Open-source models and n8n create an approachable path for teams that want control and lower costs. Use
a retrieval-augmented generation (RAG) pattern: retrieve relevant docs from a vector DB, then call an LLM to
generate the response.
A simple assistant flow: – User message triggers webhook. – Fetch user context and perform a semantic
search on your vector DB. – Compose a prompt with the retrieved context and send to the LLM. – Post
process and send the response back to the user (chat UI, email, or Slack).
Advantages of combining n8n with open models: – Cost control: self-host smaller models for PII-sensitive
tasks. – Flexibility: swap models or change prompts without rewriting infrastructure. – Rapid iteration: n8n
lets you modify orchestration quickly.
Want a starter n8n RAG workflow that integrates a vector DB and an open-source LLM? I can provide
JSON for import.
