sap-ai-core

# 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

## When to Use This Skill

Use this skill when provisioning SAP AI Core, using SAP AI Launchpad, configuring Generative AI Hub orchestration, choosing model providers, building RAG or grounding flows, managing prompt templates, deploying training/inference workloads, or wiring AI capabilities into SAP applications.

## Table of Contents

1. [Overview](#overview)
2. [Quick Start](#quick-start)
3. [Service Plans](#service-plans)
4. [Model Providers](#model-providers)
5. [Orchestration](#orchestration)
6. [Content Filtering](#content-filtering)
7. [Data Masking](#data-masking)
8. [Grounding (RAG)](#grounding-rag)
9. [Tool Calling](#tool-calling)
10. [Structured Output](#structured-output)
11. [Embeddings](#embeddings)
12. [ML Training](#ml-training)
13. [Deployments](#deployments)
14. [Bundled Resources](#bundled-resources)
15. [SAP AI Launchpad](#sap-ai-launchpad)
16. [Prompt Registry](#prompt-registry)
17. [API Reference](#api-reference)
18. [Common Patterns](#common-patterns)
19. [Troubleshooting](#troubleshooting)
20. [References](#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

1. **SAP AI Core**: Execution engine for AI workflows and model serving
2. **SAP AI Launchpad**: Management UI for AI runtimes and GenAI Hub
3. **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 model providers through a tenant-specific catalog. Treat exact model names and versions as examples until verified in the target tenant with `GET /v2/lm/scenarios/foundation-models/models` or SAP AI Launchpad Model Library.
- **Azure OpenAI**: GPT-family chat, vision, reasoning, realtime, and embedding models where entitled
- **SAP Open Source**: Llama/Falcon/Mistral-family open source models where enabled
- **Google Vertex AI**: Gemini-family chat, vision, code, and embedding models where entitled
- **AWS Bedrock**: Anthropic Claude and Amazon model families where entitled
- **Mistral AI**: Mistral Large/Small/Codestral-family models where enabled
- **IBM**: Granite models
- **Perplexity**: Sonar-family web-grounded models where enabled

For detailed provider configurations and model lists, see `references/model-providers.md`.

## Orchestration

The orchestration service provides unified access to multiple models