Connector for SAP Leonardo Machine Learning Foundation

1 Introduction

SAP Leonardo Machine Learning Foundation is a toolbox of intelligent technologies, services, and industry expertise provided by SAP. It helps companies transform into Intelligent Enterprises.

The Connector for SAP Leonardo Machine Learning Foundation allows you to use a number of the models which are available in the SAP Leonardo Machine Learning Foundation. The connector provides access to pre-trained and customizable models.

The models which are supported by the Connector for SAP Leonardo Machine Learning Foundation are:

• Classify Image (Customizable Image Classification)
• Classify Product from Image (Product Image Classification)
• Detect Face (Face Detection)
• Detect Topic (Topic Detection)
• Extract Image Features (Customizable Image Feature Extraction)
• Recognize Optical Character (OCR)
• Score Similarity (Similarity Scoring)
• Translate (Machine Translation)

2 Prerequisites

To use the Connector for SAP Leonardo Machine Learning Foundation, you need the following prerequisites:

• The SAP Leonardo Machine Learning Foundation connector downloaded into your app from the Mendix Marketplace

• An API key obtained from the SAP API Business Hub

  1. Go to the SAP Leonardo Machine Learning Foundation - Functional Services page here: https://api.sap.com/package/SAPLeonardoMLFunctionalServices.

  2. Select one of the models shown on the page:

     

  3. Click Show API Key.

  4. Click Copy Key and Close.

     

  5. Use the API key in your app, as described in the next section.

3 Setting up the API Key

The Connector for SAP Leonardo Machine Learning Foundation actions are available in your microflows, once you have imported the Connector for SAP Leonardo Machine Learning Foundation module from the Mendix Marketplace.

Every call to SAP Leonardo Machine Learning Foundation needs to be authenticated by a valid API key. Instructions for obtaining this are in the Prerequisites section above.

The API key needs to be pasted as the value of the constant, APIKey which is found in App > Marketplace modules > LeonardoMachineLearning > Constants.

4 Microflow Actions

Each action from the Connector for SAP Leonardo Machine Learning Foundation is described below.

4.1 ClassifyImage

This implements the Inference Service for Customizable Image Classification (classification) service of SAP Leonardo Machine Learning Foundation. It takes an image and detects the dominant objects present in it from a set of 1000 categories such as trees, animals, food, vehicles, people, and more. A description of the service can be found on the SAP site here: Inference Service for Customizable Image Classification.

Input

There are two inputs to this action.

• File

  This is an image object which is a System.Image entity, or an entity which is based on the System.Image entity.

• Options

  This is an object of type LeonardoMachineLearning.ImageClassifierOptions. This contains two attributes, ModelName and ModelVersion (corresponding to modelName and version in the Leonardo API), which identify a specific model to be used in the inference.

  For the default inference model, and when using SAP API Business Hub, this value should be empty.

Limitations

The ClassifyImage action does not currently support a list of image objects, or images in an archive (ZIP) file.

Options are not available on the SAP API Business Hub.

Output

The ClassifyImage action returns a list of objects of entity type LeonardoMachineLearning.ImageClassificationResponse. Note that this list will contain a single object.

4.2 ClassifyProductFromImage

This implements the Product Image Classification API (inference_sync) service of SAP Leonardo Machine Learning Foundation. It takes an image or archive of images and classifies them into a fixed set of categories of products that are common in eCommerce. A description of the service can be found on the SAP site here: Inference Service for Product Image Classification.

Input

The input is an object which is a System.FileDocument entity, or an entity which is based on the System.FileDocument entity. For example, it could be an object based on System.Image which is a single image. It could also be an object containing a ZIP archive file containing images.

Limitations

The ClassifyProductFromImage action does not currently support a list of image objects.

Output

The ClassifyProductFromImage action returns a list of objects of entity type LeonardoMachineLearning.ProductClassificationResponse.

4.3 DetectFace

This implements the Inference Service for Face Detection (face-detection) service of SAP Leonardo Machine Learning Foundation. It takes an image and detects faces in it, if any. It returns information declaring a boundary box per face. A description of the service can be found on the SAP site here: Inference Service for Face Detection.

Input

The input is an image object which is a System.Image entity, or an entity which is based on the System.Image entity.

Limitations

The DetectFace action does not currently support a list of image objects, or images in an archive (ZIP) file.

Output

The DetectFace action returns a list of objects of entity type LeonardoMachineLearning.FaceDetectionResponse. Note that this list will contain a single object.

4.4 DetectTopic

This implements the Inference Service for Topic Detection (topic-detection) service of SAP Leonardo Machine Learning Foundation. It detects and ranks topics in the input documents. A description of the service can be found on the SAP site here: Inference Service for Topic Detection. At least two documents must be submitted.

Input

There are two inputs to this action.

• File

  This is an object which is a System.FileDocument entity, or an entity which is based on the System.FileDocument entity. It must be an archive containing at least two text documents.

• Options

  This is an object of type LeonardoMachineLearning.TopicDetectionOptions. This contains the following attributes:

  Attribute	Description
  NumTopics	Total number of topics to be detected
  NumTopicsPerDoc	Number of most relevant topics to be listed per document
  NumKeywordsPerTopic	Number of keywords to be listed per topic
  NumFeatures	Maximum number of keywords to be extracted from documents (optional)

  All the values for the options can be found in the API documentation on the SAP API Business Hub here: https://api.sap.com/api/topic_detection_api/resource. The parameters in the API have the same names as the attributes in the TopicDetectionOptions entity.

Output

The DetectTopic action returns an object of entity type LeonardoMachineLearning.TopicDetectionResponse.

4.5 ExtractImageFeature

This implements the Inference Service For Customizable Image Feature Extraction (feature-extraction) service of SAP Leonardo Machine Learning Foundation. It takes an image and extracts feature vectors (measurable properties of the image such as edges or objects) for comparison, information retrieval, clustering, or further processing.

The values in the feature vector extracted from the image can be used as input to the ScoreSimilarity action. See the section on ScoreSimilarity, below.

Input

An image object which is a System.Image entity, or an entity which is based on the System.Image entity.

Limitations

The ExtractImageFeature action does not currently support a list of image objects or images in an archive (ZIP) file.

ExtractImageFeature uses only the default model for inferring vectors.

Output

The ExtractImageFeature action returns a list of objects of entity type LeonardoMachineLearning.ImageFeatureExtratorResponse [sic]. Note that this list will contain a single object.

4.6 RecognizeOpticalCharacter (OCR)

This implements the Inference Service for Optical Character Recognition (OCR) service of SAP Leonardo Machine Learning Foundation. It takes one portable document file with the .pdf extension, or one image file in .jpeg, .jpe, or .png format as input. It returns the result in text or xml format.

Input

There are two inputs to this action.

• Options

  This is an object of type LeonardoMachineLearning.ocr_options. This contains the following attributes:

  Attribute	Description	Default
  Lang	The language of the text (currently only a single language can be set)	en (English)
  OutputType	The output type of the result	txt
  PageSegMode	The page segmentation mode	1 (automatic)
  ModelType	The type of the machine learning model for ocr	lstmStandard

  All the values for the options can be found in the API documentation on the SAP API Business Hub here: https://api.sap.com/api/ocr_api/resource?tag=OCR&path=post_ocr&method=post&opId=post_ocr. The parameters in the API have the same names as the attributes in the ocr_options entity.

• File

  This is an object which is a System.FileDocument entity, or an entity which is based on the System.FileDocument entity. For example, it could be an object based on System.Image which is a single image. It could also be an object containing a .pdf portable document format file.

Limitations

The RecognizeOpticalCharacter (OCR) Lang option only accepts a single language for recognition.

The ocr_options entity must be passed - the connector does not use the defaults if it is missing.

Output

The RecognizeOpticalCharacter (OCR) action returns an object of entity type LeonardoMachineLearning.OCRResponse.

4.7 ScoreSimilarity

This implements the Inference Service For Similarity Scoring (similarity-scoring) service of SAP Leonardo Machine Learning Foundation. It compares vectors using a similarity score based on the cosine similarity of the vectors. The vectors retrieved from the ExtractImageFeature microflow action or from the Inference Service for Document Feature Extraction SAP Leonardo Machine Learning Foundation service can be used as inputs to this service.

Input

An object of entity type LeonardoMachineLearning.SimilarityScoringRequest, or an entity which is based on the LeonardoMachineLearning.SimilarityScoringRequest entity.

For each vector in VectorSets, ScoreSimilarity will return the best numSimilarVectors comparisons.

VectorSets must be a string in JSON format which, in the simplest case, contains an array (called 0) of vectors. Each vector contains an identifier and an array of values. See the example, below: example. It is also possible to compare two sets of vectors. More information is in the API documentation here: Inference Service For Similarity Scoring.

Limitations

The vectors to be scored must be provided as a string in JSON format. The action does not accept files as input.

Output

The ScoreSimilarity action returns an object of entity type LeonardoMachineLearning.SimilarityScoringResponse.

Example

You have three vectors, v0, v1, and v2, containing the following values: [1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], and [1.0, 2.1, 3.1, 4.0]. In this case the JSON needs to be constructed as shown in the example, below.

 {
 	"0": [{
 		"id": "v0",
 		"vector": [1.0, 2.0, 3.0, 4.0]
 	}, {
 		"id": "v1",
 		"vector": [5.0, 6.0, 7.0, 8.0]
 	}, {
 		"id": "v2",
 		"vector": [1.0, 2.1, 3.1, 4.0]
 	}]
 }

If numSimilarVectors is set to 1 then the action will return just the best similarity score for each vector, and the id of the vector which is most similar.

4.8 Translate

This implements the Inference Service For Machine Translation (translation) service of SAP Leonardo Machine Learning Foundation. It translates one or more strings from a source language to one or more target languages. The Inference Service for Machine Translation is powered by SAP Translation Hub and is trained on SAP-related content to provide accurate domain adaptation and terminology coverage. A description of the service can be found on the SAP site here: Inference Service for Machine Translation.

Input

An object of entity type LeonardoMachineLearning.Translation. This must be associated with a number of other entities, as described below.

For example, to translate the two texts Hello and Goodbye from English to both Spanish and German, you need to create objects as shown below:

Limitations

There is no validation that there is a translation model for any requested language pair. The languages supported are documented here: Inference Service for Machine Translation

Passing the key value is not supported.

Output

The Translate action returns an object of entity type LeonardoMachineLearning.TranslationResponse.

5 Binding SAP Leonardo Machine Learning Foundation Services to Your App

You can use the SAP Leonardo Machine Learning (ML) Foundation Connector to access the services on SAP API Business Hub. Here, you can explore the services in an “as-simple-as-possible deployment”. However, to use the full abilities of the services, you need to bind a ML Foundation Service to your app running in SAP Business technology Platform (SAP BTP). More information on the distinction is available in the SAP blog post Production-ready integration of SAP Leonardo Machine Learning Foundation services with an SAP S/4HANA side-by-side extension built with the SAP S/4HANA Cloud SDK.

The following sections describe how to bind a ML Foundation Service to your app.

5.1 Adding ML Foundation Trial Entitlement

The ML Foundation Trial is not added automatically to your quota. Therefore, you need to add it manually.

1. Go to the SAP BTP cockpit for your account.

2. Choose the Entitlements and choose the ML Foundation Trial entitlement.

3. Click the Subaccount where you want to use the ML Foundation Trial services (this will usually be the trial subaccount).

   

4. You will see your subaccount overview, and can see that ML Foundation Trial has been added.

5.2 Binding an Instance of a ML Foundation Service to Your App

1. Go to the Space where your app is deployed on SAP BTP.

2. Select ml-foundation-trial-beta from the Service Marketplace

   

3. Go to Instances and click New Instance to add an instance of the service.

4. Choose the desired Service Plan and click Next.

5. Add parameters, if required, and click Next. In most cases additional parameters are not required.

6. Choose your app from the drop-down list of Applications. In most cases you will not need to add any parameters.

7. Click Next.

8. Type an Instance Name for the instance and click Finish.

   

6 Read More

• SAP Leonardo Machine Learning Foundation - Functional Services