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 πŸš€ πŸ“Œ Introduction Have you ever wanted to chat with your P...

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

Source: DEV Community

🧠 Building a Chat with PDF App (From Scratch using NumPy) – Part 1 Turning a simple PDF into a conversational AI system using local LLMs πŸš€ πŸ“Œ 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. πŸ‘‰ In this first part, we’ll build a naive RAG (Retrieval-Augmented Generation) system using only NumPy β€” no FAISS, no vector databases, just pure fundamentals. 🎯 What We’ll Build By the end of this article, you’ll have: πŸ“„ A system that reads a PDF βœ‚οΈ Splits it into meaningful chunks πŸ”’ Converts text into embeddings using a local model πŸ” Searches relevant content using vector similarity πŸ’¬ Generates answers using an LLM βš™οΈ Tech Stack pdfplumber β†’ Extract text from PDFs numpy β†’ Perform vector similarity search ollama β†’ Run local embedding + LLM models 🧩 How It Works (High Leve

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