Add tokenizer list (#6205)

* Add tokenizer list

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Minor rewording

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Apply suggestions from code review

Co-authored-by: Nathan Bower <nbower@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>

* Implement editorial comments

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

---------

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com>
Co-authored-by: Nathan Bower <nbower@amazon.com>

_analyzers/index.md

@@ -45,7 +45,7 @@ Analyzer | Analysis performed | Analyzer output
 **Simple** | - Parses strings into tokens on any non-letter character <br> - Removes non-letter characters <br> - Converts tokens to lowercase  | [`it`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `pr`, `or`, `to`, `opensearch`]
 **Whitespace** | - Parses strings into tokens on white space | [`It’s`, `fun`, `to`, `contribute`, `a`,`brand-new`, `PR`, `or`, `2`, `to`, `OpenSearch!`]
 **Stop** | - Parses strings into tokens on any non-letter character <br> - Removes non-letter characters <br> - Removes stop words <br> - Converts tokens to lowercase | [`s`, `fun`, `contribute`, `brand`, `new`, `pr`, `opensearch`]
-**Keyword** (noop) | - Outputs the entire string unchanged | [`It’s fun to contribute a brand-new PR or 2 to OpenSearch!`]
+**Keyword** (no-op) | - Outputs the entire string unchanged | [`It’s fun to contribute a brand-new PR or 2 to OpenSearch!`]
 **Pattern** | - Parses strings into tokens using regular expressions <br> - Supports converting strings to lowercase <br> - Supports removing stop words | [`it`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `pr`, `or`, `2`, `to`, `opensearch`]
 [**Language**]({{site.url}}{{site.baseurl}}/analyzers/language-analyzers/) | Performs analysis specific to a certain language (for example, `english`). | [`fun`, `contribut`, `brand`, `new`, `pr`, `2`, `opensearch`]
 **Fingerprint** | - Parses strings on any non-letter character <br> - Normalizes characters by converting them to ASCII <br> - Converts tokens to lowercase <br> - Sorts, deduplicates, and concatenates tokens into a single token <br> - Supports removing stop words | [`2 a brand contribute fun it's new opensearch or pr to`] <br> Note that the apostrophe was converted to its ASCII counterpart.

_analyzers/tokenizers/index.md

@@ -0,0 +1,61 @@
+---
+layout: default
+title: Tokenizers
+nav_order: 60
+has_children: false
+has_toc: false
+---
+
+# Tokenizers
+
+A tokenizer receives a stream of characters and splits the text into individual _tokens_. A token consists of a term (usually, a word) and metadata about this term. For example, a tokenizer can split text on white space so that the text `Actions speak louder than words.` becomes [`Actions`, `speak`, `louder`, `than`, `words.`]. 
+
+The output of a tokenizer is a stream of tokens. Tokenizers also maintain the following metadata about tokens:
+
+- The **order** or **position** of each token: This information is used for word and phrase proximity queries. 
+- The starting and ending positions (**offsets**) of the tokens in the text: This information is used for highlighting search terms. 
+- The token **type**: Some tokenizers (for example, `standard`) classify tokens by type, for example, <ALPHANUM> or <NUM>. Simpler tokenizers (for example, `letter`) only classify tokens as type `word`.
+
+You can use tokenizers to define custom analyzers. 
+
+## Built-in tokenizers
+
+The following tables list the built-in tokenizers that OpenSearch provides. 
+
+### Word tokenizers
+
+Word tokenizers parse full text into words.
+
+Tokenizer | Description | Example
+:--- | :--- | :---
+`standard` | - Parses strings into tokens at word boundaries <br> - Removes most punctuation | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`] 
+`letter` | - Parses strings into tokens on any non-letter character <br> - Removes non-letter characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`It`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `to`, `OpenSearch`]
+`lowercase` | - Parses strings into tokens on any non-letter character <br> - Removes non-letter characters <br> - Converts terms to lowercase | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`it`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `pr`, `or`, `to`, `opensearch`]
+`whitespace` | - Parses strings into tokens at white space characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!` <br>becomes<br> [`It’s`, `fun`, `to`, `contribute`, `a`,`brand-new`, `PR`, `or`, `2`, `to`, `OpenSearch!`] 
+`uax_url_email` | - Similar to the standard tokenizer <br> - Unlike the standard tokenizer, leaves URLs and email addresses as single terms | `It’s fun to contribute a brand-new PR or 2 to OpenSearch opensearch-project@github.com!` <br>becomes<br> [`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`, `opensearch-project@github.com`] 
+`classic` | - Parses strings into tokens on: <br> &emsp; - Punctuation characters that are followed by a white space character <br> &emsp; - Hyphens if the term does not contain numbers <br> - Removes punctuation <br>  - Leaves URLs and email addresses as single terms | `Part number PA-35234, single-use product (128.32)` <br>becomes<br> [`Part`, `number`, `PA-35234`, `single`, `use`, `product`, `128.32`]
+`thai` | - Parses Thai text into terms | `สวัสดีและยินดีต` <br>becomes<br> [`สวัสด`, `และ`, `ยินดี`, `ต`] 
+
+### Partial word tokenizers
+
+Partial word tokenizers parse text into words and generate fragments of those words for partial word matching.
+
+Tokenizer | Description | Example
+:--- | :--- | :---
+`ngram`| - Parses strings into words on specified characters (for example, punctuation or white space characters) and generates n-grams of each word | `My repo` <br>becomes<br> [`M`, `My`, `y`, `y `, <code>&nbsp;</code>, <code>&nbsp;r</code>, `r`, `re`, `e`, `ep`, `p`, `po`, `o`] <br> because the default n-gram length is 1--2 characters 
+`edge_ngram` | - Parses strings into words on specified characters (for example, punctuation or white space characters) and generates edge n-grams of each word (n-grams that start at the beginning of the word) | `My repo` <br>becomes<br> [`M`, `My`] <br> because the default n-gram length is 1--2 characters 
+
+### Structured text tokenizers
+
+Structured text tokenizers parse structured text, such as identifiers, email addresses, paths, or ZIP Codes.
+
+Tokenizer | Description | Example
+:--- | :--- | :---
+`keyword` | - No-op tokenizer <br> - Outputs the entire string unchanged <br> - Can be combined with token filters, like lowercase, to normalize terms | `My repo` <br>becomes<br> `My repo`
+`pattern` | - Uses a regular expression pattern to parse text into terms on a word separator or to capture matching text as terms <br> - Uses [Java regular expressions](https://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html) | `https://opensearch.org/forum` <br>becomes<br> [`https`, `opensearch`, `org`, `forum`] because by default the tokenizer splits terms at word boundaries (`\W+`)<br>  Can be configured with a regex pattern
+`simple_pattern` | - Uses a regular expression pattern to return matching text as terms <br>  - Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)  <br> - Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions |  Returns an empty array by default <br> Must be configured with a pattern because the pattern defaults to an empty string
+`simple_pattern_split` | - Uses a regular expression pattern to split the text at matches rather than returning the matches as terms  <br>  - Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)  <br> - Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions | No-op by default<br> Must be configured with a pattern
+`char_group` | - Parses on a set of configurable characters <br> - Faster than tokenizers that run regular expressions | No-op by default<br> Must be configured with a list of characters
+`path_hierarchy` | - Parses text on the path separator (by default, `/`) and returns a full path to each component in the tree hierarchy | `one/two/three` <br>becomes<br> [`one`, `one/two`, `one/two/three`]
+
+