Knowledge Base #1
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Fields of get and post methodsIn OpenAPI Specification (OAS), parameters for GET methods (query parameters) and request bodies for POST methods (body parameters) can have different fields. Here's a summary of the valid fields for each: GET Method ParametersQuery parameters can have the following fields:
POST Method Schema PropertiesFor the request body of POST methods, you would specify a schema for the content type application/json. Inside the schema, you can have
Both parameter lists and schema properties can include additional fields based on extensions (fields that start with Keep in mind that The OpenAPI Specification may evolve, so always refer to the official OAS documentation for the most up-to-date and detailed information. |
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Schema of "schema"The Schema Object Fields
Example Schema Object for a GET Query Parameter:parameters:
- in: query
name: id
schema:
type: integer
format: int64
minimum: 1
required: true
description: Unique identifier of the resourceExample Schema Object for a POST Request Body:requestBody:
content:
application/json:
schema:
type: object
required:
- name
properties:
name:
type: string
age:
type: integer
format: int32
minimum: 0
additionalProperties: falseIn both examples, the The OAS documentation provides a detailed reference on data types and the structure of the |
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typeCan the value of the type field be anything? The
Specifying Schema for an ObjectWhen the type: object
required: [id, name]
properties:
id:
type: integer
format: int64
name:
type: stringSpecifying Schema for an Array with Mixed TypesIf you want an array to contain elements of different types in a specific order, you can use the Here's an example of how you might describe a simple array with a single type and a string with a pattern: type: object
properties:
settings:
type: array
minItems: 3
maxItems: 3
items:
- type: boolean
- type: integer
- type: string
pattern: '^[a-zA-Z_].*'In this example, For more complex structures, you could use a combination of For very complex validation rules that cannot be described using OpenAPI's schema object, you may need to document the constraint in the |
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With the Edge browser I went to the github API OpenAPI specification: Then I asked bing chat:
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I asked it for the full list of GET (highest level) endpoints. Here is the full list of GET endpoints:
I hope this helps! Source: Conversation with Bing, 11/17/2023 |
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Third party toolsMight there be some third party python libraries that might help me do this translation from json schemas to python objects? Yes, there are several third-party Python libraries designed to help with the translation from JSON schemas to Python objects. Here are a few notable ones:
To use any of these libraries, you would typically install them via pip and follow their documentation for schema definition and object serialization/deserialization. For instance, if you were to use Pydantic, you could define a model based on your schema, and Pydantic would handle the conversion from JSON to the Pydantic model (Python object) automatically. Here is a simple example using Pydantic: from pydantic import BaseModel
class Item(BaseModel):
type: str
limit: int
item = Item.parse_obj({
'type': 'SomeType',
'limit': 10
})In this example, For more complex JSON schemas that include nested objects, Pydantic supports models within models, and you can also use Examplefrom pydantic import BaseModel, create_model
from i2 import Sig, sort_params
from inspect import Parameter, Signature
from typing import List, Type, Dict, Any, Optional
# Default mapping from JSON types to Pydantic fields
default_type_mapping = {
'array': (List[Any], ...),
'integer': (int, ...),
'object': (Dict[Any, Any], ...),
'string': (str, ...),
'boolean': (bool, ...),
'number': (float, ...),
}
def json_to_pydantic_model(params: List[Dict[str, Any]], type_mapping: Dict[str, Type] = default_type_mapping) -> Type[BaseModel]:
fields = {}
for param in params:
field_info = type_mapping[param['schema']['type']]
# Set the default value if it's not required and a default is provided
if not param.get('required', False) and 'default' in param['schema']:
field_info = (field_info[0], param['schema']['default'])
fields[param['name']] = field_info
return create_model('DynamicModel', **fields)
def pydantic_model_to_sig(model: Type[BaseModel], return_annotation: Any = None) -> Signature:
parameters = [
Parameter(
name=field.alias,
kind=Parameter.POSITIONAL_OR_KEYWORD,
default=field.default if not field.required else Parameter.empty,
annotation=field.outer_type_ if field.outer_type_ != Any else field.type_,
)
for field in model.__fields__.values()
]
sorted_parameters = sort_params(parameters) # Assuming this sorts parameters suitably for your use-case
return Signature(parameters=sorted_parameters, return_annotation=return_annotation)
# Example usage
params = [
{
'name': 'type',
'schema': {'type': 'string'},
'required': True,
'description': 'Type of items to list',
},
{
'name': 'limit',
'schema': {'type': 'integer', 'default': 10},
'required': False,
'description': 'Maximum number of items to return',
},
]
# Create a Pydantic model from the JSON schema parameters
DynamicModel = json_to_pydantic_model(params)
# Convert the Pydantic model to an inspect.Signature
signature = pydantic_model_to_sig(DynamicModel)
# Wrap the Signature in a Sig from the 'i2' package
sig = Sig.from_signature(signature) |
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json to pydantic toolsIf you're looking for tools to automatically generate Pydantic models from JSON or Python dictionaries, there are a couple of excellent options available:
These tools can save you a significant amount of time, especially when dealing with complex or large JSON structures that need to be validated in your Python projects. |
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This is to collect information that we'd like to have at our finger tips and/or reference.
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