Add vector search documentation

_config.yml

@@ -124,6 +124,9 @@ collections:
   workspace:
     permalink: /:collection/:path/
     output: true
+  vector-search:
+    permalink: /:collection/:path/
+    output: true
 
 opensearch_collection:
   # Define the collections used in the theme
@@ -173,6 +176,9 @@ opensearch_collection:
     search-plugins:
       name: Search features
       nav_fold: true
+    vector-search:
+      name: Vector search 
+      nav_fold: true
     ml-commons-plugin:
       name: Machine learning
       nav_fold: true

_field-types/supported-field-types/knn-vector.md

@@ -1,17 +1,17 @@
 ---
 layout: default
 title: k-NN vector
-nav_order: 58
+nav_order: 20
 has_children: false
 parent: Supported field types
 has_math: true
 ---
 
-# k-NN vector field type
+# k-NN vector
 **Introduced 1.0**
 {: .label .label-purple }
 
-The [k-NN plugin]({{site.url}}{{site.baseurl}}/search-plugins/knn/index/) introduces a custom data type, the `knn_vector`, that allows users to ingest their k-NN vectors into an OpenSearch index and perform different kinds of k-NN search. The `knn_vector` field is highly configurable and can serve many different k-NN workloads. In general, a `knn_vector` field can be built either by providing a method definition or specifying a model id.
+The `knn_vector` data type allows you to ingest vectors into an OpenSearch index and perform different kinds of vector search. The `knn_vector` field is highly configurable and can serve many different vector workloads. In general, a `knn_vector` field can be built either by providing a method definition or specifying a model id.
 
 ## Example
 
@@ -53,7 +53,7 @@
 | `in_memory` (Default) | `nmslib`       | Prioritizes low-latency search. This mode uses the `nmslib` engine without any quantization applied. It is configured with the default parameter values for vector search in OpenSearch.                                                            |
 | `on_disk`             | `faiss`        | Prioritizes low-cost vector search while maintaining strong recall. By default, the `on_disk` mode uses quantization and rescoring to execute a two-pass approach to retrieve the top neighbors. The `on_disk` mode supports only `float` vector types. |
 
-To create a k-NN index that uses the `on_disk` mode for low-cost search, send the following request:
+To create a vector index that uses the `on_disk` mode for low-cost search, send the following request:
 
 ```json
 PUT test-index
@@ -130,7 +130,7 @@
 
 ## Method definitions
 
-[Method definitions]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index#method-definitions) are used when the underlying [approximate k-NN]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/) algorithm does not require training. For example, the following `knn_vector` field specifies that *nmslib*'s implementation of *hnsw* should be used for approximate k-NN search. During indexing, *nmslib* will build the corresponding *hnsw* segment files.
+[Method definitions]({{site.url}}{{site.baseurl}}/vector-search/creating-vector-index/method/) are used when the underlying [approximate k-NN]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/) algorithm does not require training. For example, the following `knn_vector` field specifies that NMSLIB's implementation of HNSW should be used for approximate k-NN search. During indexing, NMSLIB will build the corresponding HNSW segment files.
 
 ```json
 "my_vector": {
@@ -150,7 +150,7 @@
 
 ## Model IDs
 
-Model IDs are used when the underlying Approximate k-NN algorithm requires a training step. As a prerequisite, the model must be created with the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-a-model). The
+Model IDs are used when the underlying approximate k-NN algorithm requires a training step. As a prerequisite, the model must be created with the [Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-a-model). The
 model contains the information needed to initialize the native library segment files.
 
 ```json
@@ -180,7 +180,7 @@
 When using `byte` vectors, expect some loss of precision in the recall compared to using `float` vectors. Byte vectors are useful in large-scale applications and use cases that prioritize a reduced memory footprint in exchange for a minimal loss of recall.
 {: .important}
 
-When using `byte` vectors with the `faiss` engine, we recommend using [SIMD optimization]({{site.url}}{{site.baseurl}}/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), which helps to significantly reduce search latencies and improve indexing throughput.
+When using `byte` vectors with the `faiss` engine, we recommend using [SIMD optimization]({{site.url}}{{site.baseurl}}/vector-search/creating-vector-index/vector-field/#simd-optimization-for-the-faiss-engine), which helps to significantly reduce search latencies and improve indexing throughput.
 {: .important} 
 
 Introduced in k-NN plugin version 2.9, the optional `data_type` parameter defines the data type of a vector. The default value of this parameter is `float`.

_includes/cards.html

@@ -1,43 +1,17 @@
 <div class="card-container-wrapper">
-  <p class="heading-main">Explore OpenSearch documentation</p>
-  <div class="card-container">
-    <div class="card">
-      <a href="{{site.url}}{{site.baseurl}}/about/" class='card-link'></a>
-        <p class="heading">OpenSearch and OpenSearch Dashboards</p>
-        <p class="description">Build your OpenSearch solution using core tooling and visualizations</p>
-        <p class="last-link">Documentation &#x2192;</p>
-      </div>
-
-
-    <div class="card">
-      <a href="{{site.url}}/docs/latest/data-prepper/" class='card-link'></a>
-        <p class="heading">OpenSearch Data Prepper</p>
-        <p class="description">Filter, mutate, and sample your data for ingestion into OpenSearch</p>
-        <p class="last-link" >Documentation &#x2192;</p>
-      </div>
-
-    <div class="card">
-      <a href="{{site.url}}/docs/latest/clients/" class='card-link'></a>
-        <p class="heading">Clients</p>
-        <p class="description">Interact with OpenSearch from your application using language APIs</p>
-        <p class="last-link">Documentation &#x2192;</p>
-      </div>
-
-
-    <div class="card">
-      <a href="{{site.url}}/docs/latest/benchmark/" class='card-link'></a>
-        <p class="heading">OpenSearch Benchmark</p>
-        <p class="description">Measure performance metrics for your OpenSearch cluster</p>
-        <p class="last-link">Documentation &#x2192;</p>
-      </div>  
-
-    <div class="card">
-      <a href="{{site.url}}/docs/latest/migration-assistant/" class='card-link'></a>
-        <p class="heading">Migration Assistant</p>
-        <p class="description">Migrate to OpenSearch</p>
-        <p class="last-link">Documentation &#x2192;</p>
-      </div>  
+    <div class="card-container">
+      {% for card in include.cards %}
+        <div class="card">
+          <a href="{{ site.url }}{{ site.baseurl }}{{ card.link }}" class="card-link"></a>
+          <p class="heading">{{ card.heading }}</p>
+          {% if card.description %}
+            <p class="description">{{ card.description }}</p>
+          {% endif %}
+          {% if include.documentation_link %}
+            <p class="last-link">Documentation &#x2192;</p>
+          {% endif %}
+        </div>
+      {% endfor %}
+    </div>
   </div>
-
-</div>
-
+

_includes/home_cards.html

@@ -0,0 +1,72 @@
+<div class="home-card-container-wrapper">
+  <p class="heading-main">OpenSearch and OpenSearch Dashboards</p>
+  <div class="home-card-container">
+    <div class="home-card">
+      <a href="{{site.url}}{{site.baseurl}}/about/" class='card-link'></a>
+        <p class="heading">All documentation</p>
+        <p class="description">Build your OpenSearch solution using core tooling and visualizations.</p>
+        <p class="last-link">Documentation &#x2192;</p>
+      </div>
+
+
+    <div class="home-card">
+      <a href="{{site.url}}{{site.baseurl}}/vector-search/" class='card-link'></a>
+        <p class="heading">Vector search</p>
+        <p class="description">Use vector database capabilities for more relevant search results.</p>
+        <p class="last-link" >Documentation &#x2192;</p>
+      </div>
+
+    <div class="home-card">
+      <a href="{{site.url}}{{site.baseurl}}/ml-commons-plugin/" class='card-link'></a>
+        <p class="heading">Machine learning</p>
+        <p class="description">Power your applications with machine learning model integration.</p>
+        <p class="last-link">Documentation &#x2192;</p>
+      </div>
+
+
+    <div class="home-card">
+      <a href="{{site.url}}{{site.baseurl}}/dashboards/" class='card-link'></a>
+        <p class="heading">OpenSearch Dashboards</p>
+        <p class="description">Explore and visualize your data using interactive dashboards.</p>
+        <p class="last-link">Documentation &#x2192;</p>
+      </div>  
+  </div>
+
+</div>
+
+<div class="home-card-container-wrapper">
+  <p class="heading-main">Supporting tools</p>
+  <div class="home-card-container">
+
+    <div class="home-card">
+      <a href="{{site.url}}/docs/latest/data-prepper/" class='card-link'></a>
+        <p class="heading">Data Prepper</p>
+        <p class="description">Filter, mutate, and sample your data for ingestion into OpenSearch.</p>
+        <p class="last-link" >Documentation &#x2192;</p>
+      </div>
+
+    <div class="home-card">
+      <a href="{{site.url}}/docs/latest/clients/" class='card-link'></a>
+        <p class="heading">Clients</p>
+        <p class="description">Interact with OpenSearch from your application using language APIs.</p>
+        <p class="last-link">Documentation &#x2192;</p>
+      </div>
+
+
+    <div class="home-card">
+      <a href="{{site.url}}/docs/latest/benchmark/" class='card-link'></a>
+        <p class="heading">OpenSearch Benchmark</p>
+        <p class="description">Measure performance metrics for your OpenSearch cluster.</p>
+        <p class="last-link">Documentation &#x2192;</p>
+      </div>  
+
+    <div class="home-card">
+      <a href="{{site.url}}/docs/latest/migration-assistant/" class='card-link'></a>
+        <p class="heading">Migration Assistant</p>
+        <p class="description">Migrate to OpenSearch.</p>
+        <p class="last-link">Documentation &#x2192;</p>
+      </div>  
+  </div>
+
+</div>
+

_includes/list.html

@@ -0,0 +1,22 @@
+<div class="numbered-list">
+  {% if include.list_title %}
+    <div class="heading">{{ include.list_title }}</div>
+  {% endif %}
+    {% assign counter = 0 %}
+    {% for item in include.list_items %}
+      {% assign counter = counter | plus: 1 %}
+      <div class="list-item">
+        <div class="number-circle">{{ counter }}</div>
+        <div class="list-content">
+          <div class="list-heading">
+            {% if item.link %}
+              <a href="{{ site.url }}{{ site.baseurl }}{{ item.link }}">{{ item.heading }}</a>
+            {% else %}
+              {{ item.heading }}
+            {% endif %} 
+          </div>
+          <p class="description">{{ item.description | markdownify }}</p>
+        </div>
+      </div>
+    {% endfor %}
+</div>

_ml-commons-plugin/custom-local-models.md

@@ -320,7 +320,7 @@ The response contains the tokens and weights:
 
 ## Step 5: Use the model for search
 
-To learn how to use the model for vector search, see [Using an ML model for neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/#using-an-ml-model-for-neural-search).
+To learn how to use the model for vector search, see [ML-powered search methods]({{site.url}}{{site.baseurl}}/vector-search/ml-powered-search/index/#ml-powered-search-methods).
 
 ## Question answering models
 

_ml-commons-plugin/remote-models/index.md

@@ -323,7 +323,7 @@ To learn how to use the model for batch ingestion in order to improve ingestion
 
 ## Step 7: Use the model for search
 
-To learn how to use the model for vector search, see [Using an ML model for neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/#using-an-ml-model-for-neural-search).
+To learn how to use the model for vector search, see [ML-powered search methods]({{site.url}}{{site.baseurl}}/vector-search/ml-powered-search/index/#ml-powered-search-methods).
 
 ## Step 8 (Optional): Undeploy the model 
 

_sass/_home.scss

@@ -22,11 +22,16 @@
 
 // Card style
 
-.card-container-wrapper {
+.home-card-container-wrapper {
     @include gradient-open-sky;
+    margin-bottom: 2rem;
 }
 
-.card-container {
+.card-container-wrapper {
+    margin-bottom: 0;
+}
+
+.home-card-container {
     display: grid;
     grid-template-columns: 1fr;
     margin: 0 auto;
@@ -42,11 +47,27 @@
     }
 }
 
-.card {
+.card-container {
+    display: grid;
+    grid-template-columns: 1fr;
+    margin: 0 auto;
+    padding: 2rem 0;
+    grid-row-gap: 1rem;
+    grid-column-gap: 1rem;
+    grid-auto-rows: 1fr;
+    @include mq(md) {
+        grid-template-columns: repeat(1, 1fr);
+    }
+    @include mq(lg) {
+        grid-template-columns: repeat(2, 1fr);
+    }
+}
+
+.home-card {
     @extend .panel;
     @include thick-edge-left;
     padding: 1rem;
-    margin-bottom: 4rem;
+    margin-bottom: 2rem;
     text-align: left;
     background-color: white;
     display: flex;
@@ -67,6 +88,11 @@
     }
 }
 
+.card {
+    @extend .home-card;
+    margin-bottom: 0;
+}
+
 @mixin heading-font {
     @include heading-sans-serif;
     font-size: 1.5rem;
@@ -110,6 +136,53 @@
     width: 100%;
 }
 
+// List layout
+
+.numbered-list {
+    display: flex;
+    flex-direction: column;
+    gap: 2rem;
+    padding: 1rem;
+}
+
+.list-item {
+    display: flex;
+    align-items: flex-start;
+    gap: 1rem;
+}
+
+.number-circle {
+    width: 2.5rem;
+    height: 2.5rem;
+    border-radius: 50%;
+    background-color: $blue-lt-100; 
+    color: $blue-dk-300; 
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-weight: bold;
+    font-size: 1.2rem;
+    flex-shrink: 0;
+}
+
+.list-content {
+    max-width: 100%;
+}
+
+.list-heading {
+    @include heading-font; 
+    margin: 0 0 0.75rem 0; 
+    font-size: 1.2rem;
+    color: $blue-dk-300;
+    font-weight: bold;
+}
+
+.list-content p {
+    margin: 0.5rem 0;
+    font-size: 1rem;
+    line-height: 1.5;
+}
+
 // Banner style
 
 .os-banner {