Unlock Instant Knowledge with AI-Powered Retrieval Augmented Generation

This n8n workflow automates the process of building an intelligent Q&A system powered by Retrieval Augmented Generation (RAG). It dynamically ingests 'living data' from sources like Notion, converts it into searchable embeddings using OpenAI, and then uses these embeddings to answer questions with unprecedented accuracy.

The workflow begins by triggering on changes to your Notion pages or on a schedule, fetching the latest content. This content is then processed and split into manageable chunks, enriched with relevant metadata, and converted into vector embeddings. These embeddings are stored for efficient retrieval. When a question is posed, the system retrieves the most relevant information chunks from your stored embeddings and uses a powerful language model (like GPT-4o) to generate a concise and accurate answer, referencing your live data. This ensures your AI always has access to the most current information, eliminating outdated responses and boosting productivity.