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Val Brodsky
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Add test for mmc data rows
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SPLIT_SCHEMA_ID = "cko8sbczn0002h2dkdaxb5kal"
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TEST_SPLIT_ID = "cko8scbz70005h2dkastwhgqt"
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TEXT_SCHEMA_ID = "cko8s9r5v0001h2dk9elqdidh"
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CAPTURE_DT_SCHEMA_ID = "cko8sdzv70006h2dk8jg64zvb"
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EXPECTED_METADATA_SCHEMA_IDS = [
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SPLIT_SCHEMA_ID,
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TEST_SPLIT_ID,
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TEXT_SCHEMA_ID,
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CAPTURE_DT_SCHEMA_ID,
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]
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CUSTOM_TEXT_SCHEMA_NAME = "custom_text"
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import json
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import random
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import pytest
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from constants import EXPECTED_METADATA_SCHEMA_IDS
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@pytest.fixture
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def mmc_data_row(dataset, make_metadata_fields, embedding):
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row_data = {
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"type": "application/vnd.labelbox.conversational.model-chat-evaluation",
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"draft": True,
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"rootMessageIds": ["root1"],
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"actors": {},
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"messages": {},
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}
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vector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]
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embeddings = [{"embedding_id": embedding.id, "vector": vector}]
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content_all = {
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"row_data": row_data,
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"attachments": [{"type": "RAW_TEXT", "value": "attachment value"}],
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"metadata_fields": make_metadata_fields,
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"embeddings": embeddings,
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}
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task = dataset.create_data_rows([content_all])
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task.wait_till_done()
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assert task.status == "COMPLETE"
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data_row = list(dataset.data_rows())[0]
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yield data_row
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data_row.delete()
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def test_mmc(mmc_data_row, embedding):
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data_row = mmc_data_row
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assert json.loads(data_row.row_data) == {
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"type": "application/vnd.labelbox.conversational.model-chat-evaluation",
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"draft": True,
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"rootMessageIds": ["root1"],
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"actors": {},
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"messages": {},
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}
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metadata_fields = data_row.metadata_fields
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metadata = data_row.metadata
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assert len(metadata_fields) == 3
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assert len(metadata) == 3
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assert [
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m["schemaId"] for m in metadata_fields
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].sort() == EXPECTED_METADATA_SCHEMA_IDS.sort()
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attachments = list(data_row.attachments())
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assert len(attachments) == 1
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assert embedding.get_imported_vector_count() == 1

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