{"id":7344,"date":"2019-08-06T07:02:34","date_gmt":"2019-08-06T14:02:34","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=7344"},"modified":"2024-02-07T11:56:40","modified_gmt":"2024-02-07T19:56:40","slug":"building-autocomplete-or-typeahead-functionality-using-full-text-search","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/building-autocomplete-or-typeahead-functionality-using-full-text-search\/","title":{"rendered":"Build autocomplete or typeAhead using Full Text Search"},"content":{"rendered":"

What is Autocomplete vs Type Ahead?<\/b><\/h3>\n

Autocomplete as the<\/span> wikipedia<\/span><\/a> says<\/span><\/p>\n

\u201cAutocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing\u201d<\/span><\/i><\/p>\n

It is also known as\u00a0 Suggestions or Type Ahead or Search as you Type. This helps in navigating or guiding a user by prompting them with likely completions and alternatives to the text as they are typing it. And It reduces the amount of character a user needs to type for getting the final search results, thereby enhancing the search experience of users.<\/span><\/p>\n

Let\u2019s explore a couple of functional approaches on how to implement autocomplete the autocomplete feature using Couchbase FTS.<\/span><\/p>\n

\u00a0<\/b><\/h4>\n

Using Edge NGram approach.<\/b><\/h4>\n

This approach involves the use of different index and query time analysers.<\/span><\/p>\n

Step1<\/b> – is to have the right FTS index definition for the fields that needs to be autocompleted. So during indexing, the autocomplete intended fields have to be analysed with a custom analyser making use of the edge-ngram token filter<\/a>. It can also be chained with `to_lower` token filter if concerned about the case sensitivity.\u00a0 <\/span>store <\/b><\/a>– option has to be enabled for the autocomplete field in the index definition to preserve the field contents intact within the FTS index so that this stored field value will be fetched explicitly during the query phase to perform the actual auto complete or suggestions for the user.<\/span><\/p>\n

How does an edge ngram token filter works?<\/b><\/h5>\n

An edge ngram tokeniser will tokenise a given autocomplete text value into sub tokens of length ranging from a given min and max length parameters. For example,\u00a0 an edge-ngram tokeniser of min length 2 and max length 6 would tokenise given the text \u201c<\/span>jurassic park<\/b>\u201d like below.<\/span><\/p>\n

ju, jur, jura, juras, jurass, pa, par, park.\u00a0<\/b><\/p>\n

The idea here is that these tokenised texts of the indexed field would serve as the future potential partial text entries from the user on the user interface.<\/span><\/p>\n

\u00a0<\/span><\/p>\n

 <\/p>\n

Step2<\/b> – Later when the end user actually starts typing on the text box in the user interface,\u00a0 the client application can trigger search queries in the background with the partial text available in the text box. Clients should specifically use the match query<\/a> as it has the provisions to explicitly provide,<\/span><\/p>\n