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[8.x] Mark semantic text as GA in docs #124670

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Mar 20, 2025
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10 changes: 10 additions & 0 deletions docs/changelog/124670.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,10 @@
pr: 124670
summary: Release semantic_text as a GA feature
area: Mapping
type: feature
issues: []
highlight:
title: Release semantic_text as a GA feature
body: semantic_text is now an official GA (generally available) feature!
This field type allows you to easily set up and perform semantic search with minimal ramp up time.
notable: true
8 changes: 3 additions & 5 deletions docs/reference/mapping/types/semantic-text.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -5,14 +5,12 @@
<titleabbrev>Semantic text</titleabbrev>
++++

beta[]

The `semantic_text` field type automatically generates embeddings for text content using an inference endpoint.
Long passages are <<auto-text-chunking, automatically chunked>> to smaller sections to enable the processing of larger corpuses of text.

The `semantic_text` field type specifies an inference endpoint identifier that will be used to generate embeddings.
You can create the inference endpoint by using the <<put-inference-api>>.
This field type and the <<query-dsl-semantic-query,`semantic` query>> type make it simpler to perform semantic search on your data.
This field type and the <<query-dsl-semantic-query,`semantic` query>> type make it simpler to perform semantic search on your data.
The `semantic_text` field type may also be queried with <<query-dsl-match-query, match>>, <<query-dsl-sparse-vector-query, sparse_vector>> or <<query-dsl-knn-query, knn>> queries.

If you don’t specify an inference endpoint, the `inference_id` field defaults to `.elser-2-elasticsearch`, a preconfigured endpoint for the elasticsearch service.
Expand Down Expand Up @@ -193,8 +191,8 @@ types and create an ingest pipeline with an
<<inference-processor, {infer} processor>> to generate the embeddings.
<<semantic-search-inference,This tutorial>> walks you through the process. In
these cases - when you use `sparse_vector` or `dense_vector` field types instead
of the `semantic_text` field type to customize indexing - using the
<<query-dsl-semantic-query,`semantic_query`>> is not supported for querying the
of the `semantic_text` field type to customize indexing - using the
<<query-dsl-semantic-query,`semantic_query`>> is not supported for querying the
field data.


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2 changes: 0 additions & 2 deletions docs/reference/query-dsl/semantic-query.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,6 @@
<titleabbrev>Semantic</titleabbrev>
++++

beta[]

The `semantic` query type enables you to perform <<semantic-search,semantic search>> on data stored in a <<semantic-text,`semantic_text`>> field.


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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,6 @@
<titleabbrev>Semantic search with `semantic_text`</titleabbrev>
++++

beta[]

This tutorial shows you how to use the semantic text feature to perform semantic search on your data.

Semantic text simplifies the {infer} workflow by providing {infer} at ingestion time and sensible default values automatically.
Expand All @@ -21,7 +19,7 @@ This tutorial uses the <<infer-service-elasticsearch,`elasticsearch` service>> f
[[semantic-text-requirements]]
==== Requirements

This tutorial uses the <<infer-service-elasticsearch,`elasticsearch` service>> for demonstration, which is created automatically as needed.
This tutorial uses the <<infer-service-elasticsearch,`elasticsearch` service>> for demonstration, which is created automatically as needed.
To use the `semantic_text` field type with an {infer} service other than `elasticsearch` service, you must create an inference endpoint using the <<put-inference-api>>.


Expand Down Expand Up @@ -92,7 +90,7 @@ The reindexed data will be processed by the {infer} endpoint associated with the
------------------------------------------------------------
POST _reindex?wait_for_completion=false
{
"source": {
"source": {
"index": "test-data",
"size": 10 <1>
},
Expand Down Expand Up @@ -139,7 +137,7 @@ The {infer} endpoint used to generate the embeddings for the `semantic_text` fie
GET semantic-embeddings/_search
{
"query": {
"semantic": {
"semantic": {
"field": "content", <1>
"query": "How to avoid muscle soreness while running?" <2>
}
Expand Down