feat: add bigquery.ml.generate_embedding function

Author: GarrettWu

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/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/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/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")