Restructure the search section

_config.yml

@@ -184,7 +184,7 @@ opensearch_collection:
       name: Aggregations
       nav_fold: true
     search-plugins:
-      name: Search features
+      name: Search
       nav_fold: true
     vector-search:
       name: Vector search 

_search-plugins/async/index.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Asynchronous search
-nav_order: 51
+nav_order: 40
 parent: Improving search performance
 has_children: true
 redirect_from:

_search-plugins/caching/index.md

@@ -3,7 +3,7 @@ layout: default
 title: Caching
 parent: Improving search performance
 has_children: true
-nav_order: 100
+nav_order: 10
 redirect_from:
   - /search-plugins/caching/
 ---
@@ -18,9 +18,9 @@ Understanding how your data uses the cache can help improve your cluster's perfo
 
 OpenSearch supports the following on-heap cache types:
 
-- [**Index request cache**]({{site.url}}{{site.baseurl}}/search-plugins/caching/request-cache/): Caches the local results on each shard. This allows frequently used and potentially resource-heavy search requests to return results almost instantaneously. 
+- [**Index request cache**]({{site.url}}{{site.baseurl}}/search-plugins/caching/request-cache/): Caches the local results on each shard. This allows frequently used and potentially resource-heavy search requests to return results almost instantaneously.
 - **Query cache**: Caches common data from similar queries at the shard level. The query cache is more granular than the request cache and can cache data to be reused in different queries.
-- **Field data cache**: Caches field data and global ordinals, which are both used to support aggregations on certain field types.
+- [**Field data cache**]({{site.url}}{{site.baseurl}}/search-plugins/caching/field-data-cache/): Caches field data and global ordinals, which are both used to support aggregations on certain field types.
 
 ## Additional cache stores
 

_search-plugins/concurrent-segment-search.md

@@ -2,7 +2,7 @@
 layout: default
 title: Concurrent segment search
 parent: Improving search performance
-nav_order: 53
+nav_order: 20
 ---
 
 # Concurrent segment search

_search-plugins/cross-cluster-search.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Cross-cluster search
-nav_order: 65
+nav_order: 230
 redirect_from:
  - /security/access-control/cross-cluster-search/
  - /security-plugin/access-control/cross-cluster-search/

_search-plugins/filter-search.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Filter results
-parent: Search options
+parent: Customizing search results
 nav_order: 36
 ---
 

_search-plugins/improving-search-performance.md

@@ -3,14 +3,19 @@ layout: default
 title: Improving search performance
 nav_order: 220
 has_children: true
+has_toc: false
 ---
 
 # Improving search performance
 
-OpenSearch offers several ways to improve search performance:
+OpenSearch offers several ways to improve search performance, from foundational optimizations to specialized techniques:
 
-- Run resource-intensive queries asynchronously with [asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/).
+- Store frequently accessed data in memory with [caching]({{site.url}}{{site.baseurl}}/search-plugins/caching/) for faster retrieval.
 
-- Search segments concurrently using [concurrent segment search]({{site.url}}{{site.baseurl}}/search-plugins/concurrent-segment-search/).
+- Search segments concurrently using [concurrent segment search]({{site.url}}{{site.baseurl}}/search-plugins/concurrent-segment-search/) for better resource utilization.
 
-- Improve aggregation performance using a [star-tree index]({{site.url}}{{site.baseurl}}/search-plugins/star-tree-index/).
+- Control shard selection for optimized query routing with [search shard routing]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/search-shard-routing/).
+
+- Run resource-intensive queries asynchronously with [asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/) to avoid timeouts.
+
+- Improve aggregation performance using a [star-tree index]({{site.url}}{{site.baseurl}}/search-plugins/star-tree-index/) for analytical workloads.

_search-plugins/index.md

@@ -1,98 +1,64 @@
 ---
 layout: default
-title: Search features
+title: Search
 nav_order: 1
 has_children: false
 has_toc: false
 nav_exclude: true
 permalink: /search-plugins/
 redirect_from:
   - /search-plugins/index/
-keyword:
+search_methods:
   - heading: "Keyword (BM25) search"
-    description: "Find exact and close matches using traditional text search"
+    description: "Lexical text search using the BM25 algorithm to match and rank documents based on term frequency and document length. Find exact and close matches using traditional text search."
     link: "/search-plugins/keyword-search/"
-vector:
   - heading: "Vector search"
-    description: "Search by similarity using dense or sparse vector embeddings"
+    description: "Similarity (k-nearest neighbor) search using dense and sparse vector embeddings to power semantic search, retrieval-augmented generation, and multimodal image search."
     link: "/vector-search/"
-ai:
   - heading: "AI search"
-    description: "Build intelligent search applications using AI models"
+    description: "AI-powered search capabilities beyond vector embeddings. Enrich search and ingestion flows with any AI service to power the full range of AI-enhanced search use cases."
     link: "/vector-search/ai-search/"
 ---
 
-# Search features
+# Search
 
 OpenSearch provides many features for customizing your search use cases and improving search relevance. 
 
 ## Search methods
 
-OpenSearch supports the following search methods.
+OpenSearch supports multiple search methods to meet different use cases and requirements.
 
-### Exact matching and keywords
-
-OpenSearch implements lexical (keyword) text search using the BM25 algorithm to match and rank documents based on term frequency and document length.
-
-{% include cards.html cards=page.keyword %}
-
-### Similarity and meaning
-
-OpenSearch supports similarity (k-nearest neighbor) search using dense and sparse vector embeddings to power use cases such as semantic search, retrieval-augmented generation, and multimodal image search. 
-
-{% include cards.html cards=page.vector %}
-
-### AI-powered search
-
-OpenSearch supports AI-powered search capabilities beyond vector embeddings. OpenSearch's AI search enables search and ingestion flows to be enriched by any AI service to power the full range of AI-enhanced search use cases.
-
-{% include cards.html cards=page.ai %}
+{% include cards.html cards=page.search_methods %}
 
 ## Query languages
 
-In OpenSearch, you can use the following query languages to search your data:
-
-- [Query domain-specific language (DSL)]({{site.url}}{{site.baseurl}}/query-dsl/index/): The primary OpenSearch query language that supports creating complex, fully customizable queries.
+In OpenSearch, you can use the following query languages to search your data.
 
-- [Query string query language]({{site.url}}{{site.baseurl}}/query-dsl/full-text/query-string/): A scaled-down query language that you can use in a query parameter of a search request or in OpenSearch Dashboards.
+Language | Where you can use it | Description
+:--- | :--- | :---
+[Query domain-specific language (DSL)]({{site.url}}{{site.baseurl}}/query-dsl/index/) | Search API, Dev Tools | The primary OpenSearch query language that supports creating complex, fully customizable queries.
+[Query string query language]({{site.url}}{{site.baseurl}}/query-dsl/full-text/query-string/) | Search API `q` parameter, Discover and Dashboard search bar | A scaled-down query language whose syntax is based on Apache Lucene.
+[SQL]({{site.url}}{{site.baseurl}}/search-plugins/sql/sql/index/) | SQL API, Query Workbench | A traditional query language that bridges the gap between relational database concepts and the flexibility of OpenSearch's document-oriented data storage.
+[Piped Processing Language (PPL)]({{site.url}}{{site.baseurl}}/search-plugins/sql/ppl/index/) | PPL API, Query Workbench | The primary language used with observability in OpenSearch. PPL uses a pipe syntax that chains commands into a query.
 
-- [SQL]({{site.url}}{{site.baseurl}}/search-plugins/sql/sql/index/): A traditional query language that bridges the gap between traditional relational database concepts and the flexibility of OpenSearch’s document-oriented data storage.
+For information about querying data in OpenSearch Dashboards, including the Dashboards Query Language (DQL), see [OpenSearch Dashboards query languages]({{site.url}}{{site.baseurl}}/dashboards/#query-languages).
 
-- [Piped Processing Language (PPL)]({{site.url}}{{site.baseurl}}/search-plugins/sql/ppl/index/): The primary language used with observability in OpenSearch. PPL uses a pipe syntax that chains commands into a query.
+## Customizing search results
 
-- [Dashboards Query Language (DQL)]({{site.url}}{{site.baseurl}}/dashboards/dql/): A simple text-based query language for filtering data in OpenSearch Dashboards. 
+OpenSearch provides fundamental result handling capabilities that work with all search types.  You can customize result navigation (pagination, sorting), result formatting (highlighting, field selection), query enhancement (autocomplete, did-you-mean), and result filtering. For more information, see [Customizing search results]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/).
 
-## Search performance
+## Optimizing search quality
 
-OpenSearch offers several ways to improve search performance:
+OpenSearch provides comprehensive tools and features to help you measure, analyze, and improve the quality of your search results. These integrated features work together to optimize search relevance based on user behavior and machine learning. For more information, see [Optimizing search quality]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/).
 
-- [Asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/): Runs resource-intensive queries asynchronously.
-
-- [Concurrent segment search]({{site.url}}{{site.baseurl}}/search-plugins/concurrent-segment-search/): Searches segments concurrently.
-
-## Search relevance
-
-*Search relevance* is a measure of how well a document matches a search query. When you run a search query, OpenSearch compares the words in your query to the words in each document and assigns a relevance score based on factors such as how frequently the words appear and how closely they match. For more information, see [Relevance]({{site.url}}{{site.baseurl}}/getting-started/intro/#relevance).
-
-To help you fine-tune and improve search relevance, OpenSearch provides several specialized features:
-
-- [Search Relevance Workbench]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/using-search-relevance-workbench/): A suite of tools that support search quality improvements through experimentation. 
-
-- [Querqy]({{site.url}}{{site.baseurl}}/search-plugins/querqy/): Offers query rewriting capability.
+## Search pipelines
 
-- [User Behavior Insights]({{site.url}}{{site.baseurl}}/search-plugins/ubi/): Links user behavior to user queries to improve search quality.
-
-## Search results
+Search pipelines are the foundational infrastructure that enables OpenSearch's AI and vector search capabilities. They provide modular processors that can transform queries (text-to-vector conversion, ML inference, query rewriting), enhance results (reranking, RAG, field manipulation), and orchestrate complex AI workflows. For more information, see [Search pipelines]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/).
 
-OpenSearch supports the following commonly used operations on search results:
+## Improving search performance
 
-- [Paginate]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/paginate/)
-- [Paginate with Point in Time]({{site.url}}{{site.baseurl}}/search-plugins/point-in-time/)
-- [Sort]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/sort/)
-- [Highlight search terms]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/highlight/) 
-- [Autocomplete]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/autocomplete/)
-- [Did-you-mean]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/did-you-mean/) 
+OpenSearch provides various features to optimize search performance from foundational caching techniques to specialized optimizations. For more information, see [Improving search performance]({{site.url}}{{site.baseurl}}/search-plugins/improving-search-performance/).
 
-## Search pipelines
+## Cross-cluster search
 
-You can process search queries and search results with [search pipelines]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/index/).
+OpenSearch supports searching across multiple clusters to scale your search infrastructure for large deployments. For more information, see [Cross-cluster search]({{site.url}}{{site.baseurl}}/search-plugins/cross-cluster-search/).

_search-plugins/ltr/advanced-functionality.md

@@ -3,6 +3,7 @@ layout: default
 title: Advanced functionality
 nav_order: 80
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/core-concepts.md

@@ -3,6 +3,7 @@ layout: default
 title: ML ranking core concepts
 nav_order: 10
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/faq.md

@@ -3,6 +3,7 @@ layout: default
 title: Common issues
 nav_order: 1000
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/feature-engineering.md

@@ -3,6 +3,7 @@ layout: default
 title: Feature engineering
 nav_order: 40
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/fits-in.md

@@ -3,6 +3,7 @@ layout: default
 title: Scope of the plugin
 nav_order: 20
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/index.md

@@ -1,7 +1,8 @@
 ---
 layout: default
 title: Learning to Rank
-nav_order: 20
+parent: Optimizing search quality
+nav_order: 40
 has_children: true
 has_toc: false
 redirect_from:
@@ -22,3 +23,20 @@
 - If you are new to LTR, start with the [ML ranking core concepts]({{site.url}}{{site.baseurl}}/search-plugins/ltr/core-concepts/) documentation.
 - For a quick introduction, see the demo in [hello-ltr](https://github.com/o19s/hello-ltr).
 - If you are familiar with LTR, start with the [Integrating the plugin]({{site.url}}{{site.baseurl}}/search-plugins/ltr/fits-in/) documentation.
+
+## Documentation
+
+### Core concepts and setup
+- [Core concepts]({{site.url}}{{site.baseurl}}/search-plugins/ltr/core-concepts/): Understand the fundamental concepts behind Learning to Rank.
+- [Fits in]({{site.url}}{{site.baseurl}}/search-plugins/ltr/fits-in/): Learn how LTR integrates with your OpenSearch infrastructure.
+
+### Feature engineering and model development
+- [Feature engineering]({{site.url}}{{site.baseurl}}/search-plugins/ltr/feature-engineering/): Design effective features for your ranking models.
+- [Working with features]({{site.url}}{{site.baseurl}}/search-plugins/ltr/working-with-features/): Create and manage feature sets.
+- [Logging features]({{site.url}}{{site.baseurl}}/search-plugins/ltr/logging-features/): Collect feature data for model training.
+- [Training models]({{site.url}}{{site.baseurl}}/search-plugins/ltr/training-models/): Build and train your ranking models.
+
+### Deployment and advanced topics
+- [Searching with your model]({{site.url}}{{site.baseurl}}/search-plugins/ltr/searching-with-your-model/): Deploy models in production search.
+- [Advanced functionality]({{site.url}}{{site.baseurl}}/search-plugins/ltr/advanced-functionality/): Explore advanced LTR features and techniques.
+- [FAQ]({{site.url}}{{site.baseurl}}/search-plugins/ltr/faq/): Common questions and troubleshooting.

_search-plugins/ltr/logging-features.md

@@ -3,6 +3,7 @@ layout: default
 title: Logging feature scores
 nav_order: 50
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/searching-with-your-model.md

@@ -3,6 +3,7 @@ layout: default
 title: Optimizing search with LTR
 nav_order: 70
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/training-models.md

@@ -3,6 +3,7 @@ layout: default
 title: Uploading trained models
 nav_order: 60
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/ltr/working-with-features.md

@@ -3,6 +3,7 @@ layout: default
 title: Working with features
 nav_order: 30
 parent: Learning to Rank
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/querqy/index.md

@@ -2,11 +2,11 @@
 layout: default
 title: Querqy
 parent: Query rewriting
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 has_children: false
 redirect_from:
   - /search-plugins/querqy/
-nav_order: 210
+nav_order: 60
 ---
 
 # Querqy

_search-plugins/search-pipelines/index.md

@@ -72,6 +72,10 @@ Alternatively, you can use a temporary pipeline with a request or set a default
 
 To learn about retrieving details for an existing search pipeline, see [Retrieving search pipelines]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/retrieving-search-pipeline/).
 
+For information about troubleshooting search pipelines, see [Debugging search pipelines]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/debugging-search-pipeline/).
+
+To delete an existing search pipeline, see [Deleting search pipelines]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/deleting-search-pipeline/).
+
 ## Manual and automatic processor creation
 
 Search processors can be created manually or automatically:

_search-plugins/search-relevance/compare-query-sets.md

@@ -3,7 +3,7 @@ layout: default
 title: Comparing query sets
 nav_order: 12
 parent: Search Relevance Workbench
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/search-relevance/compare-search-results.md

@@ -3,7 +3,7 @@ layout: default
 title: Comparing single queries
 nav_order: 10
 parent: Search Relevance Workbench
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/search-relevance/comparing-search-results.md

@@ -3,7 +3,7 @@ layout: default
 title: Comparing search results
 nav_order: 11
 parent: Search Relevance Workbench
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 ---
 
 # Comparing search results

_search-plugins/search-relevance/evaluate-search-quality.md

@@ -3,7 +3,7 @@ layout: default
 title: Evaluating search quality
 nav_order: 50
 parent: Search Relevance Workbench
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 has_children: false
 ---
 

_search-plugins/search-relevance/experiments.md

@@ -3,7 +3,7 @@ layout: default
 title: Experiments
 nav_order: 9
 parent: Search Relevance Workbench
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 has_children: false
 has_toc: false
 ---

_search-plugins/search-relevance/explore-experiment-results.md

@@ -3,7 +3,7 @@ layout: default
 title: Exploring search evaluation results
 nav_order: 65
 parent: Search Relevance Workbench
-grand_parent: Search relevance
+grand_parent: Optimizing search quality
 has_children: false
 ---