1.13.0

Dockerfile_minicpm45v_cpu

@@ -1,27 +0,0 @@
-# MiniCPM-V 4.5 CPU Variant
-# Vision-Language Model optimized for CPU-only inference
-FROM ollama/ollama:latest
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="MiniCPM-V 4.5 Vision-Language Model - CPU optimized (GGUF)"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration for CPU-only mode
-ENV MODEL_NAME="minicpm-v"
-ENV OLLAMA_HOST="0.0.0.0"
-ENV OLLAMA_ORIGINS="*"
-# Disable GPU usage for CPU-only variant
-ENV CUDA_VISIBLE_DEVICES=""
-
-# Copy and setup entrypoint
-COPY image_support_files/minicpm45v_entrypoint.sh /usr/local/bin/docker-entrypoint.sh
-RUN chmod +x /usr/local/bin/docker-entrypoint.sh
-
-# Expose Ollama API port
-EXPOSE 11434
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=3 \
-    CMD curl -f http://localhost:11434/api/tags || exit 1
-
-ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"]

Dockerfile_paddleocr_vl_cpu

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

Dockerfile_paddleocr_vl_full

@@ -1,90 +0,0 @@
-# PaddleOCR-VL Full Pipeline (PP-DocLayoutV2 + PaddleOCR-VL + Structured Output)
-# Self-contained GPU image with complete document parsing pipeline
-FROM nvidia/cuda:12.4.0-devel-ubuntu22.04
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL Full Pipeline - Layout Detection + VL Recognition + JSON/Markdown Output"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV HF_HOME=/root/.cache/huggingface
-ENV PADDLEOCR_HOME=/root/.paddleocr
-ENV SERVER_PORT=8000
-ENV SERVER_HOST=0.0.0.0
-ENV VLM_PORT=8080
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.11 \
-    python3.11-venv \
-    python3.11-dev \
-    python3-pip \
-    libgl1-mesa-glx \
-    libglib2.0-0 \
-    libgomp1 \
-    libsm6 \
-    libxext6 \
-    libxrender1 \
-    curl \
-    git \
-    wget \
-    && rm -rf /var/lib/apt/lists/* \
-    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
-
-# Create and activate virtual environment
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Upgrade pip
-RUN pip install --no-cache-dir --upgrade pip setuptools wheel
-
-# Install PaddlePaddle GPU (CUDA 12.x)
-RUN pip install --no-cache-dir \
-    paddlepaddle-gpu==3.2.1 \
-    --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
-
-# Install PaddleOCR with doc-parser (includes PP-DocLayoutV2)
-RUN pip install --no-cache-dir \
-    "paddleocr[doc-parser]" \
-    safetensors
-
-# Install PyTorch with CUDA support
-RUN pip install --no-cache-dir \
-    torch==2.5.1 \
-    torchvision \
-    --index-url https://download.pytorch.org/whl/cu124
-
-# Install transformers for PaddleOCR-VL inference (no vLLM - use local inference)
-# PaddleOCR-VL requires transformers>=4.55.0 for use_kernel_forward_from_hub
-RUN pip install --no-cache-dir \
-    transformers>=4.55.0 \
-    accelerate \
-    hf-kernels
-
-# Install our API server dependencies
-RUN pip install --no-cache-dir \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    httpx \
-    pillow
-
-# Copy server files
-COPY image_support_files/paddleocr_vl_full_server.py /app/server.py
-COPY image_support_files/paddleocr_vl_full_entrypoint.sh /usr/local/bin/entrypoint.sh
-RUN chmod +x /usr/local/bin/entrypoint.sh
-
-# Expose ports (8000 = API, 8080 = internal VLM server)
-EXPOSE 8000
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
-
-ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]

Dockerfile_paddleocr_vl_gpu

@@ -1,71 +0,0 @@
-# PaddleOCR-VL GPU Variant (Transformers-based, not vLLM)
-# Vision-Language Model for document parsing using transformers with CUDA
-FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04
-
-LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
-LABEL description="PaddleOCR-VL 0.9B GPU - Vision-Language Model using transformers"
-LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
-
-# Environment configuration
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV HF_HOME=/root/.cache/huggingface
-ENV SERVER_PORT=8000
-ENV SERVER_HOST=0.0.0.0
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.11 \
-    python3.11-venv \
-    python3.11-dev \
-    python3-pip \
-    libgl1-mesa-glx \
-    libglib2.0-0 \
-    libgomp1 \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/* \
-    && update-alternatives --install /usr/bin/python python /usr/bin/python3.11 1 \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
-
-# Create and activate virtual environment
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# Install PyTorch with CUDA support
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir \
-    torch==2.5.1 \
-    torchvision \
-    --index-url https://download.pytorch.org/whl/cu124
-
-# Install Python dependencies (transformers-based, not vLLM)
-RUN pip install --no-cache-dir \
-    transformers \
-    accelerate \
-    safetensors \
-    pillow \
-    fastapi \
-    uvicorn[standard] \
-    python-multipart \
-    httpx \
-    protobuf \
-    sentencepiece \
-    einops
-
-# Copy server files (same as CPU variant - it auto-detects CUDA)
-COPY image_support_files/paddleocr_vl_server.py /app/paddleocr_vl_server.py
-COPY image_support_files/paddleocr_vl_entrypoint.sh /usr/local/bin/paddleocr-vl-entrypoint.sh
-RUN chmod +x /usr/local/bin/paddleocr-vl-entrypoint.sh
-
-# Expose API port
-EXPOSE 8000
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=10s --start-period=300s --retries=3 \
-    CMD curl -f http://localhost:8000/health || exit 1
-
-ENTRYPOINT ["/usr/local/bin/paddleocr-vl-entrypoint.sh"]

Dockerfile_qwen3vl

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

changelog.md

@@ -1,5 +1,62 @@
 # Changelog
 
+## 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
 

image_support_files/paddleocr_vl_entrypoint.sh

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

image_support_files/paddleocr_vl_full_entrypoint.sh

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

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@host.today/ht-docker-ai",
-  "version": "1.8.0",
+  "version": "1.13.0",
   "type": "module",
   "private": false,
   "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",

test/helpers/docker.ts

@@ -311,9 +311,8 @@ export async function ensureOllamaModel(modelName: string): Promise<boolean> {
     if (response.ok) {
       const data = await response.json();
       const models = data.models || [];
-      const exists = models.some((m: { name: string }) =>
+      // Exact match required - don't match on prefix
-        m.name === modelName || m.name.startsWith(modelName.split(':')[0])
+      const exists = models.some((m: { name: string }) => m.name === modelName);
-      );
 
       if (exists) {
         console.log(`[Ollama] Model already available: ${modelName}`);
@@ -358,3 +357,29 @@ export async function ensureQwen25(): Promise<boolean> {
   // Then ensure the Qwen2.5 model is pulled
   return ensureOllamaModel('qwen2.5:7b');
 }
+
+/**
+ * Ensure Ministral 3 8B model is available (for structured JSON extraction)
+ * Ministral 3 has native JSON output support and OCR-style document extraction
+ */
+export async function ensureMinistral3(): Promise<boolean> {
+  // First ensure the Ollama service (MiniCPM container) is running
+  const ollamaOk = await ensureMiniCpm();
+  if (!ollamaOk) return false;
+
+  // Then ensure the Ministral 3 8B model is pulled
+  return ensureOllamaModel('ministral-3:8b');
+}
+
+/**
+ * Ensure Qwen3-VL 8B model is available (vision-language model)
+ * Q4_K_M quantization (~5GB) - fits in 15GB VRAM with room to spare
+ */
+export async function ensureQwen3Vl(): Promise<boolean> {
+  // First ensure the Ollama service is running
+  const ollamaOk = await ensureMiniCpm();
+  if (!ollamaOk) return false;
+
+  // Then ensure Qwen3-VL 8B is pulled
+  return ensureOllamaModel('qwen3-vl:8b');
+}

test/test.bankstatements.combined.ts

@@ -1,549 +0,0 @@
-/**
- * Bank statement extraction test using MiniCPM-V (visual) + PaddleOCR-VL (table recognition)
- *
- * This is the combined/dual-VLM approach that uses both models for consensus:
- *   - MiniCPM-V for visual extraction
- *   - PaddleOCR-VL for table recognition
- */
-import { tap, expect } from '@git.zone/tstest/tapbundle';
-import * as fs from 'fs';
-import * as path from 'path';
-import { execSync } from 'child_process';
-import * as os from 'os';
-import { ensurePaddleOcrVl, ensureMiniCpm } from './helpers/docker.js';
-
-// Service URLs
-const OLLAMA_URL = 'http://localhost:11434';
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-
-// Models
-const MINICPM_MODEL = 'minicpm-v:latest';
-const PADDLEOCR_VL_MODEL = 'paddleocr-vl';
-
-// Prompt for MiniCPM-V visual extraction
-const MINICPM_EXTRACT_PROMPT = `/nothink
-You are a bank statement parser. Extract EVERY transaction from the table.
-
-Read the Amount column carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point
-
-For each row output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Do not skip any rows. Return ONLY the JSON array, no explanation.`;
-
-// Prompt for PaddleOCR-VL table extraction
-const PADDLEOCR_VL_TABLE_PROMPT = `Table Recognition:`;
-
-// Post-processing prompt to convert PaddleOCR-VL output to JSON
-const PADDLEOCR_VL_CONVERT_PROMPT = `/nothink
-Convert the following bank statement table data to JSON.
-
-Read the Amount values carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point
-
-For each transaction output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Return ONLY the JSON array, no explanation.
-
-Table data:
----
-{TABLE_DATA}
----`;
-
-interface ITransaction {
-  date: string;
-  counterparty: string;
-  amount: number;
-}
-
-/**
- * Convert PDF to PNG images using ImageMagick
- */
-function convertPdfToImages(pdfPath: string): string[] {
-  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
-  const outputPattern = path.join(tempDir, 'page-%d.png');
-
-  try {
-    execSync(
-      `convert -density 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
-      { stdio: 'pipe' }
-    );
-
-    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
-    const images: string[] = [];
-
-    for (const file of files) {
-      const imagePath = path.join(tempDir, file);
-      const imageData = fs.readFileSync(imagePath);
-      images.push(imageData.toString('base64'));
-    }
-
-    return images;
-  } finally {
-    fs.rmSync(tempDir, { recursive: true, force: true });
-  }
-}
-
-/**
- * Extract using MiniCPM-V via Ollama
- */
-async function extractWithMiniCPM(images: string[], passLabel: string): Promise<ITransaction[]> {
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt: MINICPM_EXTRACT_PROMPT,
-    images,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
-
-  const reader = response.body?.getReader();
-  if (!reader) {
-    throw new Error('No response body');
-  }
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-  let lineBuffer = '';
-
-  console.log(`[${passLabel}] Extracting with MiniCPM-V...`);
-
-  while (true) {
-    const { done, value } = await reader.read();
-    if (done) break;
-
-    const chunk = decoder.decode(value, { stream: true });
-    const lines = chunk.split('\n').filter((l) => l.trim());
-
-    for (const line of lines) {
-      try {
-        const json = JSON.parse(line);
-        if (json.response) {
-          fullText += json.response;
-          lineBuffer += json.response;
-
-          if (lineBuffer.includes('\n')) {
-            const parts = lineBuffer.split('\n');
-            for (let i = 0; i < parts.length - 1; i++) {
-              console.log(parts[i]);
-            }
-            lineBuffer = parts[parts.length - 1];
-          }
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  if (lineBuffer) {
-    console.log(lineBuffer);
-  }
-  console.log('');
-
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error('No JSON array found in response');
-  }
-
-  return JSON.parse(fullText.substring(startIdx, endIdx));
-}
-
-/**
- * Extract table using PaddleOCR-VL via OpenAI-compatible API
- */
-async function extractTableWithPaddleOCRVL(imageBase64: string): Promise<string> {
-  const payload = {
-    model: PADDLEOCR_VL_MODEL,
-    messages: [
-      {
-        role: 'user',
-        content: [
-          {
-            type: 'image_url',
-            image_url: { url: `data:image/png;base64,${imageBase64}` },
-          },
-          {
-            type: 'text',
-            text: PADDLEOCR_VL_TABLE_PROMPT,
-          },
-        ],
-      },
-    ],
-    temperature: 0.0,
-    max_tokens: 8192,
-  };
-
-  const response = await fetch(`${PADDLEOCR_VL_URL}/v1/chat/completions`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    const text = await response.text();
-    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
-  }
-
-  const data = await response.json();
-  return data.choices?.[0]?.message?.content || '';
-}
-
-/**
- * Convert PaddleOCR-VL table output to transactions using MiniCPM-V
- */
-async function convertTableToTransactions(
-  tableData: string,
-  passLabel: string
-): Promise<ITransaction[]> {
-  const prompt = PADDLEOCR_VL_CONVERT_PROMPT.replace('{TABLE_DATA}', tableData);
-
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
-
-  const reader = response.body?.getReader();
-  if (!reader) {
-    throw new Error('No response body');
-  }
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-
-  console.log(`[${passLabel}] Converting table data to JSON...`);
-
-  while (true) {
-    const { done, value } = await reader.read();
-    if (done) break;
-
-    const chunk = decoder.decode(value, { stream: true });
-    const lines = chunk.split('\n').filter((l) => l.trim());
-
-    for (const line of lines) {
-      try {
-        const json = JSON.parse(line);
-        if (json.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error('No JSON array found in response');
-  }
-
-  return JSON.parse(fullText.substring(startIdx, endIdx));
-}
-
-/**
- * Extract using PaddleOCR-VL (table recognition) + conversion
- */
-async function extractWithPaddleOCRVL(
-  images: string[],
-  passLabel: string
-): Promise<ITransaction[]> {
-  console.log(`[${passLabel}] Extracting tables with PaddleOCR-VL...`);
-
-  // Extract table data from each page
-  const tableDataParts: string[] = [];
-  for (let i = 0; i < images.length; i++) {
-    console.log(`[${passLabel}] Processing page ${i + 1}/${images.length}...`);
-    const tableData = await extractTableWithPaddleOCRVL(images[i]);
-    if (tableData.trim()) {
-      tableDataParts.push(`--- Page ${i + 1} ---\n${tableData}`);
-    }
-  }
-
-  const combinedTableData = tableDataParts.join('\n\n');
-  console.log(`[${passLabel}] Got ${combinedTableData.length} chars of table data`);
-
-  // Convert to transactions
-  return convertTableToTransactions(combinedTableData, passLabel);
-}
-
-/**
- * Create a hash of transactions for comparison
- */
-function hashTransactions(transactions: ITransaction[]): string {
-  return transactions
-    .map((t) => `${t.date}|${t.amount.toFixed(2)}`)
-    .sort()
-    .join(';');
-}
-
-/**
- * Check if PaddleOCR-VL service is available
- */
-async function isPaddleOCRVLAvailable(): Promise<boolean> {
-  try {
-    const response = await fetch(`${PADDLEOCR_VL_URL}/health`, {
-      method: 'GET',
-      signal: AbortSignal.timeout(5000),
-    });
-    return response.ok;
-  } catch {
-    return false;
-  }
-}
-
-/**
- * Extract with dual-VLM consensus
- * Strategy:
- *   Pass 1 = MiniCPM-V visual extraction
- *   Pass 2 = PaddleOCR-VL table recognition (if available)
- *   Pass 3+ = MiniCPM-V visual (fallback)
- */
-async function extractWithConsensus(
-  images: string[],
-  maxPasses: number = 5
-): Promise<ITransaction[]> {
-  const results: Array<{ transactions: ITransaction[]; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
-
-  const addResult = (transactions: ITransaction[], passLabel: string): number => {
-    const hash = hashTransactions(transactions);
-    results.push({ transactions, hash });
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(
-      `[${passLabel}] Got ${transactions.length} transactions (hash: ${hash.substring(0, 20)}...)`
-    );
-    return hashCounts.get(hash)!;
-  };
-
-  // Check if PaddleOCR-VL is available
-  const paddleOCRVLAvailable = await isPaddleOCRVLAvailable();
-  if (paddleOCRVLAvailable) {
-    console.log('[Setup] PaddleOCR-VL service available - using dual-VLM consensus');
-  } else {
-    console.log('[Setup] PaddleOCR-VL not available - using MiniCPM-V only');
-  }
-
-  // Pass 1: MiniCPM-V visual extraction
-  try {
-    const pass1Result = await extractWithMiniCPM(images, 'Pass 1 MiniCPM-V');
-    addResult(pass1Result, 'Pass 1 MiniCPM-V');
-  } catch (err) {
-    console.log(`[Pass 1] Error: ${err}`);
-  }
-
-  // Pass 2: PaddleOCR-VL table recognition (if available)
-  if (paddleOCRVLAvailable) {
-    try {
-      const pass2Result = await extractWithPaddleOCRVL(images, 'Pass 2 PaddleOCR-VL');
-      const count = addResult(pass2Result, 'Pass 2 PaddleOCR-VL');
-      if (count >= 2) {
-        console.log('[Consensus] MiniCPM-V and PaddleOCR-VL extractions match!');
-        return pass2Result;
-      }
-    } catch (err) {
-      console.log(`[Pass 2 PaddleOCR-VL] Error: ${err}`);
-    }
-  }
-
-  // Pass 3+: Continue with MiniCPM-V visual passes
-  const startPass = paddleOCRVLAvailable ? 3 : 2;
-  for (let pass = startPass; pass <= maxPasses; pass++) {
-    try {
-      const transactions = await extractWithMiniCPM(images, `Pass ${pass} MiniCPM-V`);
-      const count = addResult(transactions, `Pass ${pass} MiniCPM-V`);
-
-      if (count >= 2) {
-        console.log(`[Consensus] Reached after ${pass} passes`);
-        return transactions;
-      }
-
-      console.log(`[Pass ${pass}] No consensus yet, trying again...`);
-    } catch (err) {
-      console.log(`[Pass ${pass}] Error: ${err}`);
-    }
-  }
-
-  // No consensus reached - return the most common result
-  let bestHash = '';
-  let bestCount = 0;
-  for (const [hash, count] of hashCounts) {
-    if (count > bestCount) {
-      bestCount = count;
-      bestHash = hash;
-    }
-  }
-
-  if (!bestHash) {
-    throw new Error('No valid results obtained');
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`[No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.transactions;
-}
-
-/**
- * Compare extracted transactions against expected
- */
-function compareTransactions(
-  extracted: ITransaction[],
-  expected: ITransaction[]
-): { matches: number; total: number; errors: string[] } {
-  const errors: string[] = [];
-  let matches = 0;
-
-  for (let i = 0; i < expected.length; i++) {
-    const exp = expected[i];
-    const ext = extracted[i];
-
-    if (!ext) {
-      errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
-      continue;
-    }
-
-    const dateMatch = ext.date === exp.date;
-    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
-
-    if (dateMatch && amountMatch) {
-      matches++;
-    } else {
-      errors.push(
-        `Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`
-      );
-    }
-  }
-
-  if (extracted.length > expected.length) {
-    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
-  }
-
-  return { matches, total: expected.length, errors };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL is running (auto-detects GPU/CPU)
-  const paddleOk = await ensurePaddleOcrVl();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
-  const response = await fetch(`${OLLAMA_URL}/api/tags`);
-  const data = await response.json();
-  const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
-});
-
-tap.test('should check PaddleOCR-VL availability', async () => {
-  const available = await isPaddleOCRVLAvailable();
-  console.log(`PaddleOCR-VL available: ${available}`);
-  expect(available).toBeTrue();
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-for (const testCase of testCases) {
-  tap.test(`should extract transactions from ${testCase.name}`, async () => {
-    // Load expected transactions
-    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
-    console.log(`\n=== ${testCase.name} ===`);
-    console.log(`Expected: ${expected.length} transactions`);
-
-    // Convert PDF to images
-    console.log('Converting PDF to images...');
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`Converted: ${images.length} pages\n`);
-
-    // Extract with dual-VLM consensus
-    const extracted = await extractWithConsensus(images);
-    console.log(`\nFinal: ${extracted.length} transactions`);
-
-    // Compare results
-    const result = compareTransactions(extracted, expected);
-    console.log(`Accuracy: ${result.matches}/${result.total}`);
-
-    if (result.errors.length > 0) {
-      console.log('Errors:');
-      result.errors.forEach((e) => console.log(`  - ${e}`));
-    }
-
-    // Assert high accuracy
-    const accuracy = result.matches / result.total;
-    expect(accuracy).toBeGreaterThan(0.95);
-    expect(extracted.length).toEqual(expected.length);
-  });
-}
-
-export default tap.start();

test/test.bankstatements.minicpm.ts

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

test/test.bankstatements.paddleocr-vl.ts

@@ -1,346 +0,0 @@
-/**
- * Bank statement extraction test using PaddleOCR-VL Full Pipeline
- *
- * This tests the complete PaddleOCR-VL pipeline for bank statements:
- *   1. PP-DocLayoutV2 for layout detection
- *   2. PaddleOCR-VL for recognition (tables with proper structure)
- *   3. Structured Markdown output with tables
- *   4. MiniCPM extracts transactions from structured tables
- *
- * The structured Markdown has properly formatted tables,
- * making it much easier for MiniCPM to extract transaction data.
- */
-import { tap, expect } from '@git.zone/tstest/tapbundle';
-import * as fs from 'fs';
-import * as path from 'path';
-import { execSync } from 'child_process';
-import * as os from 'os';
-import { ensurePaddleOcrVlFull, ensureMiniCpm } from './helpers/docker.js';
-
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-const OLLAMA_URL = 'http://localhost:11434';
-const MINICPM_MODEL = 'minicpm-v:latest';
-
-interface ITransaction {
-  date: string;
-  counterparty: string;
-  amount: number;
-}
-
-/**
- * Convert PDF to PNG images using ImageMagick
- */
-function convertPdfToImages(pdfPath: string): string[] {
-  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
-  const outputPattern = path.join(tempDir, 'page-%d.png');
-
-  try {
-    execSync(
-      `convert -density 300 -quality 100 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
-      { stdio: 'pipe' }
-    );
-
-    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
-    const images: string[] = [];
-
-    for (const file of files) {
-      const imagePath = path.join(tempDir, file);
-      const imageData = fs.readFileSync(imagePath);
-      images.push(imageData.toString('base64'));
-    }
-
-    return images;
-  } finally {
-    fs.rmSync(tempDir, { recursive: true, force: true });
-  }
-}
-
-/**
- * Parse document using PaddleOCR-VL Full Pipeline (returns structured Markdown)
- */
-async function parseDocument(imageBase64: string): Promise<string> {
-  const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      image: imageBase64,
-      output_format: 'markdown',
-    }),
-  });
-
-  if (!response.ok) {
-    const text = await response.text();
-    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
-  }
-
-  const data = await response.json();
-
-  if (!data.success) {
-    throw new Error(`PaddleOCR-VL error: ${data.error}`);
-  }
-
-  return data.result?.markdown || '';
-}
-
-/**
- * Extract transactions from structured Markdown using MiniCPM
- */
-async function extractTransactionsFromMarkdown(markdown: string): Promise<ITransaction[]> {
-  console.log(`    [Extract] Processing ${markdown.length} chars of Markdown`);
-
-  const prompt = `/nothink
-Convert this bank statement to a JSON array of transactions.
-
-Read the Amount values carefully:
-- "- 21,47 €" means DEBIT, output as: -21.47
-- "+ 1.000,00 €" means CREDIT, output as: 1000.00
-- European format: comma = decimal point, dot = thousands
-
-For each transaction output: {"date":"YYYY-MM-DD","counterparty":"NAME","amount":-21.47}
-
-Return ONLY the JSON array, no explanation.
-
-Document:
-${markdown}`;
-
-  const payload = {
-    model: MINICPM_MODEL,
-    prompt,
-    stream: true,
-    options: {
-      num_predict: 16384,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
-
-  const reader = response.body?.getReader();
-  if (!reader) {
-    throw new Error('No response body');
-  }
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-
-  while (true) {
-    const { done, value } = await reader.read();
-    if (done) break;
-
-    const chunk = decoder.decode(value, { stream: true });
-    const lines = chunk.split('\n').filter((l) => l.trim());
-
-    for (const line of lines) {
-      try {
-        const json = JSON.parse(line);
-        if (json.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  // Extract JSON array from response
-  const startIdx = fullText.indexOf('[');
-  const endIdx = fullText.lastIndexOf(']') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error(`No JSON array found in response: ${fullText.substring(0, 200)}`);
-  }
-
-  const jsonStr = fullText.substring(startIdx, endIdx);
-  return JSON.parse(jsonStr);
-}
-
-/**
- * Extract transactions from all pages of a bank statement
- */
-async function extractAllTransactions(images: string[]): Promise<ITransaction[]> {
-  const allTransactions: ITransaction[] = [];
-
-  for (let i = 0; i < images.length; i++) {
-    console.log(`  Processing page ${i + 1}/${images.length}...`);
-
-    // Parse with full pipeline
-    const markdown = await parseDocument(images[i]);
-    console.log(`    [Parse] Got ${markdown.split('\n').length} lines of Markdown`);
-
-    // Extract transactions
-    try {
-      const transactions = await extractTransactionsFromMarkdown(markdown);
-      console.log(`    [Extracted] ${transactions.length} transactions`);
-      allTransactions.push(...transactions);
-    } catch (err) {
-      console.log(`    [Error] ${err}`);
-    }
-  }
-
-  return allTransactions;
-}
-
-/**
- * Compare transactions - find matching transaction in expected list
- */
-function findMatchingTransaction(
-  tx: ITransaction,
-  expectedList: ITransaction[]
-): ITransaction | undefined {
-  return expectedList.find((exp) => {
-    const dateMatch = tx.date === exp.date;
-    const amountMatch = Math.abs(tx.amount - exp.amount) < 0.02;
-    const counterpartyMatch =
-      tx.counterparty?.toLowerCase().includes(exp.counterparty?.toLowerCase().slice(0, 10)) ||
-      exp.counterparty?.toLowerCase().includes(tx.counterparty?.toLowerCase().slice(0, 10));
-    return dateMatch && amountMatch && counterpartyMatch;
-  });
-}
-
-/**
- * Calculate extraction accuracy
- */
-function calculateAccuracy(
-  extracted: ITransaction[],
-  expected: ITransaction[]
-): { matched: number; total: number; accuracy: number } {
-  let matched = 0;
-  const usedExpected = new Set<number>();
-
-  for (const tx of extracted) {
-    for (let i = 0; i < expected.length; i++) {
-      if (usedExpected.has(i)) continue;
-
-      const exp = expected[i];
-      const dateMatch = tx.date === exp.date;
-      const amountMatch = Math.abs(tx.amount - exp.amount) < 0.02;
-
-      if (dateMatch && amountMatch) {
-        matched++;
-        usedExpected.add(i);
-        break;
-      }
-    }
-  }
-
-  return {
-    matched,
-    total: expected.length,
-    accuracy: expected.length > 0 ? (matched / expected.length) * 100 : 0,
-  };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/bankstatements/
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit/bankstatements');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  testCases.sort((a, b) => a.name.localeCompare(b.name));
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL Full Pipeline is running
-  const paddleOk = await ensurePaddleOcrVlFull();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running (for field extraction from Markdown)
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} bank statement test cases (PaddleOCR-VL Full Pipeline)\n`);
-
-const results: Array<{ name: string; accuracy: number; matched: number; total: number }> = [];
-
-for (const testCase of testCases) {
-  tap.test(`should extract bank statement: ${testCase.name}`, async () => {
-    // Load expected data
-    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
-    console.log(`\n=== ${testCase.name} ===`);
-    console.log(`Expected: ${expected.length} transactions`);
-
-    const startTime = Date.now();
-
-    // Convert PDF to images
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`  Pages: ${images.length}`);
-
-    // Extract all transactions
-    const extracted = await extractAllTransactions(images);
-
-    const endTime = Date.now();
-    const elapsedMs = endTime - startTime;
-
-    // Calculate accuracy
-    const accuracy = calculateAccuracy(extracted, expected);
-    results.push({
-      name: testCase.name,
-      accuracy: accuracy.accuracy,
-      matched: accuracy.matched,
-      total: accuracy.total,
-    });
-
-    console.log(`  Extracted: ${extracted.length} transactions`);
-    console.log(`  Matched: ${accuracy.matched}/${accuracy.total} (${accuracy.accuracy.toFixed(1)}%)`);
-    console.log(`  Time: ${(elapsedMs / 1000).toFixed(1)}s`);
-
-    // We expect at least 50% accuracy
-    expect(accuracy.accuracy).toBeGreaterThan(50);
-  });
-}
-
-tap.test('summary', async () => {
-  const totalStatements = results.length;
-  const avgAccuracy =
-    results.length > 0 ? results.reduce((a, b) => a + b.accuracy, 0) / results.length : 0;
-  const totalMatched = results.reduce((a, b) => a + b.matched, 0);
-  const totalExpected = results.reduce((a, b) => a + b.total, 0);
-
-  console.log(`\n======================================================`);
-  console.log(`  Bank Statement Extraction Summary (PaddleOCR-VL Full)`);
-  console.log(`======================================================`);
-  console.log(`  Method:      PaddleOCR-VL Full Pipeline -> MiniCPM`);
-  console.log(`  Statements:  ${totalStatements}`);
-  console.log(`  Transactions: ${totalMatched}/${totalExpected} matched`);
-  console.log(`  Avg accuracy: ${avgAccuracy.toFixed(1)}%`);
-  console.log(`======================================================\n`);
-});
-
-export default tap.start();

test/test.bankstatements.qwen3vl.ts

@@ -0,0 +1,345 @@
+/**
+ * Bank statement extraction using Qwen3-VL 8B Vision (Direct)
+ *
+ * Multi-query approach:
+ * 1. First ask how many transactions on each page
+ * 2. Then query each transaction individually
+ * Single pass, no consensus voting.
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensureMiniCpm } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const VISION_MODEL = 'qwen3-vl:8b';
+
+interface ITransaction {
+  date: string;
+  counterparty: string;
+  amount: number;
+}
+
+/**
+ * Convert PDF to PNG images
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    execSync(
+      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Query Qwen3-VL with a simple prompt
+ */
+async function queryVision(image: string, prompt: string): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: VISION_MODEL,
+      messages: [{
+        role: 'user',
+        content: prompt,
+        images: [image],
+      }],
+      stream: false,
+      options: {
+        num_predict: 500,
+        temperature: 0.1,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const data = await response.json();
+  return (data.message?.content || '').trim();
+}
+
+/**
+ * Count transactions on a page
+ */
+async function countTransactions(image: string, pageNum: number): Promise<number> {
+  const response = await queryVision(image,
+    `How many transaction rows are in this bank statement table?
+Count only the data rows (with dates like "01.01.2024" and amounts like "- 50,00 €").
+Do NOT count the header row or summary/total rows.
+Answer with just the number, for example: 7`
+  );
+
+  console.log(`    [Page ${pageNum}] Count query response: "${response}"`);
+  const match = response.match(/(\d+)/);
+  const count = match ? parseInt(match[1], 10) : 0;
+  console.log(`    [Page ${pageNum}] Parsed count: ${count}`);
+  return count;
+}
+
+/**
+ * Get a single transaction by index (logs immediately when complete)
+ */
+async function getTransaction(image: string, index: number, pageNum: number): Promise<ITransaction | null> {
+  const response = await queryVision(image,
+    `This is a bank statement. Look at transaction row #${index} in the table (counting from top, excluding headers).
+
+Extract this transaction's details:
+- Date in YYYY-MM-DD format
+- Counterparty/description name
+- Amount as number (negative for debits like "- 21,47 €" = -21.47, positive for credits like "+ 100,00 €" = 100.00)
+
+Answer in format: DATE|COUNTERPARTY|AMOUNT
+Example: 2024-01-15|Amazon|−25.99`
+  );
+
+  // Parse the response
+  const lines = response.split('\n').filter(l => l.includes('|'));
+  const line = lines[lines.length - 1] || response;
+  const parts = line.split('|').map(p => p.trim());
+
+  if (parts.length >= 3) {
+    // Parse amount - handle various formats
+    let amountStr = parts[2].replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
+    // European format: comma is decimal
+    if (amountStr.includes(',')) {
+      amountStr = amountStr.replace(/\./g, '').replace(',', '.');
+    }
+    const amount = parseFloat(amountStr) || 0;
+
+    const tx = {
+      date: parts[0],
+      counterparty: parts[1],
+      amount: amount,
+    };
+    // Log immediately as this transaction completes
+    console.log(`    [P${pageNum} Tx${index.toString().padStart(2, ' ')}] ${tx.date} | ${tx.counterparty.substring(0, 25).padEnd(25)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
+    return tx;
+  }
+
+  // Log raw response on parse failure
+  console.log(`    [P${pageNum} Tx${index.toString().padStart(2, ' ')}] PARSE FAILED: "${response.replace(/\n/g, ' ').substring(0, 60)}..."`);
+  return null;
+}
+
+/**
+ * Extract transactions from a single page using multi-query approach
+ */
+async function extractTransactionsFromPage(image: string, pageNum: number): Promise<ITransaction[]> {
+  // Step 1: Count transactions
+  const count = await countTransactions(image, pageNum);
+
+  if (count === 0) {
+    return [];
+  }
+
+  // Step 2: Query each transaction (in batches to avoid overwhelming)
+  // Each transaction logs itself as it completes
+  const transactions: ITransaction[] = [];
+  const batchSize = 5;
+
+  for (let start = 1; start <= count; start += batchSize) {
+    const end = Math.min(start + batchSize - 1, count);
+    const indices = Array.from({ length: end - start + 1 }, (_, i) => start + i);
+
+    // Query batch in parallel - each logs as it completes
+    const results = await Promise.all(
+      indices.map(i => getTransaction(image, i, pageNum))
+    );
+
+    for (const tx of results) {
+      if (tx) {
+        transactions.push(tx);
+      }
+    }
+  }
+
+  console.log(`    [Page ${pageNum}] Complete: ${transactions.length}/${count} extracted`);
+  return transactions;
+}
+
+/**
+ * Extract all transactions from bank statement
+ */
+async function extractTransactions(images: string[]): Promise<ITransaction[]> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with Qwen3-VL (multi-query)`);
+
+  const allTransactions: ITransaction[] = [];
+
+  for (let i = 0; i < images.length; i++) {
+    const pageTransactions = await extractTransactionsFromPage(images[i], i + 1);
+    allTransactions.push(...pageTransactions);
+  }
+
+  console.log(`    [Vision] Total: ${allTransactions.length} transactions`);
+  return allTransactions;
+}
+
+/**
+ * Compare transactions
+ */
+function compareTransactions(
+  extracted: ITransaction[],
+  expected: ITransaction[]
+): { matches: number; total: number; errors: string[] } {
+  const errors: string[] = [];
+  let matches = 0;
+
+  for (let i = 0; i < expected.length; i++) {
+    const exp = expected[i];
+    const ext = extracted[i];
+
+    if (!ext) {
+      errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
+      continue;
+    }
+
+    const dateMatch = ext.date === exp.date;
+    const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
+
+    if (dateMatch && amountMatch) {
+      matches++;
+    } else {
+      errors.push(`Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
+    }
+  }
+
+  if (extracted.length > expected.length) {
+    errors.push(`Extra transactions: ${extracted.length - expected.length}`);
+  }
+
+  return { matches, total: expected.length, errors };
+}
+
+/**
+ * Find test cases in .nogit/
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of files.filter((f: string) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  return testCases.sort((a, b) => a.name.localeCompare(b.name));
+}
+
+/**
+ * Ensure Qwen3-VL model is available
+ */
+async function ensureQwen3Vl(): Promise<boolean> {
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      if (models.some((m: { name: string }) => m.name === VISION_MODEL)) {
+        console.log(`[Ollama] Model available: ${VISION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    return false;
+  }
+
+  console.log(`[Ollama] Pulling ${VISION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: VISION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+// Tests
+
+tap.test('setup: ensure Qwen3-VL is running', async () => {
+  console.log('\n[Setup] Checking Qwen3-VL 8B...\n');
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+  const visionOk = await ensureQwen3Vl();
+  expect(visionOk).toBeTrue();
+  console.log('\n[Setup] Ready!\n');
+});
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} bank statement test cases (Qwen3-VL)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
+
+for (const testCase of testCases) {
+  tap.test(`should extract: ${testCase.name}`, async () => {
+    const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.length} transactions`);
+
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    const extracted = await extractTransactions(images);
+    console.log(`  Extracted: ${extracted.length} transactions`);
+
+    const result = compareTransactions(extracted, expected);
+    const accuracy = result.total > 0 ? result.matches / result.total : 0;
+
+    if (accuracy >= 0.95 && extracted.length === expected.length) {
+      passedCount++;
+      console.log(`  Result: PASS (${result.matches}/${result.total})`);
+    } else {
+      failedCount++;
+      console.log(`  Result: FAIL (${result.matches}/${result.total})`);
+      result.errors.slice(0, 5).forEach((e) => console.log(`    - ${e}`));
+    }
+
+    expect(accuracy).toBeGreaterThan(0.95);
+    expect(extracted.length).toEqual(expected.length);
+  });
+}
+
+tap.test('summary', async () => {
+  const total = testCases.length;
+  console.log(`\n======================================================`);
+  console.log(`   Bank Statement Summary (Qwen3-VL Vision)`);
+  console.log(`======================================================`);
+  console.log(`  Method:   Multi-query (count then extract each)`);
+  console.log(`  Passed:   ${passedCount}/${total}`);
+  console.log(`  Failed:   ${failedCount}/${total}`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();

test/test.invoices.combined.ts

@@ -1,455 +0,0 @@
-/**
- * Invoice extraction test using MiniCPM-V (visual) + PaddleOCR-VL (OCR augmentation)
- *
- * This is the combined approach that uses both models for best accuracy:
- *   - MiniCPM-V for visual understanding
- *   - PaddleOCR-VL for OCR text to augment prompts
- */
-import { tap, expect } from '@git.zone/tstest/tapbundle';
-import * as fs from 'fs';
-import * as path from 'path';
-import { execSync } from 'child_process';
-import * as os from 'os';
-import { ensurePaddleOcrVl, ensureMiniCpm } from './helpers/docker.js';
-
-const OLLAMA_URL = 'http://localhost:11434';
-const MODEL = 'minicpm-v:latest';
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-
-interface IInvoice {
-  invoice_number: string;
-  invoice_date: string;
-  vendor_name: string;
-  currency: string;
-  net_amount: number;
-  vat_amount: number;
-  total_amount: number;
-}
-
-/**
- * Extract OCR text from an image using PaddleOCR-VL (OpenAI-compatible API)
- */
-async function extractOcrText(imageBase64: string): Promise<string> {
-  try {
-    const response = await fetch(`${PADDLEOCR_VL_URL}/v1/chat/completions`, {
-      method: 'POST',
-      headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify({
-        model: 'paddleocr-vl',
-        messages: [{
-          role: 'user',
-          content: [
-            { type: 'image_url', image_url: { url: `data:image/png;base64,${imageBase64}` } },
-            { type: 'text', text: 'OCR:' }
-          ]
-        }],
-        temperature: 0.0,
-        max_tokens: 4096
-      }),
-    });
-
-    if (!response.ok) return '';
-
-    const data = await response.json();
-    return data.choices?.[0]?.message?.content || '';
-  } catch {
-    // PaddleOCR-VL unavailable
-  }
-  return '';
-}
-
-/**
- * Build prompt with optional OCR text
- */
-function buildPrompt(ocrText: string): string {
-  const base = `/nothink
-You are an invoice parser. Extract the following fields from this invoice:
-
-1. invoice_number: The invoice/receipt number
-2. invoice_date: Date in YYYY-MM-DD format
-3. vendor_name: Company that issued the invoice
-4. currency: EUR, USD, etc.
-5. net_amount: Amount before tax (if shown)
-6. vat_amount: Tax/VAT amount (if shown, 0 if reverse charge or no tax)
-7. total_amount: Final amount due
-
-Return ONLY valid JSON in this exact format:
-{"invoice_number":"XXX","invoice_date":"YYYY-MM-DD","vendor_name":"Company Name","currency":"EUR","net_amount":100.00,"vat_amount":19.00,"total_amount":119.00}
-
-If a field is not visible, use null for strings or 0 for numbers.
-No explanation, just the JSON object.`;
-
-  if (ocrText) {
-    // Limit OCR text to prevent context overflow
-    const maxOcrLength = 4000;
-    const truncatedOcr = ocrText.length > maxOcrLength
-      ? ocrText.substring(0, maxOcrLength) + '\n... (truncated)'
-      : ocrText;
-
-    return `${base}
-
-OCR text extracted from the invoice (use for reference):
----
-${truncatedOcr}
----
-
-Cross-reference the image with the OCR text above for accuracy.`;
-  }
-  return base;
-}
-
-/**
- * Convert PDF to PNG images using ImageMagick
- */
-function convertPdfToImages(pdfPath: string): string[] {
-  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
-  const outputPattern = path.join(tempDir, 'page-%d.png');
-
-  try {
-    execSync(
-      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
-      { stdio: 'pipe' }
-    );
-
-    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
-    const images: string[] = [];
-
-    for (const file of files) {
-      const imagePath = path.join(tempDir, file);
-      const imageData = fs.readFileSync(imagePath);
-      images.push(imageData.toString('base64'));
-    }
-
-    return images;
-  } finally {
-    fs.rmSync(tempDir, { recursive: true, force: true });
-  }
-}
-
-/**
- * Single extraction pass
- */
-async function extractOnce(images: string[], passNum: number, ocrText: string = ''): Promise<IInvoice> {
-  const payload = {
-    model: MODEL,
-    prompt: buildPrompt(ocrText),
-    images,
-    stream: true,
-    options: {
-      num_predict: 2048,
-      temperature: 0.1,
-    },
-  };
-
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
-  });
-
-  if (!response.ok) {
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
-
-  const reader = response.body?.getReader();
-  if (!reader) {
-    throw new Error('No response body');
-  }
-
-  const decoder = new TextDecoder();
-  let fullText = '';
-
-  while (true) {
-    const { done, value } = await reader.read();
-    if (done) break;
-
-    const chunk = decoder.decode(value, { stream: true });
-    const lines = chunk.split('\n').filter((l) => l.trim());
-
-    for (const line of lines) {
-      try {
-        const json = JSON.parse(line);
-        if (json.response) {
-          fullText += json.response;
-        }
-      } catch {
-        // Skip invalid JSON lines
-      }
-    }
-  }
-
-  // Extract JSON from response
-  const startIdx = fullText.indexOf('{');
-  const endIdx = fullText.lastIndexOf('}') + 1;
-
-  if (startIdx < 0 || endIdx <= startIdx) {
-    throw new Error(`No JSON object found in response: ${fullText.substring(0, 200)}`);
-  }
-
-  const jsonStr = fullText.substring(startIdx, endIdx);
-  return JSON.parse(jsonStr);
-}
-
-/**
- * Create a hash of invoice for comparison (using key fields)
- */
-function hashInvoice(invoice: IInvoice): string {
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
-}
-
-/**
- * Extract with majority voting - run until 2 passes match
- * Optimization: Run Pass 1, OCR, and Pass 2 (after OCR) in parallel
- */
-async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
-  const results: Array<{ invoice: IInvoice; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
-
-  const addResult = (invoice: IInvoice, passLabel: string): number => {
-    const hash = hashInvoice(invoice);
-    results.push({ invoice, hash });
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
-    return hashCounts.get(hash)!;
-  };
-
-  // OPTIMIZATION: Run Pass 1 (no OCR) in parallel with OCR -> Pass 2 (with OCR)
-  let ocrText = '';
-  const pass1Promise = extractOnce(images, 1, '').catch((err) => ({ error: err }));
-
-  // OCR then immediately Pass 2
-  const ocrThenPass2Promise = (async () => {
-    ocrText = await extractOcrText(images[0]);
-    if (ocrText) {
-      console.log(`  [OCR] Extracted ${ocrText.split('\n').length} text lines`);
-    }
-    return extractOnce(images, 2, ocrText).catch((err) => ({ error: err }));
-  })();
-
-  // Wait for both to complete
-  const [pass1Result, pass2Result] = await Promise.all([pass1Promise, ocrThenPass2Promise]);
-
-  // Process Pass 1 result
-  if ('error' in pass1Result) {
-    console.log(`  [Pass 1] Error: ${(pass1Result as {error: unknown}).error}`);
-  } else {
-    const count = addResult(pass1Result as IInvoice, 'Pass 1');
-    if (count >= 2) {
-      console.log(`  [Consensus] Reached after parallel passes`);
-      return pass1Result as IInvoice;
-    }
-  }
-
-  // Process Pass 2 result
-  if ('error' in pass2Result) {
-    console.log(`  [Pass 2+OCR] Error: ${(pass2Result as {error: unknown}).error}`);
-  } else {
-    const count = addResult(pass2Result as IInvoice, 'Pass 2+OCR');
-    if (count >= 2) {
-      console.log(`  [Consensus] Reached after parallel passes`);
-      return pass2Result as IInvoice;
-    }
-  }
-
-  // Continue with passes 3+ using OCR text if no consensus yet
-  for (let pass = 3; pass <= maxPasses; pass++) {
-    try {
-      const invoice = await extractOnce(images, pass, ocrText);
-      const count = addResult(invoice, `Pass ${pass}+OCR`);
-
-      if (count >= 2) {
-        console.log(`  [Consensus] Reached after ${pass} passes`);
-        return invoice;
-      }
-    } catch (err) {
-      console.log(`  [Pass ${pass}] Error: ${err}`);
-    }
-  }
-
-  // No consensus reached - return the most common result
-  let bestHash = '';
-  let bestCount = 0;
-  for (const [hash, count] of hashCounts) {
-    if (count > bestCount) {
-      bestCount = count;
-      bestHash = hash;
-    }
-  }
-
-  if (!bestHash) {
-    throw new Error(`No valid results for ${invoiceName}`);
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.invoice;
-}
-
-/**
- * Compare extracted invoice against expected
- */
-function compareInvoice(
-  extracted: IInvoice,
-  expected: IInvoice
-): { match: boolean; errors: string[] } {
-  const errors: string[] = [];
-
-  // Compare invoice number (normalize by removing spaces and case)
-  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
-  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
-  if (extNum !== expNum) {
-    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
-  }
-
-  // Compare date
-  if (extracted.invoice_date !== expected.invoice_date) {
-    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
-  }
-
-  // Compare total amount (with tolerance)
-  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
-    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
-  }
-
-  // Compare currency
-  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
-    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
-  }
-
-  return { match: errors.length === 0, errors };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/invoices/
- * Priority invoices (like vodafone) run first for quick feedback
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit/invoices');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  // Sort with priority invoices first, then alphabetically
-  const priorityPrefixes = ['vodafone'];
-  testCases.sort((a, b) => {
-    const aPriority = priorityPrefixes.findIndex((p) => a.name.startsWith(p));
-    const bPriority = priorityPrefixes.findIndex((p) => b.name.startsWith(p));
-
-    // Both have priority - sort by priority order
-    if (aPriority >= 0 && bPriority >= 0) return aPriority - bPriority;
-    // Only a has priority - a comes first
-    if (aPriority >= 0) return -1;
-    // Only b has priority - b comes first
-    if (bPriority >= 0) return 1;
-    // Neither has priority - alphabetical
-    return a.name.localeCompare(b.name);
-  });
-
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL is running (auto-detects GPU/CPU)
-  const paddleOk = await ensurePaddleOcrVl();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure MiniCPM is running
-  const minicpmOk = await ensureMiniCpm();
-  expect(minicpmOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
-  const response = await fetch(`${OLLAMA_URL}/api/tags`);
-  const data = await response.json();
-  const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} invoice test cases\n`);
-
-let passedCount = 0;
-let failedCount = 0;
-const processingTimes: number[] = [];
-
-for (const testCase of testCases) {
-  tap.test(`should extract invoice: ${testCase.name}`, async () => {
-    // Load expected data
-    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
-    console.log(`\n=== ${testCase.name} ===`);
-    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
-
-    const startTime = Date.now();
-
-    // Convert PDF to images
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`  Pages: ${images.length}`);
-
-    // Extract with consensus voting
-    const extracted = await extractWithConsensus(images, testCase.name);
-
-    const endTime = Date.now();
-    const elapsedMs = endTime - startTime;
-    processingTimes.push(elapsedMs);
-
-    // Compare results
-    const result = compareInvoice(extracted, expected);
-
-    if (result.match) {
-      passedCount++;
-      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-    } else {
-      failedCount++;
-      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-      result.errors.forEach((e) => console.log(`    - ${e}`));
-    }
-
-    // Assert match
-    expect(result.match).toBeTrue();
-  });
-}
-
-tap.test('summary', async () => {
-  const totalInvoices = testCases.length;
-  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
-  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
-  const avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
-  const avgTimeSec = avgTimeMs / 1000;
-  const totalTimeSec = totalTimeMs / 1000;
-
-  console.log(`\n========================================`);
-  console.log(`        Invoice Extraction Summary`);
-  console.log(`========================================`);
-  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
-  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
-  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
-  console.log(`----------------------------------------`);
-  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
-  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
-  console.log(`========================================\n`);
-});
-
-export default tap.start();

test/test.invoices.minicpm.ts

@@ -1,8 +1,10 @@
 /**
- * Invoice extraction test using MiniCPM-V only (visual extraction)
+ * Invoice extraction test using MiniCPM-V (visual extraction)
  *
- * This tests MiniCPM-V's ability to extract invoice data directly from images
+ * Consensus approach:
- * without any OCR augmentation.
+ * 1. Pass 1: Fast JSON extraction
+ * 2. Pass 2: Confirm with thinking enabled
+ * 3. If mismatch: repeat until consensus or max attempts
  */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
@@ -12,7 +14,7 @@ import * as os from 'os';
 import { ensureMiniCpm } from './helpers/docker.js';
 
 const OLLAMA_URL = 'http://localhost:11434';
-const MODEL = 'minicpm-v:latest';
+const MODEL = 'openbmb/minicpm-v4.5:q8_0';
 
 interface IInvoice {
   invoice_number: string;
@@ -24,28 +26,6 @@ interface IInvoice {
   total_amount: number;
 }
 
-/**
- * Build extraction prompt (MiniCPM-V only, no OCR augmentation)
- */
-function buildPrompt(): string {
-  return `/nothink
-You are an invoice parser. Extract the following fields from this invoice:
-
-1. invoice_number: The invoice/receipt number
-2. invoice_date: Date in YYYY-MM-DD format
-3. vendor_name: Company that issued the invoice
-4. currency: EUR, USD, etc.
-5. net_amount: Amount before tax (if shown)
-6. vat_amount: Tax/VAT amount (if shown, 0 if reverse charge or no tax)
-7. total_amount: Final amount due
-
-Return ONLY valid JSON in this exact format:
-{"invoice_number":"XXX","invoice_date":"YYYY-MM-DD","vendor_name":"Company Name","currency":"EUR","net_amount":100.00,"vat_amount":19.00,"total_amount":119.00}
-
-If a field is not visible, use null for strings or 0 for numbers.
-No explanation, just the JSON object.`;
-}
-
 /**
  * Convert PDF to PNG images using ImageMagick
  */
@@ -55,7 +35,7 @@ function convertPdfToImages(pdfPath: string): string[] {
 
   try {
     execSync(
-      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      `convert -density 300 -quality 95 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
       { stdio: 'pipe' }
     );
 
@@ -74,123 +54,288 @@ function convertPdfToImages(pdfPath: string): string[] {
   }
 }
 
-/**
+const JSON_PROMPT = `Extract invoice data from this image. Return ONLY a JSON object with these exact fields:
- * Single extraction pass with MiniCPM-V
+{
- */
+  "invoice_number": "the invoice number (not VAT ID, not customer ID)",
-async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
+  "invoice_date": "YYYY-MM-DD format",
-  const payload = {
+  "vendor_name": "company that issued the invoice",
-    model: MODEL,
+  "currency": "EUR, USD, or GBP",
-    prompt: buildPrompt(),
+  "net_amount": 0.00,
-    images,
+  "vat_amount": 0.00,
-    stream: true,
+  "total_amount": 0.00
-    options: {
+}
-      num_predict: 2048,
+Return only the JSON, no explanation.`;
-      temperature: 0.1,
-    },
-  };
 
-  const response = await fetch(`${OLLAMA_URL}/api/generate`, {
+/**
+ * Query MiniCPM-V for JSON output (fast, no thinking)
+ */
+async function queryJsonFast(images: string[]): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify(payload),
+    body: JSON.stringify({
+      model: MODEL,
+      messages: [{
+        role: 'user',
+        content: JSON_PROMPT,
+        images: images,
+      }],
+      stream: false,
+      options: {
+        num_predict: 1000,
+        temperature: 0.1,
+      },
+    }),
   });
 
   if (!response.ok) {
     throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const reader = response.body?.getReader();
+  const data = await response.json();
-  if (!reader) {
+  return (data.message?.content || '').trim();
-    throw new Error('No response body');
+}
+
+/**
+ * Query MiniCPM-V for JSON output with thinking enabled (slower, more accurate)
+ */
+async function queryJsonWithThinking(images: string[]): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: MODEL,
+      messages: [{
+        role: 'user',
+        content: `Think carefully about this invoice image, then ${JSON_PROMPT}`,
+        images: images,
+      }],
+      stream: false,
+      options: {
+        num_predict: 2000,
+        temperature: 0.1,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const decoder = new TextDecoder();
+  const data = await response.json();
-  let fullText = '';
+  return (data.message?.content || '').trim();
+}
 
-  while (true) {
+/**
-    const { done, value } = await reader.read();
+ * Parse amount from string (handles European format)
-    if (done) break;
+ */
+function parseAmount(s: string | number | undefined): number {
+  if (s === undefined || s === null) return 0;
+  if (typeof s === 'number') return s;
+  const match = s.match(/([\d.,]+)/);
+  if (!match) return 0;
+  const numStr = match[1];
+  // Handle European format: 1.234,56 → 1234.56
+  const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+    ? numStr.replace(/\./g, '').replace(',', '.')
+    : numStr.replace(/,/g, '');
+  return parseFloat(normalized) || 0;
+}
 
-    const chunk = decoder.decode(value, { stream: true });
+/**
-    const lines = chunk.split('\n').filter((l) => l.trim());
+ * Extract invoice number from potentially verbose response
+ */
+function extractInvoiceNumber(s: string | undefined): string {
+  if (!s) return '';
+  let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+  const patterns = [
+    /\b([A-Z]{2,3}\d{10,})\b/i,           // IEE2022006460244
+    /\b([A-Z]\d{8,})\b/i,                  // R0014359508
+    /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,     // INV-2024-001
+    /\b(\d{7,})\b/,                        // 1579087430
+  ];
+  for (const pattern of patterns) {
+    const match = clean.match(pattern);
+    if (match) return match[1];
+  }
+  return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
+}
 
-    for (const line of lines) {
+/**
+ * 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 {
-        const json = JSON.parse(line);
+        return JSON.parse(jsonMatch[0]);
-        if (json.response) {
-          fullText += json.response;
-        }
       } catch {
-        // Skip invalid JSON lines
+        return null;
       }
     }
+    return null;
   }
-
-  // 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)
+ * Parse JSON response into IInvoice
  */
-function hashInvoice(invoice: IInvoice): string {
+function parseJsonToInvoice(response: string): IInvoice | null {
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${invoice.total_amount.toFixed(2)}`;
+  const parsed = extractJsonFromResponse(response);
+  if (!parsed) return null;
+
+  return {
+    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
+    invoice_date: extractDate(String(parsed.invoice_date || '')),
+    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
+    currency: extractCurrency(String(parsed.currency || '')),
+    net_amount: parseAmount(parsed.net_amount as string | number),
+    vat_amount: parseAmount(parsed.vat_amount as string | number),
+    total_amount: parseAmount(parsed.total_amount as string | number),
+  };
 }
 
 /**
- * Extract with consensus voting using MiniCPM-V only
+ * Compare two invoices for consensus (key fields must match)
  */
-async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
+function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
-  const results: Array<{ invoice: IInvoice; hash: string }> = [];
+  const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
-  const hashCounts: Map<string, number> = new Map();
+  const dateMatch = a.invoice_date === b.invoice_date;
+  const totalMatch = Math.abs(a.total_amount - b.total_amount) < 0.02;
+  return numMatch && dateMatch && totalMatch;
+}
 
-  const addResult = (invoice: IInvoice, passLabel: string): number => {
+/**
-    const hash = hashInvoice(invoice);
+ * Extract invoice data using consensus approach:
-    results.push({ invoice, hash });
+ * 1. Pass 1: Fast JSON extraction
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
+ * 2. Pass 2: Confirm with thinking enabled
-    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
+ * 3. If mismatch: repeat until consensus or max 5 attempts
-    return hashCounts.get(hash)!;
+ */
+async function extractInvoiceFromImages(images: string[]): Promise<IInvoice> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with ${MODEL} (consensus)`);
+
+  const MAX_ATTEMPTS = 5;
+  let attempt = 0;
+
+  while (attempt < MAX_ATTEMPTS) {
+    attempt++;
+    console.log(`    [Attempt ${attempt}/${MAX_ATTEMPTS}]`);
+
+    // PASS 1: Fast JSON extraction
+    console.log(`    [Pass 1] Fast extraction...`);
+    const fastResponse = await queryJsonFast(images);
+    const fastInvoice = parseJsonToInvoice(fastResponse);
+
+    if (!fastInvoice) {
+      console.log(`    [Pass 1] JSON parsing failed, retrying...`);
+      continue;
+    }
+    console.log(`    [Pass 1] Result: ${fastInvoice.invoice_number} | ${fastInvoice.invoice_date} | ${fastInvoice.total_amount} ${fastInvoice.currency}`);
+
+    // PASS 2: Confirm with thinking
+    console.log(`    [Pass 2] Thinking confirmation...`);
+    const thinkResponse = await queryJsonWithThinking(images);
+    const thinkInvoice = parseJsonToInvoice(thinkResponse);
+
+    if (!thinkInvoice) {
+      console.log(`    [Pass 2] JSON parsing failed, retrying...`);
+      continue;
+    }
+    console.log(`    [Pass 2] Result: ${thinkInvoice.invoice_number} | ${thinkInvoice.invoice_date} | ${thinkInvoice.total_amount} ${thinkInvoice.currency}`);
+
+    // Check consensus
+    if (invoicesMatch(fastInvoice, thinkInvoice)) {
+      console.log(`    [Consensus] MATCH - using result`);
+      return thinkInvoice; // Prefer thinking result
+    }
+
+    console.log(`    [Consensus] MISMATCH - repeating...`);
+    console.log(`      Fast:  ${fastInvoice.invoice_number} | ${fastInvoice.invoice_date} | ${fastInvoice.total_amount}`);
+    console.log(`      Think: ${thinkInvoice.invoice_number} | ${thinkInvoice.invoice_date} | ${thinkInvoice.total_amount}`);
+  }
+
+  // Max attempts reached - do one final thinking pass and use that
+  console.log(`    [Final] Max attempts reached, using final thinking pass`);
+  const finalResponse = await queryJsonWithThinking(images);
+  const finalInvoice = parseJsonToInvoice(finalResponse);
+
+  if (finalInvoice) {
+    console.log(`    [Final] Result: ${finalInvoice.invoice_number} | ${finalInvoice.invoice_date} | ${finalInvoice.total_amount} ${finalInvoice.currency}`);
+    return finalInvoice;
+  }
+
+  // Return empty invoice if all else fails
+  console.log(`    [Final] All parsing failed, returning empty`);
+  return {
+    invoice_number: '',
+    invoice_date: '',
+    vendor_name: '',
+    currency: 'EUR',
+    net_amount: 0,
+    vat_amount: 0,
+    total_amount: 0,
+  };
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
+
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
   };
 
-  for (let pass = 1; pass <= maxPasses; pass++) {
+  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
-    try {
+  if (match) {
-      const invoice = await extractOnce(images, pass);
+    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
-      const count = addResult(invoice, `Pass ${pass}`);
-
-      if (count >= 2) {
-        console.log(`  [Consensus] Reached after ${pass} passes`);
-        return invoice;
-      }
-    } catch (err) {
-      console.log(`  [Pass ${pass}] Error: ${err}`);
-    }
   }
 
-  // No consensus reached - return the most common result
+  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
-  let bestHash = '';
+  if (match) {
-  let bestCount = 0;
+    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
-  for (const [hash, count] of hashCounts) {
-    if (count > bestCount) {
-      bestCount = count;
-      bestHash = hash;
-    }
   }
 
-  if (!bestHash) {
+  return dateStr;
-    throw new Error(`No valid results for ${invoiceName}`);
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.invoice;
 }
 
 /**
@@ -210,7 +355,7 @@ function compareInvoice(
   }
 
   // Compare date
-  if (extracted.invoice_date !== expected.invoice_date) {
+  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
     errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
   }
 
@@ -252,9 +397,7 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
     }
   }
 
-  // Sort alphabetically
   testCases.sort((a, b) => a.name.localeCompare(b.name));
-
   return testCases;
 }
 
@@ -262,24 +405,20 @@ function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: strin
 
 tap.test('setup: ensure Docker containers are running', async () => {
   console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure MiniCPM is running
   const minicpmOk = await ensureMiniCpm();
   expect(minicpmOk).toBeTrue();
-
   console.log('\n[Setup] All containers ready!\n');
 });
 
-tap.test('should have MiniCPM-V 4.5 model loaded', async () => {
+tap.test('should have MiniCPM-V model loaded', async () => {
   const response = await fetch(`${OLLAMA_URL}/api/tags`);
   const data = await response.json();
   const modelNames = data.models.map((m: { name: string }) => m.name);
-  expect(modelNames.some((name: string) => name.includes('minicpm-v4.5'))).toBeTrue();
+  expect(modelNames.some((name: string) => name.includes('minicpm'))).toBeTrue();
 });
 
-// Dynamic test for each PDF/JSON pair
 const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} invoice test cases (MiniCPM-V only)\n`);
+console.log(`\nFound ${testCases.length} invoice test cases (MiniCPM-V)\n`);
 
 let passedCount = 0;
 let failedCount = 0;
@@ -287,25 +426,20 @@ const processingTimes: number[] = [];
 
 for (const testCase of testCases) {
   tap.test(`should extract invoice: ${testCase.name}`, async () => {
-    // Load expected data
     const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
     console.log(`\n=== ${testCase.name} ===`);
     console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
 
     const startTime = Date.now();
-
-    // Convert PDF to images
     const images = convertPdfToImages(testCase.pdfPath);
     console.log(`  Pages: ${images.length}`);
 
-    // Extract with consensus voting (MiniCPM-V only)
+    const extracted = await extractInvoiceFromImages(images);
-    const extracted = await extractWithConsensus(images, testCase.name);
+    console.log(`  Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
 
-    const endTime = Date.now();
+    const elapsedMs = Date.now() - startTime;
-    const elapsedMs = endTime - startTime;
     processingTimes.push(elapsedMs);
 
-    // Compare results
     const result = compareInvoice(extracted, expected);
 
     if (result.match) {
@@ -317,7 +451,6 @@ for (const testCase of testCases) {
       result.errors.forEach((e) => console.log(`    - ${e}`));
     }
 
-    // Assert match
     expect(result.match).toBeTrue();
   });
 }
@@ -326,18 +459,17 @@ tap.test('summary', async () => {
   const totalInvoices = testCases.length;
   const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
   const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
-  const avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
+  const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
-  const avgTimeSec = avgTimeMs / 1000;
-  const totalTimeSec = totalTimeMs / 1000;
 
   console.log(`\n========================================`);
-  console.log(`   Invoice Extraction Summary (MiniCPM)`);
+  console.log(`   Invoice Extraction Summary (${MODEL})`);
   console.log(`========================================`);
+  console.log(`  Method:    Consensus (fast + thinking)`);
   console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
   console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
   console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
   console.log(`----------------------------------------`);
-  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
+  console.log(`  Total time:   ${(totalTimeMs / 1000).toFixed(1)}s`);
   console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
   console.log(`========================================\n`);
 });

test/test.invoices.paddleocr-vl.ts

@@ -1,461 +0,0 @@
-/**
- * Invoice extraction test using PaddleOCR-VL Full Pipeline
- *
- * This tests the complete PaddleOCR-VL pipeline:
- *   1. PP-DocLayoutV2 for layout detection
- *   2. PaddleOCR-VL for recognition
- *   3. Structured HTML output (semantic tags with proper tables)
- *   4. Qwen2.5 extracts invoice fields from structured HTML
- *
- * HTML output is used instead of Markdown because:
- * - <table> tags are unambiguous (no parser variations)
- * - LLMs are heavily trained on web/HTML data
- * - Semantic tags (header, footer, section) provide clear structure
- */
-import { tap, expect } from '@git.zone/tstest/tapbundle';
-import * as fs from 'fs';
-import * as path from 'path';
-import { execSync } from 'child_process';
-import * as os from 'os';
-import { ensurePaddleOcrVlFull, ensureQwen25 } from './helpers/docker.js';
-
-const PADDLEOCR_VL_URL = 'http://localhost:8000';
-const OLLAMA_URL = 'http://localhost:11434';
-// Use Qwen2.5 for text-only JSON extraction (not MiniCPM which is vision-focused)
-const TEXT_MODEL = 'qwen2.5:7b';
-
-interface IInvoice {
-  invoice_number: string;
-  invoice_date: string;
-  vendor_name: string;
-  currency: string;
-  net_amount: number;
-  vat_amount: number;
-  total_amount: number;
-}
-
-/**
- * Convert PDF to PNG images using ImageMagick
- */
-function convertPdfToImages(pdfPath: string): string[] {
-  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
-  const outputPattern = path.join(tempDir, 'page-%d.png');
-
-  try {
-    execSync(
-      `convert -density 200 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
-      { stdio: 'pipe' }
-    );
-
-    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
-    const images: string[] = [];
-
-    for (const file of files) {
-      const imagePath = path.join(tempDir, file);
-      const imageData = fs.readFileSync(imagePath);
-      images.push(imageData.toString('base64'));
-    }
-
-    return images;
-  } finally {
-    fs.rmSync(tempDir, { recursive: true, force: true });
-  }
-}
-
-/**
- * Parse document using PaddleOCR-VL Full Pipeline (returns structured HTML)
- */
-async function parseDocument(imageBase64: string): Promise<string> {
-  const response = await fetch(`${PADDLEOCR_VL_URL}/parse`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      image: imageBase64,
-      output_format: 'html',
-    }),
-  });
-
-  if (!response.ok) {
-    const text = await response.text();
-    throw new Error(`PaddleOCR-VL API error: ${response.status} - ${text}`);
-  }
-
-  const data = await response.json();
-
-  if (!data.success) {
-    throw new Error(`PaddleOCR-VL error: ${data.error}`);
-  }
-
-  return data.result?.html || '';
-}
-
-/**
- * Extract invoice fields from structured HTML using Qwen2.5 (text-only model)
- */
-async function extractInvoiceFromHtml(html: string): Promise<IInvoice> {
-  // Truncate if too long (HTML is more valuable per byte, allow more)
-  const truncated = html.length > 16000 ? html.slice(0, 16000) : html;
-  console.log(`    [Extract] Processing ${truncated.length} chars of HTML`);
-
-  const prompt = `You are an invoice data extractor. Extract the following fields from this HTML document (OCR output with semantic structure) and return ONLY a valid JSON object.
-
-The HTML uses semantic tags:
-- <table> with <thead>/<tbody> for structured tables (invoice line items, totals)
-- <header> for document header (company info, invoice number)
-- <footer> for document footer (payment terms, legal text)
-- <section class="table-region"> for table regions
-- data-type and data-y attributes indicate block type and vertical position
-
-Required fields:
-- invoice_number: The invoice/receipt/document number
-- invoice_date: Date in YYYY-MM-DD format (convert from any format)
-- vendor_name: Company that issued the invoice
-- currency: EUR, USD, GBP, etc.
-- net_amount: Amount before tax (number)
-- vat_amount: Tax/VAT amount (number, use 0 if reverse charge or not shown)
-- total_amount: Final total amount (number)
-
-Example output format:
-{"invoice_number":"INV-123","invoice_date":"2022-01-28","vendor_name":"Adobe","currency":"EUR","net_amount":24.99,"vat_amount":0,"total_amount":24.99}
-
-Rules:
-- Return ONLY the JSON object, no explanation or markdown
-- Use null for missing string fields
-- Use 0 for missing numeric fields
-- Convert dates to YYYY-MM-DD format (e.g., "28-JAN-2022" becomes "2022-01-28")
-- Extract numbers without currency symbols
-- Look for totals in <table> sections, especially rows with "Total", "Amount Due", "Grand Total"
-
-HTML Document:
-${truncated}
-
-JSON:`;
-
-  const payload = {
-    model: TEXT_MODEL,
-    prompt,
-    stream: true,
-    options: {
-      num_predict: 512,
-      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);
-  const parsed = JSON.parse(jsonStr);
-
-  // Ensure numeric fields are actually numbers
-  return {
-    invoice_number: parsed.invoice_number || null,
-    invoice_date: parsed.invoice_date || null,
-    vendor_name: parsed.vendor_name || null,
-    currency: parsed.currency || 'EUR',
-    net_amount: parseFloat(parsed.net_amount) || 0,
-    vat_amount: parseFloat(parsed.vat_amount) || 0,
-    total_amount: parseFloat(parsed.total_amount) || 0,
-  };
-}
-
-/**
- * Single extraction pass: Parse with PaddleOCR-VL Full, extract with Qwen2.5 (text-only)
- */
-async function extractOnce(images: string[], passNum: number): Promise<IInvoice> {
-  // Parse document with full pipeline (PaddleOCR-VL) -> returns HTML
-  const html = await parseDocument(images[0]);
-  console.log(`    [Parse] Got ${html.split('\n').length} lines of HTML`);
-
-  // Extract invoice fields from HTML using text-only model (no images)
-  return extractInvoiceFromHtml(html);
-}
-
-/**
- * Create a hash of invoice for comparison (using key fields)
- */
-function hashInvoice(invoice: IInvoice): string {
-  // Ensure total_amount is a number
-  const amount = typeof invoice.total_amount === 'number'
-    ? invoice.total_amount.toFixed(2)
-    : String(invoice.total_amount || 0);
-  return `${invoice.invoice_number}|${invoice.invoice_date}|${amount}`;
-}
-
-/**
- * Extract with consensus voting
- */
-async function extractWithConsensus(images: string[], invoiceName: string, maxPasses: number = 5): Promise<IInvoice> {
-  const results: Array<{ invoice: IInvoice; hash: string }> = [];
-  const hashCounts: Map<string, number> = new Map();
-
-  const addResult = (invoice: IInvoice, passLabel: string): number => {
-    const hash = hashInvoice(invoice);
-    results.push({ invoice, hash });
-    hashCounts.set(hash, (hashCounts.get(hash) || 0) + 1);
-    console.log(`  [${passLabel}] ${invoice.invoice_number} | ${invoice.invoice_date} | ${invoice.total_amount} ${invoice.currency}`);
-    return hashCounts.get(hash)!;
-  };
-
-  for (let pass = 1; pass <= maxPasses; pass++) {
-    try {
-      const invoice = await extractOnce(images, pass);
-      const count = addResult(invoice, `Pass ${pass}`);
-
-      if (count >= 2) {
-        console.log(`  [Consensus] Reached after ${pass} passes`);
-        return invoice;
-      }
-    } catch (err) {
-      console.log(`  [Pass ${pass}] Error: ${err}`);
-    }
-  }
-
-  // No consensus reached - return the most common result
-  let bestHash = '';
-  let bestCount = 0;
-  for (const [hash, count] of hashCounts) {
-    if (count > bestCount) {
-      bestCount = count;
-      bestHash = hash;
-    }
-  }
-
-  if (!bestHash) {
-    throw new Error(`No valid results for ${invoiceName}`);
-  }
-
-  const best = results.find((r) => r.hash === bestHash)!;
-  console.log(`  [No consensus] Using most common result (${bestCount}/${maxPasses} passes)`);
-  return best.invoice;
-}
-
-/**
- * Normalize date to YYYY-MM-DD format
- */
-function normalizeDate(dateStr: string | null): string {
-  if (!dateStr) return '';
-
-  // Already in correct format
-  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) {
-    return dateStr;
-  }
-
-  // Handle DD-MMM-YYYY format (e.g., "28-JUN-2022")
-  const monthMap: Record<string, string> = {
-    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
-    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
-  };
-
-  const match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
-  if (match) {
-    const day = match[1].padStart(2, '0');
-    const month = monthMap[match[2].toUpperCase()] || '01';
-    const year = match[3];
-    return `${year}-${month}-${day}`;
-  }
-
-  // Handle DD/MM/YYYY or DD.MM.YYYY
-  const match2 = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
-  if (match2) {
-    const day = match2[1].padStart(2, '0');
-    const month = match2[2].padStart(2, '0');
-    const year = match2[3];
-    return `${year}-${month}-${day}`;
-  }
-
-  return dateStr;
-}
-
-/**
- * Compare extracted invoice against expected
- */
-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 (normalize format first)
-  const extDate = normalizeDate(extracted.invoice_date);
-  const expDate = normalizeDate(expected.invoice_date);
-  if (extDate !== expDate) {
-    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
-  }
-
-  // Compare total amount (with tolerance)
-  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
-    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
-  }
-
-  // Compare currency
-  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
-    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
-  }
-
-  return { match: errors.length === 0, errors };
-}
-
-/**
- * Find all test cases (PDF + JSON pairs) in .nogit/invoices/
- */
-function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
-  const testDir = path.join(process.cwd(), '.nogit/invoices');
-  if (!fs.existsSync(testDir)) {
-    return [];
-  }
-
-  const files = fs.readdirSync(testDir);
-  const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
-  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
-
-  for (const pdf of pdfFiles) {
-    const baseName = pdf.replace('.pdf', '');
-    const jsonFile = `${baseName}.json`;
-    if (files.includes(jsonFile)) {
-      testCases.push({
-        name: baseName,
-        pdfPath: path.join(testDir, pdf),
-        jsonPath: path.join(testDir, jsonFile),
-      });
-    }
-  }
-
-  // Sort alphabetically
-  testCases.sort((a, b) => a.name.localeCompare(b.name));
-
-  return testCases;
-}
-
-// Tests
-
-tap.test('setup: ensure Docker containers are running', async () => {
-  console.log('\n[Setup] Checking Docker containers...\n');
-
-  // Ensure PaddleOCR-VL Full Pipeline is running
-  const paddleOk = await ensurePaddleOcrVlFull();
-  expect(paddleOk).toBeTrue();
-
-  // Ensure Qwen2.5 is available (for text-only JSON extraction)
-  const qwenOk = await ensureQwen25();
-  expect(qwenOk).toBeTrue();
-
-  console.log('\n[Setup] All containers ready!\n');
-});
-
-// Dynamic test for each PDF/JSON pair
-const testCases = findTestCases();
-console.log(`\nFound ${testCases.length} invoice test cases (PaddleOCR-VL Full Pipeline)\n`);
-
-let passedCount = 0;
-let failedCount = 0;
-const processingTimes: number[] = [];
-
-for (const testCase of testCases) {
-  tap.test(`should extract invoice: ${testCase.name}`, async () => {
-    // Load expected data
-    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
-    console.log(`\n=== ${testCase.name} ===`);
-    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
-
-    const startTime = Date.now();
-
-    // Convert PDF to images
-    const images = convertPdfToImages(testCase.pdfPath);
-    console.log(`  Pages: ${images.length}`);
-
-    // Extract with consensus voting (PaddleOCR-VL Full -> MiniCPM)
-    const extracted = await extractWithConsensus(images, testCase.name);
-
-    const endTime = Date.now();
-    const elapsedMs = endTime - startTime;
-    processingTimes.push(elapsedMs);
-
-    // Compare results
-    const result = compareInvoice(extracted, expected);
-
-    if (result.match) {
-      passedCount++;
-      console.log(`  Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-    } else {
-      failedCount++;
-      console.log(`  Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
-      result.errors.forEach((e) => console.log(`    - ${e}`));
-    }
-
-    // Assert match
-    expect(result.match).toBeTrue();
-  });
-}
-
-tap.test('summary', async () => {
-  const totalInvoices = testCases.length;
-  const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
-  const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
-  const avgTimeMs = processingTimes.length > 0 ? totalTimeMs / processingTimes.length : 0;
-  const avgTimeSec = avgTimeMs / 1000;
-  const totalTimeSec = totalTimeMs / 1000;
-
-  console.log(`\n======================================================`);
-  console.log(`   Invoice Extraction Summary (PaddleOCR-VL Full)`);
-  console.log(`======================================================`);
-  console.log(`  Method:    PaddleOCR-VL Full Pipeline (HTML) -> Qwen2.5 (text-only)`);
-  console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
-  console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
-  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
-  console.log(`------------------------------------------------------`);
-  console.log(`  Total time:   ${totalTimeSec.toFixed(1)}s`);
-  console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
-  console.log(`======================================================\n`);
-});
-
-export default tap.start();

test/test.invoices.qwen3vl.ts

@@ -0,0 +1,351 @@
+/**
+ * Invoice extraction using Qwen3-VL 8B Vision (Direct)
+ *
+ * Multi-query approach: 5 parallel simple queries to avoid token exhaustion.
+ * Single pass, no consensus voting.
+ */
+import { tap, expect } from '@git.zone/tstest/tapbundle';
+import * as fs from 'fs';
+import * as path from 'path';
+import { execSync } from 'child_process';
+import * as os from 'os';
+import { ensureMiniCpm } from './helpers/docker.js';
+
+const OLLAMA_URL = 'http://localhost:11434';
+const VISION_MODEL = 'qwen3-vl:8b';
+
+interface IInvoice {
+  invoice_number: string;
+  invoice_date: string;
+  vendor_name: string;
+  currency: string;
+  net_amount: number;
+  vat_amount: number;
+  total_amount: number;
+}
+
+/**
+ * Convert PDF to PNG images using ImageMagick
+ */
+function convertPdfToImages(pdfPath: string): string[] {
+  const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
+  const outputPattern = path.join(tempDir, 'page-%d.png');
+
+  try {
+    // 150 DPI is sufficient for invoice extraction, reduces context size
+    execSync(
+      `convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
+      { stdio: 'pipe' }
+    );
+
+    const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
+    const images: string[] = [];
+
+    for (const file of files) {
+      const imagePath = path.join(tempDir, file);
+      const imageData = fs.readFileSync(imagePath);
+      images.push(imageData.toString('base64'));
+    }
+
+    return images;
+  } finally {
+    fs.rmSync(tempDir, { recursive: true, force: true });
+  }
+}
+
+/**
+ * Query Qwen3-VL for a single field
+ * Uses simple prompts to minimize thinking tokens
+ */
+async function queryField(images: string[], question: string): Promise<string> {
+  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({
+      model: VISION_MODEL,
+      messages: [{
+        role: 'user',
+        content: `${question} Reply with just the value, nothing else.`,
+        images: images,
+      }],
+      stream: false,
+      options: {
+        num_predict: 500,
+        temperature: 0.1,
+      },
+    }),
+  });
+
+  if (!response.ok) {
+    throw new Error(`Ollama API error: ${response.status}`);
+  }
+
+  const data = await response.json();
+  return (data.message?.content || '').trim();
+}
+
+/**
+ * Extract invoice data using multiple simple queries
+ * Each query asks for 1-2 fields to minimize thinking tokens
+ * (Qwen3's thinking mode uses all tokens on complex prompts)
+ */
+async function extractInvoiceFromImages(images: string[]): Promise<IInvoice> {
+  console.log(`    [Vision] Processing ${images.length} page(s) with Qwen3-VL (multi-query)`);
+
+  // Query each field separately to avoid excessive thinking tokens
+  // Use explicit questions to avoid confusion between similar fields
+  // Log each result as it comes in (not waiting for all to complete)
+  const queryAndLog = async (name: string, question: string): Promise<string> => {
+    const result = await queryField(images, question);
+    console.log(`    [Query] ${name}: "${result}"`);
+    return result;
+  };
+
+  const [invoiceNum, invoiceDate, vendor, currency, totalAmount, netAmount, vatAmount] = await Promise.all([
+    queryAndLog('Invoice Number', 'What is the INVOICE NUMBER (not VAT number, not customer ID)? Look for "Invoice No", "Invoice #", "Rechnung Nr", "Facture". Just the number/code.'),
+    queryAndLog('Invoice Date  ', 'What is the INVOICE DATE (not due date, not delivery date)? The date the invoice was issued. Format: YYYY-MM-DD'),
+    queryAndLog('Vendor        ', 'What company ISSUED this invoice (the seller/vendor, not the buyer)? Look at the letterhead or "From" section.'),
+    queryAndLog('Currency      ', 'What CURRENCY is used? Look for € (EUR), $ (USD), or £ (GBP). Answer with 3-letter code: EUR, USD, or GBP'),
+    queryAndLog('Total Amount  ', 'What is the TOTAL AMOUNT INCLUDING TAX (the final amount to pay, with VAT/tax included)? Just the number, e.g. 24.99'),
+    queryAndLog('Net Amount    ', 'What is the NET AMOUNT (subtotal before VAT/tax)? Just the number, e.g. 20.99'),
+    queryAndLog('VAT Amount    ', 'What is the VAT/TAX AMOUNT? Just the number, e.g. 4.00'),
+  ]);
+
+  // Parse amount from string (handles European format)
+  const parseAmount = (s: string): number => {
+    if (!s) return 0;
+    // Extract number from the response
+    const match = s.match(/([\d.,]+)/);
+    if (!match) return 0;
+    const numStr = match[1];
+    // Handle European format: 1.234,56 → 1234.56
+    const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
+      ? numStr.replace(/\./g, '').replace(',', '.')
+      : numStr.replace(/,/g, '');
+    return parseFloat(normalized) || 0;
+  };
+
+  // Extract invoice number from potentially verbose response
+  const extractInvoiceNumber = (s: string): string => {
+    let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+    // Look for common invoice number patterns
+    const patterns = [
+      /\b([A-Z]{2,3}\d{10,})\b/i,           // IEE2022006460244
+      /\b([A-Z]\d{8,})\b/i,                  // R0014359508
+      /\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i,     // INV-2024-001
+      /\b(\d{7,})\b/,                        // 1579087430
+    ];
+    for (const pattern of patterns) {
+      const match = clean.match(pattern);
+      if (match) return match[1];
+    }
+    return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
+  };
+
+  // Extract date (YYYY-MM-DD) from response
+  const extractDate = (s: string): string => {
+    let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
+    const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
+    if (isoMatch) return isoMatch[1];
+    return clean.replace(/[^\d-]/g, '').trim();
+  };
+
+  // Extract currency
+  const extractCurrency = (s: string): string => {
+    const upper = s.toUpperCase();
+    if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
+    if (upper.includes('USD') || upper.includes('$')) return 'USD';
+    if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
+    return 'EUR';
+  };
+
+  return {
+    invoice_number: extractInvoiceNumber(invoiceNum),
+    invoice_date: extractDate(invoiceDate),
+    vendor_name: vendor.replace(/\*\*/g, '').replace(/`/g, '').trim() || '',
+    currency: extractCurrency(currency),
+    net_amount: parseAmount(netAmount),
+    vat_amount: parseAmount(vatAmount),
+    total_amount: parseAmount(totalAmount),
+  };
+}
+
+/**
+ * Normalize date to YYYY-MM-DD
+ */
+function normalizeDate(dateStr: string | null): string {
+  if (!dateStr) return '';
+  if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
+
+  const monthMap: Record<string, string> = {
+    JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
+    JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
+  };
+
+  let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
+  if (match) {
+    return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
+  }
+
+  match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
+  if (match) {
+    return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
+  }
+
+  return dateStr;
+}
+
+/**
+ * Compare extracted vs expected
+ */
+function compareInvoice(extracted: IInvoice, expected: IInvoice): { match: boolean; errors: string[] } {
+  const errors: string[] = [];
+
+  const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
+  if (extNum !== expNum) {
+    errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
+  }
+
+  if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
+    errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
+  }
+
+  if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
+    errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
+  }
+
+  if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
+    errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
+  }
+
+  return { match: errors.length === 0, errors };
+}
+
+/**
+ * Find test cases
+ */
+function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
+  const testDir = path.join(process.cwd(), '.nogit/invoices');
+  if (!fs.existsSync(testDir)) return [];
+
+  const files = fs.readdirSync(testDir);
+  const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
+
+  for (const pdf of files.filter((f) => f.endsWith('.pdf'))) {
+    const baseName = pdf.replace('.pdf', '');
+    const jsonFile = `${baseName}.json`;
+    if (files.includes(jsonFile)) {
+      testCases.push({
+        name: baseName,
+        pdfPath: path.join(testDir, pdf),
+        jsonPath: path.join(testDir, jsonFile),
+      });
+    }
+  }
+
+  return testCases.sort((a, b) => a.name.localeCompare(b.name));
+}
+
+/**
+ * Ensure Qwen3-VL 8B model is available
+ */
+async function ensureQwen3Vl(): Promise<boolean> {
+  try {
+    const response = await fetch(`${OLLAMA_URL}/api/tags`);
+    if (response.ok) {
+      const data = await response.json();
+      const models = data.models || [];
+      if (models.some((m: { name: string }) => m.name === VISION_MODEL)) {
+        console.log(`[Ollama] Model already available: ${VISION_MODEL}`);
+        return true;
+      }
+    }
+  } catch {
+    console.log('[Ollama] Cannot check models');
+    return false;
+  }
+
+  console.log(`[Ollama] Pulling model: ${VISION_MODEL}...`);
+  const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
+    method: 'POST',
+    headers: { 'Content-Type': 'application/json' },
+    body: JSON.stringify({ name: VISION_MODEL, stream: false }),
+  });
+
+  return pullResponse.ok;
+}
+
+// Tests
+
+tap.test('setup: ensure Qwen3-VL is running', async () => {
+  console.log('\n[Setup] Checking Qwen3-VL 8B...\n');
+
+  // Ensure Ollama service is running
+  const ollamaOk = await ensureMiniCpm();
+  expect(ollamaOk).toBeTrue();
+
+  // Ensure Qwen3-VL 8B model
+  const visionOk = await ensureQwen3Vl();
+  expect(visionOk).toBeTrue();
+
+  console.log('\n[Setup] Ready!\n');
+});
+
+const testCases = findTestCases();
+console.log(`\nFound ${testCases.length} invoice test cases (Qwen3-VL Vision)\n`);
+
+let passedCount = 0;
+let failedCount = 0;
+const times: number[] = [];
+
+for (const testCase of testCases) {
+  tap.test(`should extract invoice: ${testCase.name}`, async () => {
+    const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
+    console.log(`\n=== ${testCase.name} ===`);
+    console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
+
+    const start = Date.now();
+    const images = convertPdfToImages(testCase.pdfPath);
+    console.log(`  Pages: ${images.length}`);
+
+    const extracted = await extractInvoiceFromImages(images);
+    console.log(`  Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
+    const elapsed = Date.now() - start;
+    times.push(elapsed);
+
+    const result = compareInvoice(extracted, expected);
+
+    if (result.match) {
+      passedCount++;
+      console.log(`  Result: MATCH (${(elapsed / 1000).toFixed(1)}s)`);
+    } else {
+      failedCount++;
+      console.log(`  Result: MISMATCH (${(elapsed / 1000).toFixed(1)}s)`);
+      result.errors.forEach((e) => console.log(`    - ${e}`));
+    }
+
+    expect(result.match).toBeTrue();
+  });
+}
+
+tap.test('summary', async () => {
+  const total = testCases.length;
+  const accuracy = total > 0 ? (passedCount / total) * 100 : 0;
+  const totalTime = times.reduce((a, b) => a + b, 0) / 1000;
+  const avgTime = times.length > 0 ? totalTime / times.length : 0;
+
+  console.log(`\n======================================================`);
+  console.log(`   Invoice Extraction Summary (Qwen3-VL Vision)`);
+  console.log(`======================================================`);
+  console.log(`  Method:    Multi-query (single pass)`);
+  console.log(`  Passed:    ${passedCount}/${total}`);
+  console.log(`  Failed:    ${failedCount}/${total}`);
+  console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
+  console.log(`------------------------------------------------------`);
+  console.log(`  Total time:   ${totalTime.toFixed(1)}s`);
+  console.log(`  Avg per inv:  ${avgTime.toFixed(1)}s`);
+  console.log(`======================================================\n`);
+});
+
+export default tap.start();