Extract Personal Data with Self-Hosted Mistral NeMo LLM via Ollama

Name: Extract Personal Data with Self-Hosted Mistral NeMo LLM via Ollama
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Author: Free N8N

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AI & Machine LearningAutomationData ExtractionLLMLangchainMistralNeMoOllamaPrivacySelf-Hosted

This n8n workflow leverages a self-hosted Mistral NeMo LLM via Ollama to extract structured personal data from chat messages. It uses the 'Basic LLM Chain' and 'Structured Output Parser' nodes to ensure accurate data extraction.

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About This Workflow

Overview

This workflow automates the extraction of personal data from incoming chat messages using a local, self-hosted LLM. It's designed to process unstructured text and convert it into a structured JSON format, adhering to a predefined schema. This is particularly useful for privacy-conscious applications where sending sensitive data to third-party APIs is not feasible. The workflow utilizes the power of Ollama to run models like Mistral NeMo locally, combined with n8n's Langchain nodes for robust LLM interaction and data parsing.

Problem it solves:

Data Privacy: Avoids sending personal data to external AI services by using a self-hosted LLM.
Structured Data Extraction: Converts unstructured chat messages into a defined JSON schema for easier processing and analysis.
Automated Data Handling: Streamlines the process of collecting and organizing personal information from communications.

Key Features

Utilizes a self-hosted Mistral NeMo LLM via Ollama for local data processing.
Employs n8n's Langchain nodes for sophisticated LLM interactions.
Implements a 'Structured Output Parser' to enforce a predefined JSON schema for extracted data.
Includes an 'Auto-fixing Output Parser' to re-prompt the LLM if the initial output doesn't conform to the schema.
Handles incoming chat messages as the trigger event.

How To Use

Set up Ollama: Ensure Ollama is installed and running locally, and that the mistral-nemo:latest model is pulled.
Configure n8n Credentials: Set up Ollama credentials within n8n, linking it to your local Ollama instance.
Define JSON Schema: In the 'Structured Output Parser' node, define the JSON schema for the personal data you wish to extract.
Configure LLM Chain: In the 'Basic LLM Chain' node, set up the prompt that instructs the LLM to extract data according to the defined schema.
Connect Nodes: Ensure the 'When chat message received' node is connected to the 'Basic LLM Chain', and the LLM nodes are connected to the output parsers.
Run Workflow: Trigger the workflow by sending a chat message that the LLM can process.

Apps Used

Automation

Data Extraction

LLM

Langchain

Mistral

NeMo

Ollama

Privacy

Self-Hosted

Workflow JSON

{
  "id": "ccf527f1-34fc-4259-aaad-2b60cf51560f",
  "name": "Extract Personal Data with Self-Hosted Mistral NeMo LLM via Ollama",
  "nodes": 0,
  "category": "AI & Machine Learning",
  "status": "active",
  "version": "1.0.0"
}

Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.