We’ve all been there: digging through a mountain of crumpled hospital printouts, blurry scans, and nested PDFs just to find out what that specific blood test result was three years ago. Medical data is messy, unstructured, and—let's be honest—doctor's handwriting is the final boss of OCR.
In this tutorial, we are building a Personal Electronic Health Record (EHR) RAG system. We will transform those chaotic PDFs and scanned images into a searchable, intelligent knowledge base. By using a Vector Database like Milvus and powerful document partitioning, we'll achieve a seamless Personal Electronic Health Record experience where you can literally "talk" to your medical history. 🚀
Why is this hard? (The Problem)
Standard RAG (Retrieval-Augmented Generation) often fails on medical documents because:
Layouts are complex: Labs use tables; prescriptions use weird grids.
