RAG Starter Template: Form Upload, Vector Store & OpenAI

This workflow provides a ready-to-use RAG (Retrieval-Augmented Generation) template in n8n, enabling users to upload knowledge files like PDFs or CSVs through a simple form trigger. It processes the documents using OpenAI embeddings, loads them into a simple vector store, and powers an AI agent for contextual conversations based on the uploaded data. No prior setup is needed—just execute, upload, and start chatting.

Key benefits include rapid prototyping of knowledge-enhanced AI agents without complex configurations. It saves hours of development time by handling data ingestion, embedding, storage, and retrieval out-of-the-box. Customization is straightforward: tweak agent prompts, swap vector stores for production tools, or integrate alternative data sources like Google Drive.

Ideal use cases span marketing campaigns (e.g., querying product docs for personalized responses), customer support chatbots with internal FAQs, or research agents analyzing reports. Perfect for teams building AI-driven tools needing domain-specific knowledge, enhancing response accuracy over generic LLMs.

For production, upgrade to robust vector stores and add ranking logic. See n8n docs for advanced RAG techniques.