Merge branch 'main' into main_chelsealin_astype

Author: chelsea-lin

bigframes/bigquery/_operations/ai.py

@@ -113,6 +113,81 @@ def generate_bool(
     return series_list[0]._apply_nary_op(operator, series_list[1:])
 
 
+@log_adapter.method_logger(custom_base_name="bigquery_ai")
+def generate_int(
+    prompt: PROMPT_TYPE,
+    *,
+    connection_id: str | None = None,
+    endpoint: str | None = None,
+    request_type: Literal["dedicated", "shared", "unspecified"] = "unspecified",
+    model_params: Mapping[Any, Any] | None = None,
+) -> series.Series:
+    """
+    Returns the AI analysis based on the prompt, which can be any combination of text and unstructured data.
+
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.bigquery as bbq
+        >>> bpd.options.display.progress_bar = None
+        >>> animal = bpd.Series(["Kangaroo", "Rabbit", "Spider"])
+        >>> bbq.ai.generate_int(("How many legs does a ", animal, " have?"))
+        0    {'result': 2, 'full_response': '{"candidates":...
+        1    {'result': 4, 'full_response': '{"candidates":...
+        2    {'result': 8, 'full_response': '{"candidates":...
+        dtype: struct<result: int64, full_response: extension<dbjson<JSONArrowType>>, status: string>[pyarrow]
+
+        >>> bbq.ai.generate_int(("How many legs does a ", animal, " have?")).struct.field("result")
+        0    2
+        1    4
+        2    8
+        Name: result, dtype: Int64
+
+    Args:
+        prompt (Series | List[str|Series] | Tuple[str|Series, ...]):
+            A mixture of Series and string literals that specifies the prompt to send to the model. The Series can be BigFrames Series
+            or pandas Series.
+        connection_id (str, optional):
+            Specifies the connection to use to communicate with the model. For example, `myproject.us.myconnection`.
+            If not provided, the connection from the current session will be used.
+        endpoint (str, optional):
+            Specifies the Vertex AI endpoint to use for the model. For example `"gemini-2.5-flash"`. You can specify any
+            generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and
+            uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML selects a recent stable
+            version of Gemini to use.
+        request_type (Literal["dedicated", "shared", "unspecified"]):
+            Specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses.
+            * "dedicated": function only uses Provisioned Throughput quota. The function returns the error Provisioned throughput is not
+            purchased or is not active if Provisioned Throughput quota isn't available.
+            * "shared": the function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota.
+            * "unspecified": If you haven't purchased Provisioned Throughput quota, the function uses DSQ quota.
+            If you have purchased Provisioned Throughput quota, the function uses the Provisioned Throughput quota first.
+            If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota.
+        model_params (Mapping[Any, Any]):
+            Provides additional parameters to the model. The MODEL_PARAMS value must conform to the generateContent request body format.
+
+    Returns:
+        bigframes.series.Series: A new struct Series with the result data. The struct contains these fields:
+        * "result": an integer (INT64) value containing the model's response to the prompt. The result is None if the request fails or is filtered by responsible AI.
+        * "full_response": a JSON value containing the response from the projects.locations.endpoints.generateContent call to the model.
+        The generated text is in the text element.
+        * "status": a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful.
+    """
+
+    prompt_context, series_list = _separate_context_and_series(prompt)
+    assert len(series_list) > 0
+
+    operator = ai_ops.AIGenerateInt(
+        prompt_context=tuple(prompt_context),
+        connection_id=_resolve_connection_id(series_list[0], connection_id),
+        endpoint=endpoint,
+        request_type=request_type,
+        model_params=json.dumps(model_params) if model_params else None,
+    )
+
+    return series_list[0]._apply_nary_op(operator, series_list[1:])
+
+
 def _separate_context_and_series(
     prompt: PROMPT_TYPE,
 ) -> Tuple[List[str | None], List[series.Series]]:

bigframes/core/compile/ibis_compiler/aggregate_compiler.py

@@ -175,15 +175,11 @@ def _(
 
 
 @compile_unary_agg.register
-@numeric_op
 def _(
     op: agg_ops.MedianOp,
     column: ibis_types.NumericColumn,
     window=None,
 ) -> ibis_types.NumericValue:
-    # TODO(swast): Allow switching between exact and approximate median.
-    # For now, the best we can do is an approximate median when we're doing
-    # an aggregation, as PERCENTILE_CONT is only an analytic function.
     return cast(ibis_types.NumericValue, column.approx_median())
 
 

bigframes/core/compile/ibis_compiler/scalar_op_registry.py

@@ -1975,23 +1975,43 @@ def ai_generate_bool(
     *values: ibis_types.Value, op: ops.AIGenerateBool
 ) -> ibis_types.StructValue:
 
+    return ai_ops.AIGenerateBool(
+        _construct_prompt(values, op.prompt_context),  # type: ignore
+        op.connection_id,  # type: ignore
+        op.endpoint,  # type: ignore
+        op.request_type.upper(),  # type: ignore
+        op.model_params,  # type: ignore
+    ).to_expr()
+
+
+@scalar_op_compiler.register_nary_op(ops.AIGenerateInt, pass_op=True)
+def ai_generate_int(
+    *values: ibis_types.Value, op: ops.AIGenerateBool
+) -> ibis_types.StructValue:
+
+    return ai_ops.AIGenerateInt(
+        _construct_prompt(values, op.prompt_context),  # type: ignore
+        op.connection_id,  # type: ignore
+        op.endpoint,  # type: ignore
+        op.request_type.upper(),  # type: ignore
+        op.model_params,  # type: ignore
+    ).to_expr()
+
+
+def _construct_prompt(
+    col_refs: tuple[ibis_types.Value], prompt_context: tuple[str | None]
+) -> ibis_types.StructValue:
     prompt: dict[str, ibis_types.Value | str] = {}
     column_ref_idx = 0
 
-    for idx, elem in enumerate(op.prompt_context):
+    for idx, elem in enumerate(prompt_context):
         if elem is None:
-            prompt[f"_field_{idx + 1}"] = values[column_ref_idx]
+            prompt[f"_field_{idx + 1}"] = col_refs[column_ref_idx]
             column_ref_idx += 1
         else:
             prompt[f"_field_{idx + 1}"] = elem
 
-    return ai_ops.AIGenerateBool(
-        ibis.struct(prompt),  # type: ignore
-        op.connection_id,  # type: ignore
-        op.endpoint,  # type: ignore
-        op.request_type.upper(),  # type: ignore
-        op.model_params,  # type: ignore
-    ).to_expr()
+    return ibis.struct(prompt)
 
 
 @scalar_op_compiler.register_nary_op(ops.RowKey, pass_op=True)

bigframes/core/compile/sqlglot/aggregations/unary_compiler.py

@@ -56,6 +56,18 @@ def _(
     return apply_window_if_present(sge.func("MAX", column.expr), window)
 
 
+@UNARY_OP_REGISTRATION.register(agg_ops.MedianOp)
+def _(
+    op: agg_ops.MedianOp,
+    column: typed_expr.TypedExpr,
+    window: typing.Optional[window_spec.WindowSpec] = None,
+) -> sge.Expression:
+    approx_quantiles = sge.func("APPROX_QUANTILES", column.expr, sge.convert(2))
+    return sge.Bracket(
+        this=approx_quantiles, expressions=[sge.func("OFFSET", sge.convert(1))]
+    )
+
+
 @UNARY_OP_REGISTRATION.register(agg_ops.MinOp)
 def _(
     op: agg_ops.MinOp,

bigframes/core/compile/sqlglot/expressions/ai_ops.py

@@ -14,6 +14,9 @@
 
 from __future__ import annotations
 
+from dataclasses import asdict
+import typing
+
 import sqlglot.expressions as sge
 
 from bigframes import operations as ops
@@ -25,41 +28,61 @@
 
 @register_nary_op(ops.AIGenerateBool, pass_op=True)
 def _(*exprs: TypedExpr, op: ops.AIGenerateBool) -> sge.Expression:
+    args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op)
+
+    return sge.func("AI.GENERATE_BOOL", *args)
+
+
+@register_nary_op(ops.AIGenerateInt, pass_op=True)
+def _(*exprs: TypedExpr, op: ops.AIGenerateInt) -> sge.Expression:
+    args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op)
+
+    return sge.func("AI.GENERATE_INT", *args)
+
 
+def _construct_prompt(
+    exprs: tuple[TypedExpr, ...], prompt_context: tuple[str | None, ...]
+) -> sge.Kwarg:
     prompt: list[str | sge.Expression] = []
     column_ref_idx = 0
 
-    for elem in op.prompt_context:
+    for elem in prompt_context:
         if elem is None:
             prompt.append(exprs[column_ref_idx].expr)
         else:
             prompt.append(sge.Literal.string(elem))
 
-    args = [sge.Kwarg(this="prompt", expression=sge.Tuple(expressions=prompt))]
+    return sge.Kwarg(this="prompt", expression=sge.Tuple(expressions=prompt))
 
+
+def _construct_named_args(op: ops.NaryOp) -> list[sge.Kwarg]:
+    args = []
+
+    op_args = asdict(op)
+
+    connection_id = typing.cast(str, op_args["connection_id"])
     args.append(
-        sge.Kwarg(this="connection_id", expression=sge.Literal.string(op.connection_id))
+        sge.Kwarg(this="connection_id", expression=sge.Literal.string(connection_id))
     )
 
-    if op.endpoint is not None:
-        args.append(
-            sge.Kwarg(this="endpoint", expression=sge.Literal.string(op.endpoint))
-        )
+    endpoit = typing.cast(str, op_args.get("endpoint", None))
+    if endpoit is not None:
+        args.append(sge.Kwarg(this="endpoint", expression=sge.Literal.string(endpoit)))
 
+    request_type = typing.cast(str, op_args["request_type"]).upper()
     args.append(
-        sge.Kwarg(
-            this="request_type", expression=sge.Literal.string(op.request_type.upper())
-        )
+        sge.Kwarg(this="request_type", expression=sge.Literal.string(request_type))
     )
 
-    if op.model_params is not None:
+    model_params = typing.cast(str, op_args.get("model_params", None))
+    if model_params is not None:
         args.append(
             sge.Kwarg(
                 this="model_params",
-                # sge.JSON requires a newer SQLGlot version than 23.6.3.
+                # sge.JSON requires the SQLGlot version to be at least 25.18.0
                 # PARSE_JSON won't work as the function requires a JSON literal.
-                expression=sge.JSON(this=sge.Literal.string(op.model_params)),
+                expression=sge.JSON(this=sge.Literal.string(model_params)),
             )
         )
 
-    return sge.func("AI.GENERATE_BOOL", *args)
+    return args

bigframes/core/indexes/base.py

@@ -740,6 +740,9 @@ def to_numpy(self, dtype=None, *, allow_large_results=None, **kwargs) -> np.ndar
 
     __array__ = to_numpy
 
+    def to_list(self, *, allow_large_results: Optional[bool] = None) -> list:
+        return self.to_pandas(allow_large_results=allow_large_results).to_list()
+
     def __len__(self):
         return self.shape[0]
 

bigframes/operations/__init__.py

@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from bigframes.operations.ai_ops import AIGenerateBool
+from bigframes.operations.ai_ops import AIGenerateBool, AIGenerateInt
 from bigframes.operations.array_ops import (
     ArrayIndexOp,
     ArrayReduceOp,
@@ -413,6 +413,7 @@
     "GeoStDistanceOp",
     # AI ops
     "AIGenerateBool",
+    "AIGenerateInt",
     # Numpy ops mapping
     "NUMPY_TO_BINOP",
     "NUMPY_TO_OP",

bigframes/operations/ai_ops.py

@@ -28,7 +28,6 @@
 class AIGenerateBool(base_ops.NaryOp):
     name: ClassVar[str] = "ai_generate_bool"
 
-    # None are the placeholders for column references.
     prompt_context: Tuple[str | None, ...]
     connection_id: str
     endpoint: str | None
@@ -45,3 +44,25 @@ def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionT
                 )
             )
         )
+
+
+@dataclasses.dataclass(frozen=True)
+class AIGenerateInt(base_ops.NaryOp):
+    name: ClassVar[str] = "ai_generate_int"
+
+    prompt_context: Tuple[str | None, ...]
+    connection_id: str
+    endpoint: str | None
+    request_type: Literal["dedicated", "shared", "unspecified"]
+    model_params: str | None
+
+    def output_type(self, *input_types: dtypes.ExpressionType) -> dtypes.ExpressionType:
+        return pd.ArrowDtype(
+            pa.struct(
+                (
+                    pa.field("result", pa.int64()),
+                    pa.field("full_response", dtypes.JSON_ARROW_TYPE),
+                    pa.field("status", pa.string()),
+                )
+            )
+        )