feature/pydantic 1 and 2

[patrick91]

Closes #3425

[codecov]

Codecov Report

Merging #3426 (4a1a498) into main (905b02e) will increase coverage by 0.00%.
Report is 1 commits behind head on main.
The diff coverage is 100.00%.

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #3426   +/-   ##
=======================================
  Coverage   96.43%   96.43%           
=======================================
  Files         507      508    +1     
  Lines       32178    32272   +94     
  Branches     5325     5344   +19     
=======================================
+ Hits        31030    31122   +92     
- Misses        937      939    +2     
  Partials      211      211           

[codspeed-hq]

CodSpeed Performance Report

Merging #3426 will not alter performance

_{Comparing feature/pydantic-1-and-2 (4a1a498) with main (905b02e)}

Summary

✅ 13 untouched benchmarks

[patrick91]

/pre-release

[botberry]

Pre-release

👋

Releasing commit [fd7e83a] to PyPi as pre-release! 📦

[botberry]

Pre-release

👋

Pre-release 0.224.0.dev.1711748192 [fd7e83a] has been released on PyPi! 🚀
You can try it by doing:

poetry add strawberry-graphql==0.224.0.dev.1711748192

[botberry]

Thanks for adding the RELEASE.md file!

Here's a preview of the changelog:

---

This release adds support for using both Pydantic v1 and v2, when importing from
pydantic.v1.

This is automatically detected and the correct version is used.

Here's the tweet text:

🆕 Release (next) is out! Thanks to @patrick91 for the PR 👏

Get it here 👉 https://strawberry.rocks/release/(next)

[sourcery-ai]

Hey @patrick91 - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟡 General issues: 1 issue found
• 🟢 Security: all looks good
• 🟡 Testing: 2 issues found
• 🟢 Complexity: all looks good
• 🟢 Docstrings: all looks good

---

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[sourcery-ai]

suggestion (code_clarification): Consider documenting the purpose of _missing_type and is_v1 attributes.

[sourcery-ai]

suggestion (testing): Consider adding negative test cases.

It's great to see positive test scenarios covered for both Pydantic v1 and v2 compatibility. However, adding negative test cases where the schema should fail (e.g., invalid types or missing required fields) would further ensure robustness.

[sourcery-ai]

suggestion (testing): Clarify reason for expected failure.

Using @pytest.mark.xfail indicates that this test is expected to fail, but it's not immediately clear why. Please add a reason to the decorator to clarify the expected failure condition for future reference.