feat: support fit() in GeminiTextGenerator

bigframes/ml/llm.py

@@ -571,6 +571,8 @@ class GeminiTextGenerator(base.BaseEstimator):
             Connection to connect with remote service. str of the format <PROJECT_NUMBER/PROJECT_ID>.<LOCATION>.<CONNECTION_ID>.
             If None, use default connection in session context. BigQuery DataFrame will try to create the connection and attach
             permission if the connection isn't fully set up.
+        max_iterations (Optional[int], Default to 300):
+            The number of steps to run when performing supervised tuning.
     """
 
     def __init__(
@@ -581,9 +583,11 @@ def __init__(
         ] = "gemini-pro",
         session: Optional[bigframes.Session] = None,
         connection_name: Optional[str] = None,
+        max_iterations: int = 300,
     ):
         self.model_name = model_name
         self.session = session or bpd.get_global_session()
+        self.max_iterations = max_iterations
         self._bq_connection_manager = self.session.bqconnectionmanager
 
         connection_name = connection_name or self.session._bq_connection
@@ -647,6 +651,55 @@ def _from_bq(
         model._bqml_model = core.BqmlModel(session, bq_model)
         return model
 
+    @property
+    def _bqml_options(self) -> dict:
+        """The model options as they will be set for BQML"""
+        options = {
+            "max_iterations": self.max_iterations,
+            "data_split_method": "NO_SPLIT",
+        }
+        return options
+
+    def fit(
+        self,
+        X: Union[bpd.DataFrame, bpd.Series],
+        y: Union[bpd.DataFrame, bpd.Series],
+    ) -> GeminiTextGenerator:
+        """Fine tune GeminiTextGenerator model. Only support "gemini-pro" model for now.
+
+        .. note::
+
+            This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
+            Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is"
+            and might have limited support. For more information, see the launch stage descriptions
+            (https://cloud.google.com/products#product-launch-stages).
+
+        Args:
+            X (bigframes.dataframe.DataFrame or bigframes.series.Series):
+                DataFrame of shape (n_samples, n_features). Training data.
+            y (bigframes.dataframe.DataFrame or bigframes.series.Series:
+                Training labels.
+
+        Returns:
+            GeminiTextGenerator: Fitted estimator.
+        """
+        if self._bqml_model.model_name.startswith("gemini-1.5"):
+            raise NotImplementedError("Fit is not supported for gemini-1.5 model.")
+
+        X, y = utils.convert_to_dataframe(X, y)
+
+        options = self._bqml_options
+        options["endpoint"] = "gemini-1.0-pro-002"
+        options["prompt_col"] = X.columns.tolist()[0]
+
+        self._bqml_model = self._bqml_model_factory.create_llm_remote_model(
+            X,
+            y,
+            options=options,
+            connection_name=self.connection_name,
+        )
+        return self
+
     def predict(
         self,
         X: Union[bpd.DataFrame, bpd.Series],

tests/system/load/test_llm.py

@@ -99,3 +99,30 @@ def test_llm_palm_score_params(llm_fine_tune_df_default_index):
         "evaluation_status",
     ]
     assert all(col in score_result_col for col in expected_col)
+
+
+@pytest.mark.flaky(retries=2)
+def test_llm_gemini_configure_fit(llm_fine_tune_df_default_index, llm_remote_text_df):
+    model = bigframes.ml.llm.GeminiTextGenerator(
+        model_name="gemini-pro", max_iterations=1
+    )
+
+    X_train = llm_fine_tune_df_default_index[["prompt"]]
+    y_train = llm_fine_tune_df_default_index[["label"]]
+    model.fit(X_train, y_train)
+
+    assert model is not None
+
+    df = model.predict(
+        llm_remote_text_df["prompt"],
+        temperature=0.5,
+        max_output_tokens=100,
+        top_k=20,
+        top_p=0.5,
+    ).to_pandas()
+    assert df.shape == (3, 4)
+    assert "ml_generate_text_llm_result" in df.columns
+    series = df["ml_generate_text_llm_result"]
+    assert all(series.str.len() == 1)
+
+    # TODO(ashleyxu b/335492787): After bqml rolled out version control: save, load, check parameters to ensure configuration was kept