phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-12-31 08:50:01 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

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

Browse source 
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-03-31 13:08:22 -04:00
parent 9b478f3756
commit 2847216efb
10 changed files with 69 additions and 32 deletions
Download patch file Download diff file Expand all files Collapse all files

5
docs/source/getting_started/index.md
View file

@ -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`.