Merge remote-tracking branch 'github/main' into iceberg_tables

Author: TrevorBergeron

.librarian/state.yaml

@@ -1,7 +1,7 @@
 image: us-central1-docker.pkg.dev/cloud-sdk-librarian-prod/images-prod/python-librarian-generator@sha256:e7cc6823efb073a8a26e7cefdd869f12ec228abfbd2a44aa9a7eacc284023677
 libraries:
   - id: bigframes
-    version: 2.33.0
+    version: 2.34.0
     last_generated_commit: ""
     apis: []
     source_roots:

CHANGELOG.md

@@ -4,6 +4,22 @@
 
 [1]: https://pypi.org/project/bigframes/#history
 
+## [2.34.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.33.0...v2.34.0) (2026-02-02)
+
+
+### Features
+
+* add `bigframes.pandas.options.experiments.sql_compiler` for switching the backend compiler (#2417) ([7eba6ee03f07938315d99e2aeaf72368c02074cf](https://github.com/googleapis/python-bigquery-dataframes/commit/7eba6ee03f07938315d99e2aeaf72368c02074cf))
+* add bigquery.ml.generate_embedding function (#2422) ([35f3f5e6f8c64b47e6e7214034f96f047785e647](https://github.com/googleapis/python-bigquery-dataframes/commit/35f3f5e6f8c64b47e6e7214034f96f047785e647))
+* add bigquery.create_external_table method (#2415) ([76db2956e505aec4f1055118ac7ca523facc10ff](https://github.com/googleapis/python-bigquery-dataframes/commit/76db2956e505aec4f1055118ac7ca523facc10ff))
+* add deprecation warnings for .blob accessor and read_gbq_object_table (#2408) ([7261a4ea5cdab6b30f5bc333501648c60e70be59](https://github.com/googleapis/python-bigquery-dataframes/commit/7261a4ea5cdab6b30f5bc333501648c60e70be59))
+* add bigquery.ml.generate_text function (#2403) ([5ac681028624de15e31f0c2ae360b47b2dcf1e8d](https://github.com/googleapis/python-bigquery-dataframes/commit/5ac681028624de15e31f0c2ae360b47b2dcf1e8d))
+
+
+### Bug Fixes
+
+* broken job url (#2411) ([fcb5bc1761c656e1aec61dbcf96a36d436833b7a](https://github.com/googleapis/python-bigquery-dataframes/commit/fcb5bc1761c656e1aec61dbcf96a36d436833b7a))
+
 ## [2.33.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.32.0...v2.33.0) (2026-01-22)
 
 

bigframes/_config/experiment_options.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional
+from typing import Literal, Optional
 import warnings
 
 import bigframes
@@ -27,6 +27,7 @@ class ExperimentOptions:
     def __init__(self):
         self._semantic_operators: bool = False
         self._ai_operators: bool = False
+        self._sql_compiler: Literal["legacy", "stable", "experimental"] = "stable"
 
     @property
     def semantic_operators(self) -> bool:
@@ -55,6 +56,24 @@ def ai_operators(self, value: bool):
             warnings.warn(msg, category=bfe.PreviewWarning)
         self._ai_operators = value
 
+    @property
+    def sql_compiler(self) -> Literal["legacy", "stable", "experimental"]:
+        return self._sql_compiler
+
+    @sql_compiler.setter
+    def sql_compiler(self, value: Literal["legacy", "stable", "experimental"]):
+        if value not in ["legacy", "stable", "experimental"]:
+            raise ValueError(
+                "sql_compiler must be one of 'legacy', 'stable', or 'experimental'"
+            )
+        if value == "experimental":
+            msg = bfe.format_message(
+                "The experimental SQL compiler is still under experiments, and is subject "
+                "to change in the future."
+            )
+            warnings.warn(msg, category=FutureWarning)
+        self._sql_compiler = value
+
     @property
     def blob(self) -> bool:
         msg = bfe.format_message(

bigframes/bigquery/__init__.py

@@ -43,6 +43,7 @@
     st_regionstats,
     st_simplify,
 )
+from bigframes.bigquery._operations.io import load_data
 from bigframes.bigquery._operations.json import (
     json_extract,
     json_extract_array,
@@ -60,7 +61,7 @@
 from bigframes.bigquery._operations.search import create_vector_index, vector_search
 from bigframes.bigquery._operations.sql import sql_scalar
 from bigframes.bigquery._operations.struct import struct
-from bigframes.bigquery.table import create_external_table
+from bigframes.bigquery._operations.table import create_external_table
 from bigframes.core.logging import log_adapter
 
 _functions = [
@@ -107,6 +108,8 @@
     struct,
     # table ops
     create_external_table,
+    # io ops
+    load_data,
 ]
 
 _module = sys.modules[__name__]
@@ -160,6 +163,8 @@
     "struct",
     # table ops
     "create_external_table",
+    # io ops
+    "load_data",
     # Modules / SQL namespaces
     "ai",
     "ml",

bigframes/bigquery/_operations/ai.py

@@ -19,7 +19,7 @@
 from __future__ import annotations
 
 import json
-from typing import Any, Iterable, List, Literal, Mapping, Tuple, Union
+from typing import Any, Dict, Iterable, List, Literal, Mapping, Optional, Tuple, Union
 
 import pandas as pd
 
@@ -28,6 +28,7 @@
 from bigframes import series, session
 from bigframes.core import convert
 from bigframes.core.logging import log_adapter
+import bigframes.core.sql.literals
 from bigframes.ml import core as ml_core
 from bigframes.operations import ai_ops, output_schemas
 
@@ -388,6 +389,113 @@ def generate_double(
     return series_list[0]._apply_nary_op(operator, series_list[1:])
 
 
+@log_adapter.method_logger(custom_base_name="bigquery_ai")
+def generate_embedding(
+    model_name: str,
+    data: Union[dataframe.DataFrame, series.Series, pd.DataFrame, pd.Series],
+    *,
+    output_dimensionality: Optional[int] = None,
+    task_type: Optional[str] = None,
+    start_second: Optional[float] = None,
+    end_second: Optional[float] = None,
+    interval_seconds: Optional[float] = None,
+    trial_id: Optional[int] = None,
+) -> dataframe.DataFrame:
+    """
+    Creates embeddings that describe an entity—for example, a piece of text or an image.
+
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.bigquery as bbq
+        >>> df = bpd.DataFrame({"content": ["apple", "bear", "pear"]})
+        >>> bbq.ai.generate_embedding(
+        ...     "project.dataset.model_name",
+        ...     df
+        ... ) # doctest: +SKIP
+
+    Args:
+        model_name (str):
+            The name of a remote model from Vertex AI, such as the
+            multimodalembedding@001 model.
+        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_dimensionality (int, optional):
+            An INT64 value that specifies the number of dimensions to use when
+            generating embeddings. For example, if you specify 256 AS
+            output_dimensionality, then the embedding output column contains a
+            256-dimensional embedding for each input value. To find the
+            supported range of output dimensions, read about the available
+            `Google text embedding models <https://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#google-models>`_.
+        task_type (str, optional):
+            A STRING literal that specifies the intended downstream application to
+            help the model produce better quality embeddings. For a list of
+            supported task types and how to choose which one to use, see `Choose an
+            embeddings task type <http://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types>`_.
+        start_second (float, optional):
+            The second in the video at which to start the embedding. The default value is 0.
+        end_second (float, optional):
+            The second in the video at which to end the embedding. The default value is 120.
+        interval_seconds (float, optional):
+            The interval to use when creating embeddings. The default value is 16.
+        trial_id (int, optional):
+            An INT64 value that identifies the hyperparameter tuning trial that
+            you want the function to evaluate. The function uses the optimal
+            trial by default. Only specify this argument if you ran
+            hyperparameter tuning when creating the model.
+
+    Returns:
+        bigframes.pandas.DataFrame:
+            A new DataFrame with the generated embeddings. See the `SQL
+            reference for AI.GENERATE_EMBEDDING
+            <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-generate-embedding#output>`_
+            for details.
+    """
+    if isinstance(data, (pd.DataFrame, pd.Series)):
+        data = bpd.read_pandas(data)
+
+    if isinstance(data, series.Series):
+        data = data.copy()
+        data.name = "content"
+        data_df = data.to_frame()
+    elif isinstance(data, dataframe.DataFrame):
+        data_df = data
+    else:
+        raise ValueError(f"Unsupported data type: {type(data)}")
+
+    # We need to get the SQL for the input data to pass as a subquery to the TVF
+    source_sql = data_df.sql
+
+    struct_fields: Dict[str, bigframes.core.sql.literals.STRUCT_VALUES] = {}
+    if output_dimensionality is not None:
+        struct_fields["OUTPUT_DIMENSIONALITY"] = output_dimensionality
+    if task_type is not None:
+        struct_fields["TASK_TYPE"] = task_type
+    if start_second is not None:
+        struct_fields["START_SECOND"] = start_second
+    if end_second is not None:
+        struct_fields["END_SECOND"] = end_second
+    if interval_seconds is not None:
+        struct_fields["INTERVAL_SECONDS"] = interval_seconds
+    if trial_id is not None:
+        struct_fields["TRIAL_ID"] = trial_id
+
+    # Construct the TVF query
+    query = f"""
+        SELECT *
+        FROM AI.GENERATE_EMBEDDING(
+            MODEL `{model_name}`,
+            ({source_sql}),
+            {bigframes.core.sql.literals.struct_literal(struct_fields)})
+        )
+    """
+
+    return data_df._session.read_gbq(query)
+
+
 @log_adapter.method_logger(custom_base_name="bigquery_ai")
 def if_(
     prompt: PROMPT_TYPE,

bigframes/bigquery/_operations/io.py

@@ -0,0 +1,94 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import Mapping, Optional, Union
+
+import pandas as pd
+
+from bigframes.bigquery._operations.table import _get_table_metadata
+import bigframes.core.logging.log_adapter as log_adapter
+import bigframes.core.sql.io
+import bigframes.session
+
+
+@log_adapter.method_logger(custom_base_name="bigquery_io")
+def load_data(
+    table_name: str,
+    *,
+    write_disposition: str = "INTO",
+    columns: Optional[Mapping[str, str]] = None,
+    partition_by: Optional[list[str]] = None,
+    cluster_by: Optional[list[str]] = None,
+    table_options: Optional[Mapping[str, Union[str, int, float, bool, list]]] = None,
+    from_files_options: Mapping[str, Union[str, int, float, bool, list]],
+    with_partition_columns: Optional[Mapping[str, str]] = None,
+    connection_name: Optional[str] = None,
+    session: Optional[bigframes.session.Session] = None,
+) -> pd.Series:
+    """
+    Loads data into a BigQuery table.
+    See the `BigQuery LOAD DATA DDL syntax
+    <https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/load-statements>`_
+    for additional reference.
+    Args:
+        table_name (str):
+            The name of the table in BigQuery.
+        write_disposition (str, default "INTO"):
+            Whether to replace the table if it already exists ("OVERWRITE") or append to it ("INTO").
+        columns (Mapping[str, str], optional):
+            The table's schema.
+        partition_by (list[str], optional):
+            A list of partition expressions to partition the table by. See https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/load-statements#partition_expression.
+        cluster_by (list[str], optional):
+            A list of columns to cluster the table by.
+        table_options (Mapping[str, Union[str, int, float, bool, list]], optional):
+            The table options.
+        from_files_options (Mapping[str, Union[str, int, float, bool, list]]):
+            The options for loading data from files.
+        with_partition_columns (Mapping[str, str], optional):
+            The table's partition columns.
+        connection_name (str, optional):
+            The connection to use for the table.
+        session (bigframes.session.Session, optional):
+            The session to use. If not provided, the default session is used.
+    Returns:
+        pandas.Series:
+            A Series with object dtype containing the table metadata. Reference
+            the `BigQuery Table REST API reference
+            <https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table>`_
+            for available fields.
+    """
+    import bigframes.pandas as bpd
+
+    sql = bigframes.core.sql.io.load_data_ddl(
+        table_name=table_name,
+        write_disposition=write_disposition,
+        columns=columns,
+        partition_by=partition_by,
+        cluster_by=cluster_by,
+        table_options=table_options,
+        from_files_options=from_files_options,
+        with_partition_columns=with_partition_columns,
+        connection_name=connection_name,
+    )
+
+    if session is None:
+        bpd.read_gbq_query(sql)
+        session = bpd.get_global_session()
+    else:
+        session.read_gbq_query(sql)
+
+    return _get_table_metadata(bqclient=session.bqclient, table_name=table_name)

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)