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)

1. Navigate to your n8n installation directory
2. Install the package:

   npm install n8n-nodes-sap-ai-core

3. Restart your n8n instance

Option 2: Development Setup

1. Clone this repository:

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

2. Install dependencies:

   npm install

3. Build the node:

   npm run build

4. 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"

5. 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:

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

2. Get Authentication Credentials

1. In SAP BTP Cockpit, navigate to Services > Instances and Subscriptions
2. Find your AI Core service instance
3. Create a service key
4. 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

1. In n8n, go to Credentials and click Add Credential

2. Search for and select SAP AI Core API

3. 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")

4. Save the credentials

2. Node Configuration

1. Add the SAP AI Core LLM node to your workflow

2. 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

1. Download the desired workflow JSON file
2. In n8n, go to Workflows > Import from File
3. Select the downloaded JSON file
4. Configure your SAP AI Core credentials
5. Update the deploymentId with your actual deployment ID
6. 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

1. Authentication Errors

   • Verify your credentials are correct
   • Check that your OAuth2 URL is accessible
   • Ensure your service key fields are properly extracted

2. 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

3. Model Errors

   • Verify the model name matches your deployment configuration
   • Check that your prompt format is compatible with the deployed model

4. 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:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: npm test
5. 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:

1. Check the troubleshooting section
2. Review SAP AI Core documentation
3. Open an issue on GitHub
4. Contact the maintainers

Changelog

v1.0.0

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