Description

n8n-nodes-supabase-namespace

This is an n8n community node that allows you to work with Supabase Vector Store with extended support for custom schemas and namespaces.

Supabase Vector Store is a vector database that allows you to store and search document embeddings efficiently, ideal for AI applications and semantic search.

n8n is a workflow automation platform with fair-code license.

Installation
Operations
Credentials
Compatibility
Usage
Resources
Version History

Installation

Follow the installation guide in the n8n community nodes documentation.

Operations

This node supports the following operations:

• Load: Load an existing vector store from Supabase
• Insert: Insert new documents into the vector store
• Retrieve: Retrieve similar documents based on queries
• Update: Update existing documents in the vector store
• Retrieve as Tool: Use retrieval as a tool in LangChain flows

Main Features

🗄️ Custom Schema Support

• Use database schemas different from the default "public" schema
• Flexible configuration for multi-tenant environments

🏷️ Namespace System

• Logical partitioning of documents
• Efficient filtering by namespace
• Option to clear namespaces before inserting new data

🔍 Custom Queries

• Configuration of custom query names
• Support for advanced metadata filters
• Native integration with LangChain

Credentials

To use this node, you need to configure Supabase credentials:

Prerequisites

1. An account on Supabase
2. A configured Supabase project
3. A PostgreSQL database with the pgvector extension enabled

Credential Configuration

1. Go to your Supabase project
2. Navigate to Settings > API
3. Copy your Project URL (host)
4. Copy your service_role key (not the anon key)
5. In n8n, configure the credentials with:
   • Host: Your Project URL
   • Service Role: Your service_role key

Required Table Structure

Your table must have the following minimum structure:

CREATE TABLE your_table_name (
  id BIGSERIAL PRIMARY KEY,
  content TEXT,
  metadata JSONB,
  embedding vector(1536), -- or the dimension of your embeddings
  namespace TEXT -- column for the namespace system
);

Compatibility

• Minimum n8n version: 1.0.0
• Minimum Node.js version: 20.15
• Tested versions: n8n 1.0.0+

Usage

Basic Configuration

1. Table Name: Select or write the name of your table
2. Use Custom Schema: Activate if you want to use a schema different from "public"
3. Schema: Specify the schema name (e.g., "ai_docs", "user_data")
4. Namespace: Define a namespace to organize your documents

Common Use Cases

📚 Client Document Management

{
  "tableName": "documents",
  "schema": "client_data",
  "namespace": "client_123",
  "options": {
    "clearNamespace": true
  }
}

🔍 Semantic Search

{
  "tableName": "knowledge_base",
  "namespace": "product_docs",
  "options": {
    "queryName": "search_products",
    "metadataFilter": {
      "category": "electronics"
    }
  }
}

🔄 Data Update

{
  "tableName": "user_preferences",
  "schema": "user_profiles",
  "namespace": "user_456"
}

LangChain Integration

This node integrates perfectly with LangChain flows in n8n, enabling:

• Complex reasoning chains
• Conversational agents
• Recommendation systems
• Document analysis

Resources

• n8n community nodes documentation
• Official Supabase documentation
• pgvector guide
• LangChain documentation
• Project repository

Version History

v0.1.0 (Current)

• ✅ Basic support for Supabase Vector Store
• ✅ Namespace system implemented
• ✅ Support for custom schemas
• ✅ Complete CRUD operations
• ✅ LangChain integration
• ✅ Advanced metadata filters

Contributing

Contributions are welcome! Please:

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

License

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

Author

Néstor Cano – [email protected]

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