Adding more retriever examples to the documentation examples

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

@@ -1,5 +1,5 @@
 [[retrievers-examples]]
-==== Retrievers examples
+=== Retrievers examples
 
 To show the full functionality, for this set of examples, we'll assume that we have access to a <<semantic-reranking-models,reranker model>> through the inference service.
 
@@ -95,8 +95,8 @@ POST /retrievers_example/_doc/5
 Now that we also have our documents in place, let's try to run some queries using retrievers.
 
 [discrete]
-[[combining-standard-knn-retrievers-with-rrf]]
-===== Combining query and kNN with RRF
+[[retrievers-examples-combining-standard-knn-retrievers-with-rrf]]
+==== Combining query and kNN with RRF
 
 First, let's examine how to combine two different types of queries: a `kNN` query and a
 `query_string` query. While these queries may produce scores in different ranges, we can use
@@ -147,8 +147,8 @@ GET /retrievers_example/_search
 //NOTCONSOLE
 
 [discrete]
-[[collapsing-retriever-results]]
-===== Grouping results by year with `collapse`
+[[retrievers-examples-collapsing-retriever-results]]
+==== Grouping results by year with `collapse`
 
 In our result set, we have many documents with the same `year` value. We can clean this
 up using the `collapse` parameter with our retriever. This enables grouping results by
@@ -202,8 +202,8 @@ GET /retrievers_example/_search
 //NOTCONSOLE
 
 [discrete]
-[[text-similarity-reranker-on-top-of-rrf]]
-=====  Rerank results of RRF retriever
+[[retrievers-examples-text-similarity-reranker-on-top-of-rrf]]
+====  Rerank results of an RRF retriever
 
 Previously, we used a `text_similarity_reranker` retriever within an `rrf` retriever.
 Because retrievers support full composability, we can also rerank the results of an
@@ -257,8 +257,8 @@ GET retrievers_example/_search
 //NOTCONSOLE
 
 [discrete]
-[[rrf-ranking-on-text-similarity-reranker-results]]
-===== RRF with semantic reranker
+[[retrievers-examples-rrf-ranking-on-text-similarity-reranker-results]]
+==== RRF with semantic reranker
 
 For this example, we'll replace our semantic query with the `my-rerank-model`
 reranker we previously configured. Since this is a reranker, it needs an initial pool of
@@ -313,8 +313,8 @@ GET /retrievers_example/_search
 //NOTCONSOLE
 
 [discrete]
-[[chaining-text-similarity-reranker-retrievers]]
-===== Chaining multiple semantic rerankers
+[[retrievers-examples-chaining-text-similarity-reranker-retrievers]]
+==== Chaining multiple semantic rerankers
 
 Full composability means we can chain together multiple retrievers of the same type. For instance, imagine we have a computationally expensive reranker that's specialized for AI content. We can rerank the results of a `text_similarity_reranker` using another `text_similarity_reranker` retriever. Each reranker can operate on different fields and/or use different inference services.
 
@@ -365,8 +365,8 @@ pick the top 10 results and rerank them using the more fine-grained
 `my-other-more-expensive-rerank-model`.
 
 [discrete]
-[[rrf-and-aggregations]]
-===== Combine RRF with aggregations
+[[retrievers-examples-rrf-and-aggregations]]
+==== Combine RRF with aggregations
 
 Retrievers support both composability and most of the standard `_search` functionality. For instance,
 we can compute aggregations with the `rrf` retriever. When using a compound retriever,

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

@@ -1,5 +1,5 @@
 [[retrievers-overview]]
-=== Retrievers
+== Retrievers
 
 A retriever is an abstraction that was added to the Search API in *8.14.0* and was made generally available in *8.16.0*.
 This abstraction enables the configuration of multi-stage retrieval pipelines within a single `_search` call.
@@ -11,7 +11,7 @@ For implementation details, including notable restrictions, check out the
 
 [discrete]
 [[retrievers-overview-types]]
-==== Retriever types
+=== Retriever types
 
 Retrievers come in various types, each tailored for different search operations.
 The following retrievers are currently available:
@@ -34,7 +34,8 @@ Used for <<semantic-reranking,semantic reranking>>.
 Requires first creating a `rerank` task using the <<put-inference-api,{es} Inference API>>.
 
 [discrete]
-==== What makes retrievers useful?
+[[retrievers-overview-why-are-they-useful]]
+=== What makes retrievers useful?
 
 Here's an overview of what makes retrievers useful and how they differ from regular queries.
 
@@ -66,7 +67,7 @@ When using compound retrievers, only the query element is allowed, which enforce
 
 [discrete]
 [[retrievers-overview-example]]
-==== Example
+=== Example
 
 The following example demonstrates how using retrievers simplify the composability of queries for RRF ranking.
 
@@ -149,7 +150,7 @@ For more examples on how to use retrievers, please refer to <<retrievers-example
 
 [discrete]
 [[retrievers-overview-glossary]]
-==== Glossary
+=== Glossary
 
 Here are some important terms:
 
@@ -168,7 +169,7 @@ Special compound retrievers that reorder hits and may adjust the number of hits,
 
 [discrete]
 [[retrievers-overview-play-in-search]]
-==== Retrievers in action
+=== Retrievers in action
 
 The Search Playground builds Elasticsearch queries using the retriever abstraction.
 It automatically detects the fields and types in your index and builds a retriever tree based on your selections.
@@ -179,7 +180,7 @@ Refer to the {kibana-ref}/playground.html[Playground documentation] for more inf
 
 [discrete]
 [[retrievers-overview-api-reference]]
-==== API reference
+=== API reference
 
 For implementation details, including notable restrictions, check out the <<retriever,reference documentation>> in the Search API docs.
 

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

@@ -530,5 +530,4 @@ 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

@@ -43,6 +43,7 @@ DSL, with a simplified user experience. Create search applications based on your
 results directly in the Kibana Search UI.
 
 include::search-api.asciidoc[]
+include::retrievers-overview.asciidoc[]
 include::knn-search.asciidoc[]
 include::semantic-search.asciidoc[]
 include::search-across-clusters.asciidoc[]