Automation platform Latenode launches AI Data Storage (RAG), a new feature that simplifies creating AI agents with access to company knowledge. RAG technology – typically requiring external vector databases, manual chunking, and multiple service integrations – now works entirely within one platform in just a few clicks.
Retrieval-Augmented Generation (RAG) enables AI models to use company-specific data for generating accurate, contextual responses. Until now, implementing RAG meant dealing with vector database configurations, managing embedding services, handling document chunking, and maintaining connections between different tools. Latenode removes these technical hurdles completely.
How it works:
No External Dependencies. Everything runs within Latenode – no vector databases to set up, no embedding APIs to manage, no separate storage solutions. Upload documents and connect RAG Search to your AI agent. That’s it.
Upload and Go. Creating a knowledge base happens automatically when you upload files. The platform chunks documents, generates embeddings, and indexes content for semantic search without manual configuration.
Any Content Works. The system processes PDFs, text files, JSON, Markdown, and images with OCR support in English. Technical documentation, customer records, or unstructured notes all become instantly searchable through natural language.
Visual Setup, No Code. Traditional RAG requires understanding vectors, embeddings, and retrieval algorithms. With Latenode, users drag files into storage and connect nodes visually. The technical complexity happens behind the scenes.
Adding RAG to AI Agents
Users upload documents to AI Data Storage, where content gets processed and indexed using Cloudflare and LlamaIndex embedding models. When connected to an AI Agent node, the system performs semantic search across the knowledge base, finding relevant chunks that help the agent generate informed responses. The entire workflow stays visual – no programming needed.
Practical Applications:
Teams across organizations are already seeing results. Support tickets get resolved faster with AI agents that understand product documentation. Marketing campaigns become more targeted through AI that learns from historical data. Legal reviews that took hours now happen in seconds as AI searches through entire contract libraries.
“RAG has always been powerful but unnecessarily complicated to set up,” notes the Latenode team. “We’ve removed the friction between businesses and this technology. If you can upload a file and connect two nodes, you can build a RAG-powered AI agent.”
The AI Data Storage feature is available in beta for all Latenode users. This democratized approach makes enterprise-grade RAG technology accessible to businesses of all sizes, removing the traditional cost barriers associated with AI implementation.
Early adopters report dramatic time savings. Tasks that required days of configuration now take minutes. More importantly, teams without technical backgrounds can build and maintain their own AI knowledge systems.
About Latenode: Latenode is a low-code AI agent builder that combines visual workflow automation with advanced AI capabilities, serving thousands of businesses worldwide with accessible automation solutions.