mirror of
https://github.com/microsoft/graphrag.git
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f5c5876dde
* Extract base docs and entity graph * Move extracted entities and text units * Move communities and community reports * Move covariates and final documents * Move entities, nodes, relationships * Move text_units and summarized entities * Assert all snapshot null cases * Remove disabled steps util * Remove incorrect use of input "others" * Convert text_embed_df to just return the embeddings, not update the df * Convert snapshot functions to noops * Semver * Remove lingering covariates_enabled param * Name consistency * Syntax cleanup
119 lines
3.1 KiB
Python
119 lines
3.1 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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import networkx as nx
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from graphrag.index.storage.memory_pipeline_storage import MemoryPipelineStorage
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from graphrag.index.workflows.v1.create_base_entity_graph import (
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build_steps,
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workflow_name,
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)
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from .util import (
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get_config_for_workflow,
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get_workflow_output,
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load_expected,
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load_input_tables,
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)
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async def test_create_base_entity_graph():
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input_tables = load_input_tables([
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"workflow:create_summarized_entities",
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])
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expected = load_expected(workflow_name)
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storage = MemoryPipelineStorage()
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config = get_config_for_workflow(workflow_name)
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steps = build_steps(config)
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actual = await get_workflow_output(
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input_tables,
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{
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"steps": steps,
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},
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storage=storage,
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)
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# the serialization of the graph may differ so we can't assert the dataframes directly
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assert actual.shape == expected.shape, "Graph dataframe shapes differ"
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# let's parse a sample of the raw graphml
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actual_graphml_0 = actual["clustered_graph"][:1][0]
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actual_graph_0 = nx.parse_graphml(actual_graphml_0)
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expected_graphml_0 = expected["clustered_graph"][:1][0]
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expected_graph_0 = nx.parse_graphml(expected_graphml_0)
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assert (
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actual_graph_0.number_of_nodes() == expected_graph_0.number_of_nodes()
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), "Graphml node count differs"
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assert (
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actual_graph_0.number_of_edges() == expected_graph_0.number_of_edges()
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), "Graphml edge count differs"
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assert len(storage.keys()) == 0, "Storage should be empty"
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async def test_create_base_entity_graph_with_embeddings():
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input_tables = load_input_tables([
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"workflow:create_summarized_entities",
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])
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expected = load_expected(workflow_name)
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config = get_config_for_workflow(workflow_name)
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config["embed_graph_enabled"] = True
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steps = build_steps(config)
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actual = await get_workflow_output(
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input_tables,
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{
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"steps": steps,
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},
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)
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assert (
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len(actual.columns) == len(expected.columns) + 1
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), "Graph dataframe missing embedding column"
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assert "embeddings" in actual.columns, "Graph dataframe missing embedding column"
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async def test_create_base_entity_graph_with_snapshots():
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input_tables = load_input_tables([
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"workflow:create_summarized_entities",
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])
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expected = load_expected(workflow_name)
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storage = MemoryPipelineStorage()
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config = get_config_for_workflow(workflow_name)
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config["graphml_snapshot"] = True
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steps = build_steps(config)
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actual = await get_workflow_output(
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input_tables,
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{
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"steps": steps,
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},
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storage=storage,
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)
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assert actual.shape == expected.shape, "Graph dataframe shapes differ"
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assert storage.keys() == [
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"clustered_graph.0.graphml",
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"clustered_graph.1.graphml",
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"clustered_graph.2.graphml",
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"clustered_graph.3.graphml",
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"embedded_graph.0.graphml",
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"embedded_graph.1.graphml",
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"embedded_graph.2.graphml",
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"embedded_graph.3.graphml",
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], "Graph snapshot keys differ"
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