Delete unused images from the site and correct image formatting

_search-plugins/ubi/ubi-dashboard-tutorial.md

@@ -22,7 +22,7 @@ To quickly view a dashboard without completing the full tutorial, do the followi
 ## 1. Start OpenSearch Dashboards
 
 Start OpenSearch Dashboards. For example, go to `http://{server}:5601/app/home#/`. For more information, see [OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/dashboards/). The following image shows the home page.
-![Dashboard Home]({{site.url}}{{site.baseurl}}/images/ubi/home.png "Dashboards")
+![Dashboard Home]({{site.url}}{{site.baseurl}}/images/ubi/home.png)
 
 ## 2. Create an index pattern
 
@@ -31,7 +31,7 @@ In OpenSearch Management, navigate to **Dashboards Management > Index patterns**
 OpenSearch Dashboards accesses your indexes using index patterns. To visualize your users' online search behavior, you must create an index pattern in order to access the indexes that UBI creates. For more information, see [Index patterns]({{site.url}}{{site.baseurl}}/dashboards/management/index-patterns/).
 
 After you select **Create index pattern**, a list of indexes in your OpenSearch instance is displayed. The UBI stores may be hidden by default, so make sure to select **Include system and hidden indexes**, as shown in the following image. 
-![Index Patterns]({{site.url}}{{site.baseurl}}/images/ubi/index_pattern2.png "Index Patterns")
+![Index Patterns]({{site.url}}{{site.baseurl}}/images/ubi/index_pattern2.png)
 
 You can group indexes into the same data source for your dashboard using wildcards. For this tutorial you'll combine the query and event stores into the `ubi_*` pattern.
 
@@ -50,7 +50,7 @@ If you haven't previously created a dashboard, you are presented with the option
 In the **New Visualization** window, select **Pie** to create a new pie chart. Then select the index pattern you created in step 2.
 
 Most visualizations require some sort of aggregate function on a bucket/facet/aggregatable field (numeric or keyword). You'll add a `Terms` aggregation to the `action_name` field so that you can view the distribution of event names. Change the **Size** to the number of slices you want to display, as shown in the following image.
-![Pie Chart]({{site.url}}{{site.baseurl}}/images/ubi/pie.png "Pie Chart")
+![Pie Chart]({{site.url}}{{site.baseurl}}/images/ubi/pie.png)
 
 Save the visualization so that it's added to your new dashboard. Now that you have a visualization displayed on your dashboard, you can save the dashboard.
 
@@ -61,34 +61,34 @@ Now you'll add a word cloud for trending searches by creating a new visualizatio
 In the **New Visualization** window, select **Tag Cloud**, and then select the index pattern you created in step 2. Choose the tag cloud visualization of the terms in the `message` field where the JavaScript client logs the raw search text. Note: The true query, as processed by OpenSearch with filters, boosting, and so on, resides in the `ubi_queries` index. However, you'll view the `message` field of the `ubi_events` index, where the JavaScript client captures the text that the user actually typed. 
 
 The following image shows the tag cloud visualization on the `message` field.
-![Word Cloud]({{site.url}}{{site.baseurl}}/images/ubi/tag_cloud1.png "Word Cloud")
+![Word Cloud]({{site.url}}{{site.baseurl}}/images/ubi/tag_cloud1.png)
 
 The underlying queries can be found at [SQL trending queries]({{site.url}}{{site.baseurl}}/search-plugins/ubi/sql-queries/#trending-queries).
 {: .note} 
 
 
 The resulting visualization may contain different information than you're looking for. The `message` field is updated with every event, and as a result, it can contain error messages, debug messages, click information, and other unwanted data.
 To view only search terms for query events, you need to add a filter to your visualization. Because during setup you provided a `message_type` of `QUERY` for each search event, you can filter by that message type to isolate the specific users' searches. To do this, select **Add filter** and then select **QUERY** in the **Edit filter** panel, as shown in the following image. 
-![Word Cloud]({{site.url}}{{site.baseurl}}/images/ubi/tag_cloud2.png "Word Cloud")
+![Word Cloud]({{site.url}}{{site.baseurl}}/images/ubi/tag_cloud2.png)
 
 There should now be two visualizations (the pie chart and the tag cloud) displayed on your dashboard, as shown in the following image.
-![UBI Dashboard]({{site.url}}{{site.baseurl}}/images/ubi/dashboard2.png "UBI Dashboard")
+![UBI Dashboard]({{site.url}}{{site.baseurl}}/images/ubi/dashboard2.png)
 
 ## 5. Add a histogram of item clicks
 
 Now you'll add a histogram visualization to your dashboard, similarly to the previous step. In the **New Visualization** window, select **Vertical Bar**. Then select the index pattern you created in step 2. 
 
 Examine the `event_attributes.position.ordinal` data field. This field contains the position of the item in a list selected by the user. For the histogram visualization, the x-axis represents the ordinal number of the selected item (n). The y-axis represents the number of times that the nth item was clicked, as shown in the following image. 
 
-![Vertical Bar Chart]({{site.url}}{{site.baseurl}}/images/ubi/histogram.png "Vertical Bar Chart")
+![Vertical Bar Chart]({{site.url}}{{site.baseurl}}/images/ubi/histogram.png)
 
-## 6) Filter the displayed data
+## 6. Filter the displayed data
 
 Now you can further filter the displayed data. For example, you can see how the click position changes when a purchase occurs. Select **Add filter** and then select the `action_name:product_purchase` field, as shown in the following image.
-![Product Purchase]({{site.url}}{{site.baseurl}}/images/ubi/product_purchase.png "Product Purchase")
+![Product Purchase]({{site.url}}{{site.baseurl}}/images/ubi/product_purchase.png)
 
 
 You can filter event messages containing the word `*laptop*` by adding wildcards, as shown in the following image.
-![Laptop]({{site.url}}{{site.baseurl}}/images/ubi/laptop.png "Laptop").
+![Laptop]({{site.url}}{{site.baseurl}}/images/ubi/laptop.png).
 
 

_tutorials/vector-search/semantic-search/semantic-search-cfn-sagemaker.md

@@ -163,7 +163,7 @@ Complete the following fields, keeping all other fields at their default values:
 
 If you already have a SageMaker inference endpoint, you can configure a model using that endpoint, as shown in the following image.  
 
-![Using an existing SageMaker inference endpoint]({{site.url}}{{site.baseurl}}/images/semantic_search/semantic_search_remote_model_Integration_3.png)
+![Using an existing SageMaker inference endpoint]({{site.url}}{{site.baseurl}}/images/vector-search-tutorials/semantic_search_remote_model_Integration_3.png)
 
 Complete the following fields, keeping all others at their default values: