Generate Pydantic Models from JSON

How does a JSON object say goodbye?

I'll be key-ping in touch!

What did the array say to the JSON object?

You've got some serious key-value problems.

Why was the JSON file so confident?

It knew its place, key by key.

What do you call a well-structured JSON document?

A true object of desire.

Why did the JSON programmer break up with the XML programmer?

It was all talk and no action, too many closing tags.

JSON Input
Paste your JSON below to get started.
JSON to Other Formats
Convert your JSON to other formats and schemas.

syntax = "proto3";

message MyMessage {
  Vessel vessel = 1;
}

message Vessel {
  string name = 1;
  string registry = 2;
  string class = 3;
  string status = 4;
  bool commissioned = 5;
  int64 launch_year = 6;
  repeated Seniorstaff senior_staff = 7;
  Systems systems = 8;
}

message Seniorstaff {
  string name = 1;
  string position = 2;
  string species = 3;
}

message Systems {
  Warpdrive warp_drive = 1;
  Shields shields = 2;
}

message Warpdrive {
  double max_speed = 1;
  string manufacturer = 2;
}

message Shields {
  string capacity = 1;
}

// Corresponding TextProto data representation:

vessel {
  name: "USS Enterprise"
  registry: "NCC-1701-D"
  class: "Galaxy-class"
  status: "Active"
  commissioned: true
  launch_year: 2363
  senior_staff {
    name: "Jean-Luc Picard"
    position: "Captain"
    species: "Human"
  }
  senior_staff {
    name: "William T. Riker"
    position: "First Officer"
    species: "Human"
  }
  senior_staff {
    name: "Data"
    position: "Second Officer"
    species: "Android"
  }
  senior_staff {
    name: "Worf"
    position: "Chief of Security"
    species: "Klingon"
  }
  systems {
    warp_drive {
      max_speed: 9.6
      manufacturer: "Yoyodyne Propulsion Systems"
    }
    shields {
      capacity: "4,590,000 TJ"
    }
  }
}

Generating Pydantic Models from JSON
Automate Python data validation and serialization by converting JSON to Pydantic models.

Pydantic is a popular Python library for data validation and settings management using Python type annotations. It provides a powerful way to define data schemas in a clear, Pythonic way. A JSON-to-Pydantic converter automates the process of creating these schemas by inferring the data types and structure from a sample JSON object. This saves developers from the tedious and error-prone task of manually writing Pydantic models that match a complex JSON structure. By generating these models, you can instantly leverage Pydantic's robust validation capabilities, ensuring that the data your Python application consumes is always correct. It also simplifies serialization and provides excellent editor support with autocompletion, making your code more reliable and easier to maintain.

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