DEPLOY Fully Private + Local AI RAG Agents (Step by Step)

Uploaded: 2025-12-15
[“This video demonstrates how to create a fully local and air-gapped AI agent using N8N and Dockling, ensuring complete control over sensitive documents without relying on external APIs.”,”It covers the process of setting up local AI systems to interrogate multimodal documents, extracting data efficiently while maintaining security and privacy.”]

channel	The AI Automators
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Professional summary: This video shows how to build a fully local, airgapped multimodal RAG agent using N8N, Dockling, Olama and Docker. It walks through document ingestion, image extraction, vectorization, and serving a local chat UI while explaining hardware and networking constraints.
– Document processing: Dockling pipelines and VLMs to extract structured markdown/JSON and images.
– Local AI stack: N8N orchestration, Quadrant vector store, Olama models, Docker volumes/networks.
– Deployment notes: GPU requirements, async ingestion, image hosting, and local-network access.

Quotes:

We’re going fully local and airgapped. No external APIs.

Dockling spits out clean structured markdown or JSON your agent can search.

You essentially need a graphics card to actually run these.