sap-ai-core

What this skill does

# 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

## 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)
14. [SAP AI Launchpad](#sap-ai-launchpad)
15. [API Reference](#api-reference)
16. [Common Patterns](#common-patterns)
17. [Troubleshooting](#troubleshooting)
18. [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 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:
1. 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_execu