prototype: use pyproject and uv to build distribution

Goals:

* remove the need of a custom tool to install a collection of python
  packages AKA `llama stack build`
* use the power of 'uv', which was designed to manage dependencies
* `llama stack build` can "probably" go away and be replaced with uv

Howto, with the pyproject, you can install an Ollama distribution in a
virtual env like so:

```
uv venv --python 3.10 ollama-distro
source ollama-distro/bin/activate
uv sync --extra ollama
llama stack run llama_stack/templates/ollama/run.yaml
```

Caveats:

* external provider, we could still use a build file or add
the known external providers to the pyproject?
* growth of the uv.lock?

We create a requirements.txt for convenience as some users are most
familiar with this format than looking at pyproject.

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/providers/registry/inference.py

@@ -40,7 +40,7 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             provider_type="inline::vllm",
             pip_packages=[
-                "vllm",
+                "vllm; sys_platform == 'linux'",
             ],
             module="llama_stack.providers.inline.inference.vllm",
             config_class="llama_stack.providers.inline.inference.vllm.VLLMConfig",
@@ -49,8 +49,9 @@ def available_providers() -> list[ProviderSpec]:
             api=Api.inference,
             provider_type="inline::sentence-transformers",
             pip_packages=[
-                "torch torchvision --index-url https://download.pytorch.org/whl/cpu",
-                "sentence-transformers --no-deps",
+                "torch",
+                "torchvision",
+                "sentence-transformers",
             ],
             module="llama_stack.providers.inline.inference.sentence_transformers",
             config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig",