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270 lines
10 KiB
Python
270 lines
10 KiB
Python
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"""
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Integration tests for GraphRAG retrieval system
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These tests verify the end-to-end functionality of the GraphRAG system,
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testing the coordination between embeddings, graph retrieval, triple querying, and prompt services.
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Following the TEST_STRATEGY.md approach for integration testing.
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NOTE: This is the first integration test file for GraphRAG (previously had only unit tests).
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"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock
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from trustgraph.retrieval.graph_rag.graph_rag import GraphRag
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@pytest.mark.integration
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class TestGraphRagIntegration:
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"""Integration tests for GraphRAG system coordination"""
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@pytest.fixture
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def mock_embeddings_client(self):
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"""Mock embeddings client that returns realistic vector embeddings"""
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client = AsyncMock()
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client.embed.return_value = [
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[0.1, 0.2, 0.3, 0.4, 0.5], # Realistic 5-dimensional embedding
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]
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return client
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@pytest.fixture
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def mock_graph_embeddings_client(self):
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"""Mock graph embeddings client that returns realistic entities"""
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client = AsyncMock()
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client.query.return_value = [
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"http://trustgraph.ai/e/machine-learning",
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"http://trustgraph.ai/e/artificial-intelligence",
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"http://trustgraph.ai/e/neural-networks"
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]
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return client
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@pytest.fixture
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def mock_triples_client(self):
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"""Mock triples client that returns realistic knowledge graph triples"""
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client = AsyncMock()
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# Mock different queries return different triples
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async def query_side_effect(s=None, p=None, o=None, limit=None, user=None, collection=None):
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# Mock label queries
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if p == "http://www.w3.org/2000/01/rdf-schema#label":
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if s == "http://trustgraph.ai/e/machine-learning":
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return [MagicMock(s=s, p=p, o="Machine Learning")]
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elif s == "http://trustgraph.ai/e/artificial-intelligence":
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return [MagicMock(s=s, p=p, o="Artificial Intelligence")]
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elif s == "http://trustgraph.ai/e/neural-networks":
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return [MagicMock(s=s, p=p, o="Neural Networks")]
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return []
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# Mock relationship queries
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if s == "http://trustgraph.ai/e/machine-learning":
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return [
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MagicMock(
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s="http://trustgraph.ai/e/machine-learning",
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p="http://trustgraph.ai/is_subset_of",
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o="http://trustgraph.ai/e/artificial-intelligence"
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),
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MagicMock(
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s="http://trustgraph.ai/e/machine-learning",
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p="http://www.w3.org/2000/01/rdf-schema#label",
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o="Machine Learning"
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)
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]
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return []
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client.query.side_effect = query_side_effect
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return client
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@pytest.fixture
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def mock_prompt_client(self):
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"""Mock prompt client that generates realistic responses"""
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client = AsyncMock()
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client.kg_prompt.return_value = (
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"Machine learning is a subset of artificial intelligence that enables computers "
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"to learn from data without being explicitly programmed. It uses algorithms "
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"and statistical models to find patterns in data."
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)
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return client
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@pytest.fixture
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def graph_rag(self, mock_embeddings_client, mock_graph_embeddings_client,
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mock_triples_client, mock_prompt_client):
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"""Create GraphRag instance with mocked dependencies"""
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return GraphRag(
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embeddings_client=mock_embeddings_client,
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graph_embeddings_client=mock_graph_embeddings_client,
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triples_client=mock_triples_client,
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prompt_client=mock_prompt_client,
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verbose=True
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)
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@pytest.mark.asyncio
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async def test_graph_rag_end_to_end_flow(self, graph_rag, mock_embeddings_client,
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mock_graph_embeddings_client, mock_triples_client,
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mock_prompt_client):
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"""Test complete GraphRAG pipeline from query to response"""
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# Arrange
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query = "What is machine learning?"
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user = "test_user"
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collection = "ml_knowledge"
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entity_limit = 50
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triple_limit = 30
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# Act
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result = await graph_rag.query(
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query=query,
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user=user,
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collection=collection,
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entity_limit=entity_limit,
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triple_limit=triple_limit
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)
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# Assert - Verify service coordination
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# 1. Should compute embeddings for query
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mock_embeddings_client.embed.assert_called_once_with(query)
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# 2. Should query graph embeddings to find relevant entities
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mock_graph_embeddings_client.query.assert_called_once()
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call_args = mock_graph_embeddings_client.query.call_args
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assert call_args.kwargs['vectors'] == [[0.1, 0.2, 0.3, 0.4, 0.5]]
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assert call_args.kwargs['limit'] == entity_limit
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assert call_args.kwargs['user'] == user
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assert call_args.kwargs['collection'] == collection
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# 3. Should query triples to build knowledge subgraph
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assert mock_triples_client.query.call_count > 0
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# 4. Should call prompt with knowledge graph
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mock_prompt_client.kg_prompt.assert_called_once()
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call_args = mock_prompt_client.kg_prompt.call_args
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assert call_args.args[0] == query # First arg is query
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assert isinstance(call_args.args[1], list) # Second arg is kg (list of triples)
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# Verify final response
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assert result is not None
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assert isinstance(result, str)
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assert "machine learning" in result.lower()
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@pytest.mark.asyncio
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async def test_graph_rag_with_different_limits(self, graph_rag, mock_embeddings_client,
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mock_graph_embeddings_client):
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"""Test GraphRAG with various entity and triple limits"""
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# Arrange
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query = "Explain neural networks"
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test_configs = [
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{"entity_limit": 10, "triple_limit": 10},
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{"entity_limit": 50, "triple_limit": 30},
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{"entity_limit": 100, "triple_limit": 100},
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]
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for config in test_configs:
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# Reset mocks
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mock_embeddings_client.reset_mock()
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mock_graph_embeddings_client.reset_mock()
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# Act
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await graph_rag.query(
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query=query,
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user="test_user",
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collection="test_collection",
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entity_limit=config["entity_limit"],
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triple_limit=config["triple_limit"]
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)
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# Assert
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call_args = mock_graph_embeddings_client.query.call_args
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assert call_args.kwargs['limit'] == config["entity_limit"]
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@pytest.mark.asyncio
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async def test_graph_rag_error_propagation(self, graph_rag, mock_embeddings_client):
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"""Test that errors from underlying services are properly propagated"""
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# Arrange
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mock_embeddings_client.embed.side_effect = Exception("Embeddings service error")
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# Act & Assert
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with pytest.raises(Exception) as exc_info:
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await graph_rag.query(
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query="test query",
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user="test_user",
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collection="test_collection"
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)
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assert "Embeddings service error" in str(exc_info.value)
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@pytest.mark.asyncio
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async def test_graph_rag_with_empty_knowledge_graph(self, graph_rag, mock_graph_embeddings_client,
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mock_triples_client, mock_prompt_client):
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"""Test GraphRAG handles empty knowledge graph gracefully"""
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# Arrange
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mock_graph_embeddings_client.query.return_value = [] # No entities found
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mock_triples_client.query.return_value = [] # No triples found
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# Act
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result = await graph_rag.query(
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query="unknown topic",
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user="test_user",
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collection="test_collection"
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)
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# Assert
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# Should still call prompt client with empty knowledge graph
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mock_prompt_client.kg_prompt.assert_called_once()
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call_args = mock_prompt_client.kg_prompt.call_args
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assert isinstance(call_args.args[1], list) # kg should be a list
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assert result is not None
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@pytest.mark.asyncio
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async def test_graph_rag_label_caching(self, graph_rag, mock_triples_client):
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"""Test that label lookups are cached to reduce redundant queries"""
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# Arrange
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query = "What is machine learning?"
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# First query
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await graph_rag.query(
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query=query,
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user="test_user",
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collection="test_collection"
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)
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first_call_count = mock_triples_client.query.call_count
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mock_triples_client.reset_mock()
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# Second identical query
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await graph_rag.query(
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query=query,
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user="test_user",
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collection="test_collection"
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)
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second_call_count = mock_triples_client.query.call_count
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# Assert - Second query should make fewer triple queries due to caching
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# Note: This is a weak assertion because caching behavior depends on
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# implementation details, but it verifies the concept
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assert second_call_count >= 0 # Should complete without errors
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@pytest.mark.asyncio
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async def test_graph_rag_multi_user_isolation(self, graph_rag, mock_graph_embeddings_client):
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"""Test that different users/collections are properly isolated"""
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# Arrange
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query = "test query"
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user1, collection1 = "user1", "collection1"
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user2, collection2 = "user2", "collection2"
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# Act
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await graph_rag.query(query=query, user=user1, collection=collection1)
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await graph_rag.query(query=query, user=user2, collection=collection2)
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# Assert - Both users should have separate queries
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assert mock_graph_embeddings_client.query.call_count == 2
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# Verify first call
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first_call = mock_graph_embeddings_client.query.call_args_list[0]
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assert first_call.kwargs['user'] == user1
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assert first_call.kwargs['collection'] == collection1
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# Verify second call
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second_call = mock_graph_embeddings_client.query.call_args_list[1]
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assert second_call.kwargs['user'] == user2
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assert second_call.kwargs['collection'] == collection2
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