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Stop writing regex parsers that break on every new invoice format. Build an extraction pipeline that sends images to a vision LLM and returns validated, typed data with confidence scores.
Message a mentor about fit, prerequisites, or where to start. Replies come on WhatsApp, usually within a day.
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Build a production invoice parser that extracts structured data from images using vision LLMs, Pydantic schemas, multi-currency support, and confidence scoring.
Extract structured data from images with vision LLMs and Pydantic validation.
What you'll ship
What you'll learn
Curriculum
Vision LLM fundamentals
Set up your environment, make your first vision API call, and define Pydantic extraction schemas
Production extraction
Handle multi-currency invoices, add confidence scoring, and build the upload endpoint
Who it's for
who have used text-based LLM APIs but never sent an image to a model
who need to extract data from scanned documents, receipts, or invoices
who want to go beyond chatbots and build structured extraction pipelines
FAQ
No. If you have used a text-based LLM API, you have the foundation. This course teaches the multimodal extension from scratch.
The project supports Gemini, GPT-4o via OpenRouter, and Fireworks. You only need one API key. Gemini has the best PDF support.
The patterns apply to any document: receipts, contracts, medical forms, shipping labels. Invoices are the teaching example because they have rich structured data.
You should be comfortable with FastAPI, Pydantic, and async Python. The streaming course covers those fundamentals if you need a refresher.
Pricing
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One-time purchase. Lifetime access to every lesson, exercise, and update.
You save 41% with regional pricing
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Structured data extraction with vision LLMs and Pydantic
$29 one-time