Improve RAG with Metadata in n8n (3 Examples)

Video Description

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-automations-4579 Hire me for N8N Automations: https://ryanandmattdatascience.com/hire-n8n-automation-engineer/ *Get 10% off your Hostinger n8n Self Hosted plan here: https://hostinger.com/datascience *Get n8n Cloud Here: https://n8n.partnerlinks.io/zbf786z9qbko 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🍿 WATCH NEXT n8n Course: https://www.youtube.com/playlist?list=PLcQVY5V2UY4K0mpuJ-oYO_LI25w5VDUD5 In this video, I walk through three powerful methods for adding metadata to your RAG pipeline using n8n. We start with basic manual metadata extraction, covering document properties like author, publication date, country, and card type. Then I demonstrate how to use AI agents to automatically extract keywords and generate document summaries with structured output parsers. Finally, I show the most advanced technique: dynamic metadata extraction using regex patterns to pull section-specific information like manufacturers, year ranges, and groupings from different parts of a document. Throughout the tutorial, I use a real-world example of Malta cigarette trading card documentation to demonstrate how thoughtful metadata design dramatically improves retrieval accuracy. I explain the importance of thinking about potential user queries when designing your metadata schema and show you exactly how to implement each approach in n8n with Pine Cone vector storage. By the end, you'll understand when to use manual, AI-generated, or dynamic metadata extraction and how to structure metadata that actually improves your RAG system's performance instead of just adding noise. TIMESTAMPS 00:00 Introduction to Metadata in RAG 01:15 What is Metadata? 03:06 Example 1: Basic Manual Metadata 05:17 Document Overview: Malta Cigarette Cards 06:50 Setting Up Pine Cone Vector Store 09:32 Reviewing Metadata in Vector Database 10:18 Example 2: AI-Generated Keywords and Summaries 13:27 Example 3: Dynamic Metadata from Document Parts 15:30 Using Claude for Regular Expressions 18:23 Testing and Refining the Code 20:03 Final Results and Vector Store Setup OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/matt-payne-ceo/ Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.