Revert "image paths"

leemthompo · leemthompo · commit 538ea6a6da0c · 2025-02-10T15:18:02.000+01:00
This reverts commit 066694b.
diff --git a/solutions/search/rag/playground-query.md b/solutions/search/rag/playground-query.md
@@ -23,7 +23,7 @@ The `{{query}}` variable represents the user’s question, rewritten as an {{es}
 
 The following screenshot shows the query editor in the Playground UI. In this simple example, the `books` index has two fields: `author` and `name`. Selecting a field adds it to the `fields` array in the query.
 
-:::{image} ../../images/kibana-query-interface.png
+:::{image} ../../../images/kibana-query-interface.png
 :alt: View and modify queries
 :class: screenshot
 :::
diff --git a/solutions/search/rag/playground.md b/solutions/search/rag/playground.md
@@ -112,7 +112,7 @@ You can also use locally hosted LLMs that are compatible with the OpenAI SDK. On
 
 ## Getting started [playground-getting-started]
 
-:::{image} ../../images/kibana-get-started.png
+:::{image} ../../../images/kibana-get-started.png
 :alt: get started
 :class: screenshot
 :::
@@ -183,7 +183,7 @@ Once you’ve connected to your LLM provider, it’s time to choose the data you
 :::::{tip}
 You can always add or remove indices later by selecting the **Data** button from the main Playground UI.
 
-:::{image} ../../images/kibana-data-button.png
+:::{image} ../../../images/kibana-data-button.png
 :alt: data button
 :class: screenshot
 :::
@@ -201,14 +201,14 @@ Since 8.15.0 (and earlier for {{es}} Serverless), the main Playground UI has two
 
 The **chat mode** is selected when you first set up your Playground instance.
 
-:::{image} ../../images/kibana-chat-interface.png
+:::{image} ../../../images/kibana-chat-interface.png
 :alt: chat interface
 :class: screenshot
 :::
 
 To switch to **query mode**, select **Query** from the main UI.
 
-:::{image} ../../images/kibana-query-interface.png
+:::{image} ../../../images/kibana-query-interface.png
 :alt: query interface
 :class: screenshot
 :::
@@ -250,7 +250,7 @@ Use the **View code** button to see the Python code that powers the chat interfa
 * {{es}} Python Client + LLM provider
 * LangChain + LLM provider
 
-:::{image} ../../images/kibana-view-code-button.png
+:::{image} ../../../images/kibana-view-code-button.png
 :alt: view code button
 :class: screenshot
 :::
diff --git a/solutions/search/ranking/learning-to-rank-ltr.md b/solutions/search/ranking/learning-to-rank-ltr.md
@@ -12,7 +12,7 @@ This feature was introduced in version 8.12.0 and is only available to certain s
 
 Learning To Rank (LTR) uses a trained machine learning (ML) model to build a ranking function for your search engine. Typically, the model is used as a second stage re-ranker, to improve the relevance of search results returned by a simpler, first stage retrieval algorithm. The LTR function takes a list of documents and a search context and outputs ranked documents:
 
-:::{image} ../../images/elasticsearch-reference-learning-to-rank-overview.png
+:::{image} ../../../images/elasticsearch-reference-learning-to-rank-overview.png
 :alt: Learning To Rank overview
 :title: Learning To Rank overview
 :name: learning-to-rank-overview-diagram
@@ -30,7 +30,7 @@ The LTR model is usually trained on a judgment list, which is a set of queries a
 
 The judgment list is the main input used to train the model. It consists of a dataset that contains pairs of queries and documents, along with their corresponding relevance labels. The relevance judgment is typically either a binary (relevant/irrelevant) or a more granular label, such as a grade between 0 (completely irrelevant) to 4 (highly relevant). The example below uses a graded relevance judgment.
 
-:::{image} ../../images/elasticsearch-reference-learning-to-rank-judgment-list.png
+:::{image} ../../../images/elasticsearch-reference-learning-to-rank-judgment-list.png
 :alt: Judgment list example
 :title: Judgment list example
 :name: learning-to-rank-judgment-list-example
@@ -59,7 +59,7 @@ These features fall into one of three main categories:
 
 To prepare the dataset for training, the features are added to the judgment list:
 
-:::{image} ../../images/elasticsearch-reference-learning-to-rank-feature-extraction.png
+:::{image} ../../../images/elasticsearch-reference-learning-to-rank-feature-extraction.png
 :alt: Judgment list with features
 :title: Judgment list with features
 :name: learning-to-rank-judgement-feature-extraction
diff --git a/solutions/search/search-approaches/near-real-time-search.md b/solutions/search/search-approaches/near-real-time-search.md
@@ -11,15 +11,15 @@ Lucene, the Java libraries on which {{es}} is based, introduced the concept of p
 
 Sitting between {{es}} and the disk is the filesystem cache. Documents in the in-memory indexing buffer ([Figure 1](#img-pre-refresh)) are written to a new segment ([Figure 2](#img-post-refresh)). The new segment is written to the filesystem cache first (which is cheap) and only later is it flushed to disk (which is expensive). However, after a file is in the cache, it can be opened and read just like any other file.
 
-:::{image} ../../images/elasticsearch-reference-lucene-in-memory-buffer.png
+:::{image} ../../../images/elasticsearch-reference-lucene-in-memory-buffer.png
 :alt: A Lucene index with new documents in the in-memory buffer
 :title: A Lucene index with new documents in the in-memory buffer
 :name: img-pre-refresh
 :::
 
 Lucene allows new segments to be written and opened, making the documents they contain visible to search ​without performing a full commit. This is a much lighter process than a commit to disk, and can be done frequently without degrading performance.
 
-:::{image} ../../images/elasticsearch-reference-lucene-written-not-committed.png
+:::{image} ../../../images/elasticsearch-reference-lucene-written-not-committed.png
 :alt: The buffer contents are written to a segment, which is searchable, but is not yet committed
 :title: The buffer contents are written to a segment, which is searchable, but is not yet committed
 :name: img-post-refresh
diff --git a/solutions/search/search-connection-details.md b/solutions/search/search-connection-details.md
@@ -21,14 +21,14 @@ To connect to your {{es}} deployment, you need either a Cloud ID or an {{es}} en
 1. Navigate to the Elastic Cloud home page.
 2. In the main menu, click **Manage this deployment**.
 
-    :::{image} ../../images/kibana-manage-deployment.png
+    :::{image} ../../../images/kibana-manage-deployment.png
     :alt: manage deployment
     :class: screenshot
     :::
 
 3. The Cloud ID is displayed on the right side of the page.
 
-    :::{image} ../../images/kibana-cloud-id.png
+    :::{image} ../../../images/kibana-cloud-id.png
     :alt: cloud id
     :class: screenshot
     :::
@@ -39,14 +39,14 @@ To connect to your {{es}} deployment, you need either a Cloud ID or an {{es}} en
 
 1. To navigate to **API keys**, use the [**global search bar**](../../get-started/the-stack.md#kibana-navigation-search).
 
-    :::{image} ../../images/kibana-api-keys-search-bar.png
+    :::{image} ../../../images/kibana-api-keys-search-bar.png
     :alt: api keys search bar
     :class: screenshot
     :::
 
 2. Click **Create API key**.
 
-    :::{image} ../../images/kibana-click-create-api-key.png
+    :::{image} ../../../images/kibana-click-create-api-key.png
     :alt: click create api key
     :class: screenshot
     :::
@@ -62,7 +62,7 @@ To connect to your {{es}} deployment, you need either a Cloud ID or an {{es}} en
 1. Navigate to the serverless project’s home page.
 2. Scroll down to the **Copy your connection details** section, and copy the **Elasticsearch endpoint**.
 
-    :::{image} ../../images/kibana-serverless-connection-details.png
+    :::{image} ../../../images/kibana-serverless-connection-details.png
     :alt: serverless connection details
     :class: screenshot
     :::
@@ -79,7 +79,7 @@ The **Cloud ID** is also displayed in the Copy your connection details section,
 1. Navigate to the serverless project’s home page.
 2. Scroll down to the **Add an API Key** section, and click **New**.
 
-    :::{image} ../../images/kibana-serverless-create-an-api-key.png
+    :::{image} ../../../images/kibana-serverless-create-an-api-key.png
     :alt: serverless create an api key
     :class: screenshot
     :::
diff --git a/solutions/search/serverless-elasticsearch-get-started.md b/solutions/search/serverless-elasticsearch-get-started.md
@@ -60,7 +60,7 @@ Once your project is set up, you’ll be directed to a page where you can create
 1. Enter a name for your index.
 2. Click **Create my index**. You can also create the index by clicking on **Code** to view and run code through the command line.
 
-    :::{image} ../../images/serverless-get-started-create-an-index.png
+    :::{image} ../../../images/serverless-get-started-create-an-index.png
     :alt: Create an index.
     :::
 
diff --git a/solutions/search/site-or-app/behavioral-analytics-event.md b/solutions/search/site-or-app/behavioral-analytics-event.md
@@ -31,7 +31,7 @@ This allows you to quickly check both absolute numbers and trends (tracked in pe
 
 The following screenshot shows an example **Overview** dashboard:
 
-:::{image} ../../images/elasticsearch-reference-analytics-overview-dashboard.png
+:::{image} ../../../images/elasticsearch-reference-analytics-overview-dashboard.png
 :alt: Analytics Overview dashboard showing the number of searches
 :class: screenshot
 :::
@@ -53,7 +53,7 @@ You can also easily sort in ascending or descending order by clicking on the hea
 
 The following screenshot shows the **Locations** tab of an **Explorer** dashboard, with a list of top locations in descending order:
 
-:::{image} ../../images/elasticsearch-reference-analytics-explorer-dashboard.png
+:::{image} ../../../images/elasticsearch-reference-analytics-explorer-dashboard.png
 :alt: Explorer dashboard showing the top locations in descending order
 :class: screenshot
 :::
@@ -67,7 +67,7 @@ Discover works with [data views^](../../../explore-analyze/find-and-organize/dat
 
 The following screenshot shows you where to find the data view dropdown menu in Discover:
 
-:::{image} ../../images/elasticsearch-reference-discover-data-view-analytics.png
+:::{image} ../../../images/elasticsearch-reference-discover-data-view-analytics.png
 :alt: Analytics Discover app showing the data view dropdown menu
 :class: screenshot
 :::
@@ -84,7 +84,7 @@ Discover has a lot of options, but here’s a quick overview of how to get start
 
 The following screenshot shows a Lens visualization of an `event.action` distribution:
 
-:::{image} ../../images/elasticsearch-reference-discover-lens-analytics.png
+:::{image} ../../../images/elasticsearch-reference-discover-lens-analytics.png
 :alt: Analytics Discover app showing a Lens visualization of an event action distribution
 :class: screenshot
 :::
