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bd06d8b4f0
* Add pipeline state property bag to run context * Move state creation out of context util * Move callbacks into PipelineRunContext * Semver * Rename state.json to context.json to avoid confusion with stats.json * Expand smoke test row count * Add util to create storage and cache
82 lines
2.5 KiB
Python
82 lines
2.5 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.config.enums import ModelType
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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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llm_settings = config.get_language_model_config(
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config.community_reports.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_RESPONSES
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llm_settings["parse_json"] = True
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config.community_reports.strategy = {
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"type": "graph_intelligence",
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"llm": llm_settings,
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"graph_prompt": "",
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}
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await run_workflow(config, context)
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actual = await load_table_from_storage("community_reports", context.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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