Tool for generating language specific schemas and interfaces code from JSON Type Definition IDL files in yaml format. Powered by jtd-codegen.
Project description
JSON Type Definition Code Build
jtd-codebuild is a tool for generating language specific schemas and interfaces code from JSON Type Definition IDL files in yaml format.
This tool is built on top of jtd-codegen
so check out the documentation if you don't have a clue about JSON Type Definition.
Prerequisites
- Python
- jtd-codegen
Installation
pip install jtd-codebuild
Usage
jtd-codebuild path/to/the/folder/where/jtd-codebuild.json/is/located
Required conventions
Configuration
The script will find jtd-codebuild.json
which is the configuration file of this tooling.
root-schema-path
The path to the root schema file. Root schema file will be the entry point of the code generation, where every definition files will be merged into.
Defaults to schema.jtd.yaml
definitions-path
The path to the definitions directory. This directory will be recursively searched for definition files.
Definition file is a file that contains a single or multiple definitions. Checkout the documentation below for more information.
Defaults to definitions
output-schema-path
The path for the merged schema file converted in json format.
Defaults to gen/schema.jtd.json
includes
Array of JTD package paths to include.
The path should have jtd-codebuild.json
file in it
so that this tool can find the codebuild configuration.
If you specifiy a package path, it will reference the package's schema definitions when generating schema file you are working on.
Defaults to []
targets
Compile targets.
It's a JSONRecord contains the object having following properties:
language (string)
: The language of the target. We essentially inject this value tojtd-codegen
as target language option which is provided as a flag which is like--{language}-out
. Available languages can be found in the documentation ofjtd-codegen
. (See: https://jsontypedef.com/)path (string)
: The path to the directory where the generated code will be placed.
targets
- Language Specific Options - Python
use-pydantic (boolean)
: Whether to use pydantic as adataclass
decorator provider. If this is set to true, the generated code will usepydantic.dataclasses.dataclass
as adataclass
decorator so that you can use pydantic's validation features. Otherwise, the generated code will be plain python dataclasses. Defaults tofalse
.use-typeddict (boolean)
: Whether to useTypedDict
instead ofdataclass
. If this is set to true, the generated code will useTypedDict
instead ofdataclass
. This property cannot be set to true ifuse-pydantic
is set to true. Also, subscriptable option will be ignored if this is set to true.subscriptable (boolean)
: Whether to make the generated class subscriptable. If this is set to true, the generated class will be subscriptable so that you can access the properties of the class likeobj["property"]
. Otherwise, the generated class will not be subscriptable. Defaults tofalse
.
targets
- Language Specific Options - TypeScript
tsconfig-path (string)
: The path to the tsconfig file. This will be used to compile typescript code to javascript code and type declarations. If you want to automatically generate plain javascript artifact with type declarations, you should also provide this option.
Configuration Example
Example congfiguration file is provided below. Copy it and modify it to your needs.
{
"root-schema-path": "schema.jtd.yaml",
"definitions-path": "definitions",
"output-schema-path": "gen/schema.jtd.json",
"includes": ["../path/to/another/folder/that/contains/jtd-codebuild.json"],
"targets": [
{
"language": "python",
"path": "gen/python"
},
{
"language": "python",
"path": "gen/python-pydantic",
"use-pydantic": true,
"subscriptable": true
},
{
"language": "python",
"path": "gen/python-typeddict",
"use-typeddict": true,
"subscriptable": true
},
{
"language": "typescript",
"path": "gen/typescript",
"tsconfig-path": "tsconfig.build.json"
}
]
}
Root Schema File
Root schema file is the entry point of the code generation.
It will be the file where every definition files will be merged into.
If you don't need a root Schema
type, you can just create an empty file.
Definition files
Definition files are sharable files of which each of them contains a single or multiple definitions.
Each declared keys as a root key in the definition file will be merged as a key of definitions
object in the root schema file, and those symbols can be shared across the other definition files.
For example, let's say you have a definition file whose code is like below.
book:
properties:
id:
type: string
title:
type: string
This can be referenced in other definition files like below.
user:
properties:
id:
type: string
name:
type: string
books:
elements:
ref: book
This will be merged as a single schema like below.
{
"definitions": {
"book": {
"properties": {
"id": {
"type": "string"
},
"title": {
"type": "string"
}
}
},
"user": {
"properties": {
"id": {
"type": "string"
},
"name": {
"type": "string"
},
"books": {
"elements": {
"ref": "book"
}
}
}
}
}
}
Checkout more about ref
if you don't have a clue about it. https://jsontypedef.com/docs/jtd-in-5-minutes/#ref-schemas
Manual dependency management
Since IDL files are basically just a bunch of JSON objects, we need to manually manage the dependency between the definition files.
For example, assume you have a folder structure like the below:
definitions
├── book
│ └── book.jtd.yaml
└── user
└── user.jtd.yaml
And assume that book.jtd.yaml
and user.jtd.yaml
are the root definition files of each module.
In this case, you need to annotate that user.jtd.yaml
depends on book.jtd.yaml
like below.
# user.jtd.yaml
#
# Depends on:
# - book (at ../book/book.jtd.yaml)
user:
properties:
id:
type: string
name:
type: string
books:
elements:
ref: book
Contributing
Any contribution is welcome! Check out CONTRIBUTING.md and CODE_OF_CONDUCT.md for more information on how to get started.
License
jtd-codebuild
is licensed under a MIT License.
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