Build a RAG Agent in 20 Minutes with N8N¶

Video Metadata¶

Target Duration: 20 minutes
Target Audience: Developers, AI enthusiasts, automation engineers
Publishing Platform: YouTube / LinkedIn
Thumbnail Status: [ ] Created [ ] Uploaded
SEO Score: /100

Hook (0-15 seconds)¶

"What if I told you that you could build a production-ready RAG agent that reads your documentation, answers questions, and even executes code - all without writing a single line of code? Let me show you."

Show N8N workflow with documents flowing into vector DB and AI responding

Video Outline¶

Introduction (30-45 seconds)¶

RAG is revolutionizing how we interact with documents
N8N makes it accessible to everyone
Today: Build a complete RAG system with UI

Main Content¶

Section 1: What is RAG? (2 minutes)¶

Key Points: - Retrieval Augmented Generation explained - Why it's better than fine-tuning - Real-world use cases (support bots, doc search, knowledge bases)

Visuals: - RAG architecture diagram - Comparison with traditional chatbots - Cost comparison chart

Script Notes: "RAG lets AI use YOUR data without expensive training. Perfect for documentation, customer support, and knowledge management."

Section 2: Setting Up N8N Locally (3 minutes)¶

Key Points: - Docker compose setup - N8N configuration - Connecting to local services - Installing community nodes

Visuals: - Terminal commands - Docker Desktop - N8N dashboard tour - Node installation process

# docker-compose.yml for local N8N + PostgreSQL + Qdrant
version: '3.8'
services:
  n8n:
    image: n8nio/n8n
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=password
    ports:
      - "5678:5678"
    volumes:
      - n8n_data:/home/node/.n8n

  postgres:
    image: postgres:15
    environment:
      POSTGRES_PASSWORD: password
    volumes:
      - postgres_data:/var/lib/postgresql/data

  qdrant:
    image: qdrant/qdrant
    ports:
      - "6333:6333"
    volumes:
      - qdrant_data:/qdrant/storage

Section 3: Building the Document Ingestion Pipeline (5 minutes)¶

Key Points: - Document loader node setup - Text splitting strategies - Embedding generation with OpenAI - Storing in Qdrant vector database

Visuals: - N8N workflow builder - Node configuration screens - Document processing flow - Vector database visualization

Script Notes: "We'll ingest my entire EasyChamp documentation - that's 100+ pages of technical docs. Watch how N8N handles different formats automatically."

Section 4: Creating the RAG Retrieval Flow (5 minutes)¶

Key Points: - Query embedding - Vector similarity search - Context window management - Prompt engineering for accuracy

Visuals: - Query flow diagram - Similarity search results - Prompt template examples - Testing with real questions

Section 5: Adding AI Agent Capabilities (4 minutes)¶

Key Points: - Claude/GPT integration - Tool calling setup - Code execution node - Error handling

Visuals: - Agent configuration - Tool definitions - Live code execution - Error recovery flow

Script Notes: "Now for the magic - our agent can not only answer questions but also execute code, query databases, and call APIs based on the documentation."

Section 6: Building a Chat Interface (3 minutes)¶

Key Points: - Webhook trigger setup - Chat UI with n8n form - Session management - Deployment options

Visuals: - Form builder - Chat interface demo - Mobile responsiveness - Embedding options

Advanced Tips (2 minutes)¶

Optimizing chunk size for better retrieval
Using metadata filters
Implementing feedback loops
Monitoring and analytics

Call to Action (30 seconds)¶

Download the workflow template (link below)
Join the Discord for RAG discussions
Next video: "Production RAG with Evaluation Metrics"

Full Script¶

[HOOK]
What if I told you that you could build a production-ready RAG agent that reads your documentation, answers questions, and even executes code - all without writing a single line of code? Let me show you.

[INTRODUCTION]
RAG - Retrieval Augmented Generation - is revolutionizing how we interact with our data. Instead of training expensive models, we give AI access to our documents in real-time. And with N8N, you can build this in minutes, not months.

[WHAT IS RAG]
Think of RAG as giving AI a perfect memory of your documents. When someone asks a question, it finds relevant information and generates an accurate answer. No hallucinations, no outdated information.

[SETUP]
Let's start by setting up N8N locally. I'm using Docker Compose to run N8N, PostgreSQL for metadata, and Qdrant for vector storage...

[DOCUMENT INGESTION]
Now the fun part. We'll ingest my entire EasyChamp documentation. Watch how N8N automatically handles PDFs, Markdown, even Jupyter notebooks...

[RETRIEVAL FLOW]
When a user asks a question, we convert it to an embedding, search for similar content, and return the most relevant chunks. But here's where it gets interesting...

[AGENT CAPABILITIES]
Our agent doesn't just answer questions. It can execute the code it finds, validate examples, even fix errors. This is where N8N really shines...

[CHAT INTERFACE]
Finally, let's add a beautiful chat interface. N8N's form builder gives us a production-ready UI in seconds...

[ADVANCED TIPS]
Quick pro tips: Use 512 token chunks for technical docs, add metadata filters for version-specific answers, and always implement a feedback loop...

[CONCLUSION]
You just built a RAG agent that would cost thousands on OpenAI's platform, and it's running entirely on your machine. Your data never leaves your control.

[CTA]
The complete workflow template is in the description. Join our Discord to share your RAG implementations. Next week, I'll show you how to add evaluation metrics to ensure accuracy. See you then!

B-Roll & Assets Needed¶

N8N workflow screenshots
Architecture diagrams
Screen recordings:
Docker setup
N8N workflow building
Document processing
Chat interface testing
Performance metrics
Cost comparison chart

SEO Optimization¶

Title Options¶

Build a RAG Agent in 20 Minutes with N8N (No Code Required)
The Easiest Way to Build RAG in 2025 - N8N Tutorial
I Built ChatGPT for My Docs in 20 Minutes (Here's How)

Keywords¶

Primary: RAG, N8N, retrieval augmented generation Secondary: vector database, AI agents, no-code AI Long-tail: how to build RAG with n8n, retrieval augmented generation tutorial

Cross-Platform Distribution¶

LinkedIn Post¶

Built a complete RAG system in 20 minutes using N8N 🤖

No code. No cloud costs. Complete control.

The system:
→ Ingests 100+ documents automatically
→ Answers questions with citations
→ Executes code from documentation
→ Runs entirely locally

Full tutorial: [link]

#AI #RAG #Automation #NoCode

Created: 2025-09-09