Extract Structured Data Privately with Self-Hosted Mistral NeMo LLM
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Automate the extraction of personal data from chat messages into a structured JSON format using a self-hosted Mistral NeMo Large Language Model. This workflow ensures data privacy and accuracy through robust parsing and auto-correction mechanisms.
About This Workflow
This powerful n8n workflow leverages the cutting-edge capabilities of self-hosted LLMs to transform unstructured chat messages into meticulously structured data. By integrating with Ollama to run the Mistral NeMo model locally, you gain unparalleled control over data privacy and security, avoiding reliance on external AI services. The workflow automatically listens for incoming chat messages, prompts the LLM to extract specific personal details according to a predefined JSON schema, and critically, employs an auto-fixing output parser to guarantee the consistency and validity of the extracted information. This makes it ideal for applications requiring precise data capture from dynamic conversations.
Key Features
- Private & Self-Hosted AI: Utilize the Mistral NeMo LLM via Ollama on your own infrastructure, ensuring maximum data privacy and control.
- Structured Data Extraction: Automatically parse incoming chat messages and extract specific data points (e.g., name, contact type, subject) into a well-defined JSON format.
- Robust Output Validation: Employs a 'Structured Output Parser' to validate LLM responses against your custom JSON schema for accuracy.
- Intelligent Auto-Correction: An 'Auto-fixing Output Parser' re-prompts the LLM to correct any outputs that fail schema validation, significantly improving reliability.
- Chat Message Trigger: Seamlessly initiate the data extraction process upon receiving new chat messages.
How To Use
- Configure Local LLM: Ensure Ollama is running with the
mistral-nemo:latestmodel. Configure the 'Ollama Chat Model' node with your Ollama API credentials and desired settings likekeepAliveandtemperature. - Define Your Data Schema: Customize the JSON schema within the 'Structured Output Parser' node to precisely define the personal data points you wish to extract (e.g., add
address, removesurname, etc.). - Update LLM Prompt (If Necessary): If your input data source or extraction requirements change, adjust the system message in the 'Basic LLM Chain' node to guide the LLM effectively.
- Set Up Chat Trigger: Configure the 'When chat message received' node to connect to your preferred chat platform (e.g., Slack, Telegram, or a custom webhook) to act as the workflow's entry point.
- Understand Auto-Fixing: Be aware that the 'Auto-fixing Output Parser' will automatically re-engage the LLM if the initial output does not conform to your defined schema, ensuring high data quality and schema compliance.
Apps Used
Workflow JSON
{
"id": "fe383e83-821d-4747-8a05-5b58ed0bf150",
"name": "Extract Structured Data Privately with Self-Hosted Mistral NeMo LLM",
"nodes": 11,
"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: fe383e83-821d...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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