[json schema] Do not throw errors when parsing non annotated parameters

[h-mayorquin]

This comes from the IBL project.

Summary:
If someone creates their own interface (inherits from BaseDataInterface) but does not annotate the __init__ function of the json validation will throw an obscure error:

self = <pydantic.json_schema.GenerateJsonSchema object at 0x77dfd6ff52d0>, schema = {'cls': <class 'inspect._empty'>, 'metadata': {}, 'type': 'is-instance'}
error_info = "core_schema.IsInstanceSchema (<class 'inspect._empty'>)"

    def handle_invalid_for_json_schema(self, schema: CoreSchemaOrField, error_info: str) -> JsonSchemaValue:
>       raise PydanticInvalidForJsonSchema(f'Cannot generate a JsonSchema for {error_info}')
E       pydantic.errors.PydanticInvalidForJsonSchema: Cannot generate a JsonSchema for core_schema.IsInstanceSchema (<class 'inspect._empty'>)
E       
E       For further information visit https://errors.pydantic.dev/2.7/u/invalid-for-json-schema

../../miniconda3/envs/work/lib/python3.11/site-packages/pydantic/json_schema.py:2152: PydanticInvalidForJsonSchema
================================================================================================ short test summary info =================================================================================================
FAILED tests/test_minimal/test_utils/test_get_json_schema_from_method_signature.py::test_get_json_schema_from_method_signature_unannotated - pydantic.errors.PydanticInvalidForJsonSchema: Cannot generate a JsonSchema for core_schema.IsInstanceSchema (<class 'inspect._empty'>)
==============================================================

The fix, in my view, is to omit validation when the user does not annotate their signature parameters as there is nothing to validate. I added a fix and a test here.

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 90.75%. Comparing base (96dfdff) to head (8fad6a9).
Report is 7 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1157      +/-   ##
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+ Coverage   90.69%   90.75%   +0.06%     
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  Files         129      129              
  Lines        8189     8286      +97     
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+ Hits         7427     7520      +93     
- Misses        762      766       +4     

Flag	Coverage Δ
unittests	90.75% <100.00%> (+0.06%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
...nterfaces/ecephys/basesortingextractorinterface.py	80.19% <100.00%> (ø)
src/neuroconv/utils/json_schema.py	95.03% <100.00%> (+0.48%)	⬆️

... and 3 files with indirect coverage changes

[bendichter]

I don't really see the point of leaving an arg un-annotated, especially if one is going to use the get_json_schema_from_method_signature method. I agree that the error message is obscure, but if that is the problem I think the best solution would be to improve the error message. If they really have a compelling use-case for leaving an arg fully unconstrained then I guess I would be OK with supporting that by throwing a warning instead of an error.

[h-mayorquin]

I don't really see the point of leaving an arg un-annotated, especially if one is going to use the get_json_schema_from_method_signature method. I agree that the error message is obscure, but if that is the problem I think the best solution would be to improve the error message. If they really have a compelling use-case for leaving an arg fully unconstrained then I guess I would be OK with supporting that by throwing a warning instead of an error.

Important context: the method get_json_schema_from_method_signature is always used when initializing interfaces. Currently, there is no option to avoid source validation, either at the interface or converter level, so they need to use it.

The argument to leave arguments unannotated is the one that you have made before: lowering the barriers to contribution for users by not adding hurdles. Forcing them to use type annotations to inherit from BaseDataInterface is a contentious hurdle. My impression is that the Python community is still quite divided about using annotations or not.

If you still prefer to force the user to use annotations, then the error can be something like:

"The arguments x, y, and z lack type annotations. Add them to initialize the interface."

What do you think?

[bendichter]

the method get_json_schema_from_method_signature is always used when initializing interfaces

Why are we always calling this function? Is this necessary?

[bendichter]

If we want to enforce typing, we could use beartype: https://github.com/beartype/beartype

[h-mayorquin]

the method get_json_schema_from_method_signature is always used when initializing interfaces

Why are we always calling this function? Is this necessary?

To validate source data. To me it seems like a nice feature to have and it has been like that since the nwb-conversion-tools times. At least for the NWBConverter.

If we were to disable source validation under which conditions we would?

I personally would prefer the outcome of this PR. Once this PR is merged it will work like this for interfaces and converters:

• If someone does annotate their interfaces __init__ then we do the source schema validation.
• If someone does not or only annotates some of them then we respect their intent and we don't validate the ones without type annotations (that is, we don't force them to annotate or add typing.Any)

beartype looks nice. We have something similar with Pydantic validate_call:
https://docs.pydantic.dev/latest/api/validate_call/

[bendichter]

I think these issues are separable:

1. We want to generate a json schema for input args. This is useful for interfacing with other applications like NWB GUIDE
2. We want to validate inputs to the init function.

We don't necessarily need to do validation with the json schema, when working within Python. There are some nuances in the conversion from args to json schema that can cause complications here.

1. It currently requires annotation for every arg, which is cumbersome
2. There are some inconsistencies in requirements between Python and JSON. For example there is this pydantic issue where JSON schema dates require timezones and Pydantic-validated dates do not.

The pydantic library has come a long way since we originally wrote this code, and now facilitates a much simpler approach. Now we can change the code so that instead of attempting to create a json schema and then validate every call with it, we use the method you mentioned, @validated_call. In so doing, we are removing the get_json_schema_from_method_signature method from this workflow, and we can have stricter requirements for this without worrying about creating a burden for users.