SAP RPT-1 (Beta)

important

This package is experimental and subject to change. Do not use in production.

The @sap-ai-sdk/rpt package provides a client to interact with SAP-RPT-1, SAP’s Relational Pretrained Transformer model.

Installation

To add the package to your project, run the following command in your terminal:

npm install @sap-ai-sdk/rpt

Usage

SAP-RPT-1 is a relational pretrained transformer model that delivers accurate predictive insights from structured business data. The following shows the different ways to use the RPT client to interact with the model.

Client Initialization

Initialize a client using RptClient constructor with optional parameters. By default, the 'sap-rpt-1-small' model is chosen as the prediction model.

import { RptClient } from '@sap-ai-sdk/rpt';

const client = new RptClient();

To use the 'sap-rpt-1-large' model, specify the model name in the constructor:

const client = new RptClient('sap-rpt-1-large');

Follow the instructions in the Foundation Models documentation to specify a model version, use a model with an explicit deployment ID, select a specific resource group, or target a custom AI Core instance.

Predict With Schema

Use the predictWithSchema() method to make predictions with a schema explicitly defined. Pass the data schema, and the prediction data including the prediction configuration and input data to the method. Prefer providing the data schema if it is known instead of relying on the auto-inference solely based on the input data.

const prediction = await client.predictWithSchema(
  // Data schema
  [
    { name: 'PRODUCT', dtype: 'string' },
    { name: 'PRICE', dtype: 'numeric' },
    { name: 'PRODUCTION_DATE', dtype: 'date' },
    { name: 'SALESGROUP', dtype: 'string' }
  ],
  // Prediction data
  {
    prediction_config: {
      target_columns: [
        {
          name: 'SALESGROUP',
          prediction_placeholder: '[PREDICT]',
          task_type: 'classification'
        }
      ]
    },
    index_column: '__row_idx__',
    rows: [
      {
        PRODUCT: 'Laptop',
        PRICE: 999.99,
        PRODUCTION_DATE: '2025-01-15',
        __row_idx__: '35',
        SALESGROUP: '[PREDICT]'
      },
      {
        PRODUCT: 'Desktop Computer',
        PRICE: 750.5,
        PRODUCTION_DATE: '2024-12-02',
        __row_idx__: '42',
        SALESGROUP: 'Enterprise Solutions'
      }
      // Additional rows...
    ]
  }
);

The data schema is a list of column definitions, each containing a name and a dtype properties. The prediction data contains the following properties:

• prediction_config: A mandatory configuration to specify the columns to predict, including their names, placeholder values, and task types. The task type can be either classification or regression. If not specified, the model infers the task type based on the column data type.
• index_column: The name of the column that serves as the unique identifier for each row. The index column is returned in the prediction results to allow mapping predictions back to the input data.
• rows or columns: The input data to make predictions on. Use rows for row-based format or columns for column-based format.
• parse_data_types: Optional boolean to control data type parsing. When set to true (default), numeric columns are parsed to float or integer, and dates (YYYY-MM-DD) are parsed automatically.

important

When you store the schema and prediction data in variables, declare the schema with as const and use the PredictionData<typeof schema> type. This enables type inference and accurate IDE autocompletion. If you pass the data directly to predictWithSchema(), you do not need additional annotations.

Example:

const schema = [...] as const;
const data: PredictionData<typeof schema> = {...};

client.predictWithSchema(schema, data);

Predict Without Schema

tip

If the data schema is known, it is recommended to use the predictWithSchema() method instead.

To make predictions without providing a schema, use the predictWithoutSchema() method. Pass only the prediction data as a parameter. The data schema will be inferred from the input data.

const prediction = await client.predictWithoutSchema(
  // Prediction data
  {
    prediction_config: {
      target_columns: [
        {
          name: 'SALESGROUP',
          prediction_placeholder: '[PREDICT]',
          task_type: 'classification'
        }
      ]
    },
    index_column: '__row_idx__',
    rows: [
      {
        PRODUCT: 'Laptop',
        PRICE: 999.99,
        PRODUCTION_DATE: '2025-01-15',
        __row_idx__: '35',
        SALESGROUP: '[PREDICT]'
      },
      {
        PRODUCT: 'Desktop Computer',
        PRICE: 750.5,
        PRODUCTION_DATE: '2024-12-02',
        __row_idx__: '42',
        SALESGROUP: 'Enterprise Solutions'
      }
      // Additional rows...
    ]
  }
);