Apa yang dilakukan JSON Data Model Generator?
JSON Data Model Generator membantu menghasilkan keluaran langsung di browser serta menjelaskan hasil dan batasannya dengan jelas. Kemampuan yang telah diverifikasi: Ten output targets, Nested type inference, Array element inference, Copyable starter models.
Generated models are a typed starting point
Use output to accelerate scaffolding, then reconcile it with the authoritative schema, nullability rules, naming conventions, and serialization library.
Bagaimana cara menggunakannya?
- Paste representative JSON and choose the target language or JSON Schema.
- Enter a root type name and generate the model.
- Review inferred nullability and collection types before copying the starter code.
Contoh masukan dan keluaran nyata
Infer a TypeScript interface
Masukan
{"id":1,"name":"Ada","active":true}Keluaran
export interface Root {
id: number;
name: string;
active: boolean;
}Each sample property becomes a field whose type is inferred from its value.
Kesalahan umum dan perbaikannya
| Gejala | Kemungkinan penyebab | Perbaikan |
|---|---|---|
| A field should be optional but is required. | One sample object cannot prove that a property may be absent. | Review the API contract and mark optional fields manually. |
| A numeric type is too broad or too narrow. | JSON has one number syntax and does not declare integer width or precision. | Choose a target type using the real value range and serialization contract. |
Important behavior of Data Model Generator
Batas khusus alat ini: A sample cannot reveal every optional field, union, integer width, validation rule, or API compatibility constraint.
Standar dan referensi implementasi
- RFC 8259 — JSON (opens in a new tab)Defines the source data types available to inference.
- JSON Schema 2020-12 (opens in a new tab)Defines the schema vocabulary used by the JSON Schema target.
Data Model Generator — FAQ
Does the Data Model Generator upload my input?
No. Processing runs in the current browser tab, and the page does not send tool input to a KitLumi processing endpoint.
What input does the Data Model Generator accept?
It accepts a valid JSON object or array. The workspace checks the input and reports malformed or unsupported values before it produces a result.
What does the Data Model Generator produce?
It produces source declarations for nine languages or a draft JSON Schema. Copy and download controls appear when the output format supports them.
What limitation should I know?
A sample cannot reveal every optional field, union, integer width, validation rule, or API compatibility constraint.
Apa yang berubah?
Pembaruan substantif: Data Model Generator: Added Ten output targets, Nested type inference, Array element inference, Copyable starter models, responsive controls, explicit runtime disclosure, and task-specific examples and limitations.