Advanced Multi-Query RAG System with Supabase & GPT-5

This advanced n8n workflow elevates basic Retrieval-Augmented Generation (RAG) by introducing multi-query decomposition, relevance-based filtering, and intermediate reasoning. It decouples the AI agent from the knowledge base via a smart sub-workflow, enabling handling of complex questions like molecular, organismal, and population-level natural selection. Ideal for healthcare applications in medical records analysis, it retrieves precise data from Supabase, filters noise, and generates comprehensive, high-quality responses with GPT-5.

Key benefits include preventing low-quality answers from irrelevant matches, supporting production-ready architectures for Q&A bots and knowledge base assistants, and dramatically improving performance over simple RAG setups. Users save significant development time by leveraging this robust pattern for AI agents in n8n.

Use cases span AI developers building research agents, n8n power users pushing AI boundaries, and teams in healthcare extracting insights from medical records. Whether for internal assistants or complex data synthesis, this workflow ensures accurate, multifaceted answers from your Supabase data.