mirror of
https://github.com/microsoft/graphrag.git
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94f1e62e5c
* Rename pipeline_storage file * Add runtime storage option to context * Fix import * Switch to memory storage for runtime * Infra for workflow runtime storage * Migrate base_text_units to runtime storage * Fix comment * Semver * Remove whitespace * Remove subflow smoke tests and ignore transient artifacts * Remove entity graph from transient list (not yet implemented) * Increase smoke runtime allotment for create_base_entity_graph * Revert format fix * Remove noqa
41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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from graphrag.index.run.utils import create_run_context
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from graphrag.index.workflows.v1.create_base_text_units 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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compare_outputs,
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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_text_units():
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input_tables = load_input_tables(inputs=[])
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expected = load_expected(workflow_name)
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context = create_run_context(None, None, None)
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config = get_config_for_workflow(workflow_name)
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# test data was created with 4o, so we need to match the encoding for chunks to be identical
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config["text_chunk"]["strategy"]["encoding_name"] = "o200k_base"
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steps = build_steps(config)
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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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context,
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)
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actual = await context.runtime_storage.get("base_text_units")
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compare_outputs(actual, expected)
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