docs: Reorganize documentation on the webpage (#2651)

# What does this PR do?
Reorganizes the Llama stack webpage into more concise index pages,
introduce more of a workflow, and reduce repetition of content.

New nav structure so far based on #2637 

Further discussions in
https://github.com/meta-llama/llama-stack/discussions/2585

**Preview:**
![Screenshot 2025-07-09 at 2 31
53 PM](https://github.com/user-attachments/assets/4c1f3845-b328-4f12-9f20-3f09375007af)

You can also build a full local preview locally 

 **Feedback**
Looking for feedback on page titles and general feedback on the new
structure

**Follow up documentation**
I plan on reducing some sections and standardizing some terminology in a
follow up PR.
More discussions on that in
https://github.com/meta-llama/llama-stack/discussions/2585

docs/source/getting_started/detailed_tutorial.md

@@ -1,4 +1,4 @@
-# Detailed Tutorial
+## Detailed Tutorial
 
 In this guide, we'll walk through how you can use the Llama Stack (server and client SDK) to test a simple agent.
 A Llama Stack agent is a simple integrated system that can perform tasks by combining a Llama model for reasoning with
@@ -10,7 +10,7 @@ Llama Stack is a stateful service with REST APIs to support seamless transition
 In this guide, we'll walk through how to build a RAG agent locally using Llama Stack with [Ollama](https://ollama.com/)
 as the inference [provider](../providers/index.md#inference) for a Llama Model.
 
-## Step 1: Installation and Setup
+### Step 1: Installation and Setup
 
 Install Ollama by following the instructions on the [Ollama website](https://ollama.com/download), then
 download Llama 3.2 3B model, and then start the Ollama service.
@@ -45,7 +45,7 @@ Setup your virtual environment.
 uv sync --python 3.12
 source .venv/bin/activate
 ```
-## Step 2:  Run Llama Stack
+### Step 2:  Run Llama Stack
 Llama Stack is a server that exposes multiple APIs, you connect with it using the Llama Stack client SDK.
 
 ::::{tab-set}
@@ -132,7 +132,7 @@ Now you can use the Llama Stack client to run inference and build agents!
 You can reuse the server setup or use the [Llama Stack Client](https://github.com/meta-llama/llama-stack-client-python/).
 Note that the client package is already included in the `llama-stack` package.
 
-## Step 3: Run Client CLI
+### Step 3: Run Client CLI
 
 Open a new terminal and navigate to the same directory you started the server from. Then set up a new or activate your
 existing server virtual environment.
@@ -232,7 +232,7 @@ OpenAIChatCompletion(
 )
 ```
 
-## Step 4: Run the Demos
+### Step 4: Run the Demos
 
 Note that these demos show the [Python Client SDK](../references/python_sdk_reference/index.md).
 Other SDKs are also available, please refer to the [Client SDK](../index.md#client-sdks) list for the complete options.
@@ -242,7 +242,7 @@ Other SDKs are also available, please refer to the [Client SDK](../index.md#clie
 :::{tab-item} Basic Inference
 Now you can run inference using the Llama Stack client SDK.
 
-### i. Create the Script
+#### i. Create the Script
 
 Create a file `inference.py` and add the following code:
 ```python
@@ -269,7 +269,7 @@ response = client.chat.completions.create(
 print(response)
 ```
 
-### ii. Run the Script
+#### ii. Run the Script
 Let's run the script using `uv`
 ```bash
 uv run python inference.py
@@ -283,7 +283,7 @@ OpenAIChatCompletion(id='chatcmpl-30cd0f28-a2ad-4b6d-934b-13707fc60ebf', choices
 
 :::{tab-item} Build a Simple Agent
 Next we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server.
-### i. Create the Script
+#### i. Create the Script
 Create a file `agent.py` and add the following code:
 
 ```python
@@ -455,7 +455,7 @@ uv run python agent.py
 
 For our last demo, we can build a RAG agent that can answer questions about the Torchtune project using the documents
 in a vector database.
-### i. Create the Script
+#### i. Create the Script
 Create a file `rag_agent.py` and add the following code:
 
 ```python
@@ -533,7 +533,7 @@ for t in turns:
     for event in AgentEventLogger().log(stream):
         event.print()
 ```
-### ii. Run the Script
+#### ii. Run the Script
 Let's run the script using `uv`
 ```bash
 uv run python rag_agent.py