Create adaptive RAG chat agent with Google Gemini and Qdrant
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) Query Classification (v1.8), Factual Strategy - Focus on Precision (v1.7), Analytical Strategy - Comprehensive Coverage (v1.7), Opinion Strategy - Diverse Perspectives (v1.7), Contextual Strategy - User Context Integration (v1.7), Answer (v1.8) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (11)
- AI Agent
@n8n/n8n-nodes-langchain.agent - Switch
n8n-nodes-base.switch - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Edit Fields (Set)
n8n-nodes-base.set - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Summarize
n8n-nodes-base.summarize - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - Respond to Webhook
n8n-nodes-base.respondToWebhook - Execute Workflow Trigger
n8n-nodes-base.executeWorkflowTrigger
What it does
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! 🧩 What’s Included - Full n8n workflow (.json) - Step-by-step setup instructions - Sample prompts and system messages for each strategy - Lifetime updates (as the workflow evolves) 💡 Use Cases - Chatbots that adapt to every user’s intent - Internal/external FAQ and helpdesk automations - AI research and summarization agents - Product support and onboarding flows Any scenario where smarter, more relevant answers = better user experience Ready to build smarter automations? Import this template, connect your Gemini & Qdrant accounts, and let your AI agent adapt to every conversation.