docs: add additional guidance around using virtualenv (#1642)

# What does this PR do?
current docs are very tailored to `conda`

also adds guidance around running code examples within virtual
environment for both `conda` and `virtualenv`

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
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@ -88,11 +88,19 @@ docker run -it \
:::{dropdown} Installing the Llama Stack client CLI and SDK :::{dropdown} Installing the Llama Stack client CLI and SDK
You can interact with the Llama Stack server using various client SDKs. We will use the Python SDK which you can install using the following command. Note that you must be using Python 3.10 or newer: You can interact with the Llama Stack server using various client SDKs. Note that you must be using Python 3.10 or newer. We will use the Python SDK which you can install via `conda` or `virtualenv`.
For `conda`:
```bash ```bash
yes | conda create -n stack-client python=3.10 yes | conda create -n stack-client python=3.10
conda activate stack-client conda activate stack-client
pip install llama-stack-client
```
For `virtualenv`:
```bash
python -m venv stack-client
source stack-client/bin/activate
pip install llama-stack-client pip install llama-stack-client
``` ```
@ -173,6 +181,13 @@ response = client.inference.chat_completion(
print(response.completion_message.content) print(response.completion_message.content)
``` ```
To run the above example, put the code in a file called `inference.py`, ensure your `conda` or `virtualenv` environment is active, and run the following:
```bash
pip install llama_stack
llama stack build --template ollama --image-type <conda|venv>
python inference.py
```
### 4. Your first RAG agent ### 4. Your first RAG agent
Here is an example of a simple RAG (Retrieval Augmented Generation) chatbot agent which can answer questions about TorchTune documentation. Here is an example of a simple RAG (Retrieval Augmented Generation) chatbot agent which can answer questions about TorchTune documentation.
@ -273,6 +288,13 @@ for prompt in user_prompts:
log.print() log.print()
``` ```
To run the above example, put the code in a file called `rag.py`, ensure your `conda` or `virtualenv` environment is active, and run the following:
```bash
pip install llama_stack
llama stack build --template ollama --image-type <conda|venv>
python rag.py
```
## Next Steps ## Next Steps
- Learn more about Llama Stack [Concepts](../concepts/index.md) - Learn more about Llama Stack [Concepts](../concepts/index.md)