RAG Data Pipelines

The operational layer around a RAG knowledge base — trigger, inputs, processing, destination — that keeps the vector store fresh. Covers create, update, and delete flows (including the n8n recycling-bin workaround for missing delete triggers), metadata tagging for stale-vector removal, and file-type switches for mixed document sources. The core claim: a stale or duplicate-heavy database silently degrades agent accuracy, so ingestion plumbing matters as much as retrieval.

Related entities

Source references

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

– Videos referenced: 5uw1wE6niGc

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 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 retrieval
  2. 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 retrieval
  3. Wiki concept Multimodal Embeddings Embedding models that place text, images, video, audio, and documents into a single shared vector space, allowing cross-modal retrieval from one query. Related by ingestion