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chore(misc): update datasets, benchmarks to use alpha, beta prefixes (#3891)

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This will be landed together with
https://github.com/llamastack/llama-stack-client-python/pull/282 (hence
CI will be red on this one.)

I have verified locally that tests pass with the updated version of the
client-sdk.
This commit is contained in:
Ashwin Bharambe 2025-10-22 15:26:35 -07:00 • committed by GitHub
parent 7918188f1e
commit 0e57233a0a
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3 changed files with 15 additions and 13 deletions
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14
tests/integration/eval/test_eval.py
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@ -17,17 +17,17 @@ from ..datasets.test_datasets import data_url_from_file
@pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])
def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
dataset = llama_stack_client.datasets.register(
dataset = llama_stack_client.beta.datasets.register(
purpose="eval/messages-answer",
source={
"type": "uri",
"uri": data_url_from_file(Path(__file__).parent.parent / "datasets" / "test_dataset.csv"),
},
)
response = llama_stack_client.datasets.list()
response = llama_stack_client.beta.datasets.list()
assert any(x.identifier == dataset.identifier for x in response)
rows = llama_stack_client.datasets.iterrows(
rows = llama_stack_client.beta.datasets.iterrows(
dataset_id=dataset.identifier,
limit=3,
)
@ -37,12 +37,12 @@ def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
scoring_fn_id,
]
benchmark_id = str(uuid.uuid4())
llama_stack_client.benchmarks.register(
llama_stack_client.alpha.benchmarks.register(
benchmark_id=benchmark_id,
dataset_id=dataset.identifier,
scoring_functions=scoring_functions,
)
list_benchmarks = llama_stack_client.benchmarks.list()
list_benchmarks = llama_stack_client.alpha.benchmarks.list()
assert any(x.identifier == benchmark_id for x in list_benchmarks)
response = llama_stack_client.alpha.eval.evaluate_rows(
@ -66,7 +66,7 @@ def test_evaluate_rows(llama_stack_client, text_model_id, scoring_fn_id):
@pytest.mark.parametrize("scoring_fn_id", ["basic::subset_of"])
def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
dataset = llama_stack_client.datasets.register(
dataset = llama_stack_client.beta.datasets.register(
purpose="eval/messages-answer",
source={
"type": "uri",
@ -74,7 +74,7 @@ def test_evaluate_benchmark(llama_stack_client, text_model_id, scoring_fn_id):
},
)
benchmark_id = str(uuid.uuid4())
llama_stack_client.benchmarks.register(
llama_stack_client.alpha.benchmarks.register(
benchmark_id=benchmark_id,
dataset_id=dataset.identifier,
scoring_functions=[scoring_fn_id],