Understanding RAG by Building a ChatPDF App with NumPy (Part 1)

🧠 Building a Chat with PDF App (From Scratch using NumPy) – Part 1 Turning a simple PDF into a conversational AI system using local LLMs 🚀 📌 Introduction Have you ever wanted to chat with your PDF documents like you chat with ChatGPT? In this series, I’ll walk you through building a ChatPDF application from scratch , starting from the absolute basics and gradually improving it into a production-ready system. 👉 In this first part, we’ll build a naive RAG (Retrieval-Augmented Generation) system using only NumPy — no FAISS, no vector databases, just pure fundamentals. 🎯 What We’ll Build By the end of this article, you’ll have: 📄 A system that reads a PDF ✂️ Splits it into meaningful chunks 🔢 Converts text into embeddings using a local model 🔍 Searches relevant content using vector similarity 💬 Generates answers using an LLM ⚙️ Tech Stack pdfplumber → Extract text from PDFs numpy → Perform vector similarity search ollama → Run local embedding + LLM models 🧩 How It Works (High Level) Our pi