# What does this PR do?
This commit significantly improves the environment variable substitution
functionality in Llama Stack configuration files:
* The version field in configuration files has been changed from string
to integer type for better type consistency across build and run
configurations.
* The environment variable substitution system for ${env.FOO:} was fixed
and properly returns an error
* The environment variable substitution system for ${env.FOO+} returns
None instead of an empty strings, it better matches type annotations in
config fields
* The system includes automatic type conversion for boolean, integer,
and float values.
* The error messages have been enhanced to provide clearer guidance when
environment variables are missing, including suggestions for using
default values or conditional syntax.
* Comprehensive documentation has been added to the configuration guide
explaining all supported syntax patterns, best practices, and runtime
override capabilities.
* Multiple provider configurations have been updated to use the new
conditional syntax for optional API keys, making the system more
flexible for different deployment scenarios. The telemetry configuration
has been improved to properly handle optional endpoints with appropriate
validation, ensuring that required endpoints are specified when their
corresponding sinks are enabled.
* There were many instances of ${env.NVIDIA_API_KEY:} that should have
caused the code to fail. However, due to a bug, the distro server was
still being started, and early validation wasn’t triggered. As a result,
failures were likely being handled downstream by the providers. I’ve
maintained similar behavior by using ${env.NVIDIA_API_KEY:+}, though I
believe this is incorrect for many configurations. I’ll leave it to each
provider to correct it as needed.
* Environment variable substitution now uses the same syntax as Bash
parameter expansion.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR contains two sets of notebooks that serve as reference material
for developers getting started with Llama Stack using the NVIDIA
Provider. Developers should be able to execute these notebooks
end-to-end, pointing to their NeMo Microservices deployment.
1. `beginner_e2e/`: Notebook that walks through a beginner end-to-end
workflow that covers creating datasets, running inference, customizing
and evaluating models, and running safety checks.
2. `tool_calling/`: Notebook that is ported over from the [Data Flywheel
& Tool Calling
notebook](https://github.com/NVIDIA/GenerativeAIExamples/tree/main/nemo/data-flywheel)
that is referenced in the NeMo Microservices docs. I updated the
notebook to use the Llama Stack client wherever possible, and added
relevant instructions.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
- Both notebook folders contain READMEs with pre-requisites. To manually
test these notebooks, you'll need to have a deployment of the NeMo
Microservices Platform and update the `config.py` file with your
deployment's information.
- I've run through these notebooks manually end-to-end to verify each
step works.
[//]: # (## Documentation)
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
This is not part of the official OpenAI API, but we'll use this for the
logs UI.
In order to support more filtering options, I'm adopting the newly
introduced sql store in in place of the kv store.
## Test Plan
Added integration/unit tests.
# What does this PR do?
* Provide sqlite implementation of the APIs introduced in
https://github.com/meta-llama/llama-stack/pull/2145.
* Introduced a SqlStore API: llama_stack/providers/utils/sqlstore/api.py
and the first Sqlite implementation
* Pagination support will be added in a future PR.
## Test Plan
Unit test on sql store:
<img width="1005" alt="image"
src="https://github.com/user-attachments/assets/9b8b7ec8-632b-4667-8127-5583426b2e29"
/>
Integration test:
```
INFERENCE_MODEL="llama3.2:3b-instruct-fp16" llama stack build --template ollama --image-type conda --run
```
```
LLAMA_STACK_CONFIG=http://localhost:5001 INFERENCE_MODEL="llama3.2:3b-instruct-fp16" python -m pytest -v tests/integration/inference/test_openai_completion.py --text-model "llama3.2:3b-instruct-fp16" -k 'inference_store and openai'
```
# What does this PR do?
It may not always be desirable to listen on all interfaces, which is the
default. As an example, by listening instead only on a loopback
interface, the server cannot be reached except from within the host it
is run on. This PR makes this configurable, through a CLI option, an env
var or an entry on the config file.
## Test Plan
I ran a server with and without the added CLI argument to verify that
the argument is used if provided, but the default is as it was before if
not.
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
We are dropping configuration via CLI flag almost entirely. If any
server configuration has to be tweak it must be done through the server
section in the run.yaml.
This is unfortunately a breaking change for whover was using:
* `--tls-*`
* `--disable_ipv6`
`--port` stays around and get a special treatment since we believe, it's
common for user dev to change port for quick experimentations.
Closes: https://github.com/meta-llama/llama-stack/issues/1076
## Test Plan
Simply do `llama stack run <config>` nothing should break :)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Replaced `${env.OTEL_SERVICE_NAME:\u200B}` and similar variants with
properly formatted `${env.OTEL_SERVICE_NAME:}` across all YAML templates
and TelemetryConfig. This prevents silent parsing issues and ensures
consistent environment variable resolution.
Slipped in https://github.com/meta-llama/llama-stack/pull/2058
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
The telemetry provider configs is the only one who leverages the env var
`SQLITE_DB_PATH` for pointing to persistent data in the respective
templates, whereas usually `SQLITE_STORE_DIR` is used.
This PR modifies the `sqlite_db_path` in various telemetry configuration
files to use the environment variable `SQLITE_STORE_DIR` instead of
`SQLITE_DB_PATH`. This change ensures that _only_ the SQLITE_STORE_DIR
needs to be set to point to a different persistence location for
providers.
All references to `SQLITE_DB_PATH` have been removed.
Another improvement could be to move `sqlite_db_path` to `db_path` in
the telemetry provider config, to align with the other provider
configurations. That could be done by another PR (if wanted).
# What does this PR do?
Implemetation of NeMO Datastore register, unregister API.
Open Issues:
- provider_id gets set to `localfs` in client.datasets.register() as it
is specified in routing_tables.py: DatasetsRoutingTable
see: #1860
Currently I have passed `"provider_id":"nvidia"` in metadata and have
parsed that in `DatasetsRoutingTable`
(Not the best approach, but just a quick workaround to make it work for
now.)
## Test Plan
- Unit test cases: `pytest
tests/unit/providers/nvidia/test_datastore.py`
```bash
========================================================== test session starts ===========================================================
platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: anyio-4.9.0, asyncio-0.26.0, nbval-0.11.0, metadata-3.1.1, html-4.1.1, cov-6.1.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 2 items
tests/unit/providers/nvidia/test_datastore.py .. [100%]
============================================================ warnings summary ============================================================
====================================================== 2 passed, 1 warning in 0.84s ======================================================
```
cc: @dglogo, @mattf, @yanxi0830
# What does this PR do?
Adds custom model registration functionality to NVIDIAInferenceAdapter
which let's the inference happen on:
- post-training model
- non-llama models in API Catalogue(behind
https://integrate.api.nvidia.com and endpoints compatible with
AyncOpenAI)
## Example Usage:
```python
from llama_stack.apis.models import Model, ModelType
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
client = LlamaStackAsLibraryClient("nvidia")
_ = client.initialize()
client.models.register(
model_id=model_name,
model_type=ModelType.llm,
provider_id="nvidia"
)
response = client.inference.chat_completion(
model_id=model_name,
messages=[{"role":"system","content":"You are a helpful assistant."},{"role":"user","content":"Write a limerick about the wonders of GPU computing."}],
)
```
## Test Plan
```bash
pytest tests/unit/providers/nvidia/test_supervised_fine_tuning.py
========================================================== test session starts ===========================================================
platform linux -- Python 3.10.0, pytest-8.3.5, pluggy-1.5.0
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: anyio-4.9.0
collected 6 items
tests/unit/providers/nvidia/test_supervised_fine_tuning.py ...... [100%]
============================================================ warnings summary ============================================================
../miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076
/home/ubuntu/miniconda/envs/nvidia-1/lib/python3.10/site-packages/pydantic/fields.py:1076: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'contentEncoding'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.11/migration/
warn(
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
====================================================== 6 passed, 1 warning in 1.51s ======================================================
```
[//]: # (## Documentation)
Updated Readme.md
cc: @dglogo, @sumitb, @mattf
# What does this PR do?
This PR adds support for NVIDIA's NeMo Evaluator API to the Llama Stack
eval module. The integration enables users to evaluate models via the
Llama Stack interface.
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
1. Added unit tests and successfully ran from root of project:
`./scripts/unit-tests.sh tests/unit/providers/nvidia/test_eval.py`
```
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_cancel PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_result PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_job_status PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_register_benchmark PASSED
tests/unit/providers/nvidia/test_eval.py::TestNVIDIAEvalImpl::test_run_eval PASSED
```
2. Verified I could build the Llama Stack image: `LLAMA_STACK_DIR=$(pwd)
llama stack build --template nvidia --image-type venv`
Documentation added to
`llama_stack/providers/remote/eval/nvidia/README.md`
---------
Co-authored-by: Jash Gulabrai <jgulabrai@nvidia.com>
# What does this PR do?
This PR adds support for NVIDIA's NeMo Customizer API to the Llama Stack
post-training module. The integration enables users to fine-tune models
using NVIDIA's cloud-based customization service through a consistent
Llama Stack interface.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
Yet to be done
Things pending under this PR:
- [x] Integration of fine-tuned model(new checkpoint) for inference with
nvidia llm distribution
- [x] distribution integration of API
- [x] Add test cases for customizer(In Progress)
- [x] Documentation
```
LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/post_training/test_supervised_fine_tuning.py
============================================================================================================================================================================ test session starts =============================================================================================================================================================================
platform linux -- Python 3.10.0, pytest-8.3.4, pluggy-1.5.0 -- /home/ubuntu/llama-stack/.venv/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.0', 'Platform': 'Linux-6.8.0-1021-gcp-x86_64-with-glibc2.35', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'nbval': '0.11.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'html': '4.1.1', 'asyncio': '0.25.3'}}
rootdir: /home/ubuntu/llama-stack
configfile: pyproject.toml
plugins: nbval-0.11.0, metadata-3.1.1, anyio-4.8.0, html-4.1.1, asyncio-0.25.3
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 2 items
tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_post_training_provider_registration[txt=8B] PASSED [ 50%]
tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_list_training_jobs[txt=8B] PASSED [100%]
======================================================================================================================================================================== 2 passed, 1 warning in 0.10s ========================================================================================================================================================================
```
cc: @mattf @dglogo @sumitb
---------
Co-authored-by: Ubuntu <ubuntu@llama-stack-customizer-dev-inst-2tx95fyisatvlic4we8hidx5tfj.us-central1-a.c.brevdevprod.internal>
# What does this PR do?
## Test Plan
LLAMA_STACK_CONFIG=dev pytest -s -v
tests/integration/agents/test_agents.py::test_custom_tool
--safety-shield meta-llama/Llama-Guard-3-8B --text-model
accounts/fireworks/models/llama-v3p1-8b-instruct
and verify trace in jaeger UI
https://llama-stack.readthedocs.io/en/latest/building_applications/telemetry.html#
Each model known to the system has two identifiers:
- the `provider_resource_id` (what the provider calls it) -- e.g.,
`accounts/fireworks/models/llama-v3p1-8b-instruct`
- the `identifier` (`model_id`) under which it is registered and gets
routed to the appropriate provider.
We have so far used the HuggingFace repo alias as the standardized
identifier you can use to refer to the model. So in the above example,
we'd use `meta-llama/Llama-3.1-8B-Instruct` as the name under which it
gets registered. This makes it convenient for users to refer to these
models across providers.
However, we forgot to register the _actual_ provider model ID also. You
should be able to route via `provider_resource_id` also, of course.
This change fixes this (somewhat grave) omission.
*Note*: this change is additive -- more aliases work now compared to
before.
## Test Plan
Run the following for distro=(ollama fireworks together)
```
LLAMA_STACK_CONFIG=$distro \
pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--inference-model=meta-llama/Llama-3.1-8B-Instruct --vision-inference-model=""
```
# What does this PR do?
- Updates ImageContentItemImageURL import
- fixes `embedding_dimensions` metadata param
## Test Plan
- Ran pytest locally, verified embedding tests pass with new types
cc: @dglogo @sumitb
# What does this PR do?
add /v1/inference/embeddings implementation to NVIDIA provider
**open topics** -
- *asymmetric models*. NeMo Retriever includes asymmetric models, which
are models that embed differently depending on if the input is destined
for storage or lookup against storage. the /v1/inference/embeddings api
does not allow the user to indicate the type of embedding to perform.
see https://github.com/meta-llama/llama-stack/issues/934
- *truncation*. embedding models typically have a limited context
window, e.g. 1024 tokens is common though newer models have 8k windows.
when the input is larger than this window the endpoint cannot perform
its designed function. two options: 0. return an error so the user can
reduce the input size and retry; 1. perform truncation for the user and
proceed (common strategies are left or right truncation). many users
encounter context window size limits and will struggle to write reliable
programs. this struggle is especially acute without access to the
model's tokenizer. the /v1/inference/embeddings api does not allow the
user to delegate truncation policy. see
https://github.com/meta-llama/llama-stack/issues/933
- *dimensions*. "Matryoshka" embedding models are available. they allow
users to control the number of embedding dimensions the model produces.
this is a critical feature for managing storage constraints. embeddings
of 1024 dimensions what achieve 95% recall for an application may not be
worth the storage cost if a 512 dimensions can achieve 93% recall.
controlling embedding dimensions allows applications to determine their
recall and storage tradeoffs. the /v1/inference/embeddings api does not
allow the user to control the output dimensions. see
https://github.com/meta-llama/llama-stack/issues/932
## Test Plan
- `llama stack run llama_stack/templates/nvidia/run.yaml`
- `LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -v
tests/client-sdk/inference/test_embedding.py --embedding-model
baai/bge-m3`
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
- Update `/eval-tasks` to `/benchmarks`
- ⚠️ Remove differentiation between `app` v.s. `benchmark` eval task
config. Now we only have `BenchmarkConfig`. The overloaded `benchmark`
is confusing and do not add any value. Backward compatibility is being
kept as the "type" is not being used anywhere.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
- This change is backward compatible
- Run notebook test with
```
pytest -v -s --nbval-lax ./docs/getting_started.ipynb
pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
<img width="846" alt="image"
src="https://github.com/user-attachments/assets/d2fc06a7-593a-444f-bc1f-10ab9b0c843d"
/>
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
---------
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Signed-off-by: Sébastien Han <seb@redhat.com>
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
Co-authored-by: Ben Browning <ben324@gmail.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Reid <61492567+reidliu41@users.noreply.github.com>
Co-authored-by: reidliu <reid201711@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
Catches docs up to source with:
```
python llama_stack/scripts/distro_codegen.py
```
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
Manually checked
```
sphinx-autobuild docs/source build/html
```
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
# What does this PR do?
allows template distribution connect to hosted or local NIM:
use --env NVIDIA_BASE_URL=http://localhost:8000 to connect to a local
NIM running at localhost:8000
use --env NVIDIA_API_KEY=blah when connecting to hosted NIM, e.g.
NVIDIA_BASE_URL=https://integrate.api.nvidia.com
## Test Plan
- `llama stack run ./llama_stack/templates/nvidia/run.yaml` -> error,
e.g. API key is required for hosted NVIDIA NIM
- `llama stack run ./llama_stack/templates/nvidia/run.yaml --env
NVIDIA_BASE_URL=https://integrate.api.nvidia.com` -> error, e.g. API key
is required for hosted NVIDIA NIM
- `llama stack run ./llama_stack/templates/nvidia/run.yaml --env
NVIDIA_API_KEY=REDACTED` -> successful connection to NIM on
https://integrate.api.nvidia.com
- `llama stack run ./llama_stack/templates/nvidia/run.yaml --env
NVIDIA_BASE_URL=https://integrate.api.nvidia.com --env
NVIDIA_API_KEY=REDACTED` -> successful connection to NIM running on
integrate.api.nvidia.com
- `llama stack run ./llama_stack/templates/nvidia/run.yaml --env
NVIDIA_BASE_URL=http://localhost:8000` -> successful connection to NIM
running on localhost:8000
- `llama stack run ./llama_stack/templates/nvidia/run.yaml --env
NVIDIA_BASE_URL=http://localhost:8000 --env NVIDIA_API_KEY=REDACTED` ->
successful connection to NIM running on http://localhost:8000
- `llama stack run ./llama_stack/templates/nvidia/run.yaml --env
NVIDIA_BASE_URL=http://bogus` -> runtime error, e.g. ConnectionError
(TODO: this should be a startup error)
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
- we no longer have meta-reference as memory provider, update cerebras
template
## Test Plan
```
python llama_stack/scripts/distro_codegen.py
```
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
- fixes to nvidia inference provider to account for strategy update
- update nvidia templates
## Test Plan
```
llama stack run ./llama_stack/templates/nvidia/run.yaml --port 5000
LLAMA_STACK_BASE_URL="http://localhost:5000" pytest -v tests/client-sdk/inference/test_inference.py --html=report.html --self-contained-html
```
<img width="1288" alt="image"
src="https://github.com/user-attachments/assets/d20f9aea-525e-47de-a5be-586e022e0d55"
/>
**NOTE**
- vision inference broken
- tool calling broken
- /completion broken
cc @mattf @cdgamarose-nv for improving NVIDIA inference adapter
## Sources
Please link relevant resources if necessary.
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
adds nvidia template for creating a distribution using inference adapter
for NVIDIA NIMs.
## Test Plan
Please describe:
Build llama stack distribution for nvidia using the template, docker and
conda.
```bash
(.venv) local-cdgamarose@a4u8g-0006:~/llama-stack$ llama-stack-client configure --endpoint http://localhost:5000
Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5000
(.venv) local-cdgamarose@a4u8g-0006:~/llama-stack$ llama-stack-client models list
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ provider_resource_id ┃ metadata ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ Llama3.1-8B-Instruct │ nvidia │ meta/llama-3.1-8b-instruct │ {} │
│ meta-llama/Llama-3.2-3B-Instruct │ nvidia │ meta/llama-3.2-3b-instruct │ {} │
└──────────────────────────────────┴─────────────┴────────────────────────────┴──────────┘
(.venv) local-cdgamarose@a4u8g-0006:~/llama-stack$ llama-stack-client inference chat-completion --message "hello, write me a 2 sentence poem"
ChatCompletionResponse(
completion_message=CompletionMessage(
content='Here is a 2 sentence poem:\n\nThe sun sets slow and paints the sky, \nA gentle hue of pink that makes me sigh.',
role='assistant',
stop_reason='end_of_turn',
tool_calls=[]
),
logprobs=None
)
```
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [x] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.
---------
Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>
