A package to convert Python type annotations into JSON schemas
Package that uses static analysis -
ast - to convert Python 3 function type annotations to JSON schemas.
This allows you to auto-generate the validation schemas for JSON-RPC backend functions written in Python.
Current support is for Python 3.8+ and JSON schema draft 7+.
From a Python 3.8+ environment, run
pip install pytojsonschema.
Scan a package
After installing the package, you can open a python terminal from the root of the repo and run:
import os import pprint from pytojsonschema.functions import process_package pprint.pprint(process_package(os.path.join("test", "example")))
The example package will be scanned and JSON schemas will be generated for all the top level functions it can find.
Scan a file
You can also target specific files, which won't include the package namespacing in the result value. Following on the same terminal:
from pytojsonschema.functions import process_file pprint.pprint(process_file(os.path.join("test", "example", "service.py")))
Include and exclude patterns
Include and exclude unix-like patterns can be used to filter function and module names we want to allow/disallow for scanning. See the difference when you now run this instead:
pprint.pprint(process_package(os.path.join("test", "example"), exclude_patterns=["_*"]))
Similarly, but applied to specific files:
pprint.pprint(process_file(os.path.join("test", "example", "service.py"), exclude_patterns=["_*"]))
Things to take into account:
- Exclude pattern matching overwrite include matches.
__init__.pyfiles are not affected by pattern rules and are always scanned. However, you can still filter its internal functions.
Type annotation rules
Fitting Python's typing model to JSON means not everything is allowed in your function signatures. This is a natural restriction that comes with JSON data serialization. Hopefully, most of the useful stuff you need is allowed.
typing.Any are allowed. Also, you can build more complex, nested
structures with the usage of
str keys are allowed) and
typing.List. All these types have a direct, non-ambiguous representation in both JSON and JSON schema.
Your functions can also use custom types like the ones defined using an assignment of
typing.Optional, as in:
ServicePort = typing.Union[int, float] ServiceConfig = typing.Dict[str, typing.Any]
You can also use one of the new Python 3.8 features,
typing.TypedDict, to build stronger validation on dict-like
objects (Only class-based syntax). As you can see, you can chain types with no restrictions:
class Service(typing.TypedDict): address: str port: ServicePort config: ServiceConfig tags: typing.List[str] debug: bool
Importing types from other files
You can import these custom types within your package and they will be picked up. However, due to the static nature of the scan, custom types coming from external packages can't be followed and hence not supported. In other words, you can only share these types within your package, using relative imports.
Other static analysis tools like
mypy use a repository with stub files to solve this issue, see
https://mypy.readthedocs.io/en/stable/stubs.html. This is out of the
scope for a tiny project like this, at least for now.
The functions you want to scan need to be type annotated. Kind of obvious requirement, right?
Only the types defined in the previous section can be used. They are the types that can be safely serialised as JSON.
Function arguments are meant to be passed in key-value format, like a json object. This puts a couple of restrictions regarding *args, **kwargs, positional-only and keyword-only arguments:
- :heavy_check_mark: **kwargs
def func(**kwargs): passare valid.
- :heavy_check_mark: keyword-only arguments
def func(*, a): passare valid.
- :x: *args
def func(*args): passare not valid.
- :x: positional-only arguments
def func(a, /): passare not valid.
- :heavy_check_mark: **kwargs
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