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docs: Updated documentation and Sphinx configuration (#1845)

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# What does this PR do?

The goal of this PR is to make the pages easier to navigate by surfacing
the child pages on the navbar, updating some of the copy, moving some of
the files around.

Some changes:
1. Clarifying Titles
2. Restructuring "Distributions" more formally in its own page to be
consistent with Providers and adding some clarity to the child pages to
surface them and make them easier to navigate
3. Updated sphinx config to not collapse navigation by default
4. Updated copyright year to be calculated dynamically 
5. Moved `docs/source/distributions/index.md` ->
`docs/source/distributions/starting_llama_stack_server.md`

Another for https://github.com/meta-llama/llama-stack/issues/1815

## Test Plan
Tested locally and pages build (screen shots for example).

## Documentation
###  Before:
![Screenshot 2025-03-31 at 1 09
21 PM](https://github.com/user-attachments/assets/98e34f76-f0d9-4055-8e2c-441b1e7d8f6a)

### After:
![Screenshot 2025-03-31 at 1 08
52 PM](https://github.com/user-attachments/assets/dfb6b8ad-3a1d-46b6-8f54-0c553664093f)

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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Francisco Arceo 2025-03-31 14:08:05 -06:00 • committed by GitHub
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5
docs/source/getting_started/index.md
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@ -1,10 +1,11 @@
# Quick Start
In this guide, we'll walk through how you can use the Llama Stack (server and client SDK) to test a simple RAG agent.
In this guide, we'll walk through how you can use the Llama Stack (server and client SDK) to build a simple [RAG (Retrieval Augmented Generation)](../building_applications/rag.md) agent.
A Llama Stack agent is a simple integrated system that can perform tasks by combining a Llama model for reasoning with tools (e.g., RAG, web search, code execution, etc.) for taking actions.
In Llama Stack, we provide a server exposing multiple APIs. These APIs are backed by implementations from different providers. For this guide, we will use [Ollama](https://ollama.com/) as the inference provider.
Ollama is an LLM runtime that allows you to run Llama models locally.
### 1. Start Ollama
@ -24,7 +25,7 @@ If you do not have ollama, you can install it from [here](https://ollama.com/dow
### 2. Pick a client environment
Llama Stack has a service-oriented architecture, so every interaction with the Stack happens through an REST interface. You can interact with the Stack in two ways:
Llama Stack has a service-oriented architecture, so every interaction with the Stack happens through a REST interface. You can interact with the Stack in two ways:
* Install the `llama-stack-client` PyPI package and point `LlamaStackClient` to a local or remote Llama Stack server.
* Or, install the `llama-stack` PyPI package and use the Stack as a library using `LlamaStackAsLibraryClient`.