SAP AI Core & AI Launchpad Skill

Related Skills

sap-btp-cloud-platform: Use for platform context, BTP account setup, and service integration
sap-cap-capire: Use for building AI-powered applications with CAP or integrating AI services
sap-cloud-sdk-ai: Use for SDK integration, AI service calls, and Java/JavaScript implementations
sap-btp-best-practices: Use for production deployment patterns and AI governance guidelines

Overview
Quick Start
Service Plans
Model Providers
Orchestration
Content Filtering
Data Masking
Grounding (RAG)
Tool Calling
Structured Output
Embeddings
ML Training
Deployments
Bundled Resources
SAP AI Launchpad
API Reference
Common Patterns
Troubleshooting
References

Overview

SAP AI Core is a service on SAP Business Technology Platform (BTP) that manages AI asset execution in a standardized, scalable, hyperscaler-agnostic manner. SAP AI Launchpad provides the management UI for AI runtimes including the Generative AI Hub.

Core Capabilities

Capability	Description
Generative AI Hub	Access to LLMs from multiple providers with unified API
Orchestration	Modular pipeline for templating, filtering, grounding, masking
ML Training	Argo Workflows-based batch pipelines for model training
Inference Serving	Deploy models as HTTPS endpoints for predictions
Grounding/RAG	Vector database integration for contextual AI

Three Components

SAP AI Core: Execution engine for AI workflows and model serving
SAP AI Launchpad: Management UI for AI runtimes and GenAI Hub
AI API: Standardized lifecycle management across runtimes

Quick Start

Prerequisites

SAP BTP enterprise account
SAP AI Core service instance (Extended plan for GenAI)
Service key with credentials

1. Get Authentication Token

bash

# Set environment variables from service key
export AI_API_URL="<your-ai-api-url>"
export AUTH_URL="<your-auth-url>"
export CLIENT_ID="<your-client-id>"
export CLIENT_SECRET="<your-client-secret>"

# Get OAuth token
AUTH_TOKEN=$(curl -s -X POST "$AUTH_URL/oauth/token" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials&client_id=$CLIENT_ID&client_secret=$CLIENT_SECRET" \
  | jq -r '.access_token')

2. Create Orchestration Deployment

bash

# Check for existing orchestration deployment
curl -X GET "$AI_API_URL/v2/lm/deployments" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "AI-Resource-Group: default" \
  -H "Content-Type: application/json"

# Create orchestration deployment if needed
curl -X POST "$AI_API_URL/v2/lm/deployments" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "AI-Resource-Group: default" \
  -H "Content-Type: application/json" \
  -d '{
    "configurationId": "<orchestration-config-id>"
  }'

3. Use Harmonized API for Model Inference

bash

ORCHESTRATION_URL="<deployment-url>"

curl -X POST "$ORCHESTRATION_URL/v2/completion" \
  -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "AI-Resource-Group: default" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "module_configurations": {
        "llm_module_config": {
          "model_name": "gpt-4o",
          "model_version": "latest",
          "model_params": {
            "max_tokens": 1000,
            "temperature": 0.7
          }
        },
        "templating_module_config": {
          "template": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "{{?user_query}}"}
          ]
        }
      }
    },
    "input_params": {
      "user_query": "What is SAP AI Core?"
    }
  }'

Service Plans

Plan	Cost	GenAI Hub	Support	Resource Groups
Free	Free	No	Community only	Default only
Standard	Per resource + baseline	No	Full SLA	Multiple
Extended	Per resource + tokens	Yes	Full SLA	Multiple

Key Restrictions:

Free and Standard mutually exclusive in same subaccount
Free → Standard upgrade possible; downgrade not supported
Max 50 resource groups per tenant

Model Providers

SAP AI Core provides access to models from six providers:

Azure OpenAI: GPT-4o, GPT-4 Turbo, GPT-3.5
SAP Open Source: Llama, Falcon, Mistral variants
Google Vertex AI: Gemini Pro, PaLM 2
AWS Bedrock: Claude, Amazon Titan
Mistral AI: Mistral Large, Medium, Small
IBM: Granite models

For detailed provider configurations and model lists, see

references/model-providers.md

Orchestration

The orchestration service provides unified access to multiple models through a modular pipeline with 8 execution stages:

Grounding → 2. Templating (mandatory) → 3. Input Translation → 4. Data Masking → 5. Input Filtering → 6. Model Configuration (mandatory) → 7. Output Filtering → 8. Output Translation

For complete orchestration module configurations, examples, and advanced patterns, see

references/orchestration-modules.md

Content Filtering

Azure Content Safety: Filters content across 4 categories (Hate, Violence, Sexual, SelfHarm) with severity levels 0-6. Azure OpenAI blocks severity 4+ automatically. Additional features include PromptShield and Protected Material detection.

Llama Guard 3: Covers 14 categories including violent crimes, privacy violations, and code interpreter abuse.

Data Masking

Two PII protection methods:

Anonymization:
```
MASKED_ENTITY
```
(non-reversible)
Pseudonymization:
```
MASKED_ENTITY_ID
```
(reversible)

Supported entities (25 total): Personal data, IDs, financial information, SAP-specific IDs, and sensitive attributes. For complete entity list and implementation details, see

references/orchestration-modules.md

Grounding (RAG)

Integrate external data from SharePoint, S3, SFTP, SAP Build Work Zone, and DMS. Supports PDF, HTML, DOCX, images, and more. Limit: 2,000 documents per pipeline with daily refresh. For detailed setup, see

references/grounding-rag.md

Tool Calling

Enable LLMs to execute functions through a 5-step workflow: define tools → receive tool_calls → execute functions → return results → LLM incorporates responses. Templates available in

templates/tool-definition.json

Structured Output

Force model responses to match JSON schemas using strict validation. Useful for structured data extraction and API responses.

Embeddings

Generate semantic embeddings for RAG and similarity search via

/v2/embeddings

endpoint. Supports document, query, and text input types.

ML Training

Uses Argo Workflows for training pipelines. Key requirements: create

default

object store secret, define workflow template, create configuration with parameters, and execute training. For complete workflow patterns, see

references/ml-operations.md

Deployments

Deploy models via two-step process: create configuration (with model binding), then create deployment with TTL. Statuses: Pending → Running → Stopping → Stopped/Dead. Templates in

templates/deployment-config.json

SAP AI Launchpad

Web-based UI with 4 key applications:

Workspaces: Manage connections and resource groups
ML Operations: Train, deploy, monitor models
Generative AI Hub: Prompt experimentation and orchestration
Functions Explorer: Explore available AI functions

Required roles include

genai_manager

genai_experimenter

prompt_manager

orchestration_executor

, and

mloperations_editor

. For complete guide, see

references/ai-launchpad-guide.md

API Reference

Core Endpoints

Key endpoints:

/v2/lm/scenarios

/v2/lm/configurations

/v2/lm/deployments

/v2/lm/executions

/lm/meta

. For complete API reference with examples, see

references/api-reference.md

Common Patterns

Simple Chat: Basic model invocation with templating module RAG with Grounding: Combine vector search with LLM for context-aware responses Secure Enterprise Chat: Filtering + masking + grounding for PII protection Templates available in

templates/orchestration-workflow.json

. "masking_providers": [{

Troubleshooting

Common Issues:

401 Unauthorized: Refresh OAuth token
403 Forbidden: Check IAM roles, request quota increase
404 Not Found: Verify AI-Resource-Group header
Deployment DEAD: Check deployment logs
Training failed: Create
```
default
```
object store secret

Request quota increases via support ticket (Component:

CA-ML-AIC

Bundled Resources

Reference Documentation

```
references/orchestration-modules.md
```
- All orchestration modules in detail
```
references/generative-ai-hub.md
```
- Complete GenAI hub documentation
```
references/model-providers.md
```
- Model providers and configurations
```
references/api-reference.md
```
- Complete API endpoint reference
```
references/grounding-rag.md
```
- Grounding and RAG implementation
```
references/ml-operations.md
```
- ML operations and training
```
references/advanced-features.md
```
- Chat, applications, security, auditing
```
references/ai-launchpad-guide.md
```
- Complete SAP AI Launchpad UI guide

Templates

```
templates/deployment-config.json
```
- Deployment configuration template
```
templates/orchestration-workflow.json
```
- Orchestration workflow template
```
templates/tool-definition.json
```
- Tool calling definition template

Official Sources

SAP AI Core Guide: https://help.sap.com/docs/sap-ai-core
SAP AI Launchpad Guide: https://help.sap.com/docs/sap-ai-launchpad
SAP Note 3437766: Model token rates and limits

sap-ai-core

NPX Install

Tags

SKILL.md Content

SAP AI Core & AI Launchpad Skill

Related Skills

Table of Contents

Overview

Core Capabilities

Three Components

Quick Start

Prerequisites

1. Get Authentication Token

2. Create Orchestration Deployment

3. Use Harmonized API for Model Inference

Service Plans

Model Providers

Orchestration

Content Filtering

Data Masking

Grounding (RAG)

Tool Calling

Structured Output

Embeddings

ML Training

Deployments

SAP AI Launchpad

API Reference

Core Endpoints

Common Patterns

Troubleshooting

Bundled Resources

Reference Documentation

Templates

Official Sources