RDoc-3184 Create a dedicated directory for Vector Search

Documentation/7.0/Raven.Documentation.Pages/ai-integration/.docs.json

@@ -1,32 +1,7 @@
 [
     {
-        "Path": "ravendb-as-vector-database.markdown",
-        "Name": "RavenDB as a Vector Database",
-        "DiscussionId": "3a848621-110f-4bbd-9147-58f743b0a950",
-        "Mappings": []
-    },
-    {
-        "Path": "vector-search-using-dynamic-query.markdown",
-        "Name": "Vector Search using a Dynamic Query",
-        "DiscussionId": "2b55d124-a9ff-474c-8171-d065daf938a3",
-        "Mappings": []
-    },
-    {
-        "Path": "vector-search-using-static-index.markdown",
-        "Name": "Vector Search using a Static Index",
-        "DiscussionId": "a25c7cca-e662-401f-9b66-8ce1102a2a09",
-        "Mappings": []
-    },
-    {
-        "Path": "indexing-attachments-for-vector-search.markdown",
-        "Name": "Indexing Attachments for Vector Search",
-        "DiscussionId": "7510d989-4925-4e7d-8c48-c3608c09506c",
-        "Mappings": []
-    },
-    {
-        "Path": "data-types-for-vector-search.markdown",
-        "Name": "Data Types for Vector Search",
-        "DiscussionId": "251b7f9e-06a8-4a67-8707-4a87ee9e5d7d",
+        "Path": "/vector-search",
+        "Name": "Vector Search",
         "Mappings": []
     }
 ]

Documentation/7.0/Raven.Documentation.Pages/ai-integration/vector-search/.docs.json

@@ -0,0 +1,32 @@
+[
+    {
+        "Path": "ravendb-as-vector-database.markdown",
+        "Name": "RavenDB as a Vector Database",
+        "DiscussionId": "3a848621-110f-4bbd-9147-58f743b0a950",
+        "Mappings": []
+    },
+    {
+        "Path": "vector-search-using-dynamic-query.markdown",
+        "Name": "Vector Search using a Dynamic Query",
+        "DiscussionId": "2b55d124-a9ff-474c-8171-d065daf938a3",
+        "Mappings": []
+    },
+    {
+        "Path": "vector-search-using-static-index.markdown",
+        "Name": "Vector Search using a Static Index",
+        "DiscussionId": "a25c7cca-e662-401f-9b66-8ce1102a2a09",
+        "Mappings": []
+    },
+    {
+        "Path": "indexing-attachments-for-vector-search.markdown",
+        "Name": "Indexing Attachments for Vector Search",
+        "DiscussionId": "7510d989-4925-4e7d-8c48-c3608c09506c",
+        "Mappings": []
+    },
+    {
+        "Path": "data-types-for-vector-search.markdown",
+        "Name": "Data Types for Vector Search",
+        "DiscussionId": "251b7f9e-06a8-4a67-8707-4a87ee9e5d7d",
+        "Mappings": []
+    }
+]

Documentation/7.0/Raven.Documentation.Pages/ai-integration/data-types-for-vector-search.dotnet.markdown → Documentation/7.0/Raven.Documentation.Pages/ai-integration/vector-search/data-types-for-vector-search.dotnet.markdown

@@ -7,13 +7,13 @@
   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-using-dynamic-query#quantization-options).
+  Learn more in [Quantization options](../../ai-integration/vector-search/vector-search-using-dynamic-query#quantization-options).
 
 * In this page:
-  * [Supported data types for vector search](../ai-integration/data-types-for-vector-search#supported-data-types-for-vector-search)
-      * [Textual data](../ai-integration/data-types-for-vector-search#textual-data)
-      * [Numerical data](../ai-integration/data-types-for-vector-search#numerical-data) 
-  * [RavenVector](../ai-integration/data-types-for-vector-search#ravenvector)
+  * [Supported data types for vector search](../../ai-integration/vector-search/data-types-for-vector-search#supported-data-types-for-vector-search)
+      * [Textual data](../../ai-integration/vector-search/data-types-for-vector-search#textual-data)
+      * [Numerical data](../../ai-integration/vector-search/data-types-for-vector-search#numerical-data) 
+  * [RavenVector](../../ai-integration/vector-search/data-types-for-vector-search#ravenvector)
 
 {NOTE/}
 
@@ -41,7 +41,7 @@
   * 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/data-types-for-vector-search#ravenvector)  
+* In addition to the native types described below, we highly recommended using [RavenVector](../ai-integration/vector-search/data-types-for-vector-search#ravenvector)  
   for efficient storage and fast queries when working with numerical embeddings.
 
 {CONTENT-FRAME: }
@@ -61,7 +61,7 @@ Use when you prioritize storage efficiency and query speed.
 `byte[] / sbyte[]` - A single pre-quantized embedding vector in the _Int8_ or _Binary_ quantization format.  
 `byte[][] / sbyte[][]` - 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-using-dynamic-query#section-1).
+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#section-1).
 
 {CONTENT-FRAME/}
 {CONTENT-FRAME: }
@@ -106,7 +106,7 @@ When a class property is stored as a `RavenVector`, the vector's content will ap
 For example:
 
 {CODE-TABS}
-{CODE-TAB:csharp:CSharp_class class@AiIntegration\DataTypes.cs /}
+{CODE-TAB:csharp:CSharp_class class@AiIntegration\VectorSearch\DataTypes.cs /}
 {CODE-TABS/}
 
 ![json document](images/json-document.png "RavenVector in a JSON document")
@@ -117,14 +117,14 @@ For example:
 
 ### Vector Search
 
-- [RavenDB as a vector database](../ai-integration/ravendb-as-vector-database)
-- [Vector search using a static index](../ai-integration/vector-search-using-static-index)
-- [Vector search using a dynamic query](../ai-integration/vector-search-using-dynamic-query)
+- [RavenDB as a vector database](../../ai-integration/vector-search/ravendb-as-vector-database)
+- [Vector search using a static index](../../ai-integration/vector-search/vector-search-using-static-index)
+- [Vector search using a dynamic query](../../ai-integration/vector-search/vector-search-using-dynamic-query)
 
 ### Querying
 
-- [Query overview](../client-api/session/querying/how-to-query)
-- [Full-text search](../client-api/session/querying/text-search/full-text-search)
+- [Query overview](../../client-api/session/querying/how-to-query)
+- [Full-text search](../../client-api/session/querying/text-search/full-text-search)
 
 
 

Documentation/7.0/Raven.Documentation.Pages/ai-integration/indexing-attachments-for-vector-search.dotnet.markdown → Documentation/7.0/Raven.Documentation.Pages/ai-integration/vector-search/indexing-attachments-for-vector-search.dotnet.markdown

@@ -4,18 +4,18 @@
 {NOTE: }
 
 * This article explains how to index attachments using a **static-index** to enable vector search on their content.  
-  Note: Vector search on attachment content is not available when making a [dynamic query](../ai-integration/vector-search-using-dynamic-query).  
+  Note: Vector search on attachment content is not available when making a [dynamic query](../../ai-integration/vector-search/vector-search-using-dynamic-query).  
 
-* **Prior to this article**, refer to the [Vector search using a static index](../ai-integration/vector-search-using-static-index) article for general knowledge about  
+* **Prior to this article**, refer to the [Vector search using a static index](../../ai-integration/vector-search/vector-search-using-static-index) article for general knowledge about  
   indexing a vector field.
 
 * In this page:
-    * [Overview](../ai-integration/indexing-attachments-for-vector-search#overview)
-    * [Indexing TEXT attachments](../ai-integration/indexing-attachments-for-vector-search#indexing-text-attachments)
-    * [Indexing NUMERICAL attachments](../ai-integration/indexing-attachments-for-vector-search#indexing-numerical-attachments)
-      * [LINQ index](../ai-integration/indexing-attachments-for-vector-search#linq-index)
-      * [JS index](../ai-integration/indexing-attachments-for-vector-search#js-index)
-    * [Indexing ALL attachments](../ai-integration/indexing-attachments-for-vector-search#indexing-all-attachments)
+    * [Overview](../../ai-integration/vector-search/indexing-attachments-for-vector-search#overview)
+    * [Indexing TEXT attachments](../../ai-integration/vector-search/indexing-attachments-for-vector-search#indexing-text-attachments)
+    * [Indexing NUMERICAL attachments](../../ai-integration/vector-search/indexing-attachments-for-vector-search#indexing-numerical-attachments)
+      * [LINQ index](../../ai-integration/vector-search/indexing-attachments-for-vector-search#linq-index)
+      * [JS index](../../ai-integration/vector-search/indexing-attachments-for-vector-search#js-index)
+    * [Indexing ALL attachments](../../ai-integration/vector-search/indexing-attachments-for-vector-search#indexing-all-attachments)
 
 {NOTE/}
 
@@ -36,7 +36,7 @@
   They are stored as **binary data**, regardless of content type.
 
 * Attachments are handled as streams, allowing efficient upload and retrieval.  
-  Learn more in: [What are attachments](../document-extensions/attachments/what-are-attachments).
+  Learn more in: [What are attachments](../../document-extensions/attachments/what-are-attachments).
 
 {CONTENT-FRAME/}
 {CONTENT-FRAME: }
@@ -55,7 +55,7 @@ enabling vector search on text or numerical data that is stored in the attachmen
 * **Attachments with NUMERICAL data**:  
     * While attachments can store any file type, RavenDB does Not generate embeddings from images, videos, or other non-textual content.  
       Each attachment must contain a **single** precomputed embedding vector, generated externally.  
-    * RavenDB indexes the embedding vector from the attachment in and can apply [quantization](../ai-integration/vector-search-using-dynamic-query#quantization-options)
+    * RavenDB indexes the embedding vector from the attachment in and can apply [quantization](../../ai-integration/vector-search/vector-search-using-dynamic-query#quantization-options)
       (e.g., index it in _Int8_ format) if this is configured.  
     * All embeddings indexed within the same vector-field in the static-index must be vectors of the **same dimension** to ensure consistency in indexing and search.
       They must also be created using the **same model**.
@@ -71,22 +71,22 @@ enabling vector search on text or numerical data that is stored in the attachmen
   This applies to all _Company_ documents that contain an attachment with that name.
 
 {CODE-TABS}
-{CODE-TAB:csharp:LINQ_index index_1@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:JS_index index_2@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:IndexDefinition index_3@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Storing_text_attachments store_attachments_1@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:LINQ_index index_1@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:JS_index index_2@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:IndexDefinition index_3@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Storing_text_attachments store_attachments_1@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TABS/}
 
 Execute a vector search using the index:  
 Results will include _Company_ documents whose attachment contains text similar to `"chinese food"`.
 
 {CODE-TABS}
-{CODE-TAB:csharp:Query query_1@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Query_async query_1_async@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:DocumentQuery query_2@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:DocumentQuery_async query_2_async@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery query_3@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery_async query_3_async@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Query query_1@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Query_async query_1_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:DocumentQuery query_2@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:DocumentQuery_async query_2_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery query_3@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery_async query_3_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TAB-BLOCK:sql:RQL}
 from index "Companies/ByVector/FromTextAttachment"
 where vector.search(VectorFromAttachment, $searchTerm, 0.8)
@@ -96,7 +96,7 @@ where vector.search(VectorFromAttachment, $searchTerm, 0.8)
 
 You can now extract the text from the attachments of the resulting documents:
 
-{CODE:csharp extract_attachment_content@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE:csharp extract_attachment_content@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 
 {PANEL/}
 
@@ -112,21 +112,21 @@ You can now extract the text from the attachments of the resulting documents:
 * Each attachment contains raw numerical data in 32-bit floating-point format.  
 
 {CODE-TABS}
-{CODE-TAB:csharp:LINQ_index index_4@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:IndexDefinition index_5@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Storing_numerical_attachments store_attachments_2@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:LINQ_index index_4@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:IndexDefinition index_5@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Storing_numerical_attachments store_attachments_2@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TABS/}
 
 Execute a vector search using the index:  
 Results will include _Company_ documents whose attachment contains vectors similar to the query vector.
 
 {CODE-TABS}
-{CODE-TAB:csharp:Query query_4@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Query_async query_4_async@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:DocumentQuery query_5@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:DocumentQuery_async query_5_async@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery query_6@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery_async query_6_async@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Query query_4@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Query_async query_4_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:DocumentQuery query_5@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:DocumentQuery_async query_5_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery query_6@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery_async query_6_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TAB-BLOCK:sql:RQL}
 from index "Companies/ByVector/FromNumericalAttachment"
 where vector.search(VectorFromAttachment, $queryVector)
@@ -138,23 +138,23 @@ where vector.search(VectorFromAttachment, $queryVector)
 
 ### JS index
 
-* The following is the JavaScript index format equivalent to the [LINQ index](../ai-integration/indexing-attachments-for-vector-search#linq-index) shown above.
+* The following is the JavaScript index format equivalent to the [LINQ index](../../ai-integration/vector-search/indexing-attachments-for-vector-search#linq-index) shown above.
 
 * The main difference is that JavaScript indexes do Not support `getContentAsStream()` on attachment objects:
   * Because of this, embedding vectors must be stored in attachments as **Base64-encoded strings**.  
   * Use `getContentAsString()` to retrieve the attachment content as a string, as shown in this example.
 
 {CODE-TABS}
-{CODE-TAB:csharp:JS_index index_6@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Storing_numerical_attachments_as_base64 store_attachments_3@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:JS_index index_6@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Storing_numerical_attachments_as_base64 store_attachments_3@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TABS/}
 
 Execute a vector search using the index:  
 Results will include _Company_ documents whose attachment contains vectors similar to the query vector.
 
 {CODE-TABS}
-{CODE-TAB:csharp:RawQuery query_7@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery_async query_7_async@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery query_7@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery_async query_7_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TAB-BLOCK:sql:RQL}
 from index "Companies/ByVector/FromNumericalAttachment/JS"
 where vector.search(VectorFromAttachment, $queryVector)
@@ -171,21 +171,21 @@ where vector.search(VectorFromAttachment, $queryVector)
 * It indexes embeddings generated from the numerical data stored in ALL attachments of all _Company_ documents.
 
 {CODE-TABS}
-{CODE-TAB:csharp:LINQ_index index_7@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:IndexDefinition index_8@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Storing_numerical_attachments store_attachments_4@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:LINQ_index index_7@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:IndexDefinition index_8@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Storing_numerical_attachments store_attachments_4@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TABS/}
 
 Execute a vector search using the index:  
 Results will include Company documents whose attachments contains vectors similar to the query vector.
 
 {CODE-TABS}
-{CODE-TAB:csharp:Query query_8@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:Query_async query_8_async@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:DocumentQuery query_9@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:DocumentQuery_async query_9_async@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery query_10@AiIntegration\VectorSearchWithAttachments.cs /}
-{CODE-TAB:csharp:RawQuery_async query_10_async@AiIntegration\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Query query_8@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:Query_async query_8_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:DocumentQuery query_9@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:DocumentQuery_async query_9_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery query_10@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
+{CODE-TAB:csharp:RawQuery_async query_10_async@AiIntegration\VectorSearch\VectorSearchWithAttachments.cs /}
 {CODE-TAB-BLOCK:sql:RQL}
 from index "Companies/ByVector/AllAttachments"
 where vector.search(VectorFromAttachment, $queryVector)
@@ -199,11 +199,11 @@ where vector.search(VectorFromAttachment, $queryVector)
 
 ### Vector Search
 
-- [RavenDB as a vector database](../ai-integration/ravendb-as-vector-database)
-- [Vector search using a dynamic query](../ai-integration/vector-search-using-dynamic-query)
-- [Vector search using a static index](../ai-integration/vector-search-using-static-index)
+- [RavenDB as a vector database](../../ai-integration/vector-search/ravendb-as-vector-database)
+- [Vector search using a dynamic query](../../ai-integration/vector-search/vector-search-using-dynamic-query)
+- [Vector search using a static index](../../ai-integration/vector-search/vector-search-using-static-index)
 
 ### Querying
 
-- [Query overview](../client-api/session/querying/how-to-query)
-- [Full-text search](../client-api/session/querying/text-search/full-text-search)
+- [Query overview](../../client-api/session/querying/how-to-query)
+- [Full-text search](../../client-api/session/querying/text-search/full-text-search)