Effortless Personal Data Extraction with Self-Hosted Mistral NeMo
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Automate the extraction of personal data from chat messages using a self-hosted Mistral NeMo LLM. This n8n workflow ensures accurate, structured output by leveraging powerful Langchain nodes and intelligent error handling.
About This Workflow
Unlock the power of your own private Large Language Model (LLM) for sensitive data extraction. This n8n workflow seamlessly integrates with a self-hosted Mistral NeMo model via Ollama to process incoming chat messages. It intelligently analyzes user requests, extracts specific personal information like names, contact methods, and communication details, and structures it into a defined JSON format. The workflow includes robust error handling and auto-fixing capabilities to ensure consistent and reliable data capture, making it ideal for privacy-conscious automation scenarios.
Key Features
- Self-Hosted LLM Integration: Utilize your private Mistral NeMo model for enhanced data security and control.
- Structured Data Extraction: Automatically parse and structure personal data into a predefined JSON schema.
- Intelligent Output Parsing: Employ auto-fixing mechanisms to correct LLM output and ensure schema compliance.
- Flexible Configuration: Easily customize prompts and define your JSON schema to fit specific data needs.
- Robust Error Handling: Built-in mechanisms to manage and recover from LLM processing errors.
How To Use
- Set up Ollama: Ensure Ollama is installed and the
mistral-nemo:latestmodel is available. - Configure Ollama Credentials: In n8n, set up your Ollama API credentials, referencing your local Ollama instance.
- Define Your JSON Schema: Modify the
inputSchemain the Structured Output Parser node to match the specific personal data fields you need to extract. - Customize LLM Prompts: Adjust the
messageValuesin the Basic LLM Chain node to guide the LLM's extraction process, including today's date for context. - Configure the Trigger: Set up the When chat message received node to listen for incoming chat messages.
- Connect Nodes: Link the When chat message received node to the Basic LLM Chain, then connect the LLM chain to the Structured Output Parser and subsequently the Auto-fixing Output Parser.
- Handle Output: The Extract JSON Output node will capture the final structured JSON data, and the On Error node provides a fallback for failed executions.
Apps Used
Workflow JSON
{
"id": "ac72d9e2-cca5-4aef-991c-1c6c5a7046f3",
"name": "Effortless Personal Data Extraction with Self-Hosted Mistral NeMo",
"nodes": 19,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: ac72d9e2-cca5...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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