Extract personal data with self-hosted LLM Mistral NeMo
n8n7 modulesv1.0This 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 1. Local LLM - Connect Ollama to run Mis
At a glance
Extract personal data with self-hosted LLM Mistral NeMo is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects chainLlm, chatTrigger, lmChatOllama, noOp. It's free to download. Follow the 5-step import below to go live in minutes.
- Platform
- n8n
- Connects
- chainLlm, chatTrigger, lmChatOllama, noOp, outputParserAutofixing, outputParserStructured
- Modules
- 7
- Price
- Free
- Version
- v1.0
About this workflow
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 1. Local LLM - Connect Ollama to run Mistral NeMo LLM locally - Provide a foundation for compliant data processing, keeping sensitive information on-premises 2. Data extraction - Convert unstructured text to a consistent JSON format - Adjust the JSON schema to meet your specific data extraction needs. 3. 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 1. Add the Basic LLM Chain node with system prompts. 2. Set up the Ollama Chat Model with optimized parameters. 3. 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!
How to import this n8n workflow
- 1
Download the workflow JSON file after purchase.
- 2
Open n8n → click the menu → Import from File.
- 3
Select the downloaded JSON and import.
- 4
Set up credentials for each node that requires them.
- 5
Click Execute Workflow to test, then activate.
Setup guide
Setup guide included
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- JSON blueprint — instant download
- Setup guide PDF included
- 5 downloads · valid 30 days
- Works with n8n
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