Add test for mmc data rows

Author: Val Brodsky

libs/labelbox/tests/integration/constants.py

@@ -0,0 +1,11 @@
+SPLIT_SCHEMA_ID = "cko8sbczn0002h2dkdaxb5kal"
+TEST_SPLIT_ID = "cko8scbz70005h2dkastwhgqt"
+TEXT_SCHEMA_ID = "cko8s9r5v0001h2dk9elqdidh"
+CAPTURE_DT_SCHEMA_ID = "cko8sdzv70006h2dk8jg64zvb"
+EXPECTED_METADATA_SCHEMA_IDS = [
+    SPLIT_SCHEMA_ID,
+    TEST_SPLIT_ID,
+    TEXT_SCHEMA_ID,
+    CAPTURE_DT_SCHEMA_ID,
+]
+CUSTOM_TEXT_SCHEMA_NAME = "custom_text"

libs/labelbox/tests/integration/test_mmc_data_rows.py

@@ -0,0 +1,59 @@
+import json
+import random
+
+import pytest
+from constants import EXPECTED_METADATA_SCHEMA_IDS
+
+
+@pytest.fixture
+def mmc_data_row(dataset, make_metadata_fields, embedding):
+    row_data = {
+        "type": "application/vnd.labelbox.conversational.model-chat-evaluation",
+        "draft": True,
+        "rootMessageIds": ["root1"],
+        "actors": {},
+        "messages": {},
+    }
+
+    vector = [random.uniform(1.0, 2.0) for _ in range(embedding.dims)]
+    embeddings = [{"embedding_id": embedding.id, "vector": vector}]
+
+    content_all = {
+        "row_data": row_data,
+        "attachments": [{"type": "RAW_TEXT", "value": "attachment value"}],
+        "metadata_fields": make_metadata_fields,
+        "embeddings": embeddings,
+    }
+    task = dataset.create_data_rows([content_all])
+    task.wait_till_done()
+    assert task.status == "COMPLETE"
+
+    data_row = list(dataset.data_rows())[0]
+
+    yield data_row
+
+    data_row.delete()
+
+
+def test_mmc(mmc_data_row, embedding):
+    data_row = mmc_data_row
+    assert json.loads(data_row.row_data) == {
+        "type": "application/vnd.labelbox.conversational.model-chat-evaluation",
+        "draft": True,
+        "rootMessageIds": ["root1"],
+        "actors": {},
+        "messages": {},
+    }
+
+    metadata_fields = data_row.metadata_fields
+    metadata = data_row.metadata
+    assert len(metadata_fields) == 3
+    assert len(metadata) == 3
+    assert [
+        m["schemaId"] for m in metadata_fields
+    ].sort() == EXPECTED_METADATA_SCHEMA_IDS.sort()
+
+    attachments = list(data_row.attachments())
+    assert len(attachments) == 1
+
+    assert embedding.get_imported_vector_count() == 1