n8n

Local Chatbot with Retrieval Augmented Generation (RAG)

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial !thumbnail.png Click here to view the YouTube…

The short answer

Local Chatbot with Retrieval Augmented Generation (RAG) is a n8n workflow for Internal Wiki. Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial !thumbnail.png Click here to view the YouTube… It is rated intermediate and runs locally — free and private, with 8 custom nodes required.

Import / view source
Use case
Internal Wiki
Difficulty
intermediate
Author
Thomas Janssen
Updated
Jul 23, 2026

Required custom nodes

  • agent
  • lmChatOllama
  • memoryBufferWindow
  • textSplitterRecursiveCharacterTextSplitter
  • formTrigger
  • documentDefaultDataLoader
  • chatTrigger
  • vectorStoreQdrant

Local Chatbot with Retrieval Augmented Generation (RAG)

A working n8n automation that runs against a local model via Ollama - 28,717 views on the n8n template library. Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial !thumbnail.png Click here to view the YouTube…

What it does

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial !thumbnail.png Click here to view the YouTube… It chains 10 nodes, integrating agent, lmChatOllama, memoryBufferWindow, textSplitterRecursiveCharacterTextSplitter.

Requirements

  • n8n (self-hosted, free) to run the workflow
  • Ollama serving a local model
  • A GPU with enough VRAM for your chosen model (see best model per GPU)

Import it

Open the workflow on the n8n template library and click Use workflow to import it into your self-hosted n8n, then point its model node at your local Ollama endpoint (http://localhost:11434).

Use it with

n8n · Ollama

Workflow by Thomas Janssen on the n8n template library. We link to the original to import; credit and the workflow JSON belong to its author.

Frequently asked

What does the Local Chatbot with Retrieval Augmented Generation (RAG) workflow do?

Build a 100% local RAG with n8n, Ollama and Qdrant. This agent uses a semantic database (Qdrant) to answer questions about PDF files. Tutorial !thumbnail.png Click here to view the YouTube… It is a n8n workflow built for Internal Wiki.

What do I need to run Local Chatbot with Retrieval Augmented Generation (RAG)?

Local Chatbot with Retrieval Augmented Generation (RAG) runs on n8n and needs 8 custom nodes. It runs entirely on your own machine.

Is Local Chatbot with Retrieval Augmented Generation (RAG) free to use?

Yes. Local Chatbot with Retrieval Augmented Generation (RAG) is a local, self-hosted n8n workflow — there are no per-run or API costs once your hardware is set up.

How hard is it to set up Local Chatbot with Retrieval Augmented Generation (RAG)?

Local Chatbot with Retrieval Augmented Generation (RAG) is rated intermediate. Expect some configuration; review the required components first.