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delete old tests
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providers:
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datasetio:
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- provider_id: test-meta
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provider_type: meta-reference
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config: {}
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scoring:
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- provider_id: test-meta
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provider_type: meta-reference
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config: {}
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- provider_id: test-braintrust
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provider_type: braintrust
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config: {}
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inference:
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- provider_id: tgi0
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provider_type: remote::tgi
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config:
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url: http://127.0.0.1:5009
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# 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 pytest
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import pytest_asyncio
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from llama_stack.apis.common.type_system import * # noqa: F403
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from llama_stack.apis.datasetio import * # noqa: F403
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from llama_stack.distribution.datatypes import * # noqa: F403
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from llama_stack.providers.tests.datasetio.test_datasetio import register_dataset
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from llama_stack.providers.tests.resolver import resolve_impls_for_test
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# How to run this test:
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#
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# 1. Ensure you have a conda with the right dependencies installed. This is a bit tricky
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# since it depends on the provider you are testing. On top of that you need
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# `pytest` and `pytest-asyncio` installed.
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#
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# 2. Copy and modify the provider_config_example.yaml depending on the provider you are testing.
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#
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# 3. Run:
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#
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# ```bash
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# PROVIDER_ID=<your_provider> \
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# PROVIDER_CONFIG=provider_config.yaml \
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# pytest -s llama_stack/providers/tests/scoring/test_scoring.py \
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# --tb=short --disable-warnings
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# ```
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@pytest_asyncio.fixture(scope="session")
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async def scoring_settings():
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impls = await resolve_impls_for_test(
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Api.scoring, deps=[Api.datasetio, Api.inference]
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)
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return {
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"scoring_impl": impls[Api.scoring],
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"scoring_functions_impl": impls[Api.scoring_functions],
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"datasets_impl": impls[Api.datasets],
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}
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@pytest_asyncio.fixture(scope="session")
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async def provider_scoring_functions():
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return {
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"meta-reference": {
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"meta-reference::equality",
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"meta-reference::subset_of",
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"meta-reference::llm_as_judge_8b_correctness",
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},
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"braintrust": {
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"braintrust::factuality",
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"braintrust::answer-correctness",
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},
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}
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@pytest.mark.asyncio
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async def test_scoring_functions_list(scoring_settings, provider_scoring_functions):
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scoring_impl = scoring_settings["scoring_impl"]
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scoring_functions_impl = scoring_settings["scoring_functions_impl"]
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scoring_functions = await scoring_functions_impl.list_scoring_functions()
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assert isinstance(scoring_functions, list)
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assert len(scoring_functions) > 0
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function_ids = [f.identifier for f in scoring_functions]
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# get current provider_type we're testing
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provider = scoring_impl.routing_table.get_provider_impl(function_ids[0])
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provider_type = provider.__provider_spec__.provider_type
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for x in provider_scoring_functions[provider_type]:
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assert x in function_ids
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@pytest.mark.asyncio
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async def test_scoring_functions_register(scoring_settings):
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scoring_impl = scoring_settings["scoring_impl"]
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scoring_functions_impl = scoring_settings["scoring_functions_impl"]
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datasets_impl = scoring_settings["datasets_impl"]
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# get current provider_type we're testing
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scoring_functions = await scoring_functions_impl.list_scoring_functions()
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function_ids = [f.identifier for f in scoring_functions]
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provider = scoring_impl.routing_table.get_provider_impl(function_ids[0])
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provider_type = provider.__provider_spec__.provider_type
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if provider_type not in ("meta-reference"):
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pytest.skip(
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"Other scoring providers don't support registering scoring functions."
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)
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test_prompt = """Output a number between 0 to 10. Your answer must match the format \n Number: <answer>"""
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# register the scoring function
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await scoring_functions_impl.register_scoring_function(
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ScoringFnDefWithProvider(
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identifier="meta-reference::llm_as_judge_8b_random",
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description="Llm As Judge Scoring Function",
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parameters=[],
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return_type=NumberType(),
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context=LLMAsJudgeContext(
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prompt_template=test_prompt,
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judge_model="Llama3.1-8B-Instruct",
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judge_score_regex=[r"Number: (\d+)"],
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),
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provider_id="test-meta",
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)
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)
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scoring_functions = await scoring_functions_impl.list_scoring_functions()
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assert isinstance(scoring_functions, list)
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assert len(scoring_functions) > 0
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function_ids = [f.identifier for f in scoring_functions]
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assert "meta-reference::llm_as_judge_8b_random" in function_ids
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# test score using newly registered scoring function
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await register_dataset(datasets_impl)
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response = await datasets_impl.list_datasets()
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assert len(response) == 1
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response = await scoring_impl.score_batch(
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dataset_id=response[0].identifier,
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scoring_functions=[
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"meta-reference::llm_as_judge_8b_random",
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],
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)
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assert "meta-reference::llm_as_judge_8b_random" in response.results
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@pytest.mark.asyncio
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async def test_scoring_score(scoring_settings, provider_scoring_functions):
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scoring_impl = scoring_settings["scoring_impl"]
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datasets_impl = scoring_settings["datasets_impl"]
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scoring_functions_impl = scoring_settings["scoring_functions_impl"]
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await register_dataset(datasets_impl)
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response = await datasets_impl.list_datasets()
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assert len(response) == 1
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# get current provider_type we're testing
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scoring_functions = await scoring_functions_impl.list_scoring_functions()
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function_ids = [f.identifier for f in scoring_functions]
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provider = scoring_impl.routing_table.get_provider_impl(function_ids[0])
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provider_type = provider.__provider_spec__.provider_type
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response = await scoring_impl.score_batch(
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dataset_id=response[0].identifier,
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scoring_functions=list(provider_scoring_functions[provider_type]),
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)
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assert len(response.results) == len(provider_scoring_functions[provider_type])
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for x in provider_scoring_functions[provider_type]:
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assert x in response.results
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