Effortless Personal Data Extraction with Self-Hosted Mistral NeMo LLM
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Automate the extraction of structured personal data from chat messages using a self-hosted Mistral NeMo LLM via Ollama. Ensure data privacy and compliance with precise, reliable extraction.
About This Workflow
This workflow leverages the power of a self-hosted Mistral NeMo Large Language Model, integrated through Ollama, to intelligently extract specific personal data from incoming chat messages. Designed for robust data handling, it employs Langchain nodes to create a seamless pipeline. The process begins by triggering on a chat message, which is then fed into a basic LLM chain. To ensure accuracy and adherence to a defined structure, the output is processed through a structured output parser with a custom JSON schema. For any parsing errors, an auto-fixing output parser is employed to re-evaluate and correct the response, guaranteeing reliable and structured data extraction, all while keeping your sensitive information within your own infrastructure.
Key Features
- Self-Hosted LLM Control: Utilize Mistral NeMo LLM on your own infrastructure for maximum data privacy and security.
- Precise Data Structuring: Define a custom JSON schema to extract specific personal data points (name, surname, contact type, etc.).
- Automated Error Correction: An auto-fixing output parser intelligently handles and corrects LLM response errors.
- Real-time Triggering: Initiate data extraction automatically upon receiving chat messages.
- Flexible Configuration: Easily adjust LLM parameters and prompts for tailored extraction needs.
How To Use
- Set up Ollama: Install Ollama and pull the
mistral-nemo:latestmodel. - Configure Credentials: In n8n, set up your Ollama API credentials.
- Define JSON Schema: In the Structured Output Parser node, manually define the
inputSchemato specify the exact personal data fields you wish to extract (e.g., name, surname, commtype, contacts, timestamp, subject). - Set up LLM Chain: Configure the Basic LLM Chain node with a prompt that instructs the LLM to extract data according to the defined schema. Ensure
hasOutputParseris enabled. - Integrate Parsers: Connect the output of the Basic LLM Chain to the Structured Output Parser. Subsequently, connect the Structured Output Parser to the Auto-fixing Output Parser to handle potential errors.
- Trigger: Use the When chat message received node as your trigger. Ensure its webhook is set up to receive incoming chat messages.
- Connect LLM: The Ollama Chat Model node should be connected to both the Basic LLM Chain and the Auto-fixing Output Parser.
- Error Handling: Optionally, connect the
onErroroutput of the Basic LLM Chain to a separate error handling node, such as the On Error (noOp) node provided.
Apps Used
Workflow JSON
{
"id": "35f3088a-7840-4c86-8320-34438b7afc52",
"name": "Effortless Personal Data Extraction with Self-Hosted Mistral NeMo LLM",
"nodes": 17,
"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: 35f3088a-7840...
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