RAG Agent with Memory and Supabase Vector Store
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This workflow enables a Retrieval Augmented Generation (RAG) agent to converse with memory, powered by OpenAI's chat model and leveraging Supabase for vector storage. It allows the agent to recall past interactions and access information from a vector database.
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About This Workflow
Overview
This n8n workflow sets up a sophisticated Retrieval Augmented Generation (RAG) agent. It uses OpenAI's chat model for generating responses and incorporates memory via a PostgreSQL database. The agent's knowledge base is stored and retrieved using a Supabase Vector Store. The workflow is triggered by a chat message, processes the input using the RAG agent, and responds accordingly, maintaining conversational context.
This workflow is ideal for building intelligent chatbots that need to remember past conversations and access a dynamic knowledge base, enhancing user experience and providing more relevant and context-aware responses.
Key Features
- Integrates OpenAI's advanced language models for natural language understanding and generation.
- Implements conversational memory using Postgres Chat Memory to maintain context.
- Utilizes Supabase as a vector store for efficient knowledge retrieval.
- Supports document loading and text splitting for knowledge base ingestion.
- Offers a RAG Agent for enhanced response generation based on retrieved information.
How To Use
- Set up Credentials: Configure your OpenAI API credentials, Supabase API credentials, and PostgreSQL credentials in n8n.
- Configure Trigger: Set up the 'When chat message received' node to receive incoming messages.
- Agent Configuration: Configure the 'RAG Agent' node with your desired OpenAI model and system message.
- Memory Configuration: Link the 'Postgres Chat Memory' node to the 'RAG Agent' to enable conversation history.
- Vector Store Configuration: Connect the 'Supabase Vector Store' node to the 'RAG Agent' to provide access to your knowledge base.
- Data Ingestion (Optional): If you need to populate the vector store, use the 'Download File' (e.g., from Google Drive), 'Default Data Loader', 'Embeddings OpenAI', and 'Add to Supabase' nodes to process and store your documents.
Apps Used
Workflow JSON
{
"id": "04fc5782-1380-4b4c-a5e9-8fad54ef5ac5",
"name": "RAG Agent with Memory and Supabase Vector Store",
"nodes": 0,
"category": "AI Automation",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 04fc5782-1380...
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