Description

• Create a hierarchical view of your codebase during theFetchRepo stage;
• Intelligently manage token usage throughout the process;
• Prioritize important files while maintaining context through summaries;
• Adapt to codebase size automatically;

This allows us to work with codebases of any size while maintaining high-quality analysis. The system will automatically adjust its depth of analysis based on the available token budget, ensuring you get the best possible results within the model's constraints.

Motivation and Context

1. Enable ways to work with large code bases;
2. Implement a smart strategy to keep the context relevant to the final application/tutorial;

Smart Content Selection:

• Files are prioritized based on:
• 1. Directory depth (maintaining hierarchical understanding);
• 2. File size (larger files often contain more important code);
• 3. File type (deprioritizing test files);
• This ensures the most relevant code is kept in context;
  Adaptive Content Storage:
• For each file, the system attempts to store the full content;
• If full content would exceed token limits, it creates a summary instead;
• This gives you the best of both worlds: detailed content where possible, summaries where necessary;

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Checklist

• My commits follow as good as possible the guidelines indicated inHow to Write a Git Commit Message.
• My change requires a change to the documentation.
• I have updated the documentation accordingly (if applicable).