From Paper to Insights: Build Your Own Private Medical RAG with Pinecone, Ollama, and PaddleOCR
Let’s be honest: looking at a blood test result or a medical lab report feels like trying to read ancient hieroglyphics. You see numbers like Albumin: 45 g/L or HbA1c: 5.7% , and your first instinct is to panic-search WebMD. What if you could build a private, secure RAG (Retrieval-Augmented Generation) system that understands your historical data and answers your questions using local LLMs? In…
Creating a private medical document archive using a retrieval-augmented generation (RAG) system can transform complex lab reports into an easy-to-understand conversational health assistant. By utilizing PaddleOCR for OCR, Pinecone as a vector database, Ollama for local LLM processing, and LangChain to tie everything together, you can build a solution that understands historical medical data and answers questions with relevant context.
The system works by extracting text from images, splitting it into chunks, embedding the chunks into vectors, and then retrieving and analyzing relevant data based on user queries.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.