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39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
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# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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from graphrag.callbacks.noop_workflow_callbacks import NoopWorkflowCallbacks
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from graphrag.config.create_graphrag_config import create_graphrag_config
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from graphrag.index.workflows.extract_graph_nlp import (
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run_workflow,
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)
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from graphrag.utils.storage import load_table_from_storage
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from .util import (
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DEFAULT_MODEL_CONFIG,
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create_test_context,
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)
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async def test_extract_graph_nlp():
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context = await create_test_context(
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storage=["text_units"],
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)
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config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
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await run_workflow(
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config,
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context,
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NoopWorkflowCallbacks(),
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)
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nodes_actual = await load_table_from_storage("entities", context.storage)
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edges_actual = await load_table_from_storage("relationships", context.storage)
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# this will be the raw count of entities and edges with no pruning
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# with NLP it is deterministic, so we can assert exact row counts
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assert len(nodes_actual) == 1148
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assert len(nodes_actual.columns) == 5
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assert len(edges_actual) == 29445
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assert len(edges_actual.columns) == 5
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