doms-json 1.0.0

A JSON toolkit that's particularly useful for what I'm making

doms-json

A JSON toolkit that's particularly useful for what I'm making

Installation

Install this library using pip:

pip install doms-json

Usage

I have a few projects in which I needed to do the same things, so I figured I'd make it a package. That's what this is.

Some key functionality within this package includes:

• Creating JSON Schemas
• Describing JSON schemas
• Pulling parameter descriptions from docstrings
• Generating JSON schemas from functions and objects
  • With descriptions pulled from docstrings
• Calling functions or initializing objects with a dictionary input

Creating a JSON schema

def create_json_schema(properties: list[str],
                       type_hints: dict[str, type] | None = None,
                       defaults: dict[str, Any] | None = None,
                       descriptions: dict[str, str]| None = None,
                       title: str | None = None,
                       additional_properties: bool = False,
                       pull_descriptions: bool = False,
                       pull_required: bool = False) -> dict:

The only thing required to create a JSON schema using the create_json_schema function is a list of properties.

For example:

create_json_schema(["my_variable", "my_second_variable"])

Will generate the following JSON schema:

{
    "type": "object",
    "properties": {
        "my_variable": { },
        "my_second_variable": { }
    },
    "additionalProperties": False
}

This is likely not very usefully. In most cases, you'll probably need to define types, defaults, descriptions, or a title.

Defining Types

Given the previous example, to define types for the variables we'll need to create a dictionary defining them:

types: dict = {
    "my_variable": str,
    "my_second_variable": int
}

Let's pass this into the function:

create_json_schema(["my_variable", "my_second_variable"], types)

The schema will now be generate as follows:

{
    "type": "object",
    "properties": {
        "my_variable": {
            "type": "string"
        },
        "my_second_variable": {
            "type": "integer"
        }
    },
    "additionalProperties": False
}

Setting Defaults

In some scenarios, you may want to define default values. These are handled similarly to how types are defined. We need to create a dictionary defining them:

defaults: dict = {
    "my_variable": "Hello world!"
}

Pass it into the function:

create_json_schema(["my_variable", "my_second_variable"], types, defaults)

Now it will generate with both types and defaults:

{
    "type": "object",
    "properties": {
        "my_variable": {
            "type": "string",
            "default": "Hello world!"
        },
        "my_second_variable": {
            "type": "integer"
        }
    },
    "additionalProperties": False
}

Adding Descriptions

Descriptions work the same way as setting types and defaults, we need to define them:

descriptions: dict = {
    "my_second_variable": "The number of lines of code"
}

Call the function:

create_json_schema(["my_variable", "my_second_variable"], types, defaults, descriptions)

And now it will be generated with types, defaults, and descriptions:

{
    "type": "object",
    "properties": {
        "my_variable": {
            "type": "string",
            "default": "Hello world!"
        },
        "my_second_variable": {
            "type": "integer",
            "description": "The number of lines of code"
        }
    },
    "additionalProperties": False
}

Setting Requirements

To require variables in the JSON schema, all you have to do is pass a list of the required variables:

create_json_schema(["my_variable", "my_second_variable"], types, defaults, descriptions, ["my_variable"])

The schema will now include the requirements:

{
    "type": "object",
    "properties": {
        "my_variable": {
            "type": "string",
            "default": "Hello world!"
        },
        "my_second_variable": {
            "type": "integer",
            "description": "The number of lines of code"
        }
    },
    "required": ["my_variable"],
    "additionalProperties": False
}

Setting a Title

In some case, you may want to set a title for the JSON schema. All you have to do is pass it in the function:

create_json_schema(["my_variable", "my_second_variable"], types, defaults, descriptions, ["my_variable"], "MySchema")

This will add the title to the schema:

{
    "type": "object",
    "title": "MySchema",
    "properties": {
        "my_variable": {
            "type": "string",
            "default": "Hello world!"
        },
        "my_second_variable": {
            "type": "integer",
            "description": "The number of lines of code"
        }
    },
    "required": ["my_variable"],
    "additionalProperties": False
}

All of these can be used interchangeably. For example:

• create_json_schema(["my_variable", "my_second_variable"], type_hints=types, required=["my_variable"])
• create_json_schema(["my_variable", "my_second_variable"], defaults=defaults, descriptions=descriptions)
• create_json_schema(["my_variable", "my_second_variable"], title="MySchema", type_hints=types)

Making Things Easier

In some cases, you may need to have a property is that is not a primative or array. You may need an object. Objects can significantly increase the complexity in terms of both creating and handling JSON schemas. Let's look at this example:

class MyObject:
    def __init__(self, my_variable: str, my_second_variable: int) -> None:
        self.my_variable: str = my_variable
        self.my_second_variable: int = my_second_variable

The JSON schema for this object would be:

{
    "type": "object",
    "title": "MyObject",
    "properties": {
        "my_variable": {
            "type": "string"
        },
        "my_second_variable": {
            "type": "integer"
        }
    },
    "required": ["my_variable", "my_second_variable"],
    "additionalProperties": False
}

Generating From Functions or Objects

Instead of using the create_json_schema to create a schema for this object manually, the generate_json_schema function will do it automatically:

generate_json_schema(MyObject)

This will generate the same exact JSON schema shown previously.

It checks for types, defaults and pulls the title automatically. Let's say my_variable should default to "Hello world!":

It also works with functions

def my_function(int_variable: int | None, str_variable: str = "This is a string"):...

generate_json_schema(my_function)

Notice the type given for int_variable: int | None. Specifying that a property can be None dictates that that property is not required.

{
    "type": "object",
    "title": "my_function",
    "properties": {
        "int_variable": {
            "type": "integer"
        },
        "str_variable": {
            "type": "string",
            "default": "This is a string"
        }
    },
    "required": ["str_variable"],
    "additionalProperties": False
}

And it works with class using class variables

class MyClass:
    names: list[str]

generate_json_schema(MyClass)

{
    "type": "object",
    "title": "MyClass",
    "properties": {
        "names": {
            "type": "array",
            "items": {
                "type": "string"
            }
        }
    },
    "required": ["names"],
    "additionalProperties": False
}

Using Docstrings

With docstrings, you can easily create definitions for functions and objects with parameter defintions in the reStructuredText (reST) format:

:param variable: Variable description

class MyObject:
    def __init__(self, my_variable: str) -> None:
        """
        :param my_variable: A simple string variable
        """
        self.my_variable: str = my_variable

When calling generate_json_schema(MyObject), we'll end up with the following JSON schema:

{
    "type": "object",
    "title": "MyObject",
    "properties": {
        "my_variable": {
            "type": "string",
            "description": "A simple string variable"
        }
    },
    "required": ["my_variable"],
    "additionalProperties": False
}

When using the generate_json_schema function, pull_descriptions is enabled. If this is disabled, the generation will ignore any docstrings.

This functionality is also available in the create_json_schema function, though it is disabled by default.

If for some reason you just need to retrieve the parameter descriptions from a function or object's docstring, you can use the pull_docstring_parameters function

Manually Describing a JSON Schema

If you already have a JSON schema and you need to add descriptions, use the describe_json_schema function to created a described copy of your schema.

{
    "type": "object",
    "properties": {
        "my_object": {
            "type": "object",
            "properties": {
                "my_string": {
                    "type": "string"
                }
            }
        },
        "my_number": {
            "type": "integer"
        }
    },
    "additionalProperties": False
}

To describe "my_object" and "my_number":

{
    "my_object": "An object",
    "my_number": "An integer"
}

Describing a property that belongs to an object requires some more formating. To describe "my_string" and "my_number":

{
    "my_object": {
        "my_string": "A string"
    },
    "my_number": "An integer"
}

If you want to describe the propertys within an object and the object itself, you'll need to use the "properties" and "description" keywords. To describe "my_object", "my_string", and "my_number"

{
    "my_object": {
        "properties": {
            "my_string": "A string"
        }
        "description": "An object"
    },
    "my_number": "An integer"
}

Convert a type to a JSON Schema Type

The to_json_schema_type function can be used to convert a type, union, tuple of types, or list of types into a JSON Schema type:

to_json_schema_type(str)

Returns

JSONSchemaType(
    schema_type={"type": "string"},
    requried=True
)

It can also handle objects, automatically generating JSON schemas if necessary. Enable pull_descriptions to pull the descriptions from docstrings.

If you want the basic type, use the to_direct_json_schema_type

to_direct_json_schema_type(str) == "string"

Calling Functions

Being able to call a function or initialize an object using a dictionary with the values can be useful. The json_call function simplifies the process significantly. For example:

def get_weather(date: str, city: str, state: str):...

Say the generate_json_schema function was used to generate a get_weather tool for an LLM. The LLM will respond with a JSON response of parameters to use with the function:

llm_response = {
    "date": "2025-01-01",
    "city": "Dallas",
    "state": "Texas"
}

Passing this along with the get_weather function will call properly assign the data and call the function

json_call(get_weather, llm_response)

It also handles objects:

class Location:
    def __init__(self, city: str, state: str) -> None:
        self.city: str = city
        self.state: str = state


def get_weather(date: str, location: Location):...

When used with an LLM, you'll get a response like this:

{
    "date": "2025-01-01",
    "location": {
        "city": "Dallas",
        "state": "Texas"
    }
}

The json_call function will automatically initialize a Location object from the given "location" data.

If the "location" value was already a Location object, then it will pass the data as is.

The json_call function handles type conversions to ensure that the function is called with the expected types.

Molding Values

The json_call function is built upon another useful function: mold_value

The mold_value function takes in a value and a expected_type, then converts the value to the expected type:

class Location:
    def __init__(self, city: str, state: str) -> None:
        self.city: str = city
        self.state: str = state

data = {"city": "Dallas", "state": "Texas"}

# Mold the data into a Location object
location_object: Location = mold_value(data, Location)

It works with many different types, with support for Unions and typed Lists:

data = [100, "string", 0.25, {"city": "Dallas", "state": "Texas"}]

# The 100, "string", and 0.25 will be uneffected, while the 
# location data will be converted to a Location object
molded_data = mold_value(data, list[int | str | float | Location])

Development

To contribute to this library, first checkout the code. Then create a new virtual environment:

cd doms-json
python -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

python -m pip install -e '.[test]'

To run the tests:

python -m pytest