ravendb · reebhub · Jan 27, 2026 · Jan 28, 2026 · Jan 28, 2026 · Jan 28, 2026
diff --git a/docs/ai-integration/ai-agents/_category_.json b/docs/ai-integration/ai-agents/_category_.json
@@ -1,4 +1,4 @@
 {
-  "position": 3,
+  "position": 4,
   "label": "AI Agents"
 }
diff --git a/docs/ai-integration/ai-tasks-list-view.mdx b/docs/ai-integration/ai-tasks-list-view.mdx
@@ -1,7 +1,7 @@
 ---
 title: "AI Tasks - List View"
 sidebar_label: AI Tasks - List View
-sidebar_position: 5
+sidebar_position: 6
 ---
 
 import Admonition from '@theme/Admonition';

diff --git a/docs/ai-integration/connection-strings/_category_.json b/docs/ai-integration/connection-strings/_category_.json
@@ -1,4 +1,4 @@
 {
-  "position": 4,
+  "position": 5,
   "label": "Connection Strings"
 }
diff --git a/docs/ai-integration/gen-ai-integration/_category_.json b/docs/ai-integration/gen-ai-integration/_category_.json
@@ -1,4 +1,4 @@
 {
-  "position": 2,
+  "position": 3,
   "label": "GenAI Integration"
 }
diff --git a/docs/ai-integration/generating-embeddings/_category_.json b/docs/ai-integration/generating-embeddings/_category_.json
@@ -1,4 +1,4 @@
 {
-  "position": 1,
+  "position": 2,
   "label": "Generating Embeddings"
 }
diff --git a/docs/ai-integration/start.mdx b/docs/ai-integration/start.mdx
@@ -46,31 +46,31 @@ AI agents are conversational proxy components that reside on the server and auto
 
 <ColGrid colCount={2}>
  <CardWithImage title="AI agents start page" description="Continue to the AI agents Start page." url="../ai-integration/ai-agents/start" imgIcon="ai-agents" ctaLabel="Read" />
- <CardWithImage title="Technical documentation" description="Learn how to create, deploy, manage conversations, and get results with AI agents." url="../ai-integration/ai-agents/start#technical-documentation" imgIcon="console" ctaLabel="Read" />
+ <CardWithImage title="Technical documentation" description="Learn how to create, deploy, manage conversations, and get results with AI agents." url="../ai-integration/ai-agents/overview" imgIcon="console" ctaLabel="Read" />
 </ColGrid>
 
 ### GenAI tasks
 GenAI tasks are configurable [ongoing operations](../studio/database/tasks/ongoing-tasks/general-info) that process your documents systematically in the background using an AI model. Instead of building custom AI integration pipelines yourself, you can easily create tasks that weave AI capabilities into your data flow. They can enrich documents with AI-generated content, validate and categorize data, translate documents, or execute countless other automated workflows that leverage AI capabilities.
 
 <ColGrid colCount={2}>
  <CardWithImage title="GenAI tasks start page" description="Continue to the GenAI tasks Start page." url="../ai-integration/gen-ai-integration/start" imgIcon="genai" ctaLabel="Read" />
- <CardWithImage title="Technical documentation" description="Learn to create GenAI tasks that process your data and enable intelligent workflows automation." url="../ai-integration/gen-ai-integration/start#technical-documentation" imgIcon="console" ctaLabel="Read" />
+ <CardWithImage title="Technical documentation" description="Learn to create GenAI tasks that process your data and enable intelligent workflows automation." url="../ai-integration/gen-ai-integration/overview" imgIcon="console" ctaLabel="Read" />
 </ColGrid>
 
 ### Embeddings generation
 Embeddings generation tasks transform your content into semantic vectors that enable intelligent similarity-based searches. Instead of building complex search infrastructure, you can utilize native tasks that seamlessly embed vector capabilities into your data, enabling intelligent search by meaning and context.
 
 <ColGrid colCount={2}>
  <CardWithImage title="Embeddings generation Start page" description="Continue to the embeddings generation Start page." url="../ai-integration/generating-embeddings/start" imgIcon="embeddings" ctaLabel="Read" />
- <CardWithImage title="Technical documentation" description="Learn to generate embeddings and enable intelligent searches." url="../ai-integration/generating-embeddings/start#technical-documentation" imgIcon="console" ctaLabel="Read" />
+ <CardWithImage title="Technical documentation" description="Learn to generate embeddings and enable intelligent searches." url="../ai-integration/generating-embeddings/overview" imgIcon="console" ctaLabel="Read" />
 </ColGrid>
 
 ### Vector search
 Vector search enables intelligent similarity-based discovery using embeddings rather than exact matching. Instead of developing custom similarity algorithms yourself, you can employ native vector operations for diverse applications. Whether you need to categorize content, find similar items, or automate recommendations, vector search delivers intelligent matching capabilities that understand meaning and context.
 
 <ColGrid colCount={2}>
  <CardWithImage title="Vector search start page" description="Continue to the vector search Start page." url="../ai-integration/vector-search/start" imgIcon="vector-search" ctaLabel="Read" />
- <CardWithImage title="Technical documentation" description="Learn to search your data by meaning and context." url="../ai-integration/vector-search/start#technical-documentation-links-" imgIcon="console" ctaLabel="Read" />
+ <CardWithImage title="Technical documentation" description="Learn to search your data by meaning and context." url="../ai-integration/vector-search/overview" imgIcon="console" ctaLabel="Read" />
 </ColGrid>
 
 ### Related lives & Videos

diff --git a/docs/ai-integration/vector-search/_category_.json b/docs/ai-integration/vector-search/_category_.json
@@ -1,4 +1,4 @@
 {
-  "position": 0,
+  "position": 1,
   "label": "Vector Search"
 }
diff --git a/...validation/auditing-document-compliance/auditing-document-compliance_studio.mdx b/...validation/auditing-document-compliance/auditing-document-compliance_studio.mdx
@@ -56,7 +56,7 @@ The validation schemas that you already defined are displayed in the [Document S
 
 The [Document Schema](../../../documents/schema-validation/write-validation/write-validation_studio#creating-a-collection-schema) view also provides an isolated playground environment for experimentation, allowing you to define schemas and try them out on your data without affecting your existing collections or schemas.
 
-![Document Schema view](./assets/ schema-validation_studio_audit-playground.png)
+![Document Schema view](./assets/schema-validation_studio_audit-playground.png)
 
 * Use the playground to experiment with schema definitions and test them against documents in your collections.  
 * You can validate an entire collection or a specified number of documents.  

diff --git a/docs/documents/schema-validation/schema-validation_overview.mdx b/docs/documents/schema-validation/schema-validation_overview.mdx
@@ -58,7 +58,7 @@ Validation is triggered during writing by these operations:
 * `ETL tasks`  
   Validates documents as they are transferred via an Extract/Transform/Load process.  
 
-#### Validation is **not** applied during these operations:  
+#### Validation is NOT applied during these operations:  
 * `Replication`  
   When replicating documents to cluster nodes or via external replication.  
 * `Backup.RestoreDatabase()`