RDoc-3605 [Node.js] Data types for vector search

docs/ai-integration/vector-search/content/_data-types-for-vector-search-csharp.mdx

@@ -2,6 +2,8 @@ import Admonition from '@theme/Admonition';
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import CodeBlock from '@theme/CodeBlock';
+import ContentFrame from '@site/src/components/ContentFrame';
+import Panel from '@site/src/components/Panel';
 
 <Admonition type="note" title="">
 
@@ -19,7 +21,7 @@ import CodeBlock from '@theme/CodeBlock';
 
 </Admonition>
 
-## Supported data types for vector search
+<Panel heading="Supported data types for vector search">
 
 ### Textual data
 
@@ -83,7 +85,9 @@ you can also use lists (for example, `List<float>` or `List<float[]>`) for dynam
 
 </Admonition>
 
-## RavenVector
+</Panel>
+
+<Panel heading="RavenVector">
 
 RavenVector is RavenDB's dedicated data type for storing and querying **numerical embeddings**.   
 It is highly optimized to minimize storage space and improve the speed of reading arrays from disk,  
@@ -126,3 +130,5 @@ For example:
 </Tabs>
 
 ![json document](../assets/json-document.png)
+
+</Panel>

docs/ai-integration/vector-search/content/_data-types-for-vector-search-nodejs.mdx

@@ -0,0 +1,156 @@
+import Admonition from '@theme/Admonition';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import CodeBlock from '@theme/CodeBlock';
+import ContentFrame from '@site/src/components/ContentFrame';
+import Panel from '@site/src/components/Panel';
+
+<Admonition type="note" title="">
+
+* Data for vector search can be stored in **raw** or **pre-quantized** formats using several data types,  
+  as outlined below.
+
+* Text and numerical data that is not pre-quantized can be further quantized in the generated embeddings.  
+  Learn more in [Quantization options](../../../ai-integration/vector-search/vector-search-using-dynamic-query.mdx#quantization-options).
+
+* In this article:
+  * [Supported data types for vector search](../../../ai-integration/vector-search/data-types-for-vector-search.mdx#supported-data-types-for-vector-search)
+      * [Textual data](../../../ai-integration/vector-search/data-types-for-vector-search.mdx#textual-data)
+      * [Numerical data](../../../ai-integration/vector-search/data-types-for-vector-search.mdx#numerical-data) 
+  * [RavenVector](../../../ai-integration/vector-search/data-types-for-vector-search.mdx#ravenvector)
+
+</Admonition>
+
+<Panel heading="Supported data types for vector search">
+
+### Textual data
+
+<Admonition type="note" title="">
+
+`string` - A single text entry.  
+`string[]` - An array of text entries.
+
+</Admonition>
+
+### Numerical data
+
+* You can store **pre-generated** embedding vectors in your documents,  
+  typically created by machine-learning models from text, images, or other sources.
+
+* When storing numerical embeddings in a document field:  
+  * Ensure that all vectors within this field across all documents in the collection are generated by the **same model** and model version and have the **same dimensions**.
+  * Consistency in both dimensionality and model source is crucial for meaningful comparisons in the vector space.
+
+* In addition to the native types described below, we highly recommended using [RavenVector](../../../ai-integration/vector-search/data-types-for-vector-search.mdx#ravenvector)  
+  for efficient storage and fast queries when working with numerical embeddings.
+
+<Admonition type="note" title="">
+
+**Raw embedding data**:  
+Use when precision is critical.  
+
+`number[]` - A single vector of numerical values representing raw embedding data.  
+`number[][]`- An array of vectors, where each entry is a separate embedding vector.  
+
+</Admonition>
+
+<Admonition type="note" title="">
+
+**Pre-quantized data**:   
+Use when you prioritize storage efficiency and query speed.  
+
+`number[]` - A single pre-quantized embedding vector in the _Int8_ or _Binary_ quantization format.  
+`number[][]` - An array of pre-quantized embedding vectors.  
+
+When storing data in these formats in your documents, you should use [RavenDB’s vector quantizer methods](../../../ai-integration/vector-search/vector-search-using-dynamic-query.mdx#section-1).
+
+</Admonition>
+
+<Admonition type="note" title="">
+
+**Base64-encoded data**:  
+Use when embedding data needs to be represented as a compact and easily serializable string format.
+
+`string` - A single vector encoded as a Base64 string.    
+`string[]` - An array of Base64-encoded vectors.    
+
+</Admonition>
+
+</Panel>
+
+<Panel heading="RavenVector">
+
+* `RavenVector` is a helper function that wraps a numerical array into a dedicated `@vector` object in RavenDB.  
+  This structure is highly efficient - it minimizes disk space usage and improves read performance,  
+  making it ideal for both storing and querying embeddings.    
+
+* `RavenVector` is purely structural - it does not apply any transformation or validation to the vector,  
+  it simply ensures that the vector is stored under the `@vector` field in the JSON document.  
+  All vector processing and comparisons are handled entirely by the server.    
+
+---
+
+#### Example: storing a vector using RavenVector
+
+<Tabs groupId='languageSyntax'>
+<TabItem value="Store_document" label="Store_document">
+```js
+const session = documentStore.openSession();
+
+const user = new User();
+
+// Store embedding in a 'RavenVector' format
+user.EmbeddingRavenVector = RavenVector([
+        6.599999904632568,
+        7.699999809265137
+    ]);
+
+// Store embedding in a raw numerical array
+// This won’t take advantage of RavenDB’s optimized storage format
+user.EmbeddingVector = [
+        6.599999904632568,
+        7.699999809265137
+    ];
+
+await session.store(user, "users/1");
+await session.saveChanges();
+```
+</TabItem>
+<TabItem value="Class" label="Class">
+```js
+class User {
+    constructor(embeddingRavenVector, embeddingVector) {
+        this.embeddingRavenVector = embeddingRavenVector;
+        this.embeddingVector = embeddingVector;
+    }
+}
+```
+</TabItem>
+</Tabs>
+
+When stored, the vector's content created with the `RavenVector` function will appear under the `@vector` field in the JSON document:
+
+![json document](../assets/json-document-nodejs.png)
+
+To query the `embeddingRavenVector field`, use:
+
+<TabItem value="" label="">
+```js
+const similarUsers = await session.query({ collection: "Users" })
+    .vectorSearch(
+        field => field.withEmbedding("embeddingRavenVector"),
+
+        queryVector => queryVector.byEmbedding(
+            // Use 'RavenVector' to wrap the query vector
+            RavenVector([
+                6.599999904632568,
+                7.699999809265137
+            ])
+        )
+    )
+    .all();
+```
+</TabItem>
+
+</Panel>
+

docs/ai-integration/vector-search/content/_vector-search-using-dynamic-query-nodejs.mdx

@@ -1247,15 +1247,15 @@ factory.byBase64(base64Embedding);
 
 #### `RavenVector`:  
 
-RavenVector is RavenDB’s dedicated representation for storing and querying numerical embeddings.
+RavenVector is RavenDB’s dedicated representation for storing and querying numerical embeddings.  
 Learn more in [RavenVector](../../../ai-integration/vector-search/data-types-for-vector-search.mdx#ravenvector).
 
 <TabItem>
 ```js    
 // representation of a RavenVector:
 { "@vector": number[] }
 
-// Helper to create this wrapper:
+// Helper function to create this wrapper:
 RavenVector(numberArray) // => { "@vector": numberArray }    
 ```
 </TabItem>

docs/ai-integration/vector-search/data-types-for-vector-search.mdx

@@ -8,15 +8,19 @@ import LanguageSwitcher from "@site/src/components/LanguageSwitcher";
 import LanguageContent from "@site/src/components/LanguageContent";
 
 import DataTypesForVectorSearchCsharp from './content/_data-types-for-vector-search-csharp.mdx';
+import DataTypesForVectorSearchNodejs from './content/_data-types-for-vector-search-nodejs.mdx';
 
-export const supportedLanguages = ["csharp"];
+export const supportedLanguages = ["csharp", "nodejs"];
 
 <LanguageSwitcher supportedLanguages={supportedLanguages} />
 
 <LanguageContent language="csharp">
    <DataTypesForVectorSearchCsharp />
 </LanguageContent>
 
+<LanguageContent language="nodejs">
+   <DataTypesForVectorSearchNodejs />
+</LanguageContent>
 
 <!---
 ### Vector Search

versioned_docs/version-7.1/ai-integration/vector-search/content/_data-types-for-vector-search-csharp.mdx

@@ -2,6 +2,8 @@ import Admonition from '@theme/Admonition';
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import CodeBlock from '@theme/CodeBlock';
+import ContentFrame from '@site/src/components/ContentFrame';
+import Panel from '@site/src/components/Panel';
 
 <Admonition type="note" title="">
 
@@ -19,7 +21,7 @@ import CodeBlock from '@theme/CodeBlock';
 
 </Admonition>
 
-## Supported data types for vector search
+<Panel heading="Supported data types for vector search">
 
 ### Textual data
 
@@ -83,7 +85,9 @@ you can also use lists (for example, `List<float>` or `List<float[]>`) for dynam
 
 </Admonition>
 
-## RavenVector
+</Panel>
+
+<Panel heading="RavenVector">
 
 RavenVector is RavenDB's dedicated data type for storing and querying **numerical embeddings**.   
 It is highly optimized to minimize storage space and improve the speed of reading arrays from disk,  
@@ -126,3 +130,5 @@ For example:
 </Tabs>
 
 ![json document](../assets/json-document.png)
+
+</Panel>