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abfbaf3c1b
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.
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import base64
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import mimetypes
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import os
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from pathlib import Path
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import pytest
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# How to run this test:
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#
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# pytest llama_stack/providers/tests/datasetio/test_datasetio.py
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# -m "meta_reference"
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# -v -s --tb=short --disable-warnings
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def data_url_from_file(file_path: str) -> str:
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"File not found: {file_path}")
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with open(file_path, "rb") as file:
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file_content = file.read()
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base64_content = base64.b64encode(file_content).decode("utf-8")
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mime_type, _ = mimetypes.guess_type(file_path)
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data_url = f"data:{mime_type};base64,{base64_content}"
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return data_url
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def register_dataset(llama_stack_client, for_generation=False, for_rag=False, dataset_id="test_dataset"):
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if for_rag:
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test_file = Path(os.path.abspath(__file__)).parent / "test_rag_dataset.csv"
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else:
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test_file = Path(os.path.abspath(__file__)).parent / "test_dataset.csv"
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test_url = data_url_from_file(str(test_file))
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if for_generation:
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dataset_schema = {
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"expected_answer": {"type": "string"},
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"input_query": {"type": "string"},
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"chat_completion_input": {"type": "chat_completion_input"},
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}
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elif for_rag:
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dataset_schema = {
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"expected_answer": {"type": "string"},
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"input_query": {"type": "string"},
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"generated_answer": {"type": "string"},
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"context": {"type": "string"},
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}
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else:
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dataset_schema = {
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"expected_answer": {"type": "string"},
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"input_query": {"type": "string"},
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"generated_answer": {"type": "string"},
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}
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llama_stack_client.datasets.register(
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dataset_id=dataset_id,
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dataset_schema=dataset_schema,
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url=dict(uri=test_url),
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provider_id="localfs",
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)
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def test_datasets_list(llama_stack_client):
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# NOTE: this needs you to ensure that you are starting from a clean state
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# but so far we don't have an unregister API unfortunately, so be careful
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response = llama_stack_client.datasets.list()
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assert isinstance(response, list)
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assert len(response) == 0
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def test_register_dataset(llama_stack_client):
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register_dataset(llama_stack_client)
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response = llama_stack_client.datasets.list()
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assert isinstance(response, list)
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assert len(response) == 1
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assert response[0].identifier == "test_dataset"
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with pytest.raises(ValueError):
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# unregister a dataset that does not exist
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llama_stack_client.datasets.unregister("test_dataset2")
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llama_stack_client.datasets.unregister("test_dataset")
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response = llama_stack_client.datasets.list()
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assert isinstance(response, list)
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assert len(response) == 0
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with pytest.raises(ValueError):
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llama_stack_client.datasets.unregister("test_dataset")
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def test_get_rows_paginated(llama_stack_client):
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register_dataset(llama_stack_client)
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response = llama_stack_client.datasetio.get_rows_paginated(
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dataset_id="test_dataset",
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rows_in_page=3,
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)
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assert isinstance(response.rows, list)
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assert len(response.rows) == 3
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assert response.next_page_token == "3"
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# iterate over all rows
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response = llama_stack_client.datasetio.get_rows_paginated(
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dataset_id="test_dataset",
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rows_in_page=2,
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page_token=response.next_page_token,
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)
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assert isinstance(response.rows, list)
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assert len(response.rows) == 2
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assert response.next_page_token == "5"
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