[DOCS] Add sparse-vector field type to docs, changed references (#100348)

docs/reference/mapping/types.asciidoc

@@ -83,6 +83,7 @@ as-you-type completion.
 ==== Document ranking types
 
 <<dense-vector,`dense_vector`>>::   Records dense vectors of float values.
+<<sparse-vector,`sparse_vector`>>:: Records sparse vectors of float values.
 <<rank-feature,`rank_feature`>>::   Records a numeric feature to boost hits at
                                     query time.
 <<rank-features,`rank_features`>>:: Records numeric features to boost hits at
@@ -179,6 +180,8 @@ include::types/search-as-you-type.asciidoc[]
 
 include::types/shape.asciidoc[]
 
+include::types/sparse-vector.asciidoc[]
+
 include::types/text.asciidoc[]
 
 include::types/token-count.asciidoc[]

docs/reference/mapping/types/sparse-vector.asciidoc

@@ -0,0 +1,36 @@
+[[sparse-vector]]
+=== Sparse vector field type
+++++
+<titleabbrev>Sparse vector</titleabbrev>
+++++
+
+A `sparse_vector` field can index features and weights so that they can later be used to query
+documents in queries with a <<query-dsl-text-expansion-query,`text_expansion`>> query.
+
+`sparse_vector` is the field type that should be used with <<elser-mappings, ELSER mappings>>.
+
+[source,console]
+--------------------------------------------------
+PUT my-index
+{
+  "mappings": {
+    "properties": {
+      "text.tokens": {
+        "type": "sparse_vector"
+      }
+    }
+  }
+}
+--------------------------------------------------
+
+See <<semantic-search-elser, semantic search with ELSER>> for a complete example on adding documents
+ to a `sparse_vector` mapped field using ELSER.
+
+NOTE: `sparse_vector` fields only support single-valued fields and strictly positive
+values. Multi-valued fields and negative values will be rejected.
+
+NOTE: `sparse_vector` fields do not support querying, sorting or aggregating. They may
+only be used within <<query-dsl-text-expansion-query,`text_expansion`>> queries.
+
+NOTE: `sparse_vector` fields only preserve 9 significant bits for the precision, which
+translates to a relative error of about 0.4%.

docs/reference/query-dsl/text-expansion-query.asciidoc

@@ -4,9 +4,9 @@
 <titleabbrev>Text expansion</titleabbrev>
 ++++
 
-The text expansion query uses a {nlp} model to convert the query text into a 
-list of token-weight pairs which are then used in a query against a 
-<<rank-features,rank features field>>.
+The text expansion query uses a {nlp} model to convert the query text into a
+list of token-weight pairs which are then used in a query against a
+<<sparse-vector,sparse vector>> or <<rank-features,rank features>> field.
 
 [discrete]
 [[text-expansion-query-ex-request]]
@@ -19,7 +19,7 @@ GET _search
 {
    "query":{
       "text_expansion":{
-         "<rank_features_field>":{
+         "<sparse_vector_field>":{
             "model_id":"the model to produce the token weights",
             "model_text":"the query string"
          }
@@ -33,33 +33,33 @@ GET _search
 [[text-expansion-query-params]]
 === Top level parameters for `text_expansion`
 
-`<rank_features_field>`:::
+`<sparse_vector_field>`:::
 (Required, object)
-The name of the field that contains the token-weight pairs the NLP model created 
+The name of the field that contains the token-weight pairs the NLP model created
 based on the input text.
 
 [discrete]
 [[text-expansion-rank-feature-field-params]]
-=== Top level parameters for `<rank_features_field>`
+=== Top level parameters for `<sparse_vector_field>`
 
 `model_id`::::
 (Required, string)
-The ID of the model to use to convert the query text into token-weight pairs. It 
-must be the same model ID that was used to create the tokens from the input 
+The ID of the model to use to convert the query text into token-weight pairs. It
+must be the same model ID that was used to create the tokens from the input
 text.
 
 `model_text`::::
 (Required, string)
-The query text you want to use for search. 
+The query text you want to use for search.
 
 
 [discrete]
 [[text-expansion-query-example]]
 === Example
 
-The following is an example of the `text_expansion` query that references the 
-ELSER model to perform semantic search. For a more detailed description of how 
-to perform semantic search by using ELSER and the `text_expansion` query, refer 
+The following is an example of the `text_expansion` query that references the
+ELSER model to perform semantic search. For a more detailed description of how
+to perform semantic search by using ELSER and the `text_expansion` query, refer
 to <<semantic-search-elser,this tutorial>>.
 
 [source,console]
@@ -82,25 +82,25 @@ GET my-index/_search
 [[optimizing-text-expansion]]
 === Optimizing the search performance of the text_expansion query
 
-https://www.elastic.co/blog/faster-retrieval-of-top-hits-in-elasticsearch-with-block-max-wand[Max WAND] 
-is an optimization technique used by {es} to skip documents that cannot score 
-competitively against the current best matching documents. However, the tokens 
-generated by the ELSER model don't work well with the Max WAND optimization. 
-Consequently, enabling Max WAND can actually increase query latency for 
-`text_expansion`. For datasets of a significant size, disabling Max 
+https://www.elastic.co/blog/faster-retrieval-of-top-hits-in-elasticsearch-with-block-max-wand[Max WAND]
+is an optimization technique used by {es} to skip documents that cannot score
+competitively against the current best matching documents. However, the tokens
+generated by the ELSER model don't work well with the Max WAND optimization.
+Consequently, enabling Max WAND can actually increase query latency for
+`text_expansion`. For datasets of a significant size, disabling Max
 WAND leads to lower query latencies.
 
 Max WAND is controlled by the
-<<track-total-hits, track_total_hits>> query parameter. Setting track_total_hits 
-to true forces {es} to consider all documents, resulting in lower query 
-latencies for the `text_expansion` query. However, other {es} queries run slower 
+<<track-total-hits, track_total_hits>> query parameter. Setting track_total_hits
+to true forces {es} to consider all documents, resulting in lower query
+latencies for the `text_expansion` query. However, other {es} queries run slower
 when Max WAND is disabled.
 
-If you are combining the `text_expansion` query with standard text queries in a 
-compound search, it is recommended to measure the query performance before 
+If you are combining the `text_expansion` query with standard text queries in a
+compound search, it is recommended to measure the query performance before
 deciding which setting to use.
 
-NOTE: The `track_total_hits` option applies to all queries in the search request 
-and may be optimal for some queries but not for others. Take into account the 
-characteristics of all your queries to determine the most suitable 
+NOTE: The `track_total_hits` option applies to all queries in the search request
+and may be optimal for some queries but not for others. Take into account the
+characteristics of all your queries to determine the most suitable
 configuration.

docs/reference/search/search-your-data/semantic-search-elser.asciidoc

@@ -4,18 +4,18 @@
 <titleabbrev>Semantic search with ELSER</titleabbrev>
 ++++
 
-Elastic Learned Sparse EncodeR - or ELSER - is an NLP model trained by Elastic 
-that enables you to perform semantic search by using sparse vector 
-representation. Instead of literal matching on search terms, semantic search 
-retrieves results based on the intent and the contextual meaning of a search 
+Elastic Learned Sparse EncodeR - or ELSER - is an NLP model trained by Elastic
+that enables you to perform semantic search by using sparse vector
+representation. Instead of literal matching on search terms, semantic search
+retrieves results based on the intent and the contextual meaning of a search
 query.
 
-The instructions in this tutorial shows you how to use ELSER to perform semantic 
+The instructions in this tutorial shows you how to use ELSER to perform semantic
 search on your data.
 
-NOTE: Only the first 512 extracted tokens per field are considered during 
-semantic search with ELSER. Refer to 
-{ml-docs}/ml-nlp-limitations.html#ml-nlp-elser-v1-limit-512[this page] for more 
+NOTE: Only the first 512 extracted tokens per field are considered during
+semantic search with ELSER. Refer to
+{ml-docs}/ml-nlp-limitations.html#ml-nlp-elser-v1-limit-512[this page] for more
 information.
 
 
@@ -23,18 +23,18 @@ information.
 [[requirements]]
 ==== Requirements
 
-To perform semantic search by using ELSER, you must have the NLP model deployed 
-in your cluster. Refer to the 
-{ml-docs}/ml-nlp-elser.html[ELSER documentation] to learn how to download and 
+To perform semantic search by using ELSER, you must have the NLP model deployed
+in your cluster. Refer to the
+{ml-docs}/ml-nlp-elser.html[ELSER documentation] to learn how to download and
 deploy the model.
 
-NOTE: The minimum dedicated ML node size for deploying and using the ELSER model 
-is 4 GB in Elasticsearch Service if 
-{cloud}/ec-autoscaling.html[deployment autoscaling] is turned off. Turning on 
-autoscaling is recommended because it allows your deployment to dynamically 
-adjust resources based on demand. Better performance can be achieved by using 
-more allocations or more threads per allocation, which requires bigger ML nodes. 
-Autoscaling provides bigger nodes when required. If autoscaling is turned off, 
+NOTE: The minimum dedicated ML node size for deploying and using the ELSER model
+is 4 GB in Elasticsearch Service if
+{cloud}/ec-autoscaling.html[deployment autoscaling] is turned off. Turning on
+autoscaling is recommended because it allows your deployment to dynamically
+adjust resources based on demand. Better performance can be achieved by using
+more allocations or more threads per allocation, which requires bigger ML nodes.
+Autoscaling provides bigger nodes when required. If autoscaling is turned off,
 you must provide suitably sized nodes yourself.
 
 
@@ -42,17 +42,17 @@ you must provide suitably sized nodes yourself.
 [[elser-mappings]]
 ==== Create the index mapping
 
-First, the mapping of the destination index - the index that contains the tokens 
-that the model created based on your text - must be created.  The destination 
-index must have a field with the 
-<<rank-features, `sparse_vector` or `rank_features`>> field type to index the 
+First, the mapping of the destination index - the index that contains the tokens
+that the model created based on your text - must be created.  The destination
+index must have a field with the
+<<sparse-vector, `sparse_vector`>> or <<rank-features,`rank_features`>> field type to index the
 ELSER output.
 
-NOTE: ELSER output must be ingested into a field with the `sparse_vector` or 
-`rank_features` field type. Otherwise, {es} interprets the token-weight pairs as 
-a massive amount of fields in a document. If you get an error similar to this 
-`"Limit of total fields [1000] has been exceeded while adding new fields"` then 
-the ELSER output field is not mapped properly and it has a field type different 
+NOTE: ELSER output must be ingested into a field with the `sparse_vector` or
+`rank_features` field type. Otherwise, {es} interprets the token-weight pairs as
+a massive amount of fields in a document. If you get an error similar to this
+`"Limit of total fields [1000] has been exceeded while adding new fields"` then
+the ELSER output field is not mapped properly and it has a field type different
 than `sparse_vector` or `rank_features`.
 
 [source,console]
@@ -74,19 +74,19 @@ PUT my-index
 // TEST[skip:TBD]
 <1> The name of the field to contain the generated tokens.
 <2> The field to contain the tokens is a `sparse_vector` field.
-<3> The name of the field from which to create the sparse vector representation. 
+<3> The name of the field from which to create the sparse vector representation.
 In this example, the name of the field is `text`.
 <4> The field type which is text in this example.
 
-To learn how to optimize space, refer to the <<save-space>> section. 
+To learn how to optimize space, refer to the <<save-space>> section.
 
 
 [discrete]
 [[inference-ingest-pipeline]]
 ==== Create an ingest pipeline with an inference processor
 
-Create an <<ingest,ingest pipeline>> with an 
-<<inference-processor,{infer} processor>> to use ELSER to infer against the data 
+Create an <<ingest,ingest pipeline>> with an
+<<inference-processor,{infer} processor>> to use ELSER to infer against the data
 that is being ingested in the pipeline.
 
 [source,console]
@@ -112,10 +112,10 @@ PUT _ingest/pipeline/elser-v2-test
 }
 ----
 // TEST[skip:TBD]
-<1> The `field_map` object maps the input document field name (which is `text` 
-in this example) to the name of the field that the model expects (which is 
+<1> The `field_map` object maps the input document field name (which is `text`
+in this example) to the name of the field that the model expects (which is
 always `text_field`).
-<2> The `text_expansion` inference type needs to be used in the {infer} ingest 
+<2> The `text_expansion` inference type needs to be used in the {infer} ingest
 processor.
 
 
@@ -123,19 +123,19 @@ processor.
 [[load-data]]
 ==== Load data
 
-In this step, you load the data that you later use in the {infer} ingest 
+In this step, you load the data that you later use in the {infer} ingest
 pipeline to extract tokens from it.
 
-Use the `msmarco-passagetest2019-top1000` data set, which is a subset of the MS 
-MARCO Passage Ranking data set. It consists of 200 queries, each accompanied by 
-a list of relevant text passages. All unique passages, along with their IDs, 
-have been extracted from that data set and compiled into a 
+Use the `msmarco-passagetest2019-top1000` data set, which is a subset of the MS
+MARCO Passage Ranking data set. It consists of 200 queries, each accompanied by
+a list of relevant text passages. All unique passages, along with their IDs,
+have been extracted from that data set and compiled into a
 https://github.com/elastic/stack-docs/blob/main/docs/en/stack/ml/nlp/data/msmarco-passagetest2019-unique.tsv[tsv file].
 
-Download the file and upload it to your cluster using the 
-{kibana-ref}/connect-to-elasticsearch.html#upload-data-kibana[Data Visualizer] 
-in the {ml-app} UI. Assign the name `id` to the first column and `text` to the 
-second column. The index name is `test-data`. Once the upload is complete, you 
+Download the file and upload it to your cluster using the
+{kibana-ref}/connect-to-elasticsearch.html#upload-data-kibana[Data Visualizer]
+in the {ml-app} UI. Assign the name `id` to the first column and `text` to the
+second column. The index name is `test-data`. Once the upload is complete, you
 can see an index named `test-data` with 182469 documents.
 
 
@@ -143,7 +143,7 @@ can see an index named `test-data` with 182469 documents.
 [[reindexing-data-elser]]
 ==== Ingest the data through the {infer} ingest pipeline
 
-Create the tokens from the text by reindexing the data throught the {infer} 
+Create the tokens from the text by reindexing the data throught the {infer}
 pipeline that uses ELSER as the inference model.
 
 [source,console]
@@ -161,8 +161,8 @@ POST _reindex?wait_for_completion=false
 }
 ----
 // TEST[skip:TBD]
-<1> The default batch size for reindexing is 1000. Reducing `size` to a smaller 
-number makes the update of the reindexing process quicker which enables you to 
+<1> The default batch size for reindexing is 1000. Reducing `size` to a smaller
+number makes the update of the reindexing process quicker which enables you to
 follow the progress closely and detect errors early.
 
 The call returns a task ID to monitor the progress:
@@ -173,7 +173,7 @@ GET _tasks/<task_id>
 ----
 // TEST[skip:TBD]
 
-You can also open the Trained Models UI, select the Pipelines tab under ELSER to 
+You can also open the Trained Models UI, select the Pipelines tab under ELSER to
 follow the progress.
 
 
@@ -181,9 +181,9 @@ follow the progress.
 [[text-expansion-query]]
 ==== Semantic search by using the `text_expansion` query
 
-To perform semantic search, use the `text_expansion` query, and provide the 
-query text and the ELSER model ID. The example below uses the query text "How to 
-avoid muscle soreness after running?", the `ml.tokens` field contains the 
+To perform semantic search, use the `text_expansion` query, and provide the
+query text and the ELSER model ID. The example below uses the query text "How to
+avoid muscle soreness after running?", the `ml.tokens` field contains the
 generated ELSER output:
 
 [source,console]
@@ -202,9 +202,9 @@ GET my-index/_search
 ----
 // TEST[skip:TBD]
 
-The result is the top 10 documents that are closest in meaning to your query 
-text from the `my-index` index sorted by their relevancy. The result also 
-contains the extracted tokens for each of the relevant search results with their 
+The result is the top 10 documents that are closest in meaning to your query
+text from the `my-index` index sorted by their relevancy. The result also
+contains the extracted tokens for each of the relevant search results with their
 weights.
 
 [source,consol-result]
@@ -246,7 +246,7 @@ weights.
 ----
 // NOTCONSOLE
 
-To learn about optimizing your `text_expansion` query, refer to 
+To learn about optimizing your `text_expansion` query, refer to
 <<optimizing-text-expansion>>.
 
 
@@ -254,16 +254,16 @@ To learn about optimizing your `text_expansion` query, refer to
 [[text-expansion-compound-query]]
 ==== Combining semantic search with other queries
 
-You can combine `text_expansion` with other queries in a 
-<<compound-queries,compound query>>. For example using a filter clause in a 
-<<query-dsl-bool-query>> or a full text query which may or may not use the same 
-query text as the `text_expansion` query. This enables you to combine the search 
+You can combine `text_expansion` with other queries in a
+<<compound-queries,compound query>>. For example using a filter clause in a
+<<query-dsl-bool-query>> or a full text query which may or may not use the same
+query text as the `text_expansion` query. This enables you to combine the search
 results from both queries.
 
-The search hits from the `text_expansion` query tend to score higher than other 
-{es} queries. Those scores can be regularized by increasing or decreasing the 
-relevance scores of each query by using the `boost` parameter. Recall on the 
-`text_expansion` query can be high where there is a long tail of less relevant 
+The search hits from the `text_expansion` query tend to score higher than other
+{es} queries. Those scores can be regularized by increasing or decreasing the
+relevance scores of each query by using the `boost` parameter. Recall on the
+`text_expansion` query can be high where there is a long tail of less relevant
 results. Use the `min_score` parameter to prune those less relevant documents.
 
 [source,console]
@@ -274,7 +274,7 @@ GET my-index/_search
     "bool": { <1>
       "should": [
         {
-          "text_expansion": { 
+          "text_expansion": {
             "ml.tokens": {
               "model_text": "How to avoid muscle soreness after running?",
               "model_id": ".elser_model_2",
@@ -295,13 +295,13 @@ GET my-index/_search
 }
 ----
 // TEST[skip:TBD]
-<1> Both the `text_expansion` and the `query_string` queries are in a `should` 
+<1> Both the `text_expansion` and the `query_string` queries are in a `should`
 clause of a `bool` query.
-<2> The `boost` value is `1` for the `text_expansion` query which is the default 
-value. This means that the relevance score of the results of this query are not 
+<2> The `boost` value is `1` for the `text_expansion` query which is the default
+value. This means that the relevance score of the results of this query are not
 boosted.
-<3> The `boost` value is `4` for the `query_string` query. The relevance score 
-of the results of this query is increased causing them to rank higher in the 
+<3> The `boost` value is `4` for the `query_string` query. The relevance score
+of the results of this query is increased causing them to rank higher in the
 search results.
 <4> Only the results with a score equal to or higher than `10` are displayed.
 
@@ -314,22 +314,22 @@ search results.
 [[save-space]]
 ==== Saving disk space by excluding the ELSER tokens from document source
 
-The tokens generated by ELSER must be indexed for use in the 
-<<query-dsl-text-expansion-query, text_expansion query>>. However, it is not 
-necessary to retain those terms in the document source. You can save disk space 
-by using the <<include-exclude,source exclude>> mapping to remove the ELSER 
-terms from the document source. 
-
-WARNING: Reindex uses the document source to populate the destination index. 
-Once the ELSER terms have been excluded from the source, they cannot be 
-recovered through reindexing. Excluding the tokens from the source is a 
-space-saving optimsation that should only be applied if you are certain that 
-reindexing will not be required in the future! It's important to carefully 
-consider this trade-off and make sure that excluding the ELSER terms from the 
+The tokens generated by ELSER must be indexed for use in the
+<<query-dsl-text-expansion-query, text_expansion query>>. However, it is not
+necessary to retain those terms in the document source. You can save disk space
+by using the <<include-exclude,source exclude>> mapping to remove the ELSER
+terms from the document source.
+
+WARNING: Reindex uses the document source to populate the destination index.
+Once the ELSER terms have been excluded from the source, they cannot be
+recovered through reindexing. Excluding the tokens from the source is a
+space-saving optimsation that should only be applied if you are certain that
+reindexing will not be required in the future! It's important to carefully
+consider this trade-off and make sure that excluding the ELSER terms from the
 source aligns with your specific requirements and use case.
 
-The mapping that excludes `ml.tokens` from the  `_source` field can be created 
-by the following API call: 
+The mapping that excludes `ml.tokens` from the  `_source` field can be created
+by the following API call:
 
 [source,console]
 ----
@@ -343,10 +343,10 @@ PUT my-index
     },
     "properties": {
       "ml.tokens": {
-        "type": "sparse_vector" 
+        "type": "sparse_vector"
       },
-      "text": { 
-        "type": "text" 
+      "text": {
+        "type": "text"
       }
     }
   }

docs/reference/tab-widgets/semantic-search/field-mappings.asciidoc

@@ -1,14 +1,14 @@
 // tag::elser[]
 
-ELSER produces token-weight pairs as output from the input text and the query. 
-The {es} <<rank-features,`sparse_vector`>> field type can store these 
-token-weight pairs as numeric feature vectors. The index must have a field with 
+ELSER produces token-weight pairs as output from the input text and the query.
+The {es} <<sparse-vector,`sparse_vector`>> field type can store these
+token-weight pairs as numeric feature vectors. The index must have a field with
 the `sparse_vector` field type to index the tokens that ELSER generates.
 
-To create a mapping for your ELSER index, refer to the 
-<<elser-mappings,Create the index mapping section>> of the tutorial. The example 
-shows how to create an index mapping for `my-index` that defines the 
-`my_embeddings.tokens` field - which will contain the ELSER output - as a 
+To create a mapping for your ELSER index, refer to the
+<<elser-mappings,Create the index mapping section>> of the tutorial. The example
+shows how to create an index mapping for `my-index` that defines the
+`my_embeddings.tokens` field - which will contain the ELSER output - as a
 `sparse_vector` field.
 
 [source,console]
@@ -29,7 +29,7 @@ PUT my-index
 ----
 <1> The name of the field that will contain the tokens generated by ELSER.
 <2> The field that contains the tokens must be a `sparse_vector` field.
-<3> The name of the field from which to create the sparse vector representation. 
+<3> The name of the field from which to create the sparse vector representation.
 In this example, the name of the field is `my_text_field`.
 <4> The field type is `text` in this example.
 
@@ -38,21 +38,21 @@ In this example, the name of the field is `my_text_field`.
 
 // tag::dense-vector[]
 
-The models compatible with {es} NLP generate dense vectors as output. The 
-<<dense-vector,`dense_vector`>> field type is suitable for storing dense vectors 
-of numeric values. The index must have a field with the `dense_vector` field 
-type to index the embeddings that the supported third-party model that you 
-selected generates. Keep in mind that the model produces embeddings with a 
-certain number of dimensions. The `dense_vector` field must be configured with 
-the same number of dimensions using the `dims` option. Refer to the respective 
-model documentation to get information about the number of dimensions of the 
+The models compatible with {es} NLP generate dense vectors as output. The
+<<dense-vector,`dense_vector`>> field type is suitable for storing dense vectors
+of numeric values. The index must have a field with the `dense_vector` field
+type to index the embeddings that the supported third-party model that you
+selected generates. Keep in mind that the model produces embeddings with a
+certain number of dimensions. The `dense_vector` field must be configured with
+the same number of dimensions using the `dims` option. Refer to the respective
+model documentation to get information about the number of dimensions of the
 embeddings.
 
-To review a mapping of an index for an NLP model, refer to the mapping code 
-snippet in the 
-{ml-docs}/ml-nlp-text-emb-vector-search-example.html#ex-text-emb-ingest[Add the text embedding model to an ingest inference pipeline] 
-section of the tutorial. The example shows how to create an index mapping that 
-defines the `my_embeddings.predicted_value` field - which will contain the model 
+To review a mapping of an index for an NLP model, refer to the mapping code
+snippet in the
+{ml-docs}/ml-nlp-text-emb-vector-search-example.html#ex-text-emb-ingest[Add the text embedding model to an ingest inference pipeline]
+section of the tutorial. The example shows how to create an index mapping that
+defines the `my_embeddings.predicted_value` field - which will contain the model
 output - as a `dense_vector` field.
 
 [source,console]
@@ -74,16 +74,16 @@ PUT my-index
   }
 }
 ----
-<1> The name of the field that will contain the embeddings generated by the 
+<1> The name of the field that will contain the embeddings generated by the
 model.
 <2> The field that contains the embeddings must be a `dense_vector` field.
-<3> The model produces embeddings with a certain number of dimensions. The 
-`dense_vector` field must be configured with the same number of dimensions by 
-the `dims` option. Refer to the respective model documentation to get 
+<3> The model produces embeddings with a certain number of dimensions. The
+`dense_vector` field must be configured with the same number of dimensions by
+the `dims` option. Refer to the respective model documentation to get
 information about the number of dimensions of the embeddings.
-<4> The name of the field from which to create the dense vector representation. 
+<4> The name of the field from which to create the dense vector representation.
 In this example, the name of the field is `my_text_field`.
 <5> The field type is `text` in this example.
 
 
-// end::dense-vector[]
+// end::dense-vector[]