AI API

The @sap-ai-sdk/ai-api package provides tools to manage scenarios and workflows in SAP AI Core.

• Streamline data preprocessing and model training pipelines.
• Execute batch inference jobs.
• Deploy inference endpoints for trained models.
• Register custom Docker registries, sync AI content from Git repositories, and register object storage for training data and model artifacts.

We maintain a list of currently available and tested AI Core APIs.

Installation

$ npm install @sap-ai-sdk/ai-api

important

This package contains generated code. Updates to this package may include breaking changes.

To ensure compatibility and manage updates effectively, we strongly recommend using the tilde (~) version range in your project instead of the caret (^). This approach will allow patch-level updates while preventing potentially breaking minor version changes.

Usage

The examples below demonstrate the usage of the most commonly used APIs in SAP AI Core. In addition to the examples below, you can find more sample code here.

Create an Artifact

async function createArtifact() {
  const requestBody: ArtifactPostData = {
    name: 'training-test-dataset',
    kind: 'dataset',
    url: 'https://ai.example.com',
    scenarioId: 'foundation-models'
  };

  const responseData: ArtifactCreationResponse =
    await ArtifactApi.artifactCreate(requestBody, {
      'AI-Resource-Group': 'default'
    }).execute();
  return responseData;
}

Create a Configuration

async function createConfiguration() {
  const requestBody: ConfigurationBaseData = {
    name: 'gpt-4o',
    executableId: 'azure-openai',
    scenarioId: 'foundation-models',
    parameterBindings: [
      {
        key: 'modelName',
        value: 'gpt-4o'
      },
      {
        key: 'modelVersion',
        value: 'latest'
      }
    ],
    inputArtifactBindings: []
  };

  const responseData: ConfigurationCreationResponse =
    await ConfigurationApi.configurationCreate(requestBody, {
      'AI-Resource-Group': 'default'
    }).execute();
  return responseData;
}

Create a Deployment

async function createDeployment() {
  const requestBody: DeploymentCreationRequest = {
    configurationId: '0a1b2c3d-4e5f6g7h'
  };
  const responseData: DeploymentCreationResponse =
    await DeploymentApi.deploymentCreate(requestBody, {
      'AI-Resource-Group': 'default'
    }).execute();
  return responseData;
}

Delete a Deployment

Only deployments with targetStatus: STOPPED can be deleted. Thus, a modification request must be sent before deletion can occur.

async function modifyDeployment() {
  let deploymentId: string = '0a1b2c3d4e5f';

  const deployment: DeploymentResponseWithDetails =
    await DeploymentApi.deploymentGet(
      deploymentId,
      {},
      { 'AI-Resource-Group': 'default' }
    ).execute();

  if (deployment.targetStatus === 'RUNNING') {
    // Only RUNNING deployments can be STOPPED.
    const requestBody: DeploymentModificationRequest = {
      targetStatus: 'STOPPED'
    };

    await DeploymentApi.deploymentModify(deploymentId, requestBody, {
      'AI-Resource-Group': 'default'
    }).execute();
  }
  // Wait a few seconds for the deployment to stop
  return DeploymentApi.deploymentDelete(deploymentId, {
    'AI-Resource-Group': 'default'
  }).execute();
}

Custom Destination

When calling the execute() method, it is possible to provide a custom destination for your SAP AI Core instance. For example, when querying deployments targeting a destination with the name my-destination, the following code can be used:

const queryParams = status ? { status } : {};
return DeploymentApi.deploymentQuery(queryParams, {
  'AI-Resource-Group': resourceGroup
}).execute({
  destinationName: 'my-destination'
});

By default, the fetched destination is cached. To disable caching, set the useCache parameter to false together with the destinationName parameter.

For more information about configuring a destination, refer to the Using a Destination section.