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

| 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

# 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

# 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

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

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

安装