Generate JSON Node

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Overview

The Generate JSON Node is a specialized AI component that generates structured JSON data based on user input and predefined schemas. This node is particularly useful for applications requiring consistent data structures, API integrations, and automated data formatting.

Node Type Information

Type	Description	Status
Batch Trigger	Starts the flow on a schedule or batch event. Ideal for periodic data processing.	❌ False
Event Trigger	Starts the flow based on external events (e.g., webhook, user interaction).	❌ False
Action	Executes a task or logic as part of the flow (e.g., API call, transformation).	✅ True

This node is an Action node that processes input data and generates structured JSON output based on predefined schemas.

Features

Key Functionalities

Generative Model Selection: Allows users to select credentials for their preferred generative model, ensuring flexibility and compatibility with various LLMs.
Customizable Prompts: Features a field for creating prompt templates, enabling tailored input for specific use cases.
System Prompt Definition: Includes an option to define a system-level prompt to guide the AI's behavior, ensuring context-aware and role-specific responses.
Additional Properties Management: Offers expandable sections for configuring advanced properties to fine-tune AI-generated outputs.

Benefits

Flexibility: Supports integration with multiple LLMs, allowing users to utilize different models based on their specific needs and use cases.
User-Centric Design: Provides intuitive interfaces for prompt customization and system prompt definition, making it accessible to both beginners and advanced users.
Enhanced Control: Enables precise control over the AI's behavior and output quality through customizable prompts and system configurations.
Scalability: Facilitates the creation of reusable prompt templates for diverse flow, ensuring consistency across projects.

What can I build?

Create AI-driven applications that require structured data output from natural language inputs.
Develop automated systems for generating specific data formats for use in analytics and reporting.
Build interactive tools that convert conversational inputs into structured JSON for use in various applications.
Design flow for content generation that require consistency in data formatting across different platforms.

Setup

Select the JSON Generate Node

Fill in the required parameters.
Build the desired flow
Deploy the Project
Click Setup on the workflow editor to get the automatically generated instruction and add it in your application.

Configuration Reference

Parameter	Description	Example Value
Generative Model Name	Select the model to generate text based on the prompt.	Gemini Model
Output Schema (Zod JSON)	Define the output structure and validation rules using Zod JSON.	JSON Schema
User Prompt	Define the instructions for generating the text output.Define the instructions for generating the text output.	Tell me travel from Bali to Singapore
System Prompt	System prompt to guide the LLM	You are Travel Planner

Low-Code Example

nodes:
  - nodeId: InstructorLLMNode_774
    nodeType: InstructorLLMNode
    nodeName: JSON Generator
    values:
      schema: |-
        {
          "type": "object",
          "properties": {
            "output": {
              "type": "string"
            }
          }
        }
      promptTemplate: tell me something about ${{triggerNode_1.output.topic}}
      attachments: "[]"
      messages: "[]"
      generativeModelName:
        provider_name: mistral
        type: generator/text
        credential_name: Mistral API
        credentialId: 32bf5e3b-a8fc-4697-b95a-b1af3dcf7498
        model_name: mistral/mistral-large-2402
    needs:
      - triggerNode_1

Output Schema

`_meta`

A nested object containing metadata about the processing of the generation request.

Token Usage Details

prompt_tokens: Number of tokens in the input prompt provided to the model.
completion_tokens: Number of tokens in the generated output.
total_tokens: Sum of prompt_tokens and completion_tokens.

`prompt_tokens_details`

A nested object providing a breakdown of token usage in the prompt.
- cached_tokens: Number of tokens reused from a cache.
- audio_tokens: Number of tokens associated with audio input (if applicable).

`completion_tokens_details`

A nested object detailing token usage in the generated output.
- reasoning_tokens: Number of tokens used for reasoning processes (if applicable).
- audio_tokens: Number of tokens associated with audio output (if applicable).
- accepted_prediction_tokens: Number of tokens from accepted predictions (if applicable).
- rejected_prediction_tokens: Number of tokens from rejected predictions (if applicable).

Model Information

model_name: The name of the AI model used for generation.
model_provider: The provider or organization supplying the model.

Example Output

{
  "name": "Adelaide",
  "_meta": {
    "prompt_tokens": 56,
    "completion_tokens": 7,
    "total_tokens": 63,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    },
    "model_name": "gpt-4-turbo",
    "model_provider": "openai"
  }
}

Troubleshooting

Common Issues

Problem	Solution
Invalid API Key	Ensure the API key is correct and has not expired.
Dynamic Content Not Loaded	Increase the `Wait for Page Load` time in the configuration.

Debugging

Check Lamatic Flow logs for error details.
Verify API Key.

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