Build a FREE RAG AI Agent with n8n & MongoDB

Build a FREE RAG AI Agent with n8n & MongoDB | 2026

Grab the free template from the attached file down below 👇

In this video, I will show you the easiest way to make a RAG AI agent using n8n for free. This automation allows you to chat with an AI agent that generates responses based on files you feed into a knowledge base integrated with MongoDB. We will build a system that can process PDFs and CSVs from Google Drive, embed the data using Gemini, and store it for retrieval.

💡What you'll learn

✅ How to build a custom AI agent in n8n that answers questions using your own knowledge base.

✅ Learn how to set up MongoDB Atlas as a free vector database to store chat memory and document embeddings.

✅ Discover how to configure Vector Search in MongoDB to perform semantic searches on your data.

✅ How to build an automated pipeline that downloads files from Google Drive and inserts them into your database automatically.

✅ How to verify and test your RAG agent with real-world files like inventory spreadsheets and financial PDFs.

✅ How to get MongoDB Credentials for n8n

✅ How to set up vector search in MongoDB

This tutorial guides you through the entire process of creating a Retrieval-Augmented Generation (RAG) system without writing code. You will learn how to use the "Vector Store" tool to let the AI retrieve information and how to handle different file formats by parsing text and turning it into numbers (embeddings). By the end, you'll be able to ask your AI complex questions about your specific business data and get accurate answers.

Book a free Consultation: 🔗 https://cal.com/genovaflow-ai/discovery-call

Let us build your project: 🔗 https://genovaflow.com/home/contact

14‑day FREE Trial on n8n: 🔗 https://n8n.partnerlinks.io/ox7johicleqv

Got questions about the video? Drop them in the comments below ⬇️

Sponsorship: 📧 contact@genovaflow.com

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