💬 RAG chat assistant with Claude, Supabase vector store, and Postgres memory
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Description
📺 Full walkthrough video: https://youtu.be/Z_l_T22px3U
Who it’s for
This workflow is for developers and AI builders who want to deploy a context-aware chat assistant powered by a private knowledge base. It suits teams that need persistent conversation memory and retrieval-augmented generation (RAG) on their own infrastructure.
How it works
- The Chat Trigger listens for incoming user messages and forwards them to the AI Agent.
- The AI Agent (Claude Sonnet 4) orchestrates the response generation.
- Postgres Chat Memory stores and retrieves the conversation history, enabling multi-turn dialogue.
- The Supabase Vector Store is queried as a tool to retrieve relevant documents from the knowledge base.
- OpenAI Embeddings convert the user query into vectors used for the Supabase similarity search.
- Claude synthesizes context from memory and retrieved documents to produce a final response.
How to set up
- Add your Anthropic API key to the Claude Sonnet 4 Model node
- Add your OpenAI API key to the Generate OpenAI Embeddings node
- Configure your Supabase project URL, API key, and target table name
- Configure the Postgres Chat Memory node with your PostgreSQL database credentials
- Optionally set authentication on the Chat Trigger node
Requirements
- Anthropic account (Claude Sonnet 4)
- OpenAI account (text-embedding model)
- Supabase project with a vector-enabled table
- PostgreSQL database for session memory
How to customize
- Swap Claude for OpenAI GPT-4 or another LLM supported by n8n
- Replace Supabase Vector Store with Pinecone, Qdrant, or another vector database
- Use a unique session key per user to support isolated multi-user conversations
🔗 Nodes Used
AI Agent, Embeddings OpenAI, Anthropic Chat Model, Supabase Vector Store, Chat Trigger, Postgres Chat Memory
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File