diff --git a/_analyzers/character-filters/html-character-filter.md b/_analyzers/character-filters/html-character-filter.md
index eee548d0f7..bd9f88583e 100644
--- a/_analyzers/character-filters/html-character-filter.md
+++ b/_analyzers/character-filters/html-character-filter.md
@@ -9,7 +9,7 @@ nav_order: 100
The `html_strip` character filter removes HTML tags, such as ``, `
`, and ``, from the input text and renders plain text. The filter can be configured to preserve certain tags or decode specific HTML entities, such as ` `, into spaces.
-## Example: HTML analyzer
+## Example
The following request applies an `html_strip` character filter to the provided text:
diff --git a/_analyzers/character-filters/index.md b/_analyzers/character-filters/index.md
index 0e2ce01b8c..9d4980ac80 100644
--- a/_analyzers/character-filters/index.md
+++ b/_analyzers/character-filters/index.md
@@ -14,6 +14,6 @@ Unlike token filters, which operate on tokens (words or terms), character filter
Use cases for character filters include:
-- **HTML stripping:** Removes HTML tags from content so that only the plain text is indexed.
-- **Pattern replacement:** Replaces or removes unwanted characters or patterns in text, for example, converting hyphens to spaces.
-- **Custom mappings:** Substitutes specific characters or sequences with other values, for example, to convert currency symbols into their textual equivalents.
+- **HTML stripping**: The [`html_strip`]({{site.url}}{{site.baseurl}}/analyzers/character-filters/html-character-filter/) character filter removes HTML tags from content so that only the plain text is indexed.
+- **Pattern replacement**: The [`pattern_replace`]({{site.url}}{{site.baseurl}}/analyzers/character-filters/pattern-replace-character-filter/) character filter replaces or removes unwanted characters or patterns in text, for example, converting hyphens to spaces.
+- **Custom mappings**: The [`mapping`]({{site.url}}{{site.baseurl}}/analyzers/character-filters/mapping-character-filter/) character filter substitutes specific characters or sequences with other values, for example, to convert currency symbols into their textual equivalents.
diff --git a/_analyzers/character-filters/mapping-character-filter.md b/_analyzers/character-filters/mapping-character-filter.md
index 0cd882e52e..59e516e4ec 100644
--- a/_analyzers/character-filters/mapping-character-filter.md
+++ b/_analyzers/character-filters/mapping-character-filter.md
@@ -36,6 +36,7 @@ GET /_analyze
"text": "I have III apples and IV oranges"
}
```
+{% include copy-curl.html %}
The response contains a token where Roman numerals have been replaced with Arabic numerals:
@@ -52,7 +53,6 @@ The response contains a token where Roman numerals have been replaced with Arabi
]
}
```
-{% include copy-curl.html %}
## Parameters
@@ -106,6 +106,7 @@ GET /text-index/_analyze
"text": "FYI, updates to the workout schedule are posted. IDK when it takes effect, but we have some details. BTW, the finalized schedule will be released Monday."
}
```
+{% include copy-curl.html %}
The response shows that the abbreviations were replaced:
diff --git a/_analyzers/tokenizers/index.md b/_analyzers/tokenizers/index.md
index f5b5ff0f25..cef1429778 100644
--- a/_analyzers/tokenizers/index.md
+++ b/_analyzers/tokenizers/index.md
@@ -30,13 +30,13 @@ Word tokenizers parse full text into words.
Tokenizer | Description | Example
:--- | :--- | :---
-`standard` | - Parses strings into tokens at word boundaries
- Removes most punctuation | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`]
-`letter` | - Parses strings into tokens on any non-letter character
- Removes non-letter characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`It`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `to`, `OpenSearch`]
-`lowercase` | - Parses strings into tokens on any non-letter character
- Removes non-letter characters
- Converts terms to lowercase | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`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!`
becomes
[`It’s`, `fun`, `to`, `contribute`, `a`,`brand-new`, `PR`, `or`, `2`, `to`, `OpenSearch!`]
-`uax_url_email` | - Similar to the standard tokenizer
- 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!`
becomes
[`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`, `opensearch-project@github.com`]
-`classic` | - Parses strings into tokens on:
- Punctuation characters that are followed by a white space character
- Hyphens if the term does not contain numbers
- Removes punctuation
- Leaves URLs and email addresses as single terms | `Part number PA-35234, single-use product (128.32)`
becomes
[`Part`, `number`, `PA-35234`, `single`, `use`, `product`, `128.32`]
-`thai` | - Parses Thai text into terms | `สวัสดีและยินดีต`
becomes
[`สวัสด`, `และ`, `ยินดี`, `ต`]
+[`standard`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/standard/) | - Parses strings into tokens at word boundaries
- Removes most punctuation | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`]
+[`letter`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/letter/) | - Parses strings into tokens on any non-letter character
- Removes non-letter characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`It`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `to`, `OpenSearch`]
+[`lowercase`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/lowercase/) | - Parses strings into tokens on any non-letter character
- Removes non-letter characters
- Converts terms to lowercase | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`it`, `s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `pr`, `or`, `to`, `opensearch`]
+[`whitespace`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/whitespace/) | - Parses strings into tokens at white space characters | `It’s fun to contribute a brand-new PR or 2 to OpenSearch!`
becomes
[`It’s`, `fun`, `to`, `contribute`, `a`,`brand-new`, `PR`, `or`, `2`, `to`, `OpenSearch!`]
+[`uax_url_email`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/uax-url-email/) | - Similar to the standard tokenizer
- 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!`
becomes
[`It’s`, `fun`, `to`, `contribute`, `a`,`brand`, `new`, `PR`, `or`, `2`, `to`, `OpenSearch`, `opensearch-project@github.com`]
+[`classic`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/classic/) | - Parses strings into tokens on:
- Punctuation characters that are followed by a white space character
- Hyphens if the term does not contain numbers
- Removes punctuation
- Leaves URLs and email addresses as single terms | `Part number PA-35234, single-use product (128.32)`
becomes
[`Part`, `number`, `PA-35234`, `single`, `use`, `product`, `128.32`]
+[`thai`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/thai/) | - Parses Thai text into terms | `สวัสดีและยินดีต`
becomes
[`สวัสด`, `และ`, `ยินดี`, `ต`]
### Partial word tokenizers
@@ -44,8 +44,8 @@ Partial word tokenizers parse text into words and generate fragments of those wo
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`
becomes
[`M`, `My`, `y`, `y `, , r, `r`, `re`, `e`, `ep`, `p`, `po`, `o`]
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`
becomes
[`M`, `My`]
because the default n-gram length is 1--2 characters
+[`ngram`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/ngram/)| - Parses strings into words on specified characters (for example, punctuation or white space characters) and generates n-grams of each word | `My repo`
becomes
[`M`, `My`, `y`, `y `, , r, `r`, `re`, `e`, `ep`, `p`, `po`, `o`]
because the default n-gram length is 1--2 characters
+[`edge_ngram`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/edge-n-gram/) | - 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`
becomes
[`M`, `My`]
because the default n-gram length is 1--2 characters
### Structured text tokenizers
@@ -53,11 +53,11 @@ Structured text tokenizers parse structured text, such as identifiers, email add
Tokenizer | Description | Example
:--- | :--- | :---
-`keyword` | - No-op tokenizer
- Outputs the entire string unchanged
- Can be combined with token filters, like lowercase, to normalize terms | `My repo`
becomes
`My repo`
-`pattern` | - Uses a regular expression pattern to parse text into terms on a word separator or to capture matching text as terms
- Uses [Java regular expressions](https://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html) | `https://opensearch.org/forum`
becomes
[`https`, `opensearch`, `org`, `forum`] because by default the tokenizer splits terms at word boundaries (`\W+`)
Can be configured with a regex pattern
-`simple_pattern` | - Uses a regular expression pattern to return matching text as terms
- Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)
- Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions | Returns an empty array by default
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 on matches rather than returning the matches as terms
- Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)
- Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions | No-op by default
Must be configured with a pattern
-`char_group` | - Parses on a set of configurable characters
- Faster than tokenizers that run regular expressions | No-op by default
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`
becomes
[`one`, `one/two`, `one/two/three`]
+[`keyword`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/keyword/) | - No-op tokenizer
- Outputs the entire string unchanged
- Can be combined with token filters, like lowercase, to normalize terms | `My repo`
becomes
`My repo`
+[`pattern`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/pattern/) | - Uses a regular expression pattern to parse text into terms on a word separator or to capture matching text as terms
- Uses [Java regular expressions](https://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html) | `https://opensearch.org/forum`
becomes
[`https`, `opensearch`, `org`, `forum`] because by default the tokenizer splits terms at word boundaries (`\W+`)
Can be configured with a regex pattern
+[`simple_pattern`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/simple-pattern/) | - Uses a regular expression pattern to return matching text as terms
- Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)
- Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions | Returns an empty array by default
Must be configured with a pattern because the pattern defaults to an empty string
+[`simple_pattern_split`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/simple-pattern-split/) | - Uses a regular expression pattern to split the text on matches rather than returning the matches as terms
- Uses [Lucene regular expressions](https://lucene.apache.org/core/8_7_0/core/org/apache/lucene/util/automaton/RegExp.html)
- Faster than the `pattern` tokenizer because it uses a subset of the `pattern` tokenizer regular expressions | No-op by default
Must be configured with a pattern
+[`char_group`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/character-group/) | - Parses on a set of configurable characters
- Faster than tokenizers that run regular expressions | No-op by default
Must be configured with a list of characters
+[`path_hierarchy`]({{site.url}}{{site.baseurl}}/analyzers/tokenizers/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`
becomes
[`one`, `one/two`, `one/two/three`]