Implement on-prem RAG with Qdrant and Ollama for a self-hosted KB
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 9 nodes and runs when a chat message arrives (plus 1 other trigger). Along the way it connects Embeddings Ollama, Ollama Chat Model and Qdrant Vector Store.
How it runs
Trigger: When a chat message arrives (plus 1 other trigger).
- AI Agent: runs an AI agent, with model: Ollama Chat Model; 1 tool (Read from Qdrant Vector Store); memory: Simple Memory
- Add to Qdrant Vector Store (Qdrant Vector Store): stores documents in the Qdrant vector store, with embeddings: Embeddings Ollama; document loader: Default Data Loader
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (8)
- Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Embeddings Ollama
@n8n/n8n-nodes-langchain.embeddingsOllama - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Ollama Chat Model
@n8n/n8n-nodes-langchain.lmChatOllama - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - n8n Form Trigger
n8n-nodes-base.formTrigger
From the author
Try It This n8n template provides a self hosted RAG implementation. How it works Provides one workflow to maintain the knowledge base and another one to query the knowledge base. Uploaded documents are saved into the Qdrant vector store. When a query is made,…