diff --git a/docs/docs/core-components/agents.mdx b/docs/docs/core-components/agents.mdx
index a7c5ef24..88102a60 100644
--- a/docs/docs/core-components/agents.mdx
+++ b/docs/docs/core-components/agents.mdx
@@ -52,7 +52,7 @@ This filter is the [Knowledge filter](/knowledge#create-knowledge-filters), and
 
 For an example of changing out the agent's language model in OpenRAG, see the [Quickstart](/quickstart#change-components).
 
-To restore the flow to its initial state, in OpenRAG, click <Icon name="Settings" aria-hidden="true"/> **Settings**, and then click **Restore Flow**.
+To restore the flow to its initial state, in OpenRAG, click <Icon name="Settings2" aria-hidden="true"/> **Settings**, and then click **Restore Flow**.
 OpenRAG warns you that this discards all custom settings. Click **Restore** to restore the flow.
 
 ## Additional Langflow functionality
diff --git a/docs/docs/core-components/ingestion.mdx b/docs/docs/core-components/ingestion.mdx
index 585ea6e3..f72e9ab0 100644
--- a/docs/docs/core-components/ingestion.mdx
+++ b/docs/docs/core-components/ingestion.mdx
@@ -15,7 +15,7 @@ Docling ingests documents from your local machine or OAuth connectors, splits th
 
 OpenRAG chose Docling for its support for a wide variety of file formats, high performance, and advanced understanding of tables and images.
 
-To modify OpenRAG's ingestion settings, including the Docling settings and ingestion flows, click <Icon name="Settings" aria-hidden="true"/> **Settings**.
+To modify OpenRAG's ingestion settings, including the Docling settings and ingestion flows, click 2" aria-hidden="true"/> **Settings**.
 
 ## Knowledge ingestion settings
 
diff --git a/docs/docs/core-components/knowledge.mdx b/docs/docs/core-components/knowledge.mdx
index cae39659..045918f1 100644
--- a/docs/docs/core-components/knowledge.mdx
+++ b/docs/docs/core-components/knowledge.mdx
@@ -31,10 +31,10 @@ The **Knowledge Ingest** flow uses Langflow's [**File** component](https://docs.
 
 The default path to your local folder is mounted from the `./documents` folder in your OpenRAG project directory to the `/app/documents/` directory inside the Docker container. Files added to the host or the container will be visible in both locations. To configure this location, modify the **Documents Paths** variable in either the TUI's [Advanced Setup](/install#setup) menu or in the `.env` used by Docker Compose.
 
-To load and process a single file from the mapped location, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**, and then click **Add File**.
+To load and process a single file from the mapped location, click **Add Knowledge**, and then click <Icon name="File" aria-hidden="true"/> **File**.
 The file is loaded into your OpenSearch database, and appears in the Knowledge page.
 
-To load and process a directory from the mapped location, click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**, and then click **Process Folder**. 
+To load and process a directory from the mapped location, click **Add Knowledge**, and then click <Icon name="Folder" aria-hidden="true"/> **Folder**.
 The files are loaded into your OpenSearch database, and appear in the Knowledge page.
 
 ### Ingest files through OAuth connectors {#oauth-ingestion}
@@ -61,11 +61,11 @@ If you wish to use another provider, add the secrets to another provider.
   <TabItem value=".env" label=".env">
    1. Stop the Docker deployment.
    2. Add the OAuth provider's client and secret key in the `.env` file for Docker Compose.
-   ```bash    
-   GOOGLE_OAUTH_CLIENT_ID='YOUR_OAUTH_CLIENT_ID'
-   GOOGLE_OAUTH_CLIENT_SECRET='YOUR_OAUTH_CLIENT_SECRET'
-   ```
-   3. Save your `.env`. file.
+       ```bash    
+       GOOGLE_OAUTH_CLIENT_ID='YOUR_OAUTH_CLIENT_ID'
+       GOOGLE_OAUTH_CLIENT_SECRET='YOUR_OAUTH_CLIENT_SECRET'
+       ```
+   3. Save your `.env` file.
    4. Start the Docker deployment.
   </TabItem>
 </Tabs>
@@ -75,11 +75,11 @@ A successful authentication opens OpenRAG with the required scopes for your conn
 
 To add knowledge from an OAuth-connected storage provider, do the following:
 
-1. Click <Icon name="Plus" aria-hidden="true"/> **Add Knowledge**, and then select the storage provider, for example, **Google Drive**.
+1. Click **Add Knowledge**, and then select the storage provider, for example, **Google Drive**.
 The **Add Cloud Knowledge** page opens.
-2. To add files or folders from the connected storage, click <Icon name="Plus" aria-hidden="true"/> **Add Files**.
+2. To add files or folders from the connected storage, click **Add Files**.
 Select the files or folders you want and click **Select**.
-You can select multiples.
+You can select multiple files.
 3. When your files are selected, click **Ingest Files**.
 The ingestion process may take some time, depending on the size of your documents.
 4. When ingestion is complete, your documents are available in the Knowledge screen.
@@ -104,11 +104,11 @@ Knowledge filters help agents work more efficiently with large document collecti
 
 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.
+1. Click **Knowledge**, and then click <Icon name="Plus" aria-hidden="true"/> **Knowledge Filters**.
+    The **Knowledge Filter** pane appears.
+2. Enter a **Name** and **Description**, and then click **Create Filter**.
+A new filter is created with default settings that match all documents.
+3. To modify the filter, click <Icon name="Library" aria-hidden="true"/> **Knowledge**, and then click your new filter to edit it in the **Knowledge Filter** pane.
 
    The following filter options are configurable.
    
@@ -116,15 +116,17 @@ A new filter is created with default settings that match everything.
    * **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`.
+   * **Connectors**: Filter by connector types, such as local upload or Google Drive.
+   * **Response 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**.
+4. When you're done editing the filter, click **Update Filter**.
 
-5. To apply the filter to OpenRAG globally, click <Icon name="Funnel" aria-hidden="true"/> **All Knowledge**, and then select the filter to apply.
+5. To apply the filter to OpenRAG globally, click <Icon name="Library" aria-hidden="true"/> **Knowledge**, and then select the filter to apply. One filter can be enabled at a time.
 
-    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.
+    To apply the filter to a single chat session, in the <Icon name="MessageSquare" aria-hidden="true"/> **Chat** window, click <Icon name="Funnel" aria-hidden="true"/>, and then select the filter to apply.
+
+    To delete the filter, in the **Knowledge Filter** pane, click **Delete Filter**.
 
 ## OpenRAG default configuration
 
@@ -132,7 +134,7 @@ OpenRAG automatically detects and configures the correct vector dimensions for e
 
 The complete list of supported models is available at [`models_service.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py).
 
-You can use custom embedding models by specifying them in your configuration.
+You can use custom embe*dding models by specifying them in your configuration.
 
 If you use an unknown embedding model, OpenRAG will automatically fall back to `1536` dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from `1536`.