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microsoft--graphrag/tests/verbs/test_extract_covariates.py
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chore: import upstream snapshot with attribution
2026-07-13 12:37:31 +08:00

74 lines
3.1 KiB
Python

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
from graphrag.data_model.schemas import COVARIATES_FINAL_COLUMNS
from graphrag.index.workflows.extract_covariates import (
run_workflow,
)
from graphrag_llm.config import LLMProviderType
from pandas.testing import assert_series_equal
from tests.unit.config.utils import get_default_graphrag_config
from .util import (
create_test_context,
load_test_table,
)
MOCK_LLM_RESPONSES = [
"""
(COMPANY A<|>GOVERNMENT AGENCY B<|>ANTI-COMPETITIVE PRACTICES<|>TRUE<|>2022-01-10T00:00:00<|>2022-01-10T00:00:00<|>Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10<|>According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.)
""".strip()
]
async def test_extract_covariates():
input = load_test_table("text_units")
context = await create_test_context(
storage=["text_units"],
)
config = get_default_graphrag_config()
config.extract_claims.enabled = True
config.extract_claims.description = "description"
llm_settings = config.get_completion_model_config(
config.extract_claims.completion_model_id
)
llm_settings.type = LLMProviderType.MockLLM
llm_settings.mock_responses = MOCK_LLM_RESPONSES # type: ignore
await run_workflow(config, context)
actual = await context.output_table_provider.read_dataframe("covariates")
for column in COVARIATES_FINAL_COLUMNS:
assert column in actual.columns
# our mock only returns one covariate per text unit, so that's a 1:1 mapping versus the LLM-extracted content in the test data
assert len(actual) == len(input)
# assert all of the columns that covariates copied from the input
assert_series_equal(actual["text_unit_id"], input["id"], check_names=False)
# make sure the human ids are incrementing
assert actual["human_readable_id"][0] == 0
assert actual["human_readable_id"][1] == 1
# check that the mock data is parsed and inserted into the correct columns
assert actual["covariate_type"][0] == "claim"
assert actual["subject_id"][0] == "COMPANY A"
assert actual["object_id"][0] == "GOVERNMENT AGENCY B"
assert actual["type"][0] == "ANTI-COMPETITIVE PRACTICES"
assert actual["status"][0] == "TRUE"
assert actual["start_date"][0] == "2022-01-10T00:00:00"
assert actual["end_date"][0] == "2022-01-10T00:00:00"
assert (
actual["description"][0]
== "Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10"
)
assert (
actual["source_text"][0]
== "According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B."
)