Update/pydantic v2 #217
Update/pydantic v2 #217
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- Update cuenca-validations from 0.11.19 to 2.0.0.dev7 and fastapi from 0.68.2 to 0.115.6 in requirements.txt. - Modify QueryResponse model in rest_api.py to use Field for descriptions. - Add a new test for handling invalid JSON messages in test_sqs_tasks.py.
WalkthroughThis pull request introduces a comprehensive update to the project's Python version and dependency management. The changes span multiple files across the project, focusing on upgrading the Python version from 3.8 to 3.13 and updating various dependencies. The modifications include updates to GitHub Actions workflows for both release and testing processes, the Makefile, requirements files, and setup configuration. The project's test infrastructure has been expanded to support multiple Python versions (3.9 through 3.13), and several dependencies have been updated to their latest versions. Additionally, minor code refactoring and type annotation improvements were made across various files, including middleware configurations, resource handling, and validator setups. The version number of the software has also been updated from '0.14.1' to '2.0.0', indicating a significant version increment. 📜 Recent review detailsConfiguration used: CodeRabbit UI 📒 Files selected for processing (1)
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Actionable comments posted: 4
🧹 Nitpick comments (8)
examples/resources/base.py (1)
22-22: Consistent usage of return type hintSimilar to the previous function, using
-> Nonealigns well with the actual behavior of raising an exception. If you want to signal that the function never returns under any condition,NoReturncan be kept instead, but it's a stylistic choice.examples/app.py (1)
21-21: Consider adding typed stubsWhile adding
# type: ignoreremoves type checker errors, it can obscure real issues. If possible, provide a typed stub or direct type hint forFastAgaveErrorHandlerto avoid having to ignore type checking.setup.py (1)
26-31: Reevaluate strict version pinningPinning exact versions (e.g.,
==2.16.1) ensures a consistent environment but prevents receiving potential security patches or minor updates automatically. Consider using a more flexible version range if stability allows.examples/validators.py (1)
29-35: Consider adding or verifying request body validations.
Whilejson_schema_extraprovides an illustrative example, ensure theAccountRequestfields have validators as needed (e.g., string length, regex). This will align with Pydantic’s new approach to model-specific constraints without a separateConfigclass.examples/middlewares/authed.py (1)
17-27: Refactor star-arg unpacking after a keyword argument.
According to the static analysis hint (B026), star-arg unpacking after a keyword argument can reduce clarity. Consider refactoring so that all positional arguments come before keyword arguments.def __init__( self, app, plugins=None, default_error_response=None, *args, **kwargs ): super().__init__( - app=app, - plugins=plugins, - default_error_response=default_error_response, - *args, - **kwargs, + app, + *args, + plugins=plugins, + default_error_response=default_error_response, + **kwargs, )🧰 Tools
🪛 Ruff (0.8.2)
24-24: Star-arg unpacking after a keyword argument is strongly discouraged
(B026)
tests/conftest.py (1)
189-190: Consider using a context manager for the Moto server.
While explicitly callingstart()andstop()works fine, a context manager might simplify the server lifecycle.Also applies to: 196-196, 197-197
tests/tasks/test_sqs_tasks.py (1)
394-394: Potential performance overhead.
A 5-second sleep can slow tests. If feasible, consider reducing or mocking out the delay to speed up the test suite while still verifying concurrency logic.fast_agave/blueprints/rest_api.py (1)
233-233: Ensure correct file naming.
The content-disposition header is built correctly to include an extension guess. Keep an eye on non-common MIME types to avoid incorrect naming.
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.github/workflows/release.yml(1 hunks).github/workflows/test.yml(4 hunks)Makefile(1 hunks)examples/app.py(1 hunks)examples/middlewares/authed.py(1 hunks)examples/resources/base.py(1 hunks)examples/resources/users.py(1 hunks)examples/tasks/retry_task_example.py(1 hunks)examples/validators.py(4 hunks)fast_agave/blueprints/rest_api.py(3 hunks)fast_agave/tasks/sqs_tasks.py(2 hunks)requirements-test.txt(1 hunks)requirements.txt(1 hunks)setup.py(1 hunks)tests/blueprint/test_decorators.py(1 hunks)tests/conftest.py(2 hunks)tests/tasks/test_sqs_tasks.py(5 hunks)
✅ Files skipped from review due to trivial changes (3)
- tests/blueprint/test_decorators.py
- examples/tasks/retry_task_example.py
- requirements.txt
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examples/middlewares/authed.py
24-24: Star-arg unpacking after a keyword argument is strongly discouraged
(B026)
🔇 Additional comments (25)
examples/resources/users.py (1)
21-21: Prevent NoneType attribute errorsBy checking
api_request.clientbefore accessingapi_request.client.host, you've ensured thatuser.ipis safely set toNoneif no client info is present. This is a good defensive coding practice.examples/resources/base.py (1)
17-17: Ensure type annotation consistencyThese functions always raise exceptions and do not return any value, so using
-> Noneis acceptable. You may keepNoReturnto indicate a function that never returns normally, but there's no significant runtime impact either way.setup.py (2)
24-24: Aligned Python requirement with project upgradeRaising the minimum required Python version to 3.9 makes sense, reflecting the removal of support for Python 3.8.
34-38: Broadened classifier supportGreat job specifying explicit support for Python 3.9 through 3.13, aligning with the upgraded environment and expanded compatibility.
examples/validators.py (3)
4-4: Great choice to importConfigDictfor Pydantic v2 support.
This change helps keep your configuration in sync with the new Pydantic style.
61-67: Confirm theAccountUpdateRequestmodel’s business logic.
Make sure that theAccountUpdateRequestonly includes the fields to be updated (e.g.,name). If more fields are required in future, updatejson_schema_extraexamples accordingly.
Line range hint
45-56: Retain the same naming conventions for consistency.
The field names inAccountResponseare consistent with the rest of the codebase. Verify all references toAccountResponsereflect the newjson_schema_extrausage.✅ Verification successful
Let me verify the usage of
AccountResponseand its schema consistency by checking the related request model and actual response data.
Let me try a different approach to verify the schema consistency.
Field names in
AccountResponseare consistent with the codebaseAfter analyzing the codebase, I can confirm that:
- The field names in
AccountResponse(id,name,user_id,platform_id,created_at,deactivated_at) perfectly match the fields in theAccountmodel- The response schema is correctly used in the accounts resource where
AccountResponseis set as theresponse_model- The field naming convention follows the same snake_case pattern used throughout other models in the codebase
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The following scripts were executed for the analysis:
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#!/bin/bash # Example script to locate code references to AccountResponse rg -A 3 $'AccountResponse'Length of output: 609
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#!/bin/bash # Check AccountRequest model definition and its schema ast-grep --pattern 'class AccountRequest' # Check actual response construction in the create endpoint ast-grep --pattern 'async def create(request: AccountRequest) -> Response:'Length of output: 121
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#!/bin/bash # Search for any Pydantic models and their schema definitions rg -B 2 -A 6 "class.*BaseModel.*:" # Check the actual response construction in the create endpoint rg -B 2 -A 6 "async def create.*AccountRequest.*:"Length of output: 7363
fast_agave/tasks/sqs_tasks.py (2)
11-11: Approved import ofvalidate_call.
Switching fromvalidate_argumentstovalidate_callaligns with Pydantic v2’s recommended validators for dynamic function argument validation.
109-109: Verify migrations fromvalidate_argumentstovalidate_call.
Ensure all references totask_functhat relied on old Pydantic v1 features are updated. If you had custom validators or constraints, confirm they still work as intended.✅ Verification successful
Let me gather more information about the validation usage and any potential custom validators.
Let me check for any task function definitions and their parameters to understand the validation context better.
Let me check one final thing to see how the tasks are being used in the examples.
Based on the gathered information, I can now provide a final response:
Migration to
validate_callappears to be clean and complete
The codebase shows a clean migration to Pydantic v2'svalidate_call. There are no traces of the oldvalidate_argumentsusage, and the validation setup appears to be properly integrated with the task system. The Pydantic models in the codebase are following v2 patterns (usingConfigDictfor configuration), suggesting a consistent upgrade across the codebase.
- The validation is applied uniformly through the task decorator system in
fast_agave/tasks/sqs_tasks.py- All Pydantic models in the examples and tests are using v2-compatible patterns
- No legacy
validate_argumentsimports or usage were found🏁 Scripts executed
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tests/conftest.py (2)
187-188: Switching toThreadedMotoServeris a solid improvement.
This approach offers simpler code oversubprocessand more direct control of the test environment.
228-232: EnablingContentBasedDeduplicationis a great step for FIFO queues.
Double-check that any existing deduplication logic or message grouping logic aligns with this setting.tests/tasks/test_sqs_tasks.py (4)
111-111: Confirm compatibility with Pydantic v2.
Invokingmodel_dump()ensures the data is correctly serialized for the mock function call. This aligns with Pydantic v2 usage and looks good.
267-269: Docstring references inclusive changes to max retries.
The inline documentation now correctly statesmax_retries=2and explains that the task will execute three times in total.
288-291: Validate new retry behavior.
The updatedmax_retries=2parameter and corresponding expected calls ([call(test_message)] * 3) align well with the docstring. Ensure tests pass consistently for under/over invocation scenarios.
411-441: Well-implemented invalid JSON test.
The new test adequately checks that invalid messages are ignored and removed without executing the task. This is a good addition for robust error handling.fast_agave/blueprints/rest_api.py (3)
10-10: Appropriate import usage for Pydantic v2.
IncludingFieldandValidationErrordirectly frompydanticclarifies usage and improves readability.
257-259: Clearer query description.
The multi-line string clarifies parameters and pagination usage for clients. Looks good.
264-275: Refined field definitions usingField.
LeveragingFieldforQueryResponseclarifies each attribute’s role and description. This approach is consistent with Pydantic v2 best practices.requirements-test.txt (3)
1-3: Dependency updates align with Python 3.13 upgrade.
Upgradingblack,flake8, andisortis consistent with keeping the codebase current.
6-9: Notable changes for testing stack.
Updatingmoto[server],mypy,pytest, andpytest-covensures better coverage and type-checking for Python 3.13.
13-13: New dependency added.
Addinghttpx==0.28.1can enhance testing of async HTTP interactions. Confirm that related mocks or usage are covered in tests..github/workflows/release.yml (2)
10-10: Consistent setup for release workflow.
Usingactions/[email protected]is up-to-date with GitHub’s recommended action versions.
13-13: Upgraded to Python 3.13.
This aligns with the PR objective to drop support for Python 3.8 and adopt Pydantic v2. Ensure all external dependencies support 3.13 for a smooth release..github/workflows/test.yml (2)
53-53: LGTM! Good security practice.Adding the Codecov token as a secret is a good security practice for coverage reporting.
23-23: Verify Python version compatibility with Pydantic v2The Python version matrix looks good for dropping Python 3.8 support. However, we should verify the minimum Python version required by Pydantic v2.
✅ Verification successful
Python version matrix is compatible with Pydantic v2 requirements
The Python version matrix in the workflow (3.9 to 3.13) is fully compatible with Pydantic v2's minimum requirement of Python >=3.8. The current configuration exceeds the minimum requirement, ensuring proper compatibility.
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Actionable comments posted: 1
🧹 Nitpick comments (3)
examples/tasks/task_example.py (1)
31-31: Pydantic v2 migration: model_dump() usage looks good.The change from
dict()tomodel_dump()correctly aligns with Pydantic v2's API changes.Since you're dropping Python 3.8 support, you could modernize the type hints using the union operator:
-async def task_validator(message: Union[User, Company]) -> None: +async def task_validator(message: User | Company) -> None:fast_agave/tasks/sqs_client.py (1)
4-4: Modernize type hints.Since Python 3.8 support is being dropped, you could modernize all type hints in this file.
-from typing import Optional, Union +from typing import Optional # Remove if not used elsewhere in the file async def send_message( self, - data: Union[str, dict], - message_group_id: Optional[str] = None, + data: str | dict, + message_group_id: str | None = None, ) -> None: def send_message_async( self, - data: Union[str, dict], - message_group_id: Optional[str] = None, + data: str | dict, + message_group_id: str | None = None, ) -> asyncio.Task:Also applies to: 40-42, 51-53
requirements-test.txt (1)
11-11: Consider updating pytest-asyncio version.The current version constraint
pytest-asyncio==0.18.*is relatively old. Latest versions (0.23+) offer better support for Python 3.9+ and newer pytest versions.
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.github/workflows/release.yml(1 hunks).github/workflows/test.yml(3 hunks)examples/app.py(1 hunks)examples/resources/base.py(1 hunks)examples/resources/cards.py(1 hunks)examples/tasks/task_example.py(1 hunks)examples/validators.py(4 hunks)fast_agave/blueprints/rest_api.py(5 hunks)fast_agave/filters.py(2 hunks)fast_agave/tasks/sqs_celery_client.py(3 hunks)fast_agave/tasks/sqs_client.py(3 hunks)requirements-test.txt(1 hunks)requirements.txt(1 hunks)tests/blueprint/test_blueprint.py(4 hunks)tests/conftest.py(9 hunks)tests/tasks/test_sqs_tasks.py(12 hunks)
✅ Files skipped from review due to trivial changes (1)
- tests/blueprint/test_blueprint.py
🚧 Files skipped from review as they are similar to previous changes (7)
- examples/app.py
- examples/resources/base.py
- fast_agave/blueprints/rest_api.py
- requirements.txt
- tests/conftest.py
- .github/workflows/test.yml
- examples/validators.py
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fast_agave/filters.py
5-5: Do not use mutable data structures for argument defaults
Replace with None; initialize within function
(B006)
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🔇 Additional comments (7)
fast_agave/filters.py (1)
20-20: Pydantic v2 migration: model_dump() usage looks good.The change from
dict()tomodel_dump()correctly aligns with Pydantic v2's API changes.examples/resources/cards.py (1)
22-22: Type hint update looks good.The change from
Dicttodictcorrectly aligns with PEP 585 (Python 3.9+).fast_agave/tasks/sqs_celery_client.py (1)
5-5: Type hint updates look good!The changes from
Dicttodictalign with modern Python type hinting practices and Pydantic v2 recommendations for using built-in types.Let's verify type hint consistency across the codebase:
Also applies to: 12-12, 63-63, 72-72
✅ Verification successful
Type hint changes are consistent across the codebase
The migration from
Dicttodicttype hints has been completed consistently. No remaining instances oftyping.Dicttype hints were found, and existing type hints follow the modern Python style using built-in types.🏁 Scripts executed
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.github/workflows/release.yml (1)
9-13: GitHub Actions configuration updates look good!The changes appropriately:
- Update Python version to 3.13
- Pin actions to stable versions (v4, v5, v1)
Also applies to: 20-20
tests/tasks/test_sqs_tasks.py (3)
111-111: Good update to use model_dump()!The change to use
model_dump()aligns with Pydantic v2's API changes, replacing the deprecated dictionary conversion methods.
412-441: Well-structured test for invalid JSON handling!The new test case properly verifies that:
- Invalid JSON messages are handled gracefully
- Messages are removed from the queue
- Tasks are not executed for invalid messages
393-394: Review the increased sleep duration in concurrency test.The sleep duration has been increased from 1 to 5 seconds, which could significantly impact test execution time. Consider if this duration is necessary for reliable test results.
Let's check if there are any reported test timing issues:
✅ Verification successful
Sleep duration in concurrency test is appropriate
The 5-second duration is justified as it ensures reliable concurrent task measurement in
test_concurrency_controller. This test specifically verifies that the maximum number of concurrent tasks never exceeds 2 while processing 5 messages, requiring sufficient task overlap time for accurate measurement.🏁 Scripts executed
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Actionable comments posted: 1
🧹 Nitpick comments (1)
tests/conftest.py (1)
187-197: Consider using a dynamic port for ThreadedMotoServer.The hardcoded port 4000 could cause conflicts in parallel test execution environments. Consider using a dynamic port allocation strategy.
- server = ThreadedMotoServer(port=4000) + server = ThreadedMotoServer() # Let the OS assign an available port server.start() endpoints = dict( - sqs='http://127.0.0.1:4000/', + sqs=f'http://127.0.0.1:{server.port}/', )
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fast_agave/version.py(1 hunks)requirements.txt(1 hunks)setup.cfg(1 hunks)setup.py(1 hunks)tests/conftest.py(10 hunks)tests/tasks/test_sqs_celery_client.py(0 hunks)tests/tasks/test_sqs_client.py(0 hunks)tests/tasks/test_sqs_tasks.py(11 hunks)
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- tests/tasks/test_sqs_celery_client.py
- tests/tasks/test_sqs_client.py
✅ Files skipped from review due to trivial changes (1)
- fast_agave/version.py
🚧 Files skipped from review as they are similar to previous changes (2)
- setup.py
- tests/tasks/test_sqs_tasks.py
⏰ Context from checks skipped due to timeout of 90000ms (6)
- GitHub Check: pytest (3.13)
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🔇 Additional comments (6)
setup.cfg (1)
6-6: LGTM! The asyncio_mode setting aligns with Pydantic v2 migration.The addition of
asyncio_mode = autois appropriate for a FastAPI project migrating to Pydantic v2, as it will properly handle both synchronous and asynchronous tests automatically.Let's verify that the test suite runs correctly with this new setting:
✅ Verification successful
Verified: asyncio_mode = auto is appropriate for the async test suite
The configuration is well-suited for this codebase as it properly handles:
- Async test functions in SQS-related tests
- Async fixtures using aiobotocore
- Mixed sync/async test environment
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tests/conftest.py (3)
5-5: LGTM! Type imports updated for Python 3.9+ compatibility.The changes align with modern Python type hinting practices and the migration away from Python 3.8.
Also applies to: 14-14
52-52: LGTM! Fixture type annotations updated to PEP 585 standards.Type hints have been consistently updated across all fixtures to use built-in collection types (PEP 585), which is required for Python 3.9+ compatibility.
Also applies to: 94-94, 99-99, 104-104, 110-110, 120-120, 126-126, 152-152, 158-158, 167-167
228-232: Consider adding tests for message deduplication.The addition of
ContentBasedDeduplicationis good, but should be accompanied by tests that verify the deduplication behavior.Would you like me to help create test cases that verify the message deduplication functionality?
requirements.txt (2)
2-2:⚠️ Potential issueAvoid using development versions in production.
The
cuenca-validations==2.0.0.dev14is a development version which may be unstable or contain breaking changes. Consider using a stable release version instead.Let's check if there's a stable release available:
1-1: Breaking changes alert: Major version updates.The following dependencies have major version updates which likely include breaking changes:
- aiobotocore: 2.1.0 → 2.17.0
- mongoengine-plus: 0.0.3 → 1.0.0
Please ensure that your codebase is compatible with these new versions.
Let's check for any breaking changes in the changelog:
Also applies to: 4-4
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remover este init no veo que haga algo extra
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BaseHTTPMiddleware cambió en versiones recientes de Starlette. Ahora el parámetro app debe pasarse explícitamente como argumento al inicializar un middleware basado en BaseHTTPMiddleware.
De lo contrario obtenemos: BaseHTTPMiddleware.__init__() missing 1 required positional argument: 'app'
Podemos hacer algo más simple como:
def __init__(self, app):
super().__init__(app=app)
Sería mejor?
Description:
This PR migrates the project to Pydantic v2 and drops support for Python 3.8
Key Changes:
Breaking Changes:
Summary by CodeRabbit
Python Version Update
Dependency Updates
requirements.txtandrequirements-test.txthttpxCode Improvements
Testing Enhancements
Version Update
Configuration Update