CodeChunker Pro

Large-Scale Code Processor for LLM Context Management

Project Overview

CodeChunker Pro is a cross-platform desktop application built to solve a common developer pain point: feeding large codebases into Large Language Models (LLMs) with limited context windows. The application allows users to upload or paste massive code files and intelligently split them into manageable "chunks" based on lines or specific part counts. By automating the fragmentation process and adding contextual headers/footers to each piece, it ensures that AI models can process entire projects without losing track of the file's structure.

Core Features

• Intelligent Splitting Strategies: Offers two primary modes—splitting by a fixed number of lines (e.g., 1,500 lines per chunk) or dividing a file into a specific number of equal parts.

• AI Context Wrappers: Automatically injects custom prefixes and suffixes (e.g., "// Part 1 of 5") into each chunk to maintain structural continuity during AI analysis.

• Real-time Analytics: Provides instant feedback on line count, character count, and estimated token usage to help users stay within specific model limits.

• Multi-Format Export: Users can copy individual chunks to the clipboard, download them as separate .txt files, or export all chunks as a single organized .zip archive using JSZip.

• Privacy-First Processing: Designed as a local tool where all code processing happens on the user's machine, ensuring no sensitive data is uploaded to external servers.

Technical Stack

• Frontend: React.js with Vite for a high-performance, responsive UI.

• Desktop Wrapper: Electron, enabling the web-based tool to run as a native Windows/macOS application with local file system access.

• Styling: Modern CSS with a custom-themed "One Dark" code editor interface and a mobile-responsive dashboard.

• Utilities:

  • JSZip: For on-the-fly generation of archive files.

  • React Icons: For a professional, intuitive interface.

Impact

This tool streamlines the workflow for developers using AI for code reviews, refactoring, or documentation by removing the manual effort of copy-pasting and formatting large files. It bridges the gap between massive local codebases and the constraints of current AI context windows.