CodeDocGen

A command-line tool and library that automatically generates Doxygen-style comments and documentation for functions and methods in codebases. Uses AI-powered analysis with fallback to NLTK for intelligent, context-aware documentation generation.

Features

• AI-Powered Comment Generation: Uses Groq (primary) with optional OpenAI fallback for intelligent, context-aware documentation
• Smart Fallback System: Falls back to NLTK-based analysis when AI is unavailable or fails
• Multi-language Support: C/C++ (using libclang), Python (using ast), Java (basic support), JavaScript (regex-based)
• Smart Function Analysis: Analyzes function bodies to detect recursion, loops, conditionals, regex usage, API calls, and file operations
• Git Integration: Process only changed files with --changes-only flag and auto-commit documentation with --auto-commit
• Context-Aware Descriptions: Generates specific, meaningful descriptions instead of generic templates
• Flexible Output: In-place file modification, diff generation, or new file creation
• Configurable: YAML-based configuration for custom rules, templates, and AI settings
• Language-Aware Comment Detection: Prevents duplicate documentation by detecting existing comments

Installation

Prerequisites

• Python 3.8+
• Clang (for C/C++ parsing)

Setup

1. Activate the virtual environment:

   source codedocgen/bin/activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Download NLTK data:

   python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger')"
   

From TestPyPI (Latest Version)

pip install --index-url https://test.pypi.org/simple/ code_doc_gen==1.2.0

From PyPI (Stable Version)

pip install code-doc-gen

OS-specific setup guides (highly recommended)

• Windows: usage/windows.md
• Linux: usage/linux.md
• macOS: usage/macos.md

Usage

Command Line Interface

# Generate documentation (automatically detects language from file extensions)
code_doc_gen --repo /path/to/repo --inplace

# Generate documentation for a C++ repository (preserves existing comments)
code_doc_gen --repo /path/to/cpp/repo --lang c++ --inplace

# Generate documentation for Python files with custom output
code_doc_gen --repo /path/to/python/repo --lang python --output-dir ./docs

# Use custom configuration
code_doc_gen --repo /path/to/repo --lang c++ --config custom_rules.yaml

# Process specific files only
code_doc_gen --repo /path/to/repo --lang python --files src/main.py src/utils.py

# Show diff without applying changes
code_doc_gen --repo /path/to/repo --lang c++ --diff

# Enable verbose logging
code_doc_gen --repo /path/to/repo --lang python --verbose

# Enable AI-powered documentation generation (Groq)
code_doc_gen --repo /path/to/repo --lang python --enable-ai --ai-provider groq --inplace

# Use Groq AI provider (requires API key)
code_doc_gen --repo /path/to/repo --lang c++ --enable-ai --ai-provider groq --inplace

# Process only changed files in a Git repository
code_doc_gen --repo /path/to/repo --lang python --changes-only --inplace

# Auto-commit generated documentation
code_doc_gen --repo /path/to/repo --lang python --enable-ai --inplace --auto-commit

Library Usage

from code_doc_gen import generate_docs

# Generate documentation (automatically detects language)
results = generate_docs('/path/to/repo', inplace=True)

# Process specific files
results = generate_docs('/path/to/repo', lang='python', files=['src/main.py'])

# Generate in-place documentation
generate_docs('/path/to/repo', lang='python', inplace=True)

# Generate to output directory
generate_docs('/path/to/repo', lang='c++', output_dir='./docs')

Configuration

Create a config.yaml file to customize documentation generation:

# Language-specific templates
templates:
  c++:
    brief: "/** \brief {description} */"
    param: " * \param {name} {description}"
    return: " * \return {description}"
    throws: " * \throws {exception} {description}"
  
  python:
    brief: '""" {description} """'
    param: "    :param {name}: {description}"
    return: "    :return: {description}"
    raises: "    :raises {exception}: {description}"

# Custom inference rules
rules:
  - pattern: "^validate.*"
    brief: "Validates the input {params}."
  - pattern: "^compute.*"
    brief: "Computes the {noun} based on {params}."
  - pattern: "^get.*"
    brief: "Retrieves the {noun}."

# AI configuration for intelligent comment generation
ai:
  enabled: false  # Set to true to enable AI-powered analysis
provider: "groq"  # Options: "groq" (requires API key) or "openai" (requires API key)
  groq_api_key: ""  # Get from https://console.groq.com/keys or set GROQ_API_KEY environment variable
  openai_api_key: ""  # Get from https://platform.openai.com/account/api-keys or set OPENAI_API_KEY environment variable
  max_retries: 3  # Number of retries for AI API calls
  retry_delay: 1.0  # Delay between retries in seconds

Environment Variables (Recommended for API Keys)

For security and ease of use, it's recommended to use environment variables for API keys instead of hardcoding them in config files.

Setup

1. Copy the example environment file:

   cp .env.example .env
   

2. Edit the .env file and add your API keys:

   # Groq API Key (get from https://console.groq.com/keys)
   GROQ_API_KEY=your_groq_api_key_here
   
   # OpenAI API Key (get from https://platform.openai.com/account/api-keys)
   OPENAI_API_KEY=your_openai_api_key_here
   

3. Add .env to your .gitignore file:

   echo ".env" >> .gitignore
   

Priority Order

The tool loads API keys in the following priority order:

1. Environment variables (from .env file) - Highest priority
2. Command line arguments (if provided)
3. Config file values (from config.yaml) - Lowest priority

This ensures your API keys are secure and not accidentally committed to version control.

Supported Languages

C/C++

• Uses libclang for AST parsing
• Generates Doxygen-style comments
• Detects function signatures, parameters, return types, and exceptions
• Supports both .c and .cpp files
• NEW: Recognizes existing comments (//, /* */, /** */) to prevent duplicates

Configuring libclang (Cross-Platform)

CodeDocGen auto-detects libclang with ABI validation (it probes Index.create to ensure compatibility) using this order:

1. Environment variables (from shell or .env):
   • LIBCLANG_LIBRARY_FILE or CLANG_LIBRARY_FILE (full path to libclang shared lib)
   • LIBCLANG_PATH, CLANG_LIBRARY_PATH, or LLVM_LIB_DIR (directory containing libclang)
2. config.yaml overrides:

   cpp:
     libclang:
       # Choose one
       library_file: "/absolute/path/to/libclang.dylib"  # .so on Linux, .dll on Windows
       # library_path: "/absolute/path/to/llvm/lib"
   

3. PyPI vendor locations:
   • libclang package native folder (if installed)
   • clang/native folder (if using the clang Python package that bundles a dylib)
4. find_library('clang'|'libclang')
5. OS default locations (Homebrew/Xcode on macOS, distro LLVM paths on Linux, C:\\Program Files\\LLVM on Windows)

If none succeed, AST parsing falls back to a robust regex mode.

macOS recommended setups:

• Xcode Command Line Tools (simple, stable):

  • Install Python bindings matching CLT (18.x):

    pip install 'clang==18.1.8'
    

  • Auto-detects /Library/Developer/CommandLineTools/usr/lib/libclang.dylib (no .env needed).

• Homebrew LLVM (latest toolchain):

  • brew install llvm
  • Add to .env:

    LIBCLANG_LIBRARY_FILE=/opt/homebrew/opt/llvm/lib/libclang.dylib   # Apple Silicon
    # or
    LIBCLANG_LIBRARY_FILE=/usr/local/opt/llvm/lib/libclang.dylib      # Intel
    

Linux:

• Prefer distro libclang and matching Python bindings, or set LIBCLANG_LIBRARY_FILE to the installed .so.

Windows:

• Install LLVM and set LIBCLANG_LIBRARY_FILE to the libclang.dll under Program Files\\LLVM.

Python

• Uses built-in ast module for parsing
• Generates PEP 257 compliant docstrings
• Detects function signatures, parameters, return types, and exceptions
• Supports .py files
• NEW: Recognizes existing comments (#, """, ''') and decorators to prevent duplicates

Java

• NEW: Java comment detection support (regex fallback)
• Recognizes Javadoc-style comments with @param, @return, @throws
• Fallback to regex-based parsing when javaparser is not available
• Supports .java files

AI-Powered Comment Generation

CodeDocGen now supports AI-powered comment generation with intelligent fallback to NLTK-based analysis:

AI Providers

Groq (Primary)

• Requires API key from https://console.groq.com/keys
• Multiple model support with automatic fallback
• Primary Model: llama3-8b-8192 (fastest)
• Fallback Models: llama3.1-8b-instant, llama3-70b-8192
• Fast response times with generous free tier
• Install with: pip install groq

Setup

1. Enable AI in configuration:

   ai:
     enabled: true
     provider: "groq"
   

2. For Groq/OpenAI users:

   • Get API keys from:
     • Groq: https://console.groq.com/keys
     • OpenAI: https://platform.openai.com/account/api-keys
   • Option 1: Use .env file (Recommended)

     # Copy the example file
     cp .env.example .env
     
     # Edit .env and add your API keys
     GROQ_API_KEY=your_groq_api_key_here
     OPENAI_API_KEY=your_openai_api_key_here
     

   • Option 2: Add to config.yaml

     groq_api_key: "your-api-key-here"
     openai_api_key: "your-openai-api-key-here"
     

   • Note: Environment variables (from .env) take precedence over config file values

3. Command line usage:

   # Enable AI with Groq
   code_doc_gen --repo /path/to/repo --enable-ai --ai-provider groq --inplace
   
   # Enable AI with Groq (using .env file)
   code_doc_gen --repo /path/to/repo --enable-ai --ai-provider groq --inplace
   
   # Enable AI with OpenAI (using .env file)
   code_doc_gen --repo /path/to/repo --enable-ai --ai-provider openai --inplace
   
   # Or pass API keys directly (not recommended for security)
   code_doc_gen --repo /path/to/repo --enable-ai --ai-provider groq --groq-api-key YOUR_KEY --inplace
   

Fallback System

The tool uses a smart fallback system:

1. AI Analysis: Try AI-powered comment generation first
2. NLTK Analysis: Fall back to NLTK-based intelligent analysis if AI fails
3. Rule-based: Final fallback to pattern-based rules

This ensures the tool always works, even when AI services are unavailable.

Intelligent Comment Generation (NLTK-based)

CodeDocGen v1.1.7 introduces intelligent comment generation with AST analysis and NLTK-powered descriptions:

Key Improvements

• Groq Model Fallback Support: Multiple models with priority order (llama3-8b-8192 → llama3.1-8b-instant → llama3-70b-8192)
• Context-Aware Parameter Descriptions: Smart parameter descriptions based on names and context
• Function-Specific Return Types: Intelligent return type descriptions based on function purpose
• Behavioral Detection: Detects recursion, loops, conditionals, regex usage, API calls, and file operations
• Specific Actions: Generates specific action verbs instead of generic "processes" descriptions
• Complete Coverage: All functions receive intelligent, meaningful comments

Language-Aware Comment Detection

CodeDocGen v1.1.3 maintains intelligent comment detection that prevents duplicate documentation:

Python Comment Detection

# Existing comment above function
@decorator
def commented_func():
    """This function has a docstring"""
    return True

def inline_commented_func():  # Inline comment
    return True

def next_line_commented_func():
    # Comment on next line
    return True

C++ Comment Detection

// Existing comment above function
int add(int a, int b) {
    return a + b;
}

void inline_commented_func() { // Inline comment
    std::cout << "Hello" << std::endl;
}

/* Multi-line comment above function */
void multi_line_func() {
    std::cout << "Multi-line" << std::endl;
}

/** Doxygen comment */
void doxygen_func() {
    std::cout << "Doxygen" << std::endl;
}

Java Comment Detection

/**
 * Existing Javadoc comment
 * @param input The input parameter
 * @return The result
 */
public String processInput(String input) {
    return input.toUpperCase();
}

Project Structure

CodeDocGen/
├── code_doc_gen/
│   ├── __init__.py          # Main package interface
│   ├── main.py              # CLI entry point
│   ├── scanner.py           # Repository scanning
│   ├── analyzer.py          # NLTK-based analysis
│   ├── generator.py         # Documentation generation
│   ├── config.py            # Configuration management
│   ├── models.py            # Data models
│   └── parsers/             # Language-specific parsers
│       ├── __init__.py
│       ├── cpp_parser.py    # C/C++ parser (libclang)
│       ├── python_parser.py # Python parser (ast)
│       ├── java_parser.py   # Java parser (regex fallback)
│       └── javascript_parser.py   # JavaScript parser (regex-based)
├── tests/                   # Unit tests (100+ tests)
├── requirements.txt         # Dependencies
├── setup.py                # Package setup
├── README.md               # This file
└── example.py              # Usage examples

Development

Running Tests

# Run all tests
python -m pytest tests/ -v

# Run specific test file
python -m pytest tests/test_generator.py -v

# Run tests with coverage
python -m pytest tests/ --cov=code_doc_gen

Installing in Development Mode

pip install -e .

Roadmap

Version 1.1.6 (Current Release)

• Groq Model Fallback Support: Multiple models with priority order and automatic fallback
• Intelligent Comment Generation: AST analysis and NLTK-powered documentation
• Context-Aware Descriptions: Smart parameter and return type descriptions
• Behavioral Detection: Recursion, loops, conditionals, regex, API calls, file operations
• Specific Actions: Meaningful action verbs instead of generic descriptions
• Complete Coverage: All functions receive intelligent comments

Version 1.2 (Next Release)

• Enhanced Java Support: Full javaparser integration for better Java parsing
• JavaScript/TypeScript Support: Add support for JS/TS files
• Enhanced Templates: More customization options for documentation styles
• Performance Optimizations: Parallel processing improvements

Version 1.3

• Go and Rust Support: Add support for Go and Rust files
• IDE Integration: VSCode and IntelliJ plugin support
• Batch Processing: Support for processing multiple repositories
• Documentation Quality: Enhanced analysis for better documentation

Version 1.4

• C# Support: Add C# language parser
• PHP Support: Add PHP language parser
• Web Interface: Simple web UI for documentation generation
• CI/CD Integration: GitHub Actions and GitLab CI templates

Future Versions

• Ruby Support: Add Ruby language parser
• Advanced Analysis: More sophisticated code analysis and inference
• Documentation Standards: Support for various documentation standards
• Machine Learning: Optional ML-based documentation suggestions

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• NLTK: For natural language processing capabilities
• libclang: For C/C++ AST parsing
• Python ast module: For Python code analysis
• Community: For feedback and contributions

AI Providers Setup

CodeDocGen supports multiple AI providers for intelligent documentation generation. You can configure one primary provider and set up fallback providers for reliability.

Available Providers

1. Groq (Primary)

• Status: Unofficial API - use with caution
• Cost: Free
• Setup: No configuration required
• Warning: This is an unofficial API that may be rate-limited, change, or violate terms of service. Use only for personal projects.

2. Groq (Free API Key Required)

• Status: Official API
• Cost: Free tier available
• Setup:
  1. Visit Groq Console
  2. Sign up for a free account
  3. Generate an API key
  4. Add to config.yaml:

     ai:
       groq_api_key: "your_groq_api_key_here"
     

3. OpenAI (Paid API Key Required)

• Status: Official API
• Cost: Pay-per-use
• Setup:
  1. Visit OpenAI Platform
  2. Create an account and add billing information
  3. Generate an API key
  4. Add to config.yaml:

     ai:
       openai_api_key: "your_openai_api_key_here"
     

Configuration

Configure AI providers in your config.yaml:

ai:
  enabled: true
provider: "groq"  # Primary provider: groq or openai
  fallback_providers: ["groq", "openai"]  # Fallback order
  groq_api_key: "your_groq_key"
  openai_api_key: "your_openai_key"
  max_retries: 5
  retry_delay: 1.0
  models:
groq: ["llama3-8b-8192", "llama3.1-8b-instant", "llama3-70b-8192"]
    groq: ["llama3-8b-8192", "llama3.1-8b-instant", "llama3-70b-8192"]
    openai: "gpt-4o-mini"

Usage Examples

# Use Groq
python -m code_doc_gen.main --repo . --files src/ --enable-ai --ai-provider groq

# Use Groq with fallback to OpenAI
python -m code_doc_gen.main --repo . --files src/ --enable-ai --ai-provider groq

# Use OpenAI directly
python -m code_doc_gen.main --repo . --files src/ --enable-ai --ai-provider openai

Fallback Behavior

The system automatically tries providers in this order:

1. Primary provider (from config)
2. Fallback providers (in order specified)

If all AI providers fail, the system falls back to NLTK-based analysis.

Rate Limiting and Reliability

• Groq: Ensure API key is set via CLI or environment
• Groq: Official rate limits; exponential backoff retry
• OpenAI: Official rate limits; exponential backoff retry

All providers use intelligent retry logic with exponential backoff to handle temporary failures.