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docs: Updated documentation and configuration to make things easier for the unfamiliar

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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docs/source/getting_started/index.md
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# 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`.