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1df89727c3
* Move covariate run conditional * All pipeline registration * Fix method name construction * Rename context storage -> output_storage * Rename OutputConfig as generic StorageConfig * Reuse Storage model under InputConfig * Move input storage creation out of document loading * Move document loading into workflows * Semver * Fix smoke test config for new workflows * Fix unit tests --------- Co-authored-by: Alonso Guevara <alonsog@microsoft.com>
80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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from pandas.testing import assert_series_equal
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from graphrag.config.create_graphrag_config import create_graphrag_config
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from graphrag.config.enums import ModelType
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from graphrag.data_model.schemas import COVARIATES_FINAL_COLUMNS
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from graphrag.index.workflows.extract_covariates 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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load_test_table,
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)
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MOCK_LLM_RESPONSES = [
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"""
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(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.)
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""".strip()
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]
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async def test_extract_covariates():
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input = load_test_table("text_units")
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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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llm_settings = config.get_language_model_config(
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config.extract_claims.model_id
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).model_dump()
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llm_settings["type"] = ModelType.MockChat
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llm_settings["responses"] = MOCK_LLM_RESPONSES
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config.extract_claims.enabled = True
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config.extract_claims.strategy = {
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"type": "graph_intelligence",
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"llm": llm_settings,
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"claim_description": "description",
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}
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await run_workflow(config, context)
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actual = await load_table_from_storage("covariates", context.output_storage)
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for column in COVARIATES_FINAL_COLUMNS:
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assert column in actual.columns
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# 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
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assert len(actual) == len(input)
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# assert all of the columns that covariates copied from the input
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assert_series_equal(actual["text_unit_id"], input["id"], check_names=False)
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# make sure the human ids are incrementing
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assert actual["human_readable_id"][0] == 1
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assert actual["human_readable_id"][1] == 2
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# check that the mock data is parsed and inserted into the correct columns
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assert actual["covariate_type"][0] == "claim"
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assert actual["subject_id"][0] == "COMPANY A"
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assert actual["object_id"][0] == "GOVERNMENT AGENCY B"
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assert actual["type"][0] == "ANTI-COMPETITIVE PRACTICES"
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assert actual["status"][0] == "TRUE"
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assert actual["start_date"][0] == "2022-01-10T00:00:00"
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assert actual["end_date"][0] == "2022-01-10T00:00:00"
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assert (
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actual["description"][0]
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== "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"
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)
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assert (
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actual["source_text"][0]
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== "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."
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)
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