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Add ValidateFrom annotation helper #11942

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@Viicos Viicos commented Jun 2, 2025

Change Summary

Fixes #11682.

Currently, the instantiation_hook is required. We could also make it optional and by default populate the arbitrary class by e.g. calling __new__(). However, by doing so, we need to make some assumptions on the from_type. It can currently be anything (supported by Pydantic: models, dataclasses, typeddicts, etc), but we will have to constrain it to some specific types (perhaps only supporting Pydantic models?) as we will most likely have to assume __dict__ is present to fetch the instance attributes and pass them to __new__().

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  • The pull request title is a good summary of the changes - it will be used in the changelog
  • Unit tests for the changes exist
  • Tests pass on CI
  • Documentation reflects the changes where applicable
  • My PR is ready to review, please add a comment including the phrase "please review" to assign reviewers

@github-actions github-actions bot added the relnotes-fix Used for bugfixes. label Jun 2, 2025
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hyperlint-ai bot commented Jun 2, 2025

PR Change Summary

Added the ValidateFrom annotation helper to enhance type validation in Pydantic.

  • Introduced the ValidateFrom annotation helper for custom type validation.
  • Provided an example demonstrating the usage of ValidateFrom with a custom class.
  • Updated documentation to reflect the new validation capabilities.

Modified Files

  • docs/concepts/validators.md
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github-actions bot commented Jun 2, 2025

Coverage report

Click to see where and how coverage changed

FileStatementsMissingCoverageCoverage
(new stmts)		Lines missing
  pydantic
  functional_validators.py
Project Total  

This report was generated by python-coverage-comment-action

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codspeed-hq bot commented Jun 2, 2025

CodSpeed Performance Report

Merging #11942 will not alter performance

Comparing validate-from (ffab3d8) with main (88c75cd)

Summary

✅ 46 untouched benchmarks

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Viicos commented Jun 5, 2025

@timkpaine @ptomecek this is an implementation of an helper that can validate from a type that is natively supported by Pydantic (your use case was using Pydantic models, but TypedDicts/dataclasses etc can also be used). Let me know if this is suitable for you (see my OP for more details on decisions that need to be made before merging).

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Successfully merging this pull request may close these issues.

2.11 Regression - Nested annotation references lead to model never being fully buildable
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