9d • I Made this
🎥 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
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🎥 Building a RAG System in n8n! 🚀
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