python-bigquery-dataframes/bigframes/core/compile/sqlglot/expressions/ai_ops.py at 12f020b402768be895d4559f96759fc3b2604e2b · googleapis/python-bigquery-dataframes · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
# Copyright 2025 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 dataclasses import asdict

import bigframes_vendored.sqlglot.expressions as sge

from bigframes import operations as ops
from bigframes.core.compile.sqlglot import expression_compiler
from bigframes.core.compile.sqlglot.expressions.typed_expr import TypedExpr

register_nary_op = expression_compiler.expression_compiler.register_nary_op


@register_nary_op(ops.AIGenerate, pass_op=True)
def _(*exprs: TypedExpr, op: ops.AIGenerate) -> sge.Expression:
    args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op)

    return sge.func("AI.GENERATE", *args)


@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)


@register_nary_op(ops.AIGenerateDouble, pass_op=True)
def _(*exprs: TypedExpr, op: ops.AIGenerateDouble) -> sge.Expression:
    args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op)

    return sge.func("AI.GENERATE_DOUBLE", *args)


@register_nary_op(ops.AIIf, pass_op=True)
def _(*exprs: TypedExpr, op: ops.AIIf) -> sge.Expression:
    args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op)

    return sge.func("AI.IF", *args)


@register_nary_op(ops.AIClassify, pass_op=True)
def _(*exprs: TypedExpr, op: ops.AIClassify) -> sge.Expression:
    category_literals = [sge.Literal.string(cat) for cat in op.categories]
    categories_arg = sge.Kwarg(
        this="categories", expression=sge.array(*category_literals)
    )

    args = [
        _construct_prompt(exprs, op.prompt_context, param_name="input"),
        categories_arg,
    ] + _construct_named_args(op)

    return sge.func("AI.CLASSIFY", *args)


@register_nary_op(ops.AIScore, pass_op=True)
def _(*exprs: TypedExpr, op: ops.AIScore) -> sge.Expression:
    args = [_construct_prompt(exprs, op.prompt_context)] + _construct_named_args(op)

    return sge.func("AI.SCORE", *args)


def _construct_prompt(
    exprs: tuple[TypedExpr, ...],
    prompt_context: tuple[str | None, ...],
    param_name: str = "prompt",
) -> sge.Kwarg:
    prompt: list[str | sge.Expression] = []
    column_ref_idx = 0

    for elem in prompt_context:
        if elem is None:
            prompt.append(exprs[column_ref_idx].expr)
            column_ref_idx += 1
        else:
            prompt.append(sge.Literal.string(elem))

    return sge.Kwarg(this=param_name, expression=sge.Tuple(expressions=prompt))


def _construct_named_args(op: ops.NaryOp) -> list[sge.Kwarg]:
    args = []

    op_args = asdict(op)

    connection_id = op_args.get("connection_id", None)
    if connection_id is not None:
        args.append(
            sge.Kwarg(
                this="connection_id", expression=sge.Literal.string(connection_id)
            )
        )

    endpoint = op_args.get("endpoint", None)
    if endpoint is not None:
        args.append(sge.Kwarg(this="endpoint", expression=sge.Literal.string(endpoint)))

    request_type = op_args.get("request_type", None)
    if request_type is not None:
        args.append(
            sge.Kwarg(
                this="request_type", expression=sge.Literal.string(request_type.upper())
            )
        )

    model_params = op_args.get("model_params", None)
    if model_params is not None:
        args.append(
            sge.Kwarg(
                this="model_params",
                # 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(model_params)),
            )
        )

    output_schema = op_args.get("output_schema", None)
    if output_schema is not None:
        args.append(
            sge.Kwarg(
                this="output_schema",
                expression=sge.Literal.string(output_schema),
            )
        )

    return args