feat: add bigquery.ml.generate_embedding function

bigframes/bigquery/_operations/ml.py

@@ -520,3 +520,63 @@ def generate_text(
         return bpd.read_gbq_query(sql)
     else:
         return session.read_gbq_query(sql)
+
+
+@log_adapter.method_logger(custom_base_name="bigquery_ml")
+def generate_embedding(
+    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
+    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
+    *,
+    flatten_json_output: Optional[bool] = None,
+    task_type: Optional[str] = None,
+    output_dimensionality: Optional[int] = None,
+) -> dataframe.DataFrame:
+    """
+    Generates text embedding using a BigQuery ML model.
+
+    See the `BigQuery ML GENERATE_EMBEDDING function syntax
+    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding>`_
+    for additional reference.
+
+    Args:
+        model (bigframes.ml.base.BaseEstimator or str):
+            The model to use for text embedding.
+        input_ (Union[bigframes.pandas.DataFrame, str]):
+            The DataFrame or query to use for text embedding.
+        flatten_json_output (bool, optional):
+            A BOOL value that determines the content of the generated JSON column.
+        task_type (str, optional):
+            A STRING value that specifies the intended downstream application task.
+            Supported values are:
+            - `RETRIEVAL_QUERY`
+            - `RETRIEVAL_DOCUMENT`
+            - `SEMANTIC_SIMILARITY`
+            - `CLASSIFICATION`
+            - `CLUSTERING`
+            - `QUESTION_ANSWERING`
+            - `FACT_VERIFICATION`
+            - `CODE_RETRIEVAL_QUERY`
+        output_dimensionality (int, optional):
+            An INT64 value that specifies the size of the output embedding.
+
+    Returns:
+        bigframes.pandas.DataFrame:
+            The generated text embedding.
+    """
+    import bigframes.pandas as bpd
+
+    model_name, session = _get_model_name_and_session(model, input_)
+    table_sql = _to_sql(input_)
+
+    sql = bigframes.core.sql.ml.generate_embedding(
+        model_name=model_name,
+        table=table_sql,
+        flatten_json_output=flatten_json_output,
+        task_type=task_type,
+        output_dimensionality=output_dimensionality,
+    )
+
+    if session is None:
+        return bpd.read_gbq_query(sql)
+    else:
+        return session.read_gbq_query(sql)

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",
 ]

bigframes/core/sql/ml.py

@@ -296,3 +296,31 @@ def generate_text(
     sql += _build_struct_sql(struct_options)
     sql += ")\n"
     return sql
+
+
+def generate_embedding(
+    model_name: str,
+    table: str,
+    *,
+    flatten_json_output: Optional[bool] = None,
+    task_type: Optional[str] = None,
+    output_dimensionality: Optional[int] = None,
+) -> str:
+    """Encode the ML.GENERATE_EMBEDDING statement.
+    See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-embedding for reference.
+    """
+    struct_options: Dict[
+        str,
+        Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]],
+    ] = {}
+    if flatten_json_output is not None:
+        struct_options["flatten_json_output"] = flatten_json_output
+    if task_type is not None:
+        struct_options["task_type"] = task_type
+    if output_dimensionality is not None:
+        struct_options["output_dimensionality"] = output_dimensionality
+
+    sql = f"SELECT * FROM ML.GENERATE_EMBEDDING(MODEL {googlesql.identifier(model_name)}, ({table})"
+    sql += _build_struct_sql(struct_options)
+    sql += ")\n"
+    return sql

tests/system/large/bigquery/test_ml.py

@@ -0,0 +1,64 @@
+# Copyright 2026 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

tests/unit/bigquery/test_ml.py

@@ -200,3 +200,32 @@ def test_generate_text_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mo
     assert "['a', 'b'] AS stop_sequences" in generated_sql
     assert "true AS ground_with_google_search" in generated_sql
     assert "'TYPE' AS request_type" in generated_sql
+
+
+@mock.patch("bigframes.pandas.read_gbq_query")
+@mock.patch("bigframes.pandas.read_pandas")
+def test_generate_embedding_with_pandas_dataframe(
+    read_pandas_mock, read_gbq_query_mock
+):
+    df = pd.DataFrame({"col1": [1, 2, 3]})
+    read_pandas_mock.return_value._to_sql_query.return_value = (
+        "SELECT * FROM `pandas_df`",
+        [],
+        [],
+    )
+    ml_ops.generate_embedding(
+        MODEL_SERIES,
+        input_=df,
+        flatten_json_output=True,
+        task_type="RETRIEVAL_DOCUMENT",
+        output_dimensionality=256,
+    )
+    read_pandas_mock.assert_called_once()
+    read_gbq_query_mock.assert_called_once()
+    generated_sql = read_gbq_query_mock.call_args[0][0]
+    assert "ML.GENERATE_EMBEDDING" in generated_sql
+    assert f"MODEL `{MODEL_NAME}`" in generated_sql
+    assert "(SELECT * FROM `pandas_df`)" in generated_sql
+    assert "true AS flatten_json_output" in generated_sql
+    assert "'RETRIEVAL_DOCUMENT' AS task_type" in generated_sql
+    assert "256 AS output_dimensionality" in generated_sql

tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_basic/generate_embedding_model_basic.sql

@@ -0,0 +1 @@
+SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data))

tests/unit/core/sql/snapshots/test_ml/test_generate_embedding_model_with_options/generate_embedding_model_with_options.sql

@@ -0,0 +1 @@
+SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data), STRUCT(true AS flatten_json_output, 'RETRIEVAL_DOCUMENT' AS task_type, 256 AS output_dimensionality))

tests/unit/core/sql/test_ml.py

@@ -201,3 +201,22 @@ def test_generate_text_model_with_options(snapshot):
         request_type="TYPE",
     )
     snapshot.assert_match(sql, "generate_text_model_with_options.sql")
+
+
+def test_generate_embedding_model_basic(snapshot):
+    sql = bigframes.core.sql.ml.generate_embedding(
+        model_name="my_project.my_dataset.my_model",
+        table="SELECT * FROM new_data",
+    )
+    snapshot.assert_match(sql, "generate_embedding_model_basic.sql")
+
+
+def test_generate_embedding_model_with_options(snapshot):
+    sql = bigframes.core.sql.ml.generate_embedding(
+        model_name="my_project.my_dataset.my_model",
+        table="SELECT * FROM new_data",
+        flatten_json_output=True,
+        task_type="RETRIEVAL_DOCUMENT",
+        output_dimensionality=256,
+    )
+    snapshot.assert_match(sql, "generate_embedding_model_with_options.sql")