Extract personal data with a self-hosted LLM Mistral NeMo

For the platform Mistral NeMo, this automated workflow extracts personal data from chat messages using a self-hosted language model. It efficiently analyzes incoming requests, ensuring accurate data extraction based on a defined JSON schema. The process includes error handling and auto-correction to enhance output quality, ultimately streamlining data management and improving user interactions.

7/8/2025
13 nodes
Medium
manualmediumlangchainnoopsticky noteadvanced
Categories:
Manual TriggeredMedium Workflow
Integrations:
LangChainNoOpSticky Note

Target Audience

This workflow is designed for:
- Data Analysts: Individuals who need to extract structured personal data from chat interactions.
- Customer Support Teams: Professionals who manage customer communications and require automatic data extraction for better service.
- Developers: Those who are integrating AI models into applications and need a reliable way to parse and structure data.
- Business Intelligence Professionals: Users who analyze communication data for insights and reporting.

Problem Solved

This workflow addresses the challenge of manually extracting and structuring personal data from chat messages. It automates the process, ensuring accuracy and efficiency in capturing essential information such as names, contact methods, and timestamps. This reduces human error and saves time, allowing teams to focus on more strategic tasks.

Workflow Steps

  • Trigger: The workflow begins when a chat message is received through a webhook.
    2. Basic LLM Chain: The incoming message is analyzed by the Basic LLM Chain, which prompts the model to extract information according to a defined JSON schema. The current date is also included in the analysis.
    3. Ollama Chat Model: This step utilizes the Mistral NeMo model to generate a response based on the user input.
    4. Structured Output Parser: The output from the model is checked against a structured schema to ensure it meets the required format.
    5. Auto-fixing Output Parser: If the output does not satisfy the constraints, this parser re-engages the model with a different prompt to correct the response.
    6. Extract JSON Output: Once valid data is confirmed, it is set in a raw JSON format for further use.
    7. Error Handling: If any errors occur during the process, they are handled gracefully to ensure the workflow continues running smoothly.

    • Customization Guide

    To customize this workflow:
    - Adjust the JSON Schema: Modify the inputSchema in the Structured Output Parser to include or exclude fields based on your requirements.
    - Change Model Parameters: In the Ollama Chat Model node, adjust parameters like temperature or keepAlive to optimize model performance for your specific use case.
    - Update Prompts: Edit the prompt in the Basic LLM Chain to refine how data is extracted from user messages.
    - Integrate Additional Nodes: Add more nodes as needed to extend functionality, such as integrating with databases or other APIs for data storage or further processing.