[Updated] Deploy a RAG Pipeline and Chatbot with App Platform for LKE

docs/guides/kubernetes/deploy-rag-pipeline-and-chatbot-on-apl/index.md

@@ -5,14 +5,18 @@ description: "This guide expands on a previously built LLM and AI inferencing ar
 authors: ["Akamai"]
 contributors: ["Akamai"]
 published: 2025-03-25
-modified: 2025-06-26
+modified: 2025-07-23
 keywords: ['ai','ai inference','ai inferencing','llm','large language model','app platform','lke','linode kubernetes engine','rag pipeline','retrieval augmented generation','open webui','kubeflow']
 license: '[CC BY-ND 4.0](https://creativecommons.org/licenses/by-nd/4.0)'
 external_resources:
 - '[Akamai App Platform for LKE](https://techdocs.akamai.com/cloud-computing/docs/application-platform)'
 - '[Akamai App Platform Documentation](https://techdocs.akamai.com/app-platform/docs/welcome)'
 ---
 
+{{< note title="This guide is being updated" type="warning" >}}
+This guide is currently undergoing updates due to ongoing development of App Platform and may not function as expected. An updated version will be available soon that incorporates these changes. Check back for the revised guide before proceeding with this deployment.
+{{< /note >}}
+
 This guide builds on the LLM (Large Language Model) architecture built in our [Deploy an LLM for AI Inferencing with App Platform for LKE](/docs/guides/deploy-llm-for-ai-inferencing-on-apl) guide by deploying a RAG (Retrieval-Augmented Generation) pipeline that indexes a custom data set. RAG is a particular method of context augmentation that attaches relevant data as context when users send queries to an LLM.
 
 Follow the steps in this tutorial to install Kubeflow Pipelines and deploy a RAG pipeline using Akamai App Platform for LKE. The deployment in this guide uses the previously deployed Open WebUI chatbot to respond to queries using a custom data set. The data set you use may vary depending on your use case. For example purposes, this guide uses a sample data set from Linode Docs in Markdown format.