Building a Search Engine: From Document Store to Relevance Algorithm

In this tutorial, we will explore the process of building a search engine from scratch. No, we won't be reinventing Google, but we will develop a lightweight, feature-centric search engine that incorporates key components such as a document store, an inverted index, distributed search, faceted search, and a relevance algorithm.

Throughout the tutorial, we will use Python to illustrate the concepts and implementation of each component. Starting with the basics, we will create a document store to store and retrieve our documents. We will then dive into the world of indexing by building an inverted index that maps unique words to their respective document identifiers. Next, we will explore the power of distributed search, enabling us to search across multiple data sources or servers simultaneously. We will also implement faceted search, a technique that allows users to filter their search results using multiple facets or categories. Lastly, we will develop a simple relevance algorithm to rank our search results based on their alignment with the search query.

By the end of this tutorial, you will have a solid understanding of the inner workings of a search engine and how each component contributes to its functionality. You will have built a basic search engine from scratch, gaining knowledge and confidence in implementing similar features in real-world scenarios. Are you ready to embark on this search engine building journey? Let's get started!