I Built a Self-Learning RAG Agent with n8n | Complete Automation Tutorial

Get all workflows to accelerate your business or start your AI Automation agency: https://go.jahidh.me/community Get this template: https://jahidhme.gumroad.com/ Buy hosting through our affiliate link and get agency website design (FREE): https://go.jahidh.me/bluehost-wp Send your invoice to get it for FREE: [email protected] My software, tools, & deals (some give me kickbacks—thank you!) Try n8n: https://go.jahidh.me/n8n Apify (Free $5): https://go.jahidh.me/apify Lifetime deal on Appsumo: https://go.jahidh.me/Appsumo Useful Tools I use daily: Screen Recorder (Best): https://go.jahidh.me/screen-recorder Auto Captions: https://go.jahidh.me/submagic Business Inquiry - [email protected] —————————————————— X: https://x.com/imJahidH Instagram: https://www.instagram.com/m.jahidh.me/ LinkedIn: https://linkedin.com/in/jahidh-me Build a Self-Learning RAG Agent with n8n | Complete Automation Tutorial Learn how to create a powerful self-learning RAG (Retrieval-Augmented Generation) agent using n8n that can both chat with users AND update its knowledge base through a single Telegram interface. Unlike traditional RAG agents that require separate workflows for database updates, this n8n automation handles everything in one streamlined process. In this comprehensive n8n tutorial, I'll show you how to build an intelligent agent that can process documents and images sent via Telegram, automatically extract information using AI, store it in a Pinecone vector database, and retrieve relevant knowledge when asked questions—all within a single n8n workflow! The n8n automation platform makes this possible by combining AI capabilities with powerful workflow automation. Whether you're new to n8n or an experienced automation developer, this tutorial breaks down the entire process into easy-to-follow steps. What You'll Learn: ✅ How to set up a complete n8n RAG agent workflow ✅ Processing documents and images with n8n and AI ✅ Integrating Telegram as both input and output interface ✅ Configuring Pinecone vector database with n8n ✅ Implementing conditional routing based on message type ✅ Building an AI assistant that retrieves knowledge automatically Time Stamps: 00:28 - RAG Agent Demo 01:47 - Pushing Data to Pinecone 02:53 - Retrieving Pinecone Data 04:44 - Download Workflow 05:05 - Workflow Logic 05:52 - Workflow Explained Hashtags: #n8n, #RAGagent, #AIautomation, #Telegram, #Pinecone, #automation, #AI, #MachineLearning, #workflow, #NoCode, #ChatbotTutorial, #AIassistant, #vectordatabase, #knowledgebase, #n8ntutorial, #AIproject, #selflearningAI, #botdevelopment, #lowcode, #documentprocessing, #automationworkflow, #telegrambot, #n8nworkflow, #vectorsearch, #aipowered Keywords: n8n, RAG agent, automation workflow, self-learning AI, Telegram bot, Pinecone database, vector search, AI automation, chatbot tutorial, document processing, image processing, n8n tutorial, AI assistant, workflow automation, no-code AI, knowledge retrieval, vector database, AI project, Telegram automation, n8n workflow, AI integration, document parsing, AI database, RAG implementation, knowledge base, n8n, RAG, n8n tutorial, AI Agent, Retrieval Augmented Generation, Automation, Telegram Bot, Pinecone, Vector Database, AI Chatbot, n8n workflow, Self-Learning AI, Workflow Automation, Google Gemini, AI Vision, Low Code Automation, No Code AI, n8n automation, Pinecone tutorial, Telegram automation, Build AI Agent, Document Processing AI, Image Processing AI, n8n RAG