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Test suite executed from CI pipeline (#433)
* Test strategy & test cases * Unit tests * Integration tests
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386
tests/integration/conftest.py
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386
tests/integration/conftest.py
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"""
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Shared fixtures and configuration for integration tests
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This file provides common fixtures and test configuration for integration tests.
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Following the TEST_STRATEGY.md patterns for integration testing.
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"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock
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@pytest.fixture
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def mock_pulsar_client():
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"""Mock Pulsar client for integration tests"""
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client = MagicMock()
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client.create_producer.return_value = AsyncMock()
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client.subscribe.return_value = AsyncMock()
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return client
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@pytest.fixture
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def mock_flow_context():
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"""Mock flow context for testing service coordination"""
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context = MagicMock()
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# Mock flow producers/consumers
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context.return_value.send = AsyncMock()
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context.return_value.receive = AsyncMock()
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return context
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@pytest.fixture
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def integration_config():
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"""Common configuration for integration tests"""
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return {
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"pulsar_host": "localhost",
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"pulsar_port": 6650,
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"test_timeout": 30.0,
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"max_retries": 3,
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"doc_limit": 10,
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"embedding_dim": 5,
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}
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@pytest.fixture
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def sample_documents():
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"""Sample document collection for testing"""
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return [
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{
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"id": "doc1",
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"content": "Machine learning is a subset of artificial intelligence that focuses on algorithms that learn from data.",
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"collection": "ml_knowledge",
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"user": "test_user"
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},
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{
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"id": "doc2",
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"content": "Deep learning uses neural networks with multiple layers to model complex patterns in data.",
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"collection": "ml_knowledge",
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"user": "test_user"
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},
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{
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"id": "doc3",
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"content": "Supervised learning algorithms learn from labeled training data to make predictions on new data.",
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"collection": "ml_knowledge",
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"user": "test_user"
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}
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]
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@pytest.fixture
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def sample_embeddings():
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"""Sample embedding vectors for testing"""
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return [
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[0.1, 0.2, 0.3, 0.4, 0.5],
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[0.6, 0.7, 0.8, 0.9, 1.0],
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[0.2, 0.3, 0.4, 0.5, 0.6],
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[0.7, 0.8, 0.9, 1.0, 0.1],
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[0.3, 0.4, 0.5, 0.6, 0.7]
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]
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@pytest.fixture
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def sample_queries():
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"""Sample queries for testing"""
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return [
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"What is machine learning?",
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"How does deep learning work?",
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"Explain supervised learning",
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"What are neural networks?",
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"How do algorithms learn from data?"
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]
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@pytest.fixture
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def sample_text_completion_requests():
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"""Sample text completion requests for testing"""
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return [
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{
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"system": "You are a helpful assistant.",
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"prompt": "What is artificial intelligence?",
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"expected_keywords": ["artificial intelligence", "AI", "machine learning"]
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},
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{
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"system": "You are a technical expert.",
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"prompt": "Explain neural networks",
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"expected_keywords": ["neural networks", "neurons", "layers"]
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},
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{
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"system": "You are a teacher.",
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"prompt": "What is supervised learning?",
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"expected_keywords": ["supervised learning", "training", "labels"]
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}
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]
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@pytest.fixture
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def mock_openai_response():
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"""Mock OpenAI API response structure"""
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return {
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"id": "chatcmpl-test123",
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"object": "chat.completion",
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"created": 1234567890,
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"model": "gpt-3.5-turbo",
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": "This is a test response from the AI model."
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},
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"finish_reason": "stop"
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}
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],
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"usage": {
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"prompt_tokens": 50,
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"completion_tokens": 100,
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"total_tokens": 150
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}
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}
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@pytest.fixture
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def text_completion_configs():
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"""Various text completion configurations for testing"""
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return [
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{
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"model": "gpt-3.5-turbo",
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"temperature": 0.0,
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"max_output": 1024,
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"description": "Conservative settings"
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},
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{
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"model": "gpt-4",
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"temperature": 0.7,
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"max_output": 2048,
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"description": "Balanced settings"
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},
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{
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"model": "gpt-4-turbo",
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"temperature": 1.0,
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"max_output": 4096,
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"description": "Creative settings"
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}
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]
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@pytest.fixture
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def sample_agent_tools():
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"""Sample agent tools configuration for testing"""
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return {
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"knowledge_query": {
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"name": "knowledge_query",
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"description": "Query the knowledge graph for information",
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"type": "knowledge-query",
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"arguments": [
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{
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"name": "question",
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"type": "string",
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"description": "The question to ask the knowledge graph"
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}
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]
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},
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"text_completion": {
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"name": "text_completion",
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"description": "Generate text completion using LLM",
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"type": "text-completion",
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"arguments": [
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{
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"name": "question",
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"type": "string",
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"description": "The question to ask the LLM"
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}
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]
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},
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"web_search": {
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"name": "web_search",
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"description": "Search the web for information",
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"type": "mcp-tool",
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"arguments": [
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{
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"name": "query",
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"type": "string",
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"description": "The search query"
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}
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]
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}
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}
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@pytest.fixture
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def sample_agent_requests():
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"""Sample agent requests for testing"""
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return [
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{
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"question": "What is machine learning?",
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"plan": "",
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"state": "",
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"history": [],
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"expected_tool": "knowledge_query"
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},
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{
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"question": "Can you explain neural networks in simple terms?",
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"plan": "",
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"state": "",
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"history": [],
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"expected_tool": "text_completion"
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},
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{
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"question": "Search for the latest AI research papers",
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"plan": "",
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"state": "",
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"history": [],
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"expected_tool": "web_search"
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}
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]
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@pytest.fixture
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def sample_agent_responses():
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"""Sample agent responses for testing"""
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return [
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{
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"thought": "I need to search for information about machine learning",
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"action": "knowledge_query",
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"arguments": {"question": "What is machine learning?"}
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},
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{
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"thought": "I can provide a direct answer about neural networks",
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"final-answer": "Neural networks are computing systems inspired by biological neural networks."
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},
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{
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"thought": "I should search the web for recent research",
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"action": "web_search",
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"arguments": {"query": "latest AI research papers 2024"}
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}
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]
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@pytest.fixture
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def sample_conversation_history():
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"""Sample conversation history for testing"""
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return [
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{
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"thought": "I need to search for basic information first",
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"action": "knowledge_query",
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"arguments": {"question": "What is artificial intelligence?"},
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"observation": "AI is the simulation of human intelligence in machines."
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},
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{
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"thought": "Now I can provide more specific information",
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"action": "text_completion",
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"arguments": {"question": "Explain machine learning within AI"},
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"observation": "Machine learning is a subset of AI that enables computers to learn from data."
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}
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]
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@pytest.fixture
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def sample_kg_extraction_data():
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"""Sample knowledge graph extraction data for testing"""
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return {
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"text_chunks": [
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"Machine Learning is a subset of Artificial Intelligence that enables computers to learn from data.",
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"Neural Networks are computing systems inspired by biological neural networks.",
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"Deep Learning uses neural networks with multiple layers to model complex patterns."
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],
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"expected_entities": [
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"Machine Learning",
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"Artificial Intelligence",
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"Neural Networks",
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"Deep Learning"
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],
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"expected_relationships": [
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{
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"subject": "Machine Learning",
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"predicate": "is_subset_of",
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"object": "Artificial Intelligence"
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},
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{
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"subject": "Deep Learning",
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"predicate": "uses",
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"object": "Neural Networks"
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}
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]
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}
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@pytest.fixture
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def sample_kg_definitions():
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"""Sample knowledge graph definitions for testing"""
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return [
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{
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"entity": "Machine Learning",
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"definition": "A subset of artificial intelligence that enables computers to learn from data without explicit programming."
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},
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{
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"entity": "Artificial Intelligence",
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"definition": "The simulation of human intelligence in machines that are programmed to think and act like humans."
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},
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{
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"entity": "Neural Networks",
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"definition": "Computing systems inspired by biological neural networks that process information using interconnected nodes."
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},
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{
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"entity": "Deep Learning",
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"definition": "A subset of machine learning that uses neural networks with multiple layers to model complex patterns in data."
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}
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]
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@pytest.fixture
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def sample_kg_relationships():
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"""Sample knowledge graph relationships for testing"""
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return [
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{
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"subject": "Machine Learning",
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"predicate": "is_subset_of",
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"object": "Artificial Intelligence",
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"object-entity": True
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},
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{
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"subject": "Deep Learning",
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"predicate": "is_subset_of",
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"object": "Machine Learning",
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"object-entity": True
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},
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{
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"subject": "Neural Networks",
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"predicate": "is_used_in",
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"object": "Deep Learning",
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"object-entity": True
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},
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{
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"subject": "Machine Learning",
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"predicate": "processes",
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"object": "data patterns",
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"object-entity": False
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}
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]
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@pytest.fixture
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def sample_kg_triples():
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"""Sample knowledge graph triples for testing"""
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return [
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{
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"subject": "http://trustgraph.ai/e/machine-learning",
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"predicate": "http://www.w3.org/2000/01/rdf-schema#label",
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"object": "Machine Learning"
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},
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{
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"subject": "http://trustgraph.ai/e/machine-learning",
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"predicate": "http://trustgraph.ai/definition",
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"object": "A subset of artificial intelligence that enables computers to learn from data."
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},
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{
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"subject": "http://trustgraph.ai/e/machine-learning",
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"predicate": "http://trustgraph.ai/e/is_subset_of",
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"object": "http://trustgraph.ai/e/artificial-intelligence"
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}
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]
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# Test markers for integration tests
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pytestmark = pytest.mark.integration
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