# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
- From old PR, it use `BUILDS_BASE_DIR` in
`llama_stack/cli/stack/configure.py`(removed).
https://github.com/meta-llama/llama-stack/pull/371/files
- Based on the current `build` code, it should only use
`DISTRIBS_BASE_DIR` to save it.
46b0a404e8/llama_stack/cli/stack/_build.py (L298)46b0a404e8/llama_stack/cli/stack/_build.py (L301)
Pls correct me if I am understand incorrectly.
So it should no need to use in `run` now.
[//]: # (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.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
Some imports were not switched to in-tree copy of the modules.
This is a follow-up to:
https://github.com/meta-llama/llama-stack/pull/1344Closes#1435
## Test Plan
Manually started the server...
[//]: # (## Documentation)
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
Inference router computes the token usage related metrics for all
providers and returns the metrics as part of response and also logs to
telemetry.
## Test Plan
LLAMA_STACK_DISABLE_VERSION_CHECK=true llama stack run
~/.llama/distributions/fireworks/fireworks-run.yaml
```
curl --request POST \
--url http://localhost:8321/v1/inference/chat-completion \
--header 'content-type: application/json' \
--data '{
"model_id": "meta-llama/Llama-3.1-70B-Instruct",
"messages": [
{
"role": "user",
"content": {
"type": "text",
"text": "where do humans live"
}
}
],
"stream": false
}' | jq .
{
"metrics": [
{
"trace_id": "yjv1tf0jS1evOyPm",
"span_id": "WqYKvg0_",
"timestamp": "2025-02-27T18:55:10.770903Z",
"attributes": {
"model_id": "meta-llama/Llama-3.1-70B-Instruct",
"provider_id": "fireworks"
},
"type": "metric",
"metric": "prompt_tokens",
"value": 10,
"unit": "tokens"
},
{
"trace_id": "yjv1tf0jS1evOyPm",
"span_id": "WqYKvg0_",
"timestamp": "2025-02-27T18:55:10.770916Z",
"attributes": {
"model_id": "meta-llama/Llama-3.1-70B-Instruct",
"provider_id": "fireworks"
},
"type": "metric",
"metric": "completion_tokens",
"value": 411,
"unit": "tokens"
},
{
"trace_id": "yjv1tf0jS1evOyPm",
"span_id": "WqYKvg0_",
"timestamp": "2025-02-27T18:55:10.770919Z",
"attributes": {
"model_id": "meta-llama/Llama-3.1-70B-Instruct",
"provider_id": "fireworks"
},
"type": "metric",
"metric": "total_tokens",
"value": 421,
"unit": "tokens"
}
],
"completion_message": {
"role": "assistant",
"content": "Humans live in various parts of the world, inhabiting almost every continent, country, and region. Here's a breakdown of where humans live:\n\n1. **Continents:** Humans inhabit all seven continents:\n\t* Africa\n\t* Antarctica (research stations only)\n\t* Asia\n\t* Australia\n\t* Europe\n\t* North America\n\t* South America\n2. **Countries:** There are 196 countries recognized by the United Nations, and humans live in almost all of them.\n3. **Regions:** Humans live in diverse regions, including:\n\t* Deserts (e.g., Sahara, Mojave)\n\t* Forests (e.g., Amazon, Congo)\n\t* Grasslands (e.g., Prairies, Steppes)\n\t* Mountains (e.g., Himalayas, Andes)\n\t* Oceans (e.g., coastal areas, islands)\n\t* Tundras (e.g., Arctic, sub-Arctic)\n4. **Cities and towns:** Many humans live in urban areas, such as cities and towns, which are often located near:\n\t* Coastlines\n\t* Rivers\n\t* Lakes\n\t* Mountains\n5. **Rural areas:** Some humans live in rural areas, such as:\n\t* Villages\n\t* Farms\n\t* Countryside\n6. **Islands:** Humans inhabit many islands, including:\n\t* Tropical islands (e.g., Hawaii, Maldives)\n\t* Arctic islands (e.g., Greenland, Iceland)\n\t* Continental islands (e.g., Great Britain, Ireland)\n7. **Extreme environments:** Humans also live in extreme environments, such as:\n\t* High-altitude areas (e.g., Tibet, Andes)\n\t* Low-altitude areas (e.g., Death Valley, Dead Sea)\n\t* Areas with extreme temperatures (e.g., Arctic, Sahara)\n\nOverall, humans have adapted to live in a wide range of environments and ecosystems around the world.",
"stop_reason": "end_of_turn",
"tool_calls": []
},
"logprobs": null
}
```
```
LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/integration/inference
======================================================================== short test summary info =========================================================================
FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=8B:vis=11B-inference:chat_completion:tool_calling_tools_absent-True] - ValueError: Unsupported tool prompt format: ToolPromptFormat.json
FAILED tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=8B:vis=11B-inference:chat_completion:tool_calling_tools_absent-False] - ValueError: Unsupported tool prompt format: ToolPromptFormat.json
FAILED tests/integration/inference/test_vision_inference.py::test_image_chat_completion_non_streaming[txt=8B:vis=11B] - fireworks.client.error.InvalidRequestError: {'error': {'object': 'error', 'type': 'invalid_request_error', 'message': 'Failed to decode image cannot identify image f...
FAILED tests/integration/inference/test_vision_inference.py::test_image_chat_completion_streaming[txt=8B:vis=11B] - fireworks.client.error.InvalidRequestError: {'error': {'object': 'error', 'type': 'invalid_request_error', 'message': 'Failed to decode image cannot identify image f...
========================================================= 4 failed, 16 passed, 23 xfailed, 17 warnings in 44.36s =========================================================
```
# What does this PR do?
When going through READMEs, I found that I had to keep editing the code
blocks since they were prefixed with `$ `. A common pattern is to triple
click (highlight all) a block and then copy paste. This minor change
will make this easier for folks to follow the READMEs.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
N/A
[//]: # (## Documentation)
# What does this PR do?
The agent API allows to query multiple DBs using the `vector_db_ids`
argument of the `rag` tool:
```py
toolgroups=[
{
"name": "builtin::rag",
"args": {"vector_db_ids": [vector_db_id]},
}
],
```
This means that multiple DBs can be used to compose an aggregated
context by executing the query on each of them.
When documents are passed to the next agent turn, there is no explicit
way to configure the vector DB where the embeddings will be ingested. In
such cases, we can assume that:
- if any `vector_db_ids` is given, we use the first one (it probably
makes sense to assume that it's the only one in the list, otherwise we
should loop on all the given DBs to have a consistent ingestion)
- if no `vector_db_ids` is given, we can use the current logic to
generate a default DB using the default provider. If multiple providers
are defined, the API will fail as expected: the user has to provide
details on where to ingest the documents.
(Closes#1270)
## Test Plan
The issue description details how to replicate the problem.
[//]: # (## Documentation)
---------
Signed-off-by: Daniele Martinoli <dmartino@redhat.com>
All of the tests from `llama_stack/providers/tests/` are now moved to
`tests/integration`.
I converted the `tools`, `scoring` and `datasetio` tests to use API.
However, `eval` and `post_training` proved to be a bit challenging to
leaving those. I think `post_training` should be relatively
straightforward also.
As part of this, I noticed that `wolfram_alpha` tool wasn't added to
some of our commonly used distros so I added it. I am going to remove a
lot of code duplication from distros next so while this looks like a
one-off right now, it will go away and be there uniformly for all
distros.
# What does this PR do?
- This was missed from previous deprecation:
https://github.com/meta-llama/llama-stack/pull/1186
- Part of https://github.com/meta-llama/llama-stack/issues/1396
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
pytest -v -s --nbval-lax ./llama-stack/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
[//]: # (## Documentation)
# What does this PR do?
- Modularized `resolve_impls` by extracting helper functions for
validation, sorting, and instantiation.
- Improved readability by introducing `validate_and_prepare_providers`,
`sort_providers_by_dependency`, and `instantiate_providers`.
- Enhanced type safety with explicit type hints (`Tuple`, `Dict`, `Set`,
etc.).
- Fixed potential issues with provider module imports and added error
handling.
- Updated `pyproject.toml` to enforce type checking on `resolver.py`
using `mypy`.
Signed-off-by: Sébastien Han <seb@redhat.com>
- [//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
Run the server.
[//]: # (## Documentation)
Signed-off-by: Sébastien Han <seb@redhat.com>
A self-respecting server needs good observability which starts with
configurable logging. Llama Stack had little until now. This PR adds a
`logcat` facility towards that. Callsites look like:
```python
logcat.debug("inference", f"params to ollama: {params}")
```
- the first parameter is a category. there is a static list of
categories in `llama_stack/logcat.py`
- each category can be associated with a log-level which can be
configured via the `LLAMA_STACK_LOGGING` env var.
- a value `LLAMA_STACK_LOGGING=inference=debug;server=info"` does the
obvious thing. there is a special key called `all` which is an alias for
all categories
## Test Plan
Ran with `LLAMA_STACK_LOGGING="all=debug" llama stack run fireworks` and
saw the following:
Hit it with a client-sdk test case and saw this:
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
Sorry for the https://github.com/meta-llama/llama-stack/pull/1340 logic,
it will cause issue if in `non-container` env.
```
Using conda <<<<<<<------ environment: stack
+ is_command_available docker
+ command -v docker
+ printf '\033[0;31mError: docker command not found. Is docker installed and in your PATH?\033[0m'
Error: docker command not found. Is docker installed and in your PATH?+ exit 1
```
[//]: # (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.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
`start_venv.sh` lifecycle should be:
025f615868
>>
34e3faa4e8
>>
4684fd3f8d
Finally replaced by `start_stack.sh`
[//]: # (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.*]
[//]: # (## Documentation)
---------
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
We want to bundle a bunch of (typically remote) providers in a distro
template and be able to configure them "on the fly" via environment
variables. So far, we have been able to do this with simple env var
replacements. However, sometimes you want to only conditionally enable
providers (because the relevant remote services may not be alive, or
relevant.) This was not possible until now.
To aid this, we add a simple (bash-like) env var replacement
enhancement: `${env.FOO+bar}` evaluates to `bar` if the variable is SET
and evaluates to empty string if it is not. On top of that, we update
our main resolver to ignore any provider whose ID is null.
This allows using the distro like this:
```bash
llama stack run dev --env CHROMADB_URL=http://localhost:6001 --env ENABLE_CHROMADB=1
```
when only Chroma is UP. This disables the other `pgvector` provider in
the run configuration.
## Test Plan
Hard code `chromadb` as the vector io provider inside
`test_vector_io.py` and run:
```bash
LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -s -v tests/client-sdk/vector_io/ --embedding-model all-MiniLM-L6-v2
```
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (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.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# Summary:
This led to extremely hard to debug messages.
Before:
llama_stack/distribution/library_client.py:275: in request
response = await self._call_non_streaming(
llama_stack/distribution/library_client.py:322: in _call_non_streaming
result = await matched_func(**body)
llama_stack/providers/utils/telemetry/trace_protocol.py:102: in
async_wrapper
result = await method(self, *args, **kwargs)
llama_stack/providers/inline/agents/meta_reference/agents.py:80: in
create_agent
value=agent_config.model_dump_json(),
E AttributeError: 'dict' object has no attribute 'model_dump_json'
After:
E ValueError: Failed to convert parameter {'model':
'meta-llama/Llama-3.1-8B-Instruct', 'instructions': 'You are a helpful
assistant', 'sampling_params': {'strategy': {'type': 'top_p',
'temperature': 0.0001, 'top_p': 0.9}}, 'toolgroups': [{'name':
'builtin::rag'}], 'input_shields': ['meta-llama/Llama-Guard-3-8B'],
'output_shields': ['meta-llama/Llama-Guard-3-8B'],
'enable_session_persistence': False} into <class
'llama_stack.apis.agents.agents.AgentConfig'>: 2 validation errors for
AgentConfig
E toolgroups.0.str
E Input should be a valid string [type=string_type, input_value={'name':
'builtin::rag'}, input_type=dict]
E For further information visit
https://errors.pydantic.dev/2.10/v/string_type
E toolgroups.0.AgentToolGroupWithArgs.args
E Field required [type=missing, input_value={'name': 'builtin::rag'},
input_type=dict]
E For further information visit
https://errors.pydantic.dev/2.10/v/missing
# Test Plan:
LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/client-sdk/
--safety-shield meta-llama/Llama-Guard-3-8B
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (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.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (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.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
check conda env name using basepath in exec.py
The current logic for finding conda prefix does a `endswith` check with
just the conda env name, but this will cause us to match incorrect if
there is a different conda env which ends with same suffix. In my case,
i had stack and llama-stack as the two conda envs.
## Test Plan
llama stack run ~/.llama/distributions/fireworks/fireworks-run.yaml
# What does this PR do?
Tool format depends on the model. @ehhuang introduced a
`get_default_tool_prompt_format` function for this purpose. We should
use that instead of hacky model ID matching we had before.
Secondly, non llama models don't have this concept so testing with those
models should work as is.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```bash
for distro in fireworks ollama; do
LLAMA_STACK_CONFIG=$distro \
pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--inference-model=meta-llama/Llama-3.2-3B-Instruct \
--vision-inference-model=""
done
LLAMA_STACK_CONFIG=dev \
pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--inference-model=openai/gpt-4o \
--vision-inference-model=""
```
[//]: # (## Documentation)
Summary:
Lets the model decide which tool it needs to call to respond to a query.
Test Plan:
```
LLAMA_STACK_CONFIG=fireworks pytest -s -v tests/client-sdk/ --safety-shield meta-llama/Llama-Guard-3-8B
```
Also evaluated on a small benchmark with 20 questions from HotpotQA.
With this PR and some prompting, the performance is 77% recall compared
to 50% currently.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with
[ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/1015).
* #1268
* #1239
* __->__ #1015
# What does this PR do?
- Introduced logging in `StackRun` to replace print-based messages
- Improved error handling for config file loading and parsing
- Replaced `cprint` with `logger.error` for consistent error messaging
- Ensured logging is used in `server.py` for startup, shutdown, and
runtime messages
- Added missing exception handling for invalid providers
Signed-off-by: Sébastien Han <seb@redhat.com>
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
currently, build_venv.sh expects a `distribution_type` as the first
argument but the only things ever passed are:
1. image name
2. pip dependencies
so distribution_type is never passed in meaning the script errors when
calling something like:
`llama stack build --image-type venv --template ollama --image-name
test`
before output:
```
llama stack build --image-type venv --template ollama --image-name venv-test
Usage: /Users/charliedoern/projects/Documents/llama-stack/llama_stack/distribution/build_venv.sh <distribution_type> <env_name> <pip_dependencies> [<special_pip_deps>]
Example: /Users/charliedoern/projects/Documents/llama-stack/llama_stack/distribution/build_venv.sh <distribution_type> mybuild ./my-stack-build.yaml 'numpy pandas scipy'
Failed to build target venv-test with return code 1
Run config path is empty
```
after:
```
llama stack build --image-type venv --template ollama --image-name venv-test
Environment 'venv-test' already exists, re-using it.
Using virtual environment venv-test
Using CPython 3.13.0 interpreter at: /opt/homebrew/opt/python@3.13/bin/python3.13
Creating virtual environment at: venv-test
Activate with: source venv-test/bin/activate
Using Python 3.13.0 environment at: venv-test
Resolved 55 packages in 640ms
Built fire==0.7.0
Prepared 54 packages in 1.14s
Installed 55 packages in 82ms
+ annotated-types==0.7.0
```
## Test Plan
ran locally with output above
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
now that llama stack supports running in venv, conda, and container
modes and the 3 scripts overlap alot, combine these three into ons
`start_stack.sh` script
## Test Plan
tested this locally on venv, conda, and container
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>
Summary:
Currently we don't set the best tool_prompt_format according to model as
promisd.
Test Plan:
Added print around raw model input and inspected manually
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with
[ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/1214).
* #1234
* __->__ #1214
# What does this PR do?
When building providers in a virtual environment or containers, special
pip dependencies may not always be provided (e.g., for Ollama). The
check should only fail if the required number of arguments is missing.
Currently, two arguments are mandatory:
1. Environment name
2. Pip dependencies
Additionally, return statements were replaced with sys.exit(1) in error
conditions to ensure immediate termination on critical failures. Error
handling in the stack build process was also improved to guarantee the
program exits with status 1 when facing configuration issues or build
failures.
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
This command shouldn't fail:
```
llama stack build --template ollama --image-type venv
```
[//]: # (## Documentation)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
--run runs the stack that was just build using the same arguments during
the build process (image-name, type, etc)
This simplifies the workflow a lot and makes the UX better for most
local users trying to get started rather than having to match the flags
of the two commands (build and then run)
Also, moved `ImageType` to distribution.utils since there were circular
import errors with its old location
## Test Plan
tested locally using the following command:
`llama stack build --run --template ollama --image-type venv`
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Addresses issues where the container is unable to run as root. Gives
write access to required folders.
[//]: # (If resolving an issue, uncomment and update the line below)
(Closes #[1207])
## Test Plan
I built locally and ran `llama stack build --template remote-vllm
--image-type container` and validated I could see my changes in the
output:
```
#11 1.186 Installed 11 packages in 61ms
#11 1.186 + llama-models==0.1.3
#11 1.186 + llama-stack==0.1.3
#11 1.186 + llama-stack-client==0.1.3
#11 1.186 + markdown-it-py==3.0.0
#11 1.186 + mdurl==0.1.2
#11 1.186 + prompt-toolkit==3.0.50
#11 1.186 + pyaml==25.1.0
#11 1.186 + pygments==2.19.1
#11 1.186 + rich==13.9.4
#11 1.186 + tiktoken==0.9.0
#11 1.186 + wcwidth==0.2.13
#11 DONE 1.6s
#12 [ 9/10] RUN mkdir -p /.llama /.cache
#12 DONE 0.3s
#13 [10/10] RUN chmod -R g+rw /app /.llama /.cache
#13 DONE 0.3s
#14 exporting to image
#14 exporting layers
#14 exporting layers 3.7s done
#14 writing image sha256:11cc8bd954db6d036037bcaf471b173ddd5261ac4b1e72074cccf85d18aefb96 done
#14 naming to docker.io/library/distribution-remote-vllm:0.1.3 done
#14 DONE 3.7s
+ set +x
Success!
```
This is what the resulting image looks like:
Also tagged the image as `0.1.3-test` and [pushed to
quay](https://quay.io/repository/jland/distribution-remote-vllm?tab=tags)
(note there are a bunch of critical vulnerabilities we may want to look
into)
And for good measure I deployed the resulting image on my Openshift
environment using the default Security Context and validated that there
were no issue with it coming up.
My validation was all done with the `vllm-remote` distribution, but if I
am understanding everything correctly the other distributions are just
different run.yaml configs.
[//]: # (## Documentation)
Please let me know if there is anything else I need to do.
Co-authored-by: Jamie Land <hokie10@gmail.com>
See Issue #922
The change is slightly backwards incompatible but no callsite (in our
client codebases or stack-apps) every passes a depth-2
`List[List[InterleavedContentItem]]` (which is now disallowed.)
## Test Plan
```bash
$ cd llama_stack/providers/tests/inference
$ pytest -s -v -k fireworks test_embeddings.py \
--inference-model nomic-ai/nomic-embed-text-v1.5 --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k together test_embeddings.py \
--inference-model togethercomputer/m2-bert-80M-8k-retrieval --env EMBEDDING_DIMENSION=784
$ pytest -s -v -k ollama test_embeddings.py \
--inference-model all-minilm:latest --env EMBEDDING_DIMENSION=784
```
Also ran `tests/client-sdk/inference/test_embeddings.py`
# What does this PR do?
- Fully deprecate eval/tasks
[//]: # (If resolving an issue, uncomment and update the line below)
Closes#1088
NOTE: this will be a breaking change. We have introduced the new API in
0.1.3 .
Notebook has been updated to use the new endpoints.
## Test Plan
```
pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
<img width="611" alt="image"
src="https://github.com/user-attachments/assets/79f6efe1-81ba-494e-bf36-1fc0c2b9bc6f"
/>
cc @SLR722 for awareness
[//]: # (## Documentation)
