[DOCS] Fix reranking IA, move retrievers to search api overview

docs/reference/index.asciidoc

@@ -32,6 +32,8 @@ include::alias.asciidoc[]
 
 include::search/search-your-data/search-your-data.asciidoc[]
 
+include::reranking/index.asciidoc[]
+
 include::query-dsl.asciidoc[]
 
 include::aggregations.asciidoc[]

docs/reference/reranking/index.asciidoc

@@ -0,0 +1,16 @@
+[[reranking-overview]]
+= Reranking
+
+Rerankers improve the relevance of results from earlier-stage retrieval mechanisms.
+First-stage rankers must be very fast and efficient because they process either the entire corpus, or all matching documents.
+
+In a multi-stage pipeline, you can progressively use more computationally intensive ranking functions and techniques, as they will operate on smaller result sets at each step.
+This helps avoid query latency degradation and keeps costs manageable.
+
+Currently, the following reranking techniques are available in {es}:
+
+* <<semantic-reranking>>: Use machine learning models to reorder search results based on their semantic similarity to a query. Models can be hosted directly in your {es} cluster, or you can use <<inference-apis,inference endpoints>> to use models provided by third-party services. Enables out-of-the-box semantic search capabilities on existing indices.
+* <<learning-to-rank>>: Train a machine learning model to build a ranking function for your search experience that updates over time. For advanced use cases.
+
+include::semantic-reranking.asciidoc[]
+include::learning-to-rank.asciidoc[]

docs/reference/search/search-your-data/learning-to-rank.asciidoc → docs/reference/reranking/learning-to-rank.asciidoc

@@ -131,4 +131,4 @@ In the next pages of this guide you will learn to:
 * <<learning-to-rank-search-usage, Search using LTR model as a rescorer>>
 
 include::learning-to-rank-model-training.asciidoc[]
-include::learning-to-rank-search-usage.asciidoc[]
+include::learning-to-rank-search-usage.asciidoc[]

docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc → docs/reference/reranking/semantic-reranking.asciidoc

@@ -1,5 +1,5 @@
 [[semantic-reranking]]
-=== Semantic reranking
+== Semantic reranking
 
 preview::[]
 
@@ -11,10 +11,6 @@ This overview focuses more on the high-level concepts and use cases for semantic
 Rerankers improve the relevance of results from earlier-stage retrieval mechanisms.
 _Semantic_ rerankers use machine learning models to reorder search results based on their semantic similarity to a query.
 
-First-stage retrievers and rankers must be very fast and efficient because they process either the entire corpus, or all matching documents.
-In a multi-stage pipeline, you can progressively use more computationally intensive ranking functions and techniques, as they will operate on smaller result sets at each step.
-This helps avoid query latency degradation and keeps costs manageable.
-
 Semantic reranking requires relatively large and complex machine learning models and operates in real-time in response to queries.
 This technique makes sense on a small _top-k_ result set, as one the of the final steps in a pipeline.
 This is a powerful technique for improving search relevance that works equally well with keyword, semantic, or hybrid retrieval algorithms.
@@ -24,7 +20,7 @@ The final sections include a practical, high-level overview of how to implement
 
 [discrete]
 [[semantic-reranking-use-cases]]
-==== Use cases
+=== Use cases
 
 Semantic reranking enables a variety of use cases:
 
@@ -45,7 +41,7 @@ Now that we've outlined the value of semantic reranking, we'll explore the speci
 
 [discrete]
 [[semantic-reranking-models]]
-==== Cross-encoder and bi-encoder models
+=== Cross-encoder and bi-encoder models
 
 At a high level, two model types are used for semantic reranking: cross-encoders and bi-encoders.
 
@@ -83,7 +79,7 @@ Note that similarity scores from bi-encoders/embedding similarities are _query-d
 
 [discrete]
 [[semantic-reranking-in-es]]
-==== Semantic reranking in {es}
+=== Semantic reranking in {es}
 
 In {es}, semantic rerankers are implemented using the {es} <<inference-apis,Inference API>> and a <<retriever,retriever>>.
 
@@ -134,7 +130,7 @@ POST _search
 
 [discrete]
 [[semantic-reranking-learn-more]]
-==== Learn more
+=== Learn more
 
 * Read the <<retriever,retriever reference documentation>> for syntax and implementation details
 * Learn more about the <<retrievers-overview,retrievers>> abstraction

docs/reference/search/search-your-data/search-api.asciidoc

@@ -530,3 +530,5 @@ include::retrieve-inner-hits.asciidoc[]
 include::search-shard-routing.asciidoc[]
 include::search-using-query-rules.asciidoc[]
 include::search-template.asciidoc[]
+include::retrievers-overview.asciidoc[]
+

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

@@ -45,8 +45,6 @@ results directly in the Kibana Search UI.
 include::search-api.asciidoc[]
 include::knn-search.asciidoc[]
 include::semantic-search.asciidoc[]
-include::retrievers-reranking/index.asciidoc[]
-include::learning-to-rank.asciidoc[]
 include::search-across-clusters.asciidoc[]
 include::search-with-synonyms.asciidoc[]
 include::search-application-overview.asciidoc[]