chore: retire /solutions/ai/genai and redirect to /solutions/ai

redirects.yaml

@@ -50,6 +50,7 @@ microcloud/roadshow?: "/microcloud"
 solutions/secure-open-source?: "/solutions/open-source-security"
 solutions/sovereign-cloud?: "/solutions/infrastructure/sovereign-cloud"
 solutions/ai/big-data-ai?: "/mlops"
+solutions/ai/genai/?: "/solutions/ai"
 java/?: "https://ubuntu.com/toolchains/java"
 
 # Juju redirects

secondary-navigation.yaml

@@ -26,8 +26,6 @@ ai:
       path: /solutions/ai/infrastructure
     - title: Edge AI
       path: /solutions/ai/edge
-    - title: GenAI infrastructure
-      path: /solutions/ai/genai
     - title: MLOps
       path: /mlops
 

templates/llms.txt

@@ -15,7 +15,6 @@
 - [Data and AI solutions](https://canonical.com/solutions/data-and-ai?format=md): Composable, cost-efficient and supported open source data and AI solutions.
 - [AI](https://canonical.com/solutions/ai?format=md): Develop and deploy AI workloads at scale with inference, model training and data solutions.
 - [AI infrastructure](https://canonical.com/solutions/ai/infrastructure?format=md): A proven and supported open source AI infrastructure stack. Start in the public cloud, then run models at scale in private infrastructure and at the edge.
-- [GenAI](https://canonical.com/solutions/ai/genai?format=md): Use open source GenAI infrastructure to take models to market faster and more securely.
 - [AI at the edge](https://canonical.com/solutions/ai/edge?format=md): Deploy AI at the edge with a supported and compliant operating system and other open source infrastructure and data solutions.
 - [Cloud-native app development](https://canonical.com/solutions/cloud-native-development?format=md): Adopt cloud-native development with reliable support and security maintenance for a broad portfolio of open source technologies.
 - [Infrastructure solutions](https://canonical.com/solutions/infrastructure?format=md): Build an enterprise-grade cloud with open source. Start quickly and scale up with full-stack automation.

templates/solutions/ai/form-data.json

@@ -2,7 +2,7 @@
   "form": {
     "/solutions/ai": {
       "templatePath": "/solutions/ai/index.html",
-      "childrenPaths": ["/solutions/ai/edge", "/solutions/ai/genai"],
+      "childrenPaths": ["/solutions/ai/edge"],
       "isModal": true,
       "modalId": "ai-modal",
       "formData": {

templates/solutions/ai/index.html

@@ -190,26 +190,6 @@ <h3 class="p-heading--5">MLOps</h3>
         </div>
       </a>
 
-      <a class="p-equal-height-row__col"
-         href="/solutions/ai/genai"
-         aria-label="GenAI with RAG">
-        <div class="p-equal-height-row__item">
-          <div class="p-image-container--16-9">
-            {{ image(url="https://assets.ubuntu.com/v1/7796e871-genai-rag.png",
-                        alt="",
-                        width="852",
-                        height="480",
-                        hi_def=True,
-                        loading="lazy",
-                        attrs={"class": "p-image-container__image"}) | safe
-            }}
-          </div>
-        </div>
-        <div class="p-equal-height-row__item">
-          <h3 class="p-heading--5">GenAI with RAG</h3>
-        </div>
-      </a>
-
       <a class="p-equal-height-row__col"
          href="/solutions/ai/big-data-ai"
          aria-label="Big data analytics">

templates/solutions/data-and-ai/index.html

@@ -159,7 +159,7 @@ <h3 class="p-heading--5">RAG</h3>
       <p>
         Retrieval augmented generation (RAG) is the leading method for enhancing the accuracy and relevance of LLM outputs – and you can easily use Canonical’s Charmed OpenSearch as your open source vector database to enable RAG capabilities.
         <div class="p-cta-block">
-          <a href="/solutions/ai/genai">Learn more about GenAI infrastructure and RAG&nbsp;&rsaquo;</a>
+          <a href="https://ubuntu.com/blog/what-is-rag-explained">Learn more about RAG&nbsp;&rsaquo;</a>
         </div>
       </p>
     {%- endif -%}