gen_ai_hub.proxy.native.sap.models module

class gen_ai_hub.proxy.native.sap.models.TargetColumn(*, name: str, prediction_placeholder: str = '[PREDICT]', task_type: Literal['classification', 'regression'] | None = None)

Bases: BaseModel

Represents a target column in data.

Parameters:

• name (str) – Name of the target column.

• prediction_placeholder (str) – Placeholder string denoting where predictions will be inserted. Defaults to "[PREDICT]".

• task_type (Optional[Literal["classification", "regression"]]) – Task type of the target column. One of "classification" or "regression". Defaults to None.

name: str

prediction_placeholder: str

task_type: Literal['classification', 'regression'] | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.PredictionConfig(*, target_columns: list[TargetColumn])

Bases: BaseModel

The configuration object specifying which columns to predict

Parameters:

target_columns (list[TargetColumn]) – List of target columns to predict.

target_columns: list[TargetColumn]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.DataType(*, dtype: Literal['string', 'numeric', 'date'])

Bases: BaseModel

Schema definition for a column.

Parameters:

dtype (Literal["string", "numeric", "date"]) – The data type of the column.

dtype: Literal['string', 'numeric', 'date']

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.RPTRequest(*, prediction_config: PredictionConfig, index_column: str | None = None, rows: list[dict] | None = None, columns: dict[str, list] | None = None, data_schema: dict[str, DataType] | None = None, parse_data_types: bool = True)

Bases: BaseModel

Request model for predictions.

Provide exactly one of rows or columns.

Parameters:

• prediction_config (PredictionConfig) – Configuration describing what to predict.

• index_column (Optional[str]) – Name of a column used to identify the row. This column is not used as an input feature and may be returned in the response objects.

• rows (Optional[list[dict]]) – Array of objects representing table rows (both context and query rows).

• columns (Optional[dict[str, list]]) – Mapping from column name to array of column values.

• data_schema (Optional[dict[str, DataType]]) – Schema definition for all columns, e.g. {"columnA": {"dtype": "string"}, "columnB": {"dtype": "numeric"}}.

• parse_data_types (bool) – Relevant when data_schema is not provided. Whether to parse data types (e.g., interpret strings as numbers or dates). Defaults to True.

prediction_config: PredictionConfig

index_column: str | None

rows: list[dict] | None

columns: dict[str, list] | None

data_schema: dict[str, DataType] | None

parse_data_types: bool

validate_rows_xor_columns()

Validate that exactly one of rows or columns is provided.

Raises:

ValueError – If neither or both of rows and columns are provided.

Returns:

The validated request instance.

Return type:

RPTRequest

model_dump(**kwargs)

Serialize the model to a dictionary.

Ensures the non-provided alternative (rows or columns) is omitted from the dump and that None values are excluded.

Parameters:

kwargs (Any) – Keyword arguments forwarded to pydantic.BaseModel.model_dump.

Returns:

Serialized dictionary representation of the model.

Return type:

dict

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.ResponseMetadata(*, num_rows: int, num_columns: int, num_predictions: int, num_query_rows: int)

Bases: BaseModel

Response metadata.

Parameters:

• num_rows (int) – Total number of input rows.

• num_columns (int) – Total number of input columns.

• num_predictions (int) – Number of table cells containing the specified placeholder values, summed over all target columns.

• num_query_rows (int) – Number of query rows for which a prediction was made.

num_rows: int

num_columns: int

num_predictions: int

num_query_rows: int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.ResponseStatus(*, code: int, message: str)

Bases: BaseModel

Status information for a prediction request.

Parameters:

• code (int) – Numeric status code.

• message (str) – Status message.

code: int

message: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.PredictionItem(*, prediction: str | float, confidence: float | None = None)

Bases: BaseModel

Single prediction result.

Parameters:

• prediction (Union[str, float]) – The predicted value.

• confidence (Optional[float]) – Confidence score for classification tasks. Defaults to None.

prediction: str | float

confidence: float | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.Prediction(root: RootModelRootType = PydanticUndefined)

Bases: RootModel[dict[str, Union[list[PredictionItem], Any]]]

Container for prediction results keyed by target column name.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

root: RootModelRootType

class gen_ai_hub.proxy.native.sap.models.RPTResponse(*, id: str, status: ResponseStatus, predictions: list[Prediction], metadata: ResponseMetadata)

Bases: BaseModel

Response model for an RPT request.

Parameters:

• id (str) – Unique identifier for the response.

• status (ResponseStatus) – Status describing the outcome of the request.

• predictions (list[Prediction]) – Prediction data returned by the service.

• metadata (ResponseMetadata) – Metadata about the request/response.

id: str

status: ResponseStatus

predictions: list[Prediction]

metadata: ResponseMetadata

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class gen_ai_hub.proxy.native.sap.models.ErrorResponseDetails(*, loc: list, msg: str, type: str)

Bases: BaseModel

Details of an error response.

Parameters:

• loc (list) – Location in the request where the error occurred.

• msg (str) – Human-readable error message.

• type (str) – Error category/type.

loc: list

msg: str

type: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

exception gen_ai_hub.proxy.native.sap.models.RPTException(status: ResponseStatus, detail: list[ErrorResponseDetails] | None = None)

Bases: Exception

Exception representing an error response from the RPT service.

Parameters:

• status (ResponseStatus) – Status indicating the error category/type.

• detail (Optional[list[ErrorResponseDetails]]) – Optional list of additional error details.

__init__(status: ResponseStatus, detail: list[ErrorResponseDetails] | None = None)