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added tabs for the tutorial output and rephrased thing based on feedback

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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Francisco Javier Arceo 2025-04-09 23:16:52 -04:00
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docs/source/getting_started/detailed_tutorial.md
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@ -45,32 +45,33 @@ Setup your virtual environment.
uv venv --python 3.10
source .venv/bin/activate
```
## Step 2: Install 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.
```bash
uv pip install llama-stack
```
Note the Llama Stack Server includes the client SDK as well.
## Step 3: Build and Run the Llama Stack Server
Llama Stack uses a [configuration file](../distributions/configuration.md) to define the stack.
The config file is a YAML file that specifies the providers and their configurations. Now let's build and run the
Llama Stack config for Ollama.
::::{tab-set}
:::{tab-item} Using Python
You can use Python to build and run the Llama Stack server. This is useful for testing and development purposes.
:::{tab-item} Using `venv`
You can use Python to build and run the Llama Stack server, which is useful for testing and development.
Llama Stack uses a [YAML configuration file](../distributions/configuration.md) to specify the stack setup,
which defines the providers and their settings.
Now let's build and run the Llama Stack config for Ollama.
```bash
INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type venv --run
```
You will see output like below:
```
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
:::
:::{tab-item} Using `conda`
You can use Python to build and run the Llama Stack server, which is useful for testing and development.
Llama Stack uses a [YAML configuration file](../distributions/configuration.md) to specify the stack setup,
which defines the providers and their settings.
Now let's build and run the Llama Stack config for Ollama.
```bash
INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type conda --run
```
:::
:::{tab-item} Using a Container
You can use a container image to run the Llama Stack server. We provide several container images for the server
component that works with different inference providers out of the box. For this guide, we will use
@ -101,8 +102,7 @@ with `host.containers.internal`.
The configuration YAML for the Ollama distribution is available at `distributions/ollama/run.yaml`.
```{admonition} Note
:class: note
```{tip}
Docker containers run in their own isolated network namespaces on Linux. To allow the container to communicate with services running on the host via `localhost`, you need `--network=host`. This makes the container use the host’s network directly so it can connect to Ollama running on `localhost:11434`.
@ -120,27 +120,32 @@ docker run -it \
```
:::
::::
You will see output like below:
```
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)
```
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.
### ii. Using the Llama Stack Client
## 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.
::::{tab-set}
:::{tab-item} Reuse the Server Setup
:::{tab-item} Reuse Server `venv`
```bash
# As mentioned, the client is included in the llama-stack package so we can just activate the server virtual environment
# The client is included in the llama-stack package so we just activate the server venv
source .venv/bin/activate
```
:::
:::{tab-item} Install the Llama Stack Client (venv)
:::{tab-item} Install with `venv`
```bash
uv venv client --python 3.10
source client/bin/activate
@ -148,7 +153,7 @@ pip install llama-stack-client
```
:::
:::{tab-item} Install the Llama Stack Client (conda)
:::{tab-item} Install with `conda`
```bash
yes | conda create -n stack-client python=3.10
conda activate stack-client
@ -157,7 +162,8 @@ pip install llama-stack-client
:::
::::
Now let's use the `llama-stack-client` CLI to check the connectivity to the server.
Now let's use the `llama-stack-client` [CLI](../references/llama_stack_client_cli_reference.md) to check the
connectivity to the server.
```bash
llama-stack-client configure --endpoint http://localhost:8321 --api-key none
@ -185,7 +191,14 @@ Total models: 2
```
## Step 4: Run Basic Inference
## 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.
::::{tab-set}
:::{tab-item} Basic Inference with the CLI
You can test basic Llama inference completion using the CLI.
```bash
@ -208,6 +221,9 @@ ChatCompletionResponse(
],
)
```
:::
:::{tab-item} Basic Inference with a Script
Alternatively, you can run inference using the Llama Stack client SDK.
### i. Create the Script
@ -250,8 +266,9 @@ Lines of code unfold
Logic flows through digital night
Beauty in the bits
```
:::
## Step 5: Build a Simple Agent
:::{tab-item} Build a Simple Agent
Now we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server.
### i. Create the Script
Create a file `agent.py` and add the following code:
@ -299,8 +316,8 @@ Let's run the script using `uv`
```bash
uv run python agent.py
```
:::{dropdown} `👋 Click here to see the sample output`
```
```{dropdown} 👋 Click here to see the sample output
Non-streaming ...
agent> I'm an artificial intelligence designed to assist and communicate with users like you. I don't have a personal identity, but I'm here to provide information, answer questions, and help with tasks to the best of my abilities.
@ -417,11 +434,11 @@ I'm a computer program designed to simulate human-like conversations, using natu
Think of me as a virtual companion, a helpful tool designed to make your interactions more efficient and enjoyable. I don't have personal opinions, emotions, or biases, but I'm here to provide accurate and informative responses to the best of my abilities.
So, who am I? I'm just a computer program designed to help you!
```
:::
## Step 6: Build a RAG Agent
:::{tab-item} Build a RAG Agent
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
@ -505,9 +522,8 @@ Let's run the script using `uv`
```bash
uv run python rag_agent.py
```
:::{dropdown} `👋 Click here to see the sample output`
```
```{dropdown} 👋 Click here to see the sample output
user> what is torchtune
inference> [knowledge_search(query='TorchTune')]
tool_execution> Tool:knowledge_search Args:{'query': 'TorchTune'}
@ -522,6 +538,8 @@ Overall, DORA is a powerful reinforcement learning algorithm that can learn comp
```
:::
::::
## Build Your Own Apps!
Congrats! 🥳 Now you're ready to [build your own Llama Stack applications](../building_applications/index)! 🚀