Implementing Fallback Models for Tokenization

[okotek]

PR Type:
Enhancement

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PR Description:
This PR introduces the ability to use fallback models for tokenization in the event that the primary model fails. It modifies the get_pr_diff and pr_generate_compressed_diff functions to accept a model parameter, and uses this parameter to determine the token limit. It also introduces a new function, retry_with_fallback_models, which attempts to generate a prediction with each model in a list until successful. The PR also updates various other functions and files to accommodate these changes.

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PR Main Files Walkthrough:
-pr_agent/algo/pr_processing.py: Modified get_pr_diff and pr_generate_compressed_diff to accept a model parameter, and added retry_with_fallback_models function.
-pr_agent/algo/token_handler.py: Removed the limit attribute from the TokenHandler class.
-pr_agent/tools/pr_code_suggestions.py: Updated to use retry_with_fallback_models and pass the model to get_pr_diff.
-pr_agent/tools/pr_description.py: Updated to use retry_with_fallback_models and pass the model to get_pr_diff.
-pr_agent/tools/pr_information_from_user.py: Updated to use retry_with_fallback_models and pass the model to get_pr_diff.
-pr_agent/tools/pr_questions.py: Updated to use retry_with_fallback_models and pass the model to get_pr_diff.
-pr_agent/tools/pr_reviewer.py: Updated to use retry_with_fallback_models and pass the model to get_pr_diff.
-pr_agent/settings/configuration.toml: Added fallback-models configuration option.

[okotek]

/describe

[github-actions]

PR Analysis

• 🎯 Main theme: Adding support for fallback models
• 📌 Type of PR: Enhancement
• 🧪 Relevant tests added: No
• ✨ Focused PR: Yes, the PR is focused as it introduces a new feature of supporting fallback models in the application. All changes in the PR are related to this feature.
• 🔒 Security concerns: No, the changes in this PR do not introduce any apparent security issues. However, it's always a good practice to ensure that any new models or data used by them are properly validated and sanitized.

PR Feedback

• 💡 General PR suggestions: The PR is well-structured and the changes are consistent with the main theme. However, it would be beneficial to add some tests to ensure the new functionality works as expected. Also, it would be helpful to provide a description in the PR to give more context about the changes and how the fallback models are used.

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