RAG chat assistant with Claude, Supabase vector store, and Postgres memory

📺 Full walkthrough video: https://youtu.be/ZlT22px3U 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. Op

Help me set up the n8n workflow "RAG chat assistant with Claude, Supabase vector store, and Postgres memory" (https://n8n.io/workflows/7381). It uses: AI Agent, Embeddings OpenAI, Anthropic Chat Model, Supabase Vector Store, Postgres Chat Memory. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.