From 15c35944a2e984d29606f8fc24089fc6e60304ab Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ignacio=20L=C3=B3pez=20Luna?= <ignasi.lopez.luna@gmail.com>
Date: Thu, 9 Oct 2025 17:05:02 +0200
Subject: [PATCH 1/3] feat(demo): add PDF data extractor demo with upload and
 extraction features

---
 demos/extractor/.gitignore   |  20 ++
 demos/extractor/README.md    |  90 +++++++
 demos/extractor/demo.html    | 447 +++++++++++++++++++++++++++++++++++
 demos/extractor/invoice.pdf  | Bin 0 -> 24762 bytes
 demos/extractor/package.json |  27 +++
 demos/extractor/server.js    | 155 ++++++++++++
 6 files changed, 739 insertions(+)
 create mode 100644 demos/extractor/.gitignore
 create mode 100644 demos/extractor/README.md
 create mode 100644 demos/extractor/demo.html
 create mode 100644 demos/extractor/invoice.pdf
 create mode 100644 demos/extractor/package.json
 create mode 100644 demos/extractor/server.js

diff --git a/demos/extractor/.gitignore b/demos/extractor/.gitignore
new file mode 100644
index 000000000..d55627f3e
--- /dev/null
+++ b/demos/extractor/.gitignore
@@ -0,0 +1,20 @@
+# Dependencies
+node_modules/
+package-lock.json
+
+# Upload directory
+uploads/
+
+# Logs
+*.log
+npm-debug.log*
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
diff --git a/demos/extractor/README.md b/demos/extractor/README.md
new file mode 100644
index 000000000..d8e78d0e7
--- /dev/null
+++ b/demos/extractor/README.md
@@ -0,0 +1,90 @@
+# PDF Data Extractor Demo
+
+This demo application allows you to extract structured data from PDF documents using JSON schemas and AI models.
+
+## Features
+
+- 📄 Upload and process PDF files
+- 📋 Define custom JSON schemas for data extraction
+- 🎯 Pre-built schema examples (Invoice, Receipt, Form)
+- 📊 View extracted data with token usage statistics
+- ⚙️ Configurable temperature and model selection
+
+## Prerequisites
+
+Before running this demo, you need:
+
+1. **Node.js** (version 18 or higher)
+2. **Docker Model Runner**
+3. **A suitable AI model** for text extraction
+
+## Setup Instructions
+
+### 1. Enable Docker Model Runner
+
+**Using Docker Desktop:**
+- Open Docker Desktop settings
+- Go to the **AI** tab
+- Select **Enable Docker Model Runner**
+- Enable **host-side TCP support** on port `12434` (default)
+
+For detailed instructions, see the [Docker Model Runner documentation](https://docs.docker.com/ai/model-runner/get-started/#enable-docker-model-runner).
+
+**Using Standalone Docker Engine:**
+TCP support is enabled by default on port `12434`.
+
+#### 2. Pull a Suitable Model
+
+You'll need a model capable of understanding and extracting text. Recommended models:
+
+```bash
+# Pull a general-purpose model
+docker model pull ai/gemma3
+```
+
+To see available models, visit [Docker Hub - AI Models](https://hub.docker.com/r/ai).
+
+## Installation
+
+1. **Navigate to the demo directory:**
+   ```bash
+   cd demos/extractor
+   ```
+
+2. **Install dependencies:**
+   ```bash
+   npm install
+   ```
+
+3. **Start the server:**
+   ```bash
+   npm start
+   ```
+
+   The server will start on `http://localhost:3000`
+
+4. **Open the demo:**
+   Open `demo.html` in your web browser (you can simply double-click the file or serve it with a local server)
+
+## Usage Guide
+
+### Basic Workflow
+
+1. **Configure API Settings**
+   - **Base API URL**: Set to `http://127.0.0.1:12434/engines/v1` for Docker Model Runner
+   - **Model**: Select from available models
+
+2. **Define Your Schema**
+   - Use the provided examples (Invoice, Receipt, Form) or create your own
+   - The schema defines what data to extract from the PDF
+   - Use standard JSON Schema format with `type`, `properties`, etc.
+
+3. **Upload a PDF**
+   - Click "Choose File" and select your PDF document
+   - Supported: Any text-based PDF (not scanned images without OCR)
+   - You can use sample PDFs [invoice.pdf](invoice.pdf)
+
+4. **Extract Data**
+   - Click "Extract Data" button
+   - Wait for processing (may take 10-30 seconds depending on PDF size and model)
+   - View extracted data in the result section
diff --git a/demos/extractor/demo.html b/demos/extractor/demo.html
new file mode 100644
index 000000000..e4d113415
--- /dev/null
+++ b/demos/extractor/demo.html
@@ -0,0 +1,447 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>PDF Data Extractor Demo</title>
+    <style>
+        body {
+            font-family: sans-serif;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            gap: 20px;
+            padding: 20px;
+            background-color: #f0f0f0;
+            max-width: 1200px;
+            margin: 0 auto;
+        }
+        h1 {
+            color: #333;
+            margin-bottom: 10px;
+        }
+        .subtitle {
+            color: #666;
+            font-size: 14px;
+            margin-top: -10px;
+            margin-bottom: 20px;
+        }
+        .config-section, .schema-section, .upload-section, .result-section {
+            background-color: #fff;
+            padding: 20px;
+            border-radius: 8px;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+            width: 100%;
+            box-sizing: border-box;
+        }
+        .section-title {
+            font-weight: bold;
+            font-size: 18px;
+            margin-bottom: 15px;
+            color: #333;
+        }
+        .form-group {
+            margin-bottom: 15px;
+        }
+        label {
+            display: block;
+            font-weight: bold;
+            margin-bottom: 5px;
+            color: #555;
+        }
+        input[type="text"], input[type="number"], select, textarea {
+            width: 100%;
+            padding: 8px;
+            border: 1px solid #ccc;
+            border-radius: 4px;
+            font-size: 14px;
+            box-sizing: border-box;
+            font-family: monospace;
+        }
+        textarea {
+            font-family: 'Courier New', monospace;
+            resize: vertical;
+            min-height: 200px;
+        }
+        input[type="file"] {
+            padding: 8px;
+            border: 1px solid #ccc;
+            border-radius: 4px;
+            width: 100%;
+            box-sizing: border-box;
+        }
+        button {
+            padding: 12px 24px;
+            font-size: 16px;
+            cursor: pointer;
+            border: none;
+            border-radius: 4px;
+            color: white;
+            font-weight: bold;
+        }
+        .btn-primary {
+            background-color: #007bff;
+        }
+        .btn-primary:hover {
+            background-color: #0056b3;
+        }
+        .btn-primary:disabled {
+            background-color: #6c757d;
+            cursor: not-allowed;
+        }
+        .btn-secondary {
+            background-color: #28a745;
+            margin-left: 10px;
+        }
+        .btn-secondary:hover {
+            background-color: #218838;
+        }
+        .button-group {
+            display: flex;
+            gap: 10px;
+            margin-top: 15px;
+        }
+        #result {
+            white-space: pre-wrap;
+            font-family: 'Courier New', monospace;
+            background-color: #f8f9fa;
+            padding: 15px;
+            border-radius: 4px;
+            border: 1px solid #dee2e6;
+            min-height: 100px;
+            max-height: 500px;
+            overflow-y: auto;
+        }
+        .error {
+            color: #dc3545;
+            font-weight: bold;
+        }
+        .success {
+            color: #28a745;
+        }
+        .info {
+            color: #17a2b8;
+            font-size: 14px;
+            margin-top: 10px;
+        }
+        .loading {
+            color: #ffc107;
+        }
+        #modelWarning {
+            background-color: #fff3cd;
+            color: #856404;
+            border: 1px solid #ffeaa7;
+            border-radius: 4px;
+            padding: 10px;
+            margin-top: 5px;
+            font-size: 14px;
+            display: none;
+        }
+        #modelWarning.show {
+            display: block;
+        }
+        #modelInfo {
+            background-color: #d1ecf1;
+            color: #0c5460;
+            border: 1px solid #bee5eb;
+            border-radius: 4px;
+            padding: 10px;
+            margin-top: 5px;
+            font-size: 14px;
+        }
+        #modelInfo a {
+            color: #0c5460;
+            text-decoration: underline;
+        }
+        .schema-examples {
+            margin-top: 10px;
+            display: flex;
+            gap: 10px;
+        }
+        .schema-examples button {
+            padding: 6px 12px;
+            font-size: 13px;
+            background-color: #6c757d;
+        }
+        .schema-examples button:hover {
+            background-color: #5a6268;
+        }
+        .grid-2 {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 15px;
+        }
+        @media (max-width: 768px) {
+            .grid-2 {
+                grid-template-columns: 1fr;
+            }
+        }
+    </style>
+</head>
+<body>
+
+<h1>PDF Data Extractor Demo</h1>
+<p class="subtitle">Extract structured data from PDF documents using JSON schemas and AI models</p>
+
+<!-- Configuration Section -->
+<div class="config-section">
+    <div class="section-title">🔧 API Configuration</div>
+    
+    <div class="form-group">
+        <label for="baseUrl">Base API URL:</label>
+        <input type="text" id="baseUrl" value="http://127.0.0.1:12434/engines/v1" placeholder="http://127.0.0.1:12434/engines/v1">
+        <div id="modelInfo">
+            ℹ️ To pull a model, run: <code>docker model pull &lt;model-name&gt;</code><br>
+            Find more models at: <a href="https://hub.docker.com/r/ai" target="_blank">https://hub.docker.com/r/ai</a>
+        </div>
+    </div>
+
+    <div class="form-group">
+        <label for="modelSelect">Model:</label>
+        <select id="modelSelect">
+            <option value="">Loading models...</option>
+        </select>
+        <div id="modelWarning"></div>
+    </div>
+
+    <div class="grid-2">
+        <div class="form-group">
+            <label for="temperature">Temperature (0-2):</label>
+            <input type="number" id="temperature" value="0.1" min="0" max="2" step="0.1">
+        </div>
+    </div>
+</div>
+
+<!-- Schema Section -->
+<div class="schema-section">
+    <div class="section-title">📋 JSON Schema</div>
+    
+    <div class="form-group">
+        <label for="schema">Define the structure to extract:</label>
+        <textarea id="schema"></textarea>
+        <div class="schema-examples">
+            <span style="margin-right: 10px; align-self: center; font-size: 14px;">Quick examples:</span>
+            <button onclick="loadInvoiceSchema()">Invoice</button>
+            <button onclick="loadReceiptSchema()">Receipt</button>
+            <button onclick="loadFormSchema()">Form</button>
+        </div>
+    </div>
+</div>
+
+<!-- Upload Section -->
+<div class="upload-section">
+    <div class="section-title">📄 PDF Upload</div>
+    
+    <div class="form-group">
+        <label for="pdfFile">Select PDF file:</label>
+        <input type="file" id="pdfFile" accept=".pdf">
+    </div>
+
+    <div class="button-group">
+        <button id="extractButton" class="btn-primary" onclick="extractData()">Extract Data</button>
+    </div>
+</div>
+
+<!-- Result Section -->
+<div class="result-section">
+    <div class="section-title">📊 Extraction Result</div>
+    <div id="result">Upload a PDF and click "Extract Data" to see results...</div>
+</div>
+
+<script>
+    const SERVER_URL = 'http://localhost:3000';
+    
+    // Default invoice schema
+    const INVOICE_SCHEMA = {
+        "invoiceNumber": { "type": "string" },
+        "date": { "type": "string" },
+        "vendorName": { "type": "string" },
+        "totalAmount": { "type": "number" },
+        "items": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "description": { "type": "string" },
+                    "quantity": { "type": "number" },
+                    "price": { "type": "number" }
+                }
+            }
+        }
+    };
+
+    const RECEIPT_SCHEMA = {
+        "storeName": { "type": "string" },
+        "date": { "type": "string" },
+        "totalAmount": { "type": "number" },
+        "items": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "name": { "type": "string" },
+                    "price": { "type": "number" }
+                }
+            }
+        },
+        "paymentMethod": { "type": "string" }
+    };
+
+    const FORM_SCHEMA = {
+        "name": { "type": "string" },
+        "email": { "type": "string" },
+        "phone": { "type": "string" },
+        "address": { "type": "string" },
+        "date": { "type": "string" }
+    };
+
+    // Load default schema on page load
+    function loadInvoiceSchema() {
+        document.getElementById('schema').value = JSON.stringify(INVOICE_SCHEMA, null, 2);
+    }
+
+    function loadReceiptSchema() {
+        document.getElementById('schema').value = JSON.stringify(RECEIPT_SCHEMA, null, 2);
+    }
+
+    function loadFormSchema() {
+        document.getElementById('schema').value = JSON.stringify(FORM_SCHEMA, null, 2);
+    }
+
+    // Fetch available models
+    async function fetchModels() {
+        const baseUrl = document.getElementById('baseUrl').value;
+        const modelSelect = document.getElementById('modelSelect');
+        const modelWarning = document.getElementById('modelWarning');
+
+        try {
+            const response = await fetch(`${SERVER_URL}/api/models`, {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({ baseUrl })
+            });
+
+            if (!response.ok) {
+                throw new Error(`HTTP error! status: ${response.status}`);
+            }
+
+            const data = await response.json();
+            const models = data.data.map(model => model.id);
+
+            modelSelect.innerHTML = '';
+
+            if (models.length === 0) {
+                modelSelect.innerHTML = '<option value="">No models available</option>';
+                modelWarning.innerHTML = '⚠️ No models found. Please ensure the model runner is active and models are loaded.';
+                modelWarning.classList.add('show');
+                return;
+            }
+
+            models.forEach(modelId => {
+                const option = document.createElement('option');
+                option.value = modelId;
+                option.textContent = modelId;
+                modelSelect.appendChild(option);
+            });
+
+            // Select first model by default
+            modelSelect.value = models[0];
+            modelWarning.classList.remove('show');
+
+        } catch (error) {
+            console.error('Error fetching models:', error);
+            modelSelect.innerHTML = '<option value="">Error loading models</option>';
+            modelWarning.innerHTML = `⚠️ Error loading models: ${error.message}`;
+            modelWarning.classList.add('show');
+        }
+    }
+
+    // Extract data from PDF
+    async function extractData() {
+        const pdfFile = document.getElementById('pdfFile').files[0];
+        const schema = document.getElementById('schema').value;
+        const baseUrl = document.getElementById('baseUrl').value;
+        const model = document.getElementById('modelSelect').value;
+        const temperature = document.getElementById('temperature').value;
+        const resultDiv = document.getElementById('result');
+        const extractButton = document.getElementById('extractButton');
+
+        // Validation
+        if (!pdfFile) {
+            resultDiv.innerHTML = '<span class="error">❌ Please select a PDF file</span>';
+            return;
+        }
+
+        if (!schema.trim()) {
+            resultDiv.innerHTML = '<span class="error">❌ Please provide a JSON schema</span>';
+            return;
+        }
+
+        if (!model) {
+            resultDiv.innerHTML = '<span class="error">❌ Please select a model</span>';
+            return;
+        }
+
+        // Validate JSON schema
+        try {
+            JSON.parse(schema);
+        } catch (error) {
+            resultDiv.innerHTML = `<span class="error">❌ Invalid JSON schema: ${error.message}</span>`;
+            return;
+        }
+
+        // Prepare form data
+        const formData = new FormData();
+        formData.append('pdf', pdfFile);
+        formData.append('schema', schema);
+        formData.append('baseUrl', baseUrl);
+        formData.append('model', model);
+        formData.append('temperature', temperature);
+
+        // Show loading state
+        extractButton.disabled = true;
+        resultDiv.innerHTML = '<span class="loading">⏳ Extracting data from PDF... This may take a moment.</span>';
+
+        try {
+            const response = await fetch(`${SERVER_URL}/api/extract`, {
+                method: 'POST',
+                body: formData
+            });
+
+            const result = await response.json();
+
+            if (!response.ok) {
+                throw new Error(result.error || result.message || 'Extraction failed');
+            }
+
+            if (result.success) {
+                let output = '<span class="success">✅ Extraction successful!</span>\n\n';
+                output += '<strong>Extracted Data:</strong>\n';
+                output += JSON.stringify(result.data, null, 2);
+                output += '\n\n<span class="info">📊 Tokens used: ' + result.tokensUsed + '</span>';
+                output += '\n<span class="info">🤖 Model: ' + result.model + '</span>';
+                resultDiv.innerHTML = output;
+            } else {
+                throw new Error(result.error || 'Unknown error occurred');
+            }
+
+        } catch (error) {
+            console.error('Error:', error);
+            resultDiv.innerHTML = `<span class="error">❌ Error: ${error.message}</span>`;
+        } finally {
+            extractButton.disabled = false;
+        }
+    }
+
+    // Initialize on page load
+    window.addEventListener('DOMContentLoaded', () => {
+        loadInvoiceSchema();
+        fetchModels();
+    });
+
+    // Refresh models when base URL changes
+    document.getElementById('baseUrl').addEventListener('change', fetchModels);
+</script>
+
+</body>
+</html>
diff --git a/demos/extractor/invoice.pdf b/demos/extractor/invoice.pdf
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literal 0
HcmV?d00001

diff --git a/demos/extractor/package.json b/demos/extractor/package.json
new file mode 100644
index 000000000..20e343aca
--- /dev/null
+++ b/demos/extractor/package.json
@@ -0,0 +1,27 @@
+{
+  "name": "pdf-data-extractor-demo",
+  "version": "1.0.0",
+  "description": "Demo application for extracting structured data from PDFs using JSON schemas",
+  "main": "server.js",
+  "scripts": {
+    "start": "node server.js",
+    "dev": "nodemon server.js"
+  },
+  "keywords": [
+    "pdf",
+    "extraction",
+    "openai",
+    "json-schema"
+  ],
+  "author": "",
+  "license": "MIT",
+  "dependencies": {
+    "express": "^4.18.2",
+    "cors": "^2.8.5",
+    "multer": "2.0.2",
+    "pdf-data-extractor": "^1.0.1"
+  },
+  "devDependencies": {
+    "nodemon": "^3.0.2"
+  }
+}
diff --git a/demos/extractor/server.js b/demos/extractor/server.js
new file mode 100644
index 000000000..7e8fe5f31
--- /dev/null
+++ b/demos/extractor/server.js
@@ -0,0 +1,155 @@
+const express = require('express');
+const cors = require('cors');
+const multer = require('multer');
+const { PdfDataExtractor } = require('pdf-data-extractor');
+const fs = require('fs').promises;
+const path = require('path');
+
+const app = express();
+const PORT = process.env.PORT || 3000;
+
+// Middleware
+app.use(cors());
+app.use(express.json());
+
+// Configure multer for file upload
+const upload = multer({ 
+  dest: 'uploads/',
+  limits: { fileSize: 10 * 1024 * 1024 } // 10MB limit
+});
+
+// Health check endpoint
+app.get('/health', (req, res) => {
+  res.json({ status: 'ok', message: 'PDF Data Extractor Demo Server' });
+});
+
+// Fetch available models from the API
+app.post('/api/models', async (req, res) => {
+  try {
+    const { baseUrl } = req.body;
+    
+    if (!baseUrl) {
+      return res.status(400).json({ error: 'Base URL is required' });
+    }
+
+    const response = await fetch(`${baseUrl}/models`);
+    
+    if (!response.ok) {
+      return res.status(response.status).json({ 
+        error: `Failed to fetch models: ${response.statusText}` 
+      });
+    }
+
+    const data = await response.json();
+    res.json(data);
+  } catch (error) {
+    console.error('Error fetching models:', error);
+    res.status(500).json({ 
+      error: 'Failed to fetch models',
+      message: error.message 
+    });
+  }
+});
+
+// Extract data from PDF
+app.post('/api/extract', upload.single('pdf'), async (req, res) => {
+  let pdfPath = null;
+  
+  try {
+    // Validate request
+    if (!req.file) {
+      return res.status(400).json({ error: 'No PDF file provided' });
+    }
+
+    const { schema, baseUrl, model, apiKey, temperature, maxTokens } = req.body;
+
+    if (!schema) {
+      return res.status(400).json({ error: 'No schema provided' });
+    }
+
+    if (!baseUrl) {
+      return res.status(400).json({ error: 'No base URL provided' });
+    }
+
+    if (!model) {
+      return res.status(400).json({ error: 'No model provided' });
+    }
+
+    // Parse schema
+    let parsedSchema;
+    try {
+      parsedSchema = JSON.parse(schema);
+    } catch (error) {
+      return res.status(400).json({ 
+        error: 'Invalid JSON schema',
+        message: error.message 
+      });
+    }
+
+    // Initialize extractor with provided configuration
+    const extractor = new PdfDataExtractor({
+      openaiApiKey: apiKey || 'not-required-for-local-models',
+      model: model,
+      baseUrl: baseUrl
+    });
+
+    // Extract data from uploaded PDF
+    pdfPath = req.file.path;
+    
+    const extractOptions = {
+      pdfPath: pdfPath,
+      schema: parsedSchema
+    };
+
+    // Add optional parameters if provided
+    if (temperature !== undefined && temperature !== '') {
+      extractOptions.temperature = parseFloat(temperature);
+    }
+    if (maxTokens !== undefined && maxTokens !== '') {
+      extractOptions.maxTokens = parseInt(maxTokens);
+    }
+
+    console.log(`Extracting data from PDF using model: ${model}`);
+    const result = await extractor.extract(extractOptions);
+
+    // Clean up uploaded file
+    await fs.unlink(pdfPath);
+    pdfPath = null;
+
+    // Return results
+    res.json({
+      success: true,
+      data: result.data,
+      tokensUsed: result.tokensUsed,
+      model: result.model
+    });
+
+  } catch (error) {
+    console.error('Error extracting data:', error);
+    
+    // Clean up file if it exists
+    if (pdfPath) {
+      try {
+        await fs.unlink(pdfPath);
+      } catch (cleanupError) {
+        console.error('Error cleaning up file:', cleanupError);
+      }
+    }
+
+    res.status(500).json({ 
+      success: false,
+      error: 'Failed to extract data from PDF',
+      message: error.message 
+    });
+  }
+});
+
+// Start server
+app.listen(PORT, () => {
+  console.log(`PDF Data Extractor Demo Server running on http://localhost:${PORT}`);
+  console.log(`Upload endpoint: http://localhost:${PORT}/api/extract`);
+});
+
+// Create uploads directory if it doesn't exist
+const uploadsDir = path.join(__dirname, 'uploads');
+fs.mkdir(uploadsDir, { recursive: true }).catch(console.error);

From 782e7ec8ef0f6bec3b42d283a71ec5413a3774c3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ignacio=20L=C3=B3pez=20Luna?= <ignasi.lopez.luna@gmail.com>
Date: Fri, 10 Oct 2025 13:04:13 +0200
Subject: [PATCH 2/3] fix(pdf-extraction): improve file cleanup and error
 handling for PDF uploads

---
 demos/extractor/server.js | 27 ++++++++++++---------------
 1 file changed, 12 insertions(+), 15 deletions(-)

diff --git a/demos/extractor/server.js b/demos/extractor/server.js
index 7e8fe5f31..8cfee7c05 100644
--- a/demos/extractor/server.js
+++ b/demos/extractor/server.js
@@ -61,6 +61,9 @@ app.post('/api/extract', upload.single('pdf'), async (req, res) => {
       return res.status(400).json({ error: 'No PDF file provided' });
     }
 
+    // Store file path for cleanup in finally block
+    pdfPath = req.file.path;
+
     const { schema, baseUrl, model, apiKey, temperature, maxTokens } = req.body;
 
     if (!schema) {
@@ -92,9 +95,6 @@ app.post('/api/extract', upload.single('pdf'), async (req, res) => {
       model: model,
       baseUrl: baseUrl
     });
-
-    // Extract data from uploaded PDF
-    pdfPath = req.file.path;
     
     const extractOptions = {
       pdfPath: pdfPath,
@@ -112,10 +112,6 @@ app.post('/api/extract', upload.single('pdf'), async (req, res) => {
     console.log(`Extracting data from PDF using model: ${model}`);
     const result = await extractor.extract(extractOptions);
 
-    // Clean up uploaded file
-    await fs.unlink(pdfPath);
-    pdfPath = null;
-
     // Return results
     res.json({
       success: true,
@@ -126,21 +122,22 @@ app.post('/api/extract', upload.single('pdf'), async (req, res) => {
 
   } catch (error) {
     console.error('Error extracting data:', error);
-    
-    // Clean up file if it exists
+
+    res.status(500).json({ 
+      success: false,
+      error: 'Failed to extract data from PDF',
+      message: error.message 
+    });
+  } finally {
+    // Always clean up uploaded file
     if (pdfPath) {
       try {
         await fs.unlink(pdfPath);
+        console.log(`Cleaned up uploaded file: ${pdfPath}`);
       } catch (cleanupError) {
         console.error('Error cleaning up file:', cleanupError);
       }
     }
-
-    res.status(500).json({ 
-      success: false,
-      error: 'Failed to extract data from PDF',
-      message: error.message 
-    });
   }
 });
 

From 2014ca4ee4cf8faf5614024836fd160a0f17209a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ignacio=20L=C3=B3pez=20Luna?= <ignasi.lopez.luna@gmail.com>
Date: Fri, 10 Oct 2025 13:17:42 +0200
Subject: [PATCH 3/3] fix(pdf-extraction): improve file cleanup and error
 handling for PDF uploads

---
 demos/extractor/server.js | 30 ++++++++++++++++++++----------
 1 file changed, 20 insertions(+), 10 deletions(-)

diff --git a/demos/extractor/server.js b/demos/extractor/server.js
index 8cfee7c05..8d5038bbb 100644
--- a/demos/extractor/server.js
+++ b/demos/extractor/server.js
@@ -7,6 +7,7 @@ const path = require('path');
 
 const app = express();
 const PORT = process.env.PORT || 3000;
+const UPLOADS_DIR = 'uploads/';
 
 // Middleware
 app.use(cors());
@@ -14,7 +15,7 @@ app.use(express.json());
 
 // Configure multer for file upload
 const upload = multer({ 
-  dest: 'uploads/',
+  dest: UPLOADS_DIR,
   limits: { fileSize: 10 * 1024 * 1024 } // 10MB limit
 });
 
@@ -141,12 +142,21 @@ app.post('/api/extract', upload.single('pdf'), async (req, res) => {
   }
 });
 
-// Start server
-app.listen(PORT, () => {
-  console.log(`PDF Data Extractor Demo Server running on http://localhost:${PORT}`);
-  console.log(`Upload endpoint: http://localhost:${PORT}/api/extract`);
-});
-
-// Create uploads directory if it doesn't exist
-const uploadsDir = path.join(__dirname, 'uploads');
-fs.mkdir(uploadsDir, { recursive: true }).catch(console.error);
+// Initialize server
+(async () => {
+  try {
+    // Create uploads directory before starting server
+    const uploadsDir = path.join(__dirname, UPLOADS_DIR);
+    await fs.mkdir(uploadsDir, { recursive: true });
+    console.log(`Uploads directory ready: ${uploadsDir}`);
+
+    // Start server only after uploads directory is ready
+    app.listen(PORT, () => {
+      console.log(`PDF Data Extractor Demo Server running on http://localhost:${PORT}`);
+      console.log(`Upload endpoint: http://localhost:${PORT}/api/extract`);
+    });
+  } catch (error) {
+    console.error('Failed to initialize server:', error);
+    process.exit(1);
+  }
+})();