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eb0dfe376b
* Remove "strategy" from community reports config/workflow * Remove extraction strategy from extract_graph * Remove summarization strategy from extract_graph * Remove strategy from claim extraction * Strongly type prompt templates * Remove strategy from embed_text * Push hydrated params into community report workflows * Push hyrdated params into extract covariates * Push hydrated params into extract graph NLP * Push hydrated params into extract graph * Push hydrated params into text embeddings * Remove a few more low-level defaults * Semver * Remove configurable prompt delimiters * Update smoke tests
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
# Copyright (c) 2024 Microsoft Corporation.
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# Licensed under the MIT License
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from graphrag.config.create_graphrag_config import create_graphrag_config
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from graphrag.data_model.schemas import COMMUNITY_REPORTS_FINAL_COLUMNS
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from graphrag.index.operations.summarize_communities.community_reports_extractor import (
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CommunityReportResponse,
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FindingModel,
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)
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from graphrag.index.workflows.create_community_reports 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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compare_outputs,
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create_test_context,
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load_test_table,
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)
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MOCK_RESPONSES = [
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CommunityReportResponse(
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title="<report_title>",
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summary="<executive_summary>",
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rating=2,
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rating_explanation="<rating_explanation>",
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findings=[
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FindingModel(
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summary="<insight_1_summary>", explanation="<insight_1_explanation"
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),
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FindingModel(
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summary="<insight_2_summary>", explanation="<insight_2_explanation"
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),
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],
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)
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]
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async def test_create_community_reports():
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expected = load_test_table("community_reports")
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context = await create_test_context(
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storage=[
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"covariates",
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"relationships",
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"entities",
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"communities",
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]
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)
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config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
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config.models["default_chat_model"].type = "mock_chat"
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config.models["default_chat_model"].responses = MOCK_RESPONSES # type: ignore
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await run_workflow(config, context)
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actual = await load_table_from_storage("community_reports", context.output_storage)
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assert len(actual.columns) == len(expected.columns)
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# only assert a couple of columns that are not mock - most of this table is LLM-generated
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compare_outputs(actual, expected, columns=["community", "level"])
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# assert a handful of mock data items to confirm they get put in the right spot
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assert actual["rank"][:1][0] == 2
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assert actual["rating_explanation"][:1][0] == "<rating_explanation>"
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for column in COMMUNITY_REPORTS_FINAL_COLUMNS:
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assert column in actual.columns
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