moving the 'improve' command to turbo mode, with auto_extended=true

[mrT23]

Type

Enhancement

---

Description

• This PR introduces enhancements to handle a new type of model, 'TURBO', in addition to the regular models.
• A new Enum 'ModelType' is introduced to differentiate between 'REGULAR' and 'TURBO' models.
• The 'retry_with_fallback_models' and '_get_all_models' functions in 'pr_processing.py' are updated to handle the new 'model_type' parameter.
• The 'run' function in 'pr_code_suggestions.py' is updated to use the 'TURBO' model type for predictions.
• The 'configuration.toml' file is updated with new configurations for the turbo model and code suggestions.

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Changes walkthrough

	Relevant files
Configuration changes	__init__.py Addition of new model 'gpt-4-0125-preview'                                              pr_agent/algo/init.py Added a new model 'gpt-4-0125-preview' to the list of models. +1/-0      configuration.toml Configuration updates for turbo model and code suggestions              pr_agent/settings/configuration.toml Added a new configuration 'model_turbo' for specifying the turbo model. - Updated several configurations under 'pr_code_suggestions' section. +7/-5
Enhancement	pr_processing.py Enhancements to model handling functions                                                  pr_agent/algo/pr_processing.py Modified the 'retry_with_fallback_models' function to accept a new parameter 'model_type'. - Updated the '_get_all_models' function to handle different types of models based on the 'model_type' parameter. +8/-5      utils.py Introduction of new Enum 'ModelType'                                                          pr_agent/algo/utils.py Introduced a new Enum 'ModelType' with two types: 'REGULAR' and 'TURBO'. +4/-0      pr_code_suggestions.py Enhancements to the 'run' function                                                              pr_agent/tools/pr_code_suggestions.py Updated the 'run' function to use the 'TURBO' model type for predictions. - Added a condition to limit context specifically for the 'improve' command. +11/-3

---

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[codiumai-pr-agent-pro]

PR Description updated to latest commit (d04d8b6)

[codiumai-pr-agent-pro]

PR Analysis

• 🎯 Main theme: Enhancement of the 'improve' command with turbo mode and auto_extended=true
• 📝 PR summary: This PR introduces enhancements to handle a new type of model, 'TURBO', in addition to the regular models. It updates the 'retry_with_fallback_models' and '_get_all_models' functions in 'pr_processing.py' to handle the new 'model_type' parameter. The 'run' function in 'pr_code_suggestions.py' is updated to use the 'TURBO' model type for predictions. The 'configuration.toml' file is also updated with new configurations for the turbo model and code suggestions.
• 📌 Type of PR: Enhancement
• 🧪 Relevant tests added: No
• ⏱️ Estimated effort to review [1-5]: 3, because the PR involves changes in multiple files and introduces a new model type which requires understanding of the existing model handling logic.
• 🔒 Security concerns: No

PR Feedback

💡 General suggestions: The PR seems to be well-structured and the changes are logically grouped. However, it would be beneficial to add comments explaining the logic behind the new 'TURBO' model type and how it differs from the 'REGULAR' model type. This would help other developers understand the changes better. Also, it would be good to add error handling for the case when an unsupported model type is provided.

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[codiumai-pr-agent-pro]

PR Code Suggestions

	Suggestions
best practice	Remove the default argument from the _get_all_models function to ensure explicit model type specification. The function _get_all_models has a default argument ModelType.REGULAR. If the function <br> is <br> called <br> without <br> any <br> arguments, <br> it <br> will <br> always <br> return <br> the <br> regular <br> model. <br> Consider <br> removing <br> the <br> default <br> argument <br> to <br> ensure <br> that <br> the <br> function <br> caller <br> explicitly <br> specifies <br> the <br> model <br> type. pr_agent/algo/pr_processing.py [246-250] -def _get_all_models(model_type: ModelType = ModelType.REGULAR) -> List[str]: +def _get_all_models(model_type: ModelType) -> List[str]: if model_type == ModelType.TURBO: model = get_settings().config.model_turbo else: model = get_settings().config.model
maintainability	Break down the run method into smaller methods to improve readability and maintainability. The run method has a lot of responsibilities. Consider breaking it down into smaller methods to improve readability and maintainability. pr_agent/tools/pr_code_suggestions.py [64-78] async def run(self): ... + data = self._prepare_data() + ... + +def _prepare_data(self): if not self.is_extended: await retry_with_fallback_models(self._prepare_prediction, ModelType.TURBO) - data = self._prepare_pr_code_suggestions() + return self._prepare_pr_code_suggestions() else: - data = await retry_with_fallback_models(self._prepare_prediction_extended, ModelType.TURBO) - ... + return await retry_with_fallback_models(self._prepare_prediction_extended, ModelType.TURBO) Remove the repetition of comments or add more specific information for each model. The comment # 128K, but may be limited by config.max_model_tokens is repeated for different models. Consider removing the repetition or adding more specific information for each model. pr_agent/algo/init.py [11-12] -'gpt-4-1106-preview': 128000, # 128K, but may be limited by config.max_model_tokens -'gpt-4-0125-preview': 128000, # 128K, but may be limited by config.max_model_tokens +'gpt-4-1106-preview': 128000, # 128K, may be limited by config.max_model_tokens +'gpt-4-0125-preview': 128000, # 128K, may also be limited by config.max_model_tokens
performance	Consider reducing the max_context_tokens setting or making it configurable to avoid potential performance issues. The max_context_tokens setting is set to 8000. If this value is too high, it might cause performance issues. Consider reducing it or making it configurable. pr_agent/settings/configuration.toml [72] [pr_code_suggestions] # /improve # -max_context_tokens=8000 +max_context_tokens=4000

---

✨ Usage guide:

---

Overview:
The improve tool scans the PR code changes, and automatically generates suggestions for improving the PR code. The tool can be triggered automatically every time a new PR is opened, or can be invoked manually by commenting on a PR.
When commenting, to edit configurations related to the improve tool (pr_code_suggestions section), use the following template:

/improve --pr_code_suggestions.some_config1=... --pr_code_suggestions.some_config2=...

With a configuration file, use the following template:

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some_config1=...
some_config2=...

Enabling\disabling automation When you first install the app, the default mode for the improve tool is: pr_commands = ["/improve --pr_code_suggestions.summarize=true", ...] meaning the improve tool will run automatically on every PR, with summarization enabled. Delete this line to disable the tool from running automatically.

Utilizing extra instructions Extra instructions are very important for the improve tool, since they enable to guide the model to suggestions that are more relevant to the specific needs of the project. Be specific, clear, and concise in the instructions. With extra instructions, you are the prompter. Specify relevant aspects that you want the model to focus on. Examples for extra instructions: [pr_code_suggestions] # /improve # extra_instructions=""" Emphasize the following aspects: - Does the code logic cover relevant edge cases? - Is the code logic clear and easy to understand? - Is the code logic efficient? ... """ Use triple quotes to write multi-line instructions. Use bullet points to make the instructions more readable.

A note on code suggestions quality While the current AI for code is getting better and better (GPT-4), it's not flawless. Not all the suggestions will be perfect, and a user should not accept all of them automatically. Suggestions are not meant to be simplistic. Instead, they aim to give deep feedback and raise questions, ideas and thoughts to the user, who can then use his judgment, experience, and understanding of the code base. Recommended to use the 'extra_instructions' field to guide the model to suggestions that are more relevant to the specific needs of the project, or use the custom suggestions 💎 tool With large PRs, best quality will be obtained by using 'improve --extended' mode.

More PR-Agent commands To invoke the PR-Agent, add a comment using one of the following commands: /review: Request a review of your Pull Request. /describe: Update the PR title and description based on the contents of the PR. /improve [--extended]: Suggest code improvements. Extended mode provides a higher quality feedback. /ask <QUESTION>: Ask a question about the PR. /update_changelog: Update the changelog based on the PR's contents. /add_docs 💎: Generate docstring for new components introduced in the PR. /generate_labels 💎: Generate labels for the PR based on the PR's contents. /analyze 💎: Automatically analyzes the PR, and presents changes walkthrough for each component. See the tools guide for more details. To list the possible configuration parameters, add a /config comment.

See the improve usage page for a more comprehensive guide on using this tool.