Store All Data Types with Agentic RAG in n8n

Learn to implement Agentic RAG systems in n8n that go beyond simple retrieval by incorporating reasoning capabilities before querying databases. Explore the evolution from traditional RAG to more intelligent systems that can handle multiple data types including tabular data, vector databases, and full document summaries. Discover why reasoning before database queries leads to more accurate and contextually relevant AI responses, and understand the differences between various RAG approaches like KAG, Graph RAG, and Agentic RAG. Follow along with a practical demonstration using Cole Medin's RAG template to test different data retrieval scenarios and see how agentic reasoning improves query results across structured and unstructured data sources.