Creates edge embeddings collection #251
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WalkthroughThe pull request introduces several changes across multiple files. Key modifications include the refactoring of the Changes
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🧹 Outside diff range and nitpick comments (3)
cognee/tasks/storage/index_graph_edges.py (2)
48-51: Use more descriptive variable names for clarityConsider renaming the variables
textandcounttorelationship_nameandedge_countto enhance readability and better reflect their purpose.Suggested change:
- for text, count in edge_types.items(): - edge = EdgeType(relationship_name=text, number_of_edges=count) + for relationship_name, edge_count in edge_types.items(): + edge = EdgeType(relationship_name=relationship_name, number_of_edges=edge_count)
66-68: Avoid variable shadowing to improve code maintainabilityThe variable
index_nameis being reused after splitting, which can lead to confusion. It's better to use a different variable name to prevent shadowing and enhance clarity.Suggested change:
- for index_name, indexable_points in index_points.items(): - index_name, field_name = index_name.split(".") + for index_key, indexable_points in index_points.items(): + index_name, field_name = index_key.split(".")cognee/modules/graph/models/EdgeType.py (1)
9-11: Redundant use ofOptionalfor an initialized class variableSince
_metadatais initialized with a default value, usingOptional[dict]is unnecessary. Typing it simply asdictimproves type clarity.Suggested change:
- _metadata: Optional[dict] = { + _metadata: dict = { "index_fields": ["relationship_name"], }
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cognee/api/v1/cognify/cognify_v2.py(2 hunks)cognee/modules/graph/models/EdgeType.py(1 hunks)cognee/tasks/storage/index_graph_edges.py(1 hunks)
🔇 Additional comments (2)
cognee/api/v1/cognify/cognify_v2.py (2)
21-21: Properly importing index_graph_edges function
The import statement correctly includes the index_graph_edges function, ensuring it's available for use in the pipeline.
98-98: Integrate index_graph_edges into the cognify pipeline
Calling await index_graph_edges() after the pipeline tasks appropriately incorporates the edge indexing step into the overall process.
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🧹 Outside diff range and nitpick comments (2)
cognee/infrastructure/databases/graph/networkx/adapter.py (2)
277-279: Consider validating existing node IDs before overwritingWhile explicitly setting node IDs is good practice, consider adding validation to detect and log cases where a node's existing 'id' attribute differs from its node_id in the graph, as this might indicate data inconsistency.
for node_id, node_data in self.graph.nodes(data=True): + if 'id' in node_data and node_data['id'] != node_id: + logger.warning(f"Node {node_id} had mismatched ID attribute: {node_data['id']}. Updating to match node_id.") node_data['id'] = node_id
Line range hint
287-289: Consider more specific error handlingThe broad Exception catch could mask specific issues. Consider handling different error types separately for better debugging.
- except Exception: - logger.error("Failed to load graph from file: %s", file_path) + except json.JSONDecodeError as e: + logger.error("Failed to parse graph file %s: %s", file_path, e) + except IOError as e: + logger.error("IO error while loading graph from %s: %s", file_path, e) + except Exception as e: + logger.error("Unexpected error loading graph from %s: %s", file_path, e)
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cognee/infrastructure/databases/graph/networkx/adapter.py(1 hunks)
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cognee/infrastructure/databases/graph/networkx/adapter.py (2)
Line range hint 280-286: LGTM: Robust empty graph initialization
The changes properly handle missing files by:
- Logging clear warning messages
- Creating necessary directories
- Maintaining graph type consistency
- Saving the empty graph state
Line range hint 277-286: Verify node ID usage across the codebase
The changes to node ID handling could affect code that assumes specific node ID behavior.
✅ Verification successful
Let me gather more information about the graph initialization and node handling to make a comprehensive assessment.
Node ID handling is consistent with codebase requirements
The changes to node ID handling in the graph loading code are safe and consistent with the codebase's requirements:
- The node ID assignment (
node_data['id'] = node_id) maintains consistency with the MultiDiGraph initialization pattern used across the codebase - UUID handling for node IDs is properly implemented, as evidenced by the UUID conversion in the adapter and its usage in edge operations
- Graph loading error handling follows the established pattern of initializing an empty MultiDiGraph when needed
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Script:
#!/bin/bash
# Description: Check for direct node ID access patterns and graph loading error handling
# Search for direct node ID access patterns
echo "Checking node ID access patterns..."
rg -A 2 "node\[.?['\"]id['\"]|node_data\[.?['\"]id['\"]"
# Search for graph loading error handling
echo "Checking graph loading error handling..."
rg -A 2 "load_graph_from_file"
Length of output: 2879
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#!/bin/bash
# Check for graph initialization and node operations
echo "Checking graph initialization patterns..."
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rg -A 2 "UUID.*node"
Length of output: 4247
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🧹 Outside diff range and nitpick comments (3)
cognee/tests/infrastructure/databases/test_index_graph_edges.py (3)
4-24: Consider enhancing test coverage with additional assertionsWhile the basic test structure is good, consider these improvements:
- Verify the actual data being passed to
index_data_points- Assert the structure of created EdgeType objects
- Add assertions for the parameters passed to
create_vector_indexHere's a suggested enhancement:
@pytest.mark.asyncio async def test_index_graph_edges_success(): """Test that index_graph_edges uses the index datapoints and creates vector index.""" mock_graph_engine = AsyncMock() + test_relationships = [ + [{"relationship_name": "rel1", "properties": {"key": "value"}}, + {"relationship_name": "rel1", "properties": {"key2": "value2"}}], + [{"relationship_name": "rel2", "properties": {"key3": "value3"}}] + ] - mock_graph_engine.get_graph_data.return_value = (None, [ - [{"relationship_name": "rel1"}, {"relationship_name": "rel1"}], - [{"relationship_name": "rel2"}] - ]) + mock_graph_engine.get_graph_data.return_value = (None, test_relationships) mock_vector_engine = AsyncMock() with patch("cognee.tasks.storage.index_graph_edges.get_graph_engine", return_value=mock_graph_engine), \ patch("cognee.tasks.storage.index_graph_edges.get_vector_engine", return_value=mock_vector_engine): from cognee.tasks.storage.index_graph_edges import index_graph_edges await index_graph_edges() mock_graph_engine.get_graph_data.assert_awaited_once() assert mock_vector_engine.create_vector_index.await_count == 1 assert mock_vector_engine.index_data_points.await_count == 1 + # Verify the data passed to create_vector_index + mock_vector_engine.create_vector_index.assert_awaited_with( + "graph_edges", dimension=1536 # Adjust dimension as per your config + ) + # Verify the structure of indexed data + calls = mock_vector_engine.index_data_points.await_args_list + assert len(calls) == 1 + indexed_data = calls[0].args[0] + assert all(isinstance(item, EdgeType) for item in indexed_data)
26-43: Enhance test documentation for clarityThe test logic is correct, but the docstring could be more descriptive about the expected behavior.
@pytest.mark.asyncio async def test_index_graph_edges_no_relationships(): - """Test that index_graph_edges handles empty relationships correctly.""" + """Test that index_graph_edges handles empty relationships correctly. + + Ensures that when no relationships are returned from the graph engine: + 1. No vector index is created + 2. No data points are indexed + 3. The function completes without errors + """
1-56: Consider adding idempotency testThe current test suite covers the basic scenarios well. However, consider adding a test to verify that the indexing operation is idempotent - i.e., running it multiple times with the same data doesn't cause issues or duplicate entries.
This would help ensure the function can be safely retried in case of partial failures in a distributed system.
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cognee/tests/infrastructure/databases/test_index_graph_edges.py (1)
1-2: LGTM! Imports are appropriate for async testing
The necessary testing utilities are imported correctly.
cognee/tests/infrastructure/databases/test_index_graph_edges.py
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🧹 Outside diff range and nitpick comments (4)
cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py (4)
8-11: Consider using type hints and constants for test dataThe test data structure could be more explicit and maintainable:
- Use type hints to clarify the expected data structure
- Define relationship names as constants
+from typing import List, Dict + +TEST_RELATIONSHIP_1 = "rel1" +TEST_RELATIONSHIP_2 = "rel2" + +test_data: List[List[Dict[str, str]]] = [ + [{"relationship_name": TEST_RELATIONSHIP_1}, {"relationship_name": TEST_RELATIONSHIP_1}], + [{"relationship_name": TEST_RELATIONSHIP_2}] +] - mock_graph_engine.get_graph_data.return_value = (None, [ - [{"relationship_name": "rel1"}, {"relationship_name": "rel1"}], - [{"relationship_name": "rel2"}] - ]) + mock_graph_engine.get_graph_data.return_value = (None, test_data)
27-29: Enhance test documentation and return value verificationThe test documentation could be more explicit about the expected behavior and verify the return value.
- """Test that index_graph_edges handles empty relationships correctly.""" + """ + Test that index_graph_edges handles empty relationships correctly. + + Expected behavior: + - Should not create vector index when no relationships exist + - Should not attempt to index any data points + - Should return None without raising exceptions + """
45-54: Consider adding more error scenarios and cleanup verificationWhile the basic error case is covered, consider adding tests for:
- Vector engine initialization failure
- Partial failures during indexing
- Cleanup/rollback behavior when errors occur
Example additional test:
@pytest.mark.asyncio async def test_index_graph_edges_vector_engine_failure(): """Test handling of vector engine initialization failure.""" mock_graph_engine = AsyncMock() with patch("cognee.tasks.storage.index_graph_edges.get_graph_engine", return_value=mock_graph_engine), \ patch("cognee.tasks.storage.index_graph_edges.get_vector_engine", side_effect=Exception("Vector engine failed")): from cognee.tasks.storage.index_graph_edges import index_graph_edges with pytest.raises(RuntimeError, match="Vector engine initialization failed"): await index_graph_edges()
1-56: Consider adding integration testsWhile the unit tests provide good coverage of the
index_graph_edgesfunction, consider adding integration tests to verify:
- Actual interaction with the vector store
- End-to-end behavior in the cognify pipeline
- Performance characteristics with larger datasets
This would help ensure the feature works correctly in a production-like environment.
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cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py (1)
1-3: LGTM: Imports are appropriate for async testing
The imports are minimal and correctly include the necessary components for async testing with pytest.
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New Features
Improvements
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Tests