Build a document-based AI chatbot with Google Drive, Llama 3, and Qdrant RAG
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 this workflow does
This AI workflow has 11 nodes and runs on Google Drive events (plus 1 other trigger). Along the way it connects Google Drive, Embeddings Ollama, Qdrant Vector Store and Ollama Chat Model.
How it runs
Trigger: On Google Drive events (plus 1 other trigger).
- Google Drive: uses Google Drive
- AI Agent: runs an AI agent, with model: Ollama Chat Model; 1 tool (Qdrant Vector Store)
- Qdrant Vector Store: uses the Qdrant vector store, with embeddings: Embeddings Ollama; document loader: Default Data Loader
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 AI 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) AI 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 (9)
- Google Drive Trigger
n8n-nodes-base.googleDriveTrigger - Google Drive
n8n-nodes-base.googleDrive - Embeddings Ollama
@n8n/n8n-nodes-langchain.embeddingsOllama - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - AI Agent
@n8n/n8n-nodes-langchain.agent - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Ollama Chat Model
@n8n/n8n-nodes-langchain.lmChatOllama
From the author
Overview This template allows users to set up an AI-powered chatbot that retrieves and processes knowledge from Google Drive documents using Retrieval-Augmented Generation (RAG). By leveraging Llama 3 for natural language responses and Qdrant vector storage…