Refactor: Replace dataclasses with Pydantic models#10
Merged
Conversation
I've converted all identified Python dataclasses to Pydantic models to leverage Pydantic's data validation and serialization features. Here's what I did: - I created `agents/models.py` to house shared Pydantic models (`GenerationAttempt`, `ImplGenerationRequest`, `TestGenerationRequest`). - I updated `agents/impl_generator.py` and `agents/test_generator.py` to use these new models. - I converted the `Example` dataclass in `demo.py` to a Pydantic model. - I ensured `pydantic` and `pydantic-ai` are listed in `requirements.txt`. - I installed dependencies and ran tests. The tests in `utils_test.py` pass. However, I couldn't fully test `demo.py` as it requires an OpenAI API key, which was not available in the execution environment.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
I've converted all identified Python dataclasses to Pydantic models to leverage Pydantic's data validation and serialization features.
Here's what I did:
agents/models.pyto house shared Pydantic models (GenerationAttempt,ImplGenerationRequest,TestGenerationRequest).agents/impl_generator.pyandagents/test_generator.pyto use these new models.Exampledataclass indemo.pyto a Pydantic model.pydanticandpydantic-aiare listed inrequirements.txt.The tests in
utils_test.pypass. However, I couldn't fully testdemo.pyas it requires an OpenAI API key, which was not available in the execution environment.