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adding server example back in and restructuring steps

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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Francisco Javier Arceo 2025-04-09 17:01:52 -04:00
parent 662483f360
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28
docs/source/getting_started/index.md
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@ -2,27 +2,32 @@
Get started with Llama Stack in minutes!
Llama Stack is a stateful service with REST APIs to support the seamless transition of AI applications across different
environments. You can build and test using a local server first and deploy to a hosted endpoint for production.
In this guide, we'll walk through how to build a RAG application locally using Llama Stack with [Ollama](https://ollama.com/)
as the inference [provider](../providers/index.md#inference) for a Llama Model.
## Step 1. Install [uv](https://docs.astral.sh/uv/) and setup your virtual environment
## Step 1. Install and Setup
Install [uv](https://docs.astral.sh/uv/), setup your virtual environment, and run inference on a Llama model with
[Ollama](https://ollama.com/download).
```bash
uv pip install llama-stack aiosqlite faiss-cpu ollama \
openai datasets opentelemetry-exporter-otlp-proto-http mcp autoevals
uv pip install llama-stack aiosqlite faiss-cpu ollama openai datasets opentelemetry-exporter-otlp-proto-http mcp autoevals
source .venv/bin/activate
export INFERENCE_MODEL="llama3.2:3b"
```
## Step 2: Run inference locally with Ollama
```bash
# make sure to run this in a separate terminal
ollama run llama3.2:3b --keepalive 60m
```
## Step 2: Run the Llama Stack Server
```bash
INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type venv --run
```
## Step 3: Run the Demo
You can run this with `uv run demo_script.py` or in an interactive shell.
Now open up a new terminal using the same virtual environment and you can run this demo as a script using `uv run demo_script.py` or in an interactive shell.
```python
from termcolor import cprint
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack_client.types import Document
from llama_stack_client import LlamaStackClient
vector_db = "faiss"
vector_db_id = "test-vector-db"
@ -37,8 +42,7 @@ documents = [
)
]
client = LlamaStackAsLibraryClient("ollama")
_ = client.initialize()
client = LlamaStackClient(base_url="http://localhost:8321")
client.vector_dbs.register(
provider_id=vector_db,
vector_db_id=vector_db_id,
@ -65,7 +69,7 @@ for chunk in response.content:
cprint("" + "-" * 50, "yellow")
```
And you should see output like below.
```bash
```
--------------------------------------------------
Query> Can you give me the arxiv link for Lora Fine Tuning in Pytorch?
--------------------------------------------------

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docs/source/index.md
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# Llama Stack
Welcome to Llama Stack, the open-source framework for building generative AI applications.
```{admonition} Llama 4 is here!
:class: tip
@ -9,7 +11,6 @@ Check out [Getting Started with Llama 4](https://colab.research.google.com/githu
Llama Stack {{ llama_stack_version }} is now available! See the {{ llama_stack_version_link }} for more details.
```
# Llama Stack
## What is Llama Stack?