[Search] Cleanup Search TOC, move/rename files

deploy-manage/production-guidance/optimize-performance/search-speed.md

@@ -424,5 +424,5 @@ The `constant_keyword` is not strictly required for this optimization: it is als
 
 ## Default search timeout [_default_search_timeout] 
 
-By default, search requests don’t time out. You can set a timeout using the [`search.default_search_timeout`](../../../solutions/search/querying-for-search.md#search-timeout) setting.
+By default, search requests don’t time out. You can set a timeout using the [`search.default_search_timeout`](../../../solutions/search/the-search-api.md#search-timeout) setting.
 

raw-migrated-files/elasticsearch/elasticsearch-reference/data-management.md

@@ -21,7 +21,7 @@ To help you manage your data, {{es}} offers you the following options:
 * Define [multiple tiers](../../../manage-data/lifecycle/data-tiers.md) of data nodes with different performance characteristics.
 * Automatically transition indices through the data tiers according to your performance needs and retention policies.
 * Leverage [searchable snapshots](../../../deploy-manage/tools/snapshot-and-restore/searchable-snapshots.md) stored in a remote repository to provide resiliency for your older indices while reducing operating costs and maintaining search performance.
-* Perform [asynchronous searches](../../../solutions/search/search-approaches/long-running-searches.md) of data stored on less-performant hardware.
+* Perform [asynchronous searches](../../../solutions/search/async-search-api.md) of data stored on less-performant hardware.
 
 **Data stream lifecycle** is less feature rich but is focused on simplicity. It allows you to do the following:
 

raw-migrated-files/toc.yml

@@ -270,8 +270,6 @@ toc:
       - file: docs-content/serverless/elasticsearch-ingest-data-through-api.md
       - file: docs-content/serverless/elasticsearch-ingest-your-data.md
       - file: docs-content/serverless/elasticsearch-manage-project.md
-      - file: docs-content/serverless/elasticsearch-search-your-data-the-search-api.md
-      - file: docs-content/serverless/elasticsearch-search-your-data.md
       - file: docs-content/serverless/endpoint-protection-rules.md
       - file: docs-content/serverless/general-billing-stop-project.md
       - file: docs-content/serverless/general-manage-organization.md
@@ -607,7 +605,6 @@ toc:
       - file: elasticsearch/elasticsearch-reference/scalability.md
       - file: elasticsearch/elasticsearch-reference/search-analyze.md
       - file: elasticsearch/elasticsearch-reference/search-with-synonyms.md
-      - file: elasticsearch/elasticsearch-reference/search-your-data.md
       - file: elasticsearch/elasticsearch-reference/secure-cluster.md
       - file: elasticsearch/elasticsearch-reference/secure-monitoring.md
       - file: elasticsearch/elasticsearch-reference/secure-settings.md
@@ -645,7 +642,6 @@ toc:
       - file: kibana/kibana/elasticsearch-mutual-tls.md
       - file: kibana/kibana/esql.md
       - file: kibana/kibana/get-started.md
-      - file: kibana/kibana/inference-endpoints.md
       - file: kibana/kibana/install.md
       - file: kibana/kibana/kibana-authentication.md
       - file: kibana/kibana/kibana-concepts-analysts.md

solutions/search/search-approaches/long-running-searches.md → solutions/search/async-search-api.md

@@ -1,11 +1,12 @@
 ---
 mapped_pages:
   - https://www.elastic.co/guide/en/elasticsearch/reference/current/async-search-intro.html
+navigation_title: The `async-search` API
 ---
 
-# Long-running searches [async-search-intro]
+# Long running searches with the Async Search API [async-search-intro]
 
-{{es}} generally allows you to quickly search across big amounts of data. There are situations where a search executes on many shards, possibly against large data sets or multiple [remote clusters](../../../deploy-manage/remote-clusters.md), for which results are not expected to be returned in milliseconds. When you need to execute long-running searches, synchronously waiting for its results to be returned is not ideal. Instead, Async search lets you submit a search request that gets executed *asynchronously*, monitor the progress of the request, and retrieve results at a later stage. You can also retrieve partial results as they become available but before the search has completed.
+{{es}} generally allows you to quickly search across big amounts of data. There are situations where a search executes on many shards, possibly against large data sets or multiple [remote clusters](../../deploy-manage/remote-clusters.md), for which results are not expected to be returned in milliseconds. When you need to execute long-running searches, synchronously waiting for its results to be returned is not ideal. Instead, Async search lets you submit a search request that gets executed *asynchronously*, monitor the progress of the request, and retrieve results at a later stage. You can also retrieve partial results as they become available but before the search has completed.
 
 You can submit an async search request using the [submit async search](https://www.elastic.co/guide/en/elasticsearch/reference/current/async-search.html#submit-async-search) API. The [get async search](https://www.elastic.co/guide/en/elasticsearch/reference/current/async-search.html#get-async-search) API allows you to monitor the progress of an async search request and retrieve its results. An ongoing async search can be deleted through the [delete async search](https://www.elastic.co/guide/en/elasticsearch/reference/current/async-search.html#delete-async-search) API.
 

solutions/search/elasticsearch-basics-quickstart.md

@@ -1,4 +1,4 @@
-# {{es}} API quickstart [getting-started]
+# Basics quickstart [getting-started]
 
 
 This quick start guide is a hands-on introduction to the fundamental concepts of Elasticsearch: [indices, documents and field type mappings](../../manage-data/data-store/index-basics.md).
@@ -10,8 +10,6 @@ The code examples in this tutorial are in [Console](../../explore-analyze/query-
 
 ::::
 
-
-
 ## Requirements [getting-started-requirements]
 
 You’ll need a running {{es}} cluster, together with {{kib}} to use the Dev Tools API Console. Run the following command in your terminal to set up a [single-node local cluster in Docker](get-started.md):

solutions/search/full-text.md

@@ -15,42 +15,10 @@ Built on decades of information retrieval research, full-text search delivers re
 
 You can combine full-text search with [semantic search using vectors](semantic-search.md) to build modern hybrid search applications. While vector search may require additional GPU resources, the full-text component remains cost-effective by leveraging existing CPU infrastructure.
 
-
-## How full-text search works [full-text-search-how-it-works]
-
-The following diagram illustrates the components of full-text search.
-
-:::{image} ../../images/elasticsearch-reference-full-text-search-overview.svg
-:alt: Components of full-text search from analysis to relevance scoring
-:::
-
-At a high level, full-text search involves the following:
-
-* [**Text analysis**](../../manage-data/data-store/text-analysis.md): Analysis consists of a pipeline of sequential transformations. Text is transformed into a format optimized for searching using techniques such as stemming, lowercasing, and stop word elimination. {{es}} contains a number of built-in [analyzers](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-analyzers.html) and tokenizers, including options to analyze specific language text. You can also create custom analyzers.
-
-    ::::{tip}
-    Refer to [Test an analyzer](../../manage-data/data-store/text-analysis/test-an-analyzer.md) to learn how to test an analyzer and inspect the tokens and metadata it generates.
-
-    ::::
-
-* **Inverted index creation**: After analysis is complete, {{es}} builds an inverted index from the resulting tokens. An inverted index is a data structure that maps each token to the documents that contain it. It’s made up of two key components:
-
-    * **Dictionary**: A sorted list of all unique terms in the collection of documents in your index.
-    * **Posting list**: For each term, a list of document IDs where the term appears, along with optional metadata like term frequency and position.
-
-* **Relevance scoring**: Results are ranked by how relevant they are to the given query. The relevance score of each document is represented by a positive floating-point number called the `_score`. The higher the `_score`, the more relevant the document.
-
-    The default [similarity algorithm](https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html) {{es}} uses for calculating relevance scores is [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25), a variation of the [TF-IDF algorithm](https://en.wikipedia.org/wiki/Tf–idf). BM25 calculates relevance scores based on term frequency, document frequency, and document length. Refer to this [technical blog post](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) for a deep dive into BM25.
-
-* **Full-text search query**: Query text is analyzed [the same way as the indexed text](../../manage-data/data-store/text-analysis/index-search-analysis.md), and the resulting tokens are used to search the inverted index.
-
-    Query DSL supports a number of [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html).
-
-    As of 8.17, {{esql}} also supports [full-text search](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions-operators.html#esql-search-functions) functions.
-
+## Getting started [full-text-search-getting-started]
 
 
-## Getting started [full-text-search-getting-started]
+For a high-level overview of how full-text search works, refer to [How full-text search works](full-text/how-full-text-works.md).
 
 For a hands-on introduction to full-text search, refer to the [full-text search tutorial](get-started.md).
 
@@ -80,5 +48,5 @@ Learn how to build full-text search queries using {{es}}'s query languages:
 
 For a technical deep dive into {{es}}'s BM25 implementation read this blog post: [The BM25 Algorithm and its Variables](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables).
 
-To learn how to optimize the relevance of your search results, refer to [Search relevance optimizations](examples.md).
+To learn how to optimize the relevance of your search results, refer to [Search relevance optimizations](full-text/search-relevance.md).
 

solutions/search/full-text/how-full-text-works.md

@@ -0,0 +1,30 @@
+# How full-text search works [full-text-search-how-it-works]
+
+The following diagram illustrates the components of full-text search.
+
+:::{image} ../../../images/elasticsearch-reference-full-text-search-overview.svg
+:alt: Components of full-text search from analysis to relevance scoring
+:width: 550px
+:::
+
+At a high level, full-text search involves the following:
+
+* [**Text analysis**](../../../manage-data/data-store/text-analysis.md): Analysis consists of a pipeline of sequential transformations. Text is transformed into a format optimized for searching using techniques such as stemming, lowercasing, and stop word elimination. {{es}} contains a number of built-in [analyzers](https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-analyzers.html) and tokenizers, including options to analyze specific language text. You can also create custom analyzers.
+::::{tip}
+Refer to [Test an analyzer](../../../manage-data/data-store/text-analysis/test-an-analyzer.md) to learn how to test an analyzer and inspect the tokens and metadata it generates.
+::::
+
+* **Inverted index creation**: After analysis is complete, {{es}} builds an inverted index from the resulting tokens. An inverted index is a data structure that maps each token to the documents that contain it. It’s made up of two key components:
+
+    * **Dictionary**: A sorted list of all unique terms in the collection of documents in your index.
+    * **Posting list**: For each term, a list of document IDs where the term appears, along with optional metadata like term frequency and position.
+
+* **Relevance scoring**: Results are ranked by how relevant they are to the given query. The relevance score of each document is represented by a positive floating-point number called the `_score`. The higher the `_score`, the more relevant the document.
+
+    The default [similarity algorithm](https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html) {{es}} uses for calculating relevance scores is [Okapi BM25](https://en.wikipedia.org/wiki/Okapi_BM25), a variation of the [TF-IDF algorithm](https://en.wikipedia.org/wiki/Tf–idf). BM25 calculates relevance scores based on term frequency, document frequency, and document length. Refer to this [technical blog post](https://www.elastic.co/blog/practical-bm25-part-2-the-bm25-algorithm-and-its-variables) for a deep dive into BM25.
+
+* **Full-text search query**: Query text is analyzed [the same way as the indexed text](../../../manage-data/data-store/text-analysis/index-search-analysis.md), and the resulting tokens are used to search the inverted index.
+
+    Query DSL supports a number of [full-text queries](https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html).
+
+    As of 8.17, {{esql}} also supports [full-text search](https://www.elastic.co/guide/en/elasticsearch/reference/current/esql-functions-operators.html#esql-search-functions) functions.

solutions/search/full-text/search-relevance.md

@@ -0,0 +1,15 @@
+---
+mapped_pages:
+  - https://www.elastic.co/guide/en/elasticsearch/reference/current/recipes.html
+---
+
+# Search relevance optimizations
+
+This section includes a few recipes to help with common search relevance issues:
+
+* [Mixing exact search with stemming](search-relevance/mixing-exact-search-with-stemming.md)
+* [Getting consistent scores](search-relevance/consistent-scoring.md)
+* [Incorporating static relevance signals into the score](search-relevance/static-scoring-signals.md)
+
+
+