add additional google models support

[R-Mathis]

User description

Add support for gemma 2 and gemini 1.5 flash models

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PR Type

Enhancement

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Description

• Added support for vertex_ai/gemini-1.5-flash model with a token limit of 1048576.
• Added support for vertex_ai/gemma2 model with a token limit of 8200.

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Changes walkthrough 📝

	Relevant files
Enhancement	__init__.py Add support for new Google models in configuration              pr_agent/algo/init.py Added support for vertex_ai/gemini-1.5-flash model. Added support for vertex_ai/gemma2 model. +2/-0

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PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 1 🔵⚪⚪⚪⚪

🏅 Score: 95

🧪 No relevant tests

🔒 No security concerns identified

🔀 No multiple PR themes

⚡ No key issues to review

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PR Code Suggestions ✨

Category	Suggestion	Score
Best practice	Improve naming consistency for model identifiers Consider using consistent naming conventions for model identifiers. The new model 'vertex_ai/gemma2' does not follow the existing pattern of including version and type information. This might lead to confusion or errors in model handling. pr_agent/algo/init.py [32] -'vertex_ai/gemma2': 8200, +'vertex_ai/gemma2-v1-standard': 8200, Apply this suggestion Suggestion importance[1-10]: 8 Why: The suggestion to use a consistent naming convention for model identifiers is valid and improves code readability and maintainability. However, it is not critical for functionality.	8
Performance	Adjust memory allocation for consistency and performance Ensure that the memory allocation for the new models is appropriate and consistent with similar models to avoid performance issues. The model 'vertex_ai/gemma2' has significantly lower memory allocation compared to other models, which might not be sufficient. pr_agent/algo/init.py [32] -'vertex_ai/gemma2': 8200, +'vertex_ai/gemma2': 100000, Apply this suggestion Suggestion importance[1-10]: 7 Why: Ensuring consistent memory allocation is important for performance, but the suggested value might need further validation to ensure it meets the model's requirements.	7
Maintainability	Refactor model storage for better scalability and manageability To maintain the scalability and manageability of the model dictionary, consider refactoring the dictionary into a separate configuration file or using a more scalable data management approach like a database or dedicated configuration management system. pr_agent/algo/init.py [31-32] -'vertex_ai/gemini-1.5-flash': 1048576, -'vertex_ai/gemma2': 8200, +# This is a conceptual suggestion and does not provide direct code replacement. Apply this suggestion Suggestion importance[1-10]: 6 Why: While the suggestion to refactor the model storage is good for long-term scalability, it is a conceptual suggestion and does not provide immediate actionable code changes.	6
Possible issue	Ensure new models are fully integrated into the system Verify that the newly added models 'vertex_ai/gemini-1.5-flash' and 'vertex_ai/gemma2' are correctly integrated into the system's model handling logic, including any necessary updates to model loading, processing, or usage functions. pr_agent/algo/init.py [31-32] -'vertex_ai/gemini-1.5-flash': 1048576, -'vertex_ai/gemma2': 8200, +# This is a conceptual suggestion and does not provide direct code replacement. Apply this suggestion Suggestion importance[1-10]: 5 Why: Verifying the integration of new models is important, but the suggestion is conceptual and lacks specific actionable steps.	5