fix(setup): preserve model name when re-running onboarding with same provider

[zmanian]

Summary

• Fix When re-running onboarding most previous settings are remembered, but not Model name #600: Re-running onboarding no longer loses the previously selected model name
• Each provider setup function (setup_api_key_provider, setup_ollama_generic, setup_openai_compatible_generic, setup_anthropic_oauth) unconditionally cleared selected_model to None, even when the user kept the same provider
• Now only clears selected_model when the backend actually changes, so Step 4 correctly shows "Keep current model?" when re-onboarding with the same provider

Test plan

• test_same_provider_preserves_selected_model -- verifies model survives re-selecting same backend
• test_different_provider_clears_selected_model -- verifies model is cleared when switching backends
• cargo clippy --all --all-features clean
• cargo fmt clean

Generated with Claude Code

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a bug in the onboarding process where the user's previously selected language model was inadvertently cleared when re-configuring the same LLM provider. The changes introduce conditional logic to ensure that the selected model is only reset if the user switches to a different provider, thereby preserving their choice when re-onboarding with the existing one. This enhancement streamlines the user experience by preventing unnecessary re-selection of models.

Highlights

• Bug Fix: Resolved an issue where the previously selected model name was lost when re-running the onboarding process with the same provider.
• Conditional Model Clearing: Updated provider setup functions to only clear the selected_model when the LLM backend provider actually changes, rather than unconditionally.
• User Experience: Improved the onboarding flow by ensuring that the "Keep current model?" option is correctly displayed when re-onboarding with the same provider.
• Test Coverage: Added new unit tests to specifically verify that the selected model is preserved when re-selecting the same backend and cleared when switching to a different backend.

Changelog

• src/setup/wizard.rs
  • Refactored the setup_anthropic_oauth, setup_api_key_provider, setup_ollama_generic, and setup_openai_compatible_generic functions to conditionally clear the selected_model based on whether the LLM backend provider has changed.
  • Added test_same_provider_preserves_selected_model to verify that the model is retained when the same provider is re-selected.
  • Added test_different_provider_clears_selected_model to confirm that the model is cleared when switching to a new provider.

Activity

• No human activity has been recorded on this pull request yet.

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[gemini-code-assist]

Code Review

This pull request correctly fixes the issue where the selected model was being cleared unconditionally when re-running the onboarding process. The logic to only clear the model when the provider changes is sound, and the changes have been applied consistently across all relevant setup functions. The addition of regression tests is also a great improvement to ensure this behavior is maintained.

I have one suggestion regarding code duplication that has been introduced. Consolidating the repeated logic into a helper method would improve the maintainability of the code, aligning with established patterns for minimizing boilerplate and maintaining consistency. Please see my specific comment for details.

[gemini-code-assist]

This logic for conditionally clearing selected_model and updating llm_backend is now duplicated in four places (setup_anthropic_oauth, setup_api_key_provider, setup_ollama_generic, setup_openai_compatible_generic) and also in the new tests. To improve maintainability and reduce code duplication, consider extracting this into a helper method on SetupWizard. This aligns with the principle of using specialized helper functions to minimize boilerplate and maintain consistency across similar operations.

For example, you could add a method like this:

    /// Sets the LLM backend, clearing the selected model if the provider has changed.
    fn set_llm_backend(&mut self, backend_id: &str) {
        if self.settings.llm_backend.as_deref() != Some(backend_id) {
            self.settings.selected_model = None;
        }
        self.settings.llm_backend = Some(backend_id.to_string());
    }

Then, you can replace the repeated blocks in all four setup_* functions with a single call, e.g., self.set_llm_backend("anthropic");.

This would also allow you to simplify the new tests by calling this helper method instead of duplicating the logic there as well. The tests would become more robust as they would test the behavior of the helper method directly, rather than being coupled to the implementation details.

References

1. Use specialized helper functions like parse_option_env<T> when resolving environment variables into Option<T> fields to minimize boilerplate and maintain consistency across configuration files.