Extract personal data with self-hosted LLM Mistral NeMo
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 7 nodes and runs when a chat message arrives. Along the way it connects Ollama Chat Model.
How it runs
Trigger: When a chat message arrives.
- Basic LLM Chain: sends a prompt to an LLM, with model: Ollama Chat Model; output parser: Auto-fixing Output Parser
- Extract JSON Output (Edit Fields (Set)): sets or maps fields
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 (7)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Ollama Chat Model
@n8n/n8n-nodes-langchain.lmChatOllama - Auto-fixing Output Parser
@n8n/n8n-nodes-langchain.outputParserAutofixing - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - No Operation, do nothing
n8n-nodes-base.noOp - Edit Fields (Set)
n8n-nodes-base.set
From the author
This workflow shows how to use a self-hosted Large Language Model (LLM) with n8n's LangChain integration to extract personal information from user input. This is particularly useful for enterprise environments where data privacy is crucial, as it allows…