How to Build a RAG Chatbot with n8n & Qdrant (Store Custom Data)

Want to make your AI chatbots smarter by letting them access your own private data? In this video, I walk you through exactly how to use Qdrant within n8n to embed and save custom data for RAG (Retrieval-Augmented Generation) workflows. By the end of this tutorial, you’ll know how to turn PDFs, text files, or database entries into vector embeddings and store them in Qdrant so your AI agents can recall specific information instantly. This is the missing link to building powerful, context-aware AI assistants without writing complex code! 👇 In this video, we cover: • How to set up the Qdrant node in n8n correctly. • Converting text into vector embeddings. • Upserting (saving) vectors into your Qdrant cluster. • Best practices for structuring your data payloads for better retrieval. Keywords: n8n tutorial, Qdrant tutorial, RAG chatbot, vector database, how to use Qdrant, n8n Qdrant integration, embed custom data, AI automation, retrieval augmented generation, n8n embeddings, no-code AI, build AI agent, vector search, Qdrant cloud, n8n workflow