TEMPLATE: Merging RAG demo staging into real LP

assets/css/demo.css

@@ -261,8 +261,8 @@
 }
 .chatbot-response-text * {
     max-width: 98%;
-    margin-bottom:0;
-    line-height: 1.2;
+    margin-bottom: 8px;
+    line-height: 1.3;
 }
 
 

content/learning-paths/servers-and-cloud-computing/gh-runners/e2e-workflow.md

@@ -162,6 +162,6 @@ Using what you have learned, navigate to the **Train, Test and Deploy** workflow
 
 The diagram below shows the end-to-end workflow, the relationship between the steps, and the time required to run each step:
 
-![#e2e-workflow](/images/e2e-workflow.png)
+![#e2e-workflow](images/e2e-workflow.png)
 
 You have run an MLOps workflow using GitHub Actions with Arm-hosted runners for managing all of the steps in your ML application's lifecycle.

content/learning-paths/servers-and-cloud-computing/aaaaaaRAGexample/_demo.md → content/learning-paths/servers-and-cloud-computing/rag/_demo.md

@@ -21,7 +21,7 @@ example_user_prompts:
   - Do Hyperscan and Snort3 work on Graviton4?
   - How can I easily build multi-architecture Docker images?
 
-rag_data_cutoff_date: 2025/01/13
+rag_data_cutoff_date: 2025/01/17
 
 title_chatbot_area: Arm RAG Demo
 

themes/arm-design-system-hugo-theme/layouts/partials/demo-components/config-rag.html

@@ -22,7 +22,7 @@
     <div class="c-row u-gap-1/2 u-flex-nowrap u-padding-top-0">
         <div class="c-col">
             <h2>RAG Vector Store Details</h2>
-            <p>This app uses all data on this site, <a href="https://www.learn.arm.com">learn.arm.com</a>, as the RAG data set. The Markdown formatted content across Learning Paths and Install Guides was segmented into labeled chunks, vector embeddings were generated using FAISS, which the LLM demo below references for your query.</p>
+            <p>This app uses all data on this site, <a href="https://www.learn.arm.com">learn.arm.com</a>, as the RAG data set. The Markdown formatted content across Learning Paths and Install Guides was segmented into labeled chunks, and vector embeddings were generated. FAISS is used for the embedded similarity search. The LLM demo below references this vector store for your query.</p>
             <p><b>Note:</b> Data was sourced on {{.Params.rag_data_cutoff_date}}.</p>
         </div>    
     </div>

themes/arm-design-system-hugo-theme/layouts/partials/demo-components/llm-chatbot/javascript--llm-chatbot.html

@@ -573,7 +573,21 @@
 
 
 (function() {
-    const server_location_encoded = '{{ getenv "HUGO_LLM_API" | base64Encode }}';
+
+    {{/* Get server location from GH Secrets */}}
+    {{ $server_location := "" }};
+
+    {{ if eq .Params.demo_template_name "llm_chatbot_rag_demo" }}
+        {{ $server_location = getenv "HUGO_RAG_API" | base64Encode }}
+        console.log('Using RAG API.');
+    {{ else if eq .Params.demo_template_name "llm_chatbot_first_demo" }}
+        {{ $server_location = getenv "HUGO_LLM_API" | base64Encode }}
+        console.log('Using LLM API.');
+    {{ else }}
+        console.log('No server location provided.');
+    {{ end }}
+
+    const server_location_encoded = '{{ $server_location }}';
 
 
     function startPingCheck() {

themes/arm-design-system-hugo-theme/layouts/partials/footer/script-includes.html

@@ -42,12 +42,6 @@
 <!-- Demo page only -->
 {{ if and $is_content (hasPrefix .File.TranslationBaseName "_demo")}}
 <script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
-<script>
-    // Your script using `marked`
-    const markdownString = "# Hello, Markdown!";
-    const htmlContent = marked(markdownString);
-    console.log(htmlContent);
-  </script>
 {{end}}
 
 <!-- Also add prism to any _demo page that needs it, set as a 'prismjs' variable -->