diff --git a/docs/ai-integration/vector-search/assets/json-document-nodejs.png b/docs/ai-integration/vector-search/assets/json-document-nodejs.png
new file mode 100644
index 0000000000..3ae5cf3146
Binary files /dev/null and b/docs/ai-integration/vector-search/assets/json-document-nodejs.png differ
diff --git a/docs/ai-integration/vector-search/content/_data-types-for-vector-search-csharp.mdx b/docs/ai-integration/vector-search/content/_data-types-for-vector-search-csharp.mdx
index 6334e4665e..dedd319a35 100644
--- a/docs/ai-integration/vector-search/content/_data-types-for-vector-search-csharp.mdx
+++ b/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';
@@ -19,7 +21,7 @@ import CodeBlock from '@theme/CodeBlock';
-## Supported data types for vector search
+
### Textual data
@@ -83,7 +85,9 @@ you can also use lists (for example, `List` or `List`) for dynam
-## 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:
+
+
\ No newline at end of file
diff --git a/docs/ai-integration/vector-search/content/_data-types-for-vector-search-nodejs.mdx b/docs/ai-integration/vector-search/content/_data-types-for-vector-search-nodejs.mdx
new file mode 100644
index 0000000000..af637aa673
--- /dev/null
+++ b/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';
+
+
+
+* 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)
+
+
+
+
+
+### Textual data
+
+
+
+`string` - A single text entry.
+`string[]` - An array of text entries.
+
+
+
+### 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.
+
+
+
+**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.
+
+
+
+
+
+**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).
+
+
+
+
+
+**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.
+
+
+
+
+
+
+
+* `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
+
+
+
+```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();
+```
+
+
+```js
+class User {
+ constructor(embeddingRavenVector, embeddingVector) {
+ this.embeddingRavenVector = embeddingRavenVector;
+ this.embeddingVector = embeddingVector;
+ }
+}
+```
+
+
+
+When stored, the vector's content created with the `RavenVector` function will appear under the `@vector` field in the JSON document:
+
+
+
+To query the `embeddingRavenVector field`, use:
+
+
+```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();
+```
+
+
+
+
diff --git a/docs/ai-integration/vector-search/content/_vector-search-using-dynamic-query-nodejs.mdx b/docs/ai-integration/vector-search/content/_vector-search-using-dynamic-query-nodejs.mdx
index f2357f7b32..e8f1df0d56 100644
--- a/docs/ai-integration/vector-search/content/_vector-search-using-dynamic-query-nodejs.mdx
+++ b/docs/ai-integration/vector-search/content/_vector-search-using-dynamic-query-nodejs.mdx
@@ -1247,7 +1247,7 @@ 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).
@@ -1255,7 +1255,7 @@ Learn more in [RavenVector](../../../ai-integration/vector-search/data-types-for
// representation of a RavenVector:
{ "@vector": number[] }
-// Helper to create this wrapper:
+// Helper function to create this wrapper:
RavenVector(numberArray) // => { "@vector": numberArray }
```
diff --git a/docs/ai-integration/vector-search/data-types-for-vector-search.mdx b/docs/ai-integration/vector-search/data-types-for-vector-search.mdx
index c8023243b6..f54d6feba7 100644
--- a/docs/ai-integration/vector-search/data-types-for-vector-search.mdx
+++ b/docs/ai-integration/vector-search/data-types-for-vector-search.mdx
@@ -8,8 +8,9 @@ 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"];
@@ -17,6 +18,9 @@ export const supportedLanguages = ["csharp"];
+
+
+