feat: support ML.GENERATE_EMBEDDING in PaLM2TextEmbeddingGenerator

bigframes/ml/core.py

@@ -152,14 +152,14 @@ def generate_text(
             ),
         )
 
-    def generate_text_embedding(
+    def generate_embedding(
         self,
         input_data: bpd.DataFrame,
         options: Mapping[str, int | float],
     ) -> bpd.DataFrame:
         return self._apply_sql(
             input_data,
-            lambda source_df: self._model_manipulation_sql_generator.ml_generate_text_embedding(
+            lambda source_df: self._model_manipulation_sql_generator.ml_generate_embedding(
                 source_df=source_df,
                 struct_options=options,
             ),

bigframes/ml/llm.py

@@ -44,7 +44,7 @@
 _GEMINI_PRO_ENDPOINT = "gemini-pro"
 
 _ML_GENERATE_TEXT_STATUS = "ml_generate_text_status"
-_ML_EMBED_TEXT_STATUS = "ml_embed_text_status"
+_ML_EMBED_TEXT_STATUS = "ml_generate_embedding_status"
 
 
 @log_adapter.class_logger
@@ -389,7 +389,7 @@ def predict(self, X: Union[bpd.DataFrame, bpd.Series]) -> bpd.DataFrame:
             "flatten_json_output": True,
         }
 
-        df = self._bqml_model.generate_text_embedding(X, options)
+        df = self._bqml_model.generate_embedding(X, options)
 
         if (df[_ML_EMBED_TEXT_STATUS] != "").any():
             warnings.warn(

bigframes/ml/sql.py

@@ -270,12 +270,12 @@ def ml_generate_text(
         return f"""SELECT * FROM ML.GENERATE_TEXT(MODEL `{self._model_name}`,
   ({self._source_sql(source_df)}), {struct_options_sql})"""
 
-    def ml_generate_text_embedding(
+    def ml_generate_embedding(
         self, source_df: bpd.DataFrame, struct_options: Mapping[str, Union[int, float]]
     ) -> str:
-        """Encode ML.GENERATE_TEXT_EMBEDDING for BQML"""
+        """Encode ML.GENERATE_EMBEDDING for BQML"""
         struct_options_sql = self.struct_options(**struct_options)
-        return f"""SELECT * FROM ML.GENERATE_TEXT_EMBEDDING(MODEL `{self._model_name}`,
+        return f"""SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `{self._model_name}`,
   ({self._source_sql(source_df)}), {struct_options_sql})"""
 
     def ml_detect_anomalies(

tests/system/small/ml/test_llm.py

@@ -261,8 +261,8 @@ def test_embedding_generator_predict_success(
 ):
     df = palm2_embedding_generator_model.predict(llm_text_df).to_pandas()
     assert df.shape == (3, 4)
-    assert "text_embedding" in df.columns
-    series = df["text_embedding"]
+    assert "ml_generate_embedding_result" in df.columns
+    series = df["ml_generate_embedding_result"]
     value = series[0]
     assert len(value) == 768
 
@@ -273,8 +273,8 @@ def test_embedding_generator_multilingual_predict_success(
 ):
     df = palm2_embedding_generator_multilingual_model.predict(llm_text_df).to_pandas()
     assert df.shape == (3, 4)
-    assert "text_embedding" in df.columns
-    series = df["text_embedding"]
+    assert "ml_generate_embedding_result" in df.columns
+    series = df["ml_generate_embedding_result"]
     value = series[0]
     assert len(value) == 768
 
@@ -285,8 +285,8 @@ def test_embedding_generator_predict_series_success(
 ):
     df = palm2_embedding_generator_model.predict(llm_text_df["prompt"]).to_pandas()
     assert df.shape == (3, 4)
-    assert "text_embedding" in df.columns
-    series = df["text_embedding"]
+    assert "ml_generate_embedding_result" in df.columns
+    series = df["ml_generate_embedding_result"]
     value = series[0]
     assert len(value) == 768
 

tests/unit/ml/test_sql.py

@@ -373,17 +373,17 @@ def test_ml_generate_text_correct(
     )
 
 
-def test_ml_generate_text_embedding_correct(
+def test_ml_generate_embedding_correct(
     model_manipulation_sql_generator: ml_sql.ModelManipulationSqlGenerator,
     mock_df: bpd.DataFrame,
 ):
-    sql = model_manipulation_sql_generator.ml_generate_text_embedding(
+    sql = model_manipulation_sql_generator.ml_generate_embedding(
         source_df=mock_df,
         struct_options={"option_key1": 1, "option_key2": 2.2},
     )
     assert (
         sql
-        == """SELECT * FROM ML.GENERATE_TEXT_EMBEDDING(MODEL `my_project_id.my_dataset_id.my_model_id`,
+        == """SELECT * FROM ML.GENERATE_EMBEDDING(MODEL `my_project_id.my_dataset_id.my_model_id`,
   (input_X_sql), STRUCT(
   1 AS option_key1,
   2.2 AS option_key2))"""