Full-text search is one of Elasticsearch's strengths. However, what if we wanted to find similar documents based on something more abstract - like the meaning of a word or the style of writing? This is where Elasticsearch's dense vector field datatype and script-score queries for vector fields come into play.

In our previous articles, we introduce the techniques to create a suggestion for typeahead searches. In this article, we do an example of how to utilise it in public transportation. In Switzerland, Public Transportation is extraordinary. It is prevalent, the service and overall reliability are extraordinary. With public transportation in Switzerland, you can nearly arrive at any destination in Switzerland comfortably.

In the previous articles, we look into Prefix Queries and Edge NGram Tokenizer to generate search-as-you-type suggestions. Suggesters are an advanced solution in Elasticsearch to return similar looking terms based on your text input. Movie, song or job titles have a widely known or popular order. In this article, we are going to complete with a hands-on example.

In the previous article, we look into the possibilities of prefix queries to create suggestions based on existing data to enhance the search experience. We experience how fast and straightforward it could help us in the beginning. We also learned that it has some drawbacks like latency and duplicates if the data-set grows more significant over time. In this article, we are going to overcome the problems with Edge NGram Tokenizer.

You know searches from Google or DuckDuckGo. As you start typing the search engines, give you some autocomplete suggestions.