Makes the following changes to the `trim` token filter docs:
* Updates description
* Adds a link to the related Lucene filter
* Adds tip about removing whitespace using tokenizers
* Adds detailed analyze snippets
* Adds custom analyzer snippet
* [DOCS] Rewrite analysis intro. Move index/search analysis content.
* Rewrites 'Text analysis' page intro as high-level definition.
Adds guidance on when users should configure text analysis
* Rewrites and splits index/search analysis content:
* Conceptual content -> 'Index and search analysis' under 'Concepts'
* Task-based content -> 'Specify an analyzer' under 'Configure...'
* Adds detailed examples for when to use the same index/search analyzer
and when not.
* Adds new example snippets for specifying search analyzers
* clarifications
* Add toc. Decrement headings.
* Reword 'When to configure' section
* Remove sentence from tip
Adds a 'Configure text analysis' page to house tutorial content for the
analysis topic.
Also relocates the following pages as children as this new page:
* 'Test an analyzer'
* 'Configuring built-in analyzers'
* 'Create a custom analyzer'
I plan to add a tutorial for specifying index-time and search-time
analyzers to this section as part of a future PR.
This helps the topic better match the structure of
our machine learning docs, e.g.
https://www.elastic.co/guide/en/machine-learning/7.5/ml-concepts.html
This PR only includes the 'Anatomy of an analyzer' page as a 'Concepts'
child page, but I plan to add other concepts, such as 'Index time vs.
search time', with later PRs.
* Changes titles to sentence case.
* Appends pages with 'reference' to differentiate their content from
conceptual overviews.
* Moves the 'Normalizers' page to end of the Analysis topic pages.
Adds a 'text analysis overview' page to the analysis topic docs.
The goals of this page are:
* Concisely summarize the analysis process while avoiding in-depth concepts, tutorials, or API examples
* Explain why analysis is important, largely through highlighting problems with full-text searches missing analysis
* Highlight how analysis can be used to improve search results
* Adds a title abbreviation
* Updates the description and adds a Lucene link
* Reformats the parameters section
* Adds analyze, custom analyzer, and custom filter snippets
Relates to #44726.
* Adds a title abbreviation
* Relocates the older name deprecation warning
* Updates the description and adds a Lucene link
* Adds a note to explain payloads and how to store them
* Adds analyze and custom analyzer snippets
* Adds a 'Return stored payloads' example
Reformats the edge n-gram and n-gram token filter docs. Changes include:
* Adds title abbreviations
* Updates the descriptions and adds Lucene links
* Reformats parameter definitions
* Adds analyze and custom analyzer snippets
* Adds notes explaining differences between the edge n-gram and n-gram
filters
Additional changes:
* Switches titles to use "n-gram" throughout.
* Fixes a typo in the edge n-gram tokenizer docs
* Adds an explicit anchor for the `index.max_ngram_diff` setting
Currently the `token_chars` setting in both `edgeNGram` and `ngram` tokenizers
only allows for a list of predefined character classes, which might not fit
every use case. For example, including underscore "_" in a token would currently
require the `punctuation` class which comes with a lot of other characters.
This change adds an additional "custom" option to the `token_chars` setting,
which requires an additional `custom_token_chars` setting to be present and
which will be interpreted as a set of characters to inlcude into a token.
Closes#25894
The `edge_ngram` tokenizer limits tokens to the `max_gram` character
length. Autocomplete searches for terms longer than this limit return
no results.
To prevent this, you can use the `truncate` token filter to truncate
tokens to the `max_gram` character length. However, this could return irrelevant results.
This commit adds some advisory text to make users aware of this limitation and outline the tradeoffs for each approach.
Closes#48956.
The first example of splitting rules for the `word_delimiter` token filter was spread across two bullet points. This makes it look like they are two separate splitting rules.
The sample code is wrong. Field type is required for the sample field.
I guess the intention was to give the sample field the name ```fingerprint```, mapping it as ```text``` using the custom analyzer ```my_analyzer```
James Rodewig
rhymes
debadair
PND
Nik Everett
Christoph Büscher
gpaimla
Julian Simioni
Peter Johnson
Wilder Pereira
Abhilash Bolla