Add processor to processor names and links to further info (#5786) (#…

…5790)

(cherry picked from commit 4acbc30)

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
Signed-off-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

_ingest-pipelines/processors/append.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/append/
 ---
 
-# Append
+# Append processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/bytes.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/bytes/
 ---
 
-# Bytes
+# Bytes processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/convert.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/convert/
 ---
 
-# Convert
+# Convert processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/csv.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/csv/
 ---
 
-# CSV
+# CSV processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/date-index-name.md

@@ -5,7 +5,7 @@ parent: Ingest processors
 nav_order: 55
 ---
 
-# Date index name
+# Date index name processor
 
 The `date_index_name` processor is used to point documents to the correct time-based index based on the date or timestamp field within the document. The processor sets the `_index` metadata field to a [date math]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/date/#date-math) index name expression. Then the processor fetches the date or timestamp from the `field` field in the document being processed and formats it into a date math index name expression. The extracted date, `index_name_prefix` value, and `date_rounding` value are then combined to create the date math index expression. For example, if the `field` field contains the value `2023-10-30T12:43:29.000Z` and `index_name_prefix` is set to `week_index-` and `date_rounding` is set to `w`, then the date math index name expression is `week_index-2023-10-30`. You can use the `date_formats` field to specify how the date in the date math index expression should be formatted.
 

_ingest-pipelines/processors/date.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/date/
 ---
 
-# Date
+# Date processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/grok.md

@@ -6,7 +6,7 @@ grand_parent: Ingest pipelines
 nav_order: 140
 ---
 
-# Grok 
+# Grok processor 
 
 The `grok` processor is used to parse and structure unstructured data using pattern matching. You can use the `grok` processor to extract fields from log messages, web server access logs, application logs, and other log data that follows a consistent format.
 

_ingest-pipelines/processors/ip2geo.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/ip2geo/
 ---
 
-# IP2Geo
+# IP2Geo processor
 **Introduced 2.10**
 {: .label .label-purple }
 

_ingest-pipelines/processors/lowercase.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/lowercase/
 ---
 
-# Lowercase
+# Lowercase processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/remove.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/remove/
 ---
 
-# Remove
+# Remove processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_ingest-pipelines/processors/sparse-encoding.md

@@ -7,9 +7,9 @@ redirect_from:
    - /api-reference/ingest-apis/processors/sparse-encoding/
 ---
 
-# Sparse encoding
+# Sparse encoding processor
 
-The `sparse_encoding` processor is used to generate a sparse vector/token and weights from text fields for [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) using sparse retrieval. 
+The `sparse_encoding` processor is used to generate a sparse vector/token and weights from text fields for [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/) using sparse retrieval. 
 
 **PREREQUISITE**<br>
 Before using the `sparse_encoding` processor, you must set up a machine learning (ML) model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
@@ -140,6 +140,8 @@ The response confirms that in addition to the `passage_text` field, the processo
 }
 ```
 
+Once you have created an ingest pipeline, you need to create an index for ingestion and ingest documents into the index. To learn more, see [Step 2: Create an index for ingestion]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/#step-2-create-an-index-for-ingestion) and [Step 3: Ingest documents into the index]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/#step-3-ingest-documents-into-the-index) of [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
+
 ---
 
 ## Next steps

_ingest-pipelines/processors/text-embedding.md

@@ -7,9 +7,9 @@ redirect_from:
    - /api-reference/ingest-apis/processors/text-embedding/
 ---
 
-# Text embedding
+# Text embedding processor
 
-The `text_embedding` processor is used to generate vector embeddings from text fields for [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/). 
+The `text_embedding` processor is used to generate vector embeddings from text fields for [semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/). 
 
 **PREREQUISITE**<br>
 Before using the `text_embedding` processor, you must set up a machine learning (ML) model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
@@ -121,9 +121,11 @@ The response confirms that in addition to the `passage_text` field, the processo
 }
 ```
 
+Once you have created an ingest pipeline, you need to create an index for ingestion and ingest documents into the index. To learn more, see [Step 2: Create an index for ingestion]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/#step-2-create-an-index-for-ingestion) and [Step 3: Ingest documents into the index]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/#step-3-ingest-documents-into-the-index) of [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/).
+
 ## Next steps
 
 - To learn how to use the `neural` query for text search, see [Neural query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/).
-- To learn more about neural text search, see [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/).
-To learn more about using models in OpenSearch, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
+- To learn more about semantic search, see [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/).
+- To learn more about using models in OpenSearch, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
 - For a comprehensive example, see [Neural search tutorial]({{site.url}}{{site.baseurl}}/search-plugins/neural-search-tutorial/).

_ingest-pipelines/processors/text-image-embedding.md

@@ -7,9 +7,9 @@ redirect_from:
    - /api-reference/ingest-apis/processors/text-image-embedding/
 ---
 
-# Text/image embedding
+# Text/image embedding processor
 
-The `text_image_embedding` processor is used to generate combined vector embeddings from text and image fields for [multimodal neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-multimodal-search/). 
+The `text_image_embedding` processor is used to generate combined vector embeddings from text and image fields for [multimodal neural search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/). 
 
 **PREREQUISITE**<br>
 Before using the `text_image_embedding` processor, you must set up a machine learning (ML) model. For more information, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
@@ -131,9 +131,11 @@ The response confirms that in addition to the `image_description` and `image_bin
 }
 ```
 
+Once you have created an ingest pipeline, you need to create an index for ingestion and ingest documents into the index. To learn more, see [Step 2: Create an index for ingestion]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/#step-2-create-an-index-for-ingestion) and [Step 3: Ingest documents into the index]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/#step-3-ingest-documents-into-the-index) of [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/).
+
 ## Next steps
 
 - To learn how to use the `neural` query for a multimodal search, see [Neural query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/).
-- To learn more about multimodal neural search, see [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/).
-To learn more about using models in OpenSearch, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
+- To learn more about multimodal search, see [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/).
+- To learn more about using models in OpenSearch, see [Choosing a model]({{site.url}}{{site.baseurl}}/ml-commons-plugin/integrating-ml-models/#choosing-a-model).
 - For a comprehensive example, see [Neural search tutorial]({{site.url}}{{site.baseurl}}/search-plugins/neural-search-tutorial/).

_ingest-pipelines/processors/uppercase.md

@@ -7,7 +7,7 @@ redirect_from:
    - /api-reference/ingest-apis/processors/uppercase/
 ---
 
-# Uppercase
+# Uppercase processor
 **Introduced 1.0**
 {: .label .label-purple }
 

_query-dsl/specialized/neural-sparse.md

@@ -10,7 +10,7 @@ nav_order: 55
 Introduced 2.11
 {: .label .label-purple }
 
-Use the `neural_sparse` query for vector field search in [sparse neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/). 
+Use the `neural_sparse` query for vector field search in [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/). 
 
 ## Request fields