# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License

from graphrag.config.models.graph_rag_config import GraphRagConfig
from graphrag.index.workflows.create_base_text_units import run_workflow
from graphrag.utils.storage import load_table_from_storage

from .util import (
    DEFAULT_MODEL_CONFIG,
    compare_outputs,
    create_test_context,
    load_test_table,
    update_document_metadata,
)


async def test_create_base_text_units():
    expected = load_test_table("text_units")

    context = await create_test_context()

    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore

    await run_workflow(config, context)

    actual = await load_table_from_storage("text_units", context.output_storage)

    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])


async def test_create_base_text_units_metadata():
    expected = load_test_table("text_units_metadata")

    context = await create_test_context()

    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
    config.input.metadata = ["title"]
    config.chunks.prepend_metadata = True

    await update_document_metadata(config.input.metadata, context)

    await run_workflow(config, context)

    actual = await load_table_from_storage("text_units", context.output_storage)
    compare_outputs(actual, expected, ["text", "document_id", "n_tokens"])


async def test_create_base_text_units_metadata_included_in_chunk():
    expected = load_test_table("text_units_metadata_included_chunk")

    context = await create_test_context()

    config = GraphRagConfig(models=DEFAULT_MODEL_CONFIG)  # type: ignore
    config.input.metadata = ["title"]
    config.chunks.prepend_metadata = True
    config.chunks.chunk_size_includes_metadata = True

    await update_document_metadata(config.input.metadata, context)

    await run_workflow(config, context)

    actual = await load_table_from_storage("text_units", context.output_storage)
    # only check the columns from the base workflow - our expected table is the final and will have more
    compare_outputs(actual, expected, columns=["text", "document_id", "n_tokens"])