Skip to content
Nappai Documentation

Schema Builder

The Schema Builder component lets you create a structured data schema from plain JSON.
You simply describe what the model should look like, list its fields, give it a name, and the component will output a ready‑to‑use schema and a JSON representation of that schema.

How it Works

When you provide the component with a Model Description, Model Fields, and Model Name, it parses the JSON fields and automatically generates a Python data model (a BaseModel).
Internally it uses the SchemaBuilderBase logic to:

  1. Read the JSON structure you supply.
  2. Infer field types (string, number, boolean, etc.).
  3. Build a schema object that can be reused in other parts of your workflow.
  4. Export the schema as a JSON string for easy sharing or storage.

No external APIs are called; everything happens locally inside the Nappai dashboard.

Inputs

  • Model Description: A brief explanation of what the model represents.
  • Model Fields: The JSON structure that defines the fields of the model.
  • Model Name: The name you want to give to the generated model.

Outputs

  • Schema: A BaseModel object that represents the data structure.
  • JSON: A plain text JSON string that contains the schema definition.

Usage Example

  1. Add the Schema Builder component to your workflow.
  2. Enter:
    • Model Description: "User profile information"
    • Model Fields: 
      {
        "id": "integer",
        "name": "string",
        "email": "string",
        "is_active": "boolean"
      }
    • Model Name: UserProfile
  3. Run the workflow.
  4. The component will output:
    • A BaseModel called UserProfile that you can use in other components.
    • A JSON string that you can copy, save, or feed into a database schema generator.
  • JSON Parser – Convert raw JSON into a format the Schema Builder can use.
  • Data Validator – Check that incoming data matches the generated schema.
  • Schema Exporter – Save the schema to a file or database for later use.

Tips and Best Practices

  • Keep the Model Fields JSON simple; avoid nested objects unless you need them.
  • Use clear, concise Model Description text so others understand the purpose of the schema.
  • If you plan to reuse the schema, give it a stable Model Name that follows your naming conventions.
  • After generating a schema, test it with sample data using the Data Validator to catch any type mismatches early.

Security Considerations

  • The component processes data locally, so no sensitive information leaves your Nappai instance.
  • Be cautious when sharing the generated JSON schema; it may reveal the structure of your data models.
  • If the schema includes fields that hold confidential data, restrict access to the component’s outputs in your workflow permissions.