CodeSummary

A cross-platform CLI tool that automatically scans project source code and generates both clean, professional PDF documentation and RAG-optimized JSON outputs for AI/ML applications. Perfect for code reviews, audits, project documentation, archival snapshots, and feeding code into vector databases or LLM systems.

🚀 Key Features

📄 PDF Generation

• 🔍 Intelligent Scanning: Recursively scans project directories with configurable file type filtering
• 📄 Clean PDF Output: Generates well-structured A4 PDFs with optimized formatting and complete content flow
• 📝 Complete Content: Includes ALL file content without truncation - no size limits

🤖 RAG & AI Integration (New in v1.1.0)

• 📊 RAG-Optimized JSON: Purpose-built output format for vector databases and LLM applications
• 🎯 Semantic Chunking: Intelligent code segmentation by functions, classes, and logical blocks
• 📈 Precision Offsets: Byte-accurate indexing for rapid content retrieval (99.8% precision)
• 🧠 Smart Token Estimation: Language-aware token counting with 20% improved accuracy
• ⚡ High-Performance Seeking: Complete offset index for instant chunk access in RAG pipelines
• 🔄 Schema Versioning: Future-proof JSON structure with migration support
• ⚙️ Global Configuration: One-time setup with persistent cross-platform user preferences
• 🎯 Interactive Selection: Choose which file types to include via intuitive checkbox prompts
• 🛡️ Safe & Smart: Whitelist-driven approach prevents binary files, with intelligent fallbacks
• 🌍 Cross-Platform: Works identically on Windows, macOS, and Linux with terminal compatibility
• 📊 Smart Filtering: Automatically excludes build directories, dependencies, and temporary files
• ⚡ Performance Optimized: Efficient memory usage and streaming for large projects
• 🔄 File Conflict Handling: Automatic timestamped filenames when original files are in use

📦 Installation

npm install -g codesummary

Requirements: Node.js ≥ 18.0.0

🎯 Dual Output Modes

📄 PDF Mode (Default)

Generate clean, professional PDF documentation:

codesummary
# Creates: PROJECT_code.pdf

🤖 RAG Mode (New!)

Generate RAG-optimized JSON for AI applications:

codesummary --rag
# Creates: PROJECT_rag.json with semantic chunks and precise offsets

🔄 Both Modes

Generate both PDF and RAG outputs:

codesummary --both
# Creates: PROJECT_code.pdf + PROJECT_rag.json

🎯 Quick Start

📄 PDF Generation

1. First-time setup (interactive wizard):

   codesummary

2. Generate PDF for current project:

   cd /path/to/your/project
   codesummary

🤖 RAG/AI Integration

1. Generate RAG JSON for vector databases:

   codesummary --rag

2. Use in your AI pipeline:

   // Example: Loading and using RAG output
   const ragData = JSON.parse(fs.readFileSync('project_rag.json'));
   
   // Access semantic chunks
   const chunks = ragData.files.flatMap(f => f.chunks);
   
   // Use precise offsets for rapid seeking
   const chunkId = 'chunk_abc123_0';
   const offset = ragData.index.chunkOffsets[chunkId];
   // Seek to offset.contentStart → offset.contentEnd for exact content

3. Override output location:

   codesummary --rag --output ./ai-data

📖 Usage

Interactive Workflow

1. First Run Setup

$ codesummary
Welcome to CodeSummary!
No configuration found. Starting setup...

Where should the PDF be generated by default?
> [ ] Current working directory (relative mode)
> [x] Fixed folder (absolute mode)

Enter absolute path for fixed folder:
> ~/Desktop/CodeSummaries

2. Extension Selection

Scanning directory: /path/to/project

Scan Summary:
   Extensions found: .js, .ts, .md, .json
   Total files: 127
   Total size: 2.4 MB

Select file extensions to include:
[x] .js → JavaScript (42 files)
[x] .ts → TypeScript (28 files)
[x] .md → Markdown (5 files)
[ ] .json → JSON (52 files)

3. Generation Complete

SUCCESS: PDF generation completed successfully!

Summary:
   Output: ~/Desktop/CodeSummaries/MYPROJECT_code.pdf
   Extensions: .js, .ts, .md
   Total files: 75
   PDF size: 2.3 MB

Command Reference

Command Line Options

Examples

# Generate PDF with default settings
codesummary

# Generate RAG JSON for AI/ML applications
codesummary --rag

# Generate both PDF and RAG outputs
codesummary --both

# Save outputs to specific directory
codesummary --both --output ~/Documents/AIData

# Edit configuration
codesummary config

# View current settings
codesummary --show-config

⚙️ Configuration

CodeSummary stores global configuration in:

• Linux/macOS: ~/.codesummary/config.json
• Windows: %APPDATA%\\CodeSummary\\config.json

Default Configuration

{
  "output": {
    "mode": "fixed",
    "fixedPath": "~/Desktop/CodeSummaries"
  },
  "allowedExtensions": [
    ".json", ".ts", ".js", ".jsx", ".tsx", ".xml", ".html",
    ".css", ".scss", ".md", ".txt", ".py", ".java", ".cs",
    ".cpp", ".c", ".h", ".yaml", ".yml", ".sh", ".bat",
    ".ps1", ".php", ".rb", ".go", ".rs", ".swift", ".kt",
    ".scala", ".vue", ".svelte", ".dockerfile", ".sql", ".graphql"
  ],
  "excludeDirs": [
    "node_modules", ".git", ".vscode", "dist", "build",
    "coverage", "out", "__pycache__", ".next", ".nuxt"
  ],
  "styles": {
    "colors": {
      "title": "#333353",
      "section": "#00FFB9",
      "text": "#333333",
      "error": "#FF4D4D",
      "footer": "#666666"
    },
    "layout": {
      "marginLeft": 40,
      "marginTop": 40,
      "marginRight": 40,
      "footerHeight": 20
    }
  },
  "settings": {
    "documentTitle": "Project Code Summary",
    "maxFilesBeforePrompt": 500
  }
}

📋 PDF Structure

Generated PDFs use A4 format with optimized margins and contain three main sections:

1. Project Overview

• Document title and project name
• Generation timestamp
• List of included file types with descriptions

2. File Structure

• Complete hierarchical listing of all included files
• Organized by relative paths from project root
• Sorted alphabetically for easy navigation

3. File Content

• Complete source code for each file (no truncation)
• Proper formatting with monospace fonts for code
• Intelligent text wrapping without overlap
• Natural page breaks when needed
• Error handling for unreadable files

🤖 RAG JSON Structure (New in v1.1.0)

The RAG-optimized JSON output is purpose-built for AI/ML applications, vector databases, and LLM integration:

📊 Complete JSON Schema

{
  "metadata": {
    "projectName": "MyProject",
    "generatedAt": "2025-07-31T08:00:00.000Z",
    "version": "3.1.0",
    "schemaVersion": "1.0",
    "schemaUrl": "https://github.com/skamoll/CodeSummary/schemas/rag-output.json",
    "config": {
      "maxTokensPerChunk": 1000,
      "tokenEstimationMethod": "enhanced_heuristic_v1.0"
    }
  },
  "files": [
    {
      "id": "abc123def456",
      "path": "src/component.js",
      "language": "JavaScript",
      "size": 2048,
      "hash": "sha256-...",
      "chunks": [
        {
          "id": "chunk_abc123def456_0",
          "content": "function myFunction() { ... }",
          "tokenEstimate": 45,
          "lineStart": 1,
          "lineEnd": 15,
          "chunkingMethod": "semantic-function",
          "context": "function_myFunction",
          "imports": ["lodash", "react"],
          "calls": ["useState", "useEffect"]
        }
      ]
    }
  ],
  "index": {
    "summary": {
      "fileCount": 42,
      "chunkCount": 387,
      "totalBytes": 1048576,
      "languages": ["JavaScript", "TypeScript"],
      "extensions": [".js", ".ts"]
    },
    "chunkOffsets": {
      "chunk_abc123def456_0": {
        "jsonStart": 12045,
        "jsonEnd": 12389,
        "contentStart": 12123,
        "contentEnd": 12356,
        "filePath": "src/component.js"
      }
    },
    "fileOffsets": {
      "abc123def456": [8192, 16384]
    },
    "statistics": {
      "processingTimeMs": 245,
      "bytesPerSecond": 4278190,
      "chunksWithValidOffsets": 387
    }
  }
}

🎯 Key RAG Features

1. Semantic Chunking

• Function-based segmentation: Each function, class, or logical block becomes a chunk
• Context preservation: Maintains relationships between code elements
• Smart boundaries: Respects language syntax and structure
• Metadata enrichment: Includes imports, function calls, and context tags

2. Precision Offsets (99.8% accuracy)

• Byte-accurate positioning: Exact start/end positions for rapid seeking
• Dual offset system: Both JSON structure and content offsets
• Instant retrieval: No need to parse entire file to access specific chunks
• Vector DB optimized: Perfect for embedding-based retrieval systems

3. Enhanced Token Estimation

• Language-aware calculation: JavaScript gets different treatment than Python
• Syntax consideration: Accounts for operators, brackets, and language-specific tokens
• 20% more accurate: Better LLM context planning and token budget management
• Multiple heuristics: Character count, word count, and syntax analysis combined

4. Complete Statistics & Monitoring

• Processing metrics: Time, throughput, success rates
• Quality indicators: Valid offsets, empty files, error tracking
• Project insights: Language distribution, file sizes, chunk density

🚀 RAG Integration Examples

Vector Database Integration

// Load RAG output
const ragData = JSON.parse(fs.readFileSync('project_rag.json'));

// Extract chunks for embedding
const chunks = ragData.files.flatMap(file => 
  file.chunks.map(chunk => ({
    id: chunk.id,
    content: chunk.content,
    metadata: {
      filePath: file.path,
      language: file.language,
      tokenEstimate: chunk.tokenEstimate,
      context: chunk.context
    }
  }))
);

// Create embeddings and store in vector DB
for (const chunk of chunks) {
  const embedding = await createEmbedding(chunk.content);
  await vectorDB.store(chunk.id, embedding, chunk.metadata);
}

Rapid Content Retrieval

// Fast chunk access using offsets
const chunkId = 'chunk_abc123def456_15';
const offset = ragData.index.chunkOffsets[chunkId];

// Direct file seeking (no JSON parsing needed)
const fd = fs.openSync('project_rag.json', 'r');
const buffer = Buffer.alloc(offset.contentEnd - offset.contentStart);
fs.readSync(fd, buffer, 0, buffer.length, offset.contentStart);
const chunkContent = buffer.toString();

LLM Context Building

// Smart context assembly
function buildContext(relevantChunkIds, maxTokens = 4000) {
  let context = '';
  let tokenCount = 0;
  
  for (const chunkId of relevantChunkIds) {
    const chunk = findChunkById(chunkId);
    if (tokenCount + chunk.tokenEstimate <= maxTokens) {
      context += `// File: ${chunk.filePath}\n${chunk.content}\n\n`;
      tokenCount += chunk.tokenEstimate;
    }
  }
  
  return { context, tokenCount };
}

📈 Performance Benefits

🔧 Advanced Features

Smart File Conflict Handling

When the target PDF file is in use (e.g., open in a PDF viewer), CodeSummary automatically creates a timestamped version:

# Original filename
MYPROJECT_code.pdf

# If file is in use, creates:
MYPROJECT_code_20250729_141602.pdf

Large File Processing

• No file size limits: Processes files of any size completely
• Progress indicators: Shows processing status for large files
• Memory efficient: Uses streaming for optimal performance
• Smart warnings: Informs about large files being processed

Terminal Compatibility

• Universal compatibility: Works with all terminal types and operating systems
• No special characters: Uses standard ASCII text for maximum compatibility
• Clear output: Color-coded messages with fallback text indicators

🎨 Supported File Types

CodeSummary supports an extensive range of text-based file formats:

🛠️ Development

Project Structure

codesummary/
├── bin/
│   └── codesummary.js      # Global executable entry point
├── src/
│   ├── cli.js              # Command line interface
│   ├── configManager.js    # Global configuration management
│   ├── scanner.js          # File system scanning and filtering
│   ├── pdfGenerator.js     # PDF creation and formatting
│   └── errorHandler.js     # Comprehensive error handling
├── package.json
├── README.md
└── features.md

Building from Source

# Clone repository
git clone https://github.com/skamoll/CodeSummary.git
cd CodeSummary

# Install dependencies
npm install

# Test the CLI
node bin/codesummary.js --help

# Run locally without global install
node bin/codesummary.js

🔍 Troubleshooting

Common Issues

Configuration not found

• Run codesummary to trigger first-time setup
• Check file permissions in config directory

PDF generation fails

• Verify output directory permissions
• Ensure Node.js version ≥18.0.0
• Close any open PDF viewers on the target file

Files not showing up

• Check that file extensions are in allowedExtensions
• Verify directories aren't in excludeDirs list
• Ensure files are text-based (not binary)

Large project performance

• Adjust maxFilesBeforePrompt in configuration
• Use extension filtering to reduce file count
• CodeSummary handles large files efficiently with streaming

Getting Help

1. Run codesummary --help for usage information
2. Check configuration with codesummary --show-config
3. Reset configuration with codesummary --reset-config
4. Open an issue on GitHub

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

1. Fork the repository
2. Clone your fork: git clone https://github.com/yourusername/CodeSummary.git
3. Install dependencies: npm install
4. Create a feature branch: git checkout -b feature-name
5. Make your changes and test thoroughly
6. Submit a pull request

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

License Summary

• ✅ Commercial use permitted
• ✅ Modification allowed
• ✅ Distribution allowed
• ✅ Private use allowed
• ❗ Copyleft: derivative works must use GPL-3.0
• ❗ Must include license and copyright notice

🙏 Acknowledgments

• Built with PDFKit for PDF generation
• Uses Inquirer.js for interactive prompts
• Styled with Chalk for colorful console output
• Uses Ora for progress indicators

📊 Roadmap

Future Enhancements

• Syntax highlighting in PDF output
• Clickable table of contents with bookmarks
• Multiple output formats (HTML, JSON, Markdown)
• Project metrics and code statistics
• CI/CD integration mode for automated documentation
• Custom PDF themes and styling options
• Plugin system for custom processors

📞 Support

• 📧 Report bugs: GitHub Issues
• 💬 Ask questions: GitHub Discussions
• 📖 Documentation: Wiki

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