feat: add bigquery.ml.generate_text function

bigframes/bigquery/_operations/ml.py

@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from typing import cast, Mapping, Optional, Union
+from typing import cast, List, Mapping, Optional, Union
 
 import bigframes_vendored.constants
 import google.cloud.bigquery
@@ -431,3 +431,92 @@ def transform(
         return bpd.read_gbq_query(sql)
     else:
         return session.read_gbq_query(sql)
+
+
+@log_adapter.method_logger(custom_base_name="bigquery_ml")
+def generate_text(
+    model: Union[bigframes.ml.base.BaseEstimator, str, pd.Series],
+    input_: Union[pd.DataFrame, dataframe.DataFrame, str],
+    *,
+    temperature: Optional[float] = None,
+    max_output_tokens: Optional[int] = None,
+    top_k: Optional[int] = None,
+    top_p: Optional[float] = None,
+    flatten_json_output: Optional[bool] = None,
+    stop_sequences: Optional[List[str]] = None,
+    ground_with_google_search: Optional[bool] = None,
+    request_type: Optional[str] = None,
+) -> dataframe.DataFrame:
+    """
+    Generates text using a BigQuery ML model.
+
+    See the `BigQuery ML GENERATE_TEXT function syntax
+    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-text>`_
+    for additional reference.
+
+    Args:
+        model (bigframes.ml.base.BaseEstimator or str):
+            The model to use for text generation.
+        input_ (Union[bigframes.pandas.DataFrame, str]):
+            The DataFrame or query to use for text generation.
+        temperature (float, optional):
+            A FLOAT64 value that is used for sampling promiscuity. The value
+            must be in the range ``[0.0, 1.0]``. A lower temperature works well
+            for prompts that expect a more deterministic and less open-ended
+            or creative response, while a higher temperature can lead to more
+            diverse or creative results. A temperature of ``0`` is
+            deterministic, meaning that the highest probability response is
+            always selected.
+        max_output_tokens (int, optional):
+            An INT64 value that sets the maximum number of tokens in the
+            generated text.
+        top_k (int, optional):
+            An INT64 value that changes how the model selects tokens for
+            output. A ``top_k`` of ``1`` means the next selected token is the
+            most probable among all tokens in the model's vocabulary. A
+            ``top_k`` of ``3`` means that the next token is selected from
+            among the three most probable tokens by using temperature. The
+            default value is ``40``.
+        top_p (float, optional):
+            A FLOAT64 value that changes how the model selects tokens for
+            output. Tokens are selected from most probable to least probable
+            until the sum of their probabilities equals the ``top_p`` value.
+            For example, if tokens A, B, and C have a probability of 0.3, 0.2,
+            and 0.1 and the ``top_p`` value is ``0.5``, then the model will
+            select either A or B as the next token by using temperature. The
+            default value is ``0.95``.
+        flatten_json_output (bool, optional):
+            A BOOL value that determines the content of the generated JSON column.
+        stop_sequences (List[str], optional):
+            An ARRAY<STRING> value that contains the stop sequences for the model.
+        ground_with_google_search (bool, optional):
+            A BOOL value that determines whether to ground the model with Google Search.
+        request_type (str, optional):
+            A STRING value that contains the request type for the model.
+
+    Returns:
+        bigframes.pandas.DataFrame:
+            The generated text.
+    """
+    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_text(
+        model_name=model_name,
+        table=table_sql,
+        temperature=temperature,
+        max_output_tokens=max_output_tokens,
+        top_k=top_k,
+        top_p=top_p,
+        flatten_json_output=flatten_json_output,
+        stop_sequences=stop_sequences,
+        ground_with_google_search=ground_with_google_search,
+        request_type=request_type,
+    )
+
+    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_text,
     global_explain,
     predict,
     transform,
@@ -35,4 +36,5 @@
     "explain_predict",
     "global_explain",
     "transform",
+    "generate_text",
 ]

bigframes/core/sql/ml.py

@@ -14,7 +14,9 @@
 
 from __future__ import annotations
 
-from typing import Dict, Mapping, Optional, Union
+import collections.abc
+import json
+from typing import Any, Dict, List, Mapping, Optional, Union
 
 import bigframes.core.compile.googlesql as googlesql
 import bigframes.core.sql
@@ -100,14 +102,41 @@ def create_model_ddl(
 
 
 def _build_struct_sql(
-    struct_options: Mapping[str, Union[str, int, float, bool]]
+    struct_options: Mapping[
+        str,
+        Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]],
+    ]
 ) -> str:
     if not struct_options:
         return ""
 
     rendered_options = []
     for option_name, option_value in struct_options.items():
-        rendered_val = bigframes.core.sql.simple_literal(option_value)
+        if option_name == "model_params":
+            json_str = json.dumps(option_value)
+            # Escape single quotes for SQL string literal
+            sql_json_str = json_str.replace("'", "''")
+            rendered_val = f"JSON'{sql_json_str}'"
+        elif isinstance(option_value, collections.abc.Mapping):
+            struct_body = ", ".join(
+                [
+                    f"{bigframes.core.sql.simple_literal(v)} AS {k}"
+                    for k, v in option_value.items()
+                ]
+            )
+            rendered_val = f"STRUCT({struct_body})"
+        elif isinstance(option_value, list):
+            rendered_val = (
+                "["
+                + ", ".join(
+                    [bigframes.core.sql.simple_literal(v) for v in option_value]
+                )
+                + "]"
+            )
+        elif isinstance(option_value, bool):
+            rendered_val = str(option_value).lower()
+        else:
+            rendered_val = bigframes.core.sql.simple_literal(option_value)
         rendered_options.append(f"{rendered_val} AS {option_name}")
     return f", STRUCT({', '.join(rendered_options)})"
 
@@ -151,7 +180,7 @@ def predict(
     """Encode the ML.PREDICT statement.
     See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict for reference.
     """
-    struct_options = {}
+    struct_options: Dict[str, Union[str, int, float, bool]] = {}
     if threshold is not None:
         struct_options["threshold"] = threshold
     if keep_original_columns is not None:
@@ -205,7 +234,7 @@ def global_explain(
     """Encode the ML.GLOBAL_EXPLAIN statement.
     See https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-global-explain for reference.
     """
-    struct_options = {}
+    struct_options: Dict[str, Union[str, int, float, bool]] = {}
     if class_level_explain is not None:
         struct_options["class_level_explain"] = class_level_explain
 
@@ -224,3 +253,46 @@ def transform(
     """
     sql = f"SELECT * FROM ML.TRANSFORM(MODEL {googlesql.identifier(model_name)}, ({table}))\n"
     return sql
+
+
+def generate_text(
+    model_name: str,
+    table: str,
+    *,
+    temperature: Optional[float] = None,
+    max_output_tokens: Optional[int] = None,
+    top_k: Optional[int] = None,
+    top_p: Optional[float] = None,
+    flatten_json_output: Optional[bool] = None,
+    stop_sequences: Optional[List[str]] = None,
+    ground_with_google_search: Optional[bool] = None,
+    request_type: Optional[str] = None,
+) -> str:
+    """Encode the ML.GENERATE_TEXT statement.
+    See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-generate-text for reference.
+    """
+    struct_options: Dict[
+        str,
+        Union[str, int, float, bool, Mapping[str, str], List[str], Mapping[str, Any]],
+    ] = {}
+    if temperature is not None:
+        struct_options["temperature"] = temperature
+    if max_output_tokens is not None:
+        struct_options["max_output_tokens"] = max_output_tokens
+    if top_k is not None:
+        struct_options["top_k"] = top_k
+    if top_p is not None:
+        struct_options["top_p"] = top_p
+    if flatten_json_output is not None:
+        struct_options["flatten_json_output"] = flatten_json_output
+    if stop_sequences is not None:
+        struct_options["stop_sequences"] = stop_sequences
+    if ground_with_google_search is not None:
+        struct_options["ground_with_google_search"] = ground_with_google_search
+    if request_type is not None:
+        struct_options["request_type"] = request_type
+
+    sql = f"SELECT * FROM ML.GENERATE_TEXT(MODEL {googlesql.identifier(model_name)}, ({table})"
+    sql += _build_struct_sql(struct_options)
+    sql += ")\n"
+    return sql

notebooks/ml/bq_dataframes_ml_cross_validation.ipynb

@@ -991,7 +991,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv",
+   "display_name": "venv (3.10.14)",
    "language": "python",
    "name": "python3"
   },
@@ -1005,7 +1005,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

tests/unit/bigquery/test_ml.py

@@ -163,3 +163,40 @@ def test_transform_with_pandas_dataframe(read_pandas_mock, read_gbq_query_mock):
     assert "ML.TRANSFORM" in generated_sql
     assert f"MODEL `{MODEL_NAME}`" in generated_sql
     assert "(SELECT * FROM `pandas_df`)" in generated_sql
+
+
+@mock.patch("bigframes.pandas.read_gbq_query")
+@mock.patch("bigframes.pandas.read_pandas")
+def test_generate_text_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_text(
+        MODEL_SERIES,
+        input_=df,
+        temperature=0.5,
+        max_output_tokens=128,
+        top_k=20,
+        top_p=0.9,
+        flatten_json_output=True,
+        stop_sequences=["a", "b"],
+        ground_with_google_search=True,
+        request_type="TYPE",
+    )
+    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_TEXT" in generated_sql
+    assert f"MODEL `{MODEL_NAME}`" in generated_sql
+    assert "(SELECT * FROM `pandas_df`)" in generated_sql
+    assert "STRUCT(0.5 AS temperature" in generated_sql
+    assert "128 AS max_output_tokens" in generated_sql
+    assert "20 AS top_k" in generated_sql
+    assert "0.9 AS top_p" in generated_sql
+    assert "true AS flatten_json_output" in generated_sql
+    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

tests/unit/core/sql/snapshots/test_ml/test_evaluate_model_with_options/evaluate_model_with_options.sql

@@ -1 +1 @@
-SELECT * FROM ML.EVALUATE(MODEL `my_model`, STRUCT(False AS perform_aggregation, 10 AS horizon, 0.95 AS confidence_level))
+SELECT * FROM ML.EVALUATE(MODEL `my_model`, STRUCT(false AS perform_aggregation, 10 AS horizon, 0.95 AS confidence_level))

tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_basic/generate_text_model_basic.sql

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

tests/unit/core/sql/snapshots/test_ml/test_generate_text_model_with_options/generate_text_model_with_options.sql

@@ -0,0 +1 @@
+SELECT * FROM ML.GENERATE_TEXT(MODEL `my_project.my_dataset.my_model`, (SELECT * FROM new_data), STRUCT(0.5 AS temperature, 128 AS max_output_tokens, 20 AS top_k, 0.9 AS top_p, true AS flatten_json_output, ['a', 'b'] AS stop_sequences, true AS ground_with_google_search, 'TYPE' AS request_type))

tests/unit/core/sql/snapshots/test_ml/test_global_explain_model_with_options/global_explain_model_with_options.sql

@@ -1 +1 @@
-SELECT * FROM ML.GLOBAL_EXPLAIN(MODEL `my_model`, STRUCT(True AS class_level_explain))
+SELECT * FROM ML.GLOBAL_EXPLAIN(MODEL `my_model`, STRUCT(true AS class_level_explain))

tests/unit/core/sql/snapshots/test_ml/test_predict_model_with_options/predict_model_with_options.sql

@@ -1 +1 @@
-SELECT * FROM ML.PREDICT(MODEL `my_model`, (SELECT * FROM new_data), STRUCT(True AS keep_original_columns))
+SELECT * FROM ML.PREDICT(MODEL `my_model`, (SELECT * FROM new_data), STRUCT(true AS keep_original_columns))

tests/unit/core/sql/test_ml.py

@@ -177,3 +177,27 @@ def test_transform_model_basic(snapshot):
         table="SELECT * FROM new_data",
     )
     snapshot.assert_match(sql, "transform_model_basic.sql")
+
+
+def test_generate_text_model_basic(snapshot):
+    sql = bigframes.core.sql.ml.generate_text(
+        model_name="my_project.my_dataset.my_model",
+        table="SELECT * FROM new_data",
+    )
+    snapshot.assert_match(sql, "generate_text_model_basic.sql")
+
+
+def test_generate_text_model_with_options(snapshot):
+    sql = bigframes.core.sql.ml.generate_text(
+        model_name="my_project.my_dataset.my_model",
+        table="SELECT * FROM new_data",
+        temperature=0.5,
+        max_output_tokens=128,
+        top_k=20,
+        top_p=0.9,
+        flatten_json_output=True,
+        stop_sequences=["a", "b"],
+        ground_with_google_search=True,
+        request_type="TYPE",
+    )
+    snapshot.assert_match(sql, "generate_text_model_with_options.sql")