A query string is passed through various filters that change its final form.

The analysis process consists of 2 parts, the Tokenisation and the Filter process.


The Tokenisation process takes a given text, "Good boy Pluto" for example, and splits it into tokens according to the chosen "Tokenizer".

The Whitespace tokenizer would split the above query on every whitespace, producing the following 3 tokens.

  • Good
  • boy
  • Pluto

There are 3 Tokenizer options for you to choose from that should cover all use cases.

Word Boundaries

Tokenizing on Word Boundaries will the text "Easy-peasy lemon squeezy" and produce the tokens below.

  • Easy
  • peasy
  • lemon
  • squeezy


On the other hand, using the Whitespace tokenizer will take "Easy-peasy" as a single token, and produce 3 tokens.

  • Easy-peasy
  • lemon
  • squeezy


The Pattern option allows you to write a Regular Expression pattern to handle any edge cases that you may have.

For example, you can specify , as a pattern to split the "Donald,Mickey, Goofy" into 3 tokens.

  • Donald
  • Mickey
  • Pluto


Once your text is tokenized, filters come to play and transform the tokens to allow better matching between them.

For example, if a user for any reason writes "mickey mickey Mouse" the tokenizer will give us 3 tokens

  • mickey
  • mickey
  • Mouse

Using the Unique filter will remove the double token mickey and we will get

  • mickey
  • Mouse

This way, the double mickey word won't affect the scoring.

Token limit

Users many times paste large texts into Search boxes and tokenize big texts is a quite expensive operation that won't help us find better matches.

Using Token limit, we can set a limit on how many tokens are created.

For example, the query "Where did Dory go ?" with a Token limit of 3 will only create the 3 tokens bellow

  • Where
  • did
  • Dory

and ignore the rest.


Removing whitespaces from the begging and from the end of any token is always good practice.

Using the Trim filter will change the

  • " Goofy "


  • "Goofy"

Decimal Digit

The Decimal Digit filter converts all digits to 0-9.

For example, the Bengali numeral will change to 3.


The Unique filter removes any duplicate tokens.

For example, the tokens:

  • Goofy
  • and
  • Pluto
  • Pluto

will change to

  • Goofy
  • and
  • Pluto

removing the double "Pluto" token.

Strip HTML

Strip HTML filter removes any HTML from even before it even tokenized.

So from the HTML

    The Lion King is a 1994 American animated musical drama film directed by
    Roger Allers and Rob Minkoff

will remain only the text

The Lion King is a 1994 American animated musical drama film directed by
Roger Allers and Rob Minkoff

and the <div> and <p> tags will disappear.

Char mapping

Same as the HTML filter, the char filter manipulates the text before it's tokenized.

Use character mapping filter if you are indexing for example, user comments that tend to have emojis within them.

You can map all happy like :) and :D emojis to happy.

By doing this, the text "To Infinity and Beyond :D" will change to "To Infinity and Beyond happy".

Now when the user searches for "happy" he get's the Buzz Lightyear's famous line.

Query Box