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docs/source/getting_started/detailed_tutorial.md
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# 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