fix: clean up test imports (#1600)

# What does this PR do?
- Clean up dead SDK code in
https://github.com/meta-llama/llama-stack-client-python/pull/198
- Regen for local cache key issue

[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])

## Test Plan
```
pytest -v -s --nbval-lax ./docs/getting_started.ipynb

LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/ --text-model meta-llama/Llama-3.3-70B-Instruct
```

- CI:
1382351211
<img width="1658" alt="image"
src="https://github.com/user-attachments/assets/1a2de383-35a2-47a0-8d80-d666d4970c34"
/>


[//]: # (## Documentation)
This commit is contained in:
Xi Yan 2025-03-13 11:01:52 -07:00 committed by GitHub
parent 5e54113b19
commit 98811cc034
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3 changed files with 35 additions and 13 deletions

View file

@ -10,8 +10,7 @@ from uuid import uuid4
import pytest
from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.lib.agents.event_logger import EventLogger
from llama_stack_client.types.agents.turn_create_params import Document as AgentDocument
from llama_stack_client.types.memory_insert_params import Document
from llama_stack_client.types.agents.turn_create_params import Document
from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
from llama_stack.apis.agents.agents import (
@ -242,7 +241,7 @@ def test_code_interpreter_for_attachments(llama_stack_client_with_mocked_inferen
codex_agent = Agent(llama_stack_client_with_mocked_inference, **agent_config)
session_id = codex_agent.create_session(f"test-session-{uuid4()}")
inflation_doc = AgentDocument(
inflation_doc = Document(
content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
mime_type="text/csv",
)

View file

@ -9,11 +9,25 @@ import mimetypes
import os
from pathlib import Path
import pytest
# How to run this test:
#
# LLAMA_STACK_CONFIG="template-name" pytest -v tests/integration/datasetio
@pytest.fixture
def dataset_for_test(llama_stack_client):
dataset_id = "test_dataset"
register_dataset(llama_stack_client, dataset_id=dataset_id)
yield
# Teardown - this always runs, even if the test fails
try:
llama_stack_client.datasets.unregister(dataset_id)
except Exception as e:
print(f"Warning: Failed to unregister test_dataset: {e}")
def data_url_from_file(file_path: str) -> str:
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
@ -80,8 +94,7 @@ def test_register_unregister_dataset(llama_stack_client):
assert len(response) == 0
def test_get_rows_paginated(llama_stack_client):
register_dataset(llama_stack_client)
def test_get_rows_paginated(llama_stack_client, dataset_for_test):
response = llama_stack_client.datasetio.get_rows_paginated(
dataset_id="test_dataset",
rows_in_page=3,

View file

@ -10,6 +10,19 @@ import pytest
from ..datasetio.test_datasetio import register_dataset
@pytest.fixture
def rag_dataset_for_test(llama_stack_client):
dataset_id = "test_dataset"
register_dataset(llama_stack_client, for_rag=True, dataset_id=dataset_id)
yield # This is where the test function will run
# Teardown - this always runs, even if the test fails
try:
llama_stack_client.datasets.unregister(dataset_id)
except Exception as e:
print(f"Warning: Failed to unregister test_dataset: {e}")
@pytest.fixture
def sample_judge_prompt_template():
return "Output a number response in the following format: Score: <number>, where <number> is the number between 0 and 9."
@ -79,9 +92,7 @@ def test_scoring_functions_register(
# TODO: add unregister api for scoring functions
def test_scoring_score(llama_stack_client):
register_dataset(llama_stack_client, for_rag=True)
def test_scoring_score(llama_stack_client, rag_dataset_for_test):
# scoring individual rows
rows = llama_stack_client.datasetio.get_rows_paginated(
dataset_id="test_dataset",
@ -115,9 +126,9 @@ def test_scoring_score(llama_stack_client):
assert len(response.results[x].score_rows) == 5
def test_scoring_score_with_params_llm_as_judge(llama_stack_client, sample_judge_prompt_template, judge_model_id):
register_dataset(llama_stack_client, for_rag=True)
def test_scoring_score_with_params_llm_as_judge(
llama_stack_client, sample_judge_prompt_template, judge_model_id, rag_dataset_for_test
):
# scoring individual rows
rows = llama_stack_client.datasetio.get_rows_paginated(
dataset_id="test_dataset",
@ -167,9 +178,8 @@ def test_scoring_score_with_params_llm_as_judge(llama_stack_client, sample_judge
],
)
def test_scoring_score_with_aggregation_functions(
llama_stack_client, sample_judge_prompt_template, judge_model_id, provider_id
llama_stack_client, sample_judge_prompt_template, judge_model_id, provider_id, rag_dataset_for_test
):
register_dataset(llama_stack_client, for_rag=True)
rows = llama_stack_client.datasetio.get_rows_paginated(
dataset_id="test_dataset",
rows_in_page=3,