Lilly Tech Systems
Build a Document Intelligence App
Build a complete AI-powered document intelligence system that can parse PDFs, invoices, handwritten notes, and complex layouts. Extract structured data from any document using OCR, GPT-4 Vision, and Pydantic validation — all in 5 hands-on steps.
What You Will Build
A fully functional document intelligence platform that extracts text, tables, and structured fields from PDFs, scanned documents, and images. Upload any document and get clean, validated JSON output ready for downstream systems.
PDF Extraction
Extract text, tables, and layout information from digital PDFs using PyMuPDF and tabula. Handle multi-column layouts, headers, footers, and embedded images.
Vision AI Analysis
Use GPT-4 Vision to understand complex documents: handwritten notes, photos of receipts, charts, and diagrams that traditional OCR cannot handle.
Structured Output
Extract specific fields (invoice number, date, line items, totals) into validated JSON using Pydantic models. No more manual data entry.
Batch Processing
Process hundreds of documents asynchronously with a queue-based pipeline, progress tracking, and error handling for production workloads.
Tech Stack
Every component is open source or has a generous free tier. Total cost to run: $0 for development, under $5/month in production.
Python 3.11+
The core language for the backend API, document processing pipeline, and extraction logic.
FastAPI
High-performance async web framework for the REST API, file uploads, and background task processing.
PyMuPDF
Fast, reliable PDF text and metadata extraction with layout analysis and image extraction capabilities.
tabula-py
Table extraction from PDFs using the tabula-java engine. Handles complex multi-row and multi-column tables.
OpenAI Vision
GPT-4 Vision for understanding complex visual documents, handwritten text, charts, and non-standard layouts.
Pydantic
Data validation and structured output parsing. Define extraction schemas and get type-safe, validated results.
Prerequisites
Make sure you have these installed before starting.
Required
- Python 3.11 or higher
- An OpenAI API key (get one at
platform.openai.com) - Java Runtime (for tabula table extraction)
- Basic Python knowledge (functions, classes, async/await)
- A terminal (bash, zsh, PowerShell, or CMD)
Helpful but Not Required
- Experience with FastAPI or Flask
- Familiarity with PDF file structure
- Basic understanding of OCR and document processing
- HTML/CSS/JavaScript basics for the upload UI
Build Steps
Follow these lessons in order. Each step builds on the previous one. By the end, you will have a fully deployable document intelligence system.
1. Project Setup
Set up the project structure, install PyMuPDF, OpenAI, FastAPI, and configure the development environment for document processing.
2. PDF Text & Table Extraction
Extract text, tables, and layout information from PDFs using PyMuPDF and tabula. Handle multi-column layouts and complex table structures.
3. Vision AI for Complex Documents
Use GPT-4 Vision to analyze handwritten notes, photos of receipts, charts, and other visual documents that text extraction cannot handle.
4. Structured Data Extraction
Extract specific fields into validated JSON using Pydantic models. Build extraction schemas for invoices, receipts, contracts, and forms.
5. Batch Processing Pipeline
Build an async processing pipeline with queuing, progress tracking, error handling, and retry logic for processing hundreds of documents.
6. Upload & Review UI
Create a drag-and-drop upload interface with extraction result display, inline editing, and correction capabilities.
7. Enhancements & Next Steps
Add OCR fallback, multi-language support, compliance features, and explore advanced document intelligence patterns. Includes a comprehensive FAQ.