n8n-nodes-sap-hana-vector-store

This is an n8n community node for integrating SAP HANA Vector Store with n8n workflows using LangChain.

n8n is a fair-code licensed workflow automation platform.

Features

• Vector Store Operations: Store, retrieve, and manage vector embeddings in SAP HANA
• LangChain Integration: Seamlessly works with LangChain for AI/ML workflows
• Document Management: Load, insert, update, and retrieve documents with embeddings
• Similarity Search: Find similar documents based on vector similarity
• Metadata Support: Store and query documents with custom metadata

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-hana-vector-store

3. Restart your n8n instance

Option 2: Development Setup

1. Clone this repository:

   git clone https://github.com/pondev1/n8n-nodes-sap-hana-vector-store.git
   cd n8n-nodes-sap-hana-vector-store

2. Install dependencies:

   npm install

3. Build the node:

   npm run build

4. Link the package locally:

   npm pack
   cd ~/.n8n
   npm install "C:\path\to\n8n-nodes-sap-ai-core\n8n-nodes-sap-hana-vector-store-1.0.0.tgz"

5. Restart your n8n instance

Operations

Available Operations

1. Load: Load documents from the vector store
2. Insert Documents: Add new documents with embeddings to the store
3. Get Documents: Retrieve specific documents by ID or metadata
4. Update Documents: Update existing documents in the store
5. Retrieve (As Tool): Use the vector store as a tool for AI agents

Credentials

You'll need to configure SAP HANA credentials with the following information:

• Host: Database hostname (from service key "host" field)
• Port: Database port (from service key "port" field, typically 443 for HANA Cloud)
• Username: Database username (from service key "user" field)
  • Use _DT suffix user for design-time operations (creating/modifying database objects) - Recommended for this node
  • Use _RT suffix user for runtime operations (reading data, SELECT queries)
• Password: Database password (from service key "password" field)
• Database: Database name (from service key "database" field, optional)
• Schema: Schema name (from service key "schema" field or your specific schema)

Additional Options

• SSL/TLS: Enable encryption for secure connections
• Validate Certificate: Certificate validation for production environments
• Connection Timeout: Customize connection timeout
• Auto Commit: Transaction auto-commit setting

Prerequisites

• SAP HANA Cloud instance or on-premise SAP HANA database
• Database user with appropriate permissions
• Vector tables configured in your HANA instance

SAP HANA Vector Table Structure

The default vector table structure expected:

• VEC_TEXT (NCLOB) - Document content
• VEC_META (NVARCHAR) - JSON metadata
• VEC_VECTOR (REAL_VECTOR) - Vector embeddings

Usage Example

Basic Document Storage

1. Connect an Embeddings node (e.g., OpenAI Embeddings)
2. Configure your SAP HANA credentials
3. Set the table name for your vector store
4. Choose operation (Insert/Load/Update)
5. Connect to your workflow

Similarity Search

Use the "Load" operation with:

• Mode: Load
• Prompt: Your search query
• Top K: Number of similar documents to retrieve

Compatibility

• Requires n8n version 1.82.0 or later
• Node.js 18.0.0 or higher
• Compatible with SAP HANA Cloud and on-premise installations

Resources

• n8n community nodes documentation
• SAP HANA Cloud Documentation
• LangChain Documentation

Sample Workflows

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

1. Document Ingestion Workflow

File: workflows/Document Ingestion.json

Comprehensive workflow for ingesting documents into SAP HANA Vector Store with embeddings generation and storage.

Features:

• Document processing and chunking
• Embedding generation using SAP AI Core
• Vector storage in HANA database
• Metadata extraction and storage
• Batch processing capabilities

2. RAG (Retrieval-Augmented Generation) Workflow

File: workflows/RAG.json

Advanced RAG workflow that combines SAP AI Core embeddings, HANA Vector Store, and chat models for intelligent document retrieval and generation.

Features:

• Chat trigger for interactive conversations
• SAP AI Core embeddings integration
• Vector store retrieval capabilities
• AI agent with RAG functionality
• Context-aware responses

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 HANA credentials
5. Update the tableName with your vector table name
6. Configure embedding function settings
7. Activate and test the workflow

Support

For issues and feature requests, please use the GitHub issues page.

License

MIT

Author

Pon Murugesh Devendren
Email: [email protected]
GitHub: @pondev1