v1.14.3

- Add tryExtractJson function to validate JSON before accepting
- Use orchestrator.continueTask() to request correction if JSON is invalid
- Retry up to 2 times for malformed JSON responses
- Remove duplicate parseJsonToInvoice function

changelog.md

@@ -1,5 +1,31 @@
 # Changelog
 
+## 2026-01-20 - 1.14.3 - fix(repo)
+no changes detected in the diff; no files modified and no release required
+
+- Diff contained no changes
+- No files were added, removed, or modified
+- No code, dependency, or documentation updates to release
+
+## 2026-01-19 - 1.14.2 - fix(readme)
+update README to document Nanonets-OCR2-3B (replaces Nanonets-OCR-s), adjust VRAM and context defaults, expand feature docs, and update examples/test command
+
+- Renamed Nanonets-OCR-s -> Nanonets-OCR2-3B throughout README and examples
+- Updated Nanonets VRAM guidance from ~10GB to ~12-16GB and documented 30K context
+- Changed documented MAX_MODEL_LEN default from 8192 to 30000
+- Updated example model identifiers (model strings and curl/example snippets) to nanonets/Nanonets-OCR2-3B
+- Added MiniCPM and Qwen feature bullets (multilingual, multi-image, flowchart support, expanded context notes)
+- Replaced README test command from ./test-images.sh to pnpm test
+
+## 2026-01-19 - 1.14.1 - fix(extraction)
+improve JSON extraction prompts and model options for invoice and bank statement tests
+
+- Refactor JSON extraction prompts to be sent after the document text and add explicit 'WHERE TO FIND DATA' and 'RULES' sections for clearer extraction guidance
+- Change chat message flow to: send document, assistant acknowledgement, then the JSON extraction prompt (avoids concatenating large prompts into one message)
+- Add model options (num_ctx: 32768, temperature: 0) to give larger context windows and deterministic JSON output
+- Simplify logging to avoid printing full prompt contents; log document and prompt lengths instead
+- Increase timeouts for large documents to 600000ms (10 minutes) where applicable
+
 ## 2026-01-19 - 1.14.0 - feat(docker-images)
 add vLLM-based Nanonets-OCR2-3B image, Qwen3-VL Ollama image and refactor build/docs/tests to use new runtime/layout
 

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@host.today/ht-docker-ai",
-  "version": "1.14.0",
+  "version": "1.14.3",
   "type": "module",
   "private": false,
   "description": "Docker images for AI vision-language models including MiniCPM-V 4.5",
@@ -14,7 +14,9 @@
   },
   "devDependencies": {
     "@git.zone/tsrun": "^2.0.1",
-    "@git.zone/tstest": "^3.1.5"
+    "@git.zone/tstest": "^3.1.5",
+    "@push.rocks/smartagent": "^1.2.8",
+    "@push.rocks/smartai": "^0.11.1"
   },
   "repository": {
     "type": "git",

readme.md

@@ -2,7 +2,7 @@
 
 Production-ready Docker images for state-of-the-art AI Vision-Language Models. Run powerful multimodal AI locally with GPU acceleration—**no cloud API keys required**.
 
-> 🔥 **Three VLMs, one registry.** From lightweight document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
+> 🔥 **Three VLMs, one registry.** From high-performance document OCR to GPT-4o-level vision understanding—pick the right tool for your task.
 
 ## Issue Reporting and Security
 
@@ -15,7 +15,7 @@ For reporting bugs, issues, or security vulnerabilities, please visit [community
 | Model | Parameters | Best For | API | Port | VRAM |
 |-------|-----------|----------|-----|------|------|
 | **MiniCPM-V 4.5** | 8B | General vision understanding, multi-image analysis | Ollama-compatible | 11434 | ~9GB |
-| **Nanonets-OCR-s** | ~4B | Document OCR with semantic markdown output | OpenAI-compatible | 8000 | ~10GB |
+| **Nanonets-OCR2-3B** | ~3B | Document OCR with semantic markdown, LaTeX, flowcharts | OpenAI-compatible | 8000 | ~12-16GB |
 | **Qwen3-VL-30B** | 30B (A3B) | Advanced visual agents, code generation from images | Ollama-compatible | 11434 | ~20GB |
 
 ---
@@ -29,7 +29,7 @@ code.foss.global/host.today/ht-docker-ai:<tag>
 | Tag | Model | Runtime | Port | VRAM |
 |-----|-------|---------|------|------|
 | `minicpm45v` / `latest` | MiniCPM-V 4.5 | Ollama | 11434 | ~9GB |
-| `nanonets-ocr` | Nanonets-OCR-s | vLLM | 8000 | ~10GB |
+| `nanonets-ocr` | Nanonets-OCR2-3B | vLLM | 8000 | ~12-16GB |
 | `qwen3vl` | Qwen3-VL-30B-A3B | Ollama | 11434 | ~20GB |
 
 ---
@@ -38,6 +38,13 @@ code.foss.global/host.today/ht-docker-ai:<tag>
 
 A GPT-4o level multimodal LLM from OpenBMB—handles image understanding, OCR, multi-image analysis, and visual reasoning across **30+ languages**.
 
+### ✨ Key Features
+
+- 🌍 **Multilingual:** 30+ languages supported
+- 🖼️ **Multi-image:** Analyze multiple images in one request
+- 📊 **Versatile:** Charts, documents, photos, diagrams
+- ⚡ **Efficient:** Runs on consumer GPUs (9GB VRAM)
+
 ### Quick Start
 
 ```bash
@@ -83,21 +90,22 @@ curl http://localhost:11434/api/chat -d '{
 
 | Mode | VRAM Required |
 |------|---------------|
-| int4 quantized | 9GB |
-| Full precision (bf16) | 18GB |
+| int4 quantized | ~9GB |
+| Full precision (bf16) | ~18GB |
 
 ---
 
-## 🔍 Nanonets-OCR-s
+## 🔍 Nanonets-OCR2-3B
 
-A **Qwen2.5-VL-3B** model fine-tuned specifically for document OCR. Outputs structured markdown with semantic HTML tags—perfect for preserving document structure.
+The **latest Nanonets document OCR model** (October 2025 release)—based on Qwen2.5-VL-3B, fine-tuned specifically for document extraction with significant improvements over the original OCR-s.
 
-### Key Features
+### ✨ Key Features
 
-- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, watermarks/page numbers → tagged
+- 📝 **Semantic output:** Tables → HTML, equations → LaTeX, flowcharts → structured markup
 - 🌍 **Multilingual:** Inherits Qwen's broad language support
-- ⚡ **Efficient:** ~10GB VRAM, runs great on consumer GPUs
+- 📄 **30K context:** Handle large, multi-page documents
 - 🔌 **OpenAI-compatible:** Drop-in replacement for existing pipelines
+- 🎯 **Improved accuracy:** Better semantic tagging and LaTeX equation extraction vs. OCR-s
 
 ### Quick Start
 
@@ -116,7 +124,7 @@ docker run -d \
 curl http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
-    "model": "nanonets/Nanonets-OCR-s",
+    "model": "nanonets/Nanonets-OCR2-3B",
     "messages": [{
       "role": "user",
       "content": [
@@ -131,7 +139,7 @@ curl http://localhost:8000/v1/chat/completions \
 
 ### Output Format
 
-Nanonets-OCR-s returns markdown with semantic tags:
+Nanonets-OCR2-3B returns markdown with semantic tags:
 
 | Element | Output Format |
 |---------|---------------|
@@ -140,13 +148,14 @@ Nanonets-OCR-s returns markdown with semantic tags:
 | Images | `<img>description</img>` |
 | Watermarks | `<watermark>OFFICIAL COPY</watermark>` |
 | Page numbers | `<page_number>14</page_number>` |
+| Flowcharts | Structured markup |
 
-### Performance
+### Hardware Requirements
 
-| Metric | Value |
-|--------|-------|
-| Speed | 3–8 seconds per page |
-| VRAM | ~10GB |
+| Config | VRAM |
+|--------|------|
+| 30K context (default) | ~12-16GB |
+| Speed | ~3-8 seconds per page |
 
 ---
 
@@ -154,7 +163,7 @@ Nanonets-OCR-s returns markdown with semantic tags:
 
 The **most powerful** Qwen vision model—30B parameters with 3B active (MoE architecture). Handles complex visual reasoning, code generation from screenshots, and visual agent capabilities.
 
-### Key Features
+### ✨ Key Features
 
 - 🚀 **256K context** (expandable to 1M tokens!)
 - 🤖 **Visual agent capabilities** — can plan and execute multi-step tasks
@@ -204,7 +213,6 @@ curl http://localhost:11434/api/chat -d '{
 Run multiple VLMs together for maximum flexibility:
 
 ```yaml
-version: '3.8'
 services:
   # General vision tasks
   minicpm:
@@ -259,10 +267,10 @@ volumes:
 
 | Variable | Default | Description |
 |----------|---------|-------------|
-| `MODEL_NAME` | `nanonets/Nanonets-OCR-s` | HuggingFace model ID |
+| `MODEL_NAME` | `nanonets/Nanonets-OCR2-3B` | HuggingFace model ID |
 | `HOST` | `0.0.0.0` | API bind address |
 | `PORT` | `8000` | API port |
-| `MAX_MODEL_LEN` | `8192` | Maximum sequence length |
+| `MAX_MODEL_LEN` | `30000` | Maximum sequence length |
 | `GPU_MEMORY_UTILIZATION` | `0.9` | GPU memory usage (0-1) |
 
 ---
@@ -283,7 +291,7 @@ This dual-VLM approach catches extraction errors that single models miss.
 ### Why Multi-Model Works
 
 - **Different architectures:** Independent models cross-validate each other
-- **Specialized strengths:** Nanonets-OCR-s excels at document structure; MiniCPM-V handles general vision
+- **Specialized strengths:** Nanonets-OCR2-3B excels at document structure; MiniCPM-V handles general vision
 - **Native processing:** All VLMs see original images—no intermediate structure loss
 
 ### Model Selection Guide
@@ -291,10 +299,11 @@ This dual-VLM approach catches extraction errors that single models miss.
 | Task | Recommended Model |
 |------|-------------------|
 | General image understanding | MiniCPM-V 4.5 |
-| Document OCR with structure preservation | Nanonets-OCR-s |
+| Document OCR with structure preservation | Nanonets-OCR2-3B |
 | Complex visual reasoning / code generation | Qwen3-VL-30B |
 | Multi-image analysis | MiniCPM-V 4.5 |
 | Visual agent tasks | Qwen3-VL-30B |
+| Large documents (30K+ tokens) | Nanonets-OCR2-3B |
 
 ---
 
@@ -309,7 +318,7 @@ cd ht-docker-ai
 ./build-images.sh
 
 # Run tests
-./test-images.sh
+pnpm test
 ```
 
 ---

test/test.bankstatements.minicpm.ts

@@ -1,9 +1,9 @@
 /**
  * Bank statement extraction using MiniCPM-V (visual extraction)
  *
- * JSON per-page approach:
+ * JSON per-page approach with streaming output:
  * 1. Ask for structured JSON of all transactions per page
- * 2. Consensus: extract twice, compare, retry if mismatch
+ * 2. Single pass extraction (no consensus)
  */
 import { tap, expect } from '@git.zone/tstest/tapbundle';
 import * as fs from 'fs';
@@ -66,11 +66,11 @@ function convertPdfToImages(pdfPath: string): string[] {
 }
 
 /**
- * Query for JSON extraction
+ * Query for JSON extraction with streaming output
  */
 async function queryJson(image: string, queryId: string): Promise<string> {
-  console.log(`      [${queryId}] Sending request to ${MODEL}...`);
   const startTime = Date.now();
+  process.stdout.write(`      [${queryId}] `);
 
   const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
@@ -82,25 +82,50 @@ async function queryJson(image: string, queryId: string): Promise<string> {
         content: JSON_PROMPT,
         images: [image],
       }],
-      stream: false,
+      stream: true,
       options: {
+        num_ctx: 32768,
         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)`);
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    process.stdout.write(`ERROR: ${response.status} (${elapsed}s)\n`);
     throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const data = await response.json();
-  const content = (data.message?.content || '').trim();
-  console.log(`      [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
-  return content;
+  let content = '';
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+          const token = json.message?.content || '';
+          if (token) {
+            process.stdout.write(token);
+            content += token;
+          }
+        } catch {
+          // Ignore parse errors for partial chunks
+        }
+      }
+    }
+  } finally {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    process.stdout.write(` (${elapsed}s)\n`);
+  }
+
+  return content.trim();
 }
 
 /**
@@ -284,102 +309,29 @@ function parseAmount(value: unknown): number {
 }
 
 /**
- * Compare two transaction arrays for consensus
- */
-function transactionArraysMatch(a: ITransaction[], b: ITransaction[]): boolean {
-  if (a.length !== b.length) return false;
-
-  for (let i = 0; i < a.length; i++) {
-    const dateMatch = a[i].date === b[i].date;
-    const amountMatch = Math.abs(a[i].amount - b[i].amount) < 0.01;
-    if (!dateMatch || !amountMatch) return false;
-  }
-
-  return true;
-}
-
-/**
- * Compare two transaction arrays and log differences
- */
-function compareAndLogDifferences(txs1: ITransaction[], txs2: ITransaction[], pageNum: number): void {
-  if (txs1.length !== txs2.length) {
-    console.log(`    [Page ${pageNum}] Length mismatch: Q1=${txs1.length}, Q2=${txs2.length}`);
-    return;
-  }
-
-  for (let i = 0; i < txs1.length; i++) {
-    const dateMatch = txs1[i].date === txs2[i].date;
-    const amountMatch = Math.abs(txs1[i].amount - txs2[i].amount) < 0.01;
-
-    if (!dateMatch || !amountMatch) {
-      console.log(`    [Page ${pageNum}] Tx ${i + 1} differs:`);
-      console.log(`      Q1: ${txs1[i].date} | ${txs1[i].amount}`);
-      console.log(`      Q2: ${txs2[i].date} | ${txs2[i].amount}`);
-    }
-  }
-}
-
-/**
- * Extract transactions from a single page with consensus
+ * Extract transactions from a single page (single pass)
  */
 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} ---`);
+  const queryId = `P${pageNum}`;
+  const response = await queryJson(image, queryId);
+  const transactions = parseJsonResponse(response, queryId);
 
-    // 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(`    [Page ${pageNum}] Extracted ${transactions.length} transactions:`);
+  for (let i = 0; i < transactions.length; i++) {
+    const tx = transactions[i];
     console.log(`      ${(i + 1).toString().padStart(2)}. ${tx.date} | ${tx.counterparty.substring(0, 30).padEnd(30)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
   }
-      return txs1;
-    }
 
-    console.log(`    [Page ${pageNum}] ✗ NO CONSENSUS`);
-    compareAndLogDifferences(txs1, txs2, pageNum);
-
-    if (attempt < MAX_ATTEMPTS) {
-      console.log(`    [Page ${pageNum}] Retrying...`);
-    }
-  }
-
-  // Fallback: use last response
-  console.log(`\n    [Page ${pageNum}] === FALLBACK (no consensus after ${MAX_ATTEMPTS} attempts) ===`);
-  const fallbackId = `P${pageNum}FALLBACK`;
-  const fallbackResponse = await queryJson(image, fallbackId);
-  const fallback = parseJsonResponse(fallbackResponse, fallbackId);
-  console.log(`    [Page ${pageNum}] ~ FALLBACK RESULT: ${fallback.length} transactions`);
-  for (let i = 0; i < fallback.length; i++) {
-    const tx = fallback[i];
-    console.log(`      ${(i + 1).toString().padStart(2)}. ${tx.date} | ${tx.counterparty.substring(0, 30).padEnd(30)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
-  }
-  return fallback;
+  return transactions;
 }
 
 /**
  * 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)`);
+  console.log(`    [Vision] Processing ${images.length} page(s) with ${MODEL} (single pass)`);
 
   const allTransactions: ITransaction[] = [];
 
@@ -527,7 +479,7 @@ tap.test('summary', async () => {
   console.log(`\n======================================================`);
   console.log(`   Bank Statement Summary (${MODEL})`);
   console.log(`======================================================`);
-  console.log(`  Method:   JSON per-page + consensus`);
+  console.log(`  Method:   JSON per-page (single pass)`);
   console.log(`  Passed:   ${passedCount}/${total}`);
   console.log(`  Failed:   ${failedCount}/${total}`);
   console.log(`======================================================\n`);

test/test.bankstatements.nanonets.ts

@@ -51,11 +51,21 @@ If there is an image in the document and image caption is not present, add a sma
 Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
 Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
 
-// JSON extraction prompt for GPT-OSS 20B
-const JSON_EXTRACTION_PROMPT = `Extract ALL transactions from this bank statement as JSON array. Each transaction: {"date": "YYYY-MM-DD", "counterparty": "NAME", "amount": -25.99}. Amount negative for debits, positive for credits. Only include actual transactions, not balances. Return ONLY JSON array, no explanation.
+// JSON extraction prompt for GPT-OSS 20B (sent AFTER the statement text is provided)
+const JSON_EXTRACTION_PROMPT = `Extract ALL transactions from the bank statement. Return ONLY valid JSON array.
 
-STATEMENT:
-`;
+WHERE TO FIND DATA:
+- Transactions are typically in TABLES with columns: Date, Description/Counterparty, Debit, Credit, Balance
+- Look for rows with actual money movements, NOT header rows or summary totals
+
+RULES:
+1. date: Convert to YYYY-MM-DD format
+2. counterparty: The name/description of who the money went to/from
+3. amount: NEGATIVE for debits/withdrawals, POSITIVE for credits/deposits
+4. Only include actual transactions, NOT opening/closing balances
+
+JSON array only:
+[{"date":"YYYY-MM-DD","counterparty":"NAME","amount":-25.99}]`;
 
 // Constants for smart batching
 const MAX_VISUAL_TOKENS = 28000;  // ~32K context minus prompt/output headroom
@@ -246,12 +256,8 @@ async function ensureExtractionModel(): Promise<boolean> {
  */
 async function extractTransactionsFromMarkdown(markdown: string, queryId: string): Promise<ITransaction[]> {
   const startTime = Date.now();
-  const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
 
-  // Log exact prompt
-  console.log(`\n      [${queryId}] ===== PROMPT =====`);
-  console.log(fullPrompt);
-  console.log(`      [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
+  console.log(`      [${queryId}] Statement: ${markdown.length} chars, Prompt: ${JSON_EXTRACTION_PROMPT.length} chars`);
 
   const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
@@ -261,9 +267,15 @@ async function extractTransactionsFromMarkdown(markdown: string, queryId: string
       messages: [
         { role: 'user', content: 'Hi there, how are you?' },
         { role: 'assistant', content: 'Good, how can I help you today?' },
-        { role: 'user', content: fullPrompt },
+        { role: 'user', content: `Here is a bank statement document:\n\n${markdown}` },
+        { role: 'assistant', content: 'I have read the bank statement document you provided. I can see all the transaction data. What would you like me to do with it?' },
+        { role: 'user', content: JSON_EXTRACTION_PROMPT },
       ],
       stream: true,
+      options: {
+        num_ctx: 32768,  // Larger context for long statements + thinking
+        temperature: 0,  // Deterministic for JSON extraction
+      },
     }),
     signal: AbortSignal.timeout(600000),  // 10 minute timeout
   });

test/test.invoices.extraction.ts

@@ -197,6 +197,10 @@ async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Pr
         { role: 'user', content: JSON_EXTRACTION_PROMPT },
       ],
       stream: true,
+      options: {
+        num_ctx: 32768,  // Larger context for long invoices + thinking
+        temperature: 0,  // Deterministic for JSON extraction
+      },
     }),
     signal: AbortSignal.timeout(120000), // 2 min timeout
   });

test/test.invoices.minicpm.ts

@@ -67,9 +67,12 @@ const JSON_PROMPT = `Extract invoice data from this image. Return ONLY a JSON ob
 Return only the JSON, no explanation.`;
 
 /**
- * Query MiniCPM-V for JSON output (fast, no thinking)
+ * Query MiniCPM-V for JSON output (fast, no thinking) with streaming
  */
 async function queryJsonFast(images: string[]): Promise<string> {
+  const startTime = Date.now();
+  process.stdout.write(`      [Fast] `);
+
   const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
@@ -80,8 +83,9 @@ async function queryJsonFast(images: string[]): Promise<string> {
         content: JSON_PROMPT,
         images: images,
       }],
-      stream: false,
+      stream: true,
       options: {
+        num_ctx: 32768,
         num_predict: 1000,
         temperature: 0.1,
       },
@@ -92,14 +96,44 @@ async function queryJsonFast(images: string[]): Promise<string> {
     throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const data = await response.json();
-  return (data.message?.content || '').trim();
+  let content = '';
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+          const token = json.message?.content || '';
+          if (token) {
+            process.stdout.write(token);
+            content += token;
+          }
+        } catch {
+          // Ignore parse errors for partial chunks
+        }
+      }
+    }
+  } finally {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    process.stdout.write(` (${elapsed}s)\n`);
+  }
+
+  return content.trim();
 }
 
 /**
- * Query MiniCPM-V for JSON output with thinking enabled (slower, more accurate)
+ * Query MiniCPM-V for JSON output with thinking enabled (slower, more accurate) with streaming
  */
 async function queryJsonWithThinking(images: string[]): Promise<string> {
+  const startTime = Date.now();
+  process.stdout.write(`      [Think] `);
+
   const response = await fetch(`${OLLAMA_URL}/api/chat`, {
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
@@ -110,8 +144,9 @@ async function queryJsonWithThinking(images: string[]): Promise<string> {
         content: `Think carefully about this invoice image, then ${JSON_PROMPT}`,
         images: images,
       }],
-      stream: false,
+      stream: true,
       options: {
+        num_ctx: 32768,
         num_predict: 2000,
         temperature: 0.1,
       },
@@ -122,8 +157,56 @@ async function queryJsonWithThinking(images: string[]): Promise<string> {
     throw new Error(`Ollama API error: ${response.status}`);
   }
 
-  const data = await response.json();
-  return (data.message?.content || '').trim();
+  let content = '';
+  let thinkingContent = '';
+  let thinkingStarted = false;
+  let outputStarted = false;
+  const reader = response.body!.getReader();
+  const decoder = new TextDecoder();
+
+  try {
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      const chunk = decoder.decode(value, { stream: true });
+      for (const line of chunk.split('\n').filter(l => l.trim())) {
+        try {
+          const json = JSON.parse(line);
+
+          // Stream thinking tokens
+          const thinking = json.message?.thinking || '';
+          if (thinking) {
+            if (!thinkingStarted) {
+              process.stdout.write(`THINKING: `);
+              thinkingStarted = true;
+            }
+            process.stdout.write(thinking);
+            thinkingContent += thinking;
+          }
+
+          // Stream content tokens
+          const token = json.message?.content || '';
+          if (token) {
+            if (!outputStarted) {
+              if (thinkingStarted) process.stdout.write('\n      [Think] ');
+              process.stdout.write(`OUTPUT: `);
+              outputStarted = true;
+            }
+            process.stdout.write(token);
+            content += token;
+          }
+        } catch {
+          // Ignore parse errors for partial chunks
+        }
+      }
+    }
+  } finally {
+    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    process.stdout.write(` (${elapsed}s)\n`);
+  }
+
+  return content.trim();
 }
 
 /**

test/test.invoices.nanonets.ts

@@ -12,6 +12,8 @@ import * as path from 'path';
 import { execSync } from 'child_process';
 import * as os from 'os';
 import { ensureNanonetsOcr, ensureMiniCpm, isContainerRunning } from './helpers/docker.js';
+import { SmartAi } from '@push.rocks/smartai';
+import { DualAgentOrchestrator } from '@push.rocks/smartagent';
 
 const NANONETS_URL = 'http://localhost:8000/v1';
 const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
@@ -19,8 +21,24 @@ const NANONETS_MODEL = 'nanonets/Nanonets-OCR2-3B';
 const OLLAMA_URL = 'http://localhost:11434';
 const EXTRACTION_MODEL = 'gpt-oss:20b';
 
-// Temp directory for storing markdown between stages
-const TEMP_MD_DIR = path.join(os.tmpdir(), 'nanonets-invoices-markdown');
+// Persistent cache directory for storing markdown between runs
+const MD_CACHE_DIR = path.join(process.cwd(), '.nogit/invoices-md');
+
+// SmartAi instance for Ollama with optimized settings
+const smartAi = new SmartAi({
+  ollama: {
+    baseUrl: OLLAMA_URL,
+    model: EXTRACTION_MODEL,
+    defaultOptions: {
+      num_ctx: 32768,   // Larger context for long invoices + thinking
+      temperature: 0,   // Deterministic for JSON extraction
+    },
+    defaultTimeout: 600000,  // 10 minute timeout for large documents
+  },
+});
+
+// DualAgentOrchestrator for structured task execution
+let orchestrator: DualAgentOrchestrator;
 
 interface IInvoice {
   invoice_number: string;
@@ -54,34 +72,30 @@ If there is an image in the document and image caption is not present, add a sma
 Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
 Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
 
-// JSON extraction prompt for GPT-OSS 20B
-const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
+// JSON extraction prompt for GPT-OSS 20B (sent AFTER the invoice text is provided)
+const JSON_EXTRACTION_PROMPT = `Extract key fields from the invoice. Return ONLY valid JSON.
 
-IMPORTANT RULES:
-1. invoice_number: The unique invoice/document number (NOT VAT ID, NOT customer ID)
-2. invoice_date: Format as YYYY-MM-DD
-3. vendor_name: The company that issued the invoice
+WHERE TO FIND DATA:
+- invoice_number, invoice_date, vendor_name: Look in the HEADER section at the TOP of PAGE 1 (near "Invoice no.", "Invoice date:", "Rechnungsnummer")
+- net_amount, vat_amount, total_amount: Look in the SUMMARY section at the BOTTOM (look for "Total", "Amount due", "Gesamtbetrag")
+
+RULES:
+1. invoice_number: Extract ONLY the value (e.g., "R0015632540"), NOT the label "Invoice no."
+2. invoice_date: Convert to YYYY-MM-DD format (e.g., "14/04/2022" → "2022-04-14")
+3. vendor_name: The company issuing the invoice
 4. currency: EUR, USD, or GBP
-5. net_amount: Amount before tax
-6. vat_amount: Tax/VAT amount
-7. total_amount: Final total (gross amount)
+5. net_amount: Total before tax
+6. vat_amount: Tax amount
+7. total_amount: Final total with tax
 
-Return ONLY this JSON format, no explanation:
-{
-  "invoice_number": "INV-2024-001",
-  "invoice_date": "2024-01-15",
-  "vendor_name": "Company Name",
-  "currency": "EUR",
-  "net_amount": 100.00,
-  "vat_amount": 19.00,
-  "total_amount": 119.00
-}
+JSON only:
+{"invoice_number":"X","invoice_date":"YYYY-MM-DD","vendor_name":"X","currency":"EUR","net_amount":0,"vat_amount":0,"total_amount":0}
+
+Double check for valid JSON syntax.
 
-INVOICE TEXT:
 `;
 
 // Constants for smart batching
-const MAX_VISUAL_TOKENS = 28000;  // ~32K context minus prompt/output headroom
 const PATCH_SIZE = 14;  // Qwen2.5-VL uses 14x14 patches
 
 /**
@@ -325,16 +339,20 @@ function extractCurrency(s: string | undefined): string {
 }
 
 /**
- * Extract JSON from response
+ * Try to extract valid JSON from a response string
  */
-function extractJsonFromResponse(response: string): Record<string, unknown> | null {
-  let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
-  const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
-  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
+function tryExtractJson(response: string): Record<string, unknown> | null {
+  // Remove thinking tags
+  let clean = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
+
+  // Try code block
+  const codeBlockMatch = clean.match(/```(?:json)?\s*([\s\S]*?)```/);
+  const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : clean;
 
   try {
     return JSON.parse(jsonStr);
   } catch {
+    // Try to find JSON object
     const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
     if (jsonMatch) {
       try {
@@ -348,111 +366,92 @@ function extractJsonFromResponse(response: string): Record<string, unknown> | nu
 }
 
 /**
- * Parse JSON response into IInvoice
- */
-function parseJsonToInvoice(response: string): IInvoice | null {
-  const parsed = extractJsonFromResponse(response);
-  if (!parsed) return null;
-
-  return {
-    invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
-    invoice_date: extractDate(String(parsed.invoice_date || '')),
-    vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
-    currency: extractCurrency(String(parsed.currency || '')),
-    net_amount: parseAmount(parsed.net_amount as string | number),
-    vat_amount: parseAmount(parsed.vat_amount as string | number),
-    total_amount: parseAmount(parsed.total_amount as string | number),
-  };
-}
-
-/**
- * Extract invoice from markdown using GPT-OSS 20B (streaming)
+ * Extract invoice from markdown using smartagent DualAgentOrchestrator
+ * Validates JSON and retries if invalid
  */
 async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
   const startTime = Date.now();
-  const fullPrompt = JSON_EXTRACTION_PROMPT + markdown;
+  const maxRetries = 2;
 
-  // Log exact prompt
-  console.log(`\n      [${queryId}] ===== PROMPT =====`);
-  console.log(fullPrompt);
-  console.log(`      [${queryId}] ===== END PROMPT (${fullPrompt.length} chars) =====\n`);
+  console.log(`      [${queryId}] Invoice: ${markdown.length} chars`);
 
-  const response = await fetch(`${OLLAMA_URL}/api/chat`, {
-    method: 'POST',
-    headers: { 'Content-Type': 'application/json' },
-    body: JSON.stringify({
-      model: EXTRACTION_MODEL,
-      messages: [
-        { role: 'user', content: 'Hi there, how are you?' },
-        { role: 'assistant', content: 'Good, how can I help you today?' },
-        { role: 'user', content: fullPrompt },
-      ],
-      stream: true,
-    }),
-    signal: AbortSignal.timeout(600000), // 10 minute timeout for large documents
-  });
+  // Build the extraction task with document context
+  const taskPrompt = `Extract the invoice data from this document and output ONLY the JSON:
 
-  if (!response.ok) {
-    const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
-    console.log(`      [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
-    throw new Error(`Ollama API error: ${response.status}`);
-  }
+${markdown}
 
-  // Stream the response
-  let content = '';
-  let thinkingContent = '';
-  let thinkingStarted = false;
-  let outputStarted = false;
-  const reader = response.body!.getReader();
-  const decoder = new TextDecoder();
+${JSON_EXTRACTION_PROMPT}`;
 
   try {
-    while (true) {
-      const { done, value } = await reader.read();
-      if (done) break;
+    let result = await orchestrator.run(taskPrompt);
+    let elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+    console.log(`      [${queryId}] Status: ${result.status}, Iterations: ${result.iterations} (${elapsed}s)`);
 
-      const chunk = decoder.decode(value, { stream: true });
+    // Try to parse JSON from result
+    let jsonData: Record<string, unknown> | null = null;
+    let responseText = result.result || '';
 
-      // Each line is a JSON object
-      for (const line of chunk.split('\n').filter(l => l.trim())) {
-        try {
-          const json = JSON.parse(line);
-
-          // Stream thinking tokens
-          const thinking = json.message?.thinking || '';
-          if (thinking) {
-            if (!thinkingStarted) {
-              process.stdout.write(`      [${queryId}] THINKING: `);
-              thinkingStarted = true;
-            }
-            process.stdout.write(thinking);
-            thinkingContent += thinking;
+    if (result.success && responseText) {
+      jsonData = tryExtractJson(responseText);
     }
 
-          // Stream content tokens
-          const token = json.message?.content || '';
-          if (token) {
-            if (!outputStarted) {
-              if (thinkingStarted) process.stdout.write('\n');
-              process.stdout.write(`      [${queryId}] OUTPUT: `);
-              outputStarted = true;
+    // Fallback: try parsing from history
+    if (!jsonData && result.history?.length > 0) {
+      const lastMessage = result.history[result.history.length - 1];
+      if (lastMessage?.content) {
+        responseText = lastMessage.content;
+        jsonData = tryExtractJson(responseText);
       }
-            process.stdout.write(token);
-            content += token;
-          }
-        } catch {
-          // Ignore parse errors for partial chunks
-        }
-      }
-    }
-  } finally {
-    if (thinkingStarted || outputStarted) process.stdout.write('\n');
     }
 
+    // If JSON is invalid, retry with correction request
+    let retries = 0;
+    while (!jsonData && retries < maxRetries) {
+      retries++;
+      console.log(`      [${queryId}] Invalid JSON, requesting correction (retry ${retries}/${maxRetries})...`);
+
+      result = await orchestrator.continueTask(
+        `Your response was not valid JSON. Please output ONLY the JSON object with no markdown, no explanation, no thinking tags. Just the raw JSON starting with { and ending with }. Format:
+{"invoice_number":"X","invoice_date":"YYYY-MM-DD","vendor_name":"X","currency":"EUR","net_amount":0,"vat_amount":0,"total_amount":0}`
+      );
+
+      elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
+      console.log(`      [${queryId}] Retry ${retries}: ${result.status} (${elapsed}s)`);
+
+      responseText = result.result || '';
+      if (responseText) {
+        jsonData = tryExtractJson(responseText);
+      }
+
+      if (!jsonData && result.history?.length > 0) {
+        const lastMessage = result.history[result.history.length - 1];
+        if (lastMessage?.content) {
+          responseText = lastMessage.content;
+          jsonData = tryExtractJson(responseText);
+        }
+      }
+    }
+
+    if (!jsonData) {
+      console.log(`      [${queryId}] Failed to get valid JSON after ${retries} retries`);
+      return null;
+    }
+
+    console.log(`      [${queryId}] Valid JSON extracted`);
+    return {
+      invoice_number: extractInvoiceNumber(String(jsonData.invoice_number || '')),
+      invoice_date: extractDate(String(jsonData.invoice_date || '')),
+      vendor_name: String(jsonData.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
+      currency: extractCurrency(String(jsonData.currency || '')),
+      net_amount: parseAmount(jsonData.net_amount as string | number),
+      vat_amount: parseAmount(jsonData.vat_amount as string | number),
+      total_amount: parseAmount(jsonData.total_amount as string | number),
+    };
+  } catch (error) {
     const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
-  console.log(`      [${queryId}] Done: ${thinkingContent.length} thinking chars, ${content.length} output chars (${elapsed}s)`);
-
-  return parseJsonToInvoice(content);
+    console.log(`      [${queryId}] ERROR: ${error} (${elapsed}s)`);
+    throw error;
+  }
 }
 
 /**
@@ -561,23 +560,45 @@ function findTestCases(): ITestCase[] {
 const testCases = findTestCases();
 console.log(`\nFound ${testCases.length} invoice test cases\n`);
 
-// Ensure temp directory exists
-if (!fs.existsSync(TEMP_MD_DIR)) {
-  fs.mkdirSync(TEMP_MD_DIR, { recursive: true });
+// Ensure cache directory exists
+if (!fs.existsSync(MD_CACHE_DIR)) {
+  fs.mkdirSync(MD_CACHE_DIR, { recursive: true });
 }
 
 // -------- STAGE 1: OCR with Nanonets --------
 
-tap.test('Stage 1: Setup Nanonets', async () => {
+tap.test('Stage 1: Convert invoices to markdown (with caching)', async () => {
   console.log('\n========== STAGE 1: Nanonets OCR ==========\n');
-  const ok = await ensureNanonetsOcr();
-  expect(ok).toBeTrue();
-});
 
-tap.test('Stage 1: Convert all invoices to markdown', async () => {
-  console.log('\n  Converting all invoice PDFs to markdown with Nanonets-OCR-s...\n');
+  // Check which invoices need OCR conversion
+  const needsConversion: ITestCase[] = [];
+  let cachedCount = 0;
 
   for (const tc of testCases) {
+    const mdPath = path.join(MD_CACHE_DIR, `${tc.name}.md`);
+    if (fs.existsSync(mdPath)) {
+      cachedCount++;
+      tc.markdownPath = mdPath;
+      console.log(`  [CACHED] ${tc.name} - using cached markdown`);
+    } else {
+      needsConversion.push(tc);
+    }
+  }
+
+  console.log(`\n  Summary: ${cachedCount} cached, ${needsConversion.length} need conversion\n`);
+
+  if (needsConversion.length === 0) {
+    console.log('  All invoices already cached, skipping Nanonets OCR\n');
+    return;
+  }
+
+  // Start Nanonets only if there are files to convert
+  console.log('  Starting Nanonets for OCR conversion...\n');
+  const ok = await ensureNanonetsOcr();
+  expect(ok).toBeTrue();
+
+  // Convert only the invoices that need conversion
+  for (const tc of needsConversion) {
     console.log(`\n    === ${tc.name} ===`);
 
     const images = convertPdfToImages(tc.pdfPath);
@@ -585,13 +606,13 @@ tap.test('Stage 1: Convert all invoices to markdown', async () => {
 
     const markdown = await convertDocumentToMarkdown(images, tc.name);
 
-    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    const mdPath = path.join(MD_CACHE_DIR, `${tc.name}.md`);
     fs.writeFileSync(mdPath, markdown);
     tc.markdownPath = mdPath;
     console.log(`      Saved: ${mdPath}`);
   }
 
-  console.log('\n  Stage 1 complete: All invoices converted to markdown\n');
+  console.log(`\n  Stage 1 complete: ${needsConversion.length} invoices converted to markdown\n`);
 });
 
 tap.test('Stage 1: Stop Nanonets', async () => {
@@ -610,6 +631,42 @@ tap.test('Stage 2: Setup Ollama + GPT-OSS 20B', async () => {
 
   const extractionOk = await ensureExtractionModel();
   expect(extractionOk).toBeTrue();
+
+  // Initialize SmartAi and DualAgentOrchestrator
+  console.log('  [SmartAgent] Starting SmartAi...');
+  await smartAi.start();
+
+  console.log('  [SmartAgent] Creating DualAgentOrchestrator...');
+  orchestrator = new DualAgentOrchestrator({
+    smartAiInstance: smartAi,
+    defaultProvider: 'ollama',
+    guardianPolicyPrompt: `
+      JSON EXTRACTION POLICY:
+      - APPROVE all JSON extraction tasks
+      - This is a read-only operation - no file system or network access needed
+      - The task is to extract structured data from document text
+    `,
+    driverSystemMessage: `You are a precise JSON extraction assistant. Your only job is to extract invoice data from documents.
+
+CRITICAL RULES:
+1. Output ONLY valid JSON - no markdown, no explanations, no thinking
+2. Use the exact format requested
+3. If you cannot find a value, use empty string "" or 0 for numbers
+
+When done, wrap your JSON in <task_complete></task_complete> tags.`,
+    maxIterations: 3,
+    // Enable streaming for real-time progress visibility
+    onToken: (token, source) => {
+      if (source === 'driver') {
+        process.stdout.write(token);
+      }
+    },
+  });
+
+  // No tools needed for JSON extraction
+  console.log('  [SmartAgent] Starting orchestrator...');
+  await orchestrator.start();
+  console.log('  [SmartAgent] Ready for extraction');
 });
 
 let passedCount = 0;
@@ -624,7 +681,7 @@ for (const tc of testCases) {
 
     const startTime = Date.now();
 
-    const mdPath = path.join(TEMP_MD_DIR, `${tc.name}.md`);
+    const mdPath = path.join(MD_CACHE_DIR, `${tc.name}.md`);
     if (!fs.existsSync(mdPath)) {
       throw new Error(`Markdown not found: ${mdPath}. Run Stage 1 first.`);
     }
@@ -654,6 +711,14 @@ for (const tc of testCases) {
 }
 
 tap.test('Summary', async () => {
+  // Cleanup orchestrator and SmartAi
+  if (orchestrator) {
+    console.log('\n  [SmartAgent] Stopping orchestrator...');
+    await orchestrator.stop();
+  }
+  console.log('  [SmartAgent] Stopping SmartAi...');
+  await smartAi.stop();
+
   const totalInvoices = testCases.length;
   const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
   const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
@@ -663,7 +728,7 @@ tap.test('Summary', async () => {
   console.log(`   Invoice Summary (Nanonets + GPT-OSS 20B)`);
   console.log(`========================================`);
   console.log(`  Stage 1:   Nanonets-OCR-s (doc -> md)`);
-  console.log(`  Stage 2:   GPT-OSS 20B (md -> JSON)`);
+  console.log(`  Stage 2:   GPT-OSS 20B + SmartAgent (md -> JSON)`);
   console.log(`  Passed:    ${passedCount}/${totalInvoices}`);
   console.log(`  Failed:    ${failedCount}/${totalInvoices}`);
   console.log(`  Accuracy:  ${accuracy.toFixed(1)}%`);
@@ -671,14 +736,7 @@ tap.test('Summary', async () => {
   console.log(`  Total time:   ${(totalTimeMs / 1000).toFixed(1)}s`);
   console.log(`  Avg per inv:  ${avgTimeSec.toFixed(1)}s`);
   console.log(`========================================\n`);
-
-  // Cleanup temp files
-  try {
-    fs.rmSync(TEMP_MD_DIR, { recursive: true, force: true });
-    console.log(`  Cleaned up temp directory: ${TEMP_MD_DIR}\n`);
-  } catch {
-    // Ignore
-  }
+  console.log(`  Cache location: ${MD_CACHE_DIR}\n`);
 });
 
 export default tap.start();