system test

Author: GarrettWu

bigframes/bigquery/ml.py

@@ -23,6 +23,7 @@
     create_model,
     evaluate,
     explain_predict,
+    generate_embedding,
     generate_text,
     global_explain,
     predict,
@@ -37,4 +38,5 @@
     "global_explain",
     "transform",
     "generate_text",
+    "generate_embedding",
 ]

tests/system/large/bigquery/test_ml.py

@@ -0,0 +1,64 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+
+import bigframes.bigquery.ml as ml
+import bigframes.pandas as bpd
+
+
+@pytest.fixture(scope="session")
+def embedding_model(bq_connection, dataset_id):
+    model_name = f"{dataset_id}.embedding_model"
+    return ml.create_model(
+        model_name=model_name,
+        options={"endpoint": "gemini-embedding-001"},
+        connection_name=bq_connection,
+    )
+
+
+def test_generate_embedding(embedding_model):
+    df = bpd.DataFrame(
+        {
+            "content": [
+                "What is BigQuery?",
+                "What is BQML?",
+            ]
+        }
+    )
+
+    result = ml.generate_embedding(embedding_model, df)
+    assert len(result) == 2
+    assert "ml_generate_embedding_result" in result.columns
+    assert "ml_generate_embedding_status" in result.columns
+
+
+def test_generate_embedding_with_options(embedding_model):
+    df = bpd.DataFrame(
+        {
+            "content": [
+                "What is BigQuery?",
+                "What is BQML?",
+            ]
+        }
+    )
+
+    result = ml.generate_embedding(
+        embedding_model, df, task_type="RETRIEVAL_DOCUMENT", output_dimensionality=256
+    )
+    assert len(result) == 2
+    assert "ml_generate_embedding_result" in result.columns
+    assert "ml_generate_embedding_status" in result.columns
+    embedding = result["ml_generate_embedding_result"].to_pandas()
+    assert len(embedding[0]) == 256