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wip tests
This commit is contained in:
parent
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commit
683a370d23
4 changed files with 227 additions and 128 deletions
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@ -51,6 +51,11 @@ class AppEvalTaskConfig(BaseModel):
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# we could optinally add any specific dataset config here
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EvalTaskConfig = Annotated[
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Union[BenchmarkEvalTaskConfig, AppEvalTaskConfig], Field(discriminator="type")
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]
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@json_schema_type
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class EvaluateResponse(BaseModel):
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generations: List[Dict[str, Any]]
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@ -70,7 +75,7 @@ class Eval(Protocol):
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async def run_eval(
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self,
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eval_task_def: EvalTaskDef, # type: ignore
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eval_task_config: AppEvalTaskConfig, # type: ignore
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eval_task_config: EvalTaskConfig, # type: ignore
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) -> Job: ...
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@webmethod(route="/eval/evaluate_rows", method="POST")
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7
llama_stack/apis/eval_tasks/__init__.py
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7
llama_stack/apis/eval_tasks/__init__.py
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@ -0,0 +1,7 @@
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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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from .eval_tasks import * # noqa: F401 F403
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@ -3,146 +3,85 @@
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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 os
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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.apis.memory import * # noqa: F403
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from llama_stack.distribution.datatypes import * # noqa: F403
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import base64
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import mimetypes
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from pathlib import Path
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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/datasetio/test_datasetio.py \
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# --tb=short --disable-warnings
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# ```
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# pytest llama_stack/providers/tests/memory/test_memory.py
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# -m "meta_reference"
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# -v -s --tb=short --disable-warnings
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@pytest_asyncio.fixture(scope="session")
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async def datasetio_settings():
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impls = await resolve_impls_for_test(
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Api.datasetio,
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)
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return {
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"datasetio_impl": impls[Api.datasetio],
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"datasets_impl": impls[Api.datasets],
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}
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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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async def register_dataset(
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datasets_impl: Datasets, for_generation=False, dataset_id="test_dataset"
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):
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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": StringType(),
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"input_query": StringType(),
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"chat_completion_input": ChatCompletionInputType(),
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}
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else:
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dataset_schema = {
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"expected_answer": StringType(),
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"input_query": StringType(),
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"generated_answer": StringType(),
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}
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dataset = DatasetDefWithProvider(
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identifier=dataset_id,
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provider_id=os.environ.get("DATASETIO_PROVIDER_ID", None)
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or os.environ["PROVIDER_ID"],
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url=URL(
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uri=test_url,
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@pytest.fixture
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def sample_documents():
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return [
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MemoryBankDocument(
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document_id="doc1",
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content="Python is a high-level programming language.",
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metadata={"category": "programming", "difficulty": "beginner"},
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),
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dataset_schema=dataset_schema,
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)
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await datasets_impl.register_dataset(dataset)
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MemoryBankDocument(
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document_id="doc2",
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content="Machine learning is a subset of artificial intelligence.",
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metadata={"category": "AI", "difficulty": "advanced"},
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),
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MemoryBankDocument(
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document_id="doc3",
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content="Data structures are fundamental to computer science.",
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metadata={"category": "computer science", "difficulty": "intermediate"},
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),
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MemoryBankDocument(
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document_id="doc4",
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content="Neural networks are inspired by biological neural networks.",
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metadata={"category": "AI", "difficulty": "advanced"},
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),
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]
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@pytest.mark.asyncio
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async def test_datasets_list(datasetio_settings):
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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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datasets_impl = datasetio_settings["datasets_impl"]
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response = await datasets_impl.list_datasets()
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assert isinstance(response, list)
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assert len(response) == 0
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@pytest.mark.asyncio
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async def test_datasets_register(datasetio_settings):
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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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datasets_impl = datasetio_settings["datasets_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 isinstance(response, list)
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assert len(response) == 1
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# register same dataset with same id again will fail
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await register_dataset(datasets_impl)
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response = await datasets_impl.list_datasets()
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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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@pytest.mark.asyncio
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async def test_get_rows_paginated(datasetio_settings):
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datasetio_impl = datasetio_settings["datasetio_impl"]
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datasets_impl = datasetio_settings["datasets_impl"]
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await register_dataset(datasets_impl)
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response = await datasetio_impl.get_rows_paginated(
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dataset_id="test_dataset",
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rows_in_page=3,
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async def register_memory_bank(banks_impl: MemoryBanks):
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bank = VectorMemoryBankDef(
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identifier="test_bank",
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embedding_model="all-MiniLM-L6-v2",
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chunk_size_in_tokens=512,
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overlap_size_in_tokens=64,
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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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await banks_impl.register_memory_bank(bank)
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# iterate over all rows
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response = await datasetio_impl.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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class TestMemory:
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@pytest.mark.asyncio
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async def test_banks_list(self, memory_stack):
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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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_, banks_impl = memory_stack
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response = await banks_impl.list_memory_banks()
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assert isinstance(response, list)
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assert len(response) == 0
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@pytest.mark.asyncio
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async def test_banks_register(self, memory_stack):
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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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_, banks_impl = memory_stack
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bank = VectorMemoryBankDef(
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identifier="test_bank_no_provider",
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embedding_model="all-MiniLM-L6-v2",
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chunk_size_in_tokens=512,
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overlap_size_in_tokens=64,
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)
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await banks_impl.register_memory_bank(bank)
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response = await banks_impl.list_memory_banks()
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assert isinstance(response, list)
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assert len(response) == 1
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# register same memory bank with same id again will fail
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await banks_impl.register_memory_bank(bank)
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response = await banks_impl.list_memory_banks()
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assert isinstance(response, list)
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assert len(response) == 1
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148
llama_stack/providers/tests/datasetio/test_datasetio_old.py
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148
llama_stack/providers/tests/datasetio/test_datasetio_old.py
Normal file
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@ -0,0 +1,148 @@
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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 os
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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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import base64
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import mimetypes
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from pathlib import Path
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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/datasetio/test_datasetio.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 datasetio_settings():
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impls = await resolve_impls_for_test(
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Api.datasetio,
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)
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return {
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"datasetio_impl": impls[Api.datasetio],
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"datasets_impl": impls[Api.datasets],
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}
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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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async def register_dataset(
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datasets_impl: Datasets, for_generation=False, dataset_id="test_dataset"
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):
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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": StringType(),
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"input_query": StringType(),
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"chat_completion_input": ChatCompletionInputType(),
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}
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else:
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dataset_schema = {
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"expected_answer": StringType(),
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"input_query": StringType(),
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"generated_answer": StringType(),
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}
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dataset = DatasetDefWithProvider(
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identifier=dataset_id,
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provider_id=os.environ.get("DATASETIO_PROVIDER_ID", None)
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or os.environ["PROVIDER_ID"],
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url=URL(
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uri=test_url,
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),
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dataset_schema=dataset_schema,
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)
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await datasets_impl.register_dataset(dataset)
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@pytest.mark.asyncio
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async def test_datasets_list(datasetio_settings):
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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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datasets_impl = datasetio_settings["datasets_impl"]
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response = await datasets_impl.list_datasets()
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assert isinstance(response, list)
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assert len(response) == 0
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@pytest.mark.asyncio
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async def test_datasets_register(datasetio_settings):
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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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datasets_impl = datasetio_settings["datasets_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 isinstance(response, list)
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assert len(response) == 1
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# register same dataset with same id again will fail
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await register_dataset(datasets_impl)
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response = await datasets_impl.list_datasets()
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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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@pytest.mark.asyncio
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async def test_get_rows_paginated(datasetio_settings):
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datasetio_impl = datasetio_settings["datasetio_impl"]
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datasets_impl = datasetio_settings["datasets_impl"]
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await register_dataset(datasets_impl)
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response = await datasetio_impl.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 = await datasetio_impl.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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