🎥 Building a RAG System in n8n! 🚀
I’m excited to share a new demo video where I walk through how I built a complete Retrieval-Augmented Generation (RAG) workflow using n8n, OpenAI, and Pinecone — all without writing a backend or managing servers. This workflow turns unstructured documents into intelligent, answer-ready knowledge using automation and AI. 🔍 What the Demo Covers 📂 Pulling documents directly from Google Drive ✂️ Splitting text using a Recursive Character Text Splitter 📄 Loading & preparing data with a Data Loader 🧠 Generating embeddings with OpenAI 📦 Storing & indexing vectors in Pinecone 🤖 Using an AI Agent connected to the vector store 💬 Answering questions with accurate, RAG-powered context 🧾 Adding memory for more natural, human-like conversations 🎯 Why This Workflow Is Powerful This setup enables you to build: 🤖 AI chatbots with custom knowledge ❓ Automated Q&A assistants 🏢 Internal knowledge search tools 📚 Document-driven AI applications All created inside n8n — visually, modularly, and with full flexibility. 📽️ Watch the Demo I’ve recorded a full walkthrough to show how everything fits together from start to finish. 👉 Video attached If you're exploring RAG, vector databases, or AI automation, feel free to connect — always happy to share ideas and learn from the community! #n8n #AI #RAG #OpenAI #Pinecone #Automation #NoCode #LowCode #VectorDatabase #LLM #ArtificialIntelligence #WorkflowAutomation