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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
69 lines
2.3 KiB
Python
69 lines
2.3 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.index.workflows.create_base_text_units import run_workflow
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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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update_document_metadata,
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)
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async def test_create_base_text_units():
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expected = load_test_table("text_units")
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context = await create_test_context()
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config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
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await run_workflow(config, context)
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actual = await load_table_from_storage("text_units", context.storage)
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compare_outputs(actual, expected, columns=["text", "document_ids", "n_tokens"])
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async def test_create_base_text_units_metadata():
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expected = load_test_table("text_units_metadata")
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context = await create_test_context()
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config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
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# test data was created with 4o, so we need to match the encoding for chunks to be identical
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config.chunks.encoding_model = "o200k_base"
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config.input.metadata = ["title"]
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config.chunks.prepend_metadata = True
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await update_document_metadata(config.input.metadata, context)
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await run_workflow(config, context)
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actual = await load_table_from_storage("text_units", context.storage)
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compare_outputs(actual, expected)
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async def test_create_base_text_units_metadata_included_in_chunk():
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expected = load_test_table("text_units_metadata_included_chunk")
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context = await create_test_context()
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config = create_graphrag_config({"models": DEFAULT_MODEL_CONFIG})
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# test data was created with 4o, so we need to match the encoding for chunks to be identical
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config.chunks.encoding_model = "o200k_base"
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config.input.metadata = ["title"]
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config.chunks.prepend_metadata = True
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config.chunks.chunk_size_includes_metadata = True
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await update_document_metadata(config.input.metadata, context)
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await run_workflow(config, context)
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actual = await load_table_from_storage("text_units", context.storage)
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# only check the columns from the base workflow - our expected table is the final and will have more
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compare_outputs(actual, expected, columns=["text", "document_ids", "n_tokens"])
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