Store Data Types as RAG in n8n

In the rapidly evolving landscape of AI applications, Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to enhance large language models with external knowledge. However, traditional RAG systems often fall short when dealing with complex queries or large knowledThis tutorial will demonstrate how to implement Agentic RAG using Vector Databases and n8n, a powerful workflow automation platform. We’ll explore why reasoning before querying is crucial and how it transforms standard RAG implementations into truly intelligent systems.

ge bases. This is where Agentic RAG comes in – a sophisticated evolution that incorporates reasonin## Understanding RAG: The Foundation

Before diving into Agentic RAG, let’s understand the basic RAG architecture:

  1. **Retrieval**: Finding relevant information from a knowledge source
  2. 2. **Augmentation**: Enhancing the retrieved information
  3. 3. **Generation**: Producing coherent, contextually relevant responses
  5. Traditional RAG systems follow a straightforward process:
  6. – User submits a query
  7. – System converts query to vector embeddings
  8. – System searches vector database for similar content
  9. – System retrieves relevant documents
  10. – LLM generates a response using the retrieved documents
  11. g capabilities before querying databases, leading to more intelligent and effective AI systems.