PDF Invoice Data Extraction to MongoDB – Node.js & React with OpenAI

By : AItoolsJS Published On Saturday, January 31, 2026, 11:23 In JavaScript

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Invoice Data Extraction – PDF to Excel , JSON and DB

A full-stack application for extracting structured data from invoices using OpenAI’s GPT models. Built with Node.js backend and React frontend, featuring multi-language support and automated database storage.

DISCLAIMER:- This item uses third-party AI services (such as OpenAI) which are not included in the purchase price. Buyers are responsible for providing their own API keys and covering any usage costs charged by these services. No AI credits, subscriptions, or usage fees are included with this item.

Features

AI-Powered Extraction: Uses OpenAI GPT-4o-mini for accurate invoice data extraction with structured JSON output
Multi-Format Support: Processes text, PDF (using pdf-parse), and image files (using Tesseract.js OCR)
Database Automation: Automatically stores extracted data in MongoDB with Mongoose ODM
Multi-Language Support: Built-in internationalization with English and Spanish translations
RESTful API: Clean API endpoints for invoice management with proper error handling
Data Validation: Comprehensive validation service with fallbacks and data cleaning
File Upload: Multi-file upload support (up to 5 files) with drag-and-drop interface
Export Functionality: Export extracted data to Excel format
Unit Testing: Comprehensive test coverage with Jest and Supertest
Modern UI: React-based frontend with responsive design and Tailwind CSS
Text Preprocessing: Intelligent text preprocessing to handle OCR artifacts and formatting issues

Tech Stack

Backend

Node.js with Express.js
MongoDB with Mongoose ODM
OpenAI API for data extraction
JWT for authentication (optional)
Jest & Supertest for testing

Frontend

React with modern hooks
Axios for API communication
i18next for internationalization
React Router for navigation
Testing Library for component testing
Tailwind CSS for styling
XLSX for Excel export functionality

Architecture Overview

Backend Architecture

PDF Invoice Data Extraction to MongoDB – Node.js & React with OpenAI - 1

Data Flow

File Upload: User uploads invoice files (PDF, image, text)
Text Extraction: Files are processed using pdf-parse or Tesseract.js OCR
AI Processing: Extracted text is sent to OpenAI with structured prompts
Data Validation: AI response is validated and cleaned
Database Storage: Structured data is saved to MongoDB
Frontend Display: Data is displayed in a responsive table with export options

Project Structure

invoice-extraction/
├── backend/                 # Node.js backend
│   ├── models/              # Mongoose models
│   ├── routes/              # API routes
│   ├── services/            # Business logic services
│   ├── __tests__/           # Unit tests
│   ├── db.js                # Database connection
│   └── index.js             # Server entry point
├── frontend/                # React frontend
│   ├── src/
│   │   ├── components/     # React components
│   │   ├── i18n/           # Internationalization setup
│   │   └── __tests__/      # Component tests
├── prompts/                # OpenAI prompt templates
├── translations/           # Language files
├── docs/                   # Documentation
├── .env                    # Environment variables
└── README.md               # This file

Prerequisites

Node.js (v16 or higher)
MongoDB (local or cloud instance)
OpenAI API key

Installation

Clone the repository

git clone <repository-url >
cd invoice-extraction

Install backend dependencies
```
cdbackend
npm install
```
Install frontend dependencies
```
cd../frontend
npm install
cd..
```
Environment Setup
- Copy .env file and update the values:
```
cp .env .env.local
```
- Update the following variables:
  - OPENAI_API_KEY: Your OpenAI API key
  - MONGO_URI: MongoDB connection string
  - PORT: Server port (default: 5000)
Start MongoDB Make sure MongoDB is running on your system or update MONGO_URI for cloud instance.

Usage

Development

Start the backend server
```
cd backend
npm run dev
```
Start the frontend
```
cd frontend
npm start
```
Access the application
- Frontend: http://localhost:3000
- Backend API: http://localhost:5000

Production

Build the frontend
```
cd frontend
npm run build
```
Start the backend
```
cd backend
npm start
```

API Documentation

Invoice Endpoints

Upload Invoice

POST /api/invoices/upload
Content-Type: multipart/formdata

Form Data:
- invoice: File (text, PDF, or image)

Response:

{
  "message": "Invoice processed successfully",
  "invoice": {
    "vendor":"Vendor Name",
    "invoiceNumber": "INV-001",
    "date": "2023-01-01T00:00:00.000Z",
    "totalAmount": 100.50,
    "currency">: "USD",
    "items": [...],
    "status": "processed" 
  }
}

Get All Invoices

GET <span class="hljs-regexp">/api/i</span>nvoices

Get Invoice by ID

GET /api/invoices/:<span class="hljs-built_in">id</span>

Delete Invoice

<span class="hljs-keyword">DELETE</span> <span class="hljs-regexp">/api/i</span>nvoices<span class="hljs-regexp">/:id</span>

AI Processing & Data Extraction

OpenAI Integration

The system uses OpenAI’s GPT-4o-mini model with structured JSON output to ensure consistent data extraction. The AI is prompted with:

System Prompt: Defines the AI’s role as an invoice data extraction expert
User Prompt: Provides the extracted text and specifies the exact JSON format required
JSON Schema: Enforces structured output with validation rules

Text Extraction Process

PDF Files: Processed using pdf-parse library to extract text content
Image Files: OCR processing using Tesseract.js with optimized parameters
Text Files: Direct UTF-8 text extraction
Preprocessing: Text cleaning to handle OCR artifacts and formatting issues

Data Validation & Cleaning

Schema Validation: Ensures all required fields are present and properly formatted
Fallback Values: Provides sensible defaults for missing data
Type Conversion: Validates dates, amounts, and other data types
Duplicate Prevention: Uses invoice number as unique identifier for upsert operations

Supported Invoice Fields

Vendor/Supplier information
Invoice number and dates
Financial amounts (total, subtotal, tax, discounts, shipping)
Customer and shipping details
Line items with descriptions, quantities, and pricing
Payment terms and currency information

Testing

Backend Tests

<span class="hljs-built_in">cd</span> backend
npm <span class="hljs-built_in">test</span>

Frontend Tests

<span class="hljs-built_in">cd</span> frontend
npm <span class="hljs-built_in">test</span>

Multi-Language Support

The application supports multiple languages through JSON-based translations.

Adding a New Language

Create a new translation file in translations/ directory
Update the i18n configuration in frontend/src/i18n/index.js
Add language option in the UI

Current Languages

English (en)
Spanish (es)

Configuration

All configuration is managed through environment variables in the .env file:

PORT: Server port
MONGO_URI: MongoDB connection string
OPENAI_API_KEY: OpenAI API key
JWT_SECRET: JWT secret for authentication
MAX_FILE_SIZE: Maximum file upload size
DEFAULT_LANGUAGE: Default application language

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Submit a pull request

Development Guidelines

Code Style

Backend: Follow Node.js best practices with async/await patterns
Frontend: Use React functional components with hooks
Naming: Use camelCase for variables/functions, PascalCase for components
Error Handling: Implement proper try-catch blocks and error responses
Comments: Add JSDoc comments for functions and complex logic

Environment Variables

Create a .env file in the root directory with:

# OpenAI Configuration
OPENAI_API_KEY =your_openai_api_key_here

# Database Configuration

MONGO_URI=mongodb://localhost:27017/invoice-extraction

# Server Configuration
PORT5000
JWT_SECRET=your_jwt_secret_here

# File Upload Configuration
MAX_FILE_SIZE=10485760
DEFAULT_LANGUAGE=en

Running Tests

# Backend tests
cd backend && npm =test

# Frontend tests
>cd frontend && npm test

Adding New Features

Create a feature branch from main
Implement the feature with proper error handling
Add unit tests for new functionality
Update documentation if needed
Submit a pull request

Deployment

Backend Deployment

Environment Setup: Configure production environment variables
Database: Set up MongoDB instance (local or cloud)
Build: Run npm install --production for dependencies
Start: Use npm start or process manager like PM2

Frontend Deployment

Build: Run npm run build in frontend directory
Serve: Deploy built files to web server (nginx, Apache, etc.)
API Configuration: Update API endpoints for production

Docker Deployment (Optional)

# Example Dockerfile for backend
FROM node:16-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
EXPOSE 5000
CMD ["npm", "start"]