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Unlocking Knowledge from PDFs: Build Your Own RAG System with Ollama and ChromaDB
Turn static documents into a smart, interactive chatbot — perfect for financial reports, research papers, or business guides. No more endless scrolling; ask and get answers.
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Hey there! If you’ve ever stared at a massive PDF — like a 100-page financial report or a dense research paper — and thought, “How do I find what I need without losing my mind?” you’re not alone. I’ve been there, flipping through pages, highlighting like crazy, and still missing key details. But what if you could chat with your PDFs? Ask questions like, “What’s the deadline for filing this report?” and get a spot-on answer in seconds.
That’s the magic of a Retrieval-Augmented Generation (RAG) system. In this guide, I’ll walk you through building one from scratch using Ollama (for AI smarts), ChromaDB (for storing and searching data), and a bit of Python. It’s simpler than it sounds, and by the end, you’ll have a tool that feels like ChatGPT but tailored to your documents. Let’s dive in — no PhD in AI required!
What Exactly is RAG?
Imagine you’re at a trivia night. Instead of relying on your foggy memory, you have a stack of reference books. You quickly flip to the relevant pages and craft…
