Fix #9794: Pydantic integration fails with __future__.annotations. #9795
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When a project uses `from __future__ import annotations`, all annotations will be stored as strings. This is fairly common, and many projects dictate that any use of type annotations must be accompanied by this import. The Pydantic integration in Celery introspects the annotations to check if any parameters and/or the return type are subclasses of `pydantic.BaseModel`. This fails when the annotations are `str` instead of the actual class. This change fixes the issue by optimistically using `typing.get_type_hints()` instead of relying on the annotations included in the result of `inspect.signature()`. This works in most cases, although there can be cases where `get_type_hints()` fails due to circular import chains. In this case, we fall back to the old implementation. A new test has been added to t/integration/test_tasks.py to validate the issue.
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## main #9795 +/- ##
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- Coverage 78.62% 78.60% -0.02%
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Files 153 153
Lines 19178 19186 +8
Branches 2540 2542 +2
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+ Hits 15079 15082 +3
- Misses 3809 3812 +3
- Partials 290 292 +2
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Pull Request Overview
This PR addresses a bug where Pydantic‐typed task annotations stored as strings (due to from __future__ import annotations) aren’t correctly recognized. It switches to using typing.get_type_hints() with a fallback and adds a new integration test to cover this case.
- Use
typing.get_type_hints()first, falling back to raw signature annotations on failure. - Add a new task
add_pydantic_string_annotationswith string annotations. - Add an integration test
test_pydantic_string_annotationsto validate the fix.
Reviewed Changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
| celery/app/base.py | Resolve string annotations via get_type_hints() with fallback. |
| t/integration/tasks.py | Define new test task add_pydantic_string_annotations. |
| t/integration/test_tasks.py | Add test_pydantic_string_annotations to cover string annotations. |
Comments suppressed due to low confidence (2)
celery/app/base.py:135
- Consider also catching
RecursionErrorfromget_type_hints(), as circular import chains can raise it. This ensures the fallback path handles all common failures.
except (NameError, AttributeError, TypeError):
t/integration/tasks.py:497
- [nitpick] Per PEP8, add two blank lines before top-level function definitions for better readability.
@shared_task(pydantic=True)
auvipy
approved these changes
Jul 5, 2025
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Description
When a project uses
from __future__ import annotations, all annotations will be stored as strings. This is fairly common, and many projects dictate that any use of type annotations must be accompanied by this import.The Pydantic integration in Celery introspects the annotations to check if any parameters and/or the return type are subclasses of
pydantic.BaseModel. This fails when the annotations arestrinstead of the actual class.This change fixes the issue by optimistically using
typing.get_type_hints()instead of relying on the annotations included in the result ofinspect.signature(). This works in most cases, although there can be cases whereget_type_hints()fails due to circular import chains. In this case, we fall back to the old implementation. A new test has been added to t/integration/test_tasks.py to validate the issue.