second pass + descriptions

Author: anupras-mohapatra-arm

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

@@ -1,34 +1,35 @@
 ---
 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.
 weight: 2
 
 layout: "learningpathall"
 ---
 
-## Google Axion C4A Arm instances for AI and RAG workloads
+## Google Cloud C4A instances for AI and RAG workloads
 
-Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for modern cloud workloads such as AI applications, vector databases, Retrieval-Augmented Generation (RAG) pipelines, and scalable inference services.
+Google Cloud C4A is a family of Arm-based virtual machines (VMs) built on Google’s custom Axion CPU, which is based on Arm Neoverse V2 cores. Designed for high-performance and energy-efficient computing, these VMs offer strong performance for modern cloud workloads.
 
 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.
 
 ## 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 RAG, document indexing, vector search, semantic retrieval, and integrating custom data sources with LLMs.
+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.
 
 LlamaIndex provides a unified framework with components such as:
 
-* Document loaders for ingesting custom data  
-* Indexing pipelines for structured retrieval workflows  
-* Query engines for context-aware question answering  
-* Vector store integrations for scalable embedding search  
-* LLM integrations for generating grounded responses  
+- Document loaders for ingesting custom data  
+- Indexing pipelines for structured retrieval workflows  
+- Query engines for context-aware question answering  
+- 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.
 
-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.
+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 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.
+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.
 
 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

@@ -1,5 +1,6 @@
 ---
 title: Build and test a browser-based RAG application with LlamaIndex
+description: Learn how to build a browser-based RAG application with LlamaIndex, ChromaDB, Ollama, and FastAPI on an Arm-based Google Cloud C4A VM.
 weight: 6
 
 ### FIXED, DO NOT MODIFY
@@ -340,10 +341,11 @@ INFO:     Waiting for application startup.
 INFO:     Application startup complete.
 INFO:     Uvicorn running on http://0.0.0.0:8000
 ```
+Keep the terminal open for testing the application. 
 
 ## Test the browser-based RAG application
 
-After starting the application, open the application UI and test the application to make sure it works. 
+After starting the application, test it by opening the UI and asking a few questions. 
 
 ### Open browser application UI
 
@@ -391,7 +393,7 @@ Copy your own files into the data directory. For example:
 cp yourfile.txt ~/llamaindex-rag/data/
 ```
 
-Stop the running FastAPI server by pressing `Ctrl+C` in the terminal where Uvicorn is running. Then restart it:
+Stop the running FastAPI server by pressing `Ctrl + C` in the terminal where Uvicorn is running. Then restart it:
 
 ```bash
 uvicorn api:app --host 0.0.0.0 --port 8000
@@ -401,6 +403,6 @@ The `build_query_engine()` function runs on startup and reads all documents from
 
 ## What you've accomplished
 
-You've successfully built a browser-based RAG application using LlamaIndex on a Google Cloud Axion Arm64 VM. You created sample documents, generated embeddings using HuggingFace models, stored vectors in ChromaDB, exposed the backend using FastAPI, and queried custom documents directly from a browser using Ollama.
+You've now built a browser-based RAG application using LlamaIndex on an Arm-based Google Cloud C4A VM. You created sample documents, generated embeddings using Hugging Face models, stored vectors in ChromaDB, exposed the backend using FastAPI, and queried custom documents directly from a browser using Ollama.
 
 You can extend this workflow for your own LlamaIndex RAG applications on Arm-based cloud infrastructure. 

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

@@ -1,5 +1,6 @@
 ---
 title: Configure Google Cloud firewall rules for LlamaIndex
+description: Learn how to create a Google Cloud firewall rule that allows browser access to a FastAPI-based LlamaIndex RAG application.
 weight: 3
 
 ### FIXED, DO NOT MODIFY
@@ -8,30 +9,30 @@ layout: learningpathall
 
 ## Allow inbound access to the LlamaIndex browser application
 
-Create a firewall rule in Google Cloud Console to expose the required port 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
 
 To configure a firewall rule:
 
-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 **VPC Network > Firewall**, and select **Create firewall rule**.
 
-![Google Cloud Console VPC Network Firewall page showing the Create firewall rule button in the top menu bar#center](images/firewall-rule.png "Create a firewall rule in Google Cloud Console")
+![Google Cloud console VPC Network Firewall page showing the Create firewall rule button in the top menu bar#center](images/firewall-rule.png "Create a firewall rule in Google Cloud console")
 
 3. Set **Name** to `allow-llamaindex-port`, then select the network you want to bind to your virtual machine.
 4. Set **Direction of traffic** to **Ingress**, set **Action on match** to **Allow**, set **Targets** to **All instances in the network**, and set **Source IPv4 ranges** to **0.0.0.0/0**.
 
-![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")
+![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:
+6. Select the **TCP** checkbox. Port `8000` is used by the FastAPI server that backs the browser-based LlamaIndex RAG application. Enter:
 
 ```text
 8000
 ```
 
-![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")
+![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. In the same **TCP** field, also add port `22` to allow SSH access to the VM.
 8. Select **Create**.
@@ -40,4 +41,4 @@ To configure a firewall rule:
 
 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.
 
-Next, you'll create a Google Cloud Axion C4A virtual machine and connect to it using SSH.
+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

@@ -1,5 +1,6 @@
 ---
-title: Create a Google Axion C4A virtual machine for LlamaIndex
+title: Create a Google Cloud C4A virtual machine for LlamaIndex
+description: Learn how to create an Arm-based Google Cloud C4A virtual machine powered by Google Axion and connect to it with browser-based SSH.
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -8,24 +9,24 @@ layout: learningpathall
 
 ## Set up the virtual machine
 
-In this section, you'll create a Google Axion C4A Arm-based virtual machine (VM). You'll use the `c4a-standard-4` machine type, which provides 4 vCPUs and 16 GB of memory. This VM will host your browser-based LlamaIndex RAG application.
+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
 
 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/).
-2. Go to **Compute Engine** > **VM Instances** and select **Create Instance**.
+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**.
-7. Increase **Size (GB)** from **10** to **100** to allocate sufficient disk space, and then click **Select**.
+7. Increase **Size (GB)** from **10** to **100** to allocate sufficient disk space, and then select **Select**.
 8. Select **Networking** from the column on the left.
-9. Under **Network tags**, enter `allow-llamaindex-port` to link the VM to the firewall rule from the previous section and allow inbound access to port `8000` for the browser-based LlamaIndex RAG application and port `22` for ssh access.
+9. Under **Network tags**, enter `allow-llamaindex-port` to link the VM to the firewall rule from the previous section and allow inbound access to port `8000` for the browser-based LlamaIndex RAG application and port `22` for SSH access.
 10. Select **Create** to launch the virtual machine.
 
 After the instance starts, select **SSH** next to the VM in the instance list to open a browser-based terminal session.
@@ -38,6 +39,6 @@ A new browser window opens with a terminal connected to your VM.
 
 ## What you've accomplished and what's next
 
-You've now provisioned a Google Axion C4A Arm VM and connected to it using SSH.
+You've now provisioned a Google Cloud C4A VM and connected to it using SSH.
 
 Next, you'll install LlamaIndex, Ollama, ChromaDB, and the required dependencies on your VM.

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

@@ -1,5 +1,6 @@
 ---
-title: Install and configure LlamaIndex on Google Cloud Axion
+title: Install and configure LlamaIndex on a Google Cloud C4A virtual machine
+description: Learn how to install Python, Ollama, LlamaIndex, ChromaDB, and FastAPI on an Arm-based Google Cloud C4A virtual machine for a browser-based RAG application.
 weight: 5
 
 ### FIXED, DO NOT MODIFY
@@ -10,12 +11,7 @@ layout: learningpathall
 
 In this section, you'll prepare a Google Cloud Axion Arm64 VM for running a browser-based RAG application using LlamaIndex.
 
-You'll install:
-
-- required system packages
-- Python 3.11
-- Ollama 
-- LlamaIndex and required Python packages
+You'll install required system packages, including Python 3.11, as well as Ollama and LLamaIndex.
 
 ### Update the virtual machine
 
@@ -64,7 +60,7 @@ Python 3.11.10
 pip 22.3.1 from /usr/lib/python3.11/site-packages/pip (python 3.11)
 ```
 
-### (Optional) Install Docker
+<!-- ### (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:
 
@@ -93,7 +89,7 @@ The output is similar to:
 ```output
 Hello from Docker!
 This message shows that your installation appears to be working correctly.
-```
+``` -->
 
 ### Create project directory
 
@@ -119,7 +115,7 @@ pip install --upgrade pip setuptools wheel
 
 ### Install Ollama
 
-Run the following command:
+Use the official Linux installer to install Ollama:
 
 ```bash
 curl -fsSL https://ollama.com/install.sh | sh
@@ -153,7 +149,7 @@ sudo systemctl start ollama
 
 ### Pull an LLM model
 
-With Ollama running, pull the `llama3.2:1b` model. This is a lightweight 1-billion parameter model suitable for local inference on a 16 GB VM:
+With Ollama running, pull the `llama3.2:1b` model. This is a lightweight 1-billion-parameter model suitable for local inference on a 16 GB VM:
 
 ```bash
 ollama pull llama3.2:1b
@@ -173,7 +169,7 @@ Retrieval-Augmented Generation (RAG) is a technique that combines a retrieval st
 
 ### Install LlamaIndex packages
 
-Install the LlamaIndex core library along with the integrations needed for Ollama, HuggingFace embeddings, and ChromaDB. You'll also install FastAPI and Uvicorn here because the browser-based application you'll build in the next section uses them as the web server:
+Install the LlamaIndex core library along with the integrations needed for Ollama, Hugging Face embeddings, and ChromaDB. You'll also install FastAPI and Uvicorn here because the browser-based application you'll build in the next section uses them as the web server:
 
 ```bash
 pip install llama-index
@@ -188,6 +184,6 @@ pip install uvicorn
 
 ## What you've accomplished and what's next
 
-You've now successfully installed and configured LlamaIndex on a Google Cloud Axion Arm64 VM running SUSE Linux with Python 3.11. You optionally installed Docker, configured Ollama for local LLM inference, and prepared the environment for building browser-based RAG applications using LlamaIndex and ChromaDB.
+You've now installed and configured LlamaIndex on a Google Cloud C4A Arm64 VM running SUSE Linux with Python 3.11. You configured Ollama for local LLM inference and prepared the environment for building browser-based RAG applications using LlamaIndex and ChromaDB.
 
 Next, you'll build the RAG engine, create the browser UI, and query custom documents using a local large language model.