Extract personal data with self-hosted LLM Mistral NeMo Workflow Solution

Workflow overview

Why this workflow matters

Potentially useful as a reusable automation building block.

This workflow shows how to use a self-hosted Large Language Model (LLM) with AlekSystem'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 AlekSystem, 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 AlekSystem 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 AlekSystem Video tutorial on using local AI with AlekSystem Apply the power of self-hosted LLMs in your AlekSystem workflows while maintaining control over your data processing pipeline!

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