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

๐Ÿง  Building a Chat with PDF App (From Scratch using NumPy) โ€“ Part 1 Turning a simple PDF into a conversational AI system using local LLMs ๐Ÿš€ ๐Ÿ“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

๐Ÿง  Building a Chat with PDF App (From Scratch using NumPy) โ€“ Part 1 Turning a simple PDF into a conversational AI system using local LLMs ๐Ÿš€ ๐Ÿ“Œ Introduction Have you ever wanted to chat with your PDF documents like you chat with ChatGPT? In this series, Iโ€™ll walk you through building a ChatPDF application from scratch, starting from the absolute basics and gradually improving it into a production-ready system. ๐Ÿ‘‰ In this first part, weโ€™ll build a naive RAG (Retrieval-Augmented Generation) system using only NumPy โ€” no FAISS, no vector databases, just pure fundamentals. ๐ŸŽฏ What Weโ€™ll Build By the end of this article, youโ€™ll have: ๐Ÿ“„ A system that reads a PDF โœ‚๏ธ Splits it into meaningful chunks ๐Ÿ”ข Converts text into embeddings using a local model ๐Ÿ” Searches relevant content using vector similarity ๐Ÿ’ฌ Generates answers using an LLM โš™๏ธ Tech Stack pdfplumber โ†’ Extract text from PDFs numpy โ†’ Perform vector similarity search ollama โ†’ Run local embedding + LLM models ๐Ÿงฉ How It Works (High Leve

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