1 Introducing AI-powered search

The search box has rapidly become the default user interface for interacting with data in most modern applications. If you think of every major app or website you visit on a daily basis, one of the first things you likely do on each visit is type or speak a query in order to find the content or actions most relevant to you in that moment.

Even in scenarios where you are not explicitly searching, you may instead be consuming streams of content customized for your particular tastes and interests. Whether these be video recommendations, items for purchase, e-mails sorted by priority or recency, news articles, or other content, you are likely still looking at filtered or ranked results and given the option to either page through or explictly filter the content with your own query.

Whereas the phrase "search engine" to most people brings up thoughts of a website like Google, Baidu, or Bing, that enables queries based upon a crawl of the entire public internet, the reality is that search is now ubiquitous - it is a tool present and available in nearly all of our digital interactions every day across the numerous websites and applications we use.

1.1  Searching for User Intent

1.1.1  Search Engines

1.1.2  Recommendations Engines

1.1.3  The Information Retrieval Continuum

1.1.4  Semantic Search and Knowledge Graphs

1.1.5  Understanding the Dimensions of User Intent

1.2  Key Technologies for AI-powered search

1.2.1  Apache Lucene: the core search library powering Apache Solr and Elasticsearch

1.2.2  Apache Solr: the open sourced, community-driven, relevance focused-search engine

Elasticsearch: the most used, anaytics-focused, full text search engine

1.2.3  Lucidworks Fusion: the out-of-the-box AI-powered search Engine

1.2.4  Apache Spark: the standard for large-scale data processing

1.2.5  Strategy for this book

1.3  Target Audience for AI-Powered Search

1.3.1  Targeted Skillsets and Occupations

1.3.2  System Requirements for Running Code Examples

1.6  Summary