What is build-your-own-search-engine?

This repository contains the hands-on materials for a "Build Your Own Search Engine" workshop. It walks you through creating a system that finds relevant answers in a collection of FAQ documents, so that instead of manually scrolling through pages of text, a user can simply type a question and get the most relevant paragraphs back in seconds. The project specifically uses FAQ documents from a series of online data science courses. The approach starts with traditional text search techniques, teaching you how to represent text as numbers so a computer can measure how much a query and a document have in common. It then moves on to more advanced "embeddings," which convert text into dense numerical vectors that can capture concepts and synonyms, rather than just exact keyword matches. Finally, the workshop explores using BERT, a modern AI language model, to create even smarter representations of the text that understand word order and context. This project is designed for people learning machine learning or building applications that need to retrieve information from custom documents. For example, if you are building a chatbot to answer questions about your company's internal handbooks, or a help-center tool for an online course, you need a reliable way to pull the right background information before generating an answer. The repository notes that these search results can be plugged directly into a larger Question-and-Answer AI system to ground its responses in real facts. The entire project is presented as a step-by-step Jupyter Notebook, mixing readable explanations directly with runnable Python code. The author is careful to point out that the search engine built here is a toy example meant for learning, not a heavy-duty system ready for production. However, the README does outline the real-world tools and databases that professional engineers use to accomplish the same tasks at a massive scale, making it a solid stepping stone from beginner concepts to practical application.