Project import generated by Copybara.

packages/bigframes/.gitignore

@@ -0,0 +1,74 @@
+*.py[cod]
+*.sw[op]
+
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+.eggs
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+lib
+lib64
+__pycache__
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.nox
+.cache
+.pytest_cache
+
+
+# Mac
+.DS_Store
+
+# JetBrains
+.idea
+
+# VS Code
+.vscode
+
+# emacs
+*~
+
+# Built documentation
+docs/_build
+**/docs/_build
+docs.metadata
+
+# Virtual environment
+env/
+venv/
+
+# Test logs
+coverage.xml
+*sponge_log.xml
+
+# System test environment variables.
+**/system_tests/local_test_setup
+**/system_tests/data
+
+# Make sure a generated file isn't accidentally committed.
+pylintrc
+pylintrc.test
+
+# Ruff cache
+.ruff_cache
+
+# Bazel (created in packages/gapic-generator)
+bazel-bin
+bazel-gapic-generator
+bazel-out
+bazel-testlogs

packages/bigframes/bigframes/_config/compute_options.py

@@ -168,6 +168,26 @@ class ComputeOptions:
         int | None: Number of rows, if set.
     """
 
+    semantic_ops_confirmation_threshold: Optional[int] = 0
+    """
+    Deprecated.
+
+    .. deprecated:: 1.42.0
+        Semantic operators are deprecated. Please use the functions in
+        :mod:`bigframes.bigquery.ai` instead.
+
+    """
+
+    semantic_ops_threshold_autofail = False
+    """
+    Deprecated.
+
+    .. deprecated:: 1.42.0
+        Semantic operators are deprecated. Please use the functions in
+        :mod:`bigframes.bigquery.ai` instead.
+
+    """
+
     def assign_extra_query_labels(self, **kwargs: Any) -> None:
         """
         Assigns additional custom labels for query configuration. The method updates the

packages/bigframes/bigframes/_config/experiment_options.py

@@ -25,9 +25,52 @@ class ExperimentOptions:
     """
 
     def __init__(self):
+        self._semantic_operators: bool = False
+        self._ai_operators: bool = False
         self._sql_compiler: Literal["legacy", "stable", "experimental"] = "stable"
         self._enable_python_transpiler: bool = False
 
+    @property
+    def semantic_operators(self) -> bool:
+        """Deprecated.
+
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.experiments.semantic_operators = True  # doctest: +SKIP
+        """
+        return self._semantic_operators
+
+    @semantic_operators.setter
+    def semantic_operators(self, value: bool):
+        if value is True:
+            msg = bfe.format_message(
+                "Semantic operators are deprecated, and will be removed in the future"
+            )
+            warnings.warn(msg, category=FutureWarning)
+        self._semantic_operators = value
+
+    @property
+    def ai_operators(self) -> bool:
+        """If True, allow using the AI operators.
+
+        **Examples:**
+
+            >>> import bigframes.pandas as bpd
+            >>> bpd.options.experiments.ai_operators = True  # doctest: +SKIP
+        """
+        return self._ai_operators
+
+    @ai_operators.setter
+    def ai_operators(self, value: bool):
+        if value is True:
+            msg = bfe.format_message(
+                "AI operators are still under experiments, and are subject "
+                "to change in the future."
+            )
+            warnings.warn(msg, category=bfe.PreviewWarning)
+        self._ai_operators = value
+
     @property
     def sql_compiler(self) -> Literal["legacy", "stable", "experimental"]:
         """Set to 'experimental' to try out the latest in compilation experiments..

packages/bigframes/bigframes/dataframe.py

@@ -80,7 +80,9 @@
 import bigframes.functions
 import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
+import bigframes.operations.ai
 import bigframes.operations.plotting as plotting
+import bigframes.operations.semantics
 import bigframes.operations.structs
 import bigframes.series
 import bigframes.session._io.bigquery
@@ -5284,3 +5286,20 @@ def _throw_if_null_index(self, opname: str):
             raise bigframes.exceptions.NullIndexError(
                 f"DataFrame cannot perform {opname} as it has no index. Set an index using set_index."
             )
+
+    @property
+    def semantics(self):
+        msg = bfe.format_message(
+            "The 'semantics' property will be removed. Please use 'bigframes.bigquery.ai' instead."
+        )
+        warnings.warn(msg, category=FutureWarning)
+        return bigframes.operations.semantics.Semantics(self)
+
+    @property
+    def ai(self):
+        """Returns the accessor for AI operators."""
+        msg = bfe.format_message(
+            "The 'ai' property will be removed. Please use 'bigframes.bigquery.ai' instead."
+        )
+        warnings.warn(msg, category=FutureWarning)
+        return bigframes.operations.ai.AIAccessor(self)

packages/bigframes/bigframes/extensions/core/series_accessor.py

@@ -31,7 +31,7 @@
 
 from bigframes import series, session
 from bigframes.core import col, sentinels
-from bigframes.extensions.core import abstract_series_accessor, series_mixins
+from bigframes.extensions.core import abstract_series_accessor, series_tvf_mixins
 
 T = TypeVar("T")
 S = TypeVar("S")
@@ -1112,7 +1112,7 @@ def unix_date(
         return self._to_series(cast(series.Series, result))
 
 
-class AiSeriesAccessor(series_mixins.AIMixin[T, S]):
+class AiSeriesAccessor(series_tvf_mixins.AITVFMixin[T, S]):
     """Series accessor for BigQuery ai functions."""
 
 

packages/bigframes/bigframes/extensions/core/series_mixins.py → packages/bigframes/bigframes/extensions/core/series_tvf_mixins.py

@@ -14,21 +14,19 @@
 
 from __future__ import annotations
 
-from typing import Any, List, Literal, Mapping, TypeVar
+from typing import List, Mapping, TypeVar
 
 import pandas as pd
 
-from bigframes import series
-from bigframes import session as bf_session
-from bigframes.bigquery import ai
+from bigframes import session
 from bigframes.extensions.core import abstract_series_accessor
 from bigframes.ml import base as ml_base
 
 T = TypeVar("T")
 S = TypeVar("S")
 
 
-class AIMixin(abstract_series_accessor.AbstractBigQuerySeriesAccessor[T, S]):
+class AITVFMixin(abstract_series_accessor.AbstractBigQuerySeriesAccessor[T, S]):
     def generate_embedding(
         self,
         model: ml_base.BaseEstimator | str | pd.Series,
@@ -39,17 +37,18 @@ def generate_embedding(
         end_second: float | None = None,
         interval_seconds: float | None = None,
         trial_id: int | None = None,
-        session: bf_session.Session | None = None,
+        session: session.Session | None = None,
     ) -> T:
         """
         Creates embeddings that describe an entity — for example, a piece of text or an image.
 
         This is an accessor for :func:`bigframes.bigquery.ai.generate_embedding`. See that
         function's documentation for detailed parameter descriptions and examples.
         """
+        import bigframes.bigquery.ai
 
         bf_series = self._bf_from_series(session)
-        result = ai.generate_embedding(
+        result = bigframes.bigquery.ai.generate_embedding(
             model,
             bf_series,
             output_dimensionality=output_dimensionality,
@@ -72,16 +71,18 @@ def generate_text(
         stop_sequences: List[str] | None = None,
         ground_with_google_search: bool | None = None,
         request_type: str | None = None,
-        session: bf_session.Session | None = None,
+        session: session.Session | None = None,
     ) -> T:
         """
         Generates text using a BigQuery ML model.
 
         This is an accessor for :func:`bigframes.bigquery.ai.generate_text`. See that
         function's documentation for detailed parameter descriptions and examples.
         """
+        import bigframes.bigquery.ai
+
         bf_series = self._bf_from_series(session)
-        result = ai.generate_text(
+        result = bigframes.bigquery.ai.generate_text(
             model,
             bf_series,
             temperature=temperature,
@@ -104,16 +105,18 @@ def generate_table(
         max_output_tokens: int | None = None,
         stop_sequences: List[str] | None = None,
         request_type: str | None = None,
-        session: bf_session.Session | None = None,
+        session: session.Session | None = None,
     ) -> T:
         """
         Generates a table using a BigQuery ML model.
 
         This is an accessor for :func:`bigframes.bigquery.ai.generate_table`. See that
         function's documentation for detailed parameter descriptions and examples.
         """
+        import bigframes.bigquery.ai
+
         bf_series = self._bf_from_series(session)
-        result = ai.generate_table(
+        result = bigframes.bigquery.ai.generate_table(
             model,
             bf_series,
             output_schema=output_schema,
@@ -124,73 +127,3 @@ def generate_table(
             request_type=request_type,
         )
         return self._to_dataframe(result)
-
-    def embed(
-        self,
-        *,
-        endpoint: str | None = None,
-        model: str | None = None,
-        task_type: (
-            Literal[
-                "retrieval_query",
-                "retrieval_document",
-                "semantic_similarity",
-                "classification",
-                "clustering",
-                "question_answering",
-                "fact_verification",
-                "code_retrieval_query",
-            ]
-            | None
-        ) = None,
-        title: str | None = None,
-        model_params: Mapping[Any, Any] | None = None,
-        connection_id: str | None = None,
-        session: bf_session.Session | None = None,
-    ) -> S:
-        """
-        Creates embeddings from text or image data in BigQuery.
-
-        This is an accessor for :func:`bigframes.bigquery.ai.embed`. See that
-        function's documentation for detailed parameter descriptions and examples.
-        """
-
-        bf_series = self._bf_from_series(session)
-        result = ai.embed(
-            bf_series,
-            endpoint=endpoint,
-            model=model,
-            task_type=task_type,
-            title=title,
-            model_params=model_params,
-            connection_id=connection_id,
-        )
-        return self._to_series(result)
-
-    def similarity(
-        self,
-        other: str | series.Series | pd.Series,
-        *,
-        endpoint: str | None = None,
-        model: str | None = None,
-        model_params: Mapping[Any, Any] | None = None,
-        connection_id: str | None = None,
-        session: bf_session.Session | None = None,
-    ) -> S:
-        """
-        Returns a FLOAT64 value that represents the cosine similarity between the two inputs.
-
-        This is an accessor for :func:`bigframes.bigquery.ai.similarity`. See that
-        function's documentation for detailed parameter descriptions and examples.
-        """
-
-        bf_series = self._bf_from_series(session)
-        result = ai.similarity(
-            bf_series,
-            other,
-            endpoint=endpoint,
-            model=model,
-            model_params=model_params,
-            connection_id=connection_id,
-        )
-        return self._to_series(result)