third pass, including removing docker steps

Author: anupras-mohapatra-arm

content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/_index.md

@@ -1,7 +1,7 @@
 ---
-title: Build RAG applications with LlamaIndex on a Google Cloud C4A Axion virtual machine
+title: Build RAG applications with LlamaIndex on a Google Cloud C4A virtual machine
 
-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.
+description: Set up LlamaIndex on Google Axion-based C4A Arm64 VMs running SUSE Linux to build browser-based Retrieval-Augmented Generation (RAG) applications using local LLMs, vector databases, and FastAPI.
 
 minutes_to_complete: 30
 

content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/background.md

@@ -1,6 +1,6 @@
 ---
-title: Learn about LlamaIndex and Google Axion C4A for RAG applications
-description: Learn how LlamaIndex supports browser-based RAG applications on Google Axion-based C4A Arm instances.
+title: Learn about LlamaIndex and Google Cloud C4A for RAG applications
+description: Learn how LlamaIndex supports browser-based RAG applications on Google Axion-based C4A instances.
 weight: 2
 
 layout: "learningpathall"
@@ -24,12 +24,12 @@ LlamaIndex provides a unified framework with components such as:
 - Vector store integrations for scalable embedding search  
 - LLM integrations for generating grounded responses  
 
-Running LlamaIndex on Google Axion C4A Arm-based infrastructure enables efficient execution of AI and RAG workloads by using multi-core Arm CPUs and optimized memory performance. This results in improved performance per watt, reduced infrastructure costs, and better scalability for browser-based AI applications and local inference pipelines.
+Running LlamaIndex on Google Cloud C4A Arm-based infrastructure enables efficient execution of AI and RAG workloads by using multi-core Arm CPUs and optimized memory performance. This results in improved performance per watt, reduced infrastructure costs, and better scalability for browser-based AI applications and local inference pipelines.
 
 In this Learning Path, you'll use these components to build a browser-based RAG application that answers questions from custom documents.
 
 ## What you've learned and what's next
 
-You've now learned about Google Cloud C4A Arm-based VMs 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.
+You've now learned about Arm-based Google Cloud C4A VMs 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.
 
 Next, you'll create a firewall rule in Google Cloud Console to enable remote access to the browser-based LlamaIndex RAG application that you'll create in this Learning Path.

content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/build-browser-rag-app.md

@@ -31,9 +31,9 @@ FastAPI
     ↓
 LlamaIndex
     ↓
-ChromaDB Vector Store
+ChromaDB vector store
     ↓
-Ollama Local LLM
+Ollama local LLM
     ↓
 Documents
 ```
@@ -383,7 +383,7 @@ What is Google Cloud Axion?
 
 The answers will appear directly in the browser interface.
 
-## Add your own documents
+## (Optional) Add your own documents
 
 After confirming that the application works, you can try adding your own documents.
 

content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/firewall.md

@@ -9,9 +9,9 @@ layout: learningpathall
 
 ## Allow inbound access to the LlamaIndex browser application
 
-Create a firewall rule in Google Cloud Console to expose port 8000 for the browser-based LlamaIndex RAG application.
+Create a firewall rule in Google Cloud console to expose port 8000 for the browser-based LlamaIndex RAG application.
 
-### Configure the firewall rule in Google Cloud Console
+### Configure the firewall rule in the Google Cloud console
 
 To configure a firewall rule:
 
@@ -26,11 +26,7 @@ To configure a firewall rule:
 ![Google Cloud console Create firewall rule form with Name set to allow-llamaindex-port and Direction of traffic set to Ingress#center](images/network-rule.png "Configuring the allow-llamaindex-port firewall rule")
 
 5. Under **Protocols and ports**, select **Specified protocols and ports**.
-6. Select the **TCP** checkbox. Port `8000` is used by the FastAPI server that backs the browser-based LlamaIndex RAG application. Enter:
-
-```text
-8000
-```
+6. Select the **TCP** checkbox. For **Ports**, enter `8000`. Port `8000` is used by the FastAPI server that backs the browser-based LlamaIndex RAG application.
 
 ![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")
 
@@ -39,6 +35,6 @@ To configure a firewall rule:
 
 ## What you've accomplished and what's next
 
-You've now created a firewall rule that exposes port 8000 for the browser-based LlamaIndex RAG application and port 22 for SSH. The firewall rule uses the network tag `allow-llamaindex-port`, which you'll attach to your virtual machine in the next section.
+You've now created a firewall rule that exposes port 8000 for the browser-based LlamaIndex RAG application and port 22 for SSH. You'll attach this firewall rule to your virtual machine in the next section.
 
 Next, you'll create a Google Cloud C4A virtual machine and connect to it using SSH.

content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/instance.md

@@ -11,16 +11,16 @@ layout: learningpathall
 
 In this section, you'll create a Google Cloud C4A Arm-based virtual machine (VM). You'll use the `c4a-standard-4` machine type, which provides four vCPUs and 16 GB of memory. This VM will host your browser-based LlamaIndex RAG application.
 
-### Configure the C4A virtual machine in Google Cloud Console
+### Configure the C4A virtual machine in the Google Cloud console
 
 To create a virtual machine based on the C4A instance type in the console:
 
-1. Navigate to the [Google Cloud Console](https://console.cloud.google.com/).
+1. Navigate to the [Google Cloud console](https://console.cloud.google.com/).
 2. Go to **Compute Engine** > **VM instances** and select **Create instance**.
 3. Under **Machine configuration**, populate fields such as **Instance name**, **Region**, and **Zone**.
 4. Set **Series** to `C4A`, then select `c4a-standard-4` for **Machine type**.
 
-![Screenshot of the Google Cloud Console showing the Machine configuration section. The Series dropdown is set to C4A and the machine type c4a-standard-4 is selected.#center](images/gcp-vm.png "Configuring machine type to C4A in Google Cloud Console")
+![Screenshot of the Google Cloud console showing the Machine configuration section. The Series dropdown is set to C4A and the machine type c4a-standard-4 is selected.#center](images/gcp-vm.png "Configuring machine type to C4A in Google Cloud Console")
 
 5. Under **OS and storage**, select **Change** and then choose an Arm64-based operating system image. For this Learning Path, select **SUSE Linux Enterprise Server**.
 6. For the license type, choose **Pay as you go**.
@@ -31,7 +31,7 @@ To create a virtual machine based on the C4A instance type in the console:
 
 After the instance starts, select **SSH** next to the VM in the instance list to open a browser-based terminal session.
 
-![Google Cloud Console VM instances page displaying running instance with green checkmark and SSH button in the Connect column#center](images/gcp-pubip-ssh.png "Connecting to a running C4A VM using SSH")
+![Google Cloud console VM instances page displaying running instance with green checkmark and SSH button in the Connect column#center](images/gcp-pubip-ssh.png "Connecting to a running C4A VM using SSH")
 
 A new browser window opens with a terminal connected to your VM.
 

content/learning-paths/servers-and-cloud-computing/llamaindex-rag-axion/setup-llamaindex-rag.md

@@ -7,7 +7,7 @@ weight: 5
 layout: learningpathall
 ---
 
-## Prepare the environment
+## Prepare the environment for a LLamaIndex RAG application
 
 In this section, you'll prepare a Google Cloud Axion Arm64 VM for running a browser-based RAG application using LlamaIndex.
 
@@ -60,37 +60,6 @@ Python 3.11.10
 pip 22.3.1 from /usr/lib/python3.11/site-packages/pip (python 3.11)
 ```
 
-<!-- ### (Optional) Install Docker
-
-For this Learning Path, ChromaDB and Ollama run natively. For extended use, you can install Docker so that you can run containerized workloads alongside the RAG pipeline if needed:
-
-```bash
-sudo zypper install -y docker
-sudo systemctl enable docker
-sudo systemctl start docker
-```
-
-Verify Docker is running and add your user to the `docker` group so you don't need `sudo` for Docker commands:
-
-```bash
-sudo systemctl status docker
-sudo usermod -aG docker $USER
-newgrp docker
-```
-
-Test Docker:
-
-```bash
-docker run hello-world
-```
-
-The output is similar to:
-
-```output
-Hello from Docker!
-This message shows that your installation appears to be working correctly.
-``` -->
-
 ### Create project directory
 
 Create a project directory and a Python virtual environment. The virtual environment isolates the Python packages for this project from your system packages: