Add processor to processor names and links to further info (#5786)
Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
This commit is contained in:
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@ -30,7 +30,7 @@ OpenSearch.FailoverNoun = YES
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OpenSearch.FailoverVerb = YES
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OpenSearch.FutureTense = NO
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OpenSearch.HeadingAcronyms = YES
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OpenSearch.HeadingCapitalization = NO
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OpenSearch.HeadingCapitalization = YES
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OpenSearch.HeadingColon = YES
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OpenSearch.HeadingPunctuation = YES
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OpenSearch.Inclusive = YES
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/append/
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---
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# Append
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# Append processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/bytes/
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---
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# Bytes
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# Bytes processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/convert/
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---
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# Convert
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# Convert processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/csv/
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---
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# CSV
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# CSV processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -5,7 +5,7 @@ parent: Ingest processors
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nav_order: 55
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---
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# Date index name
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# Date index name processor
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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.
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/date/
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---
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# Date
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# Date processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -6,7 +6,7 @@ grand_parent: Ingest pipelines
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nav_order: 140
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---
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# Grok
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# Grok processor
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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.
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/ip2geo/
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---
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# IP2Geo
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# IP2Geo processor
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**Introduced 2.10**
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{: .label .label-purple }
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/lowercase/
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---
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# Lowercase
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# Lowercase processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/remove/
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---
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# Remove
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# Remove processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -7,9 +7,9 @@ redirect_from:
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- /api-reference/ingest-apis/processors/sparse-encoding/
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---
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# Sparse encoding
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# Sparse encoding processor
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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.
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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.
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**PREREQUISITE**<br>
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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).
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@ -140,6 +140,8 @@ The response confirms that in addition to the `passage_text` field, the processo
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}
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```
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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/).
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---
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## Next steps
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@ -7,9 +7,9 @@ redirect_from:
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- /api-reference/ingest-apis/processors/text-embedding/
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---
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# Text embedding
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# Text embedding processor
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The `text_embedding` processor is used to generate vector embeddings from text fields for [neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/).
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The `text_embedding` processor is used to generate vector embeddings from text fields for [semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/).
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**PREREQUISITE**<br>
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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).
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@ -121,9 +121,11 @@ The response confirms that in addition to the `passage_text` field, the processo
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}
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```
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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/).
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## Next steps
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- To learn how to use the `neural` query for text search, see [Neural query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/).
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- To learn more about neural text search, see [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/).
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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).
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- To learn more about semantic search, see [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/).
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- 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).
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- For a comprehensive example, see [Neural search tutorial]({{site.url}}{{site.baseurl}}/search-plugins/neural-search-tutorial/).
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@ -7,9 +7,9 @@ redirect_from:
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- /api-reference/ingest-apis/processors/text-image-embedding/
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---
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# Text/image embedding
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# Text/image embedding processor
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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/).
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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/).
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**PREREQUISITE**<br>
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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).
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@ -131,9 +131,11 @@ The response confirms that in addition to the `image_description` and `image_bin
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}
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```
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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/).
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## Next steps
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- To learn how to use the `neural` query for a multimodal search, see [Neural query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/).
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- To learn more about multimodal neural search, see [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/).
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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).
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- To learn more about multimodal search, see [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/).
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- 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).
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- For a comprehensive example, see [Neural search tutorial]({{site.url}}{{site.baseurl}}/search-plugins/neural-search-tutorial/).
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@ -7,7 +7,7 @@ redirect_from:
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- /api-reference/ingest-apis/processors/uppercase/
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---
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# Uppercase
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# Uppercase processor
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**Introduced 1.0**
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{: .label .label-purple }
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@ -10,7 +10,7 @@ nav_order: 55
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Introduced 2.11
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{: .label .label-purple }
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Use the `neural_sparse` query for vector field search in [sparse neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
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Use the `neural_sparse` query for vector field search in [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/).
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## Request fields
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