Extract personal data with self-hosted LLM Mistral NeMo

Description

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 sensitive information to be processed locally.

📖 For a detailed explanation and more insights on using open-source LLMs with n8n, take a look at our comprehensive guide on open-source LLMs.

🔑 Key Features

Local LLM
Connect Ollama to run Mistral NeMo LLM locally
Provide a foundation for compliant data processing, keeping sensitive information on-premises

Data extraction
Convert unstructured text to a consistent JSON format
Adjust the JSON schema to meet your specific data extraction needs.

Error handling
Implement auto-fixing for LLM outputs
Include error output for further processing

⚙️ Setup and сonfiguration

Prerequisites

n8n AI Starter Kit installed

Configuration steps

Add the Basic LLM Chain node with system prompts.
Set up the Ollama Chat Model with optimized parameters.
Define the JSON schema in the Structured Output Parser node.

🔍 Further resources
Run LLMs locally with n8n
Video tutorial on using local AI with n8n

Apply the power of self-hosted LLMs in your n8n workflows while maintaining control over your data processing pipeline!