fix image and partial paths

serverless/pages/action-connectors.mdx

@@ -212,7 +212,7 @@ For a comparison of the Elastic subscription levels, go to
 
 Rules use connectors to route actions to different destinations like log files, ticketing systems, and messaging tools. While each ((kib)) app can offer their own types of rules, they typically share connectors. **((stack-manage-app)) → ((connectors-ui))** offers a central place to view and manage all the connectors in the current space.
 
-![Example connector listing in the ((rules-ui)) UI](images/action-types/management-connectors-connector-listing.png) */}
+![Example connector listing in the ((rules-ui)) UI](../images/action-types/management-connectors-connector-listing.png) */}
 {/* NOTE: This is an autogenerated screenshot. Do not edit it directly. */}
 {/*
 ## Required permissions
@@ -231,13 +231,13 @@ in the current space. You can use the search bar to find specific connectors by
 name and type. The **Type** dropdown also enables you to filter to a subset of
 connector types.
 
-![Filtering the connector list by types of connectors](images/action-types/management-connectors-connector-filter-by-type.png) */}
+![Filtering the connector list by types of connectors](../images/action-types/management-connectors-connector-filter-by-type.png) */}
 {/* NOTE: This is an autogenerated screenshot. Do not edit it directly. */}
 {/*
 You can delete individual connectors using the trash icon. Alternatively, select
 multiple connectors and delete them in bulk using the **Delete** button.
 
-![Deleting connectors individually or in bulk](images/action-types/management-connectors-connector-delete.png) */}
+![Deleting connectors individually or in bulk](../images/action-types/management-connectors-connector-delete.png) */}
 {/* NOTE: This is an autogenerated screenshot. Do not edit it directly. */}
 {/*
 <DocCallOut title="Note">
@@ -252,7 +252,7 @@ When this happens the action will fail to run and errors appear in the ((kib)) l
 New connectors can be created with the **Create connector** button, which guides
 you to select the type of connector and configure its properties.
 
-![Connector select type](images/action-types/management-connectors-connector-select-type.png)
+![Connector select type](../images/action-types/management-connectors-connector-select-type.png)
 
 After you create a connector, it is available for use any time you set up an
 action in the current space.
@@ -270,12 +270,12 @@ For more details, refer to the [elasticstack_kibana_action_connector](https://re
 To import and export connectors, use the
 <DocLink id="enKibanaManagingSavedObjects">Saved Objects Management UI</DocLink>.
 
-<DocImage size="l" url="images/action-types/management-connectors-connectors-import-banner.png" alt="Connectors import banner" />
+<DocImage size="l" url="../images/action-types/management-connectors-connectors-import-banner.png" alt="Connectors import banner" />
 
 If a connector is missing sensitive information after the import, a **Fix**
 button appears in **((connectors-ui))**.
 
-![Connectors with missing secrets](images/action-types/management-connectors-connectors-with-missing-secrets.png)
+![Connectors with missing secrets](../images/action-types/management-connectors-connectors-with-missing-secrets.png)
 
 ## Monitoring connectors
 

serverless/pages/api-keys.mdx

@@ -17,7 +17,7 @@ Or, you might create API keys to automate ingestion of new data from remote sour
 
 You can manage your keys in **((project-settings)) → ((manage-app)) → ((api-keys-app))**:
 
-!["API keys UI"](images/api-key-management.png)
+!["API keys UI"](../images/api-key-management.png)
 {/* TBD: This image was refreshed but should be automated */}
 
 A _personal API key_ allows external services to access the ((stack)) on behalf of a user.
@@ -36,7 +36,7 @@ cluster privileges to use API keys in Elastic.  API keys can also be seen in a r
 
 In **((api-keys-app))**, click **Create API key**:
 
-!["Create API key UI"](images/create-personal-api-key.png)
+!["Create API key UI"](../images/create-personal-api-key.png)
 
 Once created, you can copy the encoded API key and use it to send requests to the ((es)) HTTP API. For example:
 

serverless/pages/data-views.mdx

@@ -32,10 +32,10 @@ For example, a ((data-source)) can point to your log data from yesterday or all
 
 After you've loaded your data, follow these steps to create a ((data-source)):
 
-{/* <DocImage size="m" url="images/discover-find-data-view.png" alt="How to set the ((data-source)) in Discover" /> */}
+{/* <DocImage size="m" url="../images/discover-find-data-view.png" alt="How to set the ((data-source)) in Discover" /> */}
 
  1. Open {/***Lens** or*/}**Discover** then open the data view menu.
-    <DocImage size="l" url="images/discover-find-data-view.png" alt="How to set the ((data-source)) in Discover" />
+    <DocImage size="l" url="../images/discover-find-data-view.png" alt="How to set the ((data-source)) in Discover" />
     Alternatively, go to **((project-settings)) → ((manage-app)) → ((data-views-app))**.
 
 1. Click **Create a ((data-source))**.
@@ -45,7 +45,7 @@ After you've loaded your data, follow these steps to create a ((data-source)):
 1. Start typing in the **Index pattern** field, and Elastic looks for the names of
     indices, data streams, and aliases that match your input. You can
     view all available sources or only the sources that the data view targets.
-    !["Create data view"](images/discover-create-data-view.png)
+    !["Create data view"](../images/discover-create-data-view.png)
 
         * To match multiple sources, use a wildcard (*). `filebeat-*` matches
             `filebeat-apache-a`, `filebeat-apache-b`, and so on.

serverless/pages/debug-grok-expressions.mdx

@@ -65,7 +65,7 @@ For example:
     You'll see the simulated event that results from applying the grok
     pattern.
 
-    !["Grok Debugger"](images/grok-debugger-overview.png)
+    !["Grok Debugger"](../images/grok-debugger-overview.png)
 
 <div id="grokdebugger-custom-patterns"></div>
 
@@ -109,6 +109,6 @@ You must specify each pattern definition on its own line.
 
     You'll see the simulated output event that results from applying the grok pattern that contains the custom pattern:
 
-    !["Debugging a custom pattern"](images/grok-debugger-custom-pattern.png)
+    !["Debugging a custom pattern"](../images/grok-debugger-custom-pattern.png)
 
     If an error occurs, you can continue iterating over the custom pattern until the output matches your expected event.

serverless/pages/debug-painless-scripts.mdx

@@ -18,4 +18,4 @@ You can use Painless to safely write inline and stored scripts anywhere scripts
 
 To get started, open the main menu, click **Developer Tools**, and then click **Painless Lab**.
 
-![Painless Lab](images/painless-lab.png)
+![Painless Lab](../images/painless-lab.png)

serverless/pages/deploy-nlp-model-widget.mdx

@@ -1,6 +1,6 @@
 
-import Elser from './elser.mdx'
-import DenseVector from './dense-vector.mdx'
+import Elser from './deploy-nlp-model-elser.mdx'
+import DenseVector from './deploy-nlp-model-dense-vector.mdx'
 
 <DocTabs hasBorder>
 	<DocTab name="ELSER">

serverless/pages/explore-your-data-alerting.mdx

@@ -14,7 +14,7 @@ In **((alerts-app))** or **((project-settings)) → ((manage-app)) → ((rules-a
 * Drill down to rule details
 * Configure rule settings
 
-![Example rule listing in ((rules-ui))](images/alerting/rules-ui.png)
+![Example rule listing in ((rules-ui))](../images/rules-ui.png)
 
 For an overview of alerting concepts, go to <DocLink id="serverlessRules" />.
 
@@ -28,7 +28,7 @@ For more information, go to <DocBadge><DocIcon size="s" type="unlink" title="mis
 
 When you click the **Create rule** button, it launches a flyout that guides you through selecting a rule type and configuring its conditions and actions.
 
-![((rules-ui)) app](images/alerting/alerting-overview.png)
+![((rules-ui)) app](../images/alerting-overview.png)
 
 The rule types available in an ((es)) project are:
 
@@ -50,12 +50,12 @@ For more details, refer to the [elasticstack_kibana_alerting_rule](https://regis
 The rule listing enables you to quickly snooze, disable, enable, or delete individual rules.
 For example, you can change the state of a rule:
 
-![Use the rule status dropdown to enable or disable an individual rule](images/alerting/rule-enable-disable.png)
+![Use the rule status dropdown to enable or disable an individual rule](../images/rule-enable-disable.png)
 
 When you snooze a rule, the rule checks continue to run on a schedule but the alert will not trigger any actions.
 You can snooze for a specified period of time, indefinitely, or schedule single or recurring downtimes:
 
-![Snooze notifications for a rule](images/alerting/rule-snooze-panel.png)
+![Snooze notifications for a rule](../images/rule-snooze-panel.png)
 
 When a rule is in a snoozed state, you can cancel or change the duration of this state.
 
@@ -70,7 +70,7 @@ Stack monitoring rules are automatically created for you and therefore cannot be
 
 Rules are disabled on export. You are prompted to re-enable the rule on successful import.
 
-<DocImage size="l" url="images/alerting/rules-imported-banner.png" alt="Rules import banner" />
+<DocImage size="l" url="../images/rules-imported-banner.png" alt="Rules import banner" />
 
 ## View rule details
 
@@ -88,7 +88,7 @@ A rule can have one of the following responses:
 
 Click the rule name to access a rule details page:
 
-![Rule details page with multiple alerts](images/alerting/rule-details-alerts-active.png)
+![Rule details page with multiple alerts](../images/rule-details-alerts-active.png)
 
 In this example, the rule detects when a site serves more than a threshold number of bytes in a 24 hour period. Four sites are above the threshold. These are called alerts - occurrences of the condition being detected - and the alert name, status, time of detection, and duration of the condition are shown in this view. Alerts come and go from the list depending on whether the rule conditions are met.
 
@@ -109,13 +109,13 @@ The `flapping` state is possible only if you have enabled alert flapping detecti
 
 If there are rule actions that failed to run successfully, you can see the details on the **History** tab.
 In the **Message** column, click the warning or expand icon or click the number in the **Errored actions** column to open the **Errored Actions** panel.
-{/* <DocImage flatImage alt="double arrow icon to open a flyout with the document details" url="images/alerting/discover-expand-icon-2.png" /> */}
+{/* <DocImage flatImage alt="double arrow icon to open a flyout with the document details" url="../images/alerting/discover-expand-icon-2.png" /> */}
 
 {/*
 TBD: Is this setting still feasible in serverless?
 In this example, the action failed because the `xpack.actions.email.domain_allowlist` setting was updated and the action's email recipient is no longer included in the allowlist:
 
-![Rule history page with alerts that have errored actions](images/alerting/rule-details-errored-actions.png)
+![Rule history page with alerts that have errored actions](../images/rule-details-errored-actions.png)
 */}
 {/* If an alert was affected by a maintenance window, its identifier appears in the **Maintenance windows** column. */}
 
@@ -124,4 +124,4 @@ If a muted alert no longer meets the rule conditions, it stays in the list to av
 You can also disable a rule, which stops it from running checks and clears any alerts it was tracking.
 You may want to disable rules that are not currently needed to reduce the load on your cluster.
 
-![Use the disable toggle to turn off rule checks and clear alerts tracked](images/alerting/rule-details-disabling.png)
+![Use the disable toggle to turn off rule checks and clear alerts tracked](../images/rule-details-disabling.png)

serverless/pages/explore-your-data-discover-your-data.mdx

@@ -35,7 +35,7 @@ Tell ((kib)) where to find the data you want to explore, and then specify the ti
 
 2. Give your data view a name.
 
-<DocImage size="xxl" url="images/discover/create-data-view.png" alt="Create a data view" />
+<DocImage size="xxl" url="../images/create-data-view.png" alt="Create a data view" />
 
 3. Start typing in the **Index pattern** field, and the names of indices, data streams, and aliases that match your input will be displayed.
 
@@ -60,7 +60,7 @@ Tell ((kib)) where to find the data you want to explore, and then specify the ti
 
 7. Adjust the time range to view data for the **Last 40 years** to view all your book data.
 
- <DocImage size="xxl" url="images/discover/book-data.png" alt="Your book data displayed" />
+ <DocImage size="xxl" url="../images/book-data.png" alt="Your book data displayed" />
 
 <div id="explore-fields-in-your-data"></div>
 
@@ -110,7 +110,7 @@ the same way you do with other fields.
 
 8. Add `authorabbrev` to the document table.
 
-<DocImage size="xxl" url="images/discover/add-fields.png" alt="How the fields you just created should display" />
+<DocImage size="xxl" url="../images/add-fields.png" alt="How the fields you just created should display" />
 
 <div id="search-in-discover"></div>
 

serverless/pages/explore-your-data-ml-nlp-deploy-model.mdx

@@ -19,7 +19,7 @@ search speed remains unaffected by ingest workloads, and vice versa. Having
 separate deployments for search and ingest mitigates performance issues 
 resulting from interactions between the two, which can be hard to diagnose.
 
-!["Model deployment on the Trained Models UI."](images/ml/ml-nlp-deployment-id.png)
+!["Model deployment on the Trained Models UI."](../images/ml-nlp-deployment-id.png)
 
 It is recommended to fine-tune each deployment based on its specific purpose. To 
 improve ingest performance, increase throughput by adding more allocations to 

serverless/pages/explore-your-data-ml-nlp-elser.mdx

@@ -72,14 +72,14 @@ The table below shows the performance of ELSER compared to ((es)) BM25 with an
 English analyzer broken down by the 12 data sets used for the evaluation. ELSER 
 has 10 wins, 1 draw, 1 loss and an average improvement in NDCG@10 of 17%.
 
-<div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-elser-ndcg10-beir.png" alt="alt='ELSER benchmarks'" /></div>
+<div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-elser-ndcg10-beir.png" alt="alt='ELSER benchmarks'" /></div>
 _NDCG@10 for BEIR data sets for BM25 and ELSER  - higher values are better)_
 
 The following table compares the average performance of ELSER to some other 
 strong baselines. The OpenAI results are separated out because they use a 
 different subset of the BEIR suite.
 
-<div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-elser-average-ndcg.png" alt="alt='ELSER average performance compared to other baselines'" /></div>
+<div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-elser-average-ndcg.png" alt="alt='ELSER average performance compared to other baselines'" /></div>
 _Average NDCG@10 for BEIR data sets vs. various high quality baselines (higher_ 
 _is better). OpenAI chose a different subset, ELSER results on this set_ 
 _reported separately._
@@ -101,15 +101,15 @@ Dev Console.
 1. Click the **Download model** button under **Actions**. You can check the 
     download status on the **Notifications** page.
 
-    <div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-elser-download.png" alt="alt='Downloading ELSER'" /></div>
+    <div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-elser-download.png" alt="alt='Downloading ELSER'" /></div>
 
 1. After the download is finished, start the deployment by clicking the 
     **Start deployment** button.
 
 1. Provide a deployment ID, select the priority, and set the number of 
     allocations and threads per allocation values.
 
-    <div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-deployment-id.png" alt="alt='Deploying ELSER'" /></div>
+    <div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-deployment-id.png" alt="alt='Deploying ELSER'" /></div>
 
 1. Click Start.
 

serverless/pages/explore-your-data-ml-nlp-inference.mdx

@@ -23,7 +23,7 @@ you can use it to perform ((nlp)) tasks in ingest pipelines.
 
 In ((kib)), you can create and edit pipelines under **Content** → **Pipelines**.
 
-<div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-pipeline-lang.png" alt="Creating a pipeline in the Stack Management app" /></div>
+<div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-pipeline-lang.png" alt="Creating a pipeline in the Stack Management app" /></div>
 
 1. Click **Create pipeline** or edit an existing pipeline.
 1. Add an [((infer)) processor](((ref))/inference-processor.html) to your pipeline:
@@ -242,7 +242,7 @@ Before you can verify the results of the pipelines, you must
 [create ((data-sources))](((kibana-ref))/data-views.html). Then you can explore 
 your data in **Discover**:
 
-<div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-discover-ner.png" alt="A document from the NER pipeline in the Discover app" /></div>
+<div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-discover-ner.png" alt="A document from the NER pipeline in the Discover app" /></div>
 
 The `ml.inference.predicted_value` field contains the output from the ((infer))
 processor. In this NER example, there are two documents that contain the
@@ -253,7 +253,7 @@ ISO identifier of the language with the highest probability and the
 `ml.inference.top_classes` fields contain the top five most probable languages
 and their scores:
 
-<div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-discover-lang.png" alt="A document from the ((lang-ident)) pipeline in the Discover app" /></div>
+<div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-discover-lang.png" alt="A document from the ((lang-ident)) pipeline in the Discover app" /></div>
 
 To learn more about ingest pipelines and all of the other processors that you
 can add, refer to [Ingest pipelines](((ref))/ingest.html).

serverless/pages/explore-your-data-ml-nlp-ner-example.mdx

@@ -76,7 +76,7 @@ use the [sync ((ml)) objects API](((kibana-ref))/ml-sync.html).
 Deployed models can be evaluated on the **Trained Models** page by selecting the 
 **Test model** action for the respective model. 
 
-![Test trained model UI](images/ml/ml-nlp-ner-test.png)
+![Test trained model UI](../images/ml-nlp-ner-test.png)
 
 <DocAccordion buttonContent="**Test the model by using the _infer API**">
 
@@ -310,4 +310,4 @@ Optionally, adjust the time selector to cover the data points in the
 
 Update and save the visualization.
 
-<div style={{ textAlign: "center" }}><DocImage url="images/ml/ml-nlp-tag-cloud.png" alt="alt='Tag cloud created from Les Misérables'" /></div>
+<div style={{ textAlign: "center" }}><DocImage url="../images/ml-nlp-tag-cloud.png" alt="alt='Tag cloud created from Les Misérables'" /></div>

serverless/pages/explore-your-data-ml-nlp-test-inference.mdx

@@ -16,7 +16,7 @@ The simplest method to test your model against new data is to use the
 **Test model** action in ((kib)). You can either provide some input text or use a 
 field of an existing index in your cluster to test the model:
 
-![Testing a sentence with two named entities against a NER trained model in the **((ml))** app](images/ml/ml-nlp-test-ner.png)
+![Testing a sentence with two named entities against a NER trained model in the **((ml))** app](../images/ml-nlp-test-ner.png)
 
 Alternatively, you can use the
 [infer trained model API](((ref))/infer-trained-model.html).

serverless/pages/explore-your-data-ml-nlp-text-embedding-example.mdx

@@ -84,7 +84,7 @@ use the [sync ((ml)) objects API](((kibana-ref))/ml-sync.html).
 Deployed models can be evaluated on the **Trained Models** page by selecting the 
 **Test model** action for the respective model.
 
-![Test trained model UI](images/ml/ml-nlp-text-emb-test.png)
+![Test trained model UI](../images/ml-nlp-text-emb-test.png)
 
 <DocAccordion buttonContent="**Test the model by using the _infer API**">
 
@@ -148,7 +148,7 @@ Name the first column `id` and the second one `text`. The index name is
 `collection`. After the upload is done, you can see an index named `collection` 
 with 182469 documents.
 
-![Importing the data](images/ml/ml-nlp-text-emb-data.png)
+![Importing the data](../images/ml-nlp-text-emb-data.png)
 
 
 ## Add the text embedding model to an ((infer)) ingest pipeline
@@ -257,7 +257,7 @@ GET _tasks/<task_id>
 
 You can also open the model stat UI to follow the progress.
 
-![Model status UI](images/ml/ml-nlp-text-emb-reindex.png)
+![Model status UI](../images/ml-nlp-text-emb-reindex.png)
 
 After the reindexing is finished, the documents in the new index contain the 
 ((infer)) results – the vector embeddings.