Merge pull request #3361 from NeethuESim/rv-llamaindex-rag-axion-lp

fix: review changes for llamaindex-rag-axion LP

Author: pareenaverma

…d-computing/llmaidex-rag-axion/_index.md …computing/llamaindex-rag-axion/_index.mdcontent/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/_index.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md content/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/_index.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md

@@ -4,7 +4,7 @@ title: Build RAG applications with LlamaIndex on Google Cloud C4A Axion VM
 draft: true
 cascade:
     draft: true
-    
+
 description: Set up LlamaIndex on Google Cloud C4A Axion Arm VMs running SUSE Linux to build browser-based Retrieval-Augmented Generation (RAG) applications using local LLMs, vector databases, and FastAPI.
 
 minutes_to_complete: 30
@@ -42,25 +42,33 @@ tools_software_languages:
 operatingsystems:
   - Linux
 
-# ================================================================================
-#       FIXED, DO NOT MODIFY
-# ================================================================================
-
 further_reading:
-  - resource:
+    - resource:
       title: LlamaIndex official documentation
       link: https://docs.llamaindex.ai/en/stable/
       type: documentation
-
-  - resource:
+    - resource:
       title: LlamaIndex GitHub repository
       link: https://github.com/run-llama/llama_index
       type: documentation
-
-  - resource:
+    - resource:
       title: Ollama documentation
       link: https://ollama.com/library
       type: documentation
+    - resource:
+        title: Introducing Google Axion Processors, our new Arm-based CPUs
+        link: https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu
+        type: documentation
+    - resource:
+        title: Getting started with Google Cloud Platform
+        link: https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/google/
+        type: documentation
+
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
 
 weight: 1
 layout: "learningpathall"

…puting/llmaidex-rag-axion/_next-steps.md …ting/llamaindex-rag-axion/_next-steps.mdcontent/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/_next-steps.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_next-steps.md content/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/_next-steps.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_next-steps.md

@@ -11,8 +11,6 @@ Google Axion C4A is a family of Arm-based virtual machines built on Google’s c
 
 The C4A series provides a cost-effective alternative to x86 virtual machines while using the scalability and performance benefits of the Arm architecture in Google Cloud.
 
-To learn more, see the Google blog [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).
-
 ## LlamaIndex for RAG and context-aware AI applications on Arm
 
 LlamaIndex is an open-source framework designed to build context-aware AI applications using Large Language Models (LLMs). It's widely used for Retrieval-Augmented Generation (RAG), document indexing, vector search, semantic retrieval, and integrating custom data sources with LLMs.
@@ -29,8 +27,6 @@ Running LlamaIndex on Google Axion C4A Arm-based infrastructure enables efficien
 
 Common use cases include browser-based AI assistants, document search applications, semantic retrieval systems, vector database integrations, enterprise knowledge bases, and context-aware chatbot applications.
 
-To learn more, see the [LlamaIndex documentation](https://docs.llamaindex.ai/en/stable/) and the [LlamaIndex GitHub repository](https://github.com/run-llama/llama_index).
-
 ## What you've learned and what's next
 
 You've now learned about Google Axion C4A Arm-based virtual machines and their performance advantages for AI and RAG workloads. You were also introduced to core LlamaIndex components including document ingestion, indexing pipelines, query engines, vector stores, and LLM integrations.

…mputing/llmaidex-rag-axion/background.md …uting/llamaindex-rag-axion/background.mdcontent/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/background.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/background.md content/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/background.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/background.md

@@ -350,7 +350,7 @@ INFO:     Uvicorn running on http://0.0.0.0:8000
 Open a browser and navigate to:
 
 ```text
-http://<VM-IP>:8000
+http://<VM-EXTERNAL-IP>:8000
 ```
 
 This opens the browser-based RAG application UI.
@@ -389,7 +389,7 @@ Copy your own files into the data directory:
 cp yourfile.txt ~/llamaindex-rag/data/
 ```
 
-Restart FastAPI:
+First stop the server and then restart FastAPI:
 
 ```bash
 uvicorn api:app --host 0.0.0.0 --port 8000

…aidex-rag-axion/build-browser-rag-app.md …index-rag-axion/build-browser-rag-app.mdcontent/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/build-browser-rag-app.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/build-browser-rag-app.md content/learning-paths/servers-and-cloud-computing/llmaidex-rag-axion/build-browser-rag-app.md renamed to content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/build-browser-rag-app.md

@@ -10,8 +10,6 @@ layout: learningpathall
 
 Create a firewall rule in Google Cloud Console to expose the required port for the browser-based LlamaIndex RAG application.
 
-{{% notice Note %}}For help with GCP setup, see the Learning Path [Getting started with Google Cloud Platform](/learning-paths/servers-and-cloud-computing/csp/google/).{{% /notice %}}
-
 ## Configure the firewall rule in Google Cloud Console
 
 To configure a firewall rule for the LlamaIndex browser-based RAG application:
@@ -40,7 +38,9 @@ Use port mapping **8000** for the browser-based LlamaIndex RAG application runni
 
 ![Google Cloud Console Protocols and ports section with TCP selected and port 8000 entered#center](images/network-port.png "Setting the LlamaIndex browser application port in the firewall rule")
 
-7. Select **Create**.
+7. Also add port 22 in **TCP** checkbox for ssh access.
+
+8. Select **Create**.
 
 ## What you've accomplished and what's next