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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>
79 lines
2.9 KiB
Python
79 lines
2.9 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.index.workflows.extract_graph 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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)
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MOCK_LLM_ENTITY_RESPONSES = [
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"""
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("entity"<|>COMPANY_A<|>COMPANY<|>Company_A is a test company)
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##
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("entity"<|>COMPANY_B<|>COMPANY<|>Company_B owns Company_A and also shares an address with Company_A)
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##
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("entity"<|>PERSON_C<|>PERSON<|>Person_C is director of Company_A)
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##
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("relationship"<|>COMPANY_A<|>COMPANY_B<|>Company_A and Company_B are related because Company_A is 100% owned by Company_B and the two companies also share the same address)<|>2)
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##
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("relationship"<|>COMPANY_A<|>PERSON_C<|>Company_A and Person_C are related because Person_C is director of Company_A<|>1))
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""".strip()
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]
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MOCK_LLM_SUMMARIZATION_RESPONSES = [
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"""
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This is a MOCK response for the LLM. It is summarized!
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""".strip()
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]
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async def test_extract_graph():
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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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extract_claims_llm_settings = config.get_language_model_config(
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config.extract_graph.model_id
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).model_dump()
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extract_claims_llm_settings["type"] = ModelType.MockChat
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extract_claims_llm_settings["responses"] = MOCK_LLM_ENTITY_RESPONSES
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config.extract_graph.strategy = {
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"type": "graph_intelligence",
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"llm": extract_claims_llm_settings,
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}
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summarize_llm_settings = config.get_language_model_config(
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config.summarize_descriptions.model_id
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).model_dump()
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summarize_llm_settings["type"] = ModelType.MockChat
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summarize_llm_settings["responses"] = MOCK_LLM_SUMMARIZATION_RESPONSES
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config.summarize_descriptions.strategy = {
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"type": "graph_intelligence",
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"llm": summarize_llm_settings,
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"max_input_tokens": 1000,
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"max_summary_length": 100,
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}
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await run_workflow(config, context)
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nodes_actual = await load_table_from_storage("entities", context.output_storage)
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edges_actual = await load_table_from_storage(
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"relationships", context.output_storage
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)
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assert len(nodes_actual.columns) == 5
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assert len(edges_actual.columns) == 5
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# TODO: with the combined verb we can't force summarization
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# this is because the mock responses always result in a single description, which is returned verbatim rather than summarized
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# we need to update the mocking to provide somewhat unique graphs so a true merge happens
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# the assertion should grab a node and ensure the description matches the mock description, not the original as we are doing below
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assert nodes_actual["description"].to_numpy()[0] == "Company_A is a test company"
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