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ElshadHu
ElshadHu

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Code Analysis - Repomix

#cli #tooling #llm #opensource

How Analysis Started

First, I visited the web version of Repomix
to get an overall understanding of the project before diving into the code. After that, I forked the Repomix and started my analysis mainly using the git grep command .VSCode shortcuts like Ctrl+Shift+P and Ctrl+F also helped me a lot during my search.

Interesting Feature

While exploring, the repomix --token-count-tree feature caught my attention. When working with Large Language Models (LLMs), visualizing token usage across a repository is incredibly useful for understanding how much of your codebase fits into the model's context window. Running this command produces output like this:

🔢 Token Count Tree:
────────────────────
└── src/ (70,925 tokens)
    ├── cli/ (12,714 tokens)
    │   ├── actions/ (7,546 tokens)
    │   └── reporters/ (990 tokens)
    └── core/ (41,600 tokens)
        ├── file/ (10,098 tokens)
        └── output/ (5,808 tokens)
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You can also set a minimum token threshold to focus only on larger files. For example:
repomix --token-count-tree 1000

How I Investigated the Token Feature

  1. CLI Layer(cliRun.ts)

This file handles parsing command-line options, including --token-count-tree

  1. Configuration Layer (configSchema.ts)
    The tokenCountTree property stores either a boolean to enable or disable the feature or a number representing the minimum token threshold

  2. Orchestration (defaultAction.ts)
    When examining this file, I realized in my RepositoryContextPackager I have the same structure for orchestrating the packaging process.I had been thinking about optimizing this part of my code, but seeing Repomix's implementation confirmed I was on the right track.

The Most Interesting File: calculateMetricsWorker.ts

When I examined calculateMetricsWorker.ts, I was surprised by how simple and clean it is. They use OpenAI's Tiktoken library for tokenization.
Look at the code snippet below:

export interface TokenCountTask {
  content: string;
  encoding: TiktokenEncoding;
  path?: string;
}
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encoding property determines which tokenizer to use depending on the gpt model. path is used for file path and content is for file's text.They have used OpenAi's tokenization library.

The function below, we track how long this takes and and getTokenCounter() gives us the tokenizer. Also, this function returns the token count.

export const countTokens = async (task: TokenCountTask): Promise<number> => {
  const processStartAt = process.hrtime.bigint();

  try {
    const counter = getTokenCounter(task.encoding);
    const tokenCount = counter.countTokens(task.content, task.path);

    logger.trace(`Counted tokens. Count: ${tokenCount}. Took: ${getProcessDuration(processStartAt)}ms`);
    return tokenCount;
  } catch (error) {
    logger.error('Error in token counting worker:', error);
    throw error;
  }
};
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Gained Knowledge

Through this analysis, I learned several important lessons:

  • Start with documentation - Before diving into code, reading the documentation provides valuable context and understanding of the project's purpose
  • Breadth-first, then depth-first - Start with a broad overview of the project structure, then deep-dive into specific features you're interested in
  • Use the right tools - git grep for finding code patterns, VSCode shortcuts for navigation, and AI tools for understanding complex implementations
  • Architecture matters - Seeing how Repomix separates concerns (CLI parsing, configuration, orchestration, workers) showed me good patterns for organizing my own project.

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