Create adaptive RAG chat agent with Google Gemini and Qdrant

Unlock adaptive, context-aware AI chat in your automations—no coding required!

This template is a plug-and-play n8n workflow that transforms how your chatbots, support agents, and knowledge systems respond to users. Powered by Google Gemini and a Qdrant vector database, it automatically classifies every incoming query and applies a tailor-made strategy for Factual, Analytical, Opinion, or Contextual requests—delivering the right answer, every time.

🛠️ Key Features Automatic Query Classification: Seamlessly detects whether the user wants facts, a deep analysis, opinions, or context—then routes each input to the best answering strategy.

Four Dynamic Retrieval Modes: 1) Factual: Delivers precise, accurate information 2) Analytical: Breaks down complex topics for deep dives 3) Opinion: Surfaces diverse viewpoints and perspectives 4) Contextual: Connects the dots using implied or user-specific context

End-to-End RAG Pipeline: Uses Gemini to classify and answer, while Qdrant powers fast, smart knowledge retrieval.

No-Code Visual Editing: Import into n8n, connect your LLM and vector database credentials, and you’re live—customize, extend, and scale with zero backend code.

Reusable in Any Project: Perfect for customer support, research, onboarding bots, internal knowledgebases, or any adaptive AI chat interface.

🚀 How it Works 1) User submits a query (via chat or API) 2) Query is auto-classified as Factual, Analytical, Opinion, or Contextual 3) Adaptive retrieval strategy is triggered (each with its own prompt logic and memory buffer) 4) Smart knowledge search is performed using Gemini and Qdrant 5) Response is generated and sent back to the user—tailored to the query type!