Aider loves NotebookLM - Effortless Large-Repo Analysis with the /export-context Command

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AI Summary

Summary of Video Transcript

Introduction

• The video discusses integrating ADA with Notebook LM, which has a large context window of around 1 million tokens, ideal for analyzing large code repositories.
• Notebook LM’s base version is free, but transferring entire repositories can be challenging due to clipboard limits.

Export Context Command

• A new command, export context, is introduced to export files in an organized manner, either individually or in chunks, to overcome clipboard limits.

Use Cases

1. Copying Entire Repository to Notebook LM
   • The ADA Discord feature request for the /copy command to support numerical arguments is used as an example.
   • The entire ADA repository is copied to Notebook LM to determine the necessary context for the feature.
   • PyCharm is used as the IDE, and the /add command is utilized to attach all files from the git repository to the chat context.
   • The /copy context command is used to copy files to the clipboard, but limitations are encountered due to the size.
2. Viewing Repository in AI-Friendly Format
   • A method to view GitHub repositories in an AI-friendly format is shown by altering the GitHub URL, which displays the repository with a tree structure and code with line numbers.
   • The limitation of 50,000 tokens is mentioned, which is not sufficient for the entire codebase.
3. Using the /export context Command
   • The new /export context command is demonstrated, which exports files to a temporary directory with a flat structure and .txt file extensions.
   • Files are uploaded to Notebook LM, but the content is summarized, losing the file path information.
4. Chunking the Repository
   • The repository is chunked into smaller parts using the /export context command with a specified chunk size.
   • The chunks are uploaded to Notebook LM, and the AI is asked to list relevant files for implementing the feature.
   • The process is repeated for each chunk, and the responses are compiled using a reasoning model to determine the most relevant files.
5. Implementing the Feature
   • The AI is used to one-shot the feature implementation with the determined context.
   • The feature is tested in the ADA instance with various /copy command arguments, confirming its functionality.
6. Handling a Large Repository (Avalonia UI)
   • The Avalonia UI repository is used as an example of a large codebase.
   • A draft pull request is mentioned that adds the ability to read only outlines of files, which is necessary due to the size of the repository.
   • The repository is chunked, and each chunk’s outline is uploaded to Notebook LM.
   • The AI is queried chunk by chunk to determine relevant files for a feature implementation.
   • The responses are analyzed to create a list of important files and notes on the implementation approach.

Conclusion

• The video concludes with a summary of how to use the export context command with Notebook LM to manage large codebases and implement features effectively.