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# What does this PR do? - revamp and clean up datasets/scoring/eval integration tests - closes https://github.com/meta-llama/llama-stack/issues/1396 [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan **dataset** ``` LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -v tests/integration/datasetio/ ``` <img width="842" alt="image" src="https://github.com/user-attachments/assets/88fc2b6a-b496-47bf-bc0c-8fea48ba36ff" /> **scoring** ``` LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/scoring --text-model meta-llama/Llama-3.1-8B-Instruct --judge-model meta-llama/Llama-3.1-8B-Instruct ``` <img width="851" alt="image" src="https://github.com/user-attachments/assets/50f46415-b44c-4c37-a6c3-076f2767adb3" /> **eval** ``` LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/eval --text-model meta-llama/Llama-3.1-8B-Instruct --judge-model meta-llama/Llama-3.1-8B-Instruct ``` <img width="841" alt="image" src="https://github.com/user-attachments/assets/8eb1c65c-3b39-4d66-8ff4-f471ca783e49" /> [//]: # (## Documentation)
101 lines
3.2 KiB
Python
101 lines
3.2 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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# How to run this test:
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#
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# LLAMA_STACK_CONFIG="template-name" pytest -v tests/integration/datasetio
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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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dataset_providers = [x for x in llama_stack_client.providers.list() if x.api == "datasetio"]
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dataset_provider_id = dataset_providers[0].provider_id
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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=dataset_provider_id,
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)
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def test_register_unregister_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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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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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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