SAP AI Core LLM

AI & Machine Learning AI Assistants

v1.0.1

Last updated Aug 9, 2025

n8n nodes for SAP AI Core LLM and embeddings integration

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View on NPM GitHub Repository

Included Nodes

SAP AI Core LLM

SAP AI Core Chat Model

SAP AI Core Embeddings

Description

SAP AI Core LLM Node for n8n

A custom n8n community node that enables integration with SAP AI Core's LLM models for text generation and chat completion tasks.

Features

Text Generation: Generate text using deployed LLM models in SAP AI Core
Chat Completion: Interactive chat with conversation context
OAuth2 Authentication: Support for OAuth2 authentication with SAP AI Core
Flexible Configuration: Customizable parameters like temperature, max tokens, top-p, and stop sequences
Error Handling: Robust error handling with optional continue-on-fail behavior

Prerequisites

n8n instance (self-hosted or cloud)
SAP BTP account with AI Core service enabled
Deployed LLM model in SAP AI Core
Valid SAP AI Core OAuth2 credentials

Installation

⚠️ Important: This node is designed for self-hosted n8n installations only. It cannot be used with n8n Cloud due to dependency requirements.

Option 1: npm Installation (Recommended)

Navigate to your n8n installation directory
Install the package:
```
npm install n8n-nodes-sap-ai-core
```
Restart your n8n instance

Option 2: Development Setup

Clone this repository:

git clone https://github.com/pondev1/n8n-nodes-sap-ai-core.git
cd n8n-nodes-sap-ai-core

Install dependencies:
```
npm install
```
Build the node:
```
npm run build
```

Package and install locally:

npm pack
npm install "C:\path\to\n8n-nodes-sap-ai-core\n8n-nodes-sap-ai-core-1.0.0.tgz"

Set custom extensions path (Windows):

$env:N8N_CUSTOM_EXTENSIONS = "C:\Users\yourusername\.n8n\custom\node_modules"
npx n8n start

SAP AI Core Setup

1. Deploy an LLM Model

First, ensure you have an LLM model deployed in SAP AI Core:

Access SAP AI Launchpad
Navigate to ML Operations > Deployments
Create a new deployment with your desired LLM model
Note the Deployment ID – you'll need this for the n8n node configuration

2. Get Authentication Credentials

In SAP BTP Cockpit, navigate to Services > Instances and Subscriptions
Find your AI Core service instance
Create a service key
Extract the following fields from the service key JSON:
- Client ID (from service key "clientid")
- Client Secret (from service key "clientsecret")
- OAuth URL (from service key "url" field)
- Base URL (from service key "serviceurls.AI_API_URL")

Node Configuration

1. Credentials Setup

In n8n, go to Credentials and click Add Credential
Search for and select SAP AI Core API
Fill in the required fields:
- Client ID: Your OAuth2 client ID (from service key "clientid")
- Client Secret: Your OAuth2 client secret (from service key "clientsecret")
- OAuth URL: Your OAuth2 token endpoint (from service key "url")
- Base URL: Your SAP AI Core API endpoint (from service key "serviceurls.AI_API_URL")
Save the credentials

2. Node Configuration

Add the SAP AI Core LLM node to your workflow
Configure the following parameters:

Required Parameters:
- Credentials: Select your SAP AI Core API credentials
- Operation: Choose between "Generate Text" or "Chat Completion"
- Model: Your model name (e.g., "gpt-35-turbo")
- Resource Group: SAP AI Core resource group (usually "default")
- Deployment ID: The deployment ID from SAP AI Launchpad
Operation-Specific Parameters:

For Generate Text:
- Prompt: The text prompt to send to the model
For Chat Completion:
- Messages: Array of conversation messages with roles (system, user, assistant)
Optional Parameters:
- Max Tokens: Maximum number of tokens to generate (default: 100)
- Temperature: Controls randomness (0-2, default: 0.7)
- Top P: Controls diversity via nucleus sampling (0-1, default: 1)
- Stop Sequences: Comma-separated list of stop sequences

Usage Examples

Basic Text Generation

{
  "operation": "generateText",
  "model": "gpt-35-turbo",
  "resourceGroup": "default",
  "deploymentId": "dabcd1234567890", // your-deployment-id
  "prompt": "Write a brief summary of artificial intelligence."
}

Chat Completion

{
  "operation": "chatCompletion",
  "model": "gpt-35-turbo",
  "resourceGroup": "default",
  "deploymentId": "dabcd1234567890", // your-deployment-id
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What is machine learning?"
    }
  ]
}

Advanced Configuration with Custom Parameters

{
  "operation": "generateText",
  "model": "gpt-35-turbo",
  "prompt": "Generate a creative story about robots.",
  "additionalOptions": {
    "max_tokens": 500,
    "temperature": 0.8,
    "top_p": 0.9,
    "stop": "The End, END, ."
  }
}

Sample Workflows

Ready-to-use n8n workflow examples are available in the workflows/ directory:

1. AI Core Chat Model Workflow

File: workflows/AI Core Chat Model.json

Demonstrates basic chat functionality using SAP AI Core chat models. This workflow shows how to configure and use the chat model for interactive conversations.

Features:

Simple chat model configuration
Direct chat interaction
Response handling and formatting

2. AI Core LLM Agent Workflow

File: workflows/AI Core LLM Agent.json

Advanced workflow showing SAP AI Core integration with LLM agents for complex AI-powered automation tasks.

Features:

LLM agent configuration
Advanced AI workflows
Tool integration capabilities

How to Use Sample Workflows

Download the desired workflow JSON file
In n8n, go to Workflows > Import from File
Select the downloaded JSON file
Configure your SAP AI Core credentials
Update the deploymentId with your actual deployment ID
Activate and test the workflow

Response Format

The node returns a JSON object containing:

{
  "id": "response-id",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "gpt-35-turbo",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Generated text response..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 50,
    "total_tokens": 60
  },
  "operation": "chatCompletion",
  "deploymentId": "dabcd1234567890", // your-deployment-id
  "resourceGroup": "default"
}

Troubleshooting

Common Issues

Authentication Errors
- Verify your credentials are correct
- Check that your OAuth2 URL is accessible
- Ensure your service key fields are properly extracted
Deployment Not Found
- Verify the deployment ID is correct
- Check that the deployment is in "Running" status in SAP AI Launchpad
- Ensure the resource group matches your deployment
Model Errors
- Verify the model name matches your deployment configuration
- Check that your prompt format is compatible with the deployed model
Rate Limiting
- SAP AI Core may have rate limits – implement appropriate delays between requests
- Monitor your usage in SAP AI Launchpad

Error Codes

401: Authentication failed – check credentials
403: Insufficient permissions – verify resource group access
404: Deployment not found – check deployment ID
429: Rate limit exceeded – reduce request frequency
500: Internal server error – check SAP AI Core service status

Development

To contribute to this project:

Fork the repository
Create a feature branch
Make your changes
Run tests: npm test
Submit a pull request

Build Commands

npm run build: Build the project
npm run dev: Build in watch mode
npm run lint: Run linting
npm run format: Format code

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

For issues and questions:

Check the troubleshooting section
Review SAP AI Core documentation
Open an issue on GitHub
Contact the maintainers

Changelog

v1.0.0

Initial release
Support for text generation and chat completion
OAuth2 authentication
Basic error handling and configuration options