mirror of
https://github.com/trustgraph-ai/trustgraph.git
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a115ec06ab
Enhance retrieval pipelines: 4-stage GraphRAG, DocRAG grounding, consistent PROV-O GraphRAG: - Split retrieval into 4 prompt stages: extract-concepts, kg-edge-scoring, kg-edge-reasoning, kg-synthesis (was single-stage) - Add concept extraction (grounding) for per-concept embedding - Filter main query to default graph, ignoring provenance/explainability edges - Add source document edges to knowledge graph DocumentRAG: - Add grounding step with concept extraction, matching GraphRAG's pattern: Question → Grounding → Exploration → Synthesis - Per-concept embedding and chunk retrieval with deduplication Cross-pipeline: - Make PROV-O derivation links consistent: wasGeneratedBy for first entity from Activity, wasDerivedFrom for entity-to-entity chains - Update CLIs (tg-invoke-agent, tg-invoke-graph-rag, tg-invoke-document-rag) for new explainability structure - Fix all affected unit and integration tests
625 lines
22 KiB
Python
625 lines
22 KiB
Python
"""
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Tests for GraphRAG retrieval implementation
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"""
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import pytest
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import unittest.mock
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from unittest.mock import MagicMock, AsyncMock
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from trustgraph.retrieval.graph_rag.graph_rag import GraphRag, Query
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class TestGraphRag:
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"""Test cases for GraphRag class"""
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def test_graph_rag_initialization_with_defaults(self):
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"""Test GraphRag initialization with default verbose setting"""
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# Create mock clients
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mock_prompt_client = MagicMock()
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mock_embeddings_client = MagicMock()
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mock_graph_embeddings_client = MagicMock()
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mock_triples_client = MagicMock()
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# Initialize GraphRag
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graph_rag = GraphRag(
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prompt_client=mock_prompt_client,
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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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)
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# Verify initialization
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assert graph_rag.prompt_client == mock_prompt_client
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assert graph_rag.embeddings_client == mock_embeddings_client
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assert graph_rag.graph_embeddings_client == mock_graph_embeddings_client
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assert graph_rag.triples_client == mock_triples_client
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assert graph_rag.verbose is False # Default value
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# Verify label_cache is an LRUCacheWithTTL instance
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from trustgraph.retrieval.graph_rag.graph_rag import LRUCacheWithTTL
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assert isinstance(graph_rag.label_cache, LRUCacheWithTTL)
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def test_graph_rag_initialization_with_verbose(self):
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"""Test GraphRag initialization with verbose enabled"""
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# Create mock clients
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mock_prompt_client = MagicMock()
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mock_embeddings_client = MagicMock()
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mock_graph_embeddings_client = MagicMock()
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mock_triples_client = MagicMock()
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# Initialize GraphRag with verbose=True
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graph_rag = GraphRag(
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prompt_client=mock_prompt_client,
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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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verbose=True
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)
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# Verify initialization
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assert graph_rag.prompt_client == mock_prompt_client
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assert graph_rag.embeddings_client == mock_embeddings_client
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assert graph_rag.graph_embeddings_client == mock_graph_embeddings_client
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assert graph_rag.triples_client == mock_triples_client
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assert graph_rag.verbose is True
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# Verify label_cache is an LRUCacheWithTTL instance
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from trustgraph.retrieval.graph_rag.graph_rag import LRUCacheWithTTL
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assert isinstance(graph_rag.label_cache, LRUCacheWithTTL)
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class TestQuery:
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"""Test cases for Query class"""
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def test_query_initialization_with_defaults(self):
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"""Test Query initialization with default parameters"""
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# Create mock GraphRag
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mock_rag = MagicMock()
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# Initialize Query with defaults
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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# Verify initialization
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assert query.rag == mock_rag
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assert query.user == "test_user"
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assert query.collection == "test_collection"
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assert query.verbose is False
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assert query.entity_limit == 50 # Default value
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assert query.triple_limit == 30 # Default value
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assert query.max_subgraph_size == 1000 # Default value
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assert query.max_path_length == 2 # Default value
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def test_query_initialization_with_custom_params(self):
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"""Test Query initialization with custom parameters"""
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# Create mock GraphRag
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mock_rag = MagicMock()
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# Initialize Query with custom parameters
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query = Query(
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rag=mock_rag,
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user="custom_user",
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collection="custom_collection",
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verbose=True,
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entity_limit=100,
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triple_limit=60,
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max_subgraph_size=2000,
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max_path_length=3
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)
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# Verify initialization
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assert query.rag == mock_rag
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assert query.user == "custom_user"
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assert query.collection == "custom_collection"
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assert query.verbose is True
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assert query.entity_limit == 100
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assert query.triple_limit == 60
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assert query.max_subgraph_size == 2000
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assert query.max_path_length == 3
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@pytest.mark.asyncio
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async def test_get_vectors_method(self):
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"""Test Query.get_vectors method calls embeddings client correctly"""
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mock_rag = MagicMock()
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mock_embeddings_client = AsyncMock()
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mock_rag.embeddings_client = mock_embeddings_client
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# Mock embed to return vectors for a list of concepts
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expected_vectors = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
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mock_embeddings_client.embed.return_value = expected_vectors
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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concepts = ["machine learning", "neural networks"]
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result = await query.get_vectors(concepts)
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mock_embeddings_client.embed.assert_called_once_with(concepts)
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assert result == expected_vectors
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@pytest.mark.asyncio
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async def test_get_vectors_method_with_verbose(self):
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"""Test Query.get_vectors method with verbose output"""
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mock_rag = MagicMock()
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mock_embeddings_client = AsyncMock()
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mock_rag.embeddings_client = mock_embeddings_client
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expected_vectors = [[0.7, 0.8, 0.9]]
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mock_embeddings_client.embed.return_value = expected_vectors
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=True
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)
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result = await query.get_vectors(["test concept"])
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mock_embeddings_client.embed.assert_called_once_with(["test concept"])
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assert result == expected_vectors
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@pytest.mark.asyncio
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async def test_extract_concepts(self):
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"""Test Query.extract_concepts parses LLM response into concept list"""
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mock_rag = MagicMock()
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mock_prompt_client = AsyncMock()
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mock_rag.prompt_client = mock_prompt_client
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mock_prompt_client.prompt.return_value = "machine learning\nneural networks\n"
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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result = await query.extract_concepts("What is machine learning?")
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mock_prompt_client.prompt.assert_called_once_with(
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"extract-concepts",
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variables={"query": "What is machine learning?"}
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)
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assert result == ["machine learning", "neural networks"]
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@pytest.mark.asyncio
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async def test_extract_concepts_fallback_to_raw_query(self):
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"""Test extract_concepts falls back to raw query when LLM returns empty"""
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mock_rag = MagicMock()
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mock_prompt_client = AsyncMock()
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mock_rag.prompt_client = mock_prompt_client
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mock_prompt_client.prompt.return_value = ""
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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result = await query.extract_concepts("test query")
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assert result == ["test query"]
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@pytest.mark.asyncio
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async def test_get_entities_method(self):
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"""Test Query.get_entities extracts concepts, embeds, and retrieves entities"""
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mock_rag = MagicMock()
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mock_prompt_client = AsyncMock()
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mock_embeddings_client = AsyncMock()
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mock_graph_embeddings_client = AsyncMock()
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mock_rag.prompt_client = mock_prompt_client
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mock_rag.embeddings_client = mock_embeddings_client
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mock_rag.graph_embeddings_client = mock_graph_embeddings_client
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# extract_concepts returns empty -> falls back to [query]
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mock_prompt_client.prompt.return_value = ""
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# embed returns one vector set for the single concept
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test_vectors = [[0.1, 0.2, 0.3]]
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mock_embeddings_client.embed.return_value = test_vectors
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# Mock entity matches
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mock_entity1 = MagicMock()
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mock_entity1.type = "i"
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mock_entity1.iri = "entity1"
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mock_match1 = MagicMock()
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mock_match1.entity = mock_entity1
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mock_entity2 = MagicMock()
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mock_entity2.type = "i"
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mock_entity2.iri = "entity2"
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mock_match2 = MagicMock()
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mock_match2.entity = mock_entity2
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mock_graph_embeddings_client.query.return_value = [mock_match1, mock_match2]
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False,
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entity_limit=25
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)
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entities, concepts = await query.get_entities("Find related entities")
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# Verify embeddings client was called with the fallback concept
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mock_embeddings_client.embed.assert_called_once_with(["Find related entities"])
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# Verify result
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assert entities == ["entity1", "entity2"]
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assert concepts == ["Find related entities"]
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@pytest.mark.asyncio
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async def test_maybe_label_with_cached_label(self):
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"""Test Query.maybe_label method with cached label"""
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mock_rag = MagicMock()
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mock_cache = MagicMock()
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mock_cache.get.return_value = "Entity One Label"
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mock_rag.label_cache = mock_cache
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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result = await query.maybe_label("entity1")
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assert result == "Entity One Label"
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mock_cache.get.assert_called_once_with("test_user:test_collection:entity1")
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@pytest.mark.asyncio
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async def test_maybe_label_with_label_lookup(self):
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"""Test Query.maybe_label method with database label lookup"""
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mock_rag = MagicMock()
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mock_cache = MagicMock()
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mock_cache.get.return_value = None
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mock_rag.label_cache = mock_cache
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mock_triples_client = AsyncMock()
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mock_rag.triples_client = mock_triples_client
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mock_triple = MagicMock()
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mock_triple.o = "Human Readable Label"
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mock_triples_client.query.return_value = [mock_triple]
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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result = await query.maybe_label("http://example.com/entity")
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mock_triples_client.query.assert_called_once_with(
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s="http://example.com/entity",
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p="http://www.w3.org/2000/01/rdf-schema#label",
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o=None,
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limit=1,
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user="test_user",
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collection="test_collection",
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g=""
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)
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assert result == "Human Readable Label"
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cache_key = "test_user:test_collection:http://example.com/entity"
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mock_cache.put.assert_called_once_with(cache_key, "Human Readable Label")
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@pytest.mark.asyncio
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async def test_maybe_label_with_no_label_found(self):
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"""Test Query.maybe_label method when no label is found"""
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mock_rag = MagicMock()
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mock_cache = MagicMock()
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mock_cache.get.return_value = None
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mock_rag.label_cache = mock_cache
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mock_triples_client = AsyncMock()
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mock_rag.triples_client = mock_triples_client
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mock_triples_client.query.return_value = []
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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result = await query.maybe_label("unlabeled_entity")
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mock_triples_client.query.assert_called_once_with(
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s="unlabeled_entity",
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p="http://www.w3.org/2000/01/rdf-schema#label",
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o=None,
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limit=1,
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user="test_user",
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collection="test_collection",
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g=""
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)
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assert result == "unlabeled_entity"
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cache_key = "test_user:test_collection:unlabeled_entity"
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mock_cache.put.assert_called_once_with(cache_key, "unlabeled_entity")
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@pytest.mark.asyncio
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async def test_follow_edges_basic_functionality(self):
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"""Test Query.follow_edges method basic triple discovery"""
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mock_rag = MagicMock()
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mock_triples_client = AsyncMock()
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mock_rag.triples_client = mock_triples_client
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mock_triple1 = MagicMock()
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mock_triple1.s, mock_triple1.p, mock_triple1.o = "entity1", "predicate1", "object1"
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mock_triple2 = MagicMock()
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mock_triple2.s, mock_triple2.p, mock_triple2.o = "subject2", "entity1", "object2"
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mock_triple3 = MagicMock()
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mock_triple3.s, mock_triple3.p, mock_triple3.o = "subject3", "predicate3", "entity1"
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mock_triples_client.query_stream.side_effect = [
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[mock_triple1], # s=ent
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[mock_triple2], # p=ent
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[mock_triple3], # o=ent
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]
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False,
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triple_limit=10
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)
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subgraph = set()
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await query.follow_edges("entity1", subgraph, path_length=1)
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assert mock_triples_client.query_stream.call_count == 3
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mock_triples_client.query_stream.assert_any_call(
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s="entity1", p=None, o=None, limit=10,
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user="test_user", collection="test_collection", batch_size=20, g=""
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)
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mock_triples_client.query_stream.assert_any_call(
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s=None, p="entity1", o=None, limit=10,
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user="test_user", collection="test_collection", batch_size=20, g=""
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)
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mock_triples_client.query_stream.assert_any_call(
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s=None, p=None, o="entity1", limit=10,
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user="test_user", collection="test_collection", batch_size=20, g=""
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)
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expected_subgraph = {
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("entity1", "predicate1", "object1"),
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("subject2", "entity1", "object2"),
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("subject3", "predicate3", "entity1")
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}
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assert subgraph == expected_subgraph
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@pytest.mark.asyncio
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async def test_follow_edges_with_path_length_zero(self):
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"""Test Query.follow_edges method with path_length=0"""
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mock_rag = MagicMock()
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mock_triples_client = AsyncMock()
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mock_rag.triples_client = mock_triples_client
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False
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)
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subgraph = set()
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await query.follow_edges("entity1", subgraph, path_length=0)
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mock_triples_client.query_stream.assert_not_called()
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assert subgraph == set()
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@pytest.mark.asyncio
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async def test_follow_edges_with_max_subgraph_size_limit(self):
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"""Test Query.follow_edges method respects max_subgraph_size"""
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mock_rag = MagicMock()
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mock_triples_client = AsyncMock()
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mock_rag.triples_client = mock_triples_client
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False,
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max_subgraph_size=2
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)
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subgraph = {("s1", "p1", "o1"), ("s2", "p2", "o2"), ("s3", "p3", "o3")}
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await query.follow_edges("entity1", subgraph, path_length=1)
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mock_triples_client.query_stream.assert_not_called()
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assert len(subgraph) == 3
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@pytest.mark.asyncio
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async def test_get_subgraph_method(self):
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"""Test Query.get_subgraph returns (subgraph, entities, concepts) tuple"""
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mock_rag = MagicMock()
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False,
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max_path_length=1
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)
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# Mock get_entities to return (entities, concepts) tuple
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query.get_entities = AsyncMock(
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return_value=(["entity1", "entity2"], ["concept1"])
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)
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query.follow_edges_batch = AsyncMock(return_value={
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("entity1", "predicate1", "object1"),
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("entity2", "predicate2", "object2")
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})
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subgraph, entities, concepts = await query.get_subgraph("test query")
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query.get_entities.assert_called_once_with("test query")
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query.follow_edges_batch.assert_called_once_with(["entity1", "entity2"], 1)
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assert isinstance(subgraph, list)
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assert len(subgraph) == 2
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assert ("entity1", "predicate1", "object1") in subgraph
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assert ("entity2", "predicate2", "object2") in subgraph
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assert entities == ["entity1", "entity2"]
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assert concepts == ["concept1"]
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@pytest.mark.asyncio
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async def test_get_labelgraph_method(self):
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"""Test Query.get_labelgraph returns (labeled_edges, uri_map, entities, concepts)"""
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mock_rag = MagicMock()
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query = Query(
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rag=mock_rag,
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user="test_user",
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collection="test_collection",
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verbose=False,
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max_subgraph_size=100
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)
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test_subgraph = [
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("entity1", "predicate1", "object1"),
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("subject2", "http://www.w3.org/2000/01/rdf-schema#label", "Label Value"),
|
|
("entity3", "predicate3", "object3")
|
|
]
|
|
test_entities = ["entity1", "entity3"]
|
|
test_concepts = ["concept1"]
|
|
query.get_subgraph = AsyncMock(
|
|
return_value=(test_subgraph, test_entities, test_concepts)
|
|
)
|
|
|
|
async def mock_maybe_label(entity):
|
|
label_map = {
|
|
"entity1": "Human Entity One",
|
|
"predicate1": "Human Predicate One",
|
|
"object1": "Human Object One",
|
|
"entity3": "Human Entity Three",
|
|
"predicate3": "Human Predicate Three",
|
|
"object3": "Human Object Three"
|
|
}
|
|
return label_map.get(entity, entity)
|
|
|
|
query.maybe_label = AsyncMock(side_effect=mock_maybe_label)
|
|
|
|
labeled_edges, uri_map, entities, concepts = await query.get_labelgraph("test query")
|
|
|
|
query.get_subgraph.assert_called_once_with("test query")
|
|
|
|
# Label triples filtered out
|
|
assert len(labeled_edges) == 2
|
|
|
|
# maybe_label called for non-label triples
|
|
assert query.maybe_label.call_count == 6
|
|
|
|
expected_edges = [
|
|
("Human Entity One", "Human Predicate One", "Human Object One"),
|
|
("Human Entity Three", "Human Predicate Three", "Human Object Three")
|
|
]
|
|
assert labeled_edges == expected_edges
|
|
|
|
assert len(uri_map) == 2
|
|
assert entities == test_entities
|
|
assert concepts == test_concepts
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_graph_rag_query_method(self):
|
|
"""Test GraphRag.query method orchestrates full RAG pipeline with provenance"""
|
|
import json
|
|
from trustgraph.retrieval.graph_rag.graph_rag import edge_id
|
|
|
|
mock_prompt_client = AsyncMock()
|
|
mock_embeddings_client = AsyncMock()
|
|
mock_graph_embeddings_client = AsyncMock()
|
|
mock_triples_client = AsyncMock()
|
|
|
|
expected_response = "This is the RAG response"
|
|
test_labelgraph = [("Subject", "Predicate", "Object")]
|
|
test_edge_id = edge_id("Subject", "Predicate", "Object")
|
|
test_uri_map = {
|
|
test_edge_id: ("http://example.org/subject", "http://example.org/predicate", "http://example.org/object")
|
|
}
|
|
test_entities = ["http://example.org/subject"]
|
|
test_concepts = ["test concept"]
|
|
|
|
# Mock prompt responses for the multi-step process
|
|
async def mock_prompt(prompt_name, variables=None, streaming=False, chunk_callback=None):
|
|
if prompt_name == "extract-concepts":
|
|
return "" # Falls back to raw query
|
|
elif prompt_name == "kg-edge-scoring":
|
|
return json.dumps({"id": test_edge_id, "score": 0.9})
|
|
elif prompt_name == "kg-edge-reasoning":
|
|
return json.dumps({"id": test_edge_id, "reasoning": "relevant"})
|
|
elif prompt_name == "kg-synthesis":
|
|
return expected_response
|
|
return ""
|
|
|
|
mock_prompt_client.prompt = mock_prompt
|
|
|
|
graph_rag = GraphRag(
|
|
prompt_client=mock_prompt_client,
|
|
embeddings_client=mock_embeddings_client,
|
|
graph_embeddings_client=mock_graph_embeddings_client,
|
|
triples_client=mock_triples_client,
|
|
verbose=False
|
|
)
|
|
|
|
# Patch Query.get_labelgraph to return test data
|
|
original_get_labelgraph = Query.get_labelgraph
|
|
|
|
async def mock_get_labelgraph(self, query_text):
|
|
return test_labelgraph, test_uri_map, test_entities, test_concepts
|
|
|
|
Query.get_labelgraph = mock_get_labelgraph
|
|
|
|
provenance_events = []
|
|
|
|
async def collect_provenance(triples, prov_id):
|
|
provenance_events.append((triples, prov_id))
|
|
|
|
try:
|
|
response = await graph_rag.query(
|
|
query="test query",
|
|
user="test_user",
|
|
collection="test_collection",
|
|
entity_limit=25,
|
|
triple_limit=15,
|
|
explain_callback=collect_provenance
|
|
)
|
|
|
|
assert response == expected_response
|
|
|
|
# 5 events: question, grounding, exploration, focus, synthesis
|
|
assert len(provenance_events) == 5
|
|
|
|
for triples, prov_id in provenance_events:
|
|
assert isinstance(triples, list)
|
|
assert len(triples) > 0
|
|
assert prov_id.startswith("urn:trustgraph:")
|
|
|
|
# Verify order
|
|
assert "question" in provenance_events[0][1]
|
|
assert "grounding" in provenance_events[1][1]
|
|
assert "exploration" in provenance_events[2][1]
|
|
assert "focus" in provenance_events[3][1]
|
|
assert "synthesis" in provenance_events[4][1]
|
|
|
|
finally:
|
|
Query.get_labelgraph = original_get_labelgraph
|