Supabase

Postgres-based backend used throughout the cluster as the vector store (via pgvector) for n8n RAG pipelines. Metadata columns on the documents table are the primary mechanism for updating and deleting stale chunks via filename match. Also used for tabular SQL-query RAG examples.

Source references

  • [src-006] Nate Herk cluster — Nate Herk — RAG and data ingestion cluster (5 videos)

– Videos referenced: 5uw1wE6niGc, kOKavHnlPik

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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Keep reading from this thread

From 500 indexed pages and articles.

  1. Wiki concept RAG Data Pipelines The operational layer around a RAG knowledge base — trigger, inputs, processing, destination — that keeps the vector store fresh. Related by supabase
  2. Wiki concept RAG Retrieval Methods Framework of four retrieval patterns for AI agents: (1) database filters for structured rows when the answer lives in a small subset, (2) Related by vector
  3. Wiki concept Gemini File Search API Google's managed RAG service that hides chunking, embedding, and storage behind four HTTP operations: create store, upload file, import file into store Related by store