Guide students with an AI tutor chatbot using RAG, intent classification and GPT-4o-mini
Needs changesWhat this workflow does
This AI workflow has 14 nodes and runs when a chat message arrives. Along the way it connects DeepSeek Chat Model, Embeddings OpenAI, OpenAI Chat Model and Google Sheets and 1 more.
How it runs
Trigger: When a chat message arrives.
- Merge: merges the data from its input branches
- Intent Classifier (AI Agent): runs an AI agent, with model: DeepSeek LLM; memory: Classifier Memory
- Aggregate: combines items into one
- Fetch System Prompt (Google Sheets): uses Google Sheets
- AI Teacher Agent (AI Agent): runs an AI agent, with model: GPT-4o-mini LLM; 1 tool (Book Knowledge Base); memory: Teacher Session Memory
- Log Q&A to Sheets (Google Sheets): append
- Format Response Output (Edit Fields (Set)): sets or maps fields
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in Intent Classifier (v1.9), AI Teacher Agent (v1.9): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) Intent Classifier (v1.9), AI Teacher Agent (v1.9) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (11)
- DeepSeek Chat Model
@n8n/n8n-nodes-langchain.lmChatDeepSeek - AI Agent
@n8n/n8n-nodes-langchain.agent - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Edit Fields (Set)
n8n-nodes-base.set - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Aggregate
n8n-nodes-base.aggregate - Merge
n8n-nodes-base.merge - Google Sheets
n8n-nodes-base.googleSheets - Pinecone Vector Store
@n8n/n8n-nodes-langchain.vectorStorePinecone
From the author
Description Automatically guide students through personalized learning sessions using AI-powered intent classification, book-based knowledge retrieval (RAG), and full session logging — all without writing a single line of code. --- What this workflow does…