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# What does this PR do? Move pagination logic from LocalFS and HuggingFace implementations into a common helper function to ensure consistent pagination behavior across providers. This reduces code duplication and centralizes pagination logic in one place. ## Test Plan Run this script: ``` from llama_stack_client import LlamaStackClient # Initialize the client client = LlamaStackClient(base_url="http://localhost:8321") # Register a dataset response = client.datasets.register( purpose="eval/messages-answer", # or "eval/question-answer" or "post-training/messages" source={"type": "uri", "uri": "huggingface://datasets/llamastack/simpleqa?split=train"}, dataset_id="my_dataset", # optional, will be auto-generated if not provided metadata={"description": "My evaluation dataset"}, # optional ) # Verify the dataset was registered by listing all datasets datasets = client.datasets.list() print(f"Registered datasets: {[d.identifier for d in datasets]}") # You can then access the data using the datasetio API # rows = client.datasets.iterrows(dataset_id="my_dataset", start_index=1, limit=2) rows = client.datasets.iterrows(dataset_id="my_dataset") print(f"Data: {rows.data}") ``` And play with `start_index` and `limit`. [//]: # (## Documentation) Signed-off-by: Sébastien Han <seb@redhat.com> |
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| .. | ||
| agents | ||
| batch_inference | ||
| benchmarks | ||
| common | ||
| datasetio | ||
| datasets | ||
| eval | ||
| files | ||
| inference | ||
| inspect | ||
| models | ||
| post_training | ||
| providers | ||
| safety | ||
| scoring | ||
| scoring_functions | ||
| shields | ||
| synthetic_data_generation | ||
| telemetry | ||
| tools | ||
| vector_dbs | ||
| vector_io | ||
| __init__.py | ||
| datatypes.py | ||
| resource.py | ||
| version.py | ||