diff --git a/docs/docs/core-components/knowledge.mdx b/docs/docs/core-components/knowledge.mdx
index 92a285d3..7c5105b2 100644
--- a/docs/docs/core-components/knowledge.mdx
+++ b/docs/docs/core-components/knowledge.mdx
@@ -1,5 +1,5 @@
 ---
-title: Knowledge stored with OpenSearch
+title: OpenSearch Knowledge
 slug: /knowledge
 ---
 
@@ -46,9 +46,9 @@ OpenRAG supports the following enterprise-grade OAuth connectors for seamless do
 - **OneDrive**
 - **AWS**
 
-OAuth integration allows your OpenRAG server to authenticate users and applications through any OAuth 2.0 compliant service. When users or applications connect to your server, they are redirected to your chosen OAuth provider to authenticate. Upon successful authentication, they are granted access to the connector.
+OAuth integration allows individual users to connect their personal cloud storage accounts to OpenRAG. Each user must separately authorize OpenRAG to access their own cloud storage files. When a user connects a cloud service, they are redirected to authenticate with that service provider and grant OpenRAG permission to sync documents from their personal cloud storage.
 
-Before configuring OAuth in OpenRAG, you must first set up an OAuth application with an external OAuth 2.0 service provider. You must register your OpenRAG server as an OAuth client, and then obtain the `client` and `secret` keys to complete the configuration in OpenRAG.
+Before users can connect their cloud storage accounts, you must configure OAuth credentials in OpenRAG. This requires registering OpenRAG as an OAuth application with a cloud provider and obtaining client ID and secret keys for each service you want to support.
 
 To add an OAuth connector to OpenRAG, do the following.
 This example uses Google OAuth.
@@ -101,18 +101,44 @@ You can monitor the sync progress in the <Icon name="Bell" aria-hidden="true"/>
 
 Once processing is complete, the synced documents become available in your knowledge base and can be searched through the chat interface or Knowledge page.
 
-## Knowledge filters
+## Create knowledge filters
 
 OpenRAG includes a knowledge filter system for organizing and managing document collections.
-Knowledge filters are saved search configurations that allow you to create custom views of your document collection. They store search queries, filter criteria, and display settings that can be reused across different parts of the application.
+Knowledge filters are saved search configurations that allow you to create custom views of your document collection. They store search queries, filter criteria, and display settings that can be reused across different parts of OpenRAG.
+
+Knowledge filters help agents work more efficiently with large document collections by focusing their context within relevant documents sets.
+
+To create a knowledge filter, do the following:
+
+1. Click <Icon name="Funnel" aria-hidden="true"/> **All Knowledge**, and then click <Icon name="Plus" aria-hidden="true"/> **Create New Filter**.
+    The **Create New Knowledge Filter** pane appears.
+2. Enter a **Name** and **Description**, and then click <Icon name="Save" aria-hidden="true"/> **Create Filter**.
+A new filter is created with default settings that match everything.
+3. To modify the default filter, click <Icon name="Funnel" aria-hidden="true"/> **All Knowledge**, and then click your new filter to edit it in the **Knowledge Filter** pane.
+
+   The following filter options are configurable.
+   
+   * **Search Query**: Enter text for semantic search, such as "financial reports from Q4".
+   * **Data Sources**: Select specific data sources or folders to include.
+   * **Document Types**: Filter by file type.
+   * **Owners**: Filter by who uploaded the documents.
+   * **Sources**: Filter by connector types, such as local upload or Google Drive.
+   * **Result Limit**: Set maximum number of results. The default is `10`.
+   * **Score Threshold**: Set minimum relevance score. The default score is `0`.
+
+4. When you're done editing the filter, click <Icon name="Save" aria-hidden="true"/> **Save Configuration**.
+
+5. To apply the filter to OpenRAG globally, click <Icon name="Funnel" aria-hidden="true"/> **All Knowledge**, and then select the filter to apply.
+
+    To apply the filter to a single chat session, in the <Icon name="MessageSquare" aria-hidden="true"/> **Chat** window, click **@**, and then select the filter to apply.
 
 ## OpenRAG default configuration
 
 OpenRAG creates a specialized OpenSearch index called `documents` with the values defined at `src/config/settings.py`.
-- **Vector Dimensions**: 1536-dimensional embeddings using OpenAI's `text-embedding-3-small` model.
-- **KNN Vector Type**: Uses `knn_vector` field type with `disk_ann` method and `jvector` engine.
-- **Distance Metric**: L2 (Euclidean) distance for vector similarity.
-- **Performance Optimization**: Configured with `ef_construction: 100` and `m: 16` parameters.
+* **Vector Dimensions**: 1536-dimensional embeddings using OpenAI's `text-embedding-3-small` model.
+* **KNN Vector Type**: Uses `knn_vector` field type with `disk_ann` method and `jvector` engine.
+* **Distance Metric**: L2 (Euclidean) distance for vector similarity.
+* **Performance Optimization**: Configured with `ef_construction: 100` and `m: 16` parameters.
 
 OpenRAG supports hybrid search, which combines semantic and keyword search.
 
diff --git a/docs/sidebars.js b/docs/sidebars.js
index c4226946..3048cb70 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -59,7 +59,7 @@ const sidebars = {
         {
           type: "doc",
           id: "core-components/knowledge",
-          label: "Knowledge stored with OpenSearch"
+          label: "OpenSearch Knowledge"
         }
       ],
     },