caffeinism/sick-json

`Poorly written JSON` parsers

A simple tool to parse the abnormal JSON

The only function implemented in this module is parse.

parse is similar to json.loads. However, it corrects some mistakes that LLM can make.

It can be installed with pip install sick-json and used with import sick_json;sick_json.parse(something).

TL;DR

This module interpret

some description here..
[{
    "name": "Kim", // required
    'age': 13, /* optional */
},
{
    name: "Lee",
    age: "\14", 
]

to

[{"name": "Kim", "age": 13}, {"name": "Lee", "age": "14"}]

It can also be validated/calibrated via pydantic.

import sick_json
from pydantic import BaseModel

class Student(BaseModel):
    name: str
    age: int
    
sick_json.parse(r"""
some description here..
[{
    "name": "Kim", // required
    'age': 13, /* optional */
},
{
    name: "Lee",
    age: "\14", 
]
""", pydantic_model=list[Student])

[{'name': 'Kim', 'age': 13}, {'name': 'Lee', 'age': 14}]

Case by Case

By default, it uses a JSON5 parser, which solves the following problems.

• Identifiers without quotes

{
    name: "Kim"
}

• Trailing comma

{
    "name": "Kim",
}

• JS-style comment

{
    "name": "Kim" // something
}

The JSON5x parser, a slight variation of the JSON5 parser, parses the following syntax.

• Allow "True" and "False"

{
    "name": "Kim",
    "is_good_guy": True
}

• Unclosed brackets

{"name": "Kim",

[[{"some":{"name": "Kim"], "thing"]

• Ignore invalid escape sequence

{"name": "Kim", "age": "\18"}

Additionally, it heuristically solves a few problems.

• Verbose before and after JSON

blah blah blah
{
    "name": "Kim"
}
blah blah blah

• If you have multiple JSONs, it will return the longest by default, or you can specify a pydantic format.

import sick_json
from pydantic import BaseModel
maybe_json = 'blah{"name": "Kim"}blah{"names": ["Kim", "Lee"]}blah'
sick_json.parse(maybe_json)
# it return {"names": ["Kim", "Lee"]}
class MyModel(BaseModel):
    name: str
sick_json.parse(maybe_json, pydantic_model=MyModel)
# it return {"name": "Kim"}

• Type-correcting by pydantic model

import sick_json
from pydantic import BaseModel
maybe_json = '{"name": "Kim", "age": "13"}'
class MyModel(BaseModel):
    name: str
    age: int
sick_json.parse(maybe_json, pydantic_model=MyModel)
# it return {"name": "Kim", "age": 13}