internal.queries.forecast 

  1# Code generated by sqlc. DO NOT EDIT.
  2# versions:
  3#   sqlc v1.30.0
  4# source: forecast.sql
  5import datetime
  6import decimal
  7import pydantic
  8from typing import AsyncIterator, Optional
  9
 10import sqlalchemy
 11import sqlalchemy.ext.asyncio
 12
 13from internal.queries import models
 14
 15
 16CREATE_FORECAST_INPUT = """-- name: create_forecast_input \\:one
 17INSERT INTO forecast_input (bundle_id, seller_id, category_id, day_of_week, window_start, window_end, is_holiday, temperature, weather_flag, observed_reservations, observed_no_shows)
 18VALUES (:p1, :p2, :p3, :p4, :p5, :p6, :p7, :p8, :p9, :p10, :p11)
 19RETURNING input_id, bundle_id, seller_id, category_id, day_of_week, window_start, window_end, is_holiday, temperature, weather_flag, observed_reservations, observed_no_shows
 20"""
 21
 22
 23class CreateForecastInputParams(pydantic.BaseModel):
 24    bundle_id: int
 25    seller_id: int
 26    category_id: int
 27    day_of_week: models.DayOfWeek
 28    window_start: datetime.datetime
 29    window_end: datetime.datetime
 30    is_holiday: bool
 31    temperature: decimal.Decimal
 32    weather_flag: models.WeatherFlag
 33    observed_reservations: int
 34    observed_no_shows: int
 35
 36
 37GET_FORECAST_INPUTS_BY_SELLER = """-- name: get_forecast_inputs_by_seller \\:many
 38SELECT input_id, bundle_id, seller_id, category_id, day_of_week, window_start, window_end, is_holiday, temperature, weather_flag, observed_reservations, observed_no_shows
 39FROM forecast_input
 40WHERE seller_id = :p1
 41"""
 42
 43
 44GET_FORECAST_OUTPUT_BY_BUNDLE = """-- name: get_forecast_output_by_bundle \\:one
 45SELECT output_id, bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale, generated_at
 46FROM forecast_output
 47WHERE bundle_id = :p1
 48LIMIT 1
 49"""
 50
 51
 52GET_FORECAST_OUTPUTS_BY_SELLER = """-- name: get_forecast_outputs_by_seller \\:many
 53SELECT output_id, bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale, generated_at
 54FROM forecast_output
 55WHERE seller_id = :p1
 56ORDER BY window_start
 57"""
 58
 59
 60UPSERT_FORECAST_OUTPUT = """-- name: upsert_forecast_output \\:one
 61INSERT INTO forecast_output (bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale)
 62VALUES (:p1, :p2, :p3, :p4, :p5, :p6, :p7, :p8)
 63ON CONFLICT (bundle_id) DO UPDATE SET
 64    predicted_sales = EXCLUDED.predicted_sales,
 65    posted_qty = EXCLUDED.posted_qty,
 66    predicted_no_show_prob = EXCLUDED.predicted_no_show_prob,
 67    confidence = EXCLUDED.confidence,
 68    rationale = EXCLUDED.rationale,
 69    generated_at = CURRENT_TIMESTAMP
 70RETURNING output_id, bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale, generated_at
 71"""
 72
 73
 74class UpsertForecastOutputParams(pydantic.BaseModel):
 75    bundle_id: int
 76    seller_id: int
 77    window_start: datetime.datetime
 78    predicted_sales: int
 79    posted_qty: int
 80    predicted_no_show_prob: decimal.Decimal
 81    confidence: decimal.Decimal
 82    rationale: Optional[str]
 83
 84
 85class AsyncQuerier:
 86    def __init__(self, conn: sqlalchemy.ext.asyncio.AsyncConnection):
 87        self._conn = conn
 88
 89    async def create_forecast_input(self, arg: CreateForecastInputParams) -> Optional[models.ForecastInput]:
 90        row = (await self._conn.execute(sqlalchemy.text(CREATE_FORECAST_INPUT), {
 91            "p1": arg.bundle_id,
 92            "p2": arg.seller_id,
 93            "p3": arg.category_id,
 94            "p4": arg.day_of_week,
 95            "p5": arg.window_start,
 96            "p6": arg.window_end,
 97            "p7": arg.is_holiday,
 98            "p8": arg.temperature,
 99            "p9": arg.weather_flag,
100            "p10": arg.observed_reservations,
101            "p11": arg.observed_no_shows,
102        })).first()
103        if row is None:
104            return None
105        return models.ForecastInput(
106            input_id=row[0],
107            bundle_id=row[1],
108            seller_id=row[2],
109            category_id=row[3],
110            day_of_week=row[4],
111            window_start=row[5],
112            window_end=row[6],
113            is_holiday=row[7],
114            temperature=row[8],
115            weather_flag=row[9],
116            observed_reservations=row[10],
117            observed_no_shows=row[11],
118        )
119
120    async def get_forecast_inputs_by_seller(self, *, seller_id: int) -> AsyncIterator[models.ForecastInput]:
121        result = await self._conn.stream(sqlalchemy.text(GET_FORECAST_INPUTS_BY_SELLER), {"p1": seller_id})
122        async for row in result:
123            yield models.ForecastInput(
124                input_id=row[0],
125                bundle_id=row[1],
126                seller_id=row[2],
127                category_id=row[3],
128                day_of_week=row[4],
129                window_start=row[5],
130                window_end=row[6],
131                is_holiday=row[7],
132                temperature=row[8],
133                weather_flag=row[9],
134                observed_reservations=row[10],
135                observed_no_shows=row[11],
136            )
137
138    async def get_forecast_output_by_bundle(self, *, bundle_id: int) -> Optional[models.ForecastOutput]:
139        row = (await self._conn.execute(sqlalchemy.text(GET_FORECAST_OUTPUT_BY_BUNDLE), {"p1": bundle_id})).first()
140        if row is None:
141            return None
142        return models.ForecastOutput(
143            output_id=row[0],
144            bundle_id=row[1],
145            seller_id=row[2],
146            window_start=row[3],
147            predicted_sales=row[4],
148            posted_qty=row[5],
149            predicted_no_show_prob=row[6],
150            confidence=row[7],
151            rationale=row[8],
152            generated_at=row[9],
153        )
154
155    async def get_forecast_outputs_by_seller(self, *, seller_id: int) -> AsyncIterator[models.ForecastOutput]:
156        result = await self._conn.stream(sqlalchemy.text(GET_FORECAST_OUTPUTS_BY_SELLER), {"p1": seller_id})
157        async for row in result:
158            yield models.ForecastOutput(
159                output_id=row[0],
160                bundle_id=row[1],
161                seller_id=row[2],
162                window_start=row[3],
163                predicted_sales=row[4],
164                posted_qty=row[5],
165                predicted_no_show_prob=row[6],
166                confidence=row[7],
167                rationale=row[8],
168                generated_at=row[9],
169            )
170
171    async def upsert_forecast_output(self, arg: UpsertForecastOutputParams) -> Optional[models.ForecastOutput]:
172        row = (await self._conn.execute(sqlalchemy.text(UPSERT_FORECAST_OUTPUT), {
173            "p1": arg.bundle_id,
174            "p2": arg.seller_id,
175            "p3": arg.window_start,
176            "p4": arg.predicted_sales,
177            "p5": arg.posted_qty,
178            "p6": arg.predicted_no_show_prob,
179            "p7": arg.confidence,
180            "p8": arg.rationale,
181        })).first()
182        if row is None:
183            return None
184        return models.ForecastOutput(
185            output_id=row[0],
186            bundle_id=row[1],
187            seller_id=row[2],
188            window_start=row[3],
189            predicted_sales=row[4],
190            posted_qty=row[5],
191            predicted_no_show_prob=row[6],
192            confidence=row[7],
193            rationale=row[8],
194            generated_at=row[9],
195        )
CREATE_FORECAST_INPUT = '-- name: create_forecast_input \\:one\nINSERT INTO forecast_input (bundle_id, seller_id, category_id, day_of_week, window_start, window_end, is_holiday, temperature, weather_flag, observed_reservations, observed_no_shows)\nVALUES (:p1, :p2, :p3, :p4, :p5, :p6, :p7, :p8, :p9, :p10, :p11)\nRETURNING input_id, bundle_id, seller_id, category_id, day_of_week, window_start, window_end, is_holiday, temperature, weather_flag, observed_reservations, observed_no_shows\n'
class CreateForecastInputParams(pydantic.main.BaseModel): 
24class CreateForecastInputParams(pydantic.BaseModel):
25    bundle_id: int
26    seller_id: int
27    category_id: int
28    day_of_week: models.DayOfWeek
29    window_start: datetime.datetime
30    window_end: datetime.datetime
31    is_holiday: bool
32    temperature: decimal.Decimal
33    weather_flag: models.WeatherFlag
34    observed_reservations: int
35    observed_no_shows: int

!!! abstract "Usage Documentation" Models

A base class for creating Pydantic models.

Attributes:
  • __class_vars__: The names of the class variables defined on the model.
  • __private_attributes__: Metadata about the private attributes of the model.
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
  • __pydantic_core_schema__: The core schema of the model.
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
  • __pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.
  • __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
  • __pydantic_private__: Values of private attributes set on the model instance.
bundle_id: int = PydanticUndefined
seller_id: int = PydanticUndefined
category_id: int = PydanticUndefined
day_of_week: internal.queries.models.DayOfWeek = PydanticUndefined
window_start: datetime.datetime = PydanticUndefined
window_end: datetime.datetime = PydanticUndefined
is_holiday: bool = PydanticUndefined
temperature: decimal.Decimal = PydanticUndefined
weather_flag: internal.queries.models.WeatherFlag = PydanticUndefined
observed_reservations: int = PydanticUndefined
observed_no_shows: int = PydanticUndefined
GET_FORECAST_INPUTS_BY_SELLER = '-- name: get_forecast_inputs_by_seller \\:many\nSELECT input_id, bundle_id, seller_id, category_id, day_of_week, window_start, window_end, is_holiday, temperature, weather_flag, observed_reservations, observed_no_shows\nFROM forecast_input\nWHERE seller_id = :p1\n'
GET_FORECAST_OUTPUT_BY_BUNDLE = '-- name: get_forecast_output_by_bundle \\:one\nSELECT output_id, bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale, generated_at\nFROM forecast_output\nWHERE bundle_id = :p1\nLIMIT 1\n'
GET_FORECAST_OUTPUTS_BY_SELLER = '-- name: get_forecast_outputs_by_seller \\:many\nSELECT output_id, bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale, generated_at\nFROM forecast_output\nWHERE seller_id = :p1\nORDER BY window_start\n'
UPSERT_FORECAST_OUTPUT = '-- name: upsert_forecast_output \\:one\nINSERT INTO forecast_output (bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale)\nVALUES (:p1, :p2, :p3, :p4, :p5, :p6, :p7, :p8)\nON CONFLICT (bundle_id) DO UPDATE SET\n predicted_sales = EXCLUDED.predicted_sales,\n posted_qty = EXCLUDED.posted_qty,\n predicted_no_show_prob = EXCLUDED.predicted_no_show_prob,\n confidence = EXCLUDED.confidence,\n rationale = EXCLUDED.rationale,\n generated_at = CURRENT_TIMESTAMP\nRETURNING output_id, bundle_id, seller_id, window_start, predicted_sales, posted_qty, predicted_no_show_prob, confidence, rationale, generated_at\n'
class UpsertForecastOutputParams(pydantic.main.BaseModel): 
75class UpsertForecastOutputParams(pydantic.BaseModel):
76    bundle_id: int
77    seller_id: int
78    window_start: datetime.datetime
79    predicted_sales: int
80    posted_qty: int
81    predicted_no_show_prob: decimal.Decimal
82    confidence: decimal.Decimal
83    rationale: Optional[str]

!!! abstract "Usage Documentation" Models

A base class for creating Pydantic models.

Attributes:
  • __class_vars__: The names of the class variables defined on the model.
  • __private_attributes__: Metadata about the private attributes of the model.
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
  • __pydantic_core_schema__: The core schema of the model.
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
  • __pydantic_fields__: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects.
  • __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
  • __pydantic_private__: Values of private attributes set on the model instance.
bundle_id: int = PydanticUndefined
seller_id: int = PydanticUndefined
window_start: datetime.datetime = PydanticUndefined
predicted_sales: int = PydanticUndefined
posted_qty: int = PydanticUndefined
predicted_no_show_prob: decimal.Decimal = PydanticUndefined
confidence: decimal.Decimal = PydanticUndefined
rationale: str | None = PydanticUndefined
class AsyncQuerier: 
 86class AsyncQuerier:
 87    def __init__(self, conn: sqlalchemy.ext.asyncio.AsyncConnection):
 88        self._conn = conn
 89
 90    async def create_forecast_input(self, arg: CreateForecastInputParams) -> Optional[models.ForecastInput]:
 91        row = (await self._conn.execute(sqlalchemy.text(CREATE_FORECAST_INPUT), {
 92            "p1": arg.bundle_id,
 93            "p2": arg.seller_id,
 94            "p3": arg.category_id,
 95            "p4": arg.day_of_week,
 96            "p5": arg.window_start,
 97            "p6": arg.window_end,
 98            "p7": arg.is_holiday,
 99            "p8": arg.temperature,
100            "p9": arg.weather_flag,
101            "p10": arg.observed_reservations,
102            "p11": arg.observed_no_shows,
103        })).first()
104        if row is None:
105            return None
106        return models.ForecastInput(
107            input_id=row[0],
108            bundle_id=row[1],
109            seller_id=row[2],
110            category_id=row[3],
111            day_of_week=row[4],
112            window_start=row[5],
113            window_end=row[6],
114            is_holiday=row[7],
115            temperature=row[8],
116            weather_flag=row[9],
117            observed_reservations=row[10],
118            observed_no_shows=row[11],
119        )
120
121    async def get_forecast_inputs_by_seller(self, *, seller_id: int) -> AsyncIterator[models.ForecastInput]:
122        result = await self._conn.stream(sqlalchemy.text(GET_FORECAST_INPUTS_BY_SELLER), {"p1": seller_id})
123        async for row in result:
124            yield models.ForecastInput(
125                input_id=row[0],
126                bundle_id=row[1],
127                seller_id=row[2],
128                category_id=row[3],
129                day_of_week=row[4],
130                window_start=row[5],
131                window_end=row[6],
132                is_holiday=row[7],
133                temperature=row[8],
134                weather_flag=row[9],
135                observed_reservations=row[10],
136                observed_no_shows=row[11],
137            )
138
139    async def get_forecast_output_by_bundle(self, *, bundle_id: int) -> Optional[models.ForecastOutput]:
140        row = (await self._conn.execute(sqlalchemy.text(GET_FORECAST_OUTPUT_BY_BUNDLE), {"p1": bundle_id})).first()
141        if row is None:
142            return None
143        return models.ForecastOutput(
144            output_id=row[0],
145            bundle_id=row[1],
146            seller_id=row[2],
147            window_start=row[3],
148            predicted_sales=row[4],
149            posted_qty=row[5],
150            predicted_no_show_prob=row[6],
151            confidence=row[7],
152            rationale=row[8],
153            generated_at=row[9],
154        )
155
156    async def get_forecast_outputs_by_seller(self, *, seller_id: int) -> AsyncIterator[models.ForecastOutput]:
157        result = await self._conn.stream(sqlalchemy.text(GET_FORECAST_OUTPUTS_BY_SELLER), {"p1": seller_id})
158        async for row in result:
159            yield models.ForecastOutput(
160                output_id=row[0],
161                bundle_id=row[1],
162                seller_id=row[2],
163                window_start=row[3],
164                predicted_sales=row[4],
165                posted_qty=row[5],
166                predicted_no_show_prob=row[6],
167                confidence=row[7],
168                rationale=row[8],
169                generated_at=row[9],
170            )
171
172    async def upsert_forecast_output(self, arg: UpsertForecastOutputParams) -> Optional[models.ForecastOutput]:
173        row = (await self._conn.execute(sqlalchemy.text(UPSERT_FORECAST_OUTPUT), {
174            "p1": arg.bundle_id,
175            "p2": arg.seller_id,
176            "p3": arg.window_start,
177            "p4": arg.predicted_sales,
178            "p5": arg.posted_qty,
179            "p6": arg.predicted_no_show_prob,
180            "p7": arg.confidence,
181            "p8": arg.rationale,
182        })).first()
183        if row is None:
184            return None
185        return models.ForecastOutput(
186            output_id=row[0],
187            bundle_id=row[1],
188            seller_id=row[2],
189            window_start=row[3],
190            predicted_sales=row[4],
191            posted_qty=row[5],
192            predicted_no_show_prob=row[6],
193            confidence=row[7],
194            rationale=row[8],
195            generated_at=row[9],
196        )
AsyncQuerier(conn: sqlalchemy.ext.asyncio.engine.AsyncConnection) 
87    def __init__(self, conn: sqlalchemy.ext.asyncio.AsyncConnection):
88        self._conn = conn
async def create_forecast_input( self, arg: CreateForecastInputParams) -> internal.queries.models.ForecastInput | None: 
 90    async def create_forecast_input(self, arg: CreateForecastInputParams) -> Optional[models.ForecastInput]:
 91        row = (await self._conn.execute(sqlalchemy.text(CREATE_FORECAST_INPUT), {
 92            "p1": arg.bundle_id,
 93            "p2": arg.seller_id,
 94            "p3": arg.category_id,
 95            "p4": arg.day_of_week,
 96            "p5": arg.window_start,
 97            "p6": arg.window_end,
 98            "p7": arg.is_holiday,
 99            "p8": arg.temperature,
100            "p9": arg.weather_flag,
101            "p10": arg.observed_reservations,
102            "p11": arg.observed_no_shows,
103        })).first()
104        if row is None:
105            return None
106        return models.ForecastInput(
107            input_id=row[0],
108            bundle_id=row[1],
109            seller_id=row[2],
110            category_id=row[3],
111            day_of_week=row[4],
112            window_start=row[5],
113            window_end=row[6],
114            is_holiday=row[7],
115            temperature=row[8],
116            weather_flag=row[9],
117            observed_reservations=row[10],
118            observed_no_shows=row[11],
119        )
async def get_forecast_inputs_by_seller( self, *, seller_id: int) -> AsyncIterator[internal.queries.models.ForecastInput]: 
121    async def get_forecast_inputs_by_seller(self, *, seller_id: int) -> AsyncIterator[models.ForecastInput]:
122        result = await self._conn.stream(sqlalchemy.text(GET_FORECAST_INPUTS_BY_SELLER), {"p1": seller_id})
123        async for row in result:
124            yield models.ForecastInput(
125                input_id=row[0],
126                bundle_id=row[1],
127                seller_id=row[2],
128                category_id=row[3],
129                day_of_week=row[4],
130                window_start=row[5],
131                window_end=row[6],
132                is_holiday=row[7],
133                temperature=row[8],
134                weather_flag=row[9],
135                observed_reservations=row[10],
136                observed_no_shows=row[11],
137            )
async def get_forecast_output_by_bundle(self, *, bundle_id: int) -> internal.queries.models.ForecastOutput | None: 
139    async def get_forecast_output_by_bundle(self, *, bundle_id: int) -> Optional[models.ForecastOutput]:
140        row = (await self._conn.execute(sqlalchemy.text(GET_FORECAST_OUTPUT_BY_BUNDLE), {"p1": bundle_id})).first()
141        if row is None:
142            return None
143        return models.ForecastOutput(
144            output_id=row[0],
145            bundle_id=row[1],
146            seller_id=row[2],
147            window_start=row[3],
148            predicted_sales=row[4],
149            posted_qty=row[5],
150            predicted_no_show_prob=row[6],
151            confidence=row[7],
152            rationale=row[8],
153            generated_at=row[9],
154        )
async def get_forecast_outputs_by_seller( self, *, seller_id: int) -> AsyncIterator[internal.queries.models.ForecastOutput]: 
156    async def get_forecast_outputs_by_seller(self, *, seller_id: int) -> AsyncIterator[models.ForecastOutput]:
157        result = await self._conn.stream(sqlalchemy.text(GET_FORECAST_OUTPUTS_BY_SELLER), {"p1": seller_id})
158        async for row in result:
159            yield models.ForecastOutput(
160                output_id=row[0],
161                bundle_id=row[1],
162                seller_id=row[2],
163                window_start=row[3],
164                predicted_sales=row[4],
165                posted_qty=row[5],
166                predicted_no_show_prob=row[6],
167                confidence=row[7],
168                rationale=row[8],
169                generated_at=row[9],
170            )
async def upsert_forecast_output( self, arg: UpsertForecastOutputParams) -> internal.queries.models.ForecastOutput | None: 
172    async def upsert_forecast_output(self, arg: UpsertForecastOutputParams) -> Optional[models.ForecastOutput]:
173        row = (await self._conn.execute(sqlalchemy.text(UPSERT_FORECAST_OUTPUT), {
174            "p1": arg.bundle_id,
175            "p2": arg.seller_id,
176            "p3": arg.window_start,
177            "p4": arg.predicted_sales,
178            "p5": arg.posted_qty,
179            "p6": arg.predicted_no_show_prob,
180            "p7": arg.confidence,
181            "p8": arg.rationale,
182        })).first()
183        if row is None:
184            return None
185        return models.ForecastOutput(
186            output_id=row[0],
187            bundle_id=row[1],
188            seller_id=row[2],
189            window_start=row[3],
190            predicted_sales=row[4],
191            posted_qty=row[5],
192            predicted_no_show_prob=row[6],
193            confidence=row[7],
194            rationale=row[8],
195            generated_at=row[9],
196        )