Merge branch 'cohere-example' of github.com:szabosteve/elasticsearch …

…into cohere-example

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

@@ -77,7 +77,7 @@ you can see an index named `test-data` with 182469 documents.
 [[reindexing-data-infer]]
 ==== Ingest the data through the {infer} ingest pipeline
 
-Create the embeddings from the text by reindexing the data throught the {infer}
+Create the embeddings from the text by reindexing the data through the {infer}
 pipeline that uses the chosen model as the inference model.
 
 include::{es-repo-dir}/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc[]

docs/reference/tab-widgets/inference-api/infer-api-mapping.asciidoc

@@ -24,7 +24,7 @@ in the {infer} pipeline configuration in the next step.
 <3> The output dimensions of the model. Find this value in the
 https://docs.cohere.com/reference/embed[Cohere documentation] of the model you
 use.
-<4> The name of the field from which to create the sparse 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 `content`. It must be referenced in
 the {infer} pipeline configuration in the next step.
 <5> The field type which is text in this example.
@@ -63,7 +63,7 @@ of the model you use.
 because OpenAI embeddings are normalised to unit length. You can check the
 https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use[OpenAI docs]
 about which similarity function to use.
-<5> The name of the field from which to create the sparse vector representation.
+<5> The name of the field from which to create the dense vector representation.
 In this example, the name of the field is `content`. It must be referenced in
 the {infer} pipeline configuration in the next step.
 <6> The field type which is text in this example.