n8n RAG Local Chatbot Workflow (Ollama + Qdrant) Automation

Build a local RAG chat experience in n8n—powered by Ollama and Qdrant

This n8n workflow lets you create an automated RAG (Retrieval-Augmented Generation) local chatbot: users enter questions via n8n, the workflow retrieves relevant context from your Qdrant vector store, and then generates answers using an Ollama language model—fully within your own environment.

What this workflow does

• Uses a Form Trigger to capture end-user questions from an n8n form submission.
• Routes the conversation through the LangChain Chat Trigger and LangChain Agent logic (via n8nn8n-nodes-langchainchat trigger and n8nn8n-nodes-langchainagent) to manage chat flow.
• Generates embeddings locally with Ollama using the langchainembeddings ollama node.
• Performs retrieval against Qdrant (RAG) so responses are grounded in stored knowledge.
• Uses the Ollama chat model (langchainlm chat ollama) to produce the final assistant answer based on retrieved context.

Use cases

• Local knowledge assistant for SaaS teams—answer internal questions using your own Qdrant knowledge base.
• Support & operations copilot—convert form-submitted FAQs into fast, context-aware replies.
• Private RAG demo environments—test chatbot behavior with local LLMs and embeddings without external APIs.

Technical details

Designed for n8n automation engineers and operators, this workflow is built with:

• n8n nodes: sticky note, form trigger, n8nn8n-nodes-langchainagent, n8nn8n-nodes-langchainchat trigger, n8nn8n-nodes-langchainlm chat ollama, n8nn8n-nodes-langchainembeddings ollama
• RAG components: Ollama for local embeddings and chat generation; Qdrant for vector retrieval

Result: an n8n RAG Local Chatbot workflow that turns form submissions into accurate, retrieval-grounded answers using your own Ollama + Qdrant stack.