91 lines
2.1 KiB
Python
91 lines
2.1 KiB
Python
"""
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Pytest configuration and fixtures for pipeline-bridge tests.
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"""
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import asyncio
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import os
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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# Set test environment variables before importing
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os.environ.setdefault("NATS_URL", "nats://localhost:4222")
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os.environ.setdefault("OTEL_ENABLED", "false")
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os.environ.setdefault("MLFLOW_ENABLED", "false")
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@pytest.fixture(scope="session")
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def event_loop():
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"""Create event loop for async tests."""
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loop = asyncio.new_event_loop()
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yield loop
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loop.close()
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@pytest.fixture
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def mock_nats_message():
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"""Create a mock NATS message."""
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msg = MagicMock()
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msg.subject = "ai.pipeline.trigger"
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msg.reply = "ai.pipeline.status.test-123"
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return msg
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@pytest.fixture
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def argo_pipeline_request():
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"""Sample Argo pipeline trigger request."""
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return {
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"request_id": "test-request-123",
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"pipeline": "document-ingestion",
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"parameters": {
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"source_path": "s3://bucket/documents",
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"collection_name": "test_collection",
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},
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}
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@pytest.fixture
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def kubeflow_pipeline_request():
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"""Sample Kubeflow pipeline trigger request."""
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return {
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"request_id": "test-request-456",
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"pipeline": "rag-query",
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"parameters": {
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"query": "What is AI?",
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"collection": "documents",
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},
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}
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@pytest.fixture
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def unknown_pipeline_request():
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"""Request for unknown pipeline."""
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return {
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"request_id": "test-request-789",
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"pipeline": "nonexistent-pipeline",
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"parameters": {},
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}
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@pytest.fixture
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def mock_argo_response():
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"""Mock Argo Workflows API response."""
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return {
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"metadata": {
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"name": "document-ingestion-abc123",
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"namespace": "ai-ml",
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},
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"status": {"phase": "Pending"},
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}
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@pytest.fixture
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def mock_kubeflow_response():
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"""Mock Kubeflow Pipelines API response."""
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return {
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"run": {
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"id": "run-xyz-789",
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"name": "rag-query-test",
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"status": "Running",
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}
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}
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