Adaptive RAG Workflow for Personalized Responses

This advanced n8n workflow implements an Adaptive Retrieval-Augmented Generation (RAG) system designed for dynamic and context-aware information retrieval. It intelligently classifies user queries into four categories: Factual, Analytical, Opinion, and Contextual, then employs tailored strategies for each category to fetch relevant content from a vector database (Qdrant) using Google Gemini models. The workflow uses a series of nodes, including classification, strategy selection, document retrieval, context concatenation, and response generation, ensuring highly relevant and personalized answers. Practical applications include chatbots, customer support systems, and knowledge management platforms that require nuanced, category-specific responses based on user queries and contextual data.