[8.15] [8.15] creates Google vertex guide for ESS (backport #5549)

docs/AI-for-security/ai-for-security.asciidoc

@@ -15,6 +15,7 @@ include::connector-guides-landing-pg.asciidoc[leveloffset=+1]
 include::connect-to-azure-openai.asciidoc[leveloffset=+2]
 include::connect-to-bedrock.asciidoc[leveloffset=+2]
 include::connect-to-openai.asciidoc[leveloffset=+2]
+include::connect-to-vertex.asciidoc[leveloffset=+2]
 include::connect-to-byo.asciidoc[leveloffset=+2]
 
 

docs/AI-for-security/connect-to-vertex.asciidoc

@@ -0,0 +1,119 @@
+[[connect-to-vertex]]
+= Connect to Google Vertex
+
+:frontmatter-description: Set up a Google Vertex LLM connector.
+:frontmatter-tags-products: [security]
+:frontmatter-tags-content-type: [guide]
+:frontmatter-tags-user-goals: [get-started]
+
+This page provides step-by-step instructions for setting up a Google Vertex AI connector for the first time. This connector type enables you to leverage Vertex AI's large language models (LLMs) within {elastic-sec}. You'll first need to enable Vertex AI, then generate an API key, and finally configure the connector in your {elastic-sec} project.
+
+IMPORTANT: Before continuing, you should have an active project in one of Google Vertex AI's https://cloud.google.com/vertex-ai/docs/general/locations#feature-availability[supported regions]. 
+
+[discrete]
+== Enable the Vertex AI API
+
+1. Log in to the GCP console and navigate to **Vertex AI → Vertex AI Studio → Overview**. 
+2. If you're new to Vertex AI, the **Get started with Vertex AI Studio** popup appears. Click **Vertex AI API**, then click **ENABLE**.
+
+The following video demonstrates these steps.
+
+=======
+++++
+<script type="text/javascript" async src="https://play.vidyard.com/embed/v4.js"></script>
+<img
+  style="width: 100%; margin: auto; display: block;"
+  class="vidyard-player-embed"
+  src="https://play.vidyard.com/vFhtbiCZiKhvdZGy2FjyeT.jpg"
+  data-uuid="vFhtbiCZiKhvdZGy2FjyeT"
+  data-v="4"
+  data-type="inline"
+/>
+</br>
+++++
+=======
+
+NOTE: For more information about enabling the Vertex AI API, refer to https://cloud.google.com/vertex-ai/docs/start/cloud-environment[Google's documentation].
+
+[discrete] 
+== Create a Vertex AI service account
+
+1. In the GCP console, navigate to **APIs & Services → Library**.
+2. Search for **Vertex AI API**, select it, and click **MANAGE**.
+3. In the left menu, navigate to **Credentials** then click **+ CREATE CREDENTIALS** and select **Service account**.
+4. Name the new service account, then click **CREATE AND CONTINUE**. 
+5. Under **Select a role**, select **Vertex AI User**, then click **CONTINUE**.
+6. Click **Done**.
+
+The following video demonstrates these steps.
+
+=======
+++++
+<script type="text/javascript" async src="https://play.vidyard.com/embed/v4.js"></script>
+<img
+  style="width: 100%; margin: auto; display: block;"
+  class="vidyard-player-embed"
+  src="https://play.vidyard.com/tmresYYiags2w2nTv3Gac8.jpg"
+  data-uuid="tmresYYiags2w2nTv3Gac8"
+  data-v="4"
+  data-type="inline"
+/>
+</br>
+++++
+=======
+
+[discrete] 
+== Generate an API key
+
+1. Return to Vertex AI's **Credentials** menu and click **Manage service accounts**.
+2. Search for the service account you just created, select it, then click the link that appears under **Email**.
+3. Go to the **KEYS** tab, click **ADD KEY**, then select **Create new key**.
+4. Select **JSON**, then click **CREATE** to download the key. Keep it somewhere secure.
+
+The following video demonstrates these steps.
+
+=======
+++++
+<script type="text/javascript" async src="https://play.vidyard.com/embed/v4.js"></script>
+<img
+  style="width: 100%; margin: auto; display: block;"
+  class="vidyard-player-embed"
+  src="https://play.vidyard.com/hrcy3F9AodwhJcV1i2yqbG.jpg"
+  data-uuid="hrcy3F9AodwhJcV1i2yqbG"
+  data-v="4"
+  data-type="inline"
+/>
+</br>
+++++
+=======
+
+[discrete]
+== Configure the Google Gemini connector
+
+Finally, configure the connector in your Elastic deployment:
+
+1. Log in to your Elastic deployment.
+2. Navigate to **Stack Management → Connectors → Create Connector → Google Gemini**.
+3. Name your connector to help keep track of the model version you are using.
+4. Under **URL**, enter the URL for your region.
+5. Enter your **GCP Region** and **GCP Project ID**.
+6. Under **Default model**, specify either `gemini-1.5.pro` or `gemini-1.5-flash`. https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models[Learn more about the models].
+7. Under **Authentication**, enter your API key.
+8. Click **Save**.
+
+The following video demonstrates these steps.
+
+=======
+++++
+<script type="text/javascript" async src="https://play.vidyard.com/embed/v4.js"></script>
+<img
+  style="width: 100%; margin: auto; display: block;"
+  class="vidyard-player-embed"
+  src="https://play.vidyard.com/8L2WPm2HKN1cH872Gs5uvL.jpg"
+  data-uuid="8L2WPm2HKN1cH872Gs5uvL"
+  data-v="4"
+  data-type="inline"
+/>
+</br>
+++++
+=======

docs/AI-for-security/connector-guides-landing-pg.asciidoc

@@ -8,4 +8,5 @@ Setup guides are available for the following LLM providers:
 * <<assistant-connect-to-azure-openai, Azure OpenAI>>
 * <<assistant-connect-to-bedrock, Amazon Bedrock>>
 * <<assistant-connect-to-openai, OpenAI>>
+* <<connect-to-vertex, Google Vertex>>
 * <<connect-to-byo-llm, LM Studio (custom local LLM)>>