Adaptive RAG with Gemini & Qdrant: Context-Aware Queries

This n8n workflow enables adaptive Retrieval-Augmented Generation (RAG) for context-aware query answering. It automatically classifies incoming user queries into four categories—Factual (verifiable info), Analytical (in-depth explanations), Opinion (perspectives), and Contextual (user-specific)—then restructures them using tailored strategies to fetch relevant data from Qdrant vector store and generate responses via Google Gemini.

The process starts with query reception via a trigger like a chatbot or API. Classification occurs first, followed by strategy-specific embedding, retrieval, and prompting. This ensures faster, more accurate answers by avoiding generic responses and leveraging semantic search in Qdrant.

Benefits include enhanced chatbot intelligence, reduced hallucinations in AI responses, and scalability for enterprise apps. Use cases: customer support bots, knowledge bases, research assistants, or personalized Q&A systems. Setup takes 10-15 minutes: import workflow, connect Gemini/Qdrant, link trigger, and customize.

Ideal for AI enthusiasts or businesses building smart conversational agents, with sticky notes in the workflow for details.