🚀 Original Embedding Node for n8n

A powerful custom n8n node that allows you to generate embeddings using your own embedding server. Perfect for RAG (Retrieval-Augmented Generation) workflows, semantic search, and AI applications.

🌟 Features

• 🔧 Custom Embedding Server: Connect to any OpenAI-compatible embedding API
• 🔐 Flexible Authentication: Support for API Key, Basic Auth, or both combined
• 🧠 RAG Compatible: Seamlessly integrates with n8n's AI ecosystem (Vector Stores, Retrievers, etc.)
• ⚡ Batch Processing: Optional batch processing for improved performance
• 🔄 Standard Compliance: Full compatibility with n8n's ai_embedding connection type
• 🛡️ Secure: Encrypted credential storage with multiple authentication methods

📦 Installation

Option 1: npm Installation

npm install @original-land/n8n-nodes-embedding

Option 2: Manual Installation

1. Download the latest release
2. Extract to your n8n custom nodes directory
3. Restart n8n

Option 3: From Source

git clone https://github.com/noku-team/n8n-original-embedding.git
cd n8n-original-embedding
pnpm install
pnpm build

⚙️ Configuration

1. Setup Credentials

Create new Embedding Server API credentials with:

Authentication Methods:

• API Key Only: For servers requiring only API key authentication
• Basic Auth Only: For servers using username/password
• API Key + Basic Auth: For servers requiring both (recommended)

Fields:

• Authentication Method: Choose your server's auth requirements
• API Key: Your embedding server API key (if applicable)
• Username: Basic auth username (if applicable)
• Password: Basic auth password (if applicable)

2. Node Configuration

• Endpoint URL: Your embedding server endpoint

  https://your-server.com/api/v1/openai-compatible/embeddings

• Model: The embedding model to use (e.g., query, document)
• Input Text: Text to embed (supports n8n expressions like ={{ $json.text }})
• Batch Processing: Enable for processing multiple texts efficiently

🔄 Usage Examples

Basic Text Embedding

Input: {
  "text": "This is a sample text to embed"
}

Output: {
  "text": "This is a sample text to embed",
  "embedding": [0.1, 0.2, -0.3, ...],
  "model": "your-model",
  "usage": { "prompt_tokens": 7, "total_tokens": 7 }
}

RAG Workflow Integration

graph LR
    A[Document Loader] --> B[Original Embedding Node]
    B --> C[Vector Store]
    
    D[User Query] --> E[Original Embedding Node]
    E --> F[AI Retriever]
    C --> F
    
    F --> G[RAG Chain]
    H[LLM Node] --> G
    G --> I[Response]

Example RAG Setup:

1. Document Processing:

   Document → Original Embedding Node → Vector Store (Pinecone/Weaviate)

2. Query Processing:

   User Query → Original Embedding Node → AI Retriever → RAG Chain → LLM → Response

Advanced Configuration Example

// Node parameters
{
  "endpoint": "https://api.yourserver.com/v1/embeddings",
  "model": "text-embedding-3-large", 
  "inputText": "={{ $json.content }}",
  "batchProcessing": true
}

// Credential configuration  
{
  "authMethod": "both",
  "apiKey": "your-secret-key",
  "username": "your-username", 
  "password": "your-password"
}

🔗 Compatibility

Input Connections

• ✅ Main nodes: Any standard n8n node outputting data

Output Connections

• ✅ AI Vector Stores: Pinecone, Weaviate, Chroma, etc.
• ✅ AI Retrievers: For similarity search
• ✅ AI Chains: RAG chains, conversation chains
• ✅ Other AI nodes: Any node accepting ai_embedding type

Supported n8n Versions

• n8n v1.0.0+
• Node.js 20.15+

🛠️ Development

Prerequisites

• Node.js ≥ 20.15
• pnpm (recommended) or npm
• n8n development environment

Setup

git clone https://github.com/noku-team/n8n-original-embedding.git
cd n8n-original-embedding
pnpm install

Build

pnpm build

Development Mode

pnpm dev

Linting

pnpm lint          # Check for issues
pnpm lintfix       # Auto-fix issues

🔧 API Compatibility

Your embedding server should be OpenAI-compatible and support:

Request Format

POST /your-endpoint
{
  "input": "text to embed",
  "model": "your-model"
}

Response Format

{
  "object": "list",
  "data": [{
    "object": "embedding", 
    "index": 0,
    "embedding": [0.1, 0.2, -0.3, ...]
  }],
  "model": "your-model",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

Authentication Headers

The node automatically handles:

• Authorization: your-api-key (API Key mode)
• Authorization: Basic base64(username:password) (Basic Auth mode)
• Both headers simultaneously (Combined mode)

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 Changelog

See CHANGELOG.md for version history.

📄 License

This project is licensed under the MIT License - see the LICENSE.md file for details.

🆘 Support

• Documentation: n8n Custom Nodes Guide
• Issues: GitHub Issues
• Community: n8n Discord

🏷️ Keywords

n8n embedding ai rag vector-search custom-node openai machine-learning semantic-search

Made with ❤️ by Original.land