[AI/LangChain] Output Parser 4

For LangChain, this automated workflow efficiently processes user prompts to extract structured data about the five largest U.S. states by area, including their three largest cities and populations. It utilizes advanced output parsing and autofixing capabilities to ensure accurate results, enhancing data reliability and user experience.

7/8/2025
11 nodes
Medium
manualmediumlangchainsticky noteadvanced
Categories:
Manual TriggeredMedium Workflow
Integrations:
LangChainSticky Note

Target Audience

This workflow is designed for:
- Data Analysts: Who need to extract and structure information from large datasets.
- Developers: Looking to integrate LangChain with n8n for automated data processing.
- Business Analysts: Who require insights on state populations and city demographics.
- Educators: Teaching data science and automation with practical examples.
- Researchers: In need of structured data for geographic and demographic studies.

Problem Solved

This workflow addresses the challenge of retrieving and structuring complex data from a natural language prompt. It automates the process of extracting the 5 largest states by area in the USA, along with their 3 largest cities and respective populations, ensuring that the output is valid and well-structured for further analysis.

Workflow Steps

  • Manual Trigger: The workflow starts when the user clicks "Execute Workflow".
    2. Set Prompt: A prompt is defined, asking for information about the largest states and their cities.
    3. LLM Chain: This node processes the prompt using a language model to generate a response.
    4. Structured Output Parser: The response is validated against a predefined JSON schema to ensure it meets the expected format.
    5. Auto-fixing Output Parser: If the output is invalid, this node attempts to correct it using another language model.
    6. Sticky Notes: Various sticky notes provide explanations and context for each step in the workflow, enhancing understanding.

    • Customization Guide

    To customize this workflow:
    - Change the Prompt: Modify the text in the "Prompt" node to ask for different data or insights.
    - Adjust Output Schema: Update the JSON schema in the "Structured Output Parser" to reflect new output requirements.
    - Modify Language Model Settings: Change the parameters in the "OpenAI Chat Model" nodes to adjust the response style or temperature for creativity.
    - Add More Nodes: Incorporate additional nodes for further processing or analysis of the output data.
    - Change Trigger Type: If needed, adjust the trigger to allow for automatic execution based on other events.