google
diff --git a/‎bigframes/bigquery/_operations/ai.py‎
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diff --git a/‎bigframes/bigquery/ai.py‎
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diff --git a/‎tests/system/large/bigquery/test_ai.py‎
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diff --git a/‎tests/unit/bigquery/test_ai.py‎
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@@ -601,6 +601,101 @@ def generate_text(
         return session.read_gbq_query(query)
 
 
+@log_adapter.method_logger(custom_base_name="bigquery_ai")
+def generate_table(
+    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
+    data: Union[dataframe.DataFrame, series.Series, pd.DataFrame, pd.Series],
+    *,
+    output_schema: str,
+    temperature: Optional[float] = None,
+    top_p: Optional[float] = None,
+    max_output_tokens: Optional[int] = None,
+    stop_sequences: Optional[List[str]] = None,
+    request_type: Optional[str] = None,
+) -> dataframe.DataFrame:
+    """
+    Generates a table using a BigQuery ML model.
+
+    See the `AI.GENERATE_TABLE function syntax
+    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-table>`_
+    for additional reference.
+
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.bigquery as bbq
+        >>> # The user is responsible for constructing a DataFrame that contains
+        >>> # the necessary columns for the model's prompt. For example, a
+        >>> # DataFrame with a 'prompt' column for text classification.
+        >>> df = bpd.DataFrame({'prompt': ["some text to classify"]})
+        >>> result = bbq.ai.generate_table(
+        ...     "project.dataset.model_name",
+        ...     data=df,
+        ...     output_schema="category STRING"
+        ... ) # doctest: +SKIP
+
+    Args:
+        model (bigframes.ml.base.BaseEstimator or str):
+            The model to use for table generation.
+        data (bigframes.pandas.DataFrame or bigframes.pandas.Series):
+            The data to generate embeddings for. If a Series is provided, it is
+            treated as the 'content' column.  If a DataFrame is provided, it
+            must contain a 'content' column, or you must rename the column you
+            wish to embed to 'content'.
+        output_schema (str):
+            A string defining the output schema (e.g., "col1 STRING, col2 INT64").
+        temperature (float, optional):
+            A FLOAT64 value that is used for sampling promiscuity. The value
+            must be in the range ``[0.0, 1.0]``.
+        top_p (float, optional):
+            A FLOAT64 value that changes how the model selects tokens for
+            output.
+        max_output_tokens (int, optional):
+            An INT64 value that sets the maximum number of tokens in the
+            generated table.
+        stop_sequences (List[str], optional):
+            An ARRAY<STRING> value that contains the stop sequences for the model.
+        request_type (str, optional):
+            A STRING value that contains the request type for the model.
+
+    Returns:
+        bigframes.pandas.DataFrame:
+            The generated table.
+    """
+    data = _to_dataframe(data, series_rename="prompt")
+    model_name, session = bq_utils.get_model_name_and_session(model, data)
+    table_sql = bq_utils.to_sql(data)
+
+    struct_fields_bq: Dict[str, bigframes.core.sql.literals.STRUCT_VALUES] = {
+        "output_schema": output_schema
+    }
+    if temperature is not None:
+        struct_fields_bq["temperature"] = temperature
+    if top_p is not None:
+        struct_fields_bq["top_p"] = top_p
+    if max_output_tokens is not None:
+        struct_fields_bq["max_output_tokens"] = max_output_tokens
+    if stop_sequences is not None:
+        struct_fields_bq["stop_sequences"] = stop_sequences
+    if request_type is not None:
+        struct_fields_bq["request_type"] = request_type
+
+    struct_sql = bigframes.core.sql.literals.struct_literal(struct_fields_bq)
+    query = f"""
+        SELECT *
+        FROM AI.GENERATE_TABLE(
+            MODEL `{model_name}`,
+            ({table_sql}),
+            {struct_sql}
+        )
+    """
+
+    if session is None:
+        return bpd.read_gbq_query(query)
+    else:
+        return session.read_gbq_query(query)
+
+
 @log_adapter.method_logger(custom_base_name="bigquery_ai")
 def if_(
     prompt: PROMPT_TYPE,
 
@@ -24,6 +24,7 @@
     generate_double,
     generate_embedding,
     generate_int,
+    generate_table,
     generate_text,
     if_,
     score,
@@ -37,6 +38,7 @@
     "generate_double",
     "generate_embedding",
     "generate_int",
+    "generate_table",
     "generate_text",
     "if_",
     "score",
 
@@ -94,3 +94,20 @@ def test_generate_text_with_options(text_model):
 
     # It basically asserts that the results are still returned.
     assert len(result) == 2
+
+
+def test_generate_table(text_model):
+    df = bpd.DataFrame(
+        {"prompt": ["Generate a table of 2 programming languages and their creators."]}
+    )
+
+    result = ai.generate_table(
+        text_model,
+        df,
+        output_schema="language STRING, creator STRING",
+    )
+
+    assert "language" in result.columns
+    assert "creator" in result.columns
+    # The model may not always return the exact number of rows requested.
+    assert len(result) > 0
@@ -220,6 +220,55 @@ def test_generate_text_defaults(mock_dataframe, mock_session):
     assert "STRUCT()" in query
 
 
+def test_generate_table_with_dataframe(mock_dataframe, mock_session):
+    model_name = "project.dataset.model"
+
+    bbq.ai.generate_table(
+        model_name,
+        mock_dataframe,
+        output_schema="col1 STRING, col2 INT64",
+    )
+
+    mock_session.read_gbq_query.assert_called_once()
+    query = mock_session.read_gbq_query.call_args[0][0]
+
+    # Normalize whitespace for comparison
+    query = " ".join(query.split())
+
+    expected_part_1 = "SELECT * FROM AI.GENERATE_TABLE("
+    expected_part_2 = f"MODEL `{model_name}`,"
+    expected_part_3 = "(SELECT * FROM my_table),"
+    expected_part_4 = "STRUCT('col1 STRING, col2 INT64' AS output_schema)"
+
+    assert expected_part_1 in query
+    assert expected_part_2 in query
+    assert expected_part_3 in query
+    assert expected_part_4 in query
+
+
+def test_generate_table_with_options(mock_dataframe, mock_session):
+    model_name = "project.dataset.model"
+
+    bbq.ai.generate_table(
+        model_name,
+        mock_dataframe,
+        output_schema="col1 STRING",
+        temperature=0.5,
+        max_output_tokens=100,
+    )
+
+    mock_session.read_gbq_query.assert_called_once()
+    query = mock_session.read_gbq_query.call_args[0][0]
+    query = " ".join(query.split())
+
+    assert f"MODEL `{model_name}`" in query
+    assert "(SELECT * FROM my_table)" in query
+    assert (
+        "STRUCT('col1 STRING' AS output_schema, 0.5 AS temperature, 100 AS max_output_tokens)"
+        in query
+    )
+
+
 @mock.patch("bigframes.pandas.read_pandas")
 def test_generate_text_with_pandas_dataframe(
     read_pandas_mock, mock_dataframe, mock_session