Fancy Receipt Extractor

A modern web application for extracting structured information from PDF invoices and receipts using OpenAI's GPT-4o model, LLMWhisperer for PDF processing, and the Instructor Library, with robust data validation through Pydantic V2.

Features

Modern web interface with drag-and-drop PDF upload
PDF preview with multi-page navigation
Structured information extraction from PDFs
Comprehensive validation of Dutch tax rules and business logic
Support for emballage/statiegeld handling
Detailed error feedback and analysis
Multi-supplier support
SQLite database for result storage

Installation

Clone the repository:

git clone https://github.com/yourusername/fancy-receipt-extractor.git
cd fancy-receipt-extractor

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

export OPENAI_API_KEY=your_api_key_here
export LLMWHISPERER_API_KEY=your_api_key_here

Usage

Start the server:

# From the project root directory
python -m src.main

Open your browser and navigate to:

http://127.0.0.1:8000

Upload a PDF file containing invoice/receipt data
View the PDF preview and extracted structured information

Project Structure

src/
  ├── __init__.py
  ├── api/
  │   ├── __init__.py
  │   └── routes/
  │       ├── __init__.py
  │       └── invoice.py
  ├── core/
  │   ├── __init__.py
  │   ├── db/
  │   │   ├── __init__.py
  │   │   └── database.py
  │   └── extractors/
  │       ├── __init__.py
  │       ├── invoice_extractor.py
  │       └── pdf_extractor.py
  ├── models/
  │   ├── __init__.py
  │   └── pydantic/
  │       ├── __init__.py
  │       ├── invoice.py
  │       └── invoice_detail.py
  └── main.py
static/
  ├── css/
  │   └── styles.css
  └── js/
      └── main.js
templates/
  └── index.html
prompt_templates/
  ├── user_info.txt
  └── emballage_info.txt
requirements.txt

Dependencies

FastAPI
Pydantic V2
OpenAI
Instructor
LangSmith
LLMWhisperer
SQLite
Additional dependencies in requirements.txt

Error Handling

The application provides detailed error feedback for:

Invalid file formats or sizes
PDF processing issues
Invalid VAT IDs
Incorrect tax calculations
Invalid IBAN numbers
Date format issues
Amount mismatches
Citation validation

Development

The project follows a modular structure:

src/models/pydantic/: Pydantic models for data validation
src/core/extractors/: Core business logic for PDF and invoice extraction
src/core/db/: Database operations
src/api/routes/: FastAPI route handlers
static/: Frontend assets (CSS, JavaScript)
templates/: HTML templates
prompt_templates/: GPT-4o prompt templates

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Galbaz1/LLM-invoice-ETL